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url stringlengths 61 61	repository_url stringclasses 1 value	labels_url stringlengths 75 75	comments_url stringlengths 70 70	events_url stringlengths 68 68	html_url stringlengths 49 51	id int64 1.49B 2.43B	node_id stringlengths 18 19	number int64 5.35k 7.07k	title stringlengths 1 290	user dict	labels listlengths 0 3	state stringclasses 2 values	locked bool 1 class	assignee dict	assignees listlengths 0 2	milestone dict	comments sequencelengths 0 30	created_at timestamp[s]	updated_at timestamp[s]	closed_at timestamp[s]	author_association stringclasses 4 values	active_lock_reason null	draft bool 2 classes	pull_request dict	body stringlengths 1 19.9k ⌀	reactions dict	timeline_url stringlengths 70 70	performed_via_github_app null	state_reason stringclasses 3 values	is_pull_request bool 2 classes
https://api.github.com/repos/huggingface/datasets/issues/5416	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5416/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5416/comments	https://api.github.com/repos/huggingface/datasets/issues/5416/events	https://github.com/huggingface/datasets/pull/5416	1,526,988,113	PR_kwDODunzps5HDLmR	5,416	Fix RuntimeError: Sharding is ambiguous for this dataset	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "By the way, do we know how many datasets are impacted by this issue?\r\n\r\nMaybe we should do a patch release with this fix.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009256 / 0.011353 (-0.002097) \| 0.005033 / 0.011008 (-0.005975) \| 0.099346 / 0.038508 (0.060838) \| 0.035204 / 0.023109 (0.012095) \| 0.303017 / 0.275898 (0.027119) \| 0.335632 / 0.323480 (0.012152) \| 0.007953 / 0.007986 (-0.000033) \| 0.005806 / 0.004328 (0.001477) \| 0.076121 / 0.004250 (0.071871) \| 0.041164 / 0.037052 (0.004112) \| 0.305536 / 0.258489 (0.047047) \| 0.348452 / 0.293841 (0.054611) \| 0.037704 / 0.128546 (-0.090842) \| 0.011982 / 0.075646 (-0.063664) \| 0.333264 / 0.419271 (-0.086008) \| 0.047738 / 0.043533 (0.004205) \| 0.310126 / 0.255139 (0.054987) \| 0.318719 / 0.283200 (0.035519) \| 0.098933 / 0.141683 (-0.042750) \| 1.421058 / 1.452155 (-0.031096) \| 1.554771 / 1.492716 (0.062054) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.258627 / 0.018006 (0.240620) \| 0.450814 / 0.000490 (0.450324) \| 0.011288 / 0.000200 (0.011088) \| 0.000136 / 0.000054 (0.000081) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027004 / 0.037411 (-0.010407) \| 0.109067 / 0.014526 (0.094541) \| 0.120401 / 0.176557 (-0.056155) \| 0.158336 / 0.737135 (-0.578799) \| 0.126244 / 0.296338 (-0.170094) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401847 / 0.215209 (0.186638) \| 4.006003 / 2.077655 (1.928348) \| 1.806342 / 1.504120 (0.302223) \| 1.619751 / 1.541195 (0.078556) \| 1.709660 / 1.468490 (0.241170) \| 0.692444 / 4.584777 (-3.892333) \| 3.853691 / 3.745712 (0.107979) \| 2.143910 / 5.269862 (-3.125951) \| 1.471600 / 4.565676 (-3.094076) \| 0.084589 / 0.424275 (-0.339686) \| 0.012276 / 0.007607 (0.004669) \| 0.506529 / 0.226044 (0.280485) \| 5.028361 / 2.268929 (2.759432) \| 2.277660 / 55.444624 (-53.166964) \| 1.930365 / 6.876477 (-4.946112) \| 1.965494 / 2.142072 (-0.176579) \| 0.826752 / 4.805227 (-3.978475) \| 0.165050 / 6.500664 (-6.335614) \| 0.062702 / 0.075469 (-0.012767) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.234539 / 1.841788 (-0.607249) \| 15.067401 / 8.074308 (6.993093) \| 14.041920 / 10.191392 (3.850528) \| 0.162590 / 0.680424 (-0.517834) \| 0.028941 / 0.534201 (-0.505260) \| 0.438518 / 0.579283 (-0.140765) \| 0.443787 / 0.434364 (0.009423) \| 0.516671 / 0.540337 (-0.023666) \| 0.609036 / 1.386936 (-0.777900) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007535 / 0.011353 (-0.003818) \| 0.005283 / 0.011008 (-0.005725) \| 0.097116 / 0.038508 (0.058608) \| 0.033357 / 0.023109 (0.010247) \| 0.383398 / 0.275898 (0.107500) \| 0.425516 / 0.323480 (0.102037) \| 0.006039 / 0.007986 (-0.001947) \| 0.004074 / 0.004328 (-0.000255) \| 0.073207 / 0.004250 (0.068956) \| 0.052153 / 0.037052 (0.015101) \| 0.386385 / 0.258489 (0.127896) \| 0.429900 / 0.293841 (0.136059) \| 0.038341 / 0.128546 (-0.090205) \| 0.012417 / 0.075646 (-0.063230) \| 0.333859 / 0.419271 (-0.085413) \| 0.051157 / 0.043533 (0.007625) \| 0.395022 / 0.255139 (0.139883) \| 0.402462 / 0.283200 (0.119262) \| 0.105207 / 0.141683 (-0.036475) \| 1.510679 / 1.452155 (0.058524) \| 1.584205 / 1.492716 (0.091489) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225805 / 0.018006 (0.207799) \| 0.452109 / 0.000490 (0.451619) \| 0.000429 / 0.000200 (0.000229) \| 0.000057 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029653 / 0.037411 (-0.007759) \| 0.112609 / 0.014526 (0.098083) \| 0.121828 / 0.176557 (-0.054728) \| 0.159003 / 0.737135 (-0.578133) \| 0.129306 / 0.296338 (-0.167033) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.453001 / 0.215209 (0.237792) \| 4.514882 / 2.077655 (2.437228) \| 2.277494 / 1.504120 (0.773374) \| 2.073870 / 1.541195 (0.532675) \| 2.153346 / 1.468490 (0.684856) \| 0.698363 / 4.584777 (-3.886414) \| 3.921763 / 3.745712 (0.176051) \| 2.123133 / 5.269862 (-3.146729) \| 1.347618 / 4.565676 (-3.218058) \| 0.085654 / 0.424275 (-0.338621) \| 0.012059 / 0.007607 (0.004452) \| 0.568183 / 0.226044 (0.342139) \| 5.720047 / 2.268929 (3.451119) \| 2.777973 / 55.444624 (-52.666651) \| 2.453426 / 6.876477 (-4.423051) \| 2.523977 / 2.142072 (0.381905) \| 0.841979 / 4.805227 (-3.963248) \| 0.167958 / 6.500664 (-6.332706) \| 0.064929 / 0.075469 (-0.010540) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235297 / 1.841788 (-0.606491) \| 15.883598 / 8.074308 (7.809290) \| 14.395328 / 10.191392 (4.203936) \| 0.162401 / 0.680424 (-0.518022) \| 0.017806 / 0.534201 (-0.516394) \| 0.423853 / 0.579283 (-0.155430) \| 0.423266 / 0.434364 (-0.011098) \| 0.490351 / 0.540337 (-0.049986) \| 0.588116 / 1.386936 (-0.798820) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb3fbfa162bb4700e23d084826b4b7f6d97284be \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010759 / 0.011353 (-0.000594) \| 0.005748 / 0.011008 (-0.005260) \| 0.119195 / 0.038508 (0.080687) \| 0.033476 / 0.023109 (0.010367) \| 0.364081 / 0.275898 (0.088183) \| 0.422456 / 0.323480 (0.098976) \| 0.009780 / 0.007986 (0.001795) \| 0.006170 / 0.004328 (0.001841) \| 0.093242 / 0.004250 (0.088991) \| 0.041049 / 0.037052 (0.003997) \| 0.372132 / 0.258489 (0.113643) \| 0.442501 / 0.293841 (0.148660) \| 0.054889 / 0.128546 (-0.073657) \| 0.018302 / 0.075646 (-0.057345) \| 0.378899 / 0.419271 (-0.040373) \| 0.058455 / 0.043533 (0.014922) \| 0.356141 / 0.255139 (0.101002) \| 0.400866 / 0.283200 (0.117666) \| 0.103384 / 0.141683 (-0.038299) \| 1.629867 / 1.452155 (0.177713) \| 1.693939 / 1.492716 (0.201222) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.240484 / 0.018006 (0.222478) \| 0.509137 / 0.000490 (0.508648) \| 0.000450 / 0.000200 (0.000250) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025856 / 0.037411 (-0.011555) \| 0.113214 / 0.014526 (0.098689) \| 0.119420 / 0.176557 (-0.057136) \| 0.158663 / 0.737135 (-0.578473) \| 0.123542 / 0.296338 (-0.172797) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.555900 / 0.215209 (0.340691) \| 5.580295 / 2.077655 (3.502640) \| 2.216640 / 1.504120 (0.712520) \| 1.904944 / 1.541195 (0.363749) \| 1.865839 / 1.468490 (0.397349) \| 1.158325 / 4.584777 (-3.426452) \| 5.097420 / 3.745712 (1.351708) \| 2.881775 / 5.269862 (-2.388087) \| 2.068896 / 4.565676 (-2.496780) \| 0.129028 / 0.424275 (-0.295247) \| 0.014075 / 0.007607 (0.006468) \| 0.698874 / 0.226044 (0.472830) \| 7.131225 / 2.268929 (4.862296) \| 2.901686 / 55.444624 (-52.542939) \| 2.186146 / 6.876477 (-4.690330) \| 2.251172 / 2.142072 (0.109100) \| 1.342264 / 4.805227 (-3.462963) \| 0.232045 / 6.500664 (-6.268619) \| 0.073520 / 0.075469 (-0.001949) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.431314 / 1.841788 (-0.410474) \| 16.313055 / 8.074308 (8.238747) \| 18.451552 / 10.191392 (8.260160) \| 0.232875 / 0.680424 (-0.447549) \| 0.042170 / 0.534201 (-0.492031) \| 0.495261 / 0.579283 (-0.084022) \| 0.582901 / 0.434364 (0.148537) \| 0.582049 / 0.540337 (0.041712) \| 0.681122 / 1.386936 (-0.705814) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008131 / 0.011353 (-0.003222) \| 0.006162 / 0.011008 (-0.004847) \| 0.113721 / 0.038508 (0.075213) \| 0.030797 / 0.023109 (0.007688) \| 0.413108 / 0.275898 (0.137210) \| 0.449968 / 0.323480 (0.126488) \| 0.006126 / 0.007986 (-0.001860) \| 0.004848 / 0.004328 (0.000519) \| 0.085465 / 0.004250 (0.081214) \| 0.045817 / 0.037052 (0.008764) \| 0.419360 / 0.258489 (0.160871) \| 0.489077 / 0.293841 (0.195236) \| 0.050841 / 0.128546 (-0.077705) \| 0.020646 / 0.075646 (-0.055000) \| 0.379838 / 0.419271 (-0.039434) \| 0.068897 / 0.043533 (0.025365) \| 0.422182 / 0.255139 (0.167043) \| 0.435529 / 0.283200 (0.152330) \| 0.115299 / 0.141683 (-0.026384) \| 1.655134 / 1.452155 (0.202979) \| 1.835198 / 1.492716 (0.342481) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207041 / 0.018006 (0.189034) \| 0.491263 / 0.000490 (0.490773) \| 0.003554 / 0.000200 (0.003354) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030830 / 0.037411 (-0.006582) \| 0.127003 / 0.014526 (0.112477) \| 0.142901 / 0.176557 (-0.033656) \| 0.177570 / 0.737135 (-0.559565) \| 0.137758 / 0.296338 (-0.158580) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.632820 / 0.215209 (0.417611) \| 6.215535 / 2.077655 (4.137880) \| 2.615310 / 1.504120 (1.111190) \| 2.261431 / 1.541195 (0.720236) \| 2.220570 / 1.468490 (0.752080) \| 1.215820 / 4.584777 (-3.368957) \| 5.247680 / 3.745712 (1.501968) \| 3.120054 / 5.269862 (-2.149807) \| 1.950947 / 4.565676 (-2.614730) \| 0.149980 / 0.424275 (-0.274295) \| 0.015241 / 0.007607 (0.007634) \| 0.879714 / 0.226044 (0.653670) \| 7.941913 / 2.268929 (5.672984) \| 3.512456 / 55.444624 (-51.932168) \| 2.693833 / 6.876477 (-4.182644) \| 2.772780 / 2.142072 (0.630708) \| 1.459581 / 4.805227 (-3.345646) \| 0.264820 / 6.500664 (-6.235844) \| 0.076698 / 0.075469 (0.001228) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.437719 / 1.841788 (-0.404068) \| 16.750309 / 8.074308 (8.676001) \| 18.646776 / 10.191392 (8.455384) \| 0.227858 / 0.680424 (-0.452566) \| 0.024239 / 0.534201 (-0.509962) \| 0.486172 / 0.579283 (-0.093111) \| 0.574731 / 0.434364 (0.140367) \| 0.557776 / 0.540337 (0.017439) \| 0.672921 / 1.386936 (-0.714015) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb3fbfa162bb4700e23d084826b4b7f6d97284be \"CML watermark\")\n" ]	2023-01-10T08:43:19	2023-01-18T17:12:17	2023-01-18T14:09:02	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5416", "html_url": "https://github.com/huggingface/datasets/pull/5416", "diff_url": "https://github.com/huggingface/datasets/pull/5416.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5416.patch", "merged_at": "2023-01-18T14:09:02" }	This PR fixes the RuntimeError: Sharding is ambiguous for this dataset. The error for ambiguous sharding will be raised only if num_proc > 1. Fix #5415, fix #5414. Fix https://huggingface.co/datasets/ami/discussions/3.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5416/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5416/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5415	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5415/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5415/comments	https://api.github.com/repos/huggingface/datasets/issues/5415/events	https://github.com/huggingface/datasets/issues/5415	1,526,904,861	I_kwDODunzps5bArgd	5,415	RuntimeError: Sharding is ambiguous for this dataset	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	null	[]	2023-01-10T07:36:11	2023-01-18T14:09:04	2023-01-18T14:09:03	MEMBER	null	null	null	### Describe the bug When loading some datasets, a RuntimeError is raised. For example, for "ami" dataset: https://huggingface.co/datasets/ami/discussions/3 ``` .../huggingface/datasets/src/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size) 1415 fpath = path_join(self._output_dir, fname) 1416 -> 1417 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs) 1418 if num_input_shards <= 1 and num_proc is not None: 1419 logger.warning( .../huggingface/datasets/src/datasets/utils/sharding.py in _number_of_shards_in_gen_kwargs(gen_kwargs) 10 lists_lengths = {key: len(value) for key, value in gen_kwargs.items() if isinstance(value, list)} 11 if len(set(lists_lengths.values())) > 1: ---> 12 raise RuntimeError( 13 ( 14 "Sharding is ambiguous for this dataset: " RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize: - key samples_paths has length 6 - key ids has length 7 - key verification_ids has length 6 To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length. ``` This behavior was introduced when implementing multiprocessing by PR: - #5107 ### Steps to reproduce the bug ```python ds = load_dataset("ami", "microphone-single", split="train", revision="2d7620bb7c3f1aab9f329615c3bdb598069d907a") ``` ### Expected behavior No error raised. ### Environment info Since datasets 2.7.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5415/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5415/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5414	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5414/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5414/comments	https://api.github.com/repos/huggingface/datasets/issues/5414/events	https://github.com/huggingface/datasets/issues/5414	1,525,733,818	I_kwDODunzps5a8Nm6	5,414	Sharding error with Multilingual LibriSpeech	{ "login": "Nithin-Holla", "id": 19574344, "node_id": "MDQ6VXNlcjE5NTc0MzQ0", "avatar_url": "https://avatars.githubusercontent.com/u/19574344?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Nithin-Holla", "html_url": "https://github.com/Nithin-Holla", "followers_url": "https://api.github.com/users/Nithin-Holla/followers", "following_url": "https://api.github.com/users/Nithin-Holla/following{/other_user}", "gists_url": "https://api.github.com/users/Nithin-Holla/gists{/gist_id}", "starred_url": "https://api.github.com/users/Nithin-Holla/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Nithin-Holla/subscriptions", "organizations_url": "https://api.github.com/users/Nithin-Holla/orgs", "repos_url": "https://api.github.com/users/Nithin-Holla/repos", "events_url": "https://api.github.com/users/Nithin-Holla/events{/privacy}", "received_events_url": "https://api.github.com/users/Nithin-Holla/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	null	[ "Thanks for reporting, @Nithin-Holla.\r\n\r\nThis is a known issue for multiple datasets and we are investigating it:\r\n- See e.g.: https://huggingface.co/datasets/ami/discussions/3", "Main issue:\r\n- #5415", "@albertvillanova Thanks! As a workaround for now, can I use the dataset in streaming mode?", "Yes, @Nithin-Holla, in the meantime you can use this dataset in streaming mode." ]	2023-01-09T14:45:31	2023-01-18T14:09:04	2023-01-18T14:09:04	NONE	null	null	null	### Describe the bug Loading the German Multilingual LibriSpeech dataset results in a RuntimeError regarding sharding with the following stacktrace: ``` Downloading and preparing dataset multilingual_librispeech/german to /home/nithin/datadrive/cache/huggingface/datasets/facebook___multilingual_librispeech/german/2.1.0/1904af50f57a5c370c9364cc337699cfe496d4e9edcae6648a96be23086362d0... Downloading data files: 100% 3/3 [00:00<00:00, 107.23it/s] Downloading data files: 100% 1/1 [00:00<00:00, 35.08it/s] Downloading data files: 100% 6/6 [00:00<00:00, 303.36it/s] Downloading data files: 100% 3/3 [00:00<00:00, 130.37it/s] Downloading data files: 100% 1049/1049 [00:00<00:00, 4491.40it/s] Downloading data files: 100% 37/37 [00:00<00:00, 1096.78it/s] Downloading data files: 100% 40/40 [00:00<00:00, 1003.93it/s] Extracting data files: 100% 3/3 [00:11<00:00, 2.62s/it] Generating train split: 469942/0 [34:13<00:00, 273.21 examples/s] Output exceeds the size limit. Open the full output data in a text editor --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-14-74fa6d092bdc> in <module> ----> 1 mls = load_dataset(MLS_DATASET, 2 LANGUAGE, 3 cache_dir="~/datadrive/cache/huggingface/datasets", 4 ignore_verifications=True) /anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, config_kwargs) 1755 1756 # Download and prepare data -> 1757 builder_instance.download_and_prepare( 1758 download_config=download_config, 1759 download_mode=download_mode, /anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) 858 if num_proc is not None: 859 prepare_split_kwargs["num_proc"] = num_proc --> 860 self._download_and_prepare( 861 dl_manager=dl_manager, 862 verify_infos=verify_infos, /anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, prepare_splits_kwargs) 1609 1610 def _download_and_prepare(self, dl_manager, verify_infos, prepare_splits_kwargs): ... RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize: - key audio_archives has length 1049 - key local_extracted_archive has length 1049 - key limited_ids_paths has length 1 To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length. ``` ### Steps to reproduce the bug Here is the code to reproduce it: ```python from datasets import load_dataset MLS_DATASET = "facebook/multilingual_librispeech" LANGUAGE = "german" mls = load_dataset(MLS_DATASET, LANGUAGE, cache_dir="~/datadrive/cache/huggingface/datasets", ignore_verifications=True) ``` ### Expected behavior The expected behaviour is that the dataset is successfully loaded. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.4.0-1094-azure-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyArrow version: 10.0.1 - Pandas version: 1.2.4	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5414/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5414/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5413	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5413/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5413/comments	https://api.github.com/repos/huggingface/datasets/issues/5413/events	https://github.com/huggingface/datasets/issues/5413	1,524,591,837	I_kwDODunzps5a32zd	5,413	concatenate_datasets fails when two dataset with shards > 1 and unequal shard numbers	{ "login": "ZeguanXiao", "id": 38279341, "node_id": "MDQ6VXNlcjM4Mjc5MzQx", "avatar_url": "https://avatars.githubusercontent.com/u/38279341?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ZeguanXiao", "html_url": "https://github.com/ZeguanXiao", "followers_url": "https://api.github.com/users/ZeguanXiao/followers", "following_url": "https://api.github.com/users/ZeguanXiao/following{/other_user}", "gists_url": "https://api.github.com/users/ZeguanXiao/gists{/gist_id}", "starred_url": "https://api.github.com/users/ZeguanXiao/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ZeguanXiao/subscriptions", "organizations_url": "https://api.github.com/users/ZeguanXiao/orgs", "repos_url": "https://api.github.com/users/ZeguanXiao/repos", "events_url": "https://api.github.com/users/ZeguanXiao/events{/privacy}", "received_events_url": "https://api.github.com/users/ZeguanXiao/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false } ]	null	[ "Hi ! Thanks for reporting :)\r\n\r\nI managed to reproduce the hub using\r\n```python\r\n\r\nfrom datasets import concatenate_datasets, Dataset, load_from_disk\r\n\r\nDataset.from_dict({\"a\": range(9)}).save_to_disk(\"tmp/ds1\")\r\nds1 = load_from_disk(\"tmp/ds1\")\r\nds1 = concatenate_datasets([ds1, ds1])\r\n\r\nDataset.from_dict({\"b\": range(6)}).save_to_disk(\"tmp/ds2\")\r\nds2 = load_from_disk(\"tmp/ds2\")\r\nds2 = concatenate_datasets([ds2, ds2, ds2])\r\n\r\nconcatenate_datasets([ds1, ds2], axis=1)\r\n```\r\nand I get\r\n```python\r\nTraceback (most recent call last): \r\n File \"test.py\", line 98, in <module>\r\n dds = concatenate_datasets([ds1, ds2], axis=1)\r\n File \"/Users/.../datasets/combine.py\", line 182, in concatenate_datasets\r\n return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)\r\n File \"/Users/.../datasets/arrow_dataset.py\", line 5499, in _concatenate_map_style_datasets\r\n table = concat_tables([dset._data for dset in dsets], axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1778, in concat_tables\r\n return ConcatenationTable.from_tables(tables, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1483, in from_tables\r\n blocks = _extend_blocks(blocks, table_blocks, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1477, in _extend_blocks\r\n result[i].extend(row_blocks)\r\nIndexError: list index out of range\r\n```\r\n\r\nIt appears to happen when the two datasets have a number of shards that is not the same" ]	2023-01-08T17:01:52	2023-01-26T09:27:21	2023-01-26T09:27:21	NONE	null	null	null	### Describe the bug When using `concatenate_datasets([dataset1, dataset2], axis = 1)` to concatenate two datasets with shards > 1, it fails: ``` File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/combine.py", line 182, in concatenate_datasets return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 5499, in _concatenate_map_style_datasets table = concat_tables([dset._data for dset in dsets], axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1778, in concat_tables return ConcatenationTable.from_tables(tables, axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1483, in from_tables blocks = _extend_blocks(blocks, table_blocks, axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1477, in _extend_blocks result[i].extend(row_blocks) IndexError: list index out of range ``` ### Steps to reproduce the bug dataset = concatenate_datasets([dataset1, dataset2], axis = 1) ### Expected behavior The datasets are correctly concatenated. ### Environment info datasets==2.8.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5413/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5413/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5412	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5412/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5412/comments	https://api.github.com/repos/huggingface/datasets/issues/5412/events	https://github.com/huggingface/datasets/issues/5412	1,524,250,269	I_kwDODunzps5a2jad	5,412	load_dataset() cannot find dataset_info.json with multiple training runs in parallel	{ "login": "mtoles", "id": 7139344, "node_id": "MDQ6VXNlcjcxMzkzNDQ=", "avatar_url": "https://avatars.githubusercontent.com/u/7139344?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mtoles", "html_url": "https://github.com/mtoles", "followers_url": "https://api.github.com/users/mtoles/followers", "following_url": "https://api.github.com/users/mtoles/following{/other_user}", "gists_url": "https://api.github.com/users/mtoles/gists{/gist_id}", "starred_url": "https://api.github.com/users/mtoles/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mtoles/subscriptions", "organizations_url": "https://api.github.com/users/mtoles/orgs", "repos_url": "https://api.github.com/users/mtoles/repos", "events_url": "https://api.github.com/users/mtoles/events{/privacy}", "received_events_url": "https://api.github.com/users/mtoles/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi ! It fails because the dataset is already being prepared by your first run. I'd encourage you to prepare your dataset before using it for multiple trainings.\r\n\r\nYou can also specify another cache directory by passing `cache_dir=` to `load_dataset()`.", "Thank you! What do you mean by prepare it beforehand? I am unclear how to conduct dataset preparation outside of using the `load_dataset` function.", "You can have a separate script that does load_dataset + map + save_to_disk to save your prepared dataset somewhere. Then in your training script you can reload the dataset with load_from_disk", "Thank you! I believe I was running additional map steps after loading, resulting in the cache conflict. " ]	2023-01-08T00:44:32	2023-01-19T20:28:43	2023-01-19T20:28:43	NONE	null	null	null	### Describe the bug I have a custom local dataset in JSON form. I am trying to do multiple training runs in parallel. The first training run runs with no issue. However, when I start another run on another GPU, the following code throws this error. If there is a workaround to ignore the cache I think that would solve my problem too. I am using datasets version 2.8.0. ### Steps to reproduce the bug 1. Start training run of GPU 0 loading dataset from ``` load_dataset( "json", data_files=tr_dataset_path, split=f"train", download_mode="force_redownload", ) ``` 2. While GPU 0 is training, start an identical run on GPU 1. GPU 1 will produce the following error: ``` Traceback (most recent call last): File "/local-scratch1/data/mt/code/qq/train.py", line 198, in <module> main() File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1130, in __call__ return self.main(args, kwargs) File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1055, in main rv = self.invoke(ctx) File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1404, in invoke return ctx.invoke(self.callback, ctx.params) File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 760, in invoke return __callback(args, kwargs) File "/local-scratch1/data/mt/code/qq/train.py", line 113, in main load_dataset( File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/load.py", line 1734, in load_dataset builder_instance = load_dataset_builder( File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/load.py", line 1518, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/builder.py", line 366, in __init__ self.info = DatasetInfo.from_directory(self._cache_dir) File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/info.py", line 313, in from_directory with fs.open(path_join(dataset_info_dir, config.DATASET_INFO_FILENAME), "r", encoding="utf-8") as f: File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/spec.py", line 1094, in open self.open( File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/spec.py", line 1106, in open f = self._open( File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 175, in _open return LocalFileOpener(path, mode, fs=self, kwargs) File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 273, in __init__ self._open() File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 278, in _open self.f = open(self.path, mode=self.mode) FileNotFoundError: [Errno 2] No such file or directory: '/home/username/.cache/huggingface/datasets/json/default-43d06a4aedb25e6d/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/dataset_info.json' ``` ### Expected behavior Expected behavior: 2nd GPU training run should run the same as 1st GPU training run. ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.8.0 - Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.10 - Python version: 3.8.15 - PyArrow version: 9.0.0 - Pandas version: 1.5.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5412/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5412/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5411	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5411/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5411/comments	https://api.github.com/repos/huggingface/datasets/issues/5411/events	https://github.com/huggingface/datasets/pull/5411	1,523,297,786	PR_kwDODunzps5G23-T	5,411	Update docs of S3 filesystem with async aiobotocore	{ "login": "maheshpec", "id": 5677912, "node_id": "MDQ6VXNlcjU2Nzc5MTI=", "avatar_url": "https://avatars.githubusercontent.com/u/5677912?v=4", "gravatar_id": "", "url": "https://api.github.com/users/maheshpec", "html_url": "https://github.com/maheshpec", "followers_url": "https://api.github.com/users/maheshpec/followers", "following_url": "https://api.github.com/users/maheshpec/following{/other_user}", "gists_url": "https://api.github.com/users/maheshpec/gists{/gist_id}", "starred_url": "https://api.github.com/users/maheshpec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/maheshpec/subscriptions", "organizations_url": "https://api.github.com/users/maheshpec/orgs", "repos_url": "https://api.github.com/users/maheshpec/repos", "events_url": "https://api.github.com/users/maheshpec/events{/privacy}", "received_events_url": "https://api.github.com/users/maheshpec/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008587 / 0.011353 (-0.002766) \| 0.004613 / 0.011008 (-0.006395) \| 0.100446 / 0.038508 (0.061938) \| 0.029606 / 0.023109 (0.006497) \| 0.302102 / 0.275898 (0.026204) \| 0.357364 / 0.323480 (0.033884) \| 0.007031 / 0.007986 (-0.000954) \| 0.003593 / 0.004328 (-0.000735) \| 0.078110 / 0.004250 (0.073860) \| 0.035495 / 0.037052 (-0.001557) \| 0.312522 / 0.258489 (0.054033) \| 0.349336 / 0.293841 (0.055495) \| 0.033719 / 0.128546 (-0.094827) \| 0.011449 / 0.075646 (-0.064197) \| 0.321760 / 0.419271 (-0.097512) \| 0.043697 / 0.043533 (0.000165) \| 0.304476 / 0.255139 (0.049337) \| 0.333126 / 0.283200 (0.049926) \| 0.092756 / 0.141683 (-0.048927) \| 1.506734 / 1.452155 (0.054579) \| 1.547381 / 1.492716 (0.054664) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.178177 / 0.018006 (0.160171) \| 0.427814 / 0.000490 (0.427324) \| 0.002505 / 0.000200 (0.002305) \| 0.000074 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023039 / 0.037411 (-0.014372) \| 0.097113 / 0.014526 (0.082587) \| 0.105014 / 0.176557 (-0.071543) \| 0.141185 / 0.737135 (-0.595950) \| 0.108843 / 0.296338 (-0.187495) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424148 / 0.215209 (0.208939) \| 4.247599 / 2.077655 (2.169944) \| 2.130720 / 1.504120 (0.626600) \| 1.916349 / 1.541195 (0.375154) \| 1.831515 / 1.468490 (0.363025) \| 0.688301 / 4.584777 (-3.896476) \| 3.381749 / 3.745712 (-0.363963) \| 2.900045 / 5.269862 (-2.369817) \| 1.576248 / 4.565676 (-2.989428) \| 0.082354 / 0.424275 (-0.341921) \| 0.012200 / 0.007607 (0.004593) \| 0.525753 / 0.226044 (0.299709) \| 5.277672 / 2.268929 (3.008743) \| 2.603870 / 55.444624 (-52.840754) \| 2.296203 / 6.876477 (-4.580273) \| 2.308014 / 2.142072 (0.165942) \| 0.809056 / 4.805227 (-3.996171) \| 0.148122 / 6.500664 (-6.352542) \| 0.066097 / 0.075469 (-0.009372) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.214059 / 1.841788 (-0.627728) \| 13.671332 / 8.074308 (5.597024) \| 13.694554 / 10.191392 (3.503162) \| 0.151454 / 0.680424 (-0.528970) \| 0.028514 / 0.534201 (-0.505687) \| 0.391480 / 0.579283 (-0.187804) \| 0.404499 / 0.434364 (-0.029865) \| 0.458111 / 0.540337 (-0.082226) \| 0.539454 / 1.386936 (-0.847482) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006795 / 0.011353 (-0.004558) \| 0.004463 / 0.011008 (-0.006545) \| 0.099542 / 0.038508 (0.061034) \| 0.027588 / 0.023109 (0.004479) \| 0.423023 / 0.275898 (0.147125) \| 0.458459 / 0.323480 (0.134979) \| 0.004981 / 0.007986 (-0.003005) \| 0.003321 / 0.004328 (-0.001008) \| 0.075727 / 0.004250 (0.071477) \| 0.040541 / 0.037052 (0.003489) \| 0.423724 / 0.258489 (0.165235) \| 0.468334 / 0.293841 (0.174493) \| 0.031732 / 0.128546 (-0.096814) \| 0.011478 / 0.075646 (-0.064168) \| 0.319807 / 0.419271 (-0.099465) \| 0.041215 / 0.043533 (-0.002318) \| 0.423060 / 0.255139 (0.167921) \| 0.446157 / 0.283200 (0.162957) \| 0.088884 / 0.141683 (-0.052799) \| 1.553404 / 1.452155 (0.101250) \| 1.607797 / 1.492716 (0.115080) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208314 / 0.018006 (0.190307) \| 0.411627 / 0.000490 (0.411137) \| 0.002416 / 0.000200 (0.002216) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024641 / 0.037411 (-0.012770) \| 0.101047 / 0.014526 (0.086521) \| 0.108410 / 0.176557 (-0.068147) \| 0.142860 / 0.737135 (-0.594276) \| 0.112486 / 0.296338 (-0.183852) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.485520 / 0.215209 (0.270311) \| 4.864009 / 2.077655 (2.786355) \| 2.541865 / 1.504120 (1.037745) \| 2.339569 / 1.541195 (0.798374) \| 2.378258 / 1.468490 (0.909768) \| 0.698000 / 4.584777 (-3.886777) \| 3.343137 / 3.745712 (-0.402575) \| 1.842264 / 5.269862 (-3.427597) \| 1.154707 / 4.565676 (-3.410969) \| 0.082826 / 0.424275 (-0.341449) \| 0.012379 / 0.007607 (0.004772) \| 0.583335 / 0.226044 (0.357291) \| 5.885934 / 2.268929 (3.617006) \| 2.997769 / 55.444624 (-52.446856) \| 2.653681 / 6.876477 (-4.222796) \| 2.761656 / 2.142072 (0.619583) \| 0.799883 / 4.805227 (-4.005344) \| 0.151398 / 6.500664 (-6.349266) \| 0.067445 / 0.075469 (-0.008024) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.292009 / 1.841788 (-0.549779) \| 13.976180 / 8.074308 (5.901872) \| 14.219469 / 10.191392 (4.028077) \| 0.127810 / 0.680424 (-0.552614) \| 0.016919 / 0.534201 (-0.517282) \| 0.376401 / 0.579283 (-0.202882) \| 0.388563 / 0.434364 (-0.045801) \| 0.444904 / 0.540337 (-0.095433) \| 0.532290 / 1.386936 (-0.854646) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#41d4378831cac1fe5fc624bf97a97b3cf81e0b8a \"CML watermark\")\n" ]	2023-01-06T23:19:17	2023-01-18T11:18:59	2023-01-18T11:12:04	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5411", "html_url": "https://github.com/huggingface/datasets/pull/5411", "diff_url": "https://github.com/huggingface/datasets/pull/5411.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5411.patch", "merged_at": "2023-01-18T11:12:04" }	[s3fs has migrated to all async calls](https://github.com/fsspec/s3fs/commit/0de2c6fb3d87c08ea694de96dca0d0834034f8bf). Updating documentation to use `AioSession` while using s3fs for download manager as well as working with datasets	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5411/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5411/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5410	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5410/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5410/comments	https://api.github.com/repos/huggingface/datasets/issues/5410/events	https://github.com/huggingface/datasets/pull/5410	1,521,168,032	PR_kwDODunzps5GvnJH	5,410	Map-style Dataset to IterableDataset	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009812 / 0.011353 (-0.001540) \| 0.005290 / 0.011008 (-0.005719) \| 0.099728 / 0.038508 (0.061220) \| 0.036712 / 0.023109 (0.013602) \| 0.305924 / 0.275898 (0.030026) \| 0.349844 / 0.323480 (0.026365) \| 0.008353 / 0.007986 (0.000368) \| 0.004464 / 0.004328 (0.000135) \| 0.075329 / 0.004250 (0.071079) \| 0.046146 / 0.037052 (0.009094) \| 0.304197 / 0.258489 (0.045708) \| 0.354245 / 0.293841 (0.060404) \| 0.039270 / 0.128546 (-0.089276) \| 0.012496 / 0.075646 (-0.063151) \| 0.334390 / 0.419271 (-0.084882) \| 0.049428 / 0.043533 (0.005896) \| 0.297318 / 0.255139 (0.042179) \| 0.315646 / 0.283200 (0.032447) \| 0.106746 / 0.141683 (-0.034937) \| 1.443562 / 1.452155 (-0.008593) \| 1.546022 / 1.492716 (0.053305) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303419 / 0.018006 (0.285413) \| 0.536971 / 0.000490 (0.536481) \| 0.001335 / 0.000200 (0.001135) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030484 / 0.037411 (-0.006927) \| 0.110043 / 0.014526 (0.095518) \| 0.125265 / 0.176557 (-0.051291) \| 0.171410 / 0.737135 (-0.565725) \| 0.128978 / 0.296338 (-0.167361) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398354 / 0.215209 (0.183145) \| 3.984180 / 2.077655 (1.906526) \| 1.781134 / 1.504120 (0.277014) \| 1.589656 / 1.541195 (0.048462) \| 1.704192 / 1.468490 (0.235702) \| 0.682271 / 4.584777 (-3.902506) \| 3.731504 / 3.745712 (-0.014208) \| 2.243520 / 5.269862 (-3.026342) \| 1.511334 / 4.565676 (-3.054343) \| 0.084243 / 0.424275 (-0.340032) \| 0.012261 / 0.007607 (0.004654) \| 0.507499 / 0.226044 (0.281454) \| 5.066037 / 2.268929 (2.797109) \| 2.246107 / 55.444624 (-53.198517) \| 1.921032 / 6.876477 (-4.955444) \| 2.144111 / 2.142072 (0.002039) \| 0.845233 / 4.805227 (-3.959995) \| 0.165392 / 6.500664 (-6.335272) \| 0.064201 / 0.075469 (-0.011268) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.217649 / 1.841788 (-0.624138) \| 15.890487 / 8.074308 (7.816179) \| 14.772039 / 10.191392 (4.580647) \| 0.192901 / 0.680424 (-0.487523) \| 0.029119 / 0.534201 (-0.505082) \| 0.442904 / 0.579283 (-0.136380) \| 0.451035 / 0.434364 (0.016671) \| 0.520788 / 0.540337 (-0.019550) \| 0.623588 / 1.386936 (-0.763348) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007452 / 0.011353 (-0.003901) \| 0.005426 / 0.011008 (-0.005582) \| 0.096488 / 0.038508 (0.057980) \| 0.033575 / 0.023109 (0.010465) \| 0.375688 / 0.275898 (0.099790) \| 0.412393 / 0.323480 (0.088913) \| 0.006050 / 0.007986 (-0.001936) \| 0.004424 / 0.004328 (0.000095) \| 0.073102 / 0.004250 (0.068852) \| 0.052672 / 0.037052 (0.015620) \| 0.379352 / 0.258489 (0.120862) \| 0.436065 / 0.293841 (0.142224) \| 0.036594 / 0.128546 (-0.091952) \| 0.012380 / 0.075646 (-0.063266) \| 0.332899 / 0.419271 (-0.086373) \| 0.048859 / 0.043533 (0.005326) \| 0.373215 / 0.255139 (0.118076) \| 0.386990 / 0.283200 (0.103791) \| 0.105166 / 0.141683 (-0.036517) \| 1.490762 / 1.452155 (0.038607) \| 1.611310 / 1.492716 (0.118593) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.333142 / 0.018006 (0.315136) \| 0.537137 / 0.000490 (0.536647) \| 0.000452 / 0.000200 (0.000252) \| 0.000063 / 0.000054 (0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030368 / 0.037411 (-0.007043) \| 0.109608 / 0.014526 (0.095083) \| 0.124220 / 0.176557 (-0.052336) \| 0.162834 / 0.737135 (-0.574301) \| 0.128037 / 0.296338 (-0.168302) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440991 / 0.215209 (0.225782) \| 4.400825 / 2.077655 (2.323170) \| 2.158768 / 1.504120 (0.654648) \| 1.968158 / 1.541195 (0.426963) \| 2.085115 / 1.468490 (0.616625) \| 0.710757 / 4.584777 (-3.874020) \| 3.835441 / 3.745712 (0.089729) \| 2.204118 / 5.269862 (-3.065744) \| 1.378909 / 4.565676 (-3.186767) \| 0.089149 / 0.424275 (-0.335126) \| 0.013066 / 0.007607 (0.005459) \| 0.539165 / 0.226044 (0.313121) \| 5.414176 / 2.268929 (3.145248) \| 2.677020 / 55.444624 (-52.767604) \| 2.328334 / 6.876477 (-4.548143) \| 2.518933 / 2.142072 (0.376860) \| 0.840902 / 4.805227 (-3.964325) \| 0.170365 / 6.500664 (-6.330299) \| 0.063909 / 0.075469 (-0.011561) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237205 / 1.841788 (-0.604583) \| 15.678776 / 8.074308 (7.604468) \| 14.118576 / 10.191392 (3.927184) \| 0.167236 / 0.680424 (-0.513188) \| 0.018177 / 0.534201 (-0.516024) \| 0.426680 / 0.579283 (-0.152603) \| 0.425126 / 0.434364 (-0.009238) \| 0.501755 / 0.540337 (-0.038582) \| 0.592754 / 1.386936 (-0.794182) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008708 / 0.011353 (-0.002645) \| 0.004462 / 0.011008 (-0.006546) \| 0.100159 / 0.038508 (0.061651) \| 0.029543 / 0.023109 (0.006434) \| 0.304056 / 0.275898 (0.028158) \| 0.367098 / 0.323480 (0.043618) \| 0.007049 / 0.007986 (-0.000937) \| 0.003294 / 0.004328 (-0.001034) \| 0.076954 / 0.004250 (0.072703) \| 0.036850 / 0.037052 (-0.000202) \| 0.307556 / 0.258489 (0.049067) \| 0.348327 / 0.293841 (0.054486) \| 0.033520 / 0.128546 (-0.095026) \| 0.011312 / 0.075646 (-0.064334) \| 0.317588 / 0.419271 (-0.101684) \| 0.040196 / 0.043533 (-0.003337) \| 0.298330 / 0.255139 (0.043191) \| 0.333821 / 0.283200 (0.050622) \| 0.086584 / 0.141683 (-0.055099) \| 1.480205 / 1.452155 (0.028050) \| 1.520975 / 1.492716 (0.028259) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.186641 / 0.018006 (0.168635) \| 0.414420 / 0.000490 (0.413930) \| 0.003021 / 0.000200 (0.002821) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022953 / 0.037411 (-0.014458) \| 0.097338 / 0.014526 (0.082812) \| 0.104985 / 0.176557 (-0.071572) \| 0.139208 / 0.737135 (-0.597927) \| 0.108031 / 0.296338 (-0.188307) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417969 / 0.215209 (0.202759) \| 4.173189 / 2.077655 (2.095534) \| 1.862813 / 1.504120 (0.358693) \| 1.653226 / 1.541195 (0.112031) \| 1.725917 / 1.468490 (0.257426) \| 0.701038 / 4.584777 (-3.883739) \| 3.350500 / 3.745712 (-0.395213) \| 1.913156 / 5.269862 (-3.356705) \| 1.267597 / 4.565676 (-3.298079) \| 0.082197 / 0.424275 (-0.342078) \| 0.012499 / 0.007607 (0.004892) \| 0.520173 / 0.226044 (0.294128) \| 5.219981 / 2.268929 (2.951053) \| 2.306029 / 55.444624 (-53.138595) \| 1.948169 / 6.876477 (-4.928307) \| 2.013160 / 2.142072 (-0.128912) \| 0.813325 / 4.805227 (-3.991902) \| 0.149729 / 6.500664 (-6.350935) \| 0.065492 / 0.075469 (-0.009977) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.194163 / 1.841788 (-0.647625) \| 13.739562 / 8.074308 (5.665254) \| 13.881988 / 10.191392 (3.690596) \| 0.138180 / 0.680424 (-0.542244) \| 0.029031 / 0.534201 (-0.505170) \| 0.387858 / 0.579283 (-0.191425) \| 0.395171 / 0.434364 (-0.039193) \| 0.446349 / 0.540337 (-0.093988) \| 0.527073 / 1.386936 (-0.859863) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006504 / 0.011353 (-0.004849) \| 0.004564 / 0.011008 (-0.006444) \| 0.099108 / 0.038508 (0.060599) \| 0.027420 / 0.023109 (0.004311) \| 0.340712 / 0.275898 (0.064814) \| 0.391613 / 0.323480 (0.068133) \| 0.004977 / 0.007986 (-0.003009) \| 0.003375 / 0.004328 (-0.000953) \| 0.076403 / 0.004250 (0.072152) \| 0.036650 / 0.037052 (-0.000402) \| 0.341948 / 0.258489 (0.083459) \| 0.392065 / 0.293841 (0.098224) \| 0.031802 / 0.128546 (-0.096745) \| 0.011659 / 0.075646 (-0.063987) \| 0.320099 / 0.419271 (-0.099173) \| 0.041615 / 0.043533 (-0.001918) \| 0.342125 / 0.255139 (0.086986) \| 0.372833 / 0.283200 (0.089633) \| 0.089032 / 0.141683 (-0.052650) \| 1.486691 / 1.452155 (0.034536) \| 1.567326 / 1.492716 (0.074610) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193123 / 0.018006 (0.175117) \| 0.404062 / 0.000490 (0.403573) \| 0.003460 / 0.000200 (0.003260) \| 0.000079 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024565 / 0.037411 (-0.012846) \| 0.098958 / 0.014526 (0.084432) \| 0.108701 / 0.176557 (-0.067855) \| 0.142567 / 0.737135 (-0.594569) \| 0.111048 / 0.296338 (-0.185290) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474549 / 0.215209 (0.259340) \| 4.753776 / 2.077655 (2.676121) \| 2.435528 / 1.504120 (0.931409) \| 2.234491 / 1.541195 (0.693297) \| 2.269474 / 1.468490 (0.800984) \| 0.695636 / 4.584777 (-3.889141) \| 3.367816 / 3.745712 (-0.377896) \| 1.854828 / 5.269862 (-3.415034) \| 1.159729 / 4.565676 (-3.405948) \| 0.082267 / 0.424275 (-0.342008) \| 0.012483 / 0.007607 (0.004876) \| 0.578490 / 0.226044 (0.352446) \| 5.814490 / 2.268929 (3.545561) \| 2.893310 / 55.444624 (-52.551314) \| 2.540555 / 6.876477 (-4.335922) \| 2.573705 / 2.142072 (0.431633) \| 0.800545 / 4.805227 (-4.004682) \| 0.151306 / 6.500664 (-6.349358) \| 0.067925 / 0.075469 (-0.007544) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.294645 / 1.841788 (-0.547142) \| 13.641842 / 8.074308 (5.567534) \| 14.015200 / 10.191392 (3.823808) \| 0.128829 / 0.680424 (-0.551595) \| 0.016870 / 0.534201 (-0.517331) \| 0.389137 / 0.579283 (-0.190146) \| 0.388384 / 0.434364 (-0.045980) \| 0.447711 / 0.540337 (-0.092627) \| 0.540637 / 1.386936 (-0.846299) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45ad185b9040a68285080b6099ed3af58442ccb2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012282 / 0.011353 (0.000929) \| 0.006328 / 0.011008 (-0.004680) \| 0.129666 / 0.038508 (0.091158) \| 0.039403 / 0.023109 (0.016294) \| 0.375464 / 0.275898 (0.099566) \| 0.463167 / 0.323480 (0.139687) \| 0.010329 / 0.007986 (0.002344) \| 0.005111 / 0.004328 (0.000782) \| 0.108727 / 0.004250 (0.104476) \| 0.047156 / 0.037052 (0.010103) \| 0.381869 / 0.258489 (0.123380) \| 0.441936 / 0.293841 (0.148095) \| 0.054750 / 0.128546 (-0.073796) \| 0.019809 / 0.075646 (-0.055837) \| 0.436389 / 0.419271 (0.017118) \| 0.066585 / 0.043533 (0.023052) \| 0.402108 / 0.255139 (0.146969) \| 0.424571 / 0.283200 (0.141371) \| 0.118326 / 0.141683 (-0.023357) \| 1.870175 / 1.452155 (0.418020) \| 1.878720 / 1.492716 (0.386004) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012863 / 0.018006 (-0.005144) \| 0.528670 / 0.000490 (0.528181) \| 0.006057 / 0.000200 (0.005857) \| 0.000124 / 0.000054 (0.000069) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030091 / 0.037411 (-0.007320) \| 0.136143 / 0.014526 (0.121618) \| 0.148931 / 0.176557 (-0.027626) \| 0.179578 / 0.737135 (-0.557558) \| 0.144528 / 0.296338 (-0.151810) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.594080 / 0.215209 (0.378871) \| 6.029101 / 2.077655 (3.951446) \| 2.443084 / 1.504120 (0.938964) \| 2.123949 / 1.541195 (0.582754) \| 2.183021 / 1.468490 (0.714531) \| 1.235453 / 4.584777 (-3.349324) \| 5.585121 / 3.745712 (1.839408) \| 3.208510 / 5.269862 (-2.061351) \| 2.090334 / 4.565676 (-2.475342) \| 0.150353 / 0.424275 (-0.273922) \| 0.016787 / 0.007607 (0.009180) \| 0.797561 / 0.226044 (0.571516) \| 7.756291 / 2.268929 (5.487363) \| 3.283638 / 55.444624 (-52.160986) \| 2.527441 / 6.876477 (-4.349036) \| 2.590765 / 2.142072 (0.448692) \| 1.446818 / 4.805227 (-3.358409) \| 0.250563 / 6.500664 (-6.250101) \| 0.077919 / 0.075469 (0.002450) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.612022 / 1.841788 (-0.229765) \| 18.363316 / 8.074308 (10.289008) \| 22.578570 / 10.191392 (12.387178) \| 0.232801 / 0.680424 (-0.447623) \| 0.048232 / 0.534201 (-0.485969) \| 0.549518 / 0.579283 (-0.029766) \| 0.624663 / 0.434364 (0.190299) \| 0.674745 / 0.540337 (0.134408) \| 0.803489 / 1.386936 (-0.583447) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009872 / 0.011353 (-0.001481) \| 0.006593 / 0.011008 (-0.004415) \| 0.139248 / 0.038508 (0.100740) \| 0.035708 / 0.023109 (0.012598) \| 0.551335 / 0.275898 (0.275437) \| 0.544995 / 0.323480 (0.221515) \| 0.007085 / 0.007986 (-0.000900) \| 0.004742 / 0.004328 (0.000413) \| 0.095823 / 0.004250 (0.091572) \| 0.051674 / 0.037052 (0.014621) \| 0.463405 / 0.258489 (0.204916) \| 0.640392 / 0.293841 (0.346551) \| 0.055242 / 0.128546 (-0.073304) \| 0.022602 / 0.075646 (-0.053044) \| 0.419171 / 0.419271 (-0.000100) \| 0.062986 / 0.043533 (0.019453) \| 0.503683 / 0.255139 (0.248544) \| 0.568719 / 0.283200 (0.285519) \| 0.113906 / 0.141683 (-0.027777) \| 1.825248 / 1.452155 (0.373094) \| 1.985667 / 1.492716 (0.492951) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237478 / 0.018006 (0.219472) \| 0.528861 / 0.000490 (0.528371) \| 0.008507 / 0.000200 (0.008307) \| 0.000158 / 0.000054 (0.000103) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033536 / 0.037411 (-0.003875) \| 0.144202 / 0.014526 (0.129677) \| 0.139472 / 0.176557 (-0.037084) \| 0.184540 / 0.737135 (-0.552596) \| 0.147818 / 0.296338 (-0.148520) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.671654 / 0.215209 (0.456445) \| 6.616368 / 2.077655 (4.538713) \| 2.805634 / 1.504120 (1.301514) \| 2.482890 / 1.541195 (0.941695) \| 2.547686 / 1.468490 (1.079195) \| 1.289169 / 4.584777 (-3.295608) \| 5.551436 / 3.745712 (1.805724) \| 5.228500 / 5.269862 (-0.041362) \| 2.456706 / 4.565676 (-2.108970) \| 0.148556 / 0.424275 (-0.275720) \| 0.015290 / 0.007607 (0.007683) \| 0.837090 / 0.226044 (0.611045) \| 8.373561 / 2.268929 (6.104632) \| 3.663910 / 55.444624 (-51.780714) \| 2.927117 / 6.876477 (-3.949360) \| 2.976785 / 2.142072 (0.834712) \| 1.501618 / 4.805227 (-3.303609) \| 0.263321 / 6.500664 (-6.237343) \| 0.082644 / 0.075469 (0.007175) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.707419 / 1.841788 (-0.134368) \| 18.371117 / 8.074308 (10.296809) \| 22.015154 / 10.191392 (11.823762) \| 0.232066 / 0.680424 (-0.448357) \| 0.027149 / 0.534201 (-0.507052) \| 0.544450 / 0.579283 (-0.034833) \| 0.605134 / 0.434364 (0.170770) \| 0.656063 / 0.540337 (0.115725) \| 0.788121 / 1.386936 (-0.598815) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1e0ec31e07e4bc0469f4acfed601d9c71e9a459 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008952 / 0.011353 (-0.002401) \| 0.005592 / 0.011008 (-0.005416) \| 0.101138 / 0.038508 (0.062630) \| 0.035573 / 0.023109 (0.012464) \| 0.295959 / 0.275898 (0.020060) \| 0.365347 / 0.323480 (0.041867) \| 0.008136 / 0.007986 (0.000150) \| 0.004479 / 0.004328 (0.000150) \| 0.078806 / 0.004250 (0.074556) \| 0.045180 / 0.037052 (0.008127) \| 0.321687 / 0.258489 (0.063198) \| 0.345874 / 0.293841 (0.052033) \| 0.038720 / 0.128546 (-0.089826) \| 0.012534 / 0.075646 (-0.063112) \| 0.335571 / 0.419271 (-0.083700) \| 0.049048 / 0.043533 (0.005515) \| 0.294756 / 0.255139 (0.039617) \| 0.327496 / 0.283200 (0.044296) \| 0.109181 / 0.141683 (-0.032502) \| 1.417068 / 1.452155 (-0.035087) \| 1.455473 / 1.492716 (-0.037244) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267774 / 0.018006 (0.249768) \| 0.538546 / 0.000490 (0.538056) \| 0.001755 / 0.000200 (0.001555) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026839 / 0.037411 (-0.010572) \| 0.105862 / 0.014526 (0.091336) \| 0.118278 / 0.176557 (-0.058279) \| 0.157926 / 0.737135 (-0.579209) \| 0.124700 / 0.296338 (-0.171638) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399060 / 0.215209 (0.183851) \| 3.991409 / 2.077655 (1.913754) \| 1.763569 / 1.504120 (0.259449) \| 1.579602 / 1.541195 (0.038407) \| 1.652928 / 1.468490 (0.184438) \| 0.692962 / 4.584777 (-3.891815) \| 3.784635 / 3.745712 (0.038922) \| 3.249341 / 5.269862 (-2.020521) \| 1.815711 / 4.565676 (-2.749966) \| 0.084384 / 0.424275 (-0.339891) \| 0.012546 / 0.007607 (0.004939) \| 0.521397 / 0.226044 (0.295352) \| 5.075824 / 2.268929 (2.806895) \| 2.258353 / 55.444624 (-53.186272) \| 1.925220 / 6.876477 (-4.951256) \| 2.002821 / 2.142072 (-0.139252) \| 0.830507 / 4.805227 (-3.974720) \| 0.165845 / 6.500664 (-6.334819) \| 0.063905 / 0.075469 (-0.011565) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.198726 / 1.841788 (-0.643061) \| 14.804448 / 8.074308 (6.730139) \| 12.855167 / 10.191392 (2.663775) \| 0.167932 / 0.680424 (-0.512492) \| 0.028643 / 0.534201 (-0.505558) \| 0.441224 / 0.579283 (-0.138059) \| 0.434924 / 0.434364 (0.000560) \| 0.516188 / 0.540337 (-0.024150) \| 0.605017 / 1.386936 (-0.781919) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007031 / 0.011353 (-0.004322) \| 0.005157 / 0.011008 (-0.005851) \| 0.086943 / 0.038508 (0.048434) \| 0.031377 / 0.023109 (0.008268) \| 0.334810 / 0.275898 (0.058912) \| 0.368590 / 0.323480 (0.045110) \| 0.005973 / 0.007986 (-0.002013) \| 0.004173 / 0.004328 (-0.000155) \| 0.067033 / 0.004250 (0.062783) \| 0.054070 / 0.037052 (0.017018) \| 0.332232 / 0.258489 (0.073743) \| 0.384982 / 0.293841 (0.091141) \| 0.034023 / 0.128546 (-0.094524) \| 0.011301 / 0.075646 (-0.064345) \| 0.295644 / 0.419271 (-0.123628) \| 0.045589 / 0.043533 (0.002056) \| 0.330739 / 0.255139 (0.075600) \| 0.352841 / 0.283200 (0.069642) \| 0.104829 / 0.141683 (-0.036854) \| 1.329360 / 1.452155 (-0.122794) \| 1.437956 / 1.492716 (-0.054760) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299187 / 0.018006 (0.281181) \| 0.563407 / 0.000490 (0.562917) \| 0.004179 / 0.000200 (0.003979) \| 0.000114 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027405 / 0.037411 (-0.010006) \| 0.097498 / 0.014526 (0.082972) \| 0.114265 / 0.176557 (-0.062292) \| 0.146823 / 0.737135 (-0.590313) \| 0.117948 / 0.296338 (-0.178391) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.378756 / 0.215209 (0.163547) \| 3.774804 / 2.077655 (1.697150) \| 1.804149 / 1.504120 (0.300029) \| 1.626312 / 1.541195 (0.085117) \| 1.731111 / 1.468490 (0.262620) \| 0.633493 / 4.584777 (-3.951284) \| 3.488220 / 3.745712 (-0.257492) \| 3.064710 / 5.269862 (-2.205151) \| 1.690647 / 4.565676 (-2.875029) \| 0.076093 / 0.424275 (-0.348182) \| 0.010820 / 0.007607 (0.003213) \| 0.465091 / 0.226044 (0.239046) \| 4.676842 / 2.268929 (2.407913) \| 2.297381 / 55.444624 (-53.147244) \| 1.960355 / 6.876477 (-4.916122) \| 1.983742 / 2.142072 (-0.158330) \| 0.739525 / 4.805227 (-4.065702) \| 0.152663 / 6.500664 (-6.348001) \| 0.057316 / 0.075469 (-0.018153) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.104721 / 1.841788 (-0.737067) \| 14.577171 / 8.074308 (6.502863) \| 13.680402 / 10.191392 (3.489010) \| 0.182234 / 0.680424 (-0.498190) \| 0.018853 / 0.534201 (-0.515348) \| 0.426194 / 0.579283 (-0.153089) \| 0.429202 / 0.434364 (-0.005162) \| 0.543125 / 0.540337 (0.002788) \| 0.645887 / 1.386936 (-0.741049) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f830952573bdc59f8732b8f1a13f70d9187e0a65 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010055 / 0.011353 (-0.001298) \| 0.005576 / 0.011008 (-0.005432) \| 0.100059 / 0.038508 (0.061551) \| 0.038535 / 0.023109 (0.015425) \| 0.297538 / 0.275898 (0.021640) \| 0.368117 / 0.323480 (0.044637) \| 0.008540 / 0.007986 (0.000555) \| 0.004469 / 0.004328 (0.000141) \| 0.075801 / 0.004250 (0.071551) \| 0.046604 / 0.037052 (0.009552) \| 0.307242 / 0.258489 (0.048753) \| 0.343949 / 0.293841 (0.050108) \| 0.039353 / 0.128546 (-0.089194) \| 0.012446 / 0.075646 (-0.063200) \| 0.334628 / 0.419271 (-0.084643) \| 0.051628 / 0.043533 (0.008095) \| 0.298726 / 0.255139 (0.043587) \| 0.316010 / 0.283200 (0.032810) \| 0.120564 / 0.141683 (-0.021119) \| 1.459396 / 1.452155 (0.007241) \| 1.493682 / 1.492716 (0.000965) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011702 / 0.018006 (-0.006304) \| 0.570261 / 0.000490 (0.569771) \| 0.003760 / 0.000200 (0.003560) \| 0.000091 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028806 / 0.037411 (-0.008605) \| 0.112150 / 0.014526 (0.097625) \| 0.123140 / 0.176557 (-0.053417) \| 0.173055 / 0.737135 (-0.564080) \| 0.130060 / 0.296338 (-0.166279) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398216 / 0.215209 (0.183007) \| 3.978677 / 2.077655 (1.901022) \| 1.754229 / 1.504120 (0.250109) \| 1.561892 / 1.541195 (0.020697) \| 1.679138 / 1.468490 (0.210648) \| 0.690254 / 4.584777 (-3.894523) \| 3.817698 / 3.745712 (0.071986) \| 2.177854 / 5.269862 (-3.092008) \| 1.361860 / 4.565676 (-3.203816) \| 0.084108 / 0.424275 (-0.340167) \| 0.012640 / 0.007607 (0.005033) \| 0.504385 / 0.226044 (0.278341) \| 5.034103 / 2.268929 (2.765174) \| 2.254032 / 55.444624 (-53.190593) \| 1.910439 / 6.876477 (-4.966038) \| 2.003515 / 2.142072 (-0.138558) \| 0.839747 / 4.805227 (-3.965480) \| 0.165654 / 6.500664 (-6.335010) \| 0.063483 / 0.075469 (-0.011986) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.187521 / 1.841788 (-0.654267) \| 15.381121 / 8.074308 (7.306812) \| 14.579418 / 10.191392 (4.388026) \| 0.199221 / 0.680424 (-0.481202) \| 0.029335 / 0.534201 (-0.504866) \| 0.443159 / 0.579283 (-0.136124) \| 0.447772 / 0.434364 (0.013408) \| 0.545071 / 0.540337 (0.004733) \| 0.650494 / 1.386936 (-0.736442) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007675 / 0.011353 (-0.003677) \| 0.005364 / 0.011008 (-0.005644) \| 0.097921 / 0.038508 (0.059413) \| 0.033645 / 0.023109 (0.010536) \| 0.404818 / 0.275898 (0.128920) \| 0.429983 / 0.323480 (0.106503) \| 0.006106 / 0.007986 (-0.001879) \| 0.005281 / 0.004328 (0.000953) \| 0.073762 / 0.004250 (0.069512) \| 0.053065 / 0.037052 (0.016012) \| 0.400657 / 0.258489 (0.142168) \| 0.447743 / 0.293841 (0.153902) \| 0.036782 / 0.128546 (-0.091765) \| 0.012593 / 0.075646 (-0.063054) \| 0.332825 / 0.419271 (-0.086446) \| 0.049424 / 0.043533 (0.005891) \| 0.400397 / 0.255139 (0.145258) \| 0.414794 / 0.283200 (0.131594) \| 0.106555 / 0.141683 (-0.035128) \| 1.466917 / 1.452155 (0.014762) \| 1.571351 / 1.492716 (0.078635) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254337 / 0.018006 (0.236331) \| 0.568360 / 0.000490 (0.567870) \| 0.000445 / 0.000200 (0.000245) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031044 / 0.037411 (-0.006367) \| 0.112282 / 0.014526 (0.097756) \| 0.127205 / 0.176557 (-0.049352) \| 0.166551 / 0.737135 (-0.570584) \| 0.130520 / 0.296338 (-0.165818) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442906 / 0.215209 (0.227697) \| 4.430218 / 2.077655 (2.352563) \| 2.287251 / 1.504120 (0.783132) \| 2.112345 / 1.541195 (0.571150) \| 2.240952 / 1.468490 (0.772462) \| 0.713800 / 4.584777 (-3.870977) \| 3.884161 / 3.745712 (0.138449) \| 2.166901 / 5.269862 (-3.102960) \| 1.374490 / 4.565676 (-3.191187) \| 0.087548 / 0.424275 (-0.336727) \| 0.012369 / 0.007607 (0.004761) \| 0.540783 / 0.226044 (0.314739) \| 5.396187 / 2.268929 (3.127258) \| 2.779636 / 55.444624 (-52.664988) \| 2.434220 / 6.876477 (-4.442257) \| 2.508180 / 2.142072 (0.366107) \| 0.852470 / 4.805227 (-3.952757) \| 0.171266 / 6.500664 (-6.329398) \| 0.065463 / 0.075469 (-0.010006) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.241720 / 1.841788 (-0.600067) \| 15.332568 / 8.074308 (7.258260) \| 13.688723 / 10.191392 (3.497331) \| 0.145150 / 0.680424 (-0.535273) \| 0.017694 / 0.534201 (-0.516507) \| 0.426078 / 0.579283 (-0.153205) \| 0.441189 / 0.434364 (0.006825) \| 0.540284 / 0.540337 (-0.000054) \| 0.657548 / 1.386936 (-0.729388) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c47ecf71362f6b6290b6471b30e77184a5e1df31 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008604 / 0.011353 (-0.002749) \| 0.004566 / 0.011008 (-0.006442) \| 0.099607 / 0.038508 (0.061099) \| 0.029628 / 0.023109 (0.006519) \| 0.300481 / 0.275898 (0.024583) \| 0.342596 / 0.323480 (0.019116) \| 0.007003 / 0.007986 (-0.000982) \| 0.003408 / 0.004328 (-0.000920) \| 0.079076 / 0.004250 (0.074826) \| 0.034104 / 0.037052 (-0.002948) \| 0.303856 / 0.258489 (0.045367) \| 0.348729 / 0.293841 (0.054888) \| 0.033752 / 0.128546 (-0.094794) \| 0.011497 / 0.075646 (-0.064149) \| 0.321568 / 0.419271 (-0.097704) \| 0.041472 / 0.043533 (-0.002061) \| 0.303396 / 0.255139 (0.048257) \| 0.331121 / 0.283200 (0.047921) \| 0.086203 / 0.141683 (-0.055480) \| 1.476995 / 1.452155 (0.024840) \| 1.539428 / 1.492716 (0.046712) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215810 / 0.018006 (0.197803) \| 0.414292 / 0.000490 (0.413802) \| 0.000388 / 0.000200 (0.000188) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023441 / 0.037411 (-0.013970) \| 0.098463 / 0.014526 (0.083938) \| 0.105435 / 0.176557 (-0.071121) \| 0.139736 / 0.737135 (-0.597399) \| 0.109467 / 0.296338 (-0.186872) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418244 / 0.215209 (0.203035) \| 4.160693 / 2.077655 (2.083039) \| 1.878895 / 1.504120 (0.374775) \| 1.679338 / 1.541195 (0.138143) \| 1.730384 / 1.468490 (0.261894) \| 0.688603 / 4.584777 (-3.896174) \| 3.393542 / 3.745712 (-0.352170) \| 1.901337 / 5.269862 (-3.368525) \| 1.447269 / 4.565676 (-3.118408) \| 0.083003 / 0.424275 (-0.341272) \| 0.012574 / 0.007607 (0.004967) \| 0.526363 / 0.226044 (0.300318) \| 5.275159 / 2.268929 (3.006230) \| 2.323642 / 55.444624 (-53.120982) \| 1.982929 / 6.876477 (-4.893548) \| 2.014081 / 2.142072 (-0.127991) \| 0.809466 / 4.805227 (-3.995761) \| 0.149038 / 6.500664 (-6.351626) \| 0.064394 / 0.075469 (-0.011075) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.207439 / 1.841788 (-0.634349) \| 13.691048 / 8.074308 (5.616740) \| 13.880965 / 10.191392 (3.689573) \| 0.148553 / 0.680424 (-0.531871) \| 0.028397 / 0.534201 (-0.505804) \| 0.391818 / 0.579283 (-0.187465) \| 0.407181 / 0.434364 (-0.027183) \| 0.481163 / 0.540337 (-0.059175) \| 0.570689 / 1.386936 (-0.816247) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006361 / 0.011353 (-0.004992) \| 0.004520 / 0.011008 (-0.006488) \| 0.097679 / 0.038508 (0.059171) \| 0.027223 / 0.023109 (0.004113) \| 0.407966 / 0.275898 (0.132068) \| 0.439868 / 0.323480 (0.116388) \| 0.004625 / 0.007986 (-0.003360) \| 0.004039 / 0.004328 (-0.000289) \| 0.074548 / 0.004250 (0.070298) \| 0.034957 / 0.037052 (-0.002095) \| 0.412762 / 0.258489 (0.154273) \| 0.449716 / 0.293841 (0.155875) \| 0.031272 / 0.128546 (-0.097274) \| 0.011598 / 0.075646 (-0.064049) \| 0.320922 / 0.419271 (-0.098349) \| 0.041250 / 0.043533 (-0.002283) \| 0.411439 / 0.255139 (0.156300) \| 0.429722 / 0.283200 (0.146523) \| 0.087161 / 0.141683 (-0.054522) \| 1.512573 / 1.452155 (0.060418) \| 1.569385 / 1.492716 (0.076668) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222612 / 0.018006 (0.204606) \| 0.409086 / 0.000490 (0.408596) \| 0.004246 / 0.000200 (0.004046) \| 0.000083 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024324 / 0.037411 (-0.013087) \| 0.099055 / 0.014526 (0.084530) \| 0.106809 / 0.176557 (-0.069748) \| 0.141275 / 0.737135 (-0.595860) \| 0.109426 / 0.296338 (-0.186913) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.469736 / 0.215209 (0.254527) \| 4.686900 / 2.077655 (2.609246) \| 2.413392 / 1.504120 (0.909272) \| 2.217366 / 1.541195 (0.676171) \| 2.266957 / 1.468490 (0.798467) \| 0.698647 / 4.584777 (-3.886129) \| 3.389317 / 3.745712 (-0.356395) \| 1.862315 / 5.269862 (-3.407546) \| 1.160931 / 4.565676 (-3.404746) \| 0.082829 / 0.424275 (-0.341446) \| 0.012627 / 0.007607 (0.005020) \| 0.568027 / 0.226044 (0.341983) \| 5.683220 / 2.268929 (3.414291) \| 2.865701 / 55.444624 (-52.578924) \| 2.522401 / 6.876477 (-4.354076) \| 2.542395 / 2.142072 (0.400323) \| 0.801224 / 4.805227 (-4.004003) \| 0.149946 / 6.500664 (-6.350718) \| 0.065447 / 0.075469 (-0.010023) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283756 / 1.841788 (-0.558032) \| 13.903662 / 8.074308 (5.829354) \| 13.238389 / 10.191392 (3.046997) \| 0.142304 / 0.680424 (-0.538120) \| 0.016922 / 0.534201 (-0.517279) \| 0.377797 / 0.579283 (-0.201487) \| 0.382460 / 0.434364 (-0.051904) \| 0.464645 / 0.540337 (-0.075692) \| 0.556270 / 1.386936 (-0.830666) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#675cf2910c5e6f083ed6664a7bffba9a58f78309 \"CML watermark\")\n", "> I think this would be more of a Conceptual Guide doc since this is more explanatory and compares the differences between a Dataset and an IterableDataset\r\n\r\nsounds good to me !\r\n\r\n> There are definitely places in the docs where we can add a nice and link to this doc though to build up the user's understanding of this topic. For example, in the Know your dataset [tutorial](https://huggingface.co/docs/datasets/access), we only introduce the regular Dataset object and not the IterableDataset. We can add a section there for IterableDataset and then link to this doc that explains the difference between the two 🙂\r\n\r\ngood idea, thanks :)", "I'll open a PR to add a section on `IterableDataset`'s in the tutorial, and once you're done editing this doc I can give it a final polish! 😄 ", "I moved the doc page to conceptual guides and took your suggestions into account :)\r\n\r\nI think this is ready for final review now", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009890 / 0.011353 (-0.001463) \| 0.005156 / 0.011008 (-0.005852) \| 0.099493 / 0.038508 (0.060984) \| 0.036671 / 0.023109 (0.013562) \| 0.304686 / 0.275898 (0.028788) \| 0.339070 / 0.323480 (0.015590) \| 0.008466 / 0.007986 (0.000481) \| 0.005863 / 0.004328 (0.001534) \| 0.075082 / 0.004250 (0.070832) \| 0.045926 / 0.037052 (0.008874) \| 0.303157 / 0.258489 (0.044668) \| 0.363710 / 0.293841 (0.069870) \| 0.038497 / 0.128546 (-0.090049) \| 0.012063 / 0.075646 (-0.063583) \| 0.334463 / 0.419271 (-0.084808) \| 0.048161 / 0.043533 (0.004628) \| 0.300431 / 0.255139 (0.045292) \| 0.330344 / 0.283200 (0.047145) \| 0.105509 / 0.141683 (-0.036174) \| 1.475242 / 1.452155 (0.023087) \| 1.550624 / 1.492716 (0.057908) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.245749 / 0.018006 (0.227743) \| 0.575091 / 0.000490 (0.574601) \| 0.001556 / 0.000200 (0.001357) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030447 / 0.037411 (-0.006964) \| 0.110982 / 0.014526 (0.096456) \| 0.126760 / 0.176557 (-0.049797) \| 0.173375 / 0.737135 (-0.563760) \| 0.128799 / 0.296338 (-0.167539) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.392861 / 0.215209 (0.177651) \| 3.911231 / 2.077655 (1.833576) \| 1.757413 / 1.504120 (0.253293) \| 1.563287 / 1.541195 (0.022093) \| 1.658678 / 1.468490 (0.190188) \| 0.677244 / 4.584777 (-3.907533) \| 3.754917 / 3.745712 (0.009205) \| 3.779417 / 5.269862 (-1.490444) \| 1.993159 / 4.565676 (-2.572517) \| 0.084425 / 0.424275 (-0.339850) \| 0.012500 / 0.007607 (0.004893) \| 0.501788 / 0.226044 (0.275743) \| 5.003173 / 2.268929 (2.734244) \| 2.273547 / 55.444624 (-53.171077) \| 1.909766 / 6.876477 (-4.966711) \| 1.968287 / 2.142072 (-0.173785) \| 0.834895 / 4.805227 (-3.970332) \| 0.165312 / 6.500664 (-6.335352) \| 0.062202 / 0.075469 (-0.013267) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.203080 / 1.841788 (-0.638708) \| 15.158284 / 8.074308 (7.083976) \| 14.174484 / 10.191392 (3.983092) \| 0.171540 / 0.680424 (-0.508883) \| 0.028604 / 0.534201 (-0.505597) \| 0.438379 / 0.579283 (-0.140904) \| 0.429447 / 0.434364 (-0.004917) \| 0.540979 / 0.540337 (0.000642) \| 0.630322 / 1.386936 (-0.756614) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007600 / 0.011353 (-0.003753) \| 0.005400 / 0.011008 (-0.005608) \| 0.097983 / 0.038508 (0.059475) \| 0.033407 / 0.023109 (0.010297) \| 0.384429 / 0.275898 (0.108531) \| 0.415880 / 0.323480 (0.092400) \| 0.006085 / 0.007986 (-0.001900) \| 0.004330 / 0.004328 (0.000002) \| 0.074654 / 0.004250 (0.070403) \| 0.053076 / 0.037052 (0.016024) \| 0.383958 / 0.258489 (0.125469) \| 0.427289 / 0.293841 (0.133448) \| 0.036710 / 0.128546 (-0.091836) \| 0.012400 / 0.075646 (-0.063246) \| 0.332712 / 0.419271 (-0.086560) \| 0.058390 / 0.043533 (0.014857) \| 0.377747 / 0.255139 (0.122608) \| 0.398997 / 0.283200 (0.115798) \| 0.117370 / 0.141683 (-0.024313) \| 1.464211 / 1.452155 (0.012057) \| 1.596465 / 1.492716 (0.103749) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212989 / 0.018006 (0.194983) \| 0.554968 / 0.000490 (0.554479) \| 0.004305 / 0.000200 (0.004105) \| 0.000116 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029167 / 0.037411 (-0.008244) \| 0.109156 / 0.014526 (0.094631) \| 0.122575 / 0.176557 (-0.053982) \| 0.163058 / 0.737135 (-0.574077) \| 0.127431 / 0.296338 (-0.168908) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445395 / 0.215209 (0.230185) \| 4.447534 / 2.077655 (2.369879) \| 2.259186 / 1.504120 (0.755066) \| 2.082956 / 1.541195 (0.541761) \| 2.259126 / 1.468490 (0.790636) \| 0.692271 / 4.584777 (-3.892506) \| 3.795759 / 3.745712 (0.050047) \| 3.603000 / 5.269862 (-1.666862) \| 1.948556 / 4.565676 (-2.617120) \| 0.084589 / 0.424275 (-0.339687) \| 0.012751 / 0.007607 (0.005144) \| 0.544783 / 0.226044 (0.318738) \| 5.452278 / 2.268929 (3.183349) \| 2.809467 / 55.444624 (-52.635157) \| 2.479297 / 6.876477 (-4.397180) \| 2.587756 / 2.142072 (0.445683) \| 0.832258 / 4.805227 (-3.972970) \| 0.167424 / 6.500664 (-6.333240) \| 0.066064 / 0.075469 (-0.009405) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.262719 / 1.841788 (-0.579069) \| 15.917869 / 8.074308 (7.843561) \| 13.879301 / 10.191392 (3.687909) \| 0.187712 / 0.680424 (-0.492712) \| 0.018175 / 0.534201 (-0.516026) \| 0.425840 / 0.579283 (-0.153443) \| 0.426164 / 0.434364 (-0.008200) \| 0.527465 / 0.540337 (-0.012872) \| 0.629478 / 1.386936 (-0.757458) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f7e178d6373e7d66a60662a22fd60af117f0885 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009064 / 0.011353 (-0.002289) \| 0.004824 / 0.011008 (-0.006184) \| 0.100869 / 0.038508 (0.062361) \| 0.030803 / 0.023109 (0.007694) \| 0.350880 / 0.275898 (0.074982) \| 0.423816 / 0.323480 (0.100336) \| 0.007581 / 0.007986 (-0.000405) \| 0.003642 / 0.004328 (-0.000686) \| 0.077682 / 0.004250 (0.073432) \| 0.039856 / 0.037052 (0.002803) \| 0.366097 / 0.258489 (0.107608) \| 0.409226 / 0.293841 (0.115385) \| 0.033698 / 0.128546 (-0.094848) \| 0.011730 / 0.075646 (-0.063916) \| 0.321683 / 0.419271 (-0.097588) \| 0.041794 / 0.043533 (-0.001739) \| 0.351175 / 0.255139 (0.096036) \| 0.374328 / 0.283200 (0.091128) \| 0.091833 / 0.141683 (-0.049850) \| 1.507082 / 1.452155 (0.054927) \| 1.543289 / 1.492716 (0.050572) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010670 / 0.018006 (-0.007337) \| 0.429674 / 0.000490 (0.429184) \| 0.003246 / 0.000200 (0.003046) \| 0.000081 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025015 / 0.037411 (-0.012397) \| 0.102155 / 0.014526 (0.087629) \| 0.107010 / 0.176557 (-0.069546) \| 0.144265 / 0.737135 (-0.592870) \| 0.110635 / 0.296338 (-0.185703) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414211 / 0.215209 (0.199002) \| 4.125582 / 2.077655 (2.047928) \| 1.997856 / 1.504120 (0.493736) \| 1.847676 / 1.541195 (0.306481) \| 1.994100 / 1.468490 (0.525610) \| 0.694975 / 4.584777 (-3.889802) \| 3.373629 / 3.745712 (-0.372083) \| 2.863255 / 5.269862 (-2.406606) \| 1.565723 / 4.565676 (-2.999953) \| 0.082539 / 0.424275 (-0.341736) \| 0.012650 / 0.007607 (0.005043) \| 0.522989 / 0.226044 (0.296945) \| 5.205720 / 2.268929 (2.936792) \| 2.352292 / 55.444624 (-53.092332) \| 2.080467 / 6.876477 (-4.796010) \| 2.231014 / 2.142072 (0.088942) \| 0.811252 / 4.805227 (-3.993975) \| 0.149171 / 6.500664 (-6.351493) \| 0.065207 / 0.075469 (-0.010262) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.203137 / 1.841788 (-0.638651) \| 14.244903 / 8.074308 (6.170595) \| 14.454368 / 10.191392 (4.262976) \| 0.139090 / 0.680424 (-0.541334) \| 0.028738 / 0.534201 (-0.505463) \| 0.396394 / 0.579283 (-0.182889) \| 0.407207 / 0.434364 (-0.027156) \| 0.478036 / 0.540337 (-0.062302) \| 0.568488 / 1.386936 (-0.818448) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006878 / 0.011353 (-0.004475) \| 0.004636 / 0.011008 (-0.006372) \| 0.099118 / 0.038508 (0.060610) \| 0.028076 / 0.023109 (0.004967) \| 0.416097 / 0.275898 (0.140199) \| 0.451722 / 0.323480 (0.128242) \| 0.005364 / 0.007986 (-0.002622) \| 0.003506 / 0.004328 (-0.000822) \| 0.075791 / 0.004250 (0.071541) \| 0.041373 / 0.037052 (0.004321) \| 0.416358 / 0.258489 (0.157869) \| 0.458440 / 0.293841 (0.164599) \| 0.031870 / 0.128546 (-0.096676) \| 0.011751 / 0.075646 (-0.063896) \| 0.321748 / 0.419271 (-0.097524) \| 0.041780 / 0.043533 (-0.001752) \| 0.425037 / 0.255139 (0.169898) \| 0.444169 / 0.283200 (0.160969) \| 0.093145 / 0.141683 (-0.048538) \| 1.472151 / 1.452155 (0.019996) \| 1.542942 / 1.492716 (0.050226) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224287 / 0.018006 (0.206281) \| 0.415303 / 0.000490 (0.414813) \| 0.003180 / 0.000200 (0.002980) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026377 / 0.037411 (-0.011035) \| 0.106222 / 0.014526 (0.091696) \| 0.113873 / 0.176557 (-0.062684) \| 0.143255 / 0.737135 (-0.593880) \| 0.112642 / 0.296338 (-0.183697) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444149 / 0.215209 (0.228940) \| 4.421434 / 2.077655 (2.343779) \| 2.082198 / 1.504120 (0.578078) \| 1.879909 / 1.541195 (0.338715) \| 1.968526 / 1.468490 (0.500036) \| 0.697230 / 4.584777 (-3.887546) \| 3.430800 / 3.745712 (-0.314912) \| 1.893353 / 5.269862 (-3.376509) \| 1.173271 / 4.565676 (-3.392406) \| 0.082636 / 0.424275 (-0.341639) \| 0.012357 / 0.007607 (0.004750) \| 0.544008 / 0.226044 (0.317964) \| 5.465472 / 2.268929 (3.196543) \| 2.530017 / 55.444624 (-52.914608) \| 2.178462 / 6.876477 (-4.698014) \| 2.279570 / 2.142072 (0.137498) \| 0.804890 / 4.805227 (-4.000337) \| 0.152091 / 6.500664 (-6.348573) \| 0.069442 / 0.075469 (-0.006027) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.256722 / 1.841788 (-0.585065) \| 14.554131 / 8.074308 (6.479823) \| 13.499913 / 10.191392 (3.308521) \| 0.144350 / 0.680424 (-0.536074) \| 0.016977 / 0.534201 (-0.517224) \| 0.378836 / 0.579283 (-0.200447) \| 0.392004 / 0.434364 (-0.042360) \| 0.468423 / 0.540337 (-0.071914) \| 0.584711 / 1.386936 (-0.802225) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e4894fcdf2a82b3355bb6a2dc5557c8e23f8144 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008542 / 0.011353 (-0.002811) \| 0.004552 / 0.011008 (-0.006456) \| 0.100543 / 0.038508 (0.062035) \| 0.029717 / 0.023109 (0.006608) \| 0.301948 / 0.275898 (0.026050) \| 0.360211 / 0.323480 (0.036731) \| 0.006881 / 0.007986 (-0.001105) \| 0.003433 / 0.004328 (-0.000896) \| 0.077760 / 0.004250 (0.073510) \| 0.037069 / 0.037052 (0.000017) \| 0.314084 / 0.258489 (0.055595) \| 0.347759 / 0.293841 (0.053918) \| 0.033255 / 0.128546 (-0.095291) \| 0.011487 / 0.075646 (-0.064160) \| 0.323873 / 0.419271 (-0.095399) \| 0.041203 / 0.043533 (-0.002330) \| 0.298397 / 0.255139 (0.043258) \| 0.327174 / 0.283200 (0.043974) \| 0.088892 / 0.141683 (-0.052791) \| 1.560114 / 1.452155 (0.107959) \| 1.532475 / 1.492716 (0.039759) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226080 / 0.018006 (0.208074) \| 0.467492 / 0.000490 (0.467003) \| 0.002198 / 0.000200 (0.001998) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023627 / 0.037411 (-0.013784) \| 0.096696 / 0.014526 (0.082170) \| 0.106196 / 0.176557 (-0.070360) \| 0.140496 / 0.737135 (-0.596639) \| 0.108859 / 0.296338 (-0.187480) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422335 / 0.215209 (0.207126) \| 4.214879 / 2.077655 (2.137224) \| 1.865866 / 1.504120 (0.361747) \| 1.660914 / 1.541195 (0.119719) \| 1.691869 / 1.468490 (0.223379) \| 0.688164 / 4.584777 (-3.896613) \| 3.432708 / 3.745712 (-0.313004) \| 1.856852 / 5.269862 (-3.413010) \| 1.243685 / 4.565676 (-3.321991) \| 0.081552 / 0.424275 (-0.342723) \| 0.012491 / 0.007607 (0.004884) \| 0.524331 / 0.226044 (0.298287) \| 5.255090 / 2.268929 (2.986162) \| 2.269705 / 55.444624 (-53.174919) \| 1.936722 / 6.876477 (-4.939755) \| 2.018958 / 2.142072 (-0.123114) \| 0.800658 / 4.805227 (-4.004569) \| 0.148665 / 6.500664 (-6.351999) \| 0.064210 / 0.075469 (-0.011259) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235422 / 1.841788 (-0.606365) \| 14.156755 / 8.074308 (6.082447) \| 14.005916 / 10.191392 (3.814524) \| 0.150983 / 0.680424 (-0.529441) \| 0.028500 / 0.534201 (-0.505701) \| 0.393013 / 0.579283 (-0.186270) \| 0.408191 / 0.434364 (-0.026173) \| 0.481017 / 0.540337 (-0.059320) \| 0.581711 / 1.386936 (-0.805225) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006950 / 0.011353 (-0.004403) \| 0.004575 / 0.011008 (-0.006434) \| 0.076702 / 0.038508 (0.038194) \| 0.028050 / 0.023109 (0.004941) \| 0.342916 / 0.275898 (0.067018) \| 0.378861 / 0.323480 (0.055381) \| 0.005315 / 0.007986 (-0.002671) \| 0.004822 / 0.004328 (0.000494) \| 0.075560 / 0.004250 (0.071310) \| 0.040441 / 0.037052 (0.003388) \| 0.344284 / 0.258489 (0.085795) \| 0.386519 / 0.293841 (0.092678) \| 0.032122 / 0.128546 (-0.096424) \| 0.011843 / 0.075646 (-0.063803) \| 0.085798 / 0.419271 (-0.333473) \| 0.043027 / 0.043533 (-0.000506) \| 0.342910 / 0.255139 (0.087771) \| 0.366618 / 0.283200 (0.083418) \| 0.094766 / 0.141683 (-0.046917) \| 1.492981 / 1.452155 (0.040827) \| 1.566994 / 1.492716 (0.074278) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.166083 / 0.018006 (0.148076) \| 0.409315 / 0.000490 (0.408826) \| 0.003189 / 0.000200 (0.002989) \| 0.000127 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024753 / 0.037411 (-0.012658) \| 0.099112 / 0.014526 (0.084586) \| 0.106668 / 0.176557 (-0.069889) \| 0.142562 / 0.737135 (-0.594573) \| 0.110648 / 0.296338 (-0.185690) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.452668 / 0.215209 (0.237459) \| 4.501188 / 2.077655 (2.423534) \| 2.086197 / 1.504120 (0.582077) \| 1.873955 / 1.541195 (0.332761) \| 1.935610 / 1.468490 (0.467120) \| 0.708290 / 4.584777 (-3.876487) \| 3.426986 / 3.745712 (-0.318726) \| 2.805852 / 5.269862 (-2.464009) \| 1.516918 / 4.565676 (-3.048759) \| 0.084067 / 0.424275 (-0.340208) \| 0.012776 / 0.007607 (0.005169) \| 0.548853 / 0.226044 (0.322809) \| 5.488198 / 2.268929 (3.219270) \| 2.704464 / 55.444624 (-52.740161) \| 2.377817 / 6.876477 (-4.498660) \| 2.366152 / 2.142072 (0.224079) \| 0.818192 / 4.805227 (-3.987035) \| 0.152649 / 6.500664 (-6.348015) \| 0.066914 / 0.075469 (-0.008555) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.273803 / 1.841788 (-0.567985) \| 14.071633 / 8.074308 (5.997325) \| 13.655586 / 10.191392 (3.464194) \| 0.149471 / 0.680424 (-0.530953) \| 0.016745 / 0.534201 (-0.517456) \| 0.386850 / 0.579283 (-0.192434) \| 0.393595 / 0.434364 (-0.040769) \| 0.480396 / 0.540337 (-0.059942) \| 0.573708 / 1.386936 (-0.813228) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b2c7de67b326a635c0dc39ea5dd1ae982c958d6 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008173 / 0.011353 (-0.003180) \| 0.004461 / 0.011008 (-0.006547) \| 0.100284 / 0.038508 (0.061776) \| 0.028900 / 0.023109 (0.005791) \| 0.293639 / 0.275898 (0.017741) \| 0.359450 / 0.323480 (0.035971) \| 0.007567 / 0.007986 (-0.000418) \| 0.003434 / 0.004328 (-0.000894) \| 0.077913 / 0.004250 (0.073663) \| 0.036313 / 0.037052 (-0.000740) \| 0.308484 / 0.258489 (0.049995) \| 0.347575 / 0.293841 (0.053734) \| 0.033367 / 0.128546 (-0.095179) \| 0.011508 / 0.075646 (-0.064138) \| 0.323490 / 0.419271 (-0.095782) \| 0.042285 / 0.043533 (-0.001248) \| 0.295696 / 0.255139 (0.040557) \| 0.332475 / 0.283200 (0.049276) \| 0.089980 / 0.141683 (-0.051703) \| 1.461851 / 1.452155 (0.009697) \| 1.493030 / 1.492716 (0.000314) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191068 / 0.018006 (0.173062) \| 0.396768 / 0.000490 (0.396278) \| 0.002355 / 0.000200 (0.002155) \| 0.000080 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023117 / 0.037411 (-0.014294) \| 0.096155 / 0.014526 (0.081630) \| 0.102424 / 0.176557 (-0.074132) \| 0.142148 / 0.737135 (-0.594987) \| 0.105954 / 0.296338 (-0.190384) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421227 / 0.215209 (0.206018) \| 4.200403 / 2.077655 (2.122748) \| 1.899410 / 1.504120 (0.395290) \| 1.684091 / 1.541195 (0.142896) \| 1.698084 / 1.468490 (0.229594) \| 0.696195 / 4.584777 (-3.888582) \| 3.364116 / 3.745712 (-0.381596) \| 1.899133 / 5.269862 (-3.370728) \| 1.281405 / 4.565676 (-3.284272) \| 0.082958 / 0.424275 (-0.341317) \| 0.012433 / 0.007607 (0.004826) \| 0.521856 / 0.226044 (0.295812) \| 5.217626 / 2.268929 (2.948698) \| 2.309228 / 55.444624 (-53.135396) \| 1.956828 / 6.876477 (-4.919648) \| 2.018964 / 2.142072 (-0.123108) \| 0.816855 / 4.805227 (-3.988373) \| 0.152867 / 6.500664 (-6.347798) \| 0.064764 / 0.075469 (-0.010705) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.219020 / 1.841788 (-0.622768) \| 13.509058 / 8.074308 (5.434750) \| 13.637826 / 10.191392 (3.446434) \| 0.156620 / 0.680424 (-0.523804) \| 0.028518 / 0.534201 (-0.505683) \| 0.399138 / 0.579283 (-0.180146) \| 0.399931 / 0.434364 (-0.034433) \| 0.482902 / 0.540337 (-0.057435) \| 0.574089 / 1.386936 (-0.812847) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006232 / 0.011353 (-0.005121) \| 0.004467 / 0.011008 (-0.006542) \| 0.075494 / 0.038508 (0.036986) \| 0.026891 / 0.023109 (0.003782) \| 0.356603 / 0.275898 (0.080705) \| 0.371977 / 0.323480 (0.048497) \| 0.004709 / 0.007986 (-0.003276) \| 0.003230 / 0.004328 (-0.001099) \| 0.074338 / 0.004250 (0.070088) \| 0.035588 / 0.037052 (-0.001464) \| 0.349554 / 0.258489 (0.091065) \| 0.389672 / 0.293841 (0.095831) \| 0.031524 / 0.128546 (-0.097022) \| 0.011493 / 0.075646 (-0.064153) \| 0.084584 / 0.419271 (-0.334688) \| 0.041945 / 0.043533 (-0.001588) \| 0.341057 / 0.255139 (0.085918) \| 0.367876 / 0.283200 (0.084677) \| 0.090113 / 0.141683 (-0.051569) \| 1.507104 / 1.452155 (0.054949) \| 1.567810 / 1.492716 (0.075094) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210939 / 0.018006 (0.192933) \| 0.392600 / 0.000490 (0.392110) \| 0.002188 / 0.000200 (0.001988) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024294 / 0.037411 (-0.013118) \| 0.100325 / 0.014526 (0.085799) \| 0.104027 / 0.176557 (-0.072530) \| 0.141189 / 0.737135 (-0.595947) \| 0.107438 / 0.296338 (-0.188901) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.443314 / 0.215209 (0.228105) \| 4.429612 / 2.077655 (2.351957) \| 2.129275 / 1.504120 (0.625156) \| 1.940016 / 1.541195 (0.398821) \| 2.008975 / 1.468490 (0.540485) \| 0.695434 / 4.584777 (-3.889343) \| 3.355137 / 3.745712 (-0.390575) \| 2.606262 / 5.269862 (-2.663600) \| 1.451283 / 4.565676 (-3.114394) \| 0.082875 / 0.424275 (-0.341400) \| 0.012398 / 0.007607 (0.004791) \| 0.544262 / 0.226044 (0.318218) \| 5.450829 / 2.268929 (3.181900) \| 2.582074 / 55.444624 (-52.862550) \| 2.220037 / 6.876477 (-4.656439) \| 2.232473 / 2.142072 (0.090401) \| 0.802094 / 4.805227 (-4.003134) \| 0.150188 / 6.500664 (-6.350476) \| 0.066543 / 0.075469 (-0.008926) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.269098 / 1.841788 (-0.572690) \| 13.764780 / 8.074308 (5.690472) \| 13.461490 / 10.191392 (3.270098) \| 0.143841 / 0.680424 (-0.536583) \| 0.016687 / 0.534201 (-0.517514) \| 0.388548 / 0.579283 (-0.190736) \| 0.385229 / 0.434364 (-0.049135) \| 0.478966 / 0.540337 (-0.061371) \| 0.570355 / 1.386936 (-0.816581) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0ba81f5b299f0918cb0c0c071412feadd0ea3ef5 \"CML watermark\")\n", "I took your comments into account :)\r\n\r\n> Regarding the docs, I think it would be better to add this info as notes/tips/sections to the existing docs (Process/Stream; e.g. a tip under Dataset.shuffle that explains how to make this operation more performant by using to_iterable + shuffle, etc.) rather than introducing a new doc page.\r\n\r\nI added a paragraph in the Dataset.shuffle docstring, and a note in the Process doc page", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010906 / 0.011353 (-0.000447) \| 0.005995 / 0.011008 (-0.005014) \| 0.120183 / 0.038508 (0.081675) \| 0.042166 / 0.023109 (0.019057) \| 0.350945 / 0.275898 (0.075046) \| 0.433055 / 0.323480 (0.109575) \| 0.009093 / 0.007986 (0.001107) \| 0.004695 / 0.004328 (0.000366) \| 0.090362 / 0.004250 (0.086112) \| 0.051402 / 0.037052 (0.014350) \| 0.368677 / 0.258489 (0.110188) \| 0.410926 / 0.293841 (0.117086) \| 0.044471 / 0.128546 (-0.084075) \| 0.014051 / 0.075646 (-0.061595) \| 0.397765 / 0.419271 (-0.021507) \| 0.057227 / 0.043533 (0.013694) \| 0.357587 / 0.255139 (0.102448) \| 0.377470 / 0.283200 (0.094270) \| 0.119482 / 0.141683 (-0.022201) \| 1.719799 / 1.452155 (0.267645) \| 1.758228 / 1.492716 (0.265511) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224385 / 0.018006 (0.206379) \| 0.505070 / 0.000490 (0.504580) \| 0.004863 / 0.000200 (0.004663) \| 0.000379 / 0.000054 (0.000324) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030366 / 0.037411 (-0.007046) \| 0.130481 / 0.014526 (0.115955) \| 0.136429 / 0.176557 (-0.040128) \| 0.182263 / 0.737135 (-0.554872) \| 0.142871 / 0.296338 (-0.153468) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.467623 / 0.215209 (0.252414) \| 4.665522 / 2.077655 (2.587868) \| 2.130885 / 1.504120 (0.626766) \| 1.903810 / 1.541195 (0.362615) \| 2.019077 / 1.468490 (0.550587) \| 0.820868 / 4.584777 (-3.763909) \| 4.543118 / 3.745712 (0.797406) \| 2.491541 / 5.269862 (-2.778321) \| 1.585377 / 4.565676 (-2.980299) \| 0.101850 / 0.424275 (-0.322426) \| 0.014737 / 0.007607 (0.007129) \| 0.597241 / 0.226044 (0.371197) \| 5.938445 / 2.268929 (3.669516) \| 2.695799 / 55.444624 (-52.748825) \| 2.286890 / 6.876477 (-4.589587) \| 2.363064 / 2.142072 (0.220991) \| 0.986670 / 4.805227 (-3.818557) \| 0.194407 / 6.500664 (-6.306257) \| 0.074767 / 0.075469 (-0.000702) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.420630 / 1.841788 (-0.421158) \| 17.537702 / 8.074308 (9.463394) \| 16.521804 / 10.191392 (6.330412) \| 0.173622 / 0.680424 (-0.506802) \| 0.033944 / 0.534201 (-0.500257) \| 0.520461 / 0.579283 (-0.058822) \| 0.541283 / 0.434364 (0.106919) \| 0.651906 / 0.540337 (0.111569) \| 0.771724 / 1.386936 (-0.615212) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008448 / 0.011353 (-0.002905) \| 0.005893 / 0.011008 (-0.005115) \| 0.087995 / 0.038508 (0.049487) \| 0.038602 / 0.023109 (0.015493) \| 0.400048 / 0.275898 (0.124150) \| 0.436998 / 0.323480 (0.113518) \| 0.006414 / 0.007986 (-0.001572) \| 0.004478 / 0.004328 (0.000149) \| 0.086444 / 0.004250 (0.082194) \| 0.056535 / 0.037052 (0.019483) \| 0.402066 / 0.258489 (0.143577) \| 0.458730 / 0.293841 (0.164889) \| 0.041622 / 0.128546 (-0.086924) \| 0.014014 / 0.075646 (-0.061632) \| 0.101382 / 0.419271 (-0.317889) \| 0.056986 / 0.043533 (0.013453) \| 0.404527 / 0.255139 (0.149388) \| 0.428105 / 0.283200 (0.144906) \| 0.118321 / 0.141683 (-0.023361) \| 1.716940 / 1.452155 (0.264785) \| 1.834683 / 1.492716 (0.341967) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252917 / 0.018006 (0.234910) \| 0.485950 / 0.000490 (0.485461) \| 0.000489 / 0.000200 (0.000289) \| 0.000066 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035023 / 0.037411 (-0.002388) \| 0.139055 / 0.014526 (0.124529) \| 0.144165 / 0.176557 (-0.032392) \| 0.189559 / 0.737135 (-0.547577) \| 0.153213 / 0.296338 (-0.143126) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.505069 / 0.215209 (0.289860) \| 5.024620 / 2.077655 (2.946965) \| 2.429469 / 1.504120 (0.925349) \| 2.186210 / 1.541195 (0.645015) \| 2.275971 / 1.468490 (0.807481) \| 0.829432 / 4.584777 (-3.755345) \| 4.518600 / 3.745712 (0.772888) \| 2.466418 / 5.269862 (-2.803443) \| 1.558910 / 4.565676 (-3.006767) \| 0.102017 / 0.424275 (-0.322258) \| 0.015191 / 0.007607 (0.007584) \| 0.619092 / 0.226044 (0.393048) \| 6.241105 / 2.268929 (3.972176) \| 3.044213 / 55.444624 (-52.400411) \| 2.630194 / 6.876477 (-4.246282) \| 2.723685 / 2.142072 (0.581613) \| 0.994018 / 4.805227 (-3.811210) \| 0.198722 / 6.500664 (-6.301942) \| 0.075812 / 0.075469 (0.000343) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.545497 / 1.841788 (-0.296291) \| 18.305250 / 8.074308 (10.230942) \| 16.035275 / 10.191392 (5.843883) \| 0.209339 / 0.680424 (-0.471085) \| 0.020903 / 0.534201 (-0.513298) \| 0.499909 / 0.579283 (-0.079374) \| 0.488775 / 0.434364 (0.054411) \| 0.581990 / 0.540337 (0.041653) \| 0.697786 / 1.386936 (-0.689150) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78dca62e8aaddb9e0cf0212841f2c8d861fe74c8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011706 / 0.011353 (0.000353) \| 0.008406 / 0.011008 (-0.002602) \| 0.130887 / 0.038508 (0.092379) \| 0.037468 / 0.023109 (0.014359) \| 0.385043 / 0.275898 (0.109145) \| 0.458837 / 0.323480 (0.135357) \| 0.013400 / 0.007986 (0.005414) \| 0.004885 / 0.004328 (0.000557) \| 0.107156 / 0.004250 (0.102905) \| 0.046958 / 0.037052 (0.009906) \| 0.419314 / 0.258489 (0.160825) \| 0.456061 / 0.293841 (0.162220) \| 0.058859 / 0.128546 (-0.069687) \| 0.016682 / 0.075646 (-0.058965) \| 0.428401 / 0.419271 (0.009129) \| 0.062908 / 0.043533 (0.019376) \| 0.370902 / 0.255139 (0.115763) \| 0.433897 / 0.283200 (0.150697) \| 0.125672 / 0.141683 (-0.016011) \| 1.818279 / 1.452155 (0.366124) \| 1.935767 / 1.492716 (0.443050) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011928 / 0.018006 (-0.006078) \| 0.591995 / 0.000490 (0.591506) \| 0.008416 / 0.000200 (0.008216) \| 0.000122 / 0.000054 (0.000067) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029640 / 0.037411 (-0.007772) \| 0.121044 / 0.014526 (0.106518) \| 0.141840 / 0.176557 (-0.034716) \| 0.195856 / 0.737135 (-0.541280) \| 0.146460 / 0.296338 (-0.149879) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.591838 / 0.215209 (0.376629) \| 5.817309 / 2.077655 (3.739654) \| 2.411864 / 1.504120 (0.907744) \| 2.098517 / 1.541195 (0.557323) \| 2.214609 / 1.468490 (0.746119) \| 1.217542 / 4.584777 (-3.367235) \| 5.658394 / 3.745712 (1.912682) \| 5.155807 / 5.269862 (-0.114055) \| 2.797313 / 4.565676 (-1.768363) \| 0.141309 / 0.424275 (-0.282967) \| 0.014462 / 0.007607 (0.006855) \| 0.772274 / 0.226044 (0.546230) \| 7.547357 / 2.268929 (5.278429) \| 3.150178 / 55.444624 (-52.294446) \| 2.500130 / 6.876477 (-4.376347) \| 2.572036 / 2.142072 (0.429964) \| 1.434498 / 4.805227 (-3.370729) \| 0.257355 / 6.500664 (-6.243309) \| 0.087491 / 0.075469 (0.012022) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.483899 / 1.841788 (-0.357889) \| 17.990741 / 8.074308 (9.916433) \| 20.398965 / 10.191392 (10.207573) \| 0.239529 / 0.680424 (-0.440895) \| 0.046118 / 0.534201 (-0.488083) \| 0.528349 / 0.579283 (-0.050934) \| 0.614333 / 0.434364 (0.179969) \| 0.653621 / 0.540337 (0.113284) \| 0.794654 / 1.386936 (-0.592282) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008732 / 0.011353 (-0.002621) \| 0.006432 / 0.011008 (-0.004576) \| 0.090811 / 0.038508 (0.052303) \| 0.030154 / 0.023109 (0.007045) \| 0.407885 / 0.275898 (0.131987) \| 0.452457 / 0.323480 (0.128977) \| 0.006966 / 0.007986 (-0.001020) \| 0.006449 / 0.004328 (0.002120) \| 0.094439 / 0.004250 (0.090188) \| 0.050628 / 0.037052 (0.013576) \| 0.401815 / 0.258489 (0.143326) \| 0.451814 / 0.293841 (0.157973) \| 0.047456 / 0.128546 (-0.081090) \| 0.019019 / 0.075646 (-0.056628) \| 0.112941 / 0.419271 (-0.306331) \| 0.057677 / 0.043533 (0.014145) \| 0.406160 / 0.255139 (0.151021) \| 0.434469 / 0.283200 (0.151269) \| 0.110515 / 0.141683 (-0.031167) \| 1.601393 / 1.452155 (0.149238) \| 1.745581 / 1.492716 (0.252865) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280264 / 0.018006 (0.262258) \| 0.630074 / 0.000490 (0.629585) \| 0.006900 / 0.000200 (0.006700) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027338 / 0.037411 (-0.010073) \| 0.114772 / 0.014526 (0.100246) \| 0.130436 / 0.176557 (-0.046121) \| 0.168990 / 0.737135 (-0.568145) \| 0.135842 / 0.296338 (-0.160496) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.666739 / 0.215209 (0.451530) \| 6.212953 / 2.077655 (4.135298) \| 2.781716 / 1.504120 (1.277596) \| 2.369975 / 1.541195 (0.828781) \| 2.338807 / 1.468490 (0.870317) \| 1.174138 / 4.584777 (-3.410639) \| 5.420297 / 3.745712 (1.674585) \| 4.972669 / 5.269862 (-0.297192) \| 2.214294 / 4.565676 (-2.351382) \| 0.135429 / 0.424275 (-0.288846) \| 0.013877 / 0.007607 (0.006270) \| 0.750805 / 0.226044 (0.524761) \| 7.145429 / 2.268929 (4.876500) \| 3.215081 / 55.444624 (-52.229544) \| 2.598307 / 6.876477 (-4.278170) \| 2.690479 / 2.142072 (0.548406) \| 1.344673 / 4.805227 (-3.460554) \| 0.241536 / 6.500664 (-6.259128) \| 0.075544 / 0.075469 (0.000074) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.473595 / 1.841788 (-0.368192) \| 17.372237 / 8.074308 (9.297929) \| 18.586588 / 10.191392 (8.395196) \| 0.209300 / 0.680424 (-0.471124) \| 0.030878 / 0.534201 (-0.503323) \| 0.509131 / 0.579283 (-0.070152) \| 0.617884 / 0.434364 (0.183520) \| 0.633721 / 0.540337 (0.093383) \| 0.727624 / 1.386936 (-0.659312) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87f2062d47fdbec3fadf5b39bab0801f59c0f4a3 \"CML watermark\")\n", "Took your last comments into account !\r\n\r\n> so maybe a better title for it would be \"Optimize processing\" (or \"Working with datasets at scale\" as I mentioned earlier on Slack)\r\n\r\nI think the content would be slightly different, e.g. focus more on multiprocessing/sharding or what data formats to use. This can be a complementary page IMO\r\n\r\n> PS: I think it would be a good idea to add links to the Guide pages for better discoverability and to somewhat \"justify their presence in the docs\" (from the tutorial/how-to pages to the guides; some guides are not referenced at all)\r\n\r\nAdded a link in the how-to stream page. We may want to include it in the tutorial at one point at well - right now none of the tutorials mention streaming", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009167 / 0.011353 (-0.002186) \| 0.005345 / 0.011008 (-0.005663) \| 0.098302 / 0.038508 (0.059794) \| 0.035649 / 0.023109 (0.012540) \| 0.295597 / 0.275898 (0.019699) \| 0.358843 / 0.323480 (0.035364) \| 0.008011 / 0.007986 (0.000025) \| 0.004229 / 0.004328 (-0.000100) \| 0.075123 / 0.004250 (0.070872) \| 0.046098 / 0.037052 (0.009046) \| 0.310581 / 0.258489 (0.052092) \| 0.343230 / 0.293841 (0.049389) \| 0.038318 / 0.128546 (-0.090229) \| 0.011954 / 0.075646 (-0.063693) \| 0.331056 / 0.419271 (-0.088216) \| 0.052875 / 0.043533 (0.009342) \| 0.302758 / 0.255139 (0.047619) \| 0.340596 / 0.283200 (0.057396) \| 0.113676 / 0.141683 (-0.028007) \| 1.448272 / 1.452155 (-0.003883) \| 1.498008 / 1.492716 (0.005291) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.240524 / 0.018006 (0.222518) \| 0.555823 / 0.000490 (0.555333) \| 0.003143 / 0.000200 (0.002943) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027764 / 0.037411 (-0.009647) \| 0.105006 / 0.014526 (0.090480) \| 0.120550 / 0.176557 (-0.056007) \| 0.167052 / 0.737135 (-0.570084) \| 0.124521 / 0.296338 (-0.171818) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401758 / 0.215209 (0.186549) \| 3.989629 / 2.077655 (1.911974) \| 1.767307 / 1.504120 (0.263187) \| 1.579451 / 1.541195 (0.038257) \| 1.637642 / 1.468490 (0.169152) \| 0.702524 / 4.584777 (-3.882253) \| 3.714326 / 3.745712 (-0.031386) \| 2.131829 / 5.269862 (-3.138033) \| 1.487410 / 4.565676 (-3.078267) \| 0.084901 / 0.424275 (-0.339374) \| 0.012292 / 0.007607 (0.004685) \| 0.505211 / 0.226044 (0.279166) \| 5.074479 / 2.268929 (2.805551) \| 2.243068 / 55.444624 (-53.201556) \| 1.880199 / 6.876477 (-4.996278) \| 2.003757 / 2.142072 (-0.138315) \| 0.870719 / 4.805227 (-3.934508) \| 0.167626 / 6.500664 (-6.333039) \| 0.062024 / 0.075469 (-0.013445) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.192969 / 1.841788 (-0.648819) \| 14.830812 / 8.074308 (6.756504) \| 14.331178 / 10.191392 (4.139786) \| 0.199222 / 0.680424 (-0.481202) \| 0.029292 / 0.534201 (-0.504909) \| 0.440427 / 0.579283 (-0.138857) \| 0.437893 / 0.434364 (0.003529) \| 0.547155 / 0.540337 (0.006818) \| 0.645255 / 1.386936 (-0.741681) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007465 / 0.011353 (-0.003888) \| 0.005386 / 0.011008 (-0.005622) \| 0.073609 / 0.038508 (0.035100) \| 0.033550 / 0.023109 (0.010440) \| 0.341730 / 0.275898 (0.065832) \| 0.371518 / 0.323480 (0.048038) \| 0.005986 / 0.007986 (-0.001999) \| 0.004264 / 0.004328 (-0.000065) \| 0.073749 / 0.004250 (0.069498) \| 0.051452 / 0.037052 (0.014399) \| 0.347385 / 0.258489 (0.088896) \| 0.392284 / 0.293841 (0.098444) \| 0.036981 / 0.128546 (-0.091566) \| 0.012431 / 0.075646 (-0.063216) \| 0.086421 / 0.419271 (-0.332850) \| 0.053014 / 0.043533 (0.009481) \| 0.336660 / 0.255139 (0.081521) \| 0.359155 / 0.283200 (0.075956) \| 0.107666 / 0.141683 (-0.034017) \| 1.424324 / 1.452155 (-0.027830) \| 1.543027 / 1.492716 (0.050310) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260862 / 0.018006 (0.242855) \| 0.552057 / 0.000490 (0.551567) \| 0.000449 / 0.000200 (0.000249) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029184 / 0.037411 (-0.008227) \| 0.108799 / 0.014526 (0.094274) \| 0.125136 / 0.176557 (-0.051421) \| 0.157436 / 0.737135 (-0.579699) \| 0.126333 / 0.296338 (-0.170005) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424054 / 0.215209 (0.208845) \| 4.227847 / 2.077655 (2.150192) \| 2.051102 / 1.504120 (0.546983) \| 1.848651 / 1.541195 (0.307457) \| 1.922728 / 1.468490 (0.454238) \| 0.705903 / 4.584777 (-3.878874) \| 3.800977 / 3.745712 (0.055265) \| 2.099345 / 5.269862 (-3.170517) \| 1.342919 / 4.565676 (-3.222757) \| 0.086128 / 0.424275 (-0.338147) \| 0.012539 / 0.007607 (0.004932) \| 0.528767 / 0.226044 (0.302723) \| 5.299989 / 2.268929 (3.031061) \| 2.534280 / 55.444624 (-52.910345) \| 2.229532 / 6.876477 (-4.646945) \| 2.326704 / 2.142072 (0.184632) \| 0.838533 / 4.805227 (-3.966694) \| 0.168446 / 6.500664 (-6.332218) \| 0.065158 / 0.075469 (-0.010311) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.250091 / 1.841788 (-0.591697) \| 14.988651 / 8.074308 (6.914343) \| 13.655103 / 10.191392 (3.463711) \| 0.165079 / 0.680424 (-0.515345) \| 0.017829 / 0.534201 (-0.516372) \| 0.425903 / 0.579283 (-0.153381) \| 0.419771 / 0.434364 (-0.014593) \| 0.534309 / 0.540337 (-0.006028) \| 0.635563 / 1.386936 (-0.751373) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7d17ccc9b9dde2d94803b1305226c5a58d916c5 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010569 / 0.011353 (-0.000784) \| 0.005790 / 0.011008 (-0.005218) \| 0.118626 / 0.038508 (0.080118) \| 0.040455 / 0.023109 (0.017346) \| 0.342309 / 0.275898 (0.066411) \| 0.411828 / 0.323480 (0.088349) \| 0.008824 / 0.007986 (0.000839) \| 0.005426 / 0.004328 (0.001098) \| 0.088740 / 0.004250 (0.084489) \| 0.050042 / 0.037052 (0.012990) \| 0.352350 / 0.258489 (0.093861) \| 0.396030 / 0.293841 (0.102189) \| 0.043385 / 0.128546 (-0.085162) \| 0.013805 / 0.075646 (-0.061841) \| 0.396489 / 0.419271 (-0.022783) \| 0.055667 / 0.043533 (0.012135) \| 0.336165 / 0.255139 (0.081026) \| 0.372912 / 0.283200 (0.089713) \| 0.115343 / 0.141683 (-0.026340) \| 1.656412 / 1.452155 (0.204257) \| 1.708993 / 1.492716 (0.216277) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011650 / 0.018006 (-0.006357) \| 0.444415 / 0.000490 (0.443926) \| 0.003985 / 0.000200 (0.003785) \| 0.000136 / 0.000054 (0.000082) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031718 / 0.037411 (-0.005693) \| 0.119640 / 0.014526 (0.105114) \| 0.138519 / 0.176557 (-0.038037) \| 0.188847 / 0.737135 (-0.548288) \| 0.137891 / 0.296338 (-0.158448) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.447540 / 0.215209 (0.232331) \| 4.577189 / 2.077655 (2.499534) \| 2.106992 / 1.504120 (0.602872) \| 1.889631 / 1.541195 (0.348436) \| 1.972256 / 1.468490 (0.503766) \| 0.778209 / 4.584777 (-3.806568) \| 4.430279 / 3.745712 (0.684567) \| 2.401226 / 5.269862 (-2.868636) \| 1.481251 / 4.565676 (-3.084425) \| 0.094244 / 0.424275 (-0.330031) \| 0.013961 / 0.007607 (0.006354) \| 0.570962 / 0.226044 (0.344917) \| 5.809224 / 2.268929 (3.540295) \| 2.663290 / 55.444624 (-52.781334) \| 2.201228 / 6.876477 (-4.675249) \| 2.319240 / 2.142072 (0.177168) \| 0.938340 / 4.805227 (-3.866887) \| 0.185546 / 6.500664 (-6.315118) \| 0.069087 / 0.075469 (-0.006382) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.448597 / 1.841788 (-0.393191) \| 17.188573 / 8.074308 (9.114265) \| 16.197532 / 10.191392 (6.006140) \| 0.194064 / 0.680424 (-0.486360) \| 0.033694 / 0.534201 (-0.500507) \| 0.507585 / 0.579283 (-0.071699) \| 0.505470 / 0.434364 (0.071106) \| 0.623270 / 0.540337 (0.082932) \| 0.729964 / 1.386936 (-0.656972) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008529 / 0.011353 (-0.002824) \| 0.005705 / 0.011008 (-0.005304) \| 0.085594 / 0.038508 (0.047086) \| 0.038377 / 0.023109 (0.015268) \| 0.384221 / 0.275898 (0.108323) \| 0.414678 / 0.323480 (0.091199) \| 0.006195 / 0.007986 (-0.001791) \| 0.004549 / 0.004328 (0.000221) \| 0.082710 / 0.004250 (0.078460) \| 0.054899 / 0.037052 (0.017847) \| 0.404017 / 0.258489 (0.145528) \| 0.450309 / 0.293841 (0.156468) \| 0.040620 / 0.128546 (-0.087926) \| 0.013774 / 0.075646 (-0.061872) \| 0.099231 / 0.419271 (-0.320041) \| 0.057183 / 0.043533 (0.013650) \| 0.390806 / 0.255139 (0.135667) \| 0.419334 / 0.283200 (0.136134) \| 0.116449 / 0.141683 (-0.025234) \| 1.709124 / 1.452155 (0.256969) \| 1.812769 / 1.492716 (0.320052) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225206 / 0.018006 (0.207199) \| 0.440530 / 0.000490 (0.440040) \| 0.002982 / 0.000200 (0.002782) \| 0.000102 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032256 / 0.037411 (-0.005155) \| 0.127086 / 0.014526 (0.112560) \| 0.138133 / 0.176557 (-0.038424) \| 0.176168 / 0.737135 (-0.560968) \| 0.146072 / 0.296338 (-0.150267) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474374 / 0.215209 (0.259165) \| 4.785106 / 2.077655 (2.707452) \| 2.319344 / 1.504120 (0.815225) \| 2.075239 / 1.541195 (0.534045) \| 2.179231 / 1.468490 (0.710741) \| 0.832124 / 4.584777 (-3.752653) \| 4.376302 / 3.745712 (0.630590) \| 3.966837 / 5.269862 (-1.303024) \| 1.820230 / 4.565676 (-2.745446) \| 0.100692 / 0.424275 (-0.323583) \| 0.014748 / 0.007607 (0.007141) \| 0.568702 / 0.226044 (0.342657) \| 5.771548 / 2.268929 (3.502619) \| 2.747431 / 55.444624 (-52.697193) \| 2.448482 / 6.876477 (-4.427994) \| 2.497206 / 2.142072 (0.355133) \| 0.960842 / 4.805227 (-3.844385) \| 0.192855 / 6.500664 (-6.307809) \| 0.072494 / 0.075469 (-0.002975) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.474542 / 1.841788 (-0.367245) \| 17.344804 / 8.074308 (9.270496) \| 15.336082 / 10.191392 (5.144690) \| 0.200134 / 0.680424 (-0.480290) \| 0.020728 / 0.534201 (-0.513473) \| 0.488854 / 0.579283 (-0.090429) \| 0.490781 / 0.434364 (0.056418) \| 0.626288 / 0.540337 (0.085950) \| 0.721130 / 1.386936 (-0.665806) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cd7877892aa48a2470b01f52013390c54aca8a49 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008542 / 0.011353 (-0.002811) \| 0.004624 / 0.011008 (-0.006384) \| 0.100749 / 0.038508 (0.062241) \| 0.029587 / 0.023109 (0.006478) \| 0.298680 / 0.275898 (0.022782) \| 0.359659 / 0.323480 (0.036180) \| 0.007001 / 0.007986 (-0.000984) \| 0.003398 / 0.004328 (-0.000930) \| 0.078654 / 0.004250 (0.074404) \| 0.036440 / 0.037052 (-0.000612) \| 0.313245 / 0.258489 (0.054756) \| 0.342776 / 0.293841 (0.048936) \| 0.033195 / 0.128546 (-0.095352) \| 0.011500 / 0.075646 (-0.064146) \| 0.323957 / 0.419271 (-0.095314) \| 0.039878 / 0.043533 (-0.003655) \| 0.298189 / 0.255139 (0.043050) \| 0.325488 / 0.283200 (0.042289) \| 0.087276 / 0.141683 (-0.054407) \| 1.480846 / 1.452155 (0.028691) \| 1.507016 / 1.492716 (0.014300) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.189570 / 0.018006 (0.171564) \| 0.406407 / 0.000490 (0.405917) \| 0.003062 / 0.000200 (0.002862) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022865 / 0.037411 (-0.014546) \| 0.096103 / 0.014526 (0.081578) \| 0.106462 / 0.176557 (-0.070094) \| 0.140888 / 0.737135 (-0.596247) \| 0.108172 / 0.296338 (-0.188167) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415951 / 0.215209 (0.200742) \| 4.172187 / 2.077655 (2.094532) \| 1.842210 / 1.504120 (0.338090) \| 1.636997 / 1.541195 (0.095802) \| 1.706078 / 1.468490 (0.237588) \| 0.695825 / 4.584777 (-3.888952) \| 3.337354 / 3.745712 (-0.408358) \| 1.877880 / 5.269862 (-3.391982) \| 1.153882 / 4.565676 (-3.411794) \| 0.082923 / 0.424275 (-0.341352) \| 0.012814 / 0.007607 (0.005207) \| 0.521793 / 0.226044 (0.295748) \| 5.275980 / 2.268929 (3.007051) \| 2.279230 / 55.444624 (-53.165394) \| 1.941777 / 6.876477 (-4.934700) \| 1.981297 / 2.142072 (-0.160775) \| 0.809669 / 4.805227 (-3.995558) \| 0.148753 / 6.500664 (-6.351911) \| 0.064909 / 0.075469 (-0.010560) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.226757 / 1.841788 (-0.615031) \| 13.717354 / 8.074308 (5.643046) \| 12.925885 / 10.191392 (2.734493) \| 0.137926 / 0.680424 (-0.542498) \| 0.028788 / 0.534201 (-0.505413) \| 0.396654 / 0.579283 (-0.182630) \| 0.401931 / 0.434364 (-0.032432) \| 0.460515 / 0.540337 (-0.079823) \| 0.537903 / 1.386936 (-0.849033) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006757 / 0.011353 (-0.004596) \| 0.004474 / 0.011008 (-0.006534) \| 0.076571 / 0.038508 (0.038063) \| 0.027580 / 0.023109 (0.004471) \| 0.348231 / 0.275898 (0.072333) \| 0.398403 / 0.323480 (0.074923) \| 0.005089 / 0.007986 (-0.002897) \| 0.004676 / 0.004328 (0.000347) \| 0.076444 / 0.004250 (0.072194) \| 0.038508 / 0.037052 (0.001456) \| 0.348515 / 0.258489 (0.090026) \| 0.401456 / 0.293841 (0.107615) \| 0.031630 / 0.128546 (-0.096916) \| 0.011698 / 0.075646 (-0.063949) \| 0.085805 / 0.419271 (-0.333467) \| 0.041962 / 0.043533 (-0.001570) \| 0.343415 / 0.255139 (0.088276) \| 0.383001 / 0.283200 (0.099801) \| 0.090231 / 0.141683 (-0.051452) \| 1.488114 / 1.452155 (0.035960) \| 1.569039 / 1.492716 (0.076323) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.261751 / 0.018006 (0.243745) \| 0.411354 / 0.000490 (0.410865) \| 0.015103 / 0.000200 (0.014903) \| 0.000262 / 0.000054 (0.000208) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025423 / 0.037411 (-0.011988) \| 0.101334 / 0.014526 (0.086808) \| 0.108835 / 0.176557 (-0.067722) \| 0.143995 / 0.737135 (-0.593140) \| 0.111751 / 0.296338 (-0.184588) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446507 / 0.215209 (0.231298) \| 4.461543 / 2.077655 (2.383888) \| 2.104648 / 1.504120 (0.600528) \| 1.895900 / 1.541195 (0.354706) \| 1.985481 / 1.468490 (0.516991) \| 0.699029 / 4.584777 (-3.885748) \| 3.371064 / 3.745712 (-0.374648) \| 1.883445 / 5.269862 (-3.386416) \| 1.166150 / 4.565676 (-3.399527) \| 0.082639 / 0.424275 (-0.341636) \| 0.012605 / 0.007607 (0.004998) \| 0.544860 / 0.226044 (0.318815) \| 5.513223 / 2.268929 (3.244294) \| 2.570661 / 55.444624 (-52.873963) \| 2.206066 / 6.876477 (-4.670411) \| 2.256346 / 2.142072 (0.114273) \| 0.801142 / 4.805227 (-4.004085) \| 0.150412 / 6.500664 (-6.350252) \| 0.067742 / 0.075469 (-0.007727) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303477 / 1.841788 (-0.538310) \| 14.287767 / 8.074308 (6.213458) \| 13.525563 / 10.191392 (3.334171) \| 0.148202 / 0.680424 (-0.532222) \| 0.016868 / 0.534201 (-0.517333) \| 0.380729 / 0.579283 (-0.198555) \| 0.388177 / 0.434364 (-0.046187) \| 0.477410 / 0.540337 (-0.062927) \| 0.569343 / 1.386936 (-0.817593) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c18b77113da3f2e31af0570ec119877ca2a390 \"CML watermark\")\n", "> PS: I think it would be a good idea to add links to the Guide pages for better discoverability and to somewhat \"justify their presence in the docs\" (from the tutorial/how-to pages to the guides; some guides are not referenced at all)\r\n\r\nJust merged #5485, which references this new doc! Will look for other pages in the docs where it'd make sense to add them :)" ]	2023-01-05T18:12:17	2023-02-01T18:11:45	2023-02-01T16:36:01	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5410", "html_url": "https://github.com/huggingface/datasets/pull/5410", "diff_url": "https://github.com/huggingface/datasets/pull/5410.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5410.patch", "merged_at": "2023-02-01T16:36:01" }	Added `ds.to_iterable()` to get an iterable dataset from a map-style arrow dataset. It also has a `num_shards` argument to split the dataset before converting to an iterable dataset. Sharding is important to enable efficient shuffling and parallel loading of iterable datasets. TODO: - [x] tests - [x] docs Fix https://github.com/huggingface/datasets/issues/5265	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5410/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5410/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5409	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5409/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5409/comments	https://api.github.com/repos/huggingface/datasets/issues/5409/events	https://github.com/huggingface/datasets/pull/5409	1,520,374,219	PR_kwDODunzps5Gs3nL	5,409	Fix deprecation warning when use_auth_token passed to download_and_prepare	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008627 / 0.011353 (-0.002726) \| 0.004572 / 0.011008 (-0.006436) \| 0.099653 / 0.038508 (0.061145) \| 0.030010 / 0.023109 (0.006901) \| 0.300492 / 0.275898 (0.024594) \| 0.360443 / 0.323480 (0.036963) \| 0.007125 / 0.007986 (-0.000860) \| 0.003431 / 0.004328 (-0.000897) \| 0.078103 / 0.004250 (0.073852) \| 0.036884 / 0.037052 (-0.000168) \| 0.312289 / 0.258489 (0.053800) \| 0.345795 / 0.293841 (0.051954) \| 0.034001 / 0.128546 (-0.094545) \| 0.011405 / 0.075646 (-0.064242) \| 0.321258 / 0.419271 (-0.098013) \| 0.040591 / 0.043533 (-0.002942) \| 0.301114 / 0.255139 (0.045975) \| 0.337226 / 0.283200 (0.054027) \| 0.088055 / 0.141683 (-0.053628) \| 1.451892 / 1.452155 (-0.000263) \| 1.494881 / 1.492716 (0.002164) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.186749 / 0.018006 (0.168743) \| 0.414089 / 0.000490 (0.413600) \| 0.002475 / 0.000200 (0.002275) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022413 / 0.037411 (-0.014999) \| 0.097547 / 0.014526 (0.083021) \| 0.104196 / 0.176557 (-0.072361) \| 0.139819 / 0.737135 (-0.597316) \| 0.108345 / 0.296338 (-0.187994) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424750 / 0.215209 (0.209541) \| 4.261513 / 2.077655 (2.183859) \| 2.150888 / 1.504120 (0.646768) \| 1.935925 / 1.541195 (0.394730) \| 1.867456 / 1.468490 (0.398966) \| 0.694384 / 4.584777 (-3.890393) \| 3.370539 / 3.745712 (-0.375173) \| 1.886714 / 5.269862 (-3.383148) \| 1.256542 / 4.565676 (-3.309135) \| 0.082841 / 0.424275 (-0.341434) \| 0.012344 / 0.007607 (0.004737) \| 0.529801 / 0.226044 (0.303757) \| 5.315438 / 2.268929 (3.046509) \| 2.460517 / 55.444624 (-52.984107) \| 2.261840 / 6.876477 (-4.614637) \| 2.338710 / 2.142072 (0.196638) \| 0.818433 / 4.805227 (-3.986794) \| 0.150571 / 6.500664 (-6.350093) \| 0.066524 / 0.075469 (-0.008945) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253086 / 1.841788 (-0.588702) \| 13.862614 / 8.074308 (5.788306) \| 14.145149 / 10.191392 (3.953757) \| 0.165867 / 0.680424 (-0.514557) \| 0.029269 / 0.534201 (-0.504932) \| 0.397579 / 0.579283 (-0.181704) \| 0.401113 / 0.434364 (-0.033251) \| 0.463269 / 0.540337 (-0.077068) \| 0.551494 / 1.386936 (-0.835442) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006610 / 0.011353 (-0.004743) \| 0.004583 / 0.011008 (-0.006425) \| 0.096680 / 0.038508 (0.058172) \| 0.027352 / 0.023109 (0.004242) \| 0.409292 / 0.275898 (0.133394) \| 0.445790 / 0.323480 (0.122310) \| 0.004987 / 0.007986 (-0.002999) \| 0.003462 / 0.004328 (-0.000866) \| 0.074472 / 0.004250 (0.070221) \| 0.037875 / 0.037052 (0.000822) \| 0.411496 / 0.258489 (0.153007) \| 0.454721 / 0.293841 (0.160880) \| 0.031884 / 0.128546 (-0.096662) \| 0.011682 / 0.075646 (-0.063964) \| 0.318831 / 0.419271 (-0.100441) \| 0.041781 / 0.043533 (-0.001752) \| 0.411247 / 0.255139 (0.156108) \| 0.436215 / 0.283200 (0.153016) \| 0.090021 / 0.141683 (-0.051662) \| 1.492385 / 1.452155 (0.040231) \| 1.565182 / 1.492716 (0.072465) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221263 / 0.018006 (0.203257) \| 0.399074 / 0.000490 (0.398584) \| 0.000405 / 0.000200 (0.000205) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025139 / 0.037411 (-0.012272) \| 0.097952 / 0.014526 (0.083426) \| 0.106078 / 0.176557 (-0.070479) \| 0.143231 / 0.737135 (-0.593904) \| 0.109177 / 0.296338 (-0.187161) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441668 / 0.215209 (0.226459) \| 4.403247 / 2.077655 (2.325592) \| 2.072749 / 1.504120 (0.568629) \| 1.866248 / 1.541195 (0.325053) \| 1.906418 / 1.468490 (0.437927) \| 0.697234 / 4.584777 (-3.887543) \| 3.412016 / 3.745712 (-0.333696) \| 1.852572 / 5.269862 (-3.417289) \| 1.168270 / 4.565676 (-3.397407) \| 0.082132 / 0.424275 (-0.342144) \| 0.013191 / 0.007607 (0.005584) \| 0.548932 / 0.226044 (0.322888) \| 5.503891 / 2.268929 (3.234962) \| 2.539784 / 55.444624 (-52.904841) \| 2.181292 / 6.876477 (-4.695184) \| 2.242197 / 2.142072 (0.100125) \| 0.804027 / 4.805227 (-4.001200) \| 0.151649 / 6.500664 (-6.349015) \| 0.067088 / 0.075469 (-0.008381) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.296267 / 1.841788 (-0.545520) \| 13.986484 / 8.074308 (5.912176) \| 13.440705 / 10.191392 (3.249313) \| 0.140787 / 0.680424 (-0.539637) \| 0.017132 / 0.534201 (-0.517069) \| 0.381899 / 0.579283 (-0.197384) \| 0.385535 / 0.434364 (-0.048829) \| 0.439957 / 0.540337 (-0.100380) \| 0.532980 / 1.386936 (-0.853956) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]	2023-01-05T09:10:58	2023-01-06T11:06:16	2023-01-06T10:59:13	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5409", "html_url": "https://github.com/huggingface/datasets/pull/5409", "diff_url": "https://github.com/huggingface/datasets/pull/5409.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5409.patch", "merged_at": "2023-01-06T10:59:13" }	The `DatasetBuilder.download_and_prepare` argument `use_auth_token` was deprecated in: - #5302 However, `use_auth_token` is still passed to `download_and_prepare` in our built-in `io` readers (csv, json, parquet,...). This PR fixes it, so that no deprecation warning is raised. Fix #5407.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5409/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5409/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5408	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5408/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5408/comments	https://api.github.com/repos/huggingface/datasets/issues/5408/events	https://github.com/huggingface/datasets/issues/5408	1,519,890,752	I_kwDODunzps5al7FA	5,408	dataset map function could not be hash properly	{ "login": "Tungway1990", "id": 68179274, "node_id": "MDQ6VXNlcjY4MTc5Mjc0", "avatar_url": "https://avatars.githubusercontent.com/u/68179274?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Tungway1990", "html_url": "https://github.com/Tungway1990", "followers_url": "https://api.github.com/users/Tungway1990/followers", "following_url": "https://api.github.com/users/Tungway1990/following{/other_user}", "gists_url": "https://api.github.com/users/Tungway1990/gists{/gist_id}", "starred_url": "https://api.github.com/users/Tungway1990/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Tungway1990/subscriptions", "organizations_url": "https://api.github.com/users/Tungway1990/orgs", "repos_url": "https://api.github.com/users/Tungway1990/repos", "events_url": "https://api.github.com/users/Tungway1990/events{/privacy}", "received_events_url": "https://api.github.com/users/Tungway1990/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi ! On macos I tried with\r\n- py 3.9.11\r\n- datasets 2.8.0\r\n- transformers 4.25.1\r\n- dill 0.3.4\r\n\r\nand I was able to hash `prepare_dataset` correctly:\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\nHasher.hash(prepare_dataset)\r\n```\r\n\r\nWhat version of transformers do you have ? Can you try to call `Hasher.hash` on the the tokenizer and the feature extractor to see which one can't be hashed ?", "Thanks for your prompt reply.\r\n\r\nI update datasets version to 2.8.0 and the warning is gong." ]	2023-01-05T01:59:59	2023-01-06T13:22:19	2023-01-06T13:22:18	NONE	null	null	null	### Describe the bug I follow the [blog post](https://huggingface.co/blog/fine-tune-whisper#building-a-demo) to finetune a Cantonese transcribe model. When using map function to prepare dataset, following warning pop out: `common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=1)` > Parameter 'function'=<function prepare_dataset at 0x000001D1D9D79A60> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. I read https://github.com/huggingface/datasets/issues/4521 and https://github.com/huggingface/datasets/issues/3178 but cannot solve the issue. ### Steps to reproduce the bug ```python from datasets import load_dataset, DatasetDict common_voice = DatasetDict() common_voice["train"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK", split="train+validation") common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK", split="test") common_voice = common_voice.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes"]) from transformers import WhisperFeatureExtractor, WhisperTokenizer, WhisperProcessor feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small") tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="chinese", task="transcribe") processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="chinese", task="transcribe") from datasets import Audio common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000)) def prepare_dataset(batch): # load and resample audio data from 48 to 16kHz audio = batch["audio"] # compute log-Mel input features from input audio array batch["input_features"] = feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0] # encode target text to label ids batch["labels"] = tokenizer(batch["sentence"]).input_ids return batch common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=1) ``` ### Expected behavior Should be no warning shown. ### Environment info - `datasets` version: 2.7.0 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.9.12 - PyArrow version: 8.0.0 - Pandas version: 1.3.5 - dill version: 0.3.4 - multiprocess version: 0.70.12.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5408/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5408/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5407	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5407/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5407/comments	https://api.github.com/repos/huggingface/datasets/issues/5407/events	https://github.com/huggingface/datasets/issues/5407	1,519,797,345	I_kwDODunzps5alkRh	5,407	Datasets.from_sql() generates deprecation warning	{ "login": "msummerfield", "id": 21002157, "node_id": "MDQ6VXNlcjIxMDAyMTU3", "avatar_url": "https://avatars.githubusercontent.com/u/21002157?v=4", "gravatar_id": "", "url": "https://api.github.com/users/msummerfield", "html_url": "https://github.com/msummerfield", "followers_url": "https://api.github.com/users/msummerfield/followers", "following_url": "https://api.github.com/users/msummerfield/following{/other_user}", "gists_url": "https://api.github.com/users/msummerfield/gists{/gist_id}", "starred_url": "https://api.github.com/users/msummerfield/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/msummerfield/subscriptions", "organizations_url": "https://api.github.com/users/msummerfield/orgs", "repos_url": "https://api.github.com/users/msummerfield/repos", "events_url": "https://api.github.com/users/msummerfield/events{/privacy}", "received_events_url": "https://api.github.com/users/msummerfield/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	null	[ "Thanks for reporting @msummerfield. We are fixing it." ]	2023-01-05T00:43:17	2023-01-06T10:59:14	2023-01-06T10:59:14	NONE	null	null	null	### Describe the bug Calling `Datasets.from_sql()` generates a warning: `.../site-packages/datasets/builder.py:712: FutureWarning: 'use_auth_token' was deprecated in version 2.7.1 and will be removed in 3.0.0. Pass 'use_auth_token' to the initializer/'load_dataset_builder' instead.` ### Steps to reproduce the bug Any valid call to `Datasets.from_sql()` will produce the deprecation warning. ### Expected behavior No warning. The fix should be simply to remove the parameter `use_auth_token` from the call to `builder.download_and_prepare()` at line 43 of `io/sql.py` (it is set to `None` anyway, and is not needed). ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-4.15.0-169-generic-x86_64-with-glibc2.27 - Python version: 3.9.15 - PyArrow version: 10.0.1 - Pandas version: 1.5.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5407/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5407/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5406	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5406/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5406/comments	https://api.github.com/repos/huggingface/datasets/issues/5406/events	https://github.com/huggingface/datasets/issues/5406	1,519,140,544	I_kwDODunzps5ajD7A	5,406	[2.6.1][2.7.0] Upgrade `datasets` to fix `TypeError: can only concatenate str (not "int") to str`	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "I still get this error on 2.9.0\r\n<img width=\"1925\" alt=\"image\" src=\"https://user-images.githubusercontent.com/7208470/215597359-2f253c76-c472-4612-8099-d3a74d16eb29.png\">\r\n", "Hi ! I just tested locally and or colab and it works fine for 2.9 on `sst2`.\r\n\r\nAlso the code that is shown in your stack trace is not present in the 2.9 source code - so I'm wondering how you installed `datasets` that could cause this ? (you can check by searching for `[0:{label_ids[-1] + 1}]` in the [2.9 codebase](https://github.dev/huggingface/datasets/tree/b5672a956d5de864e6f5550e493527d962d6ae55) - it doesn't find anything)\r\n\r\nAnyway you can try uninstalling `datasets` and install it again", "For what it's worth, I've also gotten this error on 2.9.0, and I've tried uninstalling an reinstalling\r\n![Screenshot 2023-02-01 at 11 06 55 AM](https://user-images.githubusercontent.com/22944438/216126466-6934e8f8-0be4-41f4-9822-8436dfafd61c.png)\r\n\r\nI'm very new to this package (I was following this tutorial: https://huggingface.co/docs/transformers/training), so there's a good chance I was doing something wrong 😅 but thought I'd pass along the feedback", "@ntrpnr @mtwichel Did you install `datasets` with conda ?\r\n\r\nI suspect that `datasets` 2.9 on conda still have this issue for some reason. When I install `datasets` with `pip` I don't have this error.", "> @ntrpnr @mtwichel Did you install datasets with conda ?\r\n\r\nI did yeah, I wonder if that's the issue", "I just checked on conda at https://anaconda.org/HuggingFace/datasets/files\r\n\r\nand everything looks fine, I got\r\n```python\r\n\r\nf\"ClassLabel expected a value for all label ids [0:{int(label_ids[-1]) + 1}] but some ids are missing.\"\r\n```\r\nas expected in features.py line 1760 (notice the \"int()\") to not have the TypeError.\r\n\r\nFrom where on conda did you install `datasets` ? You should use the `HuggingFace` official channel\r\n\r\nedit: the conda-forge one [here](https://anaconda.org/conda-forge/datasets/files) seems ok as well", "Could you also try this in your notebook ? In case your python kernel doesn't match the `pip` environment in your shell\r\n```python\r\nimport datasets; datasets.__version__\r\n```\r\nand\r\n```\r\n!which python\r\n```\r\n```python\r\nimport sys; sys.executable\r\n```", "Mmmm, just a potential clue:\r\n\r\nWhere are you running your Python code? Is it the Spyder IDE?\r\n\r\nI have recently seen some users reporting conflicting Python environments while using Spyder...\r\n\r\nMaybe related:\r\n- #5487", "Other potential clue:\r\n- Had you already imported `datasets` before pip-updating it? You should first update datasets, before importing it. Otherwise, you need to restart the kernel after updating it.", "I installed `datasets` with Conda using `conda install datasets` and got this issue.\r\n\r\nThen I tried to reinstall using\r\n`\r\nconda install -c huggingface -c conda-forge datasets\r\n`\r\nThe issue is now fixed.", "I'm still getting this error on 2.13.0" ]	2023-01-04T15:10:04	2023-06-21T18:45:38	null	MEMBER	null	null	null	`datasets` 2.6.1 and 2.7.0 started to stop supporting datasets like IMDB, ConLL or MNIST datasets. When loading a dataset using 2.6.1 or 2.7.0, you may this error when loading certain datasets: ```python TypeError: can only concatenate str (not "int") to str ``` This is because we started to update the metadata of those datasets to a format that is not supported in 2.6.1 and 2.7.0 This change is required or those datasets won't be supported by the Hugging Face Hub. Therefore if you encounter this error or if you're using `datasets` 2.6.1 or 2.7.0, we encourage you to update to a newer version. For example, versions 2.6.2 and 2.7.1 patch this issue. ```python pip install -U datasets ``` All the datasets affected are the ones with a ClassLabel feature type and YAML "dataset_info" metadata. More info [here](https://github.com/huggingface/datasets/issues/5275). We apologize for the inconvenience.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5406/reactions", "total_count": 11, "+1": 11, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5406/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5405	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5405/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5405/comments	https://api.github.com/repos/huggingface/datasets/issues/5405/events	https://github.com/huggingface/datasets/issues/5405	1,517,879,386	I_kwDODunzps5aeQBa	5,405	size_in_bytes the same for all splits	{ "login": "Breakend", "id": 1609857, "node_id": "MDQ6VXNlcjE2MDk4NTc=", "avatar_url": "https://avatars.githubusercontent.com/u/1609857?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Breakend", "html_url": "https://github.com/Breakend", "followers_url": "https://api.github.com/users/Breakend/followers", "following_url": "https://api.github.com/users/Breakend/following{/other_user}", "gists_url": "https://api.github.com/users/Breakend/gists{/gist_id}", "starred_url": "https://api.github.com/users/Breakend/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Breakend/subscriptions", "organizations_url": "https://api.github.com/users/Breakend/orgs", "repos_url": "https://api.github.com/users/Breakend/repos", "events_url": "https://api.github.com/users/Breakend/events{/privacy}", "received_events_url": "https://api.github.com/users/Breakend/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Hi @Breakend,\r\n\r\nIndeed, the attribute `size_in_bytes` refers to the size of the entire dataset configuration, for all splits (size of downloaded files + Arrow files), not the specific split.\r\nThis is also the case for `download_size` (downloaded files) and `dataset_size` (Arrow files).\r\n\r\nThe size of the Arrow files for a specific split can be accessed: e.g. size of the \"test\" split only\r\n```python\r\nds[\"train\"].info.splits[\"test\"].num_bytes\r\n```\r\n\r\nI agree this is confusing and maybe we should improve it." ]	2023-01-03T20:25:48	2023-01-04T09:22:59	null	NONE	null	null	null	### Describe the bug Hi, it looks like whenever you pull a dataset and get size_in_bytes, it returns the same size for all splits (and that size is the combined size of all splits). It seems like this shouldn't be the intended behavior since it is misleading. Here's an example: ``` >>> from datasets import load_dataset >>> x = load_dataset("glue", "wnli") Found cached dataset glue (/Users/breakend/.cache/huggingface/datasets/glue/wnli/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad) 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1097.70it/s] >>> x["train"].size_in_bytes 186159 >>> x["validation"].size_in_bytes 186159 >>> x["test"].size_in_bytes 186159 >>> ``` ### Steps to reproduce the bug ``` >>> from datasets import load_dataset >>> x = load_dataset("glue", "wnli") >>> x["train"].size_in_bytes 186159 >>> x["validation"].size_in_bytes 186159 >>> x["test"].size_in_bytes 186159 ``` ### Expected behavior The expected behavior is that it should return the separate sizes for all splits. ### Environment info - `datasets` version: 2.7.1 - Platform: macOS-12.5-arm64-arm-64bit - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5405/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5405/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5404	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5404/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5404/comments	https://api.github.com/repos/huggingface/datasets/issues/5404/events	https://github.com/huggingface/datasets/issues/5404	1,517,566,331	I_kwDODunzps5adDl7	5,404	Better integration of BIG-bench	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[ "Hi, I made my version : https://huggingface.co/datasets/tasksource/bigbench" ]	2023-01-03T15:37:57	2023-02-09T20:30:26	null	MEMBER	null	null	null	### Feature request Ideally, it would be nice to have a maintained PyPI package for `bigbench`. ### Motivation We'd like to allow anyone to access, explore and use any task. ### Your contribution @lhoestq has opened an issue in their repo: - https://github.com/google/BIG-bench/issues/906	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5404/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5404/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5403	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5403/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5403/comments	https://api.github.com/repos/huggingface/datasets/issues/5403/events	https://github.com/huggingface/datasets/pull/5403	1,517,466,492	PR_kwDODunzps5Gi3d9	5,403	Replace one letter import in docs	{ "login": "MKhalusova", "id": 1065417, "node_id": "MDQ6VXNlcjEwNjU0MTc=", "avatar_url": "https://avatars.githubusercontent.com/u/1065417?v=4", "gravatar_id": "", "url": "https://api.github.com/users/MKhalusova", "html_url": "https://github.com/MKhalusova", "followers_url": "https://api.github.com/users/MKhalusova/followers", "following_url": "https://api.github.com/users/MKhalusova/following{/other_user}", "gists_url": "https://api.github.com/users/MKhalusova/gists{/gist_id}", "starred_url": "https://api.github.com/users/MKhalusova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MKhalusova/subscriptions", "organizations_url": "https://api.github.com/users/MKhalusova/orgs", "repos_url": "https://api.github.com/users/MKhalusova/repos", "events_url": "https://api.github.com/users/MKhalusova/events{/privacy}", "received_events_url": "https://api.github.com/users/MKhalusova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "> Thanks for the docs fix for consistency.\r\n> \r\n> Again for consistency, it would be nice to make the same fix across all the docs, e.g.\r\n> \r\n> https://github.com/huggingface/datasets/blob/310cdddd1c43f9658de172b85b6509d07d5e31a1/docs/source/image_classification.mdx?plain=1#L41\r\n\r\nExcellent point!", "@albertvillanova Should be all of them now :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008776 / 0.011353 (-0.002576) \| 0.004534 / 0.011008 (-0.006474) \| 0.101921 / 0.038508 (0.063413) \| 0.029995 / 0.023109 (0.006886) \| 0.307180 / 0.275898 (0.031282) \| 0.371001 / 0.323480 (0.047521) \| 0.007089 / 0.007986 (-0.000896) \| 0.003474 / 0.004328 (-0.000855) \| 0.079498 / 0.004250 (0.075248) \| 0.036522 / 0.037052 (-0.000531) \| 0.311729 / 0.258489 (0.053240) \| 0.349861 / 0.293841 (0.056020) \| 0.033815 / 0.128546 (-0.094731) \| 0.011435 / 0.075646 (-0.064211) \| 0.322924 / 0.419271 (-0.096347) \| 0.040981 / 0.043533 (-0.002552) \| 0.306174 / 0.255139 (0.051035) \| 0.331979 / 0.283200 (0.048780) \| 0.091293 / 0.141683 (-0.050389) \| 1.480935 / 1.452155 (0.028780) \| 1.522022 / 1.492716 (0.029306) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.195053 / 0.018006 (0.177047) \| 0.424898 / 0.000490 (0.424408) \| 0.003869 / 0.000200 (0.003669) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024323 / 0.037411 (-0.013088) \| 0.098061 / 0.014526 (0.083535) \| 0.105770 / 0.176557 (-0.070787) \| 0.145799 / 0.737135 (-0.591336) \| 0.109109 / 0.296338 (-0.187230) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420434 / 0.215209 (0.205225) \| 4.194781 / 2.077655 (2.117126) \| 2.030498 / 1.504120 (0.526378) \| 1.885314 / 1.541195 (0.344120) \| 1.996485 / 1.468490 (0.527995) \| 0.708540 / 4.584777 (-3.876237) \| 3.400694 / 3.745712 (-0.345018) \| 2.888704 / 5.269862 (-2.381157) \| 1.578100 / 4.565676 (-2.987577) \| 0.082150 / 0.424275 (-0.342125) \| 0.012277 / 0.007607 (0.004669) \| 0.527312 / 0.226044 (0.301268) \| 5.289566 / 2.268929 (3.020637) \| 2.369997 / 55.444624 (-53.074628) \| 2.040365 / 6.876477 (-4.836112) \| 2.298857 / 2.142072 (0.156785) \| 0.808446 / 4.805227 (-3.996781) \| 0.149355 / 6.500664 (-6.351309) \| 0.065993 / 0.075469 (-0.009477) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.231829 / 1.841788 (-0.609959) \| 13.874762 / 8.074308 (5.800454) \| 13.464379 / 10.191392 (3.272987) \| 0.151105 / 0.680424 (-0.529319) \| 0.028689 / 0.534201 (-0.505512) \| 0.398720 / 0.579283 (-0.180564) \| 0.402108 / 0.434364 (-0.032256) \| 0.463426 / 0.540337 (-0.076912) \| 0.541919 / 1.386936 (-0.845017) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006979 / 0.011353 (-0.004373) \| 0.004723 / 0.011008 (-0.006285) \| 0.099172 / 0.038508 (0.060664) \| 0.027970 / 0.023109 (0.004861) \| 0.415096 / 0.275898 (0.139198) \| 0.455916 / 0.323480 (0.132437) \| 0.005950 / 0.007986 (-0.002036) \| 0.003423 / 0.004328 (-0.000906) \| 0.075512 / 0.004250 (0.071262) \| 0.040894 / 0.037052 (0.003842) \| 0.419810 / 0.258489 (0.161321) \| 0.461913 / 0.293841 (0.168072) \| 0.033014 / 0.128546 (-0.095532) \| 0.011613 / 0.075646 (-0.064033) \| 0.320983 / 0.419271 (-0.098289) \| 0.049902 / 0.043533 (0.006369) \| 0.426378 / 0.255139 (0.171239) \| 0.445594 / 0.283200 (0.162394) \| 0.098978 / 0.141683 (-0.042705) \| 1.485724 / 1.452155 (0.033570) \| 1.563978 / 1.492716 (0.071262) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232137 / 0.018006 (0.214131) \| 0.432785 / 0.000490 (0.432296) \| 0.006173 / 0.000200 (0.005973) \| 0.000085 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024924 / 0.037411 (-0.012487) \| 0.102878 / 0.014526 (0.088352) \| 0.107976 / 0.176557 (-0.068581) \| 0.143581 / 0.737135 (-0.593554) \| 0.111644 / 0.296338 (-0.184694) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.490902 / 0.215209 (0.275693) \| 4.914060 / 2.077655 (2.836405) \| 2.569465 / 1.504120 (1.065345) \| 2.346872 / 1.541195 (0.805677) \| 2.412047 / 1.468490 (0.943557) \| 0.704975 / 4.584777 (-3.879802) \| 3.443669 / 3.745712 (-0.302043) \| 3.172055 / 5.269862 (-2.097807) \| 1.332152 / 4.565676 (-3.233525) \| 0.083023 / 0.424275 (-0.341252) \| 0.012699 / 0.007607 (0.005092) \| 0.592511 / 0.226044 (0.366466) \| 5.916376 / 2.268929 (3.647448) \| 3.028472 / 55.444624 (-52.416152) \| 2.691159 / 6.876477 (-4.185318) \| 2.786132 / 2.142072 (0.644060) \| 0.814045 / 4.805227 (-3.991182) \| 0.156630 / 6.500664 (-6.344034) \| 0.071330 / 0.075469 (-0.004139) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.277936 / 1.841788 (-0.563852) \| 14.331367 / 8.074308 (6.257059) \| 13.685694 / 10.191392 (3.494302) \| 0.138915 / 0.680424 (-0.541509) \| 0.016844 / 0.534201 (-0.517357) \| 0.390307 / 0.579283 (-0.188976) \| 0.385207 / 0.434364 (-0.049157) \| 0.448128 / 0.540337 (-0.092210) \| 0.532609 / 1.386936 (-0.854327) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]	2023-01-03T14:26:32	2023-01-03T15:06:18	2023-01-03T14:59:01	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5403", "html_url": "https://github.com/huggingface/datasets/pull/5403", "diff_url": "https://github.com/huggingface/datasets/pull/5403.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5403.patch", "merged_at": "2023-01-03T14:59:01" }	This PR updates a code example for consistency across the docs based on [feedback from this comment](https://github.com/huggingface/transformers/pull/20925/files/9fda31634d203a47d3212e4e8d43d3267faf9808#r1058769500): "In terms of style we usually stay away from one-letter imports like this (even if the community uses them) as they are not always known by beginners and one letter is very undescriptive. Here it wouldn't change anything to use albumentations instead of A."	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5403/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5403/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5402	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5402/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5402/comments	https://api.github.com/repos/huggingface/datasets/issues/5402/events	https://github.com/huggingface/datasets/issues/5402	1,517,409,429	I_kwDODunzps5acdSV	5,402	Missing state.json when creating a cloud dataset using a dataset_builder	{ "login": "danielfleischer", "id": 22022514, "node_id": "MDQ6VXNlcjIyMDIyNTE0", "avatar_url": "https://avatars.githubusercontent.com/u/22022514?v=4", "gravatar_id": "", "url": "https://api.github.com/users/danielfleischer", "html_url": "https://github.com/danielfleischer", "followers_url": "https://api.github.com/users/danielfleischer/followers", "following_url": "https://api.github.com/users/danielfleischer/following{/other_user}", "gists_url": "https://api.github.com/users/danielfleischer/gists{/gist_id}", "starred_url": "https://api.github.com/users/danielfleischer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/danielfleischer/subscriptions", "organizations_url": "https://api.github.com/users/danielfleischer/orgs", "repos_url": "https://api.github.com/users/danielfleischer/repos", "events_url": "https://api.github.com/users/danielfleischer/events{/privacy}", "received_events_url": "https://api.github.com/users/danielfleischer/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "`load_from_disk` must be used on datasets saved using `save_to_disk`: they correspond to fully serialized datasets including their state.\r\n\r\nOn the other hand, `download_and_prepare` just downloads the raw data and convert them to arrow (or parquet if you want). We are working on allowing you to reload a dataset saved on S3 with `download_and_prepare` using `load_dataset` in #5281 \r\n\r\nFor now I'd encourage you to keep using `save_to_disk`", "Thanks, I'll follow that issue. \r\n\r\nI was following the [cloud storage](https://huggingface.co/docs/datasets/filesystems) docs section and perhaps I'm missing some part of the flow; start with `load_dataset_builder` + `download_and_prepare`. You say I need an explicit `save_to_disk` but what object needs to be saved? the builder? is that related to the other issue?", "Right now `load_dataset_builder` + `download_and_prepare` is to be used with tools like dask or spark, but `load_dataset` will support private cloud storage soon as well so you'll be able to reload the dataset with `datasets`.\r\n\r\nRight now the only function that can load a dataset from a cloud storage is `load_from_disk`, that must be used with a dataset serialized with `save_to_disk`." ]	2023-01-03T13:39:59	2023-01-04T17:23:57	null	NONE	null	null	null	### Describe the bug Using `load_dataset_builder` to create a builder, run `download_and_prepare` do upload it to S3. However when trying to load it, there are missing `state.json` files. Complete example: ```python from aiobotocore.session import AioSession as Session from datasets import load_from_disk, load_datase, load_dataset_builder import s3fs storage_options = {"session": Session()} fs = s3fs.S3FileSystem(storage_options) output_dir = "s3://bucket/imdb" builder = load_dataset_builder("imdb") builder.download_and_prepare(output_dir, storage_options=storage_options) load_from_disk(output_dir, fs=fs) # ERROR # [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json' ``` As a comparison, if you use the non lazy `load_dataset`, it works and the S3 folder has different structure + state.json files. Example: ```python from aiobotocore.session import AioSession as Session from datasets import load_from_disk, load_dataset, load_dataset_builder import s3fs storage_options = {"session": Session()} fs = s3fs.S3FileSystem(storage_options) output_dir = "s3://bucket/imdb" dataset = load_dataset("imdb",) dataset.save_to_disk(output_dir, fs=fs) load_from_disk(output_dir, fs=fs) # WORKS ``` You still want the 1st option for the laziness and the parquet conversion. Thanks! ### Steps to reproduce the bug ```python from aiobotocore.session import AioSession as Session from datasets import load_from_disk, load_datase, load_dataset_builder import s3fs storage_options = {"session": Session()} fs = s3fs.S3FileSystem(**storage_options) output_dir = "s3://bucket/imdb" builder = load_dataset_builder("imdb") builder.download_and_prepare(output_dir, storage_options=storage_options) load_from_disk(output_dir, fs=fs) # ERROR # [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json' ``` BTW, you need the AioSession as s3fs is now based on aiobotocore, see https://github.com/fsspec/s3fs/issues/385. ### Expected behavior Expected to be able to load the dataset from S3. ### Environment info ``` s3fs 2022.11.0 s3transfer 0.6.0 datasets 2.8.0 aiobotocore 2.4.2 boto3 1.24.59 botocore 1.27.59 ``` python 3.7.15.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5402/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5402/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5401	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5401/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5401/comments	https://api.github.com/repos/huggingface/datasets/issues/5401/events	https://github.com/huggingface/datasets/pull/5401	1,517,160,935	PR_kwDODunzps5Gh1XQ	5,401	Support Dataset conversion from/to Spark	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5401). All of your documentation changes will be reflected on that endpoint.", "Cool thanks !\r\n\r\nSpark DataFrame are usually quite big, and I believe here `from_spark` would load everything in the driver node's RAM, which is quite limiting. Same for `to_spark` which would load everything in the driver node's RAM before sending the data to the executor. Maybe we can mention this in the docstring ?\r\n\r\nTo transfer big datasets from/into the HF ecosystem using Spark maybe we can just make sure that `pyspark` can read/write to the HF Hub, and that `datasets` can read from HDFS/S3/etc.", "Yes @lhoestq , consider this as a first integration of the Datasets library with Spark.\r\n- This PR implements the basic conversion between both.\r\n - And yes, we are using the Spark's `pandas` API (that uses `pyarrow` under the hood): everything is transferred to the driver.\r\n - Note that we are converting from/to a Datasets dataset: this is not distributed\r\n\r\nThe next step is to support the integration of the HF Hub with Spark, that I think should be done using `hffs`.", "Thinking more about it I don't really see how those two methods help in practice, since one can already do `datasets` <-> pandas <-> spark and those two methods don't add value over this.\r\n\r\nHowever I think it can be good documentation to explain that it's possible to do it and it's super simple" ]	2023-01-03T09:57:40	2023-01-05T14:21:33	null	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5401", "html_url": "https://github.com/huggingface/datasets/pull/5401", "diff_url": "https://github.com/huggingface/datasets/pull/5401.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5401.patch", "merged_at": null }	This PR implements Spark integration by supporting `Dataset` conversion from/to Spark `DataFrame`.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5401/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5401/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5400	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5400/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5400/comments	https://api.github.com/repos/huggingface/datasets/issues/5400/events	https://github.com/huggingface/datasets/pull/5400	1,517,032,972	PR_kwDODunzps5GhaGI	5,400	Support streaming datasets with os.path.exists and Path.exists	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008638 / 0.011353 (-0.002715) \| 0.004565 / 0.011008 (-0.006444) \| 0.098984 / 0.038508 (0.060476) \| 0.030118 / 0.023109 (0.007009) \| 0.321779 / 0.275898 (0.045881) \| 0.366905 / 0.323480 (0.043426) \| 0.006931 / 0.007986 (-0.001055) \| 0.004728 / 0.004328 (0.000399) \| 0.078358 / 0.004250 (0.074108) \| 0.037755 / 0.037052 (0.000702) \| 0.312694 / 0.258489 (0.054205) \| 0.351781 / 0.293841 (0.057940) \| 0.033266 / 0.128546 (-0.095280) \| 0.011397 / 0.075646 (-0.064250) \| 0.323501 / 0.419271 (-0.095771) \| 0.040779 / 0.043533 (-0.002754) \| 0.303533 / 0.255139 (0.048394) \| 0.340940 / 0.283200 (0.057740) \| 0.088701 / 0.141683 (-0.052982) \| 1.472058 / 1.452155 (0.019904) \| 1.529535 / 1.492716 (0.036818) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191803 / 0.018006 (0.173797) \| 0.409773 / 0.000490 (0.409283) \| 0.002704 / 0.000200 (0.002504) \| 0.000217 / 0.000054 (0.000163) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023520 / 0.037411 (-0.013891) \| 0.096967 / 0.014526 (0.082441) \| 0.107911 / 0.176557 (-0.068646) \| 0.146425 / 0.737135 (-0.590710) \| 0.109025 / 0.296338 (-0.187314) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418565 / 0.215209 (0.203356) \| 4.183429 / 2.077655 (2.105774) \| 1.886534 / 1.504120 (0.382414) \| 1.689015 / 1.541195 (0.147820) \| 1.710757 / 1.468490 (0.242267) \| 0.693211 / 4.584777 (-3.891566) \| 3.380062 / 3.745712 (-0.365650) \| 2.619910 / 5.269862 (-2.649952) \| 1.457512 / 4.565676 (-3.108164) \| 0.082421 / 0.424275 (-0.341854) \| 0.012126 / 0.007607 (0.004519) \| 0.525249 / 0.226044 (0.299205) \| 5.244541 / 2.268929 (2.975613) \| 2.305908 / 55.444624 (-53.138717) \| 1.945298 / 6.876477 (-4.931178) \| 2.015618 / 2.142072 (-0.126455) \| 0.816746 / 4.805227 (-3.988481) \| 0.148325 / 6.500664 (-6.352339) \| 0.063939 / 0.075469 (-0.011530) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.255790 / 1.841788 (-0.585998) \| 13.433219 / 8.074308 (5.358911) \| 13.916957 / 10.191392 (3.725565) \| 0.153468 / 0.680424 (-0.526956) \| 0.028722 / 0.534201 (-0.505479) \| 0.398245 / 0.579283 (-0.181038) \| 0.399067 / 0.434364 (-0.035296) \| 0.457525 / 0.540337 (-0.082812) \| 0.542391 / 1.386936 (-0.844545) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006411 / 0.011353 (-0.004942) \| 0.004552 / 0.011008 (-0.006456) \| 0.098036 / 0.038508 (0.059527) \| 0.026532 / 0.023109 (0.003422) \| 0.412270 / 0.275898 (0.136372) \| 0.442771 / 0.323480 (0.119291) \| 0.004891 / 0.007986 (-0.003094) \| 0.003488 / 0.004328 (-0.000841) \| 0.075437 / 0.004250 (0.071186) \| 0.036228 / 0.037052 (-0.000824) \| 0.413246 / 0.258489 (0.154757) \| 0.453546 / 0.293841 (0.159705) \| 0.031054 / 0.128546 (-0.097492) \| 0.011589 / 0.075646 (-0.064058) \| 0.318477 / 0.419271 (-0.100794) \| 0.041075 / 0.043533 (-0.002457) \| 0.411182 / 0.255139 (0.156043) \| 0.436991 / 0.283200 (0.153792) \| 0.086563 / 0.141683 (-0.055120) \| 1.511948 / 1.452155 (0.059793) \| 1.570925 / 1.492716 (0.078208) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200510 / 0.018006 (0.182504) \| 0.403450 / 0.000490 (0.402960) \| 0.000397 / 0.000200 (0.000197) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023950 / 0.037411 (-0.013461) \| 0.097334 / 0.014526 (0.082808) \| 0.105228 / 0.176557 (-0.071328) \| 0.137699 / 0.737135 (-0.599436) \| 0.107063 / 0.296338 (-0.189275) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474420 / 0.215209 (0.259211) \| 4.748212 / 2.077655 (2.670557) \| 2.407318 / 1.504120 (0.903198) \| 2.198949 / 1.541195 (0.657755) \| 2.220377 / 1.468490 (0.751887) \| 0.704022 / 4.584777 (-3.880755) \| 3.366128 / 3.745712 (-0.379584) \| 1.839454 / 5.269862 (-3.430408) \| 1.151183 / 4.565676 (-3.414493) \| 0.082818 / 0.424275 (-0.341457) \| 0.012765 / 0.007607 (0.005158) \| 0.571913 / 0.226044 (0.345868) \| 5.722544 / 2.268929 (3.453615) \| 2.858279 / 55.444624 (-52.586346) \| 2.513479 / 6.876477 (-4.362998) \| 2.574227 / 2.142072 (0.432154) \| 0.803282 / 4.805227 (-4.001945) \| 0.150603 / 6.500664 (-6.350061) \| 0.066594 / 0.075469 (-0.008875) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.301161 / 1.841788 (-0.540627) \| 13.580745 / 8.074308 (5.506436) \| 13.301551 / 10.191392 (3.110159) \| 0.141424 / 0.680424 (-0.539000) \| 0.016579 / 0.534201 (-0.517622) \| 0.380726 / 0.579283 (-0.198557) \| 0.383011 / 0.434364 (-0.051353) \| 0.438717 / 0.540337 (-0.101620) \| 0.527085 / 1.386936 (-0.859851) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]	2023-01-03T07:42:37	2023-01-06T10:42:44	2023-01-06T10:35:44	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5400", "html_url": "https://github.com/huggingface/datasets/pull/5400", "diff_url": "https://github.com/huggingface/datasets/pull/5400.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5400.patch", "merged_at": "2023-01-06T10:35:44" }	Support streaming datasets with `os.path.exists` and `pathlib.Path.exists`.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5400/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5400/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5399	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5399/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5399/comments	https://api.github.com/repos/huggingface/datasets/issues/5399/events	https://github.com/huggingface/datasets/issues/5399	1,515,548,427	I_kwDODunzps5aVW8L	5,399	Got disconnected from remote data host. Retrying in 5sec [2/20]	{ "login": "alhuri", "id": 46427957, "node_id": "MDQ6VXNlcjQ2NDI3OTU3", "avatar_url": "https://avatars.githubusercontent.com/u/46427957?v=4", "gravatar_id": "", "url": "https://api.github.com/users/alhuri", "html_url": "https://github.com/alhuri", "followers_url": "https://api.github.com/users/alhuri/followers", "following_url": "https://api.github.com/users/alhuri/following{/other_user}", "gists_url": "https://api.github.com/users/alhuri/gists{/gist_id}", "starred_url": "https://api.github.com/users/alhuri/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alhuri/subscriptions", "organizations_url": "https://api.github.com/users/alhuri/orgs", "repos_url": "https://api.github.com/users/alhuri/repos", "events_url": "https://api.github.com/users/alhuri/events{/privacy}", "received_events_url": "https://api.github.com/users/alhuri/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	2023-01-01T13:00:11	2023-01-02T07:21:52	2023-01-02T07:21:52	NONE	null	null	null	### Describe the bug While trying to upload my image dataset of a CSV file type to huggingface by running the below code. The dataset consists of a little over 100k of image-caption pairs ### Steps to reproduce the bug ``` df = pd.read_csv('x.csv', encoding='utf-8-sig') features = Features({ 'link': Image(decode=True), 'caption': Value(dtype='string'), }) #make sure u r logged in to HF ds = Dataset.from_pandas(df, features=features) ds.features ds.push_to_hub("x/x") ``` I got the below error and It always stops at the same progress ``` 100%\|██████████\| 4/4 [23:53<00:00, 358.48s/ba] 100%\|██████████\| 4/4 [24:37<00:00, 369.47s/ba]%\|▍ \| 1/22 [00:06<02:09, 6.16s/it] 100%\|██████████\| 4/4 [25:00<00:00, 375.15s/ba]%\|▉ \| 2/22 [25:54<2:36:15, 468.80s/it] 100%\|██████████\| 4/4 [24:53<00:00, 373.29s/ba]%\|█▎ \| 3/22 [51:01<4:07:07, 780.39s/it] 100%\|██████████\| 4/4 [24:01<00:00, 360.34s/ba]%\|█▊ \| 4/22 [1:17:00<5:04:07, 1013.74s/it] 100%\|██████████\| 4/4 [23:59<00:00, 359.91s/ba]%\|██▎ \| 5/22 [1:41:07<5:24:06, 1143.90s/it] 100%\|██████████\| 4/4 [24:16<00:00, 364.06s/ba]%\|██▋ \| 6/22 [2:05:14<5:29:15, 1234.74s/it] 100%\|██████████\| 4/4 [25:24<00:00, 381.10s/ba]%\|███▏ \| 7/22 [2:29:38<5:25:52, 1303.52s/it] 100%\|██████████\| 4/4 [25:24<00:00, 381.24s/ba]%\|███▋ \| 8/22 [2:56:02<5:23:46, 1387.58s/it] 100%\|██████████\| 4/4 [25:08<00:00, 377.23s/ba]%\|████ \| 9/22 [3:22:24<5:13:17, 1445.97s/it] 100%\|██████████\| 4/4 [24:11<00:00, 362.87s/ba]%\|████▌ \| 10/22 [3:48:24<4:56:02, 1480.19s/it] 100%\|██████████\| 4/4 [24:44<00:00, 371.11s/ba]%\|█████ \| 11/22 [4:12:42<4:30:10, 1473.66s/it] 100%\|██████████\| 4/4 [24:35<00:00, 368.81s/ba]%\|█████▍ \| 12/22 [4:37:34<4:06:29, 1478.98s/it] 100%\|██████████\| 4/4 [24:02<00:00, 360.67s/ba]%\|█████▉ \| 13/22 [5:03:24<3:45:04, 1500.45s/it] 100%\|██████████\| 4/4 [24:07<00:00, 361.78s/ba]%\|██████▎ \| 14/22 [5:27:33<3:17:59, 1484.97s/it] 100%\|██████████\| 4/4 [23:39<00:00, 354.85s/ba]%\|██████▊ \| 15/22 [5:51:48<2:52:10, 1475.82s/it] Pushing dataset shards to the dataset hub: 73%\|███████▎ \| 16/22 [6:16:58<2:28:37, 1486.31s/it]Got disconnected from remote data host. Retrying in 5sec [1/20] Got disconnected from remote data host. Retrying in 5sec [2/20] Got disconnected from remote data host. Retrying in 5sec [3/20] Got disconnected from remote data host. Retrying in 5sec [4/20] Got disconnected from remote data host. Retrying in 5sec [5/20] Got disconnected from remote data host. Retrying in 5sec [6/20] Got disconnected from remote data host. Retrying in 5sec [7/20] Got disconnected from remote data host. Retrying in 5sec [8/20] Got disconnected from remote data host. Retrying in 5sec [9/20] ... Got disconnected from remote data host. Retrying in 5sec [19/20] Got disconnected from remote data host. Retrying in 5sec [20/20] 75%\|███████▌ \| 3/4 [24:47<08:15, 495.86s/ba] Pushing dataset shards to the dataset hub: 73%\|███████▎ \| 16/22 [6:41:46<2:30:39, 1506.65s/it] Output exceeds the size limit. Open the full output data in a text editor --------------------------------------------------------------------------- ConnectionError Traceback (most recent call last) <ipython-input-1-dbf8530779e9> in <module> 16 ds.features ``` ### Expected behavior I was trying to upload an image dataset and expected it to be fully uploaded ### Environment info - `datasets` version: 2.8.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.9 - PyArrow version: 10.0.1 - Pandas version: 1.3.5	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5399/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5399/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5398	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5398/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5398/comments	https://api.github.com/repos/huggingface/datasets/issues/5398/events	https://github.com/huggingface/datasets/issues/5398	1,514,425,231	I_kwDODunzps5aREuP	5,398	Unpin pydantic	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	2022-12-30T10:37:31	2022-12-30T10:43:41	2022-12-30T10:43:41	MEMBER	null	null	null	Once `pydantic` fixes their issue in their 1.10.3 version, unpin it. See issue: - #5394 See temporary fix: - #5395	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5398/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5398/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5397	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5397/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5397/comments	https://api.github.com/repos/huggingface/datasets/issues/5397/events	https://github.com/huggingface/datasets/pull/5397	1,514,412,246	PR_kwDODunzps5GYirs	5,397	Unpin pydantic test dependency	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012922 / 0.011353 (0.001569) \| 0.006568 / 0.011008 (-0.004440) \| 0.139567 / 0.038508 (0.101059) \| 0.039362 / 0.023109 (0.016253) \| 0.444238 / 0.275898 (0.168340) \| 0.529102 / 0.323480 (0.205622) \| 0.010275 / 0.007986 (0.002290) \| 0.006134 / 0.004328 (0.001805) \| 0.107506 / 0.004250 (0.103255) \| 0.047948 / 0.037052 (0.010896) \| 0.460469 / 0.258489 (0.201980) \| 0.516817 / 0.293841 (0.222976) \| 0.058637 / 0.128546 (-0.069909) \| 0.019516 / 0.075646 (-0.056130) \| 0.464111 / 0.419271 (0.044839) \| 0.062140 / 0.043533 (0.018607) \| 0.445004 / 0.255139 (0.189865) \| 0.460117 / 0.283200 (0.176917) \| 0.116591 / 0.141683 (-0.025092) \| 1.936834 / 1.452155 (0.484680) \| 1.941837 / 1.492716 (0.449120) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284130 / 0.018006 (0.266124) \| 0.588109 / 0.000490 (0.587619) \| 0.004383 / 0.000200 (0.004183) \| 0.000143 / 0.000054 (0.000089) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032984 / 0.037411 (-0.004427) \| 0.132811 / 0.014526 (0.118285) \| 0.150932 / 0.176557 (-0.025625) \| 0.203759 / 0.737135 (-0.533377) \| 0.149612 / 0.296338 (-0.146726) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.677666 / 0.215209 (0.462457) \| 6.627611 / 2.077655 (4.549956) \| 2.679526 / 1.504120 (1.175406) \| 2.272536 / 1.541195 (0.731342) \| 2.371179 / 1.468490 (0.902689) \| 1.205282 / 4.584777 (-3.379495) \| 5.733537 / 3.745712 (1.987825) \| 3.165279 / 5.269862 (-2.104583) \| 2.287918 / 4.565676 (-2.277759) \| 0.144581 / 0.424275 (-0.279695) \| 0.016812 / 0.007607 (0.009205) \| 0.841719 / 0.226044 (0.615675) \| 8.379119 / 2.268929 (6.110191) \| 3.507169 / 55.444624 (-51.937456) \| 2.756666 / 6.876477 (-4.119811) \| 2.814091 / 2.142072 (0.672018) \| 1.495835 / 4.805227 (-3.309392) \| 0.253651 / 6.500664 (-6.247013) \| 0.081258 / 0.075469 (0.005789) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.651586 / 1.841788 (-0.190202) \| 19.039628 / 8.074308 (10.965320) \| 21.269814 / 10.191392 (11.078421) \| 0.241024 / 0.680424 (-0.439400) \| 0.047975 / 0.534201 (-0.486225) \| 0.563727 / 0.579283 (-0.015556) \| 0.666808 / 0.434364 (0.232445) \| 0.661065 / 0.540337 (0.120728) \| 0.762884 / 1.386936 (-0.624052) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010141 / 0.011353 (-0.001212) \| 0.006216 / 0.011008 (-0.004792) \| 0.135491 / 0.038508 (0.096983) \| 0.035439 / 0.023109 (0.012330) \| 0.482789 / 0.275898 (0.206891) \| 0.520673 / 0.323480 (0.197193) \| 0.006358 / 0.007986 (-0.001627) \| 0.005432 / 0.004328 (0.001104) \| 0.094448 / 0.004250 (0.090197) \| 0.048379 / 0.037052 (0.011326) \| 0.509359 / 0.258489 (0.250870) \| 0.539583 / 0.293841 (0.245742) \| 0.054621 / 0.128546 (-0.073925) \| 0.021382 / 0.075646 (-0.054265) \| 0.435539 / 0.419271 (0.016267) \| 0.060630 / 0.043533 (0.017097) \| 0.469593 / 0.255139 (0.214454) \| 0.507838 / 0.283200 (0.224639) \| 0.112062 / 0.141683 (-0.029621) \| 1.829694 / 1.452155 (0.377539) \| 1.972266 / 1.492716 (0.479549) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291669 / 0.018006 (0.273663) \| 0.590104 / 0.000490 (0.589614) \| 0.000661 / 0.000200 (0.000461) \| 0.000082 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034933 / 0.037411 (-0.002479) \| 0.134867 / 0.014526 (0.120341) \| 0.138892 / 0.176557 (-0.037665) \| 0.192619 / 0.737135 (-0.544516) \| 0.153787 / 0.296338 (-0.142551) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.666762 / 0.215209 (0.451553) \| 6.741736 / 2.077655 (4.664082) \| 2.988712 / 1.504120 (1.484592) \| 2.554823 / 1.541195 (1.013628) \| 2.655651 / 1.468490 (1.187161) \| 1.276603 / 4.584777 (-3.308174) \| 5.827960 / 3.745712 (2.082247) \| 5.046876 / 5.269862 (-0.222985) \| 2.829775 / 4.565676 (-1.735902) \| 0.151525 / 0.424275 (-0.272750) \| 0.016504 / 0.007607 (0.008897) \| 0.849749 / 0.226044 (0.623704) \| 8.331675 / 2.268929 (6.062747) \| 3.664529 / 55.444624 (-51.780096) \| 2.976495 / 6.876477 (-3.899982) \| 3.034737 / 2.142072 (0.892664) \| 1.499036 / 4.805227 (-3.306191) \| 0.261027 / 6.500664 (-6.239637) \| 0.088306 / 0.075469 (0.012837) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.693506 / 1.841788 (-0.148282) \| 18.939914 / 8.074308 (10.865605) \| 20.685460 / 10.191392 (10.494068) \| 0.218316 / 0.680424 (-0.462108) \| 0.029010 / 0.534201 (-0.505191) \| 0.565246 / 0.579283 (-0.014037) \| 0.633573 / 0.434364 (0.199209) \| 0.656895 / 0.540337 (0.116558) \| 0.781975 / 1.386936 (-0.604961) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]	2022-12-30T10:22:09	2022-12-30T10:53:11	2022-12-30T10:43:40	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5397", "html_url": "https://github.com/huggingface/datasets/pull/5397", "diff_url": "https://github.com/huggingface/datasets/pull/5397.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5397.patch", "merged_at": "2022-12-30T10:43:40" }	Once pydantic-1.10.3 has been yanked, we can unpin it: https://pypi.org/project/pydantic/1.10.3/ See reply by pydantic team https://github.com/pydantic/pydantic/issues/4885#issuecomment-1367819807 ``` v1.10.3 has been yanked. ``` in response to spacy request: https://github.com/pydantic/pydantic/issues/4885#issuecomment-1367810049 ``` On behalf of spacy-related packages: would it be possible for you to temporarily yank v1.10.3? To address this and be compatible with v1.10.4, we'd have to release new versions of a whole series of packages and nearly everyone (including me) is currently on vacation. Even if v1.10.4 is released with a fix, pip would still back off to v1.10.3 for spacy, etc. because of its current pins for typing_extensions. If it could instead back off to v1.10.2, we'd have a bit more breathing room to make the updates on our end. ``` Close #5398.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5397/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5397/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5396	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5396/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5396/comments	https://api.github.com/repos/huggingface/datasets/issues/5396/events	https://github.com/huggingface/datasets/pull/5396	1,514,002,934	PR_kwDODunzps5GXMhp	5,396	Fix checksum verification	{ "login": "daskol", "id": 9336514, "node_id": "MDQ6VXNlcjkzMzY1MTQ=", "avatar_url": "https://avatars.githubusercontent.com/u/9336514?v=4", "gravatar_id": "", "url": "https://api.github.com/users/daskol", "html_url": "https://github.com/daskol", "followers_url": "https://api.github.com/users/daskol/followers", "following_url": "https://api.github.com/users/daskol/following{/other_user}", "gists_url": "https://api.github.com/users/daskol/gists{/gist_id}", "starred_url": "https://api.github.com/users/daskol/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/daskol/subscriptions", "organizations_url": "https://api.github.com/users/daskol/orgs", "repos_url": "https://api.github.com/users/daskol/repos", "events_url": "https://api.github.com/users/daskol/events{/privacy}", "received_events_url": "https://api.github.com/users/daskol/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi ! If I'm not mistaken both `expected_checksums[url]` and `recorded_checksums[url]` are dictionaries with keys \"checksum\" and \"num_bytes\". So we need to check whether `expected_checksums[url] != recorded_checksums[url]` (or simply `expected_checksums[url][\"checksum\"] != recorded_checksums[url][\"checksum\"]`)\r\n\r\nBut in your fix you're checking `expected_checksums[url] != recorded_checksums[url]['checksum']`.\r\n\r\nSo I think it's fine to keep this as is", "No, the issue is that there is comparison of sclar value and dictionary.", "Acording to [`DatasetInfo`][1], we need specify a dictionary which maps a URL to a checksum as follows.\r\n\r\n```python\r\nCHECKSUMS = {\r\n URL: 'a5dc6bf63ea088ade6e98594bfa386f45211c38b2a3db3dd11b33bd530f3c481',\r\n}\r\n\r\nclass FancyDataset:\r\n def _info(self):\r\n return DatasetInfo(..., download_checksums=CHECKSUMS)\r\n```\r\n\r\nHowever, `load_dataset` fails with this checksum definition.\r\n\r\n[1]: https://github.com/huggingface/datasets/blob/main/src/datasets/info.py#L124-L125", "I think it has to be formatted like this right now. Maybe the DatasetInfo doc is unclear and we can improve it\r\n```python\r\nCHECKSUMS = {\r\n URL: {\"checksum\": checksum, \"num_bytes\": num_bytes},\r\n}\r\n```", "Right. I am not sure that this is a correct way to do it. People usually calculate sha256, md5, or whatever else but not size in bytes. Also, people use only some of checksum algorithms. This means that comparing dictionaries in `verify_checksums` is too strict (requires equality of all items) and raises compatibility issues in the future. Another issue is that a comparison of dictionaries assumes type constraints which imply type equality. \r\n\r\nSince almost noone uses checksums as far as I known, my PR suggests a minimal change to mitigate these issues except support of a specific checksum algorithm which is a separated feature and should be contributed in a separate PRs from my perspective.", "Applying this change will break the verification code, since the `expected_checksums` is a dict with those two keys.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5396). All of your documentation changes will be reflected on that endpoint." ]	2022-12-29T19:45:17	2023-02-13T11:11:22	2023-02-13T11:11:22	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5396", "html_url": "https://github.com/huggingface/datasets/pull/5396", "diff_url": "https://github.com/huggingface/datasets/pull/5396.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5396.patch", "merged_at": null }	Expected checksum was verified against checksum dict (not checksum).	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5396/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5396/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5395	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5395/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5395/comments	https://api.github.com/repos/huggingface/datasets/issues/5395/events	https://github.com/huggingface/datasets/pull/5395	1,513,997,335	PR_kwDODunzps5GXLUl	5,395	Temporarily pin pydantic test dependency	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012220 / 0.011353 (0.000867) \| 0.005943 / 0.011008 (-0.005065) \| 0.128223 / 0.038508 (0.089715) \| 0.037352 / 0.023109 (0.014242) \| 0.397143 / 0.275898 (0.121245) \| 0.483935 / 0.323480 (0.160455) \| 0.010279 / 0.007986 (0.002293) \| 0.004842 / 0.004328 (0.000513) \| 0.101403 / 0.004250 (0.097153) \| 0.042935 / 0.037052 (0.005883) \| 0.421642 / 0.258489 (0.163153) \| 0.456328 / 0.293841 (0.162487) \| 0.065639 / 0.128546 (-0.062907) \| 0.019820 / 0.075646 (-0.055826) \| 0.426090 / 0.419271 (0.006818) \| 0.069583 / 0.043533 (0.026051) \| 0.402662 / 0.255139 (0.147523) \| 0.428826 / 0.283200 (0.145626) \| 0.116760 / 0.141683 (-0.024923) \| 1.806216 / 1.452155 (0.354061) \| 1.852629 / 1.492716 (0.359913) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226555 / 0.018006 (0.208548) \| 0.584693 / 0.000490 (0.584203) \| 0.008612 / 0.000200 (0.008412) \| 0.000205 / 0.000054 (0.000150) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028393 / 0.037411 (-0.009018) \| 0.123355 / 0.014526 (0.108829) \| 0.134423 / 0.176557 (-0.042133) \| 0.188536 / 0.737135 (-0.548600) \| 0.141595 / 0.296338 (-0.154743) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.589359 / 0.215209 (0.374150) \| 5.974655 / 2.077655 (3.897001) \| 2.465580 / 1.504120 (0.961460) \| 2.007618 / 1.541195 (0.466424) \| 2.078788 / 1.468490 (0.610298) \| 1.216646 / 4.584777 (-3.368131) \| 5.217516 / 3.745712 (1.471804) \| 3.107188 / 5.269862 (-2.162674) \| 2.251641 / 4.565676 (-2.314036) \| 0.138640 / 0.424275 (-0.285635) \| 0.015046 / 0.007607 (0.007439) \| 0.780092 / 0.226044 (0.554048) \| 7.749564 / 2.268929 (5.480635) \| 3.080708 / 55.444624 (-52.363917) \| 2.393897 / 6.876477 (-4.482579) \| 2.387738 / 2.142072 (0.245665) \| 1.458844 / 4.805227 (-3.346384) \| 0.252476 / 6.500664 (-6.248188) \| 0.076594 / 0.075469 (0.001125) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.540868 / 1.841788 (-0.300919) \| 17.295684 / 8.074308 (9.221376) \| 19.669300 / 10.191392 (9.477908) \| 0.250315 / 0.680424 (-0.430109) \| 0.045068 / 0.534201 (-0.489133) \| 0.538840 / 0.579283 (-0.040443) \| 0.584443 / 0.434364 (0.150079) \| 0.614476 / 0.540337 (0.074138) \| 0.729928 / 1.386936 (-0.657008) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009218 / 0.011353 (-0.002135) \| 0.006261 / 0.011008 (-0.004747) \| 0.125541 / 0.038508 (0.087033) \| 0.034405 / 0.023109 (0.011296) \| 0.468381 / 0.275898 (0.192483) \| 0.503336 / 0.323480 (0.179856) \| 0.006839 / 0.007986 (-0.001146) \| 0.004724 / 0.004328 (0.000396) \| 0.097875 / 0.004250 (0.093625) \| 0.051278 / 0.037052 (0.014225) \| 0.473323 / 0.258489 (0.214834) \| 0.537392 / 0.293841 (0.243551) \| 0.055588 / 0.128546 (-0.072958) \| 0.021041 / 0.075646 (-0.054605) \| 0.416952 / 0.419271 (-0.002320) \| 0.070128 / 0.043533 (0.026595) \| 0.465224 / 0.255139 (0.210085) \| 0.504678 / 0.283200 (0.221478) \| 0.112504 / 0.141683 (-0.029179) \| 1.865865 / 1.452155 (0.413710) \| 1.988296 / 1.492716 (0.495580) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.314170 / 0.018006 (0.296164) \| 0.526726 / 0.000490 (0.526236) \| 0.018691 / 0.000200 (0.018491) \| 0.000128 / 0.000054 (0.000073) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033772 / 0.037411 (-0.003639) \| 0.124796 / 0.014526 (0.110270) \| 0.134700 / 0.176557 (-0.041856) \| 0.190595 / 0.737135 (-0.546541) \| 0.143205 / 0.296338 (-0.153133) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.656708 / 0.215209 (0.441499) \| 6.470503 / 2.077655 (4.392848) \| 2.866430 / 1.504120 (1.362310) \| 2.506846 / 1.541195 (0.965651) \| 2.548669 / 1.468490 (1.080179) \| 1.226695 / 4.584777 (-3.358082) \| 5.117866 / 3.745712 (1.372153) \| 3.032822 / 5.269862 (-2.237040) \| 1.999152 / 4.565676 (-2.566524) \| 0.142974 / 0.424275 (-0.281301) \| 0.015011 / 0.007607 (0.007404) \| 0.799729 / 0.226044 (0.573684) \| 8.286313 / 2.268929 (6.017385) \| 3.636482 / 55.444624 (-51.808142) \| 2.888038 / 6.876477 (-3.988439) \| 2.924982 / 2.142072 (0.782910) \| 1.471996 / 4.805227 (-3.333231) \| 0.257119 / 6.500664 (-6.243545) \| 0.077294 / 0.075469 (0.001825) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.608290 / 1.841788 (-0.233497) \| 17.599119 / 8.074308 (9.524811) \| 18.917086 / 10.191392 (8.725694) \| 0.236237 / 0.680424 (-0.444187) \| 0.026061 / 0.534201 (-0.508140) \| 0.527359 / 0.579283 (-0.051925) \| 0.589176 / 0.434364 (0.154812) \| 0.602310 / 0.540337 (0.061973) \| 0.726756 / 1.386936 (-0.660180) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "Issue reported to `pydantic`: \r\n- https://github.com/pydantic/pydantic/issues/4885\r\n\r\nFixing PR at `pydantic`:\r\n- https://github.com/pydantic/pydantic/pull/4886" ]	2022-12-29T19:34:19	2022-12-30T06:36:57	2022-12-29T21:00:26	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5395", "html_url": "https://github.com/huggingface/datasets/pull/5395", "diff_url": "https://github.com/huggingface/datasets/pull/5395.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5395.patch", "merged_at": "2022-12-29T21:00:26" }	Temporarily pin `pydantic` until a permanent solution is found. Fix #5394.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5395/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5395/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5394	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5394/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5394/comments	https://api.github.com/repos/huggingface/datasets/issues/5394/events	https://github.com/huggingface/datasets/issues/5394	1,513,976,229	I_kwDODunzps5aPXGl	5,394	CI error: TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers'	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	null	[ "I still getting the same error :\r\n\r\n`python -m spacy download fr_core_news_lg\r\n`.\r\n`import spacy`", "@MFatnassi, this issue and the corresponding fix only affect our Continuous Integration testing environment.\r\n\r\nNote that `datasets` does not depend on `spacy`." ]	2022-12-29T18:58:44	2022-12-30T10:40:51	2022-12-29T21:00:27	MEMBER	null	null	null	### Describe the bug While installing the dependencies, the CI raises a TypeError: ``` Traceback (most recent call last): File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 183, in _run_module_as_main mod_name, mod_spec, code = _get_module_details(mod_name, _Error) File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 142, in _get_module_details return _get_module_details(pkg_main_name, error) File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 109, in _get_module_details __import__(pkg_name) File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/__init__.py", line 6, in <module> from .errors import setup_default_warnings File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/errors.py", line 2, in <module> from .compat import Literal File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/compat.py", line 3, in <module> from thinc.util import copy_array File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/__init__.py", line 5, in <module> from .config import registry File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/config.py", line 2, in <module> import confection File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/confection/__init__.py", line 10, in <module> from pydantic import BaseModel, create_model, ValidationError, Extra File "pydantic/__init__.py", line 2, in init pydantic.__init__ File "pydantic/dataclasses.py", line 46, in init pydantic.dataclasses # \| None \| Attribute is set to None. \| File "pydantic/main.py", line 121, in init pydantic.main TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers' ``` See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565 ### Steps to reproduce the bug ```shell pip install .[tests,metrics-tests] python -m spacy download en_core_web_sm ``` ### Expected behavior No error. ### Environment info See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5394/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5394/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5393	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5393/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5393/comments	https://api.github.com/repos/huggingface/datasets/issues/5393/events	https://github.com/huggingface/datasets/pull/5393	1,512,908,613	PR_kwDODunzps5GTg0a	5,393	Finish deprecating the fs argument	{ "login": "dconathan", "id": 15098095, "node_id": "MDQ6VXNlcjE1MDk4MDk1", "avatar_url": "https://avatars.githubusercontent.com/u/15098095?v=4", "gravatar_id": "", "url": "https://api.github.com/users/dconathan", "html_url": "https://github.com/dconathan", "followers_url": "https://api.github.com/users/dconathan/followers", "following_url": "https://api.github.com/users/dconathan/following{/other_user}", "gists_url": "https://api.github.com/users/dconathan/gists{/gist_id}", "starred_url": "https://api.github.com/users/dconathan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/dconathan/subscriptions", "organizations_url": "https://api.github.com/users/dconathan/orgs", "repos_url": "https://api.github.com/users/dconathan/repos", "events_url": "https://api.github.com/users/dconathan/events{/privacy}", "received_events_url": "https://api.github.com/users/dconathan/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "> Thanks for the deprecation. Some minor suggested fixes below...\r\n> \r\n> Also note that the corresponding tests should be updated as well.\r\n\r\nThanks for the suggestions/typo fixes. I updated the failing test - passing locally now", "Nice thanks !\r\n\r\nI believe you also need to update `_load_info` and `_save_info` in `builder.py` - they're still passing `fs=self._fs` instead of `storage_options=self._fs.storage_options`\r\n\r\nThis should remove the remaining warnings in the CI such as \r\n\r\n```python\r\ntests/test_builder.py::test_builder_with_filesystem_download_and_prepare_reload\r\ntests/test_load.py::test_load_dataset_local[False]\r\ntests/test_load.py::test_load_dataset_local[True]\r\ntests/test_load.py::test_load_dataset_zip_csv[csv_path-False]\r\ntests/test_load.py::test_load_dataset_then_move_then_reload\r\n /opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/info.py:344: FutureWarning: 'fs' was deprecated in favor of 'storage_options' in version 2.9.0 and will be removed in 3.0.0.\r\n You can remove this warning by passing 'storage_options=fs.storage_options' instead.\r\n```", "re: docstring, I assume passing in `storage_options=s3.storage_options` is correct/necessary to pass the secrets?", "what about \r\nhttps://github.com/huggingface/datasets/blob/5b793dd8c43bf6e85f165238becb3c64f6cd3ed0/src/datasets/filesystems/__init__.py#L43-L54\r\nleave as is? Is this function no longer necessary?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008877 / 0.011353 (-0.002475) \| 0.004725 / 0.011008 (-0.006283) \| 0.100738 / 0.038508 (0.062230) \| 0.030251 / 0.023109 (0.007141) \| 0.301483 / 0.275898 (0.025585) \| 0.374161 / 0.323480 (0.050681) \| 0.007225 / 0.007986 (-0.000761) \| 0.003654 / 0.004328 (-0.000674) \| 0.078400 / 0.004250 (0.074149) \| 0.035786 / 0.037052 (-0.001267) \| 0.309744 / 0.258489 (0.051255) \| 0.355834 / 0.293841 (0.061994) \| 0.034344 / 0.128546 (-0.094202) \| 0.011584 / 0.075646 (-0.064062) \| 0.321462 / 0.419271 (-0.097810) \| 0.041201 / 0.043533 (-0.002332) \| 0.298808 / 0.255139 (0.043669) \| 0.332626 / 0.283200 (0.049426) \| 0.089131 / 0.141683 (-0.052552) \| 1.477888 / 1.452155 (0.025734) \| 1.530365 / 1.492716 (0.037649) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191647 / 0.018006 (0.173640) \| 0.424339 / 0.000490 (0.423849) \| 0.002941 / 0.000200 (0.002741) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023442 / 0.037411 (-0.013969) \| 0.097264 / 0.014526 (0.082738) \| 0.105655 / 0.176557 (-0.070901) \| 0.145055 / 0.737135 (-0.592081) \| 0.108750 / 0.296338 (-0.187588) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422925 / 0.215209 (0.207716) \| 4.216022 / 2.077655 (2.138367) \| 1.876441 / 1.504120 (0.372322) \| 1.665115 / 1.541195 (0.123920) \| 1.711105 / 1.468490 (0.242615) \| 0.701820 / 4.584777 (-3.882957) \| 3.389319 / 3.745712 (-0.356393) \| 1.909868 / 5.269862 (-3.359994) \| 1.270482 / 4.565676 (-3.295195) \| 0.083680 / 0.424275 (-0.340595) \| 0.012347 / 0.007607 (0.004740) \| 0.531076 / 0.226044 (0.305031) \| 5.344045 / 2.268929 (3.075117) \| 2.310897 / 55.444624 (-53.133728) \| 1.971953 / 6.876477 (-4.904524) \| 2.113748 / 2.142072 (-0.028325) \| 0.823766 / 4.805227 (-3.981462) \| 0.150864 / 6.500664 (-6.349800) \| 0.066263 / 0.075469 (-0.009206) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253190 / 1.841788 (-0.588598) \| 13.757887 / 8.074308 (5.683579) \| 13.888195 / 10.191392 (3.696803) \| 0.137285 / 0.680424 (-0.543139) \| 0.029151 / 0.534201 (-0.505050) \| 0.387402 / 0.579283 (-0.191881) \| 0.401673 / 0.434364 (-0.032691) \| 0.450474 / 0.540337 (-0.089863) \| 0.533757 / 1.386936 (-0.853179) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006919 / 0.011353 (-0.004434) \| 0.004655 / 0.011008 (-0.006353) \| 0.096946 / 0.038508 (0.058438) \| 0.028697 / 0.023109 (0.005588) \| 0.420020 / 0.275898 (0.144122) \| 0.460193 / 0.323480 (0.136713) \| 0.005189 / 0.007986 (-0.002796) \| 0.003425 / 0.004328 (-0.000904) \| 0.074900 / 0.004250 (0.070649) \| 0.041844 / 0.037052 (0.004792) \| 0.421538 / 0.258489 (0.163049) \| 0.468497 / 0.293841 (0.174656) \| 0.032573 / 0.128546 (-0.095973) \| 0.011731 / 0.075646 (-0.063916) \| 0.320221 / 0.419271 (-0.099050) \| 0.042113 / 0.043533 (-0.001420) \| 0.422757 / 0.255139 (0.167618) \| 0.445372 / 0.283200 (0.162172) \| 0.090300 / 0.141683 (-0.051383) \| 1.458598 / 1.452155 (0.006443) \| 1.550060 / 1.492716 (0.057344) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235489 / 0.018006 (0.217483) \| 0.418207 / 0.000490 (0.417718) \| 0.002511 / 0.000200 (0.002311) \| 0.000080 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025603 / 0.037411 (-0.011808) \| 0.100237 / 0.014526 (0.085711) \| 0.108617 / 0.176557 (-0.067939) \| 0.148417 / 0.737135 (-0.588719) \| 0.110163 / 0.296338 (-0.186176) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474804 / 0.215209 (0.259595) \| 4.745370 / 2.077655 (2.667715) \| 2.417819 / 1.504120 (0.913699) \| 2.209892 / 1.541195 (0.668697) \| 2.263296 / 1.468490 (0.794806) \| 0.695537 / 4.584777 (-3.889240) \| 3.381028 / 3.745712 (-0.364684) \| 2.952271 / 5.269862 (-2.317591) \| 1.507041 / 4.565676 (-3.058636) \| 0.083334 / 0.424275 (-0.340941) \| 0.012554 / 0.007607 (0.004947) \| 0.578861 / 0.226044 (0.352817) \| 5.795241 / 2.268929 (3.526313) \| 2.858544 / 55.444624 (-52.586080) \| 2.516270 / 6.876477 (-4.360207) \| 2.557350 / 2.142072 (0.415278) \| 0.801799 / 4.805227 (-4.003428) \| 0.151579 / 6.500664 (-6.349085) \| 0.068765 / 0.075469 (-0.006704) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.279935 / 1.841788 (-0.561853) \| 14.049065 / 8.074308 (5.974757) \| 13.972703 / 10.191392 (3.781311) \| 0.140551 / 0.680424 (-0.539873) \| 0.016831 / 0.534201 (-0.517370) \| 0.383886 / 0.579283 (-0.195397) \| 0.385661 / 0.434364 (-0.048703) \| 0.444525 / 0.540337 (-0.095813) \| 0.532197 / 1.386936 (-0.854739) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8d206848fb7afeafecf2a2581ca9a332bdedefa9 \"CML watermark\")\n" ]	2022-12-28T15:33:17	2023-01-18T12:42:33	2023-01-18T12:35:32	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5393", "html_url": "https://github.com/huggingface/datasets/pull/5393", "diff_url": "https://github.com/huggingface/datasets/pull/5393.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5393.patch", "merged_at": "2023-01-18T12:35:32" }	See #5385 for some discussion on this The `fs=` arg was depcrecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in `2.8.0` (to be removed in `3.0.0`). There are a few other places where the `fs=` arg was still used (functions/methods in `datasets.info` and `datasets.load`). This PR adds a similar behavior, warnings and the `storage_options=` arg to these functions and methods. One question: should the "deprecated" / "added" versions be `2.8.1` for the docs/warnings on these? Right now I'm going with "fs was deprecated in 2.8.0" but "storage_options= was added in 2.8.1" where appropriate. @mariosasko	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5393/reactions", "total_count": 2, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 2, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5393/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5392	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5392/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5392/comments	https://api.github.com/repos/huggingface/datasets/issues/5392/events	https://github.com/huggingface/datasets/pull/5392	1,512,712,529	PR_kwDODunzps5GS2DF	5,392	Fix Colab notebook link	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011196 / 0.011353 (-0.000157) \| 0.006039 / 0.011008 (-0.004969) \| 0.122497 / 0.038508 (0.083989) \| 0.043884 / 0.023109 (0.020774) \| 0.372982 / 0.275898 (0.097084) \| 0.444229 / 0.323480 (0.120749) \| 0.009489 / 0.007986 (0.001503) \| 0.004612 / 0.004328 (0.000284) \| 0.093921 / 0.004250 (0.089670) \| 0.052698 / 0.037052 (0.015646) \| 0.372327 / 0.258489 (0.113838) \| 0.426586 / 0.293841 (0.132745) \| 0.046755 / 0.128546 (-0.081792) \| 0.014848 / 0.075646 (-0.060799) \| 0.410474 / 0.419271 (-0.008798) \| 0.058206 / 0.043533 (0.014674) \| 0.367051 / 0.255139 (0.111912) \| 0.389950 / 0.283200 (0.106750) \| 0.120857 / 0.141683 (-0.020826) \| 1.795195 / 1.452155 (0.343040) \| 1.823938 / 1.492716 (0.331222) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215199 / 0.018006 (0.197192) \| 0.482420 / 0.000490 (0.481930) \| 0.001834 / 0.000200 (0.001634) \| 0.000099 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034483 / 0.037411 (-0.002928) \| 0.135503 / 0.014526 (0.120977) \| 0.149991 / 0.176557 (-0.026565) \| 0.198482 / 0.737135 (-0.538653) \| 0.153556 / 0.296338 (-0.142783) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.504492 / 0.215209 (0.289283) \| 4.950949 / 2.077655 (2.873294) \| 2.251186 / 1.504120 (0.747067) \| 2.049195 / 1.541195 (0.508000) \| 2.123325 / 1.468490 (0.654835) \| 0.865651 / 4.584777 (-3.719126) \| 4.652297 / 3.745712 (0.906585) \| 4.417260 / 5.269862 (-0.852602) \| 2.362390 / 4.565676 (-2.203287) \| 0.098845 / 0.424275 (-0.325430) \| 0.014675 / 0.007607 (0.007068) \| 0.608048 / 0.226044 (0.382003) \| 6.063863 / 2.268929 (3.794935) \| 2.753041 / 55.444624 (-52.691583) \| 2.340961 / 6.876477 (-4.535516) \| 2.511934 / 2.142072 (0.369862) \| 0.989297 / 4.805227 (-3.815930) \| 0.195770 / 6.500664 (-6.304894) \| 0.076027 / 0.075469 (0.000558) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.479617 / 1.841788 (-0.362170) \| 18.917860 / 8.074308 (10.843552) \| 18.219594 / 10.191392 (8.028202) \| 0.218494 / 0.680424 (-0.461930) \| 0.037207 / 0.534201 (-0.496994) \| 0.571543 / 0.579283 (-0.007741) \| 0.527884 / 0.434364 (0.093520) \| 0.658661 / 0.540337 (0.118324) \| 0.755449 / 1.386936 (-0.631487) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008762 / 0.011353 (-0.002591) \| 0.006019 / 0.011008 (-0.004989) \| 0.118756 / 0.038508 (0.080248) \| 0.039584 / 0.023109 (0.016474) \| 0.400127 / 0.275898 (0.124229) \| 0.468114 / 0.323480 (0.144634) \| 0.006771 / 0.007986 (-0.001215) \| 0.004689 / 0.004328 (0.000360) \| 0.087274 / 0.004250 (0.083023) \| 0.055548 / 0.037052 (0.018496) \| 0.419901 / 0.258489 (0.161412) \| 0.459516 / 0.293841 (0.165675) \| 0.044197 / 0.128546 (-0.084349) \| 0.014162 / 0.075646 (-0.061484) \| 0.409634 / 0.419271 (-0.009638) \| 0.058668 / 0.043533 (0.015135) \| 0.404758 / 0.255139 (0.149619) \| 0.431562 / 0.283200 (0.148363) \| 0.122361 / 0.141683 (-0.019322) \| 1.726597 / 1.452155 (0.274442) \| 1.798977 / 1.492716 (0.306260) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250831 / 0.018006 (0.232825) \| 0.489811 / 0.000490 (0.489321) \| 0.000490 / 0.000200 (0.000290) \| 0.000071 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035666 / 0.037411 (-0.001745) \| 0.134899 / 0.014526 (0.120374) \| 0.153156 / 0.176557 (-0.023401) \| 0.202409 / 0.737135 (-0.534726) \| 0.157350 / 0.296338 (-0.138989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.522464 / 0.215209 (0.307254) \| 5.204449 / 2.077655 (3.126794) \| 2.617410 / 1.504120 (1.113290) \| 2.406246 / 1.541195 (0.865052) \| 2.494487 / 1.468490 (1.025997) \| 0.834923 / 4.584777 (-3.749854) \| 4.794186 / 3.745712 (1.048474) \| 2.617939 / 5.269862 (-2.651922) \| 1.648310 / 4.565676 (-2.917367) \| 0.109785 / 0.424275 (-0.314490) \| 0.015217 / 0.007607 (0.007610) \| 0.682970 / 0.226044 (0.456926) \| 6.853894 / 2.268929 (4.584966) \| 3.277150 / 55.444624 (-52.167475) \| 2.832502 / 6.876477 (-4.043975) \| 2.984874 / 2.142072 (0.842802) \| 1.005307 / 4.805227 (-3.799921) \| 0.200623 / 6.500664 (-6.300041) \| 0.076852 / 0.075469 (0.001383) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.556656 / 1.841788 (-0.285131) \| 19.088978 / 8.074308 (11.014669) \| 16.946406 / 10.191392 (6.755014) \| 0.204419 / 0.680424 (-0.476004) \| 0.021456 / 0.534201 (-0.512745) \| 0.523603 / 0.579283 (-0.055680) \| 0.530067 / 0.434364 (0.095703) \| 0.604058 / 0.540337 (0.063721) \| 0.731531 / 1.386936 (-0.655405) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]	2022-12-28T11:44:53	2023-01-03T15:36:14	2023-01-03T15:27:31	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5392", "html_url": "https://github.com/huggingface/datasets/pull/5392", "diff_url": "https://github.com/huggingface/datasets/pull/5392.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5392.patch", "merged_at": "2023-01-03T15:27:31" }	Fix notebook link to open in Colab.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5392/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5392/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5391	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5391/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5391/comments	https://api.github.com/repos/huggingface/datasets/issues/5391/events	https://github.com/huggingface/datasets/issues/5391	1,510,350,400	I_kwDODunzps5aBh5A	5,391	Whisper Event - RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 [2:52:21<00:00, 10.34s/it]	{ "login": "catswithbats", "id": 12885107, "node_id": "MDQ6VXNlcjEyODg1MTA3", "avatar_url": "https://avatars.githubusercontent.com/u/12885107?v=4", "gravatar_id": "", "url": "https://api.github.com/users/catswithbats", "html_url": "https://github.com/catswithbats", "followers_url": "https://api.github.com/users/catswithbats/followers", "following_url": "https://api.github.com/users/catswithbats/following{/other_user}", "gists_url": "https://api.github.com/users/catswithbats/gists{/gist_id}", "starred_url": "https://api.github.com/users/catswithbats/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/catswithbats/subscriptions", "organizations_url": "https://api.github.com/users/catswithbats/orgs", "repos_url": "https://api.github.com/users/catswithbats/repos", "events_url": "https://api.github.com/users/catswithbats/events{/privacy}", "received_events_url": "https://api.github.com/users/catswithbats/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hey @catswithbats! Super sorry for the late reply! This is happening because there is data with label length (504) that exceeds the model's max length (448). \r\n\r\nThere are two options here:\r\n1. Increase the model's `max_length` parameter: \r\n```python\r\nmodel.config.max_length = 512\r\n```\r\n2. Filter data with labels longer than max length: https://discuss.huggingface.co/t/open-to-the-community-whisper-fine-tuning-event/26681/21?u=sanchit-gandhi\r\n\r\nNote that the datasets repo is reserved for issues directly related to the HF datasets library. Issues related to custom fine-tuning implementations are more applicable to the HF Forum: https://discuss.huggingface.co. You're more likely to get a response by posting your issue in the most applicable place and boost the chance of someone sharing a working solution!", "@sanchit-gandhi Thank you for all your work on this topic.\r\n\r\nI'm finding that changing the `max_length` value does not make this error go away." ]	2022-12-25T15:17:14	2023-07-21T14:29:47	2023-07-21T14:29:47	NONE	null	null	null	Done in a VM with a GPU (Ubuntu) following the [Whisper Event - PYTHON](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event#python-script) instructions. Attempted using [RuntimeError: he size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 - WEB](https://discuss.huggingface.co/t/trainer-runtimeerror-the-size-of-tensor-a-462-must-match-the-size-of-tensor-b-448-at-non-singleton-dimension-1/26010/10 ) - another person experiencing the same issue. But could not resolve the issue with the google/fleurs data. __Not clear what can be modified in the PY code to resolve the input data size mismatch, as the training data is already very small__. Tried posting on Discord, @sanchit-gandhi and @vaibhavs10. Was hoping that the event is over and some input/help is now available. [Hugging Face - whisper-small-amet](https://huggingface.co/drmeeseeks/whisper-small-amet). The paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) am_et is a low resource language (Table E), with the WER results ranging from 120-229, based on model size. (Whisper small WER=120.2). # ---> Initial Training Output /usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning warnings.warn( [INFO\|trainer.py:1641] 2022-12-18 05:23:28,799 >> *** Running training * [INFO\|trainer.py:1642] 2022-12-18 05:23:28,799 >> Num examples = 446 [INFO\|trainer.py:1643] 2022-12-18 05:23:28,799 >> Num Epochs = 72 [INFO\|trainer.py:1644] 2022-12-18 05:23:28,799 >> Instantaneous batch size per device = 16 [INFO\|trainer.py:1645] 2022-12-18 05:23:28,799 >> Total train batch size (w. parallel, distributed & accumulation) = 32 [INFO\|trainer.py:1646] 2022-12-18 05:23:28,799 >> Gradient Accumulation steps = 2 [INFO\|trainer.py:1647] 2022-12-18 05:23:28,800 >> Total optimization steps = 1000 [INFO\|trainer.py:1648] 2022-12-18 05:23:28,801 >> Number of trainable parameters = 241734912 # ---> Error 14% 9/65 [07:07<48:34, 52.04s/it][INFO\|configuration_utils.py:523] 2022-12-18 05:03:07,941 >> Generate config GenerationConfig { "begin_suppress_tokens": [ 220, 50257 ], "bos_token_id": 50257, "decoder_start_token_id": 50258, "eos_token_id": 50257, "max_length": 448, "pad_token_id": 50257, "transformers_version": "4.26.0.dev0", "use_cache": false } Traceback (most recent call last): File "run_speech_recognition_seq2seq_streaming.py", line 629, in <module> main() File "run_speech_recognition_seq2seq_streaming.py", line 578, in main train_result = trainer.train(resume_from_checkpoint=checkpoint) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1534, in train return inner_training_loop( File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1859, in _inner_training_loop self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2122, in _maybe_log_save_evaluate metrics = self.evaluate(ignore_keys=ignore_keys_for_eval) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 78, in evaluate return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2818, in evaluate output = eval_loop( File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 3000, in evaluation_loop loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 213, in prediction_step outputs = model(inputs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(input, kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1197, in forward outputs = self.model( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(input, *kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1066, in forward decoder_outputs = self.decoder( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 873, in forward hidden_states = inputs_embeds + positions RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 [2:52:21<00:00, 10.34s/it]	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5391/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5391/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5390	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5390/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5390/comments	https://api.github.com/repos/huggingface/datasets/issues/5390/events	https://github.com/huggingface/datasets/issues/5390	1,509,357,553	I_kwDODunzps5Z9vfx	5,390	Error when pushing to the CI hub	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hmmm, git bisect tells me that the behavior is the same since https://github.com/huggingface/datasets/commit/67e65c90e9490810b89ee140da11fdd13c356c9c (3 Oct), i.e. https://github.com/huggingface/datasets/pull/4926", "Maybe related to the discussions in https://github.com/huggingface/datasets/pull/5196", "Maybe the current version of moonlanding in Hub CI is the issue.\r\n\r\nI relaunched tests that were working two days ago: now they are failing. https://github.com/huggingface/datasets-server/commit/746414449cae4b311733f8a76e5b3b4ca73b38a9 for example\r\n\r\ncc @huggingface/moon-landing ", "Hi! I don't think this has anything to do with `datasets`. Hub CI seems to be the culprit - the identical failure can be found in [this](https://github.com/huggingface/datasets/pull/5389) PR (with unrelated changes) opened today.", "OK! Thanks for looking at it. Closing then." ]	2022-12-23T13:36:37	2022-12-23T20:29:02	2022-12-23T20:29:02	CONTRIBUTOR	null	null	null	### Describe the bug Note that it's a special case where the Hub URL is "https://hub-ci.huggingface.co", which does not appear if we do the same on the Hub (https://huggingface.co). The call to `dataset.push_to_hub(` fails: ``` Pushing dataset shards to the dataset hub: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:01<00:00, 1.93s/it] Traceback (most recent call last): File "reproduce_hubci.py", line 16, in <module> dataset.push_to_hub(repo_id=repo_id, private=False, token=USER_TOKEN, embed_external_files=True) File "/home/slesage/hf/datasets/src/datasets/arrow_dataset.py", line 5025, in push_to_hub HfApi(endpoint=config.HF_ENDPOINT).upload_file( File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1346, in upload_file raise err File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1337, in upload_file r.raise_for_status() File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_DATASETS_SERVER_USER__/bug-16718047265472/upload/main/README.md ``` ### Steps to reproduce the bug ```python # reproduce.py from datasets import Dataset import time USER = "__DUMMY_DATASETS_SERVER_USER__" USER_TOKEN = "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" dataset = Dataset.from_dict({"a": [1, 2, 3]}) repo_id = f"{USER}/bug-{int(time.time() * 10e3)}" dataset.push_to_hub(repo_id=repo_id, private=False, token=USER_TOKEN, embed_external_files=True) ``` ```bash $ HF_ENDPOINT="https://hub-ci.huggingface.co" python reproduce.py ``` ### Expected behavior No error and the dataset should be uploaded to the Hub with the README file (which generates the error). ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.15.0-1026-aws-x86_64-with-glibc2.35 - Python version: 3.9.15 - PyArrow version: 7.0.0 - Pandas version: 1.5.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5390/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5390/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5389	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5389/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5389/comments	https://api.github.com/repos/huggingface/datasets/issues/5389/events	https://github.com/huggingface/datasets/pull/5389	1,509,348,626	PR_kwDODunzps5GHsOo	5,389	Fix link in `load_dataset` docstring	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008935 / 0.011353 (-0.002417) \| 0.004582 / 0.011008 (-0.006426) \| 0.100950 / 0.038508 (0.062442) \| 0.030305 / 0.023109 (0.007196) \| 0.299759 / 0.275898 (0.023861) \| 0.378577 / 0.323480 (0.055097) \| 0.007834 / 0.007986 (-0.000152) \| 0.003399 / 0.004328 (-0.000930) \| 0.078568 / 0.004250 (0.074318) \| 0.037990 / 0.037052 (0.000938) \| 0.313025 / 0.258489 (0.054536) \| 0.359543 / 0.293841 (0.065702) \| 0.033631 / 0.128546 (-0.094916) \| 0.011681 / 0.075646 (-0.063966) \| 0.324542 / 0.419271 (-0.094729) \| 0.041014 / 0.043533 (-0.002519) \| 0.302884 / 0.255139 (0.047745) \| 0.337059 / 0.283200 (0.053859) \| 0.089403 / 0.141683 (-0.052280) \| 1.491262 / 1.452155 (0.039108) \| 1.521626 / 1.492716 (0.028910) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.172627 / 0.018006 (0.154621) \| 0.419406 / 0.000490 (0.418917) \| 0.001974 / 0.000200 (0.001775) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023598 / 0.037411 (-0.013814) \| 0.098127 / 0.014526 (0.083601) \| 0.105611 / 0.176557 (-0.070946) \| 0.142612 / 0.737135 (-0.594523) \| 0.121687 / 0.296338 (-0.174651) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418512 / 0.215209 (0.203303) \| 4.173099 / 2.077655 (2.095444) \| 1.865900 / 1.504120 (0.361780) \| 1.664053 / 1.541195 (0.122858) \| 1.726289 / 1.468490 (0.257799) \| 0.693214 / 4.584777 (-3.891563) \| 3.499982 / 3.745712 (-0.245730) \| 1.894278 / 5.269862 (-3.375583) \| 1.178214 / 4.565676 (-3.387463) \| 0.082391 / 0.424275 (-0.341884) \| 0.012486 / 0.007607 (0.004878) \| 0.532190 / 0.226044 (0.306145) \| 5.286612 / 2.268929 (3.017684) \| 2.316680 / 55.444624 (-53.127944) \| 1.964020 / 6.876477 (-4.912457) \| 2.016457 / 2.142072 (-0.125616) \| 0.812290 / 4.805227 (-3.992937) \| 0.149102 / 6.500664 (-6.351562) \| 0.064215 / 0.075469 (-0.011254) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.281919 / 1.841788 (-0.559869) \| 14.107509 / 8.074308 (6.033201) \| 13.892369 / 10.191392 (3.700977) \| 0.146164 / 0.680424 (-0.534260) \| 0.028740 / 0.534201 (-0.505460) \| 0.395218 / 0.579283 (-0.184066) \| 0.406321 / 0.434364 (-0.028043) \| 0.460880 / 0.540337 (-0.079458) \| 0.545975 / 1.386936 (-0.840961) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006797 / 0.011353 (-0.004556) \| 0.004522 / 0.011008 (-0.006486) \| 0.098440 / 0.038508 (0.059932) \| 0.027722 / 0.023109 (0.004613) \| 0.423995 / 0.275898 (0.148097) \| 0.456164 / 0.323480 (0.132684) \| 0.005156 / 0.007986 (-0.002830) \| 0.003439 / 0.004328 (-0.000889) \| 0.075307 / 0.004250 (0.071057) \| 0.039599 / 0.037052 (0.002547) \| 0.423671 / 0.258489 (0.165181) \| 0.463841 / 0.293841 (0.170001) \| 0.032473 / 0.128546 (-0.096073) \| 0.011674 / 0.075646 (-0.063972) \| 0.320548 / 0.419271 (-0.098723) \| 0.041618 / 0.043533 (-0.001915) \| 0.426133 / 0.255139 (0.170994) \| 0.443018 / 0.283200 (0.159819) \| 0.091103 / 0.141683 (-0.050579) \| 1.468758 / 1.452155 (0.016604) \| 1.532695 / 1.492716 (0.039978) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.255314 / 0.018006 (0.237308) \| 0.422982 / 0.000490 (0.422492) \| 0.015405 / 0.000200 (0.015205) \| 0.000103 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025260 / 0.037411 (-0.012152) \| 0.102062 / 0.014526 (0.087537) \| 0.108161 / 0.176557 (-0.068395) \| 0.144205 / 0.737135 (-0.592930) \| 0.111686 / 0.296338 (-0.184653) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.482633 / 0.215209 (0.267424) \| 4.824777 / 2.077655 (2.747123) \| 2.488626 / 1.504120 (0.984506) \| 2.285410 / 1.541195 (0.744215) \| 2.336793 / 1.468490 (0.868303) \| 0.701894 / 4.584777 (-3.882883) \| 3.506908 / 3.745712 (-0.238804) \| 3.399789 / 5.269862 (-1.870072) \| 1.536359 / 4.565676 (-3.029317) \| 0.083621 / 0.424275 (-0.340655) \| 0.012702 / 0.007607 (0.005094) \| 0.581259 / 0.226044 (0.355215) \| 5.829640 / 2.268929 (3.560711) \| 2.932201 / 55.444624 (-52.512424) \| 2.577175 / 6.876477 (-4.299301) \| 2.621782 / 2.142072 (0.479710) \| 0.812074 / 4.805227 (-3.993153) \| 0.152840 / 6.500664 (-6.347824) \| 0.067982 / 0.075469 (-0.007487) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.274915 / 1.841788 (-0.566873) \| 14.345800 / 8.074308 (6.271492) \| 14.242475 / 10.191392 (4.051083) \| 0.143636 / 0.680424 (-0.536788) \| 0.016824 / 0.534201 (-0.517377) \| 0.376449 / 0.579283 (-0.202834) \| 0.394219 / 0.434364 (-0.040145) \| 0.435368 / 0.540337 (-0.104969) \| 0.518393 / 1.386936 (-0.868544) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#187e4faa978fef267a055f6988564f922e51eaa4 \"CML watermark\")\n", "I also fixed the rest of the links that point to the markdown files. \r\n\r\nPS: the CI failures are unrelated ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008641 / 0.011353 (-0.002712) \| 0.004560 / 0.011008 (-0.006448) \| 0.100559 / 0.038508 (0.062051) \| 0.029744 / 0.023109 (0.006635) \| 0.300580 / 0.275898 (0.024682) \| 0.359100 / 0.323480 (0.035620) \| 0.007016 / 0.007986 (-0.000970) \| 0.003393 / 0.004328 (-0.000936) \| 0.078649 / 0.004250 (0.074399) \| 0.038138 / 0.037052 (0.001086) \| 0.307730 / 0.258489 (0.049241) \| 0.347678 / 0.293841 (0.053837) \| 0.033630 / 0.128546 (-0.094917) \| 0.011452 / 0.075646 (-0.064194) \| 0.320903 / 0.419271 (-0.098369) \| 0.042659 / 0.043533 (-0.000874) \| 0.298886 / 0.255139 (0.043747) \| 0.324371 / 0.283200 (0.041171) \| 0.092582 / 0.141683 (-0.049101) \| 1.490017 / 1.452155 (0.037863) \| 1.512825 / 1.492716 (0.020109) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.178965 / 0.018006 (0.160958) \| 0.420001 / 0.000490 (0.419512) \| 0.002686 / 0.000200 (0.002486) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023568 / 0.037411 (-0.013843) \| 0.097027 / 0.014526 (0.082502) \| 0.104721 / 0.176557 (-0.071836) \| 0.148757 / 0.737135 (-0.588378) \| 0.110849 / 0.296338 (-0.185489) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415034 / 0.215209 (0.199825) \| 4.155249 / 2.077655 (2.077594) \| 1.837027 / 1.504120 (0.332907) \| 1.627754 / 1.541195 (0.086559) \| 1.687958 / 1.468490 (0.219468) \| 0.699542 / 4.584777 (-3.885235) \| 3.376707 / 3.745712 (-0.369005) \| 2.900778 / 5.269862 (-2.369083) \| 1.556168 / 4.565676 (-3.009508) \| 0.082438 / 0.424275 (-0.341837) \| 0.012339 / 0.007607 (0.004732) \| 0.524952 / 0.226044 (0.298907) \| 5.269852 / 2.268929 (3.000924) \| 2.278770 / 55.444624 (-53.165854) \| 1.917987 / 6.876477 (-4.958490) \| 1.955000 / 2.142072 (-0.187072) \| 0.821169 / 4.805227 (-3.984058) \| 0.149019 / 6.500664 (-6.351645) \| 0.064604 / 0.075469 (-0.010865) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.199768 / 1.841788 (-0.642020) \| 13.760897 / 8.074308 (5.686589) \| 13.911550 / 10.191392 (3.720158) \| 0.161727 / 0.680424 (-0.518697) \| 0.028615 / 0.534201 (-0.505586) \| 0.393917 / 0.579283 (-0.185366) \| 0.392524 / 0.434364 (-0.041840) \| 0.451763 / 0.540337 (-0.088574) \| 0.536880 / 1.386936 (-0.850056) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006407 / 0.011353 (-0.004946) \| 0.004420 / 0.011008 (-0.006588) \| 0.097244 / 0.038508 (0.058736) \| 0.027114 / 0.023109 (0.004005) \| 0.412512 / 0.275898 (0.136614) \| 0.448189 / 0.323480 (0.124709) \| 0.005831 / 0.007986 (-0.002155) \| 0.005423 / 0.004328 (0.001095) \| 0.076051 / 0.004250 (0.071801) \| 0.038828 / 0.037052 (0.001776) \| 0.414586 / 0.258489 (0.156097) \| 0.457196 / 0.293841 (0.163355) \| 0.031615 / 0.128546 (-0.096931) \| 0.011542 / 0.075646 (-0.064104) \| 0.316967 / 0.419271 (-0.102304) \| 0.041278 / 0.043533 (-0.002254) \| 0.411371 / 0.255139 (0.156232) \| 0.436376 / 0.283200 (0.153177) \| 0.090212 / 0.141683 (-0.051471) \| 1.461831 / 1.452155 (0.009677) \| 1.606515 / 1.492716 (0.113799) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221453 / 0.018006 (0.203447) \| 0.404140 / 0.000490 (0.403650) \| 0.000422 / 0.000200 (0.000222) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024588 / 0.037411 (-0.012824) \| 0.098604 / 0.014526 (0.084078) \| 0.113682 / 0.176557 (-0.062874) \| 0.141141 / 0.737135 (-0.595994) \| 0.110069 / 0.296338 (-0.186270) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.477267 / 0.215209 (0.262058) \| 4.775086 / 2.077655 (2.697431) \| 2.445449 / 1.504120 (0.941329) \| 2.242220 / 1.541195 (0.701025) \| 2.303542 / 1.468490 (0.835051) \| 0.693448 / 4.584777 (-3.891329) \| 3.413319 / 3.745712 (-0.332393) \| 3.052734 / 5.269862 (-2.217127) \| 1.434075 / 4.565676 (-3.131602) \| 0.082429 / 0.424275 (-0.341846) \| 0.012594 / 0.007607 (0.004987) \| 0.584259 / 0.226044 (0.358214) \| 5.865098 / 2.268929 (3.596169) \| 2.926301 / 55.444624 (-52.518324) \| 2.572555 / 6.876477 (-4.303921) \| 2.608584 / 2.142072 (0.466512) \| 0.805029 / 4.805227 (-4.000198) \| 0.151247 / 6.500664 (-6.349417) \| 0.067142 / 0.075469 (-0.008327) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.285454 / 1.841788 (-0.556334) \| 14.296425 / 8.074308 (6.222117) \| 14.147278 / 10.191392 (3.955886) \| 0.151698 / 0.680424 (-0.528726) \| 0.016876 / 0.534201 (-0.517325) \| 0.383302 / 0.579283 (-0.195981) \| 0.388461 / 0.434364 (-0.045902) \| 0.438286 / 0.540337 (-0.102051) \| 0.525249 / 1.386936 (-0.861687) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2a3b2f04f1fd62249ac43c534761ce151ad5c269 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008677 / 0.011353 (-0.002676) \| 0.004863 / 0.011008 (-0.006145) \| 0.096606 / 0.038508 (0.058098) \| 0.034004 / 0.023109 (0.010895) \| 0.296362 / 0.275898 (0.020464) \| 0.323445 / 0.323480 (-0.000035) \| 0.007341 / 0.007986 (-0.000644) \| 0.005518 / 0.004328 (0.001189) \| 0.073584 / 0.004250 (0.069334) \| 0.041471 / 0.037052 (0.004419) \| 0.302183 / 0.258489 (0.043694) \| 0.339369 / 0.293841 (0.045528) \| 0.037375 / 0.128546 (-0.091171) \| 0.011827 / 0.075646 (-0.063819) \| 0.330723 / 0.419271 (-0.088549) \| 0.048751 / 0.043533 (0.005218) \| 0.298370 / 0.255139 (0.043231) \| 0.317781 / 0.283200 (0.034582) \| 0.097488 / 0.141683 (-0.044195) \| 1.456242 / 1.452155 (0.004088) \| 1.530149 / 1.492716 (0.037433) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207053 / 0.018006 (0.189046) \| 0.438165 / 0.000490 (0.437675) \| 0.001161 / 0.000200 (0.000961) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025353 / 0.037411 (-0.012059) \| 0.105536 / 0.014526 (0.091010) \| 0.116122 / 0.176557 (-0.060434) \| 0.151605 / 0.737135 (-0.585530) \| 0.121777 / 0.296338 (-0.174561) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402780 / 0.215209 (0.187571) \| 4.017882 / 2.077655 (1.940227) \| 1.813111 / 1.504120 (0.308991) \| 1.620000 / 1.541195 (0.078805) \| 1.649186 / 1.468490 (0.180696) \| 0.687523 / 4.584777 (-3.897254) \| 3.712595 / 3.745712 (-0.033117) \| 2.038535 / 5.269862 (-3.231326) \| 1.414794 / 4.565676 (-3.150882) \| 0.083357 / 0.424275 (-0.340918) \| 0.012032 / 0.007607 (0.004425) \| 0.502899 / 0.226044 (0.276854) \| 5.038914 / 2.268929 (2.769985) \| 2.250476 / 55.444624 (-53.194148) \| 1.919954 / 6.876477 (-4.956523) \| 1.930928 / 2.142072 (-0.211144) \| 0.826634 / 4.805227 (-3.978593) \| 0.161599 / 6.500664 (-6.339066) \| 0.061356 / 0.075469 (-0.014113) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.228998 / 1.841788 (-0.612790) \| 14.587914 / 8.074308 (6.513606) \| 14.237514 / 10.191392 (4.046122) \| 0.190913 / 0.680424 (-0.489510) \| 0.029104 / 0.534201 (-0.505097) \| 0.436160 / 0.579283 (-0.143123) \| 0.431464 / 0.434364 (-0.002900) \| 0.511670 / 0.540337 (-0.028668) \| 0.609046 / 1.386936 (-0.777890) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006980 / 0.011353 (-0.004373) \| 0.005260 / 0.011008 (-0.005748) \| 0.095288 / 0.038508 (0.056780) \| 0.032465 / 0.023109 (0.009356) \| 0.410799 / 0.275898 (0.134901) \| 0.423814 / 0.323480 (0.100334) \| 0.005533 / 0.007986 (-0.002452) \| 0.005764 / 0.004328 (0.001436) \| 0.070713 / 0.004250 (0.066462) \| 0.048193 / 0.037052 (0.011141) \| 0.405742 / 0.258489 (0.147253) \| 0.458773 / 0.293841 (0.164932) \| 0.036415 / 0.128546 (-0.092131) \| 0.012192 / 0.075646 (-0.063454) \| 0.330655 / 0.419271 (-0.088617) \| 0.055945 / 0.043533 (0.012412) \| 0.407497 / 0.255139 (0.152358) \| 0.421496 / 0.283200 (0.138296) \| 0.106285 / 0.141683 (-0.035398) \| 1.459837 / 1.452155 (0.007683) \| 1.573147 / 1.492716 (0.080431) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.205776 / 0.018006 (0.187770) \| 0.441523 / 0.000490 (0.441033) \| 0.003073 / 0.000200 (0.002873) \| 0.000092 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029207 / 0.037411 (-0.008205) \| 0.110295 / 0.014526 (0.095770) \| 0.130233 / 0.176557 (-0.046324) \| 0.157489 / 0.737135 (-0.579647) \| 0.125374 / 0.296338 (-0.170965) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440942 / 0.215209 (0.225733) \| 4.389647 / 2.077655 (2.311992) \| 2.234883 / 1.504120 (0.730763) \| 2.029510 / 1.541195 (0.488315) \| 2.082503 / 1.468490 (0.614013) \| 0.698046 / 4.584777 (-3.886731) \| 3.769127 / 3.745712 (0.023415) \| 2.058511 / 5.269862 (-3.211351) \| 1.324302 / 4.565676 (-3.241375) \| 0.085695 / 0.424275 (-0.338580) \| 0.012122 / 0.007607 (0.004515) \| 0.552406 / 0.226044 (0.326362) \| 5.527073 / 2.268929 (3.258145) \| 2.711354 / 55.444624 (-52.733270) \| 2.328848 / 6.876477 (-4.547629) \| 2.340750 / 2.142072 (0.198678) \| 0.846300 / 4.805227 (-3.958927) \| 0.167465 / 6.500664 (-6.333199) \| 0.063419 / 0.075469 (-0.012050) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.262452 / 1.841788 (-0.579336) \| 15.043537 / 8.074308 (6.969229) \| 14.212563 / 10.191392 (4.021171) \| 0.170229 / 0.680424 (-0.510194) \| 0.017696 / 0.534201 (-0.516505) \| 0.423194 / 0.579283 (-0.156089) \| 0.430908 / 0.434364 (-0.003456) \| 0.491733 / 0.540337 (-0.048604) \| 0.599267 / 1.386936 (-0.787669) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2a3b2f04f1fd62249ac43c534761ce151ad5c269 \"CML watermark\")\n", "Program enthusiastic " ]	2022-12-23T13:26:31	2023-01-25T19:00:43	2023-01-24T16:33:38	COLLABORATOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5389", "html_url": "https://github.com/huggingface/datasets/pull/5389", "diff_url": "https://github.com/huggingface/datasets/pull/5389.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5389.patch", "merged_at": "2023-01-24T16:33:38" }	Fix https://github.com/huggingface/datasets/issues/5387, fix https://github.com/huggingface/datasets/issues/4566	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5389/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5389/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5388	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5388/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5388/comments	https://api.github.com/repos/huggingface/datasets/issues/5388/events	https://github.com/huggingface/datasets/issues/5388	1,509,042,348	I_kwDODunzps5Z8iis	5,388	Getting Value Error while loading a dataset..	{ "login": "valmetisrinivas", "id": 51160232, "node_id": "MDQ6VXNlcjUxMTYwMjMy", "avatar_url": "https://avatars.githubusercontent.com/u/51160232?v=4", "gravatar_id": "", "url": "https://api.github.com/users/valmetisrinivas", "html_url": "https://github.com/valmetisrinivas", "followers_url": "https://api.github.com/users/valmetisrinivas/followers", "following_url": "https://api.github.com/users/valmetisrinivas/following{/other_user}", "gists_url": "https://api.github.com/users/valmetisrinivas/gists{/gist_id}", "starred_url": "https://api.github.com/users/valmetisrinivas/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/valmetisrinivas/subscriptions", "organizations_url": "https://api.github.com/users/valmetisrinivas/orgs", "repos_url": "https://api.github.com/users/valmetisrinivas/repos", "events_url": "https://api.github.com/users/valmetisrinivas/events{/privacy}", "received_events_url": "https://api.github.com/users/valmetisrinivas/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! I can't reproduce this error locally (Mac) or in Colab. What version of `datasets` are you using?", "Hi [mariosasko](https://github.com/mariosasko), the datasets version is '2.8.0'.", "@valmetisrinivas you get that error because you imported `datasets` (and thus `fsspec`) before installing `zstandard`.\r\n\r\nPlease, restart your Colab runtime and execute the install commands before importing `datasets`:\r\n```python\r\n!pip install datasets\r\n!pip install zstandard\r\n\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\r\n \"json\",\r\n data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\r\n split=\"train\",\r\n streaming=True,\r\n)\r\nnext(iter(ds))\r\n```", "> @valmetisrinivas you get that error because you imported `datasets` (and thus `fsspec`) before installing `zstandard`.\r\n> \r\n> Please, restart your Colab runtime and execute the install commands before importing `datasets`:\r\n> \r\n> ```python\r\n> !pip install datasets\r\n> !pip install zstandard\r\n> \r\n> from datasets import load_dataset\r\n> \r\n> ds = load_dataset(\r\n> \"json\",\r\n> data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\r\n> split=\"train\",\r\n> streaming=True,\r\n> )\r\n> next(iter(ds))\r\n> ```\r\n\r\nI guess that was the problem, importing datasets before the installation of zstandard. Thank you for the feedback. " ]	2022-12-23T08:16:43	2022-12-29T08:36:33	2022-12-27T17:59:09	NONE	null	null	null	### Describe the bug I am trying to load a dataset using Hugging Face Datasets load_dataset method. I am getting the value error as show below. Can someone help with this? I am using Windows laptop and Google Colab notebook. ``` WARNING:datasets.builder:Using custom data configuration default-a1d9e8eaedd958cd --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [<ipython-input-12-5b4fdcb8e6d5>](https://localhost:8080/#) in <module> 6 ) 7 ----> 8 next(iter(law_dataset_streamed)) 17 frames [/usr/local/lib/python3.8/dist-packages/fsspec/core.py](https://localhost:8080/#) in get_compression(urlpath, compression) 485 compression = infer_compression(urlpath) 486 if compression is not None and compression not in compr: --> 487 raise ValueError("Compression type %s not supported" % compression) 488 return compression 489 ValueError: Compression type zstd not supported ``` ### Steps to reproduce the bug ``` !pip install zstandard from datasets import load_dataset lds = load_dataset( "json", data_files="https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst", split="train", streaming=True, ) ``` ### Expected behavior I expect an iterable object as the output 'lds' to be created. ### Environment info Windows laptop with Google Colab notebook	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5388/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5388/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5387	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5387/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5387/comments	https://api.github.com/repos/huggingface/datasets/issues/5387/events	https://github.com/huggingface/datasets/issues/5387	1,508,740,177	I_kwDODunzps5Z7YxR	5,387	Missing documentation page : improve-performance	{ "login": "astariul", "id": 43774355, "node_id": "MDQ6VXNlcjQzNzc0MzU1", "avatar_url": "https://avatars.githubusercontent.com/u/43774355?v=4", "gravatar_id": "", "url": "https://api.github.com/users/astariul", "html_url": "https://github.com/astariul", "followers_url": "https://api.github.com/users/astariul/followers", "following_url": "https://api.github.com/users/astariul/following{/other_user}", "gists_url": "https://api.github.com/users/astariul/gists{/gist_id}", "starred_url": "https://api.github.com/users/astariul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/astariul/subscriptions", "organizations_url": "https://api.github.com/users/astariul/orgs", "repos_url": "https://api.github.com/users/astariul/repos", "events_url": "https://api.github.com/users/astariul/events{/privacy}", "received_events_url": "https://api.github.com/users/astariul/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! Our documentation builder does not support links to sections, hence the bug. This is the link it should point to https://huggingface.co/docs/datasets/v2.8.0/en/cache#improve-performance." ]	2022-12-23T01:12:57	2023-01-24T16:33:40	2023-01-24T16:33:40	NONE	null	null	null	### Describe the bug Trying to access https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/cache#improve-performance, the page is missing. The link is in here : https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/loading_methods#datasets.load_dataset.keep_in_memory ### Steps to reproduce the bug Access the page and see it's missing. ### Expected behavior Not missing page ### Environment info Doesn't matter	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5387/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5387/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5386	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5386/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5386/comments	https://api.github.com/repos/huggingface/datasets/issues/5386/events	https://github.com/huggingface/datasets/issues/5386	1,508,592,918	I_kwDODunzps5Z600W	5,386	`max_shard_size` in `datasets.push_to_hub()` breaks with large files	{ "login": "salieri", "id": 1086393, "node_id": "MDQ6VXNlcjEwODYzOTM=", "avatar_url": "https://avatars.githubusercontent.com/u/1086393?v=4", "gravatar_id": "", "url": "https://api.github.com/users/salieri", "html_url": "https://github.com/salieri", "followers_url": "https://api.github.com/users/salieri/followers", "following_url": "https://api.github.com/users/salieri/following{/other_user}", "gists_url": "https://api.github.com/users/salieri/gists{/gist_id}", "starred_url": "https://api.github.com/users/salieri/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/salieri/subscriptions", "organizations_url": "https://api.github.com/users/salieri/orgs", "repos_url": "https://api.github.com/users/salieri/repos", "events_url": "https://api.github.com/users/salieri/events{/privacy}", "received_events_url": "https://api.github.com/users/salieri/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! \r\n\r\nThis behavior stems from the fact that we don't always embed image bytes in the underlying arrow table, which can lead to bad size estimation (we use the first 1000 table rows to [estimate](https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L4627) the external file size). We plan to address this in the next major release by always embedding external bytes. In the meantime, you can either shuffle the dataset with `.shuffle().flatten_indices()` to make the estimation more precise or embed the bytes in the table like so:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\nformat = ds.format\r\nds = ds.with_format(\"arrow\")\r\nds = ds.map(embed_table_storage, batched=True)\r\nds = ds.with_format(**format)\r\n...\r\nds.push_to_hub(...)\r\n```", "Embedding the bytes worked like charm. Thanks @mariosasko!" ]	2022-12-22T21:50:58	2022-12-26T23:45:51	2022-12-26T23:45:51	NONE	null	null	null	### Describe the bug `max_shard_size` parameter for `datasets.push_to_hub()` works unreliably with large files, generating shard files that are way past the specified limit. In my private dataset, which contains unprocessed images of all sizes (up to `~100MB` per file), I've encountered cases where `max_shard_size='100MB'` results in shard files that are `>2GB` in size. Setting `max_shard_size` to another value, such as `1GB` or `500MB` does not fix this problem. The real problem is this: When the shard file size grows too big, the entire dataset breaks because of #4721 and ultimately https://issues.apache.org/jira/browse/ARROW-5030. Since `max_shard_size` does not let one accurately control the size of the shard files, it becomes very easy to build a large dataset without any warnings that it will be broken -- even when you think you are mitigating this problem by setting `max_shard_size`. ``` File " /path/to/sd-test-suite-v1/venv/lib/site-packages/datasets/builder.py", line 1763, in _prepare_split_single for _, table in generator: File " /path/to/sd-test-suite-v1/venv/lib/site-packages/datasets/packaged_modules/parquet/parquet.py", line 69, in _generate_tables for batch_idx, record_batch in enumerate( File "pyarrow/_parquet.pyx", line 1323, in iter_batches File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs ``` ### Steps to reproduce the bug 1. Clone [example repo](https://github.com/salieri/hf-dataset-shard-size-bug) 2. Follow steps in [README.md](https://github.com/salieri/hf-dataset-shard-size-bug/blob/main/README.md) 3. After uploading the dataset, you will see that the shard file size varies between `30MB` and `200MB` -- way beyond the `max_shard_size='75MB'` limit (example: `train-00003-of-00131...` is `155MB` in [here](https://huggingface.co/datasets/slri/shard-size-test/tree/main/data)) (Note that this example repo does not generate shard files that are so large that they would trigger #4721) ### Expected behavior The shard file size should remain below or equal to `max_shard_size`. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.10.157-139.675.amzn2.aarch64-aarch64-with-glibc2.17 - Python version: 3.7.15 - PyArrow version: 10.0.1 - Pandas version: 1.3.5	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5386/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5386/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5385	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5385/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5385/comments	https://api.github.com/repos/huggingface/datasets/issues/5385/events	https://github.com/huggingface/datasets/issues/5385	1,508,535,532	I_kwDODunzps5Z6mzs	5,385	Is `fs=` deprecated in `load_from_disk()` as well?	{ "login": "dconathan", "id": 15098095, "node_id": "MDQ6VXNlcjE1MDk4MDk1", "avatar_url": "https://avatars.githubusercontent.com/u/15098095?v=4", "gravatar_id": "", "url": "https://api.github.com/users/dconathan", "html_url": "https://github.com/dconathan", "followers_url": "https://api.github.com/users/dconathan/followers", "following_url": "https://api.github.com/users/dconathan/following{/other_user}", "gists_url": "https://api.github.com/users/dconathan/gists{/gist_id}", "starred_url": "https://api.github.com/users/dconathan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/dconathan/subscriptions", "organizations_url": "https://api.github.com/users/dconathan/orgs", "repos_url": "https://api.github.com/users/dconathan/repos", "events_url": "https://api.github.com/users/dconathan/events{/privacy}", "received_events_url": "https://api.github.com/users/dconathan/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR? ", "> Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR?\r\n\r\nYeah I can do that sometime next week. Should the storage_options be a new arg here? I’ll look around for anywhere else where fs is an arg.", "Closed by #5393." ]	2022-12-22T21:00:45	2023-01-23T10:50:05	2023-01-23T10:50:04	CONTRIBUTOR	null	null	null	### Describe the bug The `fs=` argument was deprecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in favor of automagically figuring it out via fsspec: https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L1339-L1340 Is there a reason the same thing shouldn't also apply to `datasets.load.load_from_disk()` as well ? https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/load.py#L1779 ### Steps to reproduce the bug n/a ### Expected behavior n/a ### Environment info n/a	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5385/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5385/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5384	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5384/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5384/comments	https://api.github.com/repos/huggingface/datasets/issues/5384/events	https://github.com/huggingface/datasets/pull/5384	1,508,152,598	PR_kwDODunzps5GDmR6	5,384	Handle 0-dim tensors in `cast_to_python_objects`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010576 / 0.011353 (-0.000777) \| 0.006010 / 0.011008 (-0.004998) \| 0.109375 / 0.038508 (0.070867) \| 0.037780 / 0.023109 (0.014670) \| 0.381552 / 0.275898 (0.105654) \| 0.446039 / 0.323480 (0.122559) \| 0.009004 / 0.007986 (0.001019) \| 0.005653 / 0.004328 (0.001324) \| 0.087027 / 0.004250 (0.082776) \| 0.040346 / 0.037052 (0.003293) \| 0.398827 / 0.258489 (0.140338) \| 0.407281 / 0.293841 (0.113440) \| 0.051723 / 0.128546 (-0.076824) \| 0.020254 / 0.075646 (-0.055392) \| 0.376841 / 0.419271 (-0.042430) \| 0.055505 / 0.043533 (0.011972) \| 0.383464 / 0.255139 (0.128325) \| 0.436130 / 0.283200 (0.152930) \| 0.117403 / 0.141683 (-0.024280) \| 1.569016 / 1.452155 (0.116862) \| 1.889831 / 1.492716 (0.397115) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297962 / 0.018006 (0.279956) \| 0.683699 / 0.000490 (0.683210) \| 0.000918 / 0.000200 (0.000718) \| 0.000100 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026742 / 0.037411 (-0.010669) \| 0.125293 / 0.014526 (0.110768) \| 0.128769 / 0.176557 (-0.047787) \| 0.179447 / 0.737135 (-0.557688) \| 0.142032 / 0.296338 (-0.154306) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.588389 / 0.215209 (0.373180) \| 5.943514 / 2.077655 (3.865859) \| 2.631163 / 1.504120 (1.127043) \| 1.865446 / 1.541195 (0.324252) \| 2.055610 / 1.468490 (0.587120) \| 1.090288 / 4.584777 (-3.494489) \| 5.457151 / 3.745712 (1.711439) \| 5.645614 / 5.269862 (0.375752) \| 2.849492 / 4.565676 (-1.716184) \| 0.140447 / 0.424275 (-0.283828) \| 0.015421 / 0.007607 (0.007813) \| 0.735528 / 0.226044 (0.509484) \| 7.394097 / 2.268929 (5.125169) \| 3.219714 / 55.444624 (-52.224911) \| 2.504134 / 6.876477 (-4.372342) \| 2.524291 / 2.142072 (0.382219) \| 1.452776 / 4.805227 (-3.352452) \| 0.256142 / 6.500664 (-6.244522) \| 0.093809 / 0.075469 (0.018340) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.570046 / 1.841788 (-0.271742) \| 17.360385 / 8.074308 (9.286077) \| 20.750595 / 10.191392 (10.559203) \| 0.218486 / 0.680424 (-0.461938) \| 0.048527 / 0.534201 (-0.485674) \| 0.549568 / 0.579283 (-0.029715) \| 0.633993 / 0.434364 (0.199629) \| 0.632585 / 0.540337 (0.092248) \| 0.712817 / 1.386936 (-0.674119) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010524 / 0.011353 (-0.000829) \| 0.006307 / 0.011008 (-0.004701) \| 0.129671 / 0.038508 (0.091162) \| 0.038952 / 0.023109 (0.015842) \| 0.421936 / 0.275898 (0.146038) \| 0.489911 / 0.323480 (0.166431) \| 0.007661 / 0.007986 (-0.000325) \| 0.005430 / 0.004328 (0.001102) \| 0.091851 / 0.004250 (0.087600) \| 0.059755 / 0.037052 (0.022703) \| 0.449810 / 0.258489 (0.191321) \| 0.519498 / 0.293841 (0.225657) \| 0.061644 / 0.128546 (-0.066902) \| 0.018950 / 0.075646 (-0.056696) \| 0.399149 / 0.419271 (-0.020122) \| 0.067670 / 0.043533 (0.024137) \| 0.441091 / 0.255139 (0.185952) \| 0.459327 / 0.283200 (0.176128) \| 0.122476 / 0.141683 (-0.019207) \| 1.760129 / 1.452155 (0.307974) \| 1.767945 / 1.492716 (0.275228) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276675 / 0.018006 (0.258669) \| 0.606798 / 0.000490 (0.606308) \| 0.000449 / 0.000200 (0.000249) \| 0.000078 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027762 / 0.037411 (-0.009649) \| 0.108330 / 0.014526 (0.093805) \| 0.134714 / 0.176557 (-0.041843) \| 0.175666 / 0.737135 (-0.561470) \| 0.134917 / 0.296338 (-0.161421) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.676756 / 0.215209 (0.461547) \| 6.746519 / 2.077655 (4.668864) \| 2.660869 / 1.504120 (1.156750) \| 2.273688 / 1.541195 (0.732494) \| 2.392580 / 1.468490 (0.924090) \| 1.127848 / 4.584777 (-3.456929) \| 5.356499 / 3.745712 (1.610787) \| 2.933006 / 5.269862 (-2.336855) \| 1.872877 / 4.565676 (-2.692799) \| 0.139504 / 0.424275 (-0.284771) \| 0.013501 / 0.007607 (0.005894) \| 0.749888 / 0.226044 (0.523843) \| 8.157031 / 2.268929 (5.888103) \| 3.627751 / 55.444624 (-51.816874) \| 2.713152 / 6.876477 (-4.163324) \| 2.934585 / 2.142072 (0.792512) \| 1.376398 / 4.805227 (-3.428829) \| 0.251537 / 6.500664 (-6.249127) \| 0.083995 / 0.075469 (0.008526) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.635446 / 1.841788 (-0.206342) \| 18.435807 / 8.074308 (10.361498) \| 21.395291 / 10.191392 (11.203899) \| 0.247238 / 0.680424 (-0.433186) \| 0.030503 / 0.534201 (-0.503698) \| 0.553096 / 0.579283 (-0.026187) \| 0.597583 / 0.434364 (0.163219) \| 0.594135 / 0.540337 (0.053797) \| 0.673815 / 1.386936 (-0.713122) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]	2022-12-22T16:15:30	2023-01-13T16:10:15	2023-01-13T16:00:52	COLLABORATOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5384", "html_url": "https://github.com/huggingface/datasets/pull/5384", "diff_url": "https://github.com/huggingface/datasets/pull/5384.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5384.patch", "merged_at": "2023-01-13T16:00:52" }	Fix #5229	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5384/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5384/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5383	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5383/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5383/comments	https://api.github.com/repos/huggingface/datasets/issues/5383/events	https://github.com/huggingface/datasets/issues/5383	1,507,293,968	I_kwDODunzps5Z13sQ	5,383	IterableDataset missing column_names, differs from Dataset interface	{ "login": "iceboundflame", "id": 933687, "node_id": "MDQ6VXNlcjkzMzY4Nw==", "avatar_url": "https://avatars.githubusercontent.com/u/933687?v=4", "gravatar_id": "", "url": "https://api.github.com/users/iceboundflame", "html_url": "https://github.com/iceboundflame", "followers_url": "https://api.github.com/users/iceboundflame/followers", "following_url": "https://api.github.com/users/iceboundflame/following{/other_user}", "gists_url": "https://api.github.com/users/iceboundflame/gists{/gist_id}", "starred_url": "https://api.github.com/users/iceboundflame/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/iceboundflame/subscriptions", "organizations_url": "https://api.github.com/users/iceboundflame/orgs", "repos_url": "https://api.github.com/users/iceboundflame/repos", "events_url": "https://api.github.com/users/iceboundflame/events{/privacy}", "received_events_url": "https://api.github.com/users/iceboundflame/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" }, { "id": 1935892877, "node_id": "MDU6TGFiZWwxOTM1ODkyODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue", "name": "good first issue", "color": "7057ff", "default": true, "description": "Good for newcomers" } ]	closed	false	{ "login": "patrickloeber", "id": 50772274, "node_id": "MDQ6VXNlcjUwNzcyMjc0", "avatar_url": "https://avatars.githubusercontent.com/u/50772274?v=4", "gravatar_id": "", "url": "https://api.github.com/users/patrickloeber", "html_url": "https://github.com/patrickloeber", "followers_url": "https://api.github.com/users/patrickloeber/followers", "following_url": "https://api.github.com/users/patrickloeber/following{/other_user}", "gists_url": "https://api.github.com/users/patrickloeber/gists{/gist_id}", "starred_url": "https://api.github.com/users/patrickloeber/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/patrickloeber/subscriptions", "organizations_url": "https://api.github.com/users/patrickloeber/orgs", "repos_url": "https://api.github.com/users/patrickloeber/repos", "events_url": "https://api.github.com/users/patrickloeber/events{/privacy}", "received_events_url": "https://api.github.com/users/patrickloeber/received_events", "type": "User", "site_admin": false }	[ { "login": "patrickloeber", "id": 50772274, "node_id": "MDQ6VXNlcjUwNzcyMjc0", "avatar_url": "https://avatars.githubusercontent.com/u/50772274?v=4", "gravatar_id": "", "url": "https://api.github.com/users/patrickloeber", "html_url": "https://github.com/patrickloeber", "followers_url": "https://api.github.com/users/patrickloeber/followers", "following_url": "https://api.github.com/users/patrickloeber/following{/other_user}", "gists_url": "https://api.github.com/users/patrickloeber/gists{/gist_id}", "starred_url": "https://api.github.com/users/patrickloeber/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/patrickloeber/subscriptions", "organizations_url": "https://api.github.com/users/patrickloeber/orgs", "repos_url": "https://api.github.com/users/patrickloeber/repos", "events_url": "https://api.github.com/users/patrickloeber/events{/privacy}", "received_events_url": "https://api.github.com/users/patrickloeber/received_events", "type": "User", "site_admin": false } ]	null	[ "Another example is that `IterableDataset.map` does not have `fn_kwargs`, among other arguments. It makes it harder to convert code from Dataset to IterableDataset.", "Hi! `fn_kwargs` was added to `IterableDataset.map` in `datasets 2.5.0`, so please update your installation (`pip install -U datasets`) to use it.\r\n\r\nRegarding `column_names`, I agree we should add this property to `IterableDataset`. In the meantime, you can use `list(dataset.features.keys())` instead.", "Thanks! That's great news.\n\nOn Thu, Dec 22, 2022, 07:48 Mario Šaško *@.> wrote:\n\n> Hi! fn_kwargs was added to IterableDataset.map in datasets 2.5.0, so\n> please update your installation (pip install -U datasets) to use it.\n>\n> Regarding column_names, I agree we should add this property to\n> IterableDataset. In the meantime, you can use\n> list(dataset.features.keys()) instead.\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5383#issuecomment-1362993633>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AAHD6N2EQUFEOUFDW3VHSILWORZ45ANCNFSM6AAAAAATGKWVGM>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> @.*>\n>\n", "I'm marking this issue as a \"good first issue\", as it makes sense to have `IterableDataset.column_names` in the API. Besides the case when `features` are `None` (e.g., `features` are `None` after `map`), in which we can also return `column_names` as `None`, adding this property should be straightforward,", "Hi @mariosasko, I can work on this if that's ok?", "Yes! I've assigned you the issue." ]	2022-12-22T05:27:02	2023-03-13T19:03:33	2023-03-13T19:03:33	NONE	null	null	null	### Describe the bug The documentation on [Stream](https://huggingface.co/docs/datasets/v1.18.2/stream.html) seems to imply that IterableDataset behaves just like a Dataset. However, examples like ``` dataset.map(augment_data, batched=True, remove_columns=dataset.column_names, ...) ``` will not work because `.column_names` does not exist on IterableDataset. I cannot find any clear explanation on why this is not available, is it an oversight? We do have `iterable_ds.features` available. ### Steps to reproduce the bug See above ### Expected behavior Dataset and IterableDataset would be expected to have the same interface, with any differences noted in the documentation. ### Environment info n/a	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5383/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5383/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5382	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5382/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5382/comments	https://api.github.com/repos/huggingface/datasets/issues/5382/events	https://github.com/huggingface/datasets/pull/5382	1,504,788,691	PR_kwDODunzps5F4Q0V	5,382	Raise from disconnect error in xopen	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Could you review this small PR @albertvillanova ? :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011200 / 0.011353 (-0.000153) \| 0.006156 / 0.011008 (-0.004852) \| 0.119072 / 0.038508 (0.080564) \| 0.042616 / 0.023109 (0.019507) \| 0.348329 / 0.275898 (0.072431) \| 0.418550 / 0.323480 (0.095070) \| 0.009302 / 0.007986 (0.001316) \| 0.004596 / 0.004328 (0.000267) \| 0.090111 / 0.004250 (0.085860) \| 0.053341 / 0.037052 (0.016289) \| 0.361234 / 0.258489 (0.102745) \| 0.400427 / 0.293841 (0.106586) \| 0.045601 / 0.128546 (-0.082945) \| 0.013806 / 0.075646 (-0.061841) \| 0.393178 / 0.419271 (-0.026094) \| 0.056809 / 0.043533 (0.013276) \| 0.344090 / 0.255139 (0.088951) \| 0.370610 / 0.283200 (0.087410) \| 0.125728 / 0.141683 (-0.015955) \| 1.671931 / 1.452155 (0.219776) \| 1.703143 / 1.492716 (0.210427) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226534 / 0.018006 (0.208527) \| 0.496487 / 0.000490 (0.495998) \| 0.002235 / 0.000200 (0.002035) \| 0.000094 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031298 / 0.037411 (-0.006113) \| 0.137740 / 0.014526 (0.123214) \| 0.153497 / 0.176557 (-0.023059) \| 0.204201 / 0.737135 (-0.532934) \| 0.162324 / 0.296338 (-0.134014) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.475922 / 0.215209 (0.260712) \| 4.682344 / 2.077655 (2.604689) \| 2.107387 / 1.504120 (0.603267) \| 1.884792 / 1.541195 (0.343597) \| 2.003180 / 1.468490 (0.534690) \| 0.810212 / 4.584777 (-3.774564) \| 4.631047 / 3.745712 (0.885334) \| 4.467606 / 5.269862 (-0.802256) \| 2.334196 / 4.565676 (-2.231480) \| 0.099713 / 0.424275 (-0.324562) \| 0.014732 / 0.007607 (0.007125) \| 0.604587 / 0.226044 (0.378543) \| 5.951679 / 2.268929 (3.682751) \| 2.704761 / 55.444624 (-52.739863) \| 2.280695 / 6.876477 (-4.595781) \| 2.279489 / 2.142072 (0.137417) \| 0.962474 / 4.805227 (-3.842753) \| 0.195279 / 6.500664 (-6.305385) \| 0.071503 / 0.075469 (-0.003966) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.558037 / 1.841788 (-0.283751) \| 17.722140 / 8.074308 (9.647832) \| 16.229016 / 10.191392 (6.037624) \| 0.177148 / 0.680424 (-0.503276) \| 0.034162 / 0.534201 (-0.500039) \| 0.513945 / 0.579283 (-0.065338) \| 0.533542 / 0.434364 (0.099178) \| 0.672457 / 0.540337 (0.132119) \| 0.762390 / 1.386936 (-0.624546) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009739 / 0.011353 (-0.001613) \| 0.006095 / 0.011008 (-0.004914) \| 0.105968 / 0.038508 (0.067460) \| 0.046229 / 0.023109 (0.023120) \| 0.449156 / 0.275898 (0.173258) \| 0.462182 / 0.323480 (0.138702) \| 0.006981 / 0.007986 (-0.001004) \| 0.004867 / 0.004328 (0.000539) \| 0.082142 / 0.004250 (0.077891) \| 0.058652 / 0.037052 (0.021600) \| 0.454542 / 0.258489 (0.196052) \| 0.494910 / 0.293841 (0.201069) \| 0.047159 / 0.128546 (-0.081387) \| 0.014677 / 0.075646 (-0.060969) \| 0.370819 / 0.419271 (-0.048452) \| 0.064603 / 0.043533 (0.021070) \| 0.441514 / 0.255139 (0.186375) \| 0.442802 / 0.283200 (0.159603) \| 0.138603 / 0.141683 (-0.003080) \| 1.692810 / 1.452155 (0.240655) \| 1.894596 / 1.492716 (0.401880) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281681 / 0.018006 (0.263675) \| 0.532693 / 0.000490 (0.532203) \| 0.005484 / 0.000200 (0.005284) \| 0.000156 / 0.000054 (0.000102) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032994 / 0.037411 (-0.004417) \| 0.134614 / 0.014526 (0.120088) \| 0.142286 / 0.176557 (-0.034270) \| 0.187220 / 0.737135 (-0.549916) \| 0.144897 / 0.296338 (-0.151441) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.519536 / 0.215209 (0.304327) \| 5.214429 / 2.077655 (3.136775) \| 2.612575 / 1.504120 (1.108455) \| 2.369085 / 1.541195 (0.827891) \| 2.503157 / 1.468490 (1.034667) \| 0.834827 / 4.584777 (-3.749950) \| 4.586789 / 3.745712 (0.841077) \| 4.472605 / 5.269862 (-0.797257) \| 2.314471 / 4.565676 (-2.251205) \| 0.095817 / 0.424275 (-0.328458) \| 0.014086 / 0.007607 (0.006478) \| 0.605875 / 0.226044 (0.379831) \| 6.153143 / 2.268929 (3.884214) \| 3.187456 / 55.444624 (-52.257169) \| 2.755377 / 6.876477 (-4.121100) \| 2.777118 / 2.142072 (0.635046) \| 0.967285 / 4.805227 (-3.837942) \| 0.199202 / 6.500664 (-6.301462) \| 0.075979 / 0.075469 (0.000510) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.481758 / 1.841788 (-0.360030) \| 18.053769 / 8.074308 (9.979461) \| 15.558780 / 10.191392 (5.367388) \| 0.226135 / 0.680424 (-0.454288) \| 0.021668 / 0.534201 (-0.512533) \| 0.562618 / 0.579283 (-0.016666) \| 0.518183 / 0.434364 (0.083819) \| 0.628580 / 0.540337 (0.088243) \| 0.740368 / 1.386936 (-0.646568) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4e4d46eec24c36799c0efcc1b7231f597039c497 \"CML watermark\")\n" ]	2022-12-20T15:52:44	2023-01-26T09:51:13	2023-01-26T09:42:45	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5382", "html_url": "https://github.com/huggingface/datasets/pull/5382", "diff_url": "https://github.com/huggingface/datasets/pull/5382.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5382.patch", "merged_at": "2023-01-26T09:42:45" }	this way we can know the cause of the disconnect related to https://github.com/huggingface/datasets/issues/5374	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5382/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5382/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5381	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5381/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5381/comments	https://api.github.com/repos/huggingface/datasets/issues/5381/events	https://github.com/huggingface/datasets/issues/5381	1,504,498,387	I_kwDODunzps5ZrNLT	5,381	Wrong URL for the_pile dataset	{ "login": "LeoGrin", "id": 45738728, "node_id": "MDQ6VXNlcjQ1NzM4NzI4", "avatar_url": "https://avatars.githubusercontent.com/u/45738728?v=4", "gravatar_id": "", "url": "https://api.github.com/users/LeoGrin", "html_url": "https://github.com/LeoGrin", "followers_url": "https://api.github.com/users/LeoGrin/followers", "following_url": "https://api.github.com/users/LeoGrin/following{/other_user}", "gists_url": "https://api.github.com/users/LeoGrin/gists{/gist_id}", "starred_url": "https://api.github.com/users/LeoGrin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/LeoGrin/subscriptions", "organizations_url": "https://api.github.com/users/LeoGrin/orgs", "repos_url": "https://api.github.com/users/LeoGrin/repos", "events_url": "https://api.github.com/users/LeoGrin/events{/privacy}", "received_events_url": "https://api.github.com/users/LeoGrin/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! This error can happen if there is a local file/folder with the same name as the requested dataset. And to avoid it, rename the local file/folder.\r\n\r\nSoon, it will be possible to explicitly request a Hub dataset as follows:https://github.com/huggingface/datasets/issues/5228#issuecomment-1313494020" ]	2022-12-20T12:40:14	2023-02-15T16:24:57	2023-02-15T16:24:57	NONE	null	null	null	### Describe the bug When trying to load `the_pile` dataset from the library, I get a `FileNotFound` error. ### Steps to reproduce the bug Steps to reproduce: Run: ``` from datasets import load_dataset dataset = load_dataset("the_pile") ``` I get the output: "name": "FileNotFoundError", "message": "Unable to resolve any data file that matches '['**']' at /storage/store/work/lgrinszt/memorization/the_pile with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'BLP', 'BMP', 'DIB', 'BUFR', 'CUR', 'PCX', 'DCX', 'DDS', 'PS', 'EPS', 'FIT', 'FITS', 'FLI', 'FLC', 'FTC', 'FTU', 'GBR', 'GIF', 'GRIB', 'H5', 'HDF', 'PNG', 'APNG', 'JP2', 'J2K', 'JPC', 'JPF', 'JPX', 'J2C', 'ICNS', 'ICO', 'IM', 'IIM', 'TIF', 'TIFF', 'JFIF', 'JPE', 'JPG', 'JPEG', 'MPG', 'MPEG', 'MSP', 'PCD', 'PXR', 'PBM', 'PGM', 'PPM', 'PNM', 'PSD', 'BW', 'RGB', 'RGBA', 'SGI', 'RAS', 'TGA', 'ICB', 'VDA', 'VST', 'WEBP', 'WMF', 'EMF', 'XBM', 'XPM', 'aiff', 'au', 'avr', 'caf', 'flac', 'htk', 'svx', 'mat4', 'mat5', 'mpc2k', 'ogg', 'paf', 'pvf', 'raw', 'rf64', 'sd2', 'sds', 'ircam', 'voc', 'w64', 'wav', 'nist', 'wavex', 'wve', 'xi', 'mp3', 'opus', 'AIFF', 'AU', 'AVR', 'CAF', 'FLAC', 'HTK', 'SVX', 'MAT4', 'MAT5', 'MPC2K', 'OGG', 'PAF', 'PVF', 'RAW', 'RF64', 'SD2', 'SDS', 'IRCAM', 'VOC', 'W64', 'WAV', 'NIST', 'WAVEX', 'WVE', 'XI', 'MP3', 'OPUS', 'zip']" ### Expected behavior `the_pile` dataset should be dowloaded. ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-4.15.0-112-generic-x86_64-with-glibc2.27 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5381/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5381/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5380	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5380/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5380/comments	https://api.github.com/repos/huggingface/datasets/issues/5380/events	https://github.com/huggingface/datasets/issues/5380	1,504,404,043	I_kwDODunzps5Zq2JL	5,380	Improve dataset `.skip()` speed in streaming mode	{ "login": "versae", "id": 173537, "node_id": "MDQ6VXNlcjE3MzUzNw==", "avatar_url": "https://avatars.githubusercontent.com/u/173537?v=4", "gravatar_id": "", "url": "https://api.github.com/users/versae", "html_url": "https://github.com/versae", "followers_url": "https://api.github.com/users/versae/followers", "following_url": "https://api.github.com/users/versae/following{/other_user}", "gists_url": "https://api.github.com/users/versae/gists{/gist_id}", "starred_url": "https://api.github.com/users/versae/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/versae/subscriptions", "organizations_url": "https://api.github.com/users/versae/orgs", "repos_url": "https://api.github.com/users/versae/repos", "events_url": "https://api.github.com/users/versae/events{/privacy}", "received_events_url": "https://api.github.com/users/versae/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" }, { "id": 3761482852, "node_id": "LA_kwDODunzps7gM6xk", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20second%20issue", "name": "good second issue", "color": "BDE59C", "default": false, "description": "Issues a bit more difficult than \"Good First\" issues" } ]	open	false	null	[]	null	[ "Hi! I agree `skip` can be inefficient to use in the current state.\r\n\r\nTo make it fast, we could use \"statistics\" stored in Parquet metadata and read only the chunks needed to form a dataset. \r\n\r\nAnd thanks to the \"datasets-server\" project, which aims to store the Parquet versions of the Hub datasets (only the smaller datasets are covered currently), this solution can also be applied to datasets stored in formats other than Parquet. (cc @severo)", "@mariosasko do the current parquet files created by the datasets-server already have the required \"statistics\"? If not, please open an issue on https://github.com/huggingface/datasets-server with some details to make sure we implement it.", "Yes, nothing has to be changed on the datasets-server side. What I mean by \"statistics\" is that we can use the \"row_group\" metadata embedded in a Parquet file (by default) to fetch the requested rows more efficiently.", "Glad to see the feature could be of interest. \r\n\r\nI'm sure there are many possible ways to implement this feature. I don't know enough about the datasets-server, but I guess that it is not instantaneous, in the sense that user-owned private datasets might need hours or days until they are ported to the datasets-server (if at all), which could be cumbersome. Having optionally that information in the `dataset_infos.json` file would make it easier for users to control the skip process a bit.", "re: statistics:\r\n\r\n- https://arrow.apache.org/docs/python/generated/pyarrow.parquet.FileMetaData.html\r\n- https://arrow.apache.org/docs/python/generated/pyarrow.parquet.RowGroupMetaData.html\r\n\r\n```python\r\n>>> import pyarrow.parquet as pq\r\n>>> import hffs\r\n>>> fs = hffs.HfFileSystem(\"glue\", repo_type=\"dataset\", revision=\"refs/convert/parquet\")\r\n>>> metadata = pq.read_metadata(\"ax/glue-test.parquet\", filesystem=fs)\r\n>>> metadata\r\n<pyarrow._parquet.FileMetaData object at 0x7f4537cec400>\r\n created_by: parquet-cpp-arrow version 7.0.0\r\n num_columns: 4\r\n num_rows: 1104\r\n num_row_groups: 2\r\n format_version: 1.0\r\n serialized_size: 2902\r\n>>> metadata.row_group(0)\r\n<pyarrow._parquet.RowGroupMetaData object at 0x7f45564bcbd0>\r\n num_columns: 4\r\n num_rows: 1000\r\n total_byte_size: 164474\r\n>>> metadata.row_group(1)\r\n<pyarrow._parquet.RowGroupMetaData object at 0x7f455005c400>\r\n num_columns: 4\r\n num_rows: 104\r\n total_byte_size: 13064\r\n```", "> user-owned private datasets might need hours or days until they are ported to the datasets-server (if at all)\r\n\r\nprivate datasets are not supported yet (https://github.com/huggingface/datasets-server/issues/39)", "@versae `Dataset.push_to_hub` writes shards in Parquet, so this solution would also work for such datasets (immediately after the push). ", "@mariosasko that is right. However, there are still a good amount of datasets for which the shards are created manually. In our very specific case, we create medium-sized datasets (rarely over 100-200GB) of both text and audio, we prepare the shards by hand and then upload then. It would be great to have immediate access to this download skipping feature for them too.", "From looking at Arrow's source, it seems Parquet stores metadata at the end, which means one needs to iterate over a Parquet file's data before accessing its metadata. We could mimic Dask to address this \"limitation\" and write metadata in a `_metadata`/`_common_metadata` file in `to_parquet`/`push_to_hub`, which we could then use to optimize reads (if present). Plus, it's handy that PyArrow can also parse these metadata files.", "So if Parquet metadata needs to be in its own file anyway, why not implement this skipping feature by storing the example counts per shard in `dataset_infos.json`? That would allow:\r\n- Support both private and public datasets\r\n- Immediate access to the feature upon uploading of shards\r\n- Use any dataset, not only those uploaded using `.push_to_hub()`\r\n\r\nA proper Parquet metadata file could still be created and \"overwrite\" the `dataset_infos.json` info in the datasets-server." ]	2022-12-20T11:25:23	2023-03-08T10:47:12	null	CONTRIBUTOR	null	null	null	### Feature request Add extra information to the `dataset_infos.json` file to include the number of samples/examples in each shard, for example in a new field `num_examples` alongside `num_bytes`. The `.skip()` function could use this information to ignore the download of a shard when in streaming mode, which AFAICT it should speed up the skipping process. ### Motivation When resuming from a checkpoint after a crashed run, using `dataset.skip()` is very convenient to recover the exact state of the data and to not train again over the same examples (assuming same seed, no shuffling). However, I have noticed that for audio datasets in streaming mode this is very costly in terms of time, as shards need to be downloaded every time before skipping the right number of examples. ### Your contribution I took a look already at the code, but it seems a change like this is way deeper than I am able to manage, as it touches the library in several parts. I could give it a try but might need some guidance on the internals.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5380/reactions", "total_count": 4, "+1": 4, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5380/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5379	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5379/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5379/comments	https://api.github.com/repos/huggingface/datasets/issues/5379/events	https://github.com/huggingface/datasets/pull/5379	1,504,010,639	PR_kwDODunzps5F1r2k	5,379	feat: depth estimation dataset guide.	{ "login": "sayakpaul", "id": 22957388, "node_id": "MDQ6VXNlcjIyOTU3Mzg4", "avatar_url": "https://avatars.githubusercontent.com/u/22957388?v=4", "gravatar_id": "", "url": "https://api.github.com/users/sayakpaul", "html_url": "https://github.com/sayakpaul", "followers_url": "https://api.github.com/users/sayakpaul/followers", "following_url": "https://api.github.com/users/sayakpaul/following{/other_user}", "gists_url": "https://api.github.com/users/sayakpaul/gists{/gist_id}", "starred_url": "https://api.github.com/users/sayakpaul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sayakpaul/subscriptions", "organizations_url": "https://api.github.com/users/sayakpaul/orgs", "repos_url": "https://api.github.com/users/sayakpaul/repos", "events_url": "https://api.github.com/users/sayakpaul/events{/privacy}", "received_events_url": "https://api.github.com/users/sayakpaul/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "sayakpaul", "id": 22957388, "node_id": "MDQ6VXNlcjIyOTU3Mzg4", "avatar_url": "https://avatars.githubusercontent.com/u/22957388?v=4", "gravatar_id": "", "url": "https://api.github.com/users/sayakpaul", "html_url": "https://github.com/sayakpaul", "followers_url": "https://api.github.com/users/sayakpaul/followers", "following_url": "https://api.github.com/users/sayakpaul/following{/other_user}", "gists_url": "https://api.github.com/users/sayakpaul/gists{/gist_id}", "starred_url": "https://api.github.com/users/sayakpaul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sayakpaul/subscriptions", "organizations_url": "https://api.github.com/users/sayakpaul/orgs", "repos_url": "https://api.github.com/users/sayakpaul/repos", "events_url": "https://api.github.com/users/sayakpaul/events{/privacy}", "received_events_url": "https://api.github.com/users/sayakpaul/received_events", "type": "User", "site_admin": false }	[ { "login": "sayakpaul", "id": 22957388, "node_id": "MDQ6VXNlcjIyOTU3Mzg4", "avatar_url": "https://avatars.githubusercontent.com/u/22957388?v=4", "gravatar_id": "", "url": "https://api.github.com/users/sayakpaul", "html_url": "https://github.com/sayakpaul", "followers_url": "https://api.github.com/users/sayakpaul/followers", "following_url": "https://api.github.com/users/sayakpaul/following{/other_user}", "gists_url": "https://api.github.com/users/sayakpaul/gists{/gist_id}", "starred_url": "https://api.github.com/users/sayakpaul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sayakpaul/subscriptions", "organizations_url": "https://api.github.com/users/sayakpaul/orgs", "repos_url": "https://api.github.com/users/sayakpaul/repos", "events_url": "https://api.github.com/users/sayakpaul/events{/privacy}", "received_events_url": "https://api.github.com/users/sayakpaul/received_events", "type": "User", "site_admin": false } ]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the changes, looks good to me!", "@stevhliu I have pushed some quality improvements both in terms of code and content. Would you be able to re-review? ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008325 / 0.011353 (-0.003028) \| 0.004432 / 0.011008 (-0.006576) \| 0.099794 / 0.038508 (0.061286) \| 0.029469 / 0.023109 (0.006360) \| 0.306554 / 0.275898 (0.030656) \| 0.367373 / 0.323480 (0.043893) \| 0.007532 / 0.007986 (-0.000454) \| 0.003310 / 0.004328 (-0.001018) \| 0.077453 / 0.004250 (0.073203) \| 0.034836 / 0.037052 (-0.002216) \| 0.311696 / 0.258489 (0.053207) \| 0.349683 / 0.293841 (0.055842) \| 0.033089 / 0.128546 (-0.095457) \| 0.011339 / 0.075646 (-0.064307) \| 0.321699 / 0.419271 (-0.097573) \| 0.040213 / 0.043533 (-0.003320) \| 0.304741 / 0.255139 (0.049602) \| 0.331569 / 0.283200 (0.048369) \| 0.090397 / 0.141683 (-0.051285) \| 1.526001 / 1.452155 (0.073847) \| 1.558863 / 1.492716 (0.066146) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.179446 / 0.018006 (0.161440) \| 0.416308 / 0.000490 (0.415818) \| 0.002390 / 0.000200 (0.002190) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023641 / 0.037411 (-0.013770) \| 0.096672 / 0.014526 (0.082147) \| 0.104330 / 0.176557 (-0.072227) \| 0.146338 / 0.737135 (-0.590797) \| 0.108278 / 0.296338 (-0.188060) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420194 / 0.215209 (0.204985) \| 4.196981 / 2.077655 (2.119326) \| 1.861206 / 1.504120 (0.357086) \| 1.658748 / 1.541195 (0.117554) \| 1.704309 / 1.468490 (0.235819) \| 0.691639 / 4.584777 (-3.893138) \| 3.346303 / 3.745712 (-0.399409) \| 1.932962 / 5.269862 (-3.336900) \| 1.299395 / 4.565676 (-3.266281) \| 0.081869 / 0.424275 (-0.342406) \| 0.012415 / 0.007607 (0.004808) \| 0.530805 / 0.226044 (0.304761) \| 5.293486 / 2.268929 (3.024558) \| 2.328327 / 55.444624 (-53.116297) \| 1.964956 / 6.876477 (-4.911521) \| 2.002793 / 2.142072 (-0.139280) \| 0.813380 / 4.805227 (-3.991847) \| 0.150030 / 6.500664 (-6.350634) \| 0.065194 / 0.075469 (-0.010275) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.259421 / 1.841788 (-0.582367) \| 13.667796 / 8.074308 (5.593488) \| 13.819121 / 10.191392 (3.627729) \| 0.136718 / 0.680424 (-0.543706) \| 0.028510 / 0.534201 (-0.505691) \| 0.402246 / 0.579283 (-0.177037) \| 0.405279 / 0.434364 (-0.029085) \| 0.467185 / 0.540337 (-0.073153) \| 0.554213 / 1.386936 (-0.832723) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006738 / 0.011353 (-0.004615) \| 0.004616 / 0.011008 (-0.006393) \| 0.096978 / 0.038508 (0.058470) \| 0.027750 / 0.023109 (0.004640) \| 0.411505 / 0.275898 (0.135607) \| 0.441796 / 0.323480 (0.118316) \| 0.005073 / 0.007986 (-0.002913) \| 0.003360 / 0.004328 (-0.000968) \| 0.074445 / 0.004250 (0.070194) \| 0.040654 / 0.037052 (0.003602) \| 0.414277 / 0.258489 (0.155788) \| 0.448665 / 0.293841 (0.154824) \| 0.032346 / 0.128546 (-0.096200) \| 0.011533 / 0.075646 (-0.064114) \| 0.317349 / 0.419271 (-0.101923) \| 0.041934 / 0.043533 (-0.001599) \| 0.409102 / 0.255139 (0.153963) \| 0.429977 / 0.283200 (0.146777) \| 0.089459 / 0.141683 (-0.052224) \| 1.518127 / 1.452155 (0.065973) \| 1.569902 / 1.492716 (0.077186) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232648 / 0.018006 (0.214642) \| 0.413751 / 0.000490 (0.413261) \| 0.000404 / 0.000200 (0.000204) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025468 / 0.037411 (-0.011943) \| 0.098195 / 0.014526 (0.083669) \| 0.108882 / 0.176557 (-0.067674) \| 0.150059 / 0.737135 (-0.587076) \| 0.110742 / 0.296338 (-0.185597) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445326 / 0.215209 (0.230117) \| 4.449200 / 2.077655 (2.371545) \| 2.098939 / 1.504120 (0.594819) \| 1.861207 / 1.541195 (0.320012) \| 1.901385 / 1.468490 (0.432894) \| 0.695287 / 4.584777 (-3.889490) \| 3.461775 / 3.745712 (-0.283938) \| 2.998566 / 5.269862 (-2.271296) \| 1.555036 / 4.565676 (-3.010641) \| 0.082789 / 0.424275 (-0.341486) \| 0.012772 / 0.007607 (0.005165) \| 0.564855 / 0.226044 (0.338811) \| 5.631049 / 2.268929 (3.362120) \| 2.543771 / 55.444624 (-52.900854) \| 2.194378 / 6.876477 (-4.682099) \| 2.267168 / 2.142072 (0.125095) \| 0.803330 / 4.805227 (-4.001898) \| 0.151336 / 6.500664 (-6.349328) \| 0.067015 / 0.075469 (-0.008454) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.298422 / 1.841788 (-0.543366) \| 13.933637 / 8.074308 (5.859329) \| 13.570848 / 10.191392 (3.379456) \| 0.150787 / 0.680424 (-0.529637) \| 0.016911 / 0.534201 (-0.517290) \| 0.384771 / 0.579283 (-0.194512) \| 0.397505 / 0.434364 (-0.036858) \| 0.450931 / 0.540337 (-0.089406) \| 0.534501 / 1.386936 (-0.852435) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "@lhoestq @nateraw made some changes as per the comments. PTAL and approve as necessary. ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009037 / 0.011353 (-0.002316) \| 0.004970 / 0.011008 (-0.006038) \| 0.099223 / 0.038508 (0.060715) \| 0.034935 / 0.023109 (0.011826) \| 0.297027 / 0.275898 (0.021129) \| 0.352861 / 0.323480 (0.029382) \| 0.007558 / 0.007986 (-0.000427) \| 0.003903 / 0.004328 (-0.000425) \| 0.075663 / 0.004250 (0.071413) \| 0.042577 / 0.037052 (0.005524) \| 0.307182 / 0.258489 (0.048693) \| 0.344237 / 0.293841 (0.050396) \| 0.041438 / 0.128546 (-0.087108) \| 0.012159 / 0.075646 (-0.063487) \| 0.333771 / 0.419271 (-0.085501) \| 0.047847 / 0.043533 (0.004314) \| 0.290797 / 0.255139 (0.035658) \| 0.320517 / 0.283200 (0.037318) \| 0.098334 / 0.141683 (-0.043349) \| 1.446187 / 1.452155 (-0.005968) \| 1.495506 / 1.492716 (0.002789) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203704 / 0.018006 (0.185698) \| 0.441325 / 0.000490 (0.440835) \| 0.001173 / 0.000200 (0.000973) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026694 / 0.037411 (-0.010718) \| 0.103819 / 0.014526 (0.089294) \| 0.116377 / 0.176557 (-0.060179) \| 0.158280 / 0.737135 (-0.578856) \| 0.119797 / 0.296338 (-0.176541) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405723 / 0.215209 (0.190514) \| 4.047633 / 2.077655 (1.969979) \| 1.805652 / 1.504120 (0.301532) \| 1.611382 / 1.541195 (0.070187) \| 1.663117 / 1.468490 (0.194627) \| 0.692589 / 4.584777 (-3.892188) \| 3.689970 / 3.745712 (-0.055742) \| 2.089760 / 5.269862 (-3.180101) \| 1.450576 / 4.565676 (-3.115101) \| 0.085276 / 0.424275 (-0.338999) \| 0.012042 / 0.007607 (0.004434) \| 0.513159 / 0.226044 (0.287115) \| 5.123235 / 2.268929 (2.854306) \| 2.281864 / 55.444624 (-53.162761) \| 1.926170 / 6.876477 (-4.950307) \| 2.035093 / 2.142072 (-0.106979) \| 0.857457 / 4.805227 (-3.947770) \| 0.166088 / 6.500664 (-6.334576) \| 0.062115 / 0.075469 (-0.013354) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.197776 / 1.841788 (-0.644012) \| 14.674452 / 8.074308 (6.600144) \| 14.275990 / 10.191392 (4.084598) \| 0.170848 / 0.680424 (-0.509576) \| 0.028613 / 0.534201 (-0.505588) \| 0.438650 / 0.579283 (-0.140633) \| 0.439323 / 0.434364 (0.004959) \| 0.515090 / 0.540337 (-0.025247) \| 0.614216 / 1.386936 (-0.772720) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007159 / 0.011353 (-0.004194) \| 0.005142 / 0.011008 (-0.005866) \| 0.096953 / 0.038508 (0.058445) \| 0.033036 / 0.023109 (0.009927) \| 0.391790 / 0.275898 (0.115892) \| 0.427120 / 0.323480 (0.103640) \| 0.005691 / 0.007986 (-0.002294) \| 0.004848 / 0.004328 (0.000519) \| 0.072258 / 0.004250 (0.068008) \| 0.049017 / 0.037052 (0.011965) \| 0.387267 / 0.258489 (0.128778) \| 0.437112 / 0.293841 (0.143272) \| 0.036360 / 0.128546 (-0.092186) \| 0.012249 / 0.075646 (-0.063397) \| 0.336246 / 0.419271 (-0.083025) \| 0.048777 / 0.043533 (0.005244) \| 0.397872 / 0.255139 (0.142733) \| 0.399768 / 0.283200 (0.116568) \| 0.101283 / 0.141683 (-0.040400) \| 1.443999 / 1.452155 (-0.008156) \| 1.575496 / 1.492716 (0.082779) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220952 / 0.018006 (0.202946) \| 0.442220 / 0.000490 (0.441730) \| 0.000406 / 0.000200 (0.000206) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028626 / 0.037411 (-0.008786) \| 0.109929 / 0.014526 (0.095403) \| 0.120989 / 0.176557 (-0.055568) \| 0.157377 / 0.737135 (-0.579758) \| 0.125522 / 0.296338 (-0.170816) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436565 / 0.215209 (0.221356) \| 4.380771 / 2.077655 (2.303117) \| 2.200003 / 1.504120 (0.695883) \| 2.013289 / 1.541195 (0.472094) \| 2.052658 / 1.468490 (0.584168) \| 0.703706 / 4.584777 (-3.881071) \| 3.823289 / 3.745712 (0.077577) \| 2.064882 / 5.269862 (-3.204980) \| 1.330834 / 4.565676 (-3.234842) \| 0.085945 / 0.424275 (-0.338330) \| 0.012511 / 0.007607 (0.004904) \| 0.544171 / 0.226044 (0.318127) \| 5.476059 / 2.268929 (3.207130) \| 2.695586 / 55.444624 (-52.749039) \| 2.330239 / 6.876477 (-4.546238) \| 2.429290 / 2.142072 (0.287218) \| 0.843154 / 4.805227 (-3.962073) \| 0.169334 / 6.500664 (-6.331330) \| 0.064261 / 0.075469 (-0.011209) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.268344 / 1.841788 (-0.573444) \| 14.934342 / 8.074308 (6.860034) \| 13.555389 / 10.191392 (3.363997) \| 0.142725 / 0.680424 (-0.537699) \| 0.017891 / 0.534201 (-0.516310) \| 0.424833 / 0.579283 (-0.154450) \| 0.420035 / 0.434364 (-0.014329) \| 0.491009 / 0.540337 (-0.049329) \| 0.586953 / 1.386936 (-0.799983) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "Merging this PR with approvals from @stevhliu @lhoestq. ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008586 / 0.011353 (-0.002767) \| 0.004659 / 0.011008 (-0.006350) \| 0.100343 / 0.038508 (0.061835) \| 0.029861 / 0.023109 (0.006751) \| 0.301090 / 0.275898 (0.025192) \| 0.369528 / 0.323480 (0.046048) \| 0.006920 / 0.007986 (-0.001065) \| 0.003513 / 0.004328 (-0.000815) \| 0.078514 / 0.004250 (0.074263) \| 0.035285 / 0.037052 (-0.001767) \| 0.311257 / 0.258489 (0.052768) \| 0.353995 / 0.293841 (0.060154) \| 0.033733 / 0.128546 (-0.094813) \| 0.011489 / 0.075646 (-0.064157) \| 0.323095 / 0.419271 (-0.096176) \| 0.040808 / 0.043533 (-0.002725) \| 0.301779 / 0.255139 (0.046640) \| 0.348517 / 0.283200 (0.065318) \| 0.086962 / 0.141683 (-0.054721) \| 1.496270 / 1.452155 (0.044115) \| 1.514260 / 1.492716 (0.021544) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.189502 / 0.018006 (0.171496) \| 0.419326 / 0.000490 (0.418837) \| 0.002160 / 0.000200 (0.001960) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023669 / 0.037411 (-0.013742) \| 0.096574 / 0.014526 (0.082048) \| 0.105970 / 0.176557 (-0.070587) \| 0.148531 / 0.737135 (-0.588605) \| 0.109948 / 0.296338 (-0.186391) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424968 / 0.215209 (0.209759) \| 4.246292 / 2.077655 (2.168637) \| 1.911062 / 1.504120 (0.406943) \| 1.700733 / 1.541195 (0.159538) \| 1.760756 / 1.468490 (0.292266) \| 0.696966 / 4.584777 (-3.887811) \| 3.372320 / 3.745712 (-0.373392) \| 2.886281 / 5.269862 (-2.383581) \| 1.553082 / 4.565676 (-3.012594) \| 0.082835 / 0.424275 (-0.341440) \| 0.012688 / 0.007607 (0.005081) \| 0.536352 / 0.226044 (0.310308) \| 5.382510 / 2.268929 (3.113582) \| 2.365664 / 55.444624 (-53.078960) \| 1.995631 / 6.876477 (-4.880845) \| 2.073865 / 2.142072 (-0.068207) \| 0.819109 / 4.805227 (-3.986118) \| 0.150278 / 6.500664 (-6.350386) \| 0.065201 / 0.075469 (-0.010268) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.239835 / 1.841788 (-0.601953) \| 13.911847 / 8.074308 (5.837539) \| 13.500433 / 10.191392 (3.309041) \| 0.137153 / 0.680424 (-0.543271) \| 0.028451 / 0.534201 (-0.505750) \| 0.394659 / 0.579283 (-0.184625) \| 0.404915 / 0.434364 (-0.029449) \| 0.458944 / 0.540337 (-0.081394) \| 0.542288 / 1.386936 (-0.844648) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006791 / 0.011353 (-0.004562) \| 0.004590 / 0.011008 (-0.006419) \| 0.098697 / 0.038508 (0.060189) \| 0.027634 / 0.023109 (0.004525) \| 0.344383 / 0.275898 (0.068485) \| 0.385607 / 0.323480 (0.062127) \| 0.005413 / 0.007986 (-0.002573) \| 0.003447 / 0.004328 (-0.000881) \| 0.077268 / 0.004250 (0.073018) \| 0.041823 / 0.037052 (0.004770) \| 0.342904 / 0.258489 (0.084414) \| 0.399371 / 0.293841 (0.105530) \| 0.032668 / 0.128546 (-0.095879) \| 0.011598 / 0.075646 (-0.064048) \| 0.319973 / 0.419271 (-0.099299) \| 0.041760 / 0.043533 (-0.001773) \| 0.340510 / 0.255139 (0.085371) \| 0.377929 / 0.283200 (0.094730) \| 0.090889 / 0.141683 (-0.050793) \| 1.496068 / 1.452155 (0.043913) \| 1.574884 / 1.492716 (0.082168) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230489 / 0.018006 (0.212483) \| 0.425234 / 0.000490 (0.424745) \| 0.000406 / 0.000200 (0.000206) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024650 / 0.037411 (-0.012761) \| 0.102706 / 0.014526 (0.088180) \| 0.108017 / 0.176557 (-0.068539) \| 0.143645 / 0.737135 (-0.593490) \| 0.110556 / 0.296338 (-0.185782) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.468038 / 0.215209 (0.252829) \| 4.670514 / 2.077655 (2.592860) \| 2.446620 / 1.504120 (0.942500) \| 2.241255 / 1.541195 (0.700060) \| 2.286409 / 1.468490 (0.817919) \| 0.698923 / 4.584777 (-3.885854) \| 3.401121 / 3.745712 (-0.344592) \| 1.892399 / 5.269862 (-3.377462) \| 1.163101 / 4.565676 (-3.402575) \| 0.082567 / 0.424275 (-0.341708) \| 0.012662 / 0.007607 (0.005055) \| 0.571262 / 0.226044 (0.345218) \| 5.731740 / 2.268929 (3.462812) \| 2.879649 / 55.444624 (-52.564975) \| 2.533846 / 6.876477 (-4.342631) \| 2.654789 / 2.142072 (0.512717) \| 0.811345 / 4.805227 (-3.993882) \| 0.152495 / 6.500664 (-6.348169) \| 0.067748 / 0.075469 (-0.007721) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.267852 / 1.841788 (-0.573935) \| 14.114920 / 8.074308 (6.040612) \| 14.355403 / 10.191392 (4.164011) \| 0.150393 / 0.680424 (-0.530031) \| 0.016855 / 0.534201 (-0.517346) \| 0.378710 / 0.579283 (-0.200573) \| 0.385380 / 0.434364 (-0.048984) \| 0.439054 / 0.540337 (-0.101284) \| 0.524343 / 1.386936 (-0.862593) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]	2022-12-20T05:32:11	2023-01-13T12:30:31	2023-01-13T12:23:34	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5379", "html_url": "https://github.com/huggingface/datasets/pull/5379", "diff_url": "https://github.com/huggingface/datasets/pull/5379.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5379.patch", "merged_at": "2023-01-13T12:23:34" }	This PR adds a guide for prepping datasets for depth estimation. PR to add documentation images is up here: https://huggingface.co/datasets/huggingface/documentation-images/discussions/22	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5379/reactions", "total_count": 1, "+1": 0, "-1": 0, "laugh": 0, "hooray": 1, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5379/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5378	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5378/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5378/comments	https://api.github.com/repos/huggingface/datasets/issues/5378/events	https://github.com/huggingface/datasets/issues/5378	1,503,887,508	I_kwDODunzps5Zo4CU	5,378	The dataset "the_pile", subset "enron_emails" , load_dataset() failure	{ "login": "shaoyuta", "id": 52023469, "node_id": "MDQ6VXNlcjUyMDIzNDY5", "avatar_url": "https://avatars.githubusercontent.com/u/52023469?v=4", "gravatar_id": "", "url": "https://api.github.com/users/shaoyuta", "html_url": "https://github.com/shaoyuta", "followers_url": "https://api.github.com/users/shaoyuta/followers", "following_url": "https://api.github.com/users/shaoyuta/following{/other_user}", "gists_url": "https://api.github.com/users/shaoyuta/gists{/gist_id}", "starred_url": "https://api.github.com/users/shaoyuta/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/shaoyuta/subscriptions", "organizations_url": "https://api.github.com/users/shaoyuta/orgs", "repos_url": "https://api.github.com/users/shaoyuta/repos", "events_url": "https://api.github.com/users/shaoyuta/events{/privacy}", "received_events_url": "https://api.github.com/users/shaoyuta/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Thanks for reporting @shaoyuta. We are investigating it.\r\n\r\nWe are transferring the issue to \"the_pile\" Community tab on the Hub: https://huggingface.co/datasets/the_pile/discussions/4" ]	2022-12-20T02:19:13	2022-12-20T07:52:54	2022-12-20T07:52:54	NONE	null	null	null	### Describe the bug When run "datasets.load_dataset("the_pile","enron_emails")" failure ![image](https://user-images.githubusercontent.com/52023469/208565302-cfab7b89-0b97-4fa6-a5ba-c11b0b629b1a.png) ### Steps to reproduce the bug Run below code in python cli: >>> import datasets >>> datasets.load_dataset("the_pile","enron_emails") ### Expected behavior Load dataset "the_pile", "enron_emails" successfully. ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.7.1 - Platform: Linux-5.15.0-53-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - PyArrow version: 10.0.0 - Pandas version: 1.4.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5378/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5378/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5377	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5377/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5377/comments	https://api.github.com/repos/huggingface/datasets/issues/5377/events	https://github.com/huggingface/datasets/pull/5377	1,503,477,833	PR_kwDODunzps5Fz5lw	5,377	Add a parallel implementation of to_tf_dataset()	{ "login": "Rocketknight1", "id": 12866554, "node_id": "MDQ6VXNlcjEyODY2NTU0", "avatar_url": "https://avatars.githubusercontent.com/u/12866554?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Rocketknight1", "html_url": "https://github.com/Rocketknight1", "followers_url": "https://api.github.com/users/Rocketknight1/followers", "following_url": "https://api.github.com/users/Rocketknight1/following{/other_user}", "gists_url": "https://api.github.com/users/Rocketknight1/gists{/gist_id}", "starred_url": "https://api.github.com/users/Rocketknight1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Rocketknight1/subscriptions", "organizations_url": "https://api.github.com/users/Rocketknight1/orgs", "repos_url": "https://api.github.com/users/Rocketknight1/repos", "events_url": "https://api.github.com/users/Rocketknight1/events{/privacy}", "received_events_url": "https://api.github.com/users/Rocketknight1/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Failing because the test server uses Py3.7 but the `SharedMemory` features require Py3.8! I forgot we still support 3.7 for another couple of months. I'm not sure exactly how to proceed, whether I should leave this PR until then, or just gate the feature behind a version check and skip the tests until the Python version catches up.", "I haven't played with `NumpyMultiprocessingGenerator` so I can't really help here, but this sounds promising :) Otherwise I think it's also fine to allow `num_workers` only for py>=3.8 for now. You can skip the test on 3.7 and make sure to raise an informative error if someone wants to use `num_workers` with 3.7", "Lots of comments here - I'll reply to the specific code comments underneath them, but in response to the general comments:\r\n\r\n@gante: I think this approach is much more performant than a `multiprocessing.Pool`. The reason is that when results are returned from a process `Pool`, the returned Python objects are pickled by the child processes, sent down a pipe and unpickled by the parent process. This creates a huge single-process bottleneck as the parent has to unpickle lots of large NumPy arrays, which is quite slow.\r\n\r\nWhen you use a `SharedMemory` approach, the data is just there for the parent process - the child and the parent are writing to exactly the same array in memory, and no pickling or unpickling occurs. This means the parent can just immediately copy the array (which is much faster than unpickling) and yield it to `tf.data`. We're taking advantage of the fact that we know the data is just big NumPy arrays and we don't need the full generality of `pickle`.\r\n\r\n@lhoestq: Sounds good! I'll add a clear error and skip the tests on Py<=3.7.", "Also, an extra technicality, just for information in case anyone looks at this PR later: Recent versions of Python allow [pickled objects to store out-of-band data](https://peps.python.org/pep-0574/). This allows for very efficient zero-copy unpickling of objects like NumPy arrays, with the unpickled object having a view on the same memory as the original. \r\n\r\nHowever, this explicitly does not work when the object is unpickled by a different process than the one that created it. For this to work you must explicitly allocate shared memory and create the array there, which pickle cannot handle for you. As a result, if you just benchmark unpickling vs copying of NumPy arrays it can seem like unpickling is very fast - but this is only true when the pickle was created in the unpickling process!", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008666 / 0.011353 (-0.002687) \| 0.004624 / 0.011008 (-0.006384) \| 0.099247 / 0.038508 (0.060739) \| 0.029766 / 0.023109 (0.006657) \| 0.303347 / 0.275898 (0.027449) \| 0.370022 / 0.323480 (0.046542) \| 0.007128 / 0.007986 (-0.000857) \| 0.003446 / 0.004328 (-0.000883) \| 0.076670 / 0.004250 (0.072420) \| 0.038892 / 0.037052 (0.001840) \| 0.313035 / 0.258489 (0.054546) \| 0.350503 / 0.293841 (0.056662) \| 0.033732 / 0.128546 (-0.094815) \| 0.011644 / 0.075646 (-0.064003) \| 0.323295 / 0.419271 (-0.095977) \| 0.040336 / 0.043533 (-0.003196) \| 0.302253 / 0.255139 (0.047114) \| 0.337199 / 0.283200 (0.053999) \| 0.089454 / 0.141683 (-0.052229) \| 1.624906 / 1.452155 (0.172752) \| 1.546187 / 1.492716 (0.053470) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.184614 / 0.018006 (0.166608) \| 0.427397 / 0.000490 (0.426907) \| 0.003342 / 0.000200 (0.003142) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023684 / 0.037411 (-0.013727) \| 0.100095 / 0.014526 (0.085569) \| 0.104996 / 0.176557 (-0.071560) \| 0.144719 / 0.737135 (-0.592416) \| 0.110759 / 0.296338 (-0.185579) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421108 / 0.215209 (0.205899) \| 4.214094 / 2.077655 (2.136440) \| 1.906231 / 1.504120 (0.402111) \| 1.698000 / 1.541195 (0.156806) \| 1.744856 / 1.468490 (0.276366) \| 0.693671 / 4.584777 (-3.891106) \| 3.362522 / 3.745712 (-0.383190) \| 1.878470 / 5.269862 (-3.391392) \| 1.167563 / 4.565676 (-3.398113) \| 0.082455 / 0.424275 (-0.341820) \| 0.012261 / 0.007607 (0.004654) \| 0.525196 / 0.226044 (0.299152) \| 5.257553 / 2.268929 (2.988624) \| 2.298286 / 55.444624 (-53.146339) \| 1.956106 / 6.876477 (-4.920371) \| 2.006308 / 2.142072 (-0.135764) \| 0.811069 / 4.805227 (-3.994158) \| 0.150368 / 6.500664 (-6.350296) \| 0.065699 / 0.075469 (-0.009771) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.224516 / 1.841788 (-0.617272) \| 13.619084 / 8.074308 (5.544776) \| 14.096666 / 10.191392 (3.905274) \| 0.151068 / 0.680424 (-0.529356) \| 0.028819 / 0.534201 (-0.505382) \| 0.402071 / 0.579283 (-0.177212) \| 0.408647 / 0.434364 (-0.025717) \| 0.466605 / 0.540337 (-0.073733) \| 0.547094 / 1.386936 (-0.839842) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006935 / 0.011353 (-0.004418) \| 0.004590 / 0.011008 (-0.006419) \| 0.099398 / 0.038508 (0.060890) \| 0.028145 / 0.023109 (0.005036) \| 0.426582 / 0.275898 (0.150684) \| 0.465712 / 0.323480 (0.142233) \| 0.005254 / 0.007986 (-0.002731) \| 0.004956 / 0.004328 (0.000627) \| 0.075616 / 0.004250 (0.071365) \| 0.039871 / 0.037052 (0.002819) \| 0.428859 / 0.258489 (0.170370) \| 0.470839 / 0.293841 (0.176998) \| 0.032150 / 0.128546 (-0.096396) \| 0.011778 / 0.075646 (-0.063868) \| 0.322358 / 0.419271 (-0.096913) \| 0.041974 / 0.043533 (-0.001559) \| 0.427459 / 0.255139 (0.172320) \| 0.446685 / 0.283200 (0.163485) \| 0.092000 / 0.141683 (-0.049683) \| 1.509231 / 1.452155 (0.057076) \| 1.578950 / 1.492716 (0.086234) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.168047 / 0.018006 (0.150041) \| 0.418993 / 0.000490 (0.418503) \| 0.002855 / 0.000200 (0.002655) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025652 / 0.037411 (-0.011759) \| 0.100141 / 0.014526 (0.085616) \| 0.107293 / 0.176557 (-0.069264) \| 0.142857 / 0.737135 (-0.594278) \| 0.110933 / 0.296338 (-0.185406) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.477556 / 0.215209 (0.262347) \| 4.777951 / 2.077655 (2.700296) \| 2.461885 / 1.504120 (0.957765) \| 2.252307 / 1.541195 (0.711112) \| 2.307983 / 1.468490 (0.839493) \| 0.697570 / 4.584777 (-3.887207) \| 3.370323 / 3.745712 (-0.375389) \| 3.131333 / 5.269862 (-2.138529) \| 1.594839 / 4.565676 (-2.970838) \| 0.082333 / 0.424275 (-0.341942) \| 0.012574 / 0.007607 (0.004967) \| 0.583704 / 0.226044 (0.357660) \| 5.817675 / 2.268929 (3.548746) \| 2.927054 / 55.444624 (-52.517570) \| 2.582929 / 6.876477 (-4.293548) \| 2.634275 / 2.142072 (0.492202) \| 0.806407 / 4.805227 (-3.998821) \| 0.151438 / 6.500664 (-6.349226) \| 0.067429 / 0.075469 (-0.008040) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.267011 / 1.841788 (-0.574776) \| 13.989515 / 8.074308 (5.915207) \| 14.087968 / 10.191392 (3.896576) \| 0.142130 / 0.680424 (-0.538293) \| 0.017201 / 0.534201 (-0.517000) \| 0.383394 / 0.579283 (-0.195889) \| 0.381921 / 0.434364 (-0.052443) \| 0.439169 / 0.540337 (-0.101168) \| 0.524215 / 1.386936 (-0.862721) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#be2ebc8f3cfeb532c933be2443094603bafcab04 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008489 / 0.011353 (-0.002864) \| 0.004617 / 0.011008 (-0.006391) \| 0.102035 / 0.038508 (0.063527) \| 0.029850 / 0.023109 (0.006741) \| 0.296789 / 0.275898 (0.020891) \| 0.367270 / 0.323480 (0.043790) \| 0.006934 / 0.007986 (-0.001052) \| 0.004923 / 0.004328 (0.000595) \| 0.079150 / 0.004250 (0.074900) \| 0.036884 / 0.037052 (-0.000169) \| 0.305747 / 0.258489 (0.047258) \| 0.348510 / 0.293841 (0.054669) \| 0.034074 / 0.128546 (-0.094472) \| 0.011650 / 0.075646 (-0.063997) \| 0.324226 / 0.419271 (-0.095045) \| 0.041763 / 0.043533 (-0.001770) \| 0.300887 / 0.255139 (0.045748) \| 0.333393 / 0.283200 (0.050193) \| 0.093838 / 0.141683 (-0.047844) \| 1.499801 / 1.452155 (0.047646) \| 1.505988 / 1.492716 (0.013272) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198610 / 0.018006 (0.180604) \| 0.407380 / 0.000490 (0.406891) \| 0.000367 / 0.000200 (0.000167) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022858 / 0.037411 (-0.014554) \| 0.095727 / 0.014526 (0.081202) \| 0.104014 / 0.176557 (-0.072543) \| 0.138764 / 0.737135 (-0.598371) \| 0.105860 / 0.296338 (-0.190478) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416352 / 0.215209 (0.201143) \| 4.150007 / 2.077655 (2.072352) \| 1.878727 / 1.504120 (0.374607) \| 1.678978 / 1.541195 (0.137783) \| 1.711990 / 1.468490 (0.243500) \| 0.691722 / 4.584777 (-3.893055) \| 3.386466 / 3.745712 (-0.359246) \| 1.835730 / 5.269862 (-3.434132) \| 1.149975 / 4.565676 (-3.415702) \| 0.081914 / 0.424275 (-0.342362) \| 0.012238 / 0.007607 (0.004631) \| 0.522945 / 0.226044 (0.296900) \| 5.251793 / 2.268929 (2.982864) \| 2.306907 / 55.444624 (-53.137717) \| 1.968400 / 6.876477 (-4.908076) \| 1.981154 / 2.142072 (-0.160919) \| 0.810126 / 4.805227 (-3.995101) \| 0.147876 / 6.500664 (-6.352788) \| 0.064042 / 0.075469 (-0.011428) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.199150 / 1.841788 (-0.642637) \| 13.913473 / 8.074308 (5.839165) \| 14.079132 / 10.191392 (3.887740) \| 0.137387 / 0.680424 (-0.543037) \| 0.028456 / 0.534201 (-0.505745) \| 0.394162 / 0.579283 (-0.185122) \| 0.402051 / 0.434364 (-0.032313) \| 0.461944 / 0.540337 (-0.078394) \| 0.542648 / 1.386936 (-0.844288) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006393 / 0.011353 (-0.004960) \| 0.004599 / 0.011008 (-0.006409) \| 0.097389 / 0.038508 (0.058881) \| 0.027719 / 0.023109 (0.004610) \| 0.341060 / 0.275898 (0.065162) \| 0.379604 / 0.323480 (0.056124) \| 0.004955 / 0.007986 (-0.003030) \| 0.003369 / 0.004328 (-0.000959) \| 0.075390 / 0.004250 (0.071139) \| 0.038518 / 0.037052 (0.001466) \| 0.347085 / 0.258489 (0.088596) \| 0.393468 / 0.293841 (0.099627) \| 0.031482 / 0.128546 (-0.097064) \| 0.011585 / 0.075646 (-0.064061) \| 0.317969 / 0.419271 (-0.101302) \| 0.041389 / 0.043533 (-0.002144) \| 0.343812 / 0.255139 (0.088673) \| 0.371047 / 0.283200 (0.087848) \| 0.090020 / 0.141683 (-0.051663) \| 1.461690 / 1.452155 (0.009536) \| 1.552458 / 1.492716 (0.059741) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.188691 / 0.018006 (0.170684) \| 0.415635 / 0.000490 (0.415145) \| 0.005285 / 0.000200 (0.005085) \| 0.000087 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024695 / 0.037411 (-0.012716) \| 0.098939 / 0.014526 (0.084413) \| 0.108472 / 0.176557 (-0.068085) \| 0.152635 / 0.737135 (-0.584501) \| 0.109947 / 0.296338 (-0.186391) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471975 / 0.215209 (0.256766) \| 4.716437 / 2.077655 (2.638782) \| 2.420148 / 1.504120 (0.916028) \| 2.219864 / 1.541195 (0.678669) \| 2.238647 / 1.468490 (0.770157) \| 0.697628 / 4.584777 (-3.887149) \| 3.530720 / 3.745712 (-0.214993) \| 3.327354 / 5.269862 (-1.942508) \| 1.665877 / 4.565676 (-2.899800) \| 0.082650 / 0.424275 (-0.341625) \| 0.012593 / 0.007607 (0.004986) \| 0.576109 / 0.226044 (0.350065) \| 5.744691 / 2.268929 (3.475762) \| 2.863473 / 55.444624 (-52.581152) \| 2.529616 / 6.876477 (-4.346861) \| 2.562802 / 2.142072 (0.420730) \| 0.805631 / 4.805227 (-3.999597) \| 0.150788 / 6.500664 (-6.349876) \| 0.065743 / 0.075469 (-0.009726) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.295134 / 1.841788 (-0.546654) \| 14.096046 / 8.074308 (6.021738) \| 13.901399 / 10.191392 (3.710007) \| 0.127481 / 0.680424 (-0.552943) \| 0.016666 / 0.534201 (-0.517535) \| 0.381819 / 0.579283 (-0.197464) \| 0.382629 / 0.434364 (-0.051735) \| 0.439354 / 0.540337 (-0.100984) \| 0.527662 / 1.386936 (-0.859274) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fe2ad43f59e65d39f2f2ce7442c76990493deb7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008509 / 0.011353 (-0.002844) \| 0.004523 / 0.011008 (-0.006485) \| 0.100616 / 0.038508 (0.062108) \| 0.029573 / 0.023109 (0.006464) \| 0.306414 / 0.275898 (0.030516) \| 0.377034 / 0.323480 (0.053554) \| 0.007621 / 0.007986 (-0.000365) \| 0.003335 / 0.004328 (-0.000993) \| 0.078598 / 0.004250 (0.074348) \| 0.036902 / 0.037052 (-0.000150) \| 0.318146 / 0.258489 (0.059657) \| 0.355626 / 0.293841 (0.061785) \| 0.033441 / 0.128546 (-0.095105) \| 0.011552 / 0.075646 (-0.064094) \| 0.322973 / 0.419271 (-0.096299) \| 0.040564 / 0.043533 (-0.002968) \| 0.306451 / 0.255139 (0.051312) \| 0.337591 / 0.283200 (0.054392) \| 0.086822 / 0.141683 (-0.054861) \| 1.484601 / 1.452155 (0.032447) \| 1.542777 / 1.492716 (0.050061) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201711 / 0.018006 (0.183705) \| 0.418387 / 0.000490 (0.417898) \| 0.002753 / 0.000200 (0.002553) \| 0.000263 / 0.000054 (0.000209) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023016 / 0.037411 (-0.014395) \| 0.097313 / 0.014526 (0.082787) \| 0.103435 / 0.176557 (-0.073122) \| 0.142665 / 0.737135 (-0.594470) \| 0.107397 / 0.296338 (-0.188942) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422739 / 0.215209 (0.207530) \| 4.220126 / 2.077655 (2.142471) \| 1.865447 / 1.504120 (0.361327) \| 1.649647 / 1.541195 (0.108453) \| 1.711655 / 1.468490 (0.243165) \| 0.704269 / 4.584777 (-3.880508) \| 3.407390 / 3.745712 (-0.338322) \| 1.929224 / 5.269862 (-3.340638) \| 1.281225 / 4.565676 (-3.284452) \| 0.082924 / 0.424275 (-0.341351) \| 0.012588 / 0.007607 (0.004981) \| 0.531025 / 0.226044 (0.304980) \| 5.339441 / 2.268929 (3.070512) \| 2.298969 / 55.444624 (-53.145656) \| 1.952145 / 6.876477 (-4.924332) \| 2.034754 / 2.142072 (-0.107318) \| 0.823672 / 4.805227 (-3.981555) \| 0.151465 / 6.500664 (-6.349199) \| 0.066663 / 0.075469 (-0.008807) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.258981 / 1.841788 (-0.582807) \| 13.791640 / 8.074308 (5.717332) \| 14.001514 / 10.191392 (3.810122) \| 0.149805 / 0.680424 (-0.530619) \| 0.028614 / 0.534201 (-0.505587) \| 0.400266 / 0.579283 (-0.179017) \| 0.405891 / 0.434364 (-0.028473) \| 0.471903 / 0.540337 (-0.068435) \| 0.563656 / 1.386936 (-0.823280) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006751 / 0.011353 (-0.004601) \| 0.004665 / 0.011008 (-0.006343) \| 0.098362 / 0.038508 (0.059854) \| 0.027451 / 0.023109 (0.004342) \| 0.421859 / 0.275898 (0.145961) \| 0.458089 / 0.323480 (0.134609) \| 0.004885 / 0.007986 (-0.003101) \| 0.003459 / 0.004328 (-0.000870) \| 0.075871 / 0.004250 (0.071621) \| 0.036591 / 0.037052 (-0.000462) \| 0.423307 / 0.258489 (0.164818) \| 0.467040 / 0.293841 (0.173199) \| 0.031837 / 0.128546 (-0.096710) \| 0.011604 / 0.075646 (-0.064042) \| 0.321132 / 0.419271 (-0.098140) \| 0.041806 / 0.043533 (-0.001727) \| 0.421653 / 0.255139 (0.166514) \| 0.445896 / 0.283200 (0.162696) \| 0.087998 / 0.141683 (-0.053685) \| 1.475818 / 1.452155 (0.023664) \| 1.559487 / 1.492716 (0.066770) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203096 / 0.018006 (0.185090) \| 0.401381 / 0.000490 (0.400892) \| 0.004037 / 0.000200 (0.003837) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023757 / 0.037411 (-0.013654) \| 0.099919 / 0.014526 (0.085393) \| 0.108384 / 0.176557 (-0.068173) \| 0.143780 / 0.737135 (-0.593355) \| 0.111528 / 0.296338 (-0.184811) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.475896 / 0.215209 (0.260686) \| 4.754567 / 2.077655 (2.676912) \| 2.444986 / 1.504120 (0.940866) \| 2.231055 / 1.541195 (0.689860) \| 2.283646 / 1.468490 (0.815156) \| 0.701303 / 4.584777 (-3.883474) \| 3.381597 / 3.745712 (-0.364115) \| 1.878714 / 5.269862 (-3.391148) \| 1.171566 / 4.565676 (-3.394111) \| 0.083106 / 0.424275 (-0.341169) \| 0.012575 / 0.007607 (0.004967) \| 0.582570 / 0.226044 (0.356526) \| 5.813677 / 2.268929 (3.544748) \| 2.908578 / 55.444624 (-52.536046) \| 2.548459 / 6.876477 (-4.328017) \| 2.581211 / 2.142072 (0.439139) \| 0.807925 / 4.805227 (-3.997302) \| 0.153516 / 6.500664 (-6.347148) \| 0.068763 / 0.075469 (-0.006706) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249595 / 1.841788 (-0.592193) \| 14.208573 / 8.074308 (6.134265) \| 14.179174 / 10.191392 (3.987781) \| 0.156005 / 0.680424 (-0.524419) \| 0.017045 / 0.534201 (-0.517156) \| 0.377414 / 0.579283 (-0.201869) \| 0.395291 / 0.434364 (-0.039073) \| 0.444642 / 0.540337 (-0.095695) \| 0.531626 / 1.386936 (-0.855311) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#52888645daa6854928474df6308bd997c8878ced \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008871 / 0.011353 (-0.002482) \| 0.004616 / 0.011008 (-0.006392) \| 0.100910 / 0.038508 (0.062402) \| 0.030381 / 0.023109 (0.007272) \| 0.304636 / 0.275898 (0.028737) \| 0.384258 / 0.323480 (0.060778) \| 0.007019 / 0.007986 (-0.000966) \| 0.004262 / 0.004328 (-0.000066) \| 0.077082 / 0.004250 (0.072832) \| 0.035235 / 0.037052 (-0.001817) \| 0.318293 / 0.258489 (0.059804) \| 0.356578 / 0.293841 (0.062737) \| 0.033568 / 0.128546 (-0.094978) \| 0.011583 / 0.075646 (-0.064063) \| 0.322442 / 0.419271 (-0.096830) \| 0.041941 / 0.043533 (-0.001592) \| 0.310469 / 0.255139 (0.055330) \| 0.335626 / 0.283200 (0.052427) \| 0.088195 / 0.141683 (-0.053487) \| 1.466778 / 1.452155 (0.014623) \| 1.512459 / 1.492716 (0.019743) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.184126 / 0.018006 (0.166120) \| 0.413392 / 0.000490 (0.412902) \| 0.002191 / 0.000200 (0.001992) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023426 / 0.037411 (-0.013985) \| 0.096240 / 0.014526 (0.081715) \| 0.105908 / 0.176557 (-0.070648) \| 0.146331 / 0.737135 (-0.590804) \| 0.107441 / 0.296338 (-0.188898) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420018 / 0.215209 (0.204809) \| 4.198129 / 2.077655 (2.120474) \| 1.998726 / 1.504120 (0.494606) \| 1.870410 / 1.541195 (0.329215) \| 1.925160 / 1.468490 (0.456670) \| 0.688790 / 4.584777 (-3.895987) \| 3.430629 / 3.745712 (-0.315083) \| 2.875616 / 5.269862 (-2.394246) \| 1.566269 / 4.565676 (-2.999408) \| 0.082431 / 0.424275 (-0.341844) \| 0.012409 / 0.007607 (0.004802) \| 0.536178 / 0.226044 (0.310134) \| 5.342918 / 2.268929 (3.073989) \| 2.410814 / 55.444624 (-53.033811) \| 2.056518 / 6.876477 (-4.819958) \| 2.240148 / 2.142072 (0.098075) \| 0.804848 / 4.805227 (-4.000379) \| 0.147325 / 6.500664 (-6.353340) \| 0.064217 / 0.075469 (-0.011252) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.285725 / 1.841788 (-0.556063) \| 13.909739 / 8.074308 (5.835431) \| 14.025774 / 10.191392 (3.834382) \| 0.142413 / 0.680424 (-0.538011) \| 0.028390 / 0.534201 (-0.505811) \| 0.402345 / 0.579283 (-0.176939) \| 0.404341 / 0.434364 (-0.030023) \| 0.463055 / 0.540337 (-0.077282) \| 0.556811 / 1.386936 (-0.830125) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006557 / 0.011353 (-0.004795) \| 0.004668 / 0.011008 (-0.006340) \| 0.098839 / 0.038508 (0.060331) \| 0.027618 / 0.023109 (0.004508) \| 0.409338 / 0.275898 (0.133440) \| 0.444048 / 0.323480 (0.120568) \| 0.004881 / 0.007986 (-0.003105) \| 0.003434 / 0.004328 (-0.000895) \| 0.076497 / 0.004250 (0.072247) \| 0.038932 / 0.037052 (0.001880) \| 0.411419 / 0.258489 (0.152930) \| 0.451167 / 0.293841 (0.157326) \| 0.031649 / 0.128546 (-0.096897) \| 0.011691 / 0.075646 (-0.063955) \| 0.321586 / 0.419271 (-0.097685) \| 0.041984 / 0.043533 (-0.001549) \| 0.407717 / 0.255139 (0.152578) \| 0.434687 / 0.283200 (0.151487) \| 0.086419 / 0.141683 (-0.055264) \| 1.491755 / 1.452155 (0.039601) \| 1.569081 / 1.492716 (0.076364) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231746 / 0.018006 (0.213739) \| 0.412271 / 0.000490 (0.411781) \| 0.000403 / 0.000200 (0.000203) \| 0.000063 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024264 / 0.037411 (-0.013147) \| 0.100478 / 0.014526 (0.085952) \| 0.107065 / 0.176557 (-0.069491) \| 0.140724 / 0.737135 (-0.596412) \| 0.110631 / 0.296338 (-0.185707) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472476 / 0.215209 (0.257267) \| 4.738919 / 2.077655 (2.661265) \| 2.438049 / 1.504120 (0.933929) \| 2.237855 / 1.541195 (0.696660) \| 2.282885 / 1.468490 (0.814395) \| 0.690420 / 4.584777 (-3.894357) \| 3.426487 / 3.745712 (-0.319225) \| 1.842443 / 5.269862 (-3.427418) \| 1.154466 / 4.565676 (-3.411210) \| 0.082166 / 0.424275 (-0.342109) \| 0.012309 / 0.007607 (0.004701) \| 0.574730 / 0.226044 (0.348686) \| 5.737566 / 2.268929 (3.468638) \| 2.882405 / 55.444624 (-52.562220) \| 2.540276 / 6.876477 (-4.336201) \| 2.552356 / 2.142072 (0.410283) \| 0.796413 / 4.805227 (-4.008815) \| 0.152705 / 6.500664 (-6.347959) \| 0.068273 / 0.075469 (-0.007196) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244423 / 1.841788 (-0.597365) \| 13.827750 / 8.074308 (5.753442) \| 14.074083 / 10.191392 (3.882691) \| 0.140291 / 0.680424 (-0.540133) \| 0.017337 / 0.534201 (-0.516864) \| 0.389314 / 0.579283 (-0.189969) \| 0.390914 / 0.434364 (-0.043450) \| 0.450333 / 0.540337 (-0.090004) \| 0.543860 / 1.386936 (-0.843076) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2cdcddc51d3cda24c2d79ad137af9e55d0a38044 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009490 / 0.011353 (-0.001863) \| 0.005211 / 0.011008 (-0.005798) \| 0.100884 / 0.038508 (0.062376) \| 0.035834 / 0.023109 (0.012725) \| 0.293623 / 0.275898 (0.017724) \| 0.378118 / 0.323480 (0.054638) \| 0.008106 / 0.007986 (0.000120) \| 0.005339 / 0.004328 (0.001010) \| 0.076311 / 0.004250 (0.072061) \| 0.045954 / 0.037052 (0.008902) \| 0.308163 / 0.258489 (0.049674) \| 0.353470 / 0.293841 (0.059629) \| 0.038539 / 0.128546 (-0.090008) \| 0.012174 / 0.075646 (-0.063472) \| 0.334875 / 0.419271 (-0.084396) \| 0.048602 / 0.043533 (0.005069) \| 0.295803 / 0.255139 (0.040664) \| 0.318894 / 0.283200 (0.035695) \| 0.105487 / 0.141683 (-0.036195) \| 1.433628 / 1.452155 (-0.018526) \| 1.466843 / 1.492716 (-0.025873) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203426 / 0.018006 (0.185419) \| 0.456877 / 0.000490 (0.456387) \| 0.001452 / 0.000200 (0.001252) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028308 / 0.037411 (-0.009103) \| 0.108965 / 0.014526 (0.094439) \| 0.119552 / 0.176557 (-0.057005) \| 0.156371 / 0.737135 (-0.580765) \| 0.124141 / 0.296338 (-0.172197) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400183 / 0.215209 (0.184973) \| 3.990983 / 2.077655 (1.913329) \| 1.806729 / 1.504120 (0.302609) \| 1.611944 / 1.541195 (0.070750) \| 1.740019 / 1.468490 (0.271529) \| 0.699600 / 4.584777 (-3.885177) \| 3.868711 / 3.745712 (0.122999) \| 3.249758 / 5.269862 (-2.020103) \| 1.832213 / 4.565676 (-2.733463) \| 0.085282 / 0.424275 (-0.338993) \| 0.012726 / 0.007607 (0.005119) \| 0.509385 / 0.226044 (0.283341) \| 5.066913 / 2.268929 (2.797984) \| 2.325710 / 55.444624 (-53.118914) \| 1.962238 / 6.876477 (-4.914239) \| 2.017576 / 2.142072 (-0.124496) \| 0.839444 / 4.805227 (-3.965783) \| 0.166936 / 6.500664 (-6.333728) \| 0.064546 / 0.075469 (-0.010923) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.196396 / 1.841788 (-0.645392) \| 15.077063 / 8.074308 (7.002755) \| 14.268103 / 10.191392 (4.076711) \| 0.163782 / 0.680424 (-0.516642) \| 0.028794 / 0.534201 (-0.505407) \| 0.440564 / 0.579283 (-0.138719) \| 0.439826 / 0.434364 (0.005463) \| 0.514786 / 0.540337 (-0.025551) \| 0.603353 / 1.386936 (-0.783583) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007874 / 0.011353 (-0.003479) \| 0.005347 / 0.011008 (-0.005661) \| 0.099461 / 0.038508 (0.060953) \| 0.034010 / 0.023109 (0.010901) \| 0.384650 / 0.275898 (0.108752) \| 0.423827 / 0.323480 (0.100347) \| 0.006201 / 0.007986 (-0.001784) \| 0.004212 / 0.004328 (-0.000117) \| 0.074354 / 0.004250 (0.070104) \| 0.051675 / 0.037052 (0.014623) \| 0.392488 / 0.258489 (0.133999) \| 0.425828 / 0.293841 (0.131987) \| 0.037444 / 0.128546 (-0.091103) \| 0.012388 / 0.075646 (-0.063258) \| 0.334482 / 0.419271 (-0.084789) \| 0.050715 / 0.043533 (0.007182) \| 0.378323 / 0.255139 (0.123184) \| 0.395450 / 0.283200 (0.112250) \| 0.108403 / 0.141683 (-0.033280) \| 1.426803 / 1.452155 (-0.025352) \| 1.532417 / 1.492716 (0.039701) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219989 / 0.018006 (0.201982) \| 0.454101 / 0.000490 (0.453611) \| 0.000407 / 0.000200 (0.000207) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030590 / 0.037411 (-0.006822) \| 0.113483 / 0.014526 (0.098957) \| 0.122603 / 0.176557 (-0.053954) \| 0.161031 / 0.737135 (-0.576104) \| 0.128039 / 0.296338 (-0.168300) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430458 / 0.215209 (0.215249) \| 4.286594 / 2.077655 (2.208940) \| 2.056666 / 1.504120 (0.552546) \| 1.861142 / 1.541195 (0.319948) \| 1.937185 / 1.468490 (0.468695) \| 0.701881 / 4.584777 (-3.882896) \| 3.970144 / 3.745712 (0.224432) \| 2.107118 / 5.269862 (-3.162744) \| 1.351561 / 4.565676 (-3.214115) \| 0.085470 / 0.424275 (-0.338805) \| 0.012366 / 0.007607 (0.004759) \| 0.525212 / 0.226044 (0.299168) \| 5.301553 / 2.268929 (3.032625) \| 2.593862 / 55.444624 (-52.850763) \| 2.287315 / 6.876477 (-4.589161) \| 2.368249 / 2.142072 (0.226176) \| 0.855656 / 4.805227 (-3.949571) \| 0.167846 / 6.500664 (-6.332818) \| 0.064521 / 0.075469 (-0.010948) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237008 / 1.841788 (-0.604779) \| 15.784303 / 8.074308 (7.709995) \| 14.613081 / 10.191392 (4.421689) \| 0.161012 / 0.680424 (-0.519412) \| 0.017928 / 0.534201 (-0.516273) \| 0.423905 / 0.579283 (-0.155378) \| 0.428316 / 0.434364 (-0.006048) \| 0.500226 / 0.540337 (-0.040112) \| 0.606725 / 1.386936 (-0.780211) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#08473e2ee66acb7e6f82d3591bb9b03924a661ed \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008874 / 0.011353 (-0.002479) \| 0.004581 / 0.011008 (-0.006428) \| 0.100180 / 0.038508 (0.061672) \| 0.029990 / 0.023109 (0.006880) \| 0.301616 / 0.275898 (0.025718) \| 0.343662 / 0.323480 (0.020183) \| 0.007111 / 0.007986 (-0.000875) \| 0.003428 / 0.004328 (-0.000900) \| 0.078031 / 0.004250 (0.073780) \| 0.037332 / 0.037052 (0.000279) \| 0.301977 / 0.258489 (0.043488) \| 0.345581 / 0.293841 (0.051740) \| 0.034305 / 0.128546 (-0.094241) \| 0.011660 / 0.075646 (-0.063986) \| 0.322289 / 0.419271 (-0.096982) \| 0.041488 / 0.043533 (-0.002045) \| 0.301612 / 0.255139 (0.046473) \| 0.328174 / 0.283200 (0.044974) \| 0.085561 / 0.141683 (-0.056122) \| 1.482114 / 1.452155 (0.029959) \| 1.556194 / 1.492716 (0.063478) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.186989 / 0.018006 (0.168983) \| 0.421499 / 0.000490 (0.421009) \| 0.001193 / 0.000200 (0.000993) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023551 / 0.037411 (-0.013861) \| 0.099868 / 0.014526 (0.085343) \| 0.105233 / 0.176557 (-0.071324) \| 0.141628 / 0.737135 (-0.595507) \| 0.109004 / 0.296338 (-0.187335) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415189 / 0.215209 (0.199979) \| 4.145716 / 2.077655 (2.068061) \| 1.837917 / 1.504120 (0.333797) \| 1.635043 / 1.541195 (0.093848) \| 1.683299 / 1.468490 (0.214809) \| 0.688538 / 4.584777 (-3.896239) \| 3.412628 / 3.745712 (-0.333084) \| 1.877456 / 5.269862 (-3.392405) \| 1.154129 / 4.565676 (-3.411547) \| 0.081850 / 0.424275 (-0.342425) \| 0.012309 / 0.007607 (0.004702) \| 0.522830 / 0.226044 (0.296785) \| 5.238685 / 2.268929 (2.969756) \| 2.277840 / 55.444624 (-53.166784) \| 1.941787 / 6.876477 (-4.934690) \| 1.999688 / 2.142072 (-0.142385) \| 0.807590 / 4.805227 (-3.997637) \| 0.148157 / 6.500664 (-6.352507) \| 0.064898 / 0.075469 (-0.010571) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253859 / 1.841788 (-0.587929) \| 13.676097 / 8.074308 (5.601789) \| 14.237837 / 10.191392 (4.046444) \| 0.137178 / 0.680424 (-0.543246) \| 0.028971 / 0.534201 (-0.505230) \| 0.400380 / 0.579283 (-0.178903) \| 0.409990 / 0.434364 (-0.024374) \| 0.462552 / 0.540337 (-0.077786) \| 0.552153 / 1.386936 (-0.834783) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006831 / 0.011353 (-0.004522) \| 0.004627 / 0.011008 (-0.006381) \| 0.099883 / 0.038508 (0.061375) \| 0.028072 / 0.023109 (0.004962) \| 0.343556 / 0.275898 (0.067658) \| 0.386792 / 0.323480 (0.063312) \| 0.005080 / 0.007986 (-0.002906) \| 0.003508 / 0.004328 (-0.000820) \| 0.077803 / 0.004250 (0.073552) \| 0.040038 / 0.037052 (0.002985) \| 0.345089 / 0.258489 (0.086600) \| 0.396078 / 0.293841 (0.102238) \| 0.032241 / 0.128546 (-0.096305) \| 0.011711 / 0.075646 (-0.063935) \| 0.320531 / 0.419271 (-0.098740) \| 0.043658 / 0.043533 (0.000125) \| 0.344696 / 0.255139 (0.089557) \| 0.389847 / 0.283200 (0.106648) \| 0.092328 / 0.141683 (-0.049355) \| 1.477290 / 1.452155 (0.025136) \| 1.548698 / 1.492716 (0.055982) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236073 / 0.018006 (0.218067) \| 0.422113 / 0.000490 (0.421624) \| 0.000431 / 0.000200 (0.000231) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024738 / 0.037411 (-0.012673) \| 0.100546 / 0.014526 (0.086020) \| 0.107550 / 0.176557 (-0.069006) \| 0.146056 / 0.737135 (-0.591079) \| 0.112665 / 0.296338 (-0.183674) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.490259 / 0.215209 (0.275050) \| 4.907994 / 2.077655 (2.830339) \| 2.547175 / 1.504120 (1.043055) \| 2.344419 / 1.541195 (0.803224) \| 2.403985 / 1.468490 (0.935495) \| 0.696011 / 4.584777 (-3.888766) \| 3.442426 / 3.745712 (-0.303286) \| 1.878702 / 5.269862 (-3.391159) \| 1.158280 / 4.565676 (-3.407396) \| 0.082300 / 0.424275 (-0.341975) \| 0.012513 / 0.007607 (0.004906) \| 0.602696 / 0.226044 (0.376651) \| 6.014592 / 2.268929 (3.745663) \| 3.014466 / 55.444624 (-52.430159) \| 2.669376 / 6.876477 (-4.207101) \| 2.724485 / 2.142072 (0.582412) \| 0.799795 / 4.805227 (-4.005432) \| 0.151220 / 6.500664 (-6.349444) \| 0.067486 / 0.075469 (-0.007983) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.281265 / 1.841788 (-0.560523) \| 14.362284 / 8.074308 (6.287976) \| 14.313690 / 10.191392 (4.122298) \| 0.142870 / 0.680424 (-0.537554) \| 0.017206 / 0.534201 (-0.516995) \| 0.380084 / 0.579283 (-0.199199) \| 0.388161 / 0.434364 (-0.046203) \| 0.442617 / 0.540337 (-0.097721) \| 0.528487 / 1.386936 (-0.858449) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#452b7f8ae78967dc662f5436e751233d46c62e78 \"CML watermark\")\n", "@lhoestq @amyeroberts @gante I did a substantial rewrite and all tests are passing now (Windows seems to time out or something and I can't figure out why - not sure if that's related to this PR!). I also confirmed tests are passing locally with Py==3.10. \r\n\r\nAside from incorporating everyone's comments, I also made a context manager to create and handle shared memory - this ensures that shared memory is cleaned up even if execution is interrupted. Also, shared memory names include a UUID string now to avoid collisions. Finally, string arrays are now split up into fixed-width character arrays in the workers so that they can be passed through shared memory, and the parent process reconstructs them into string arrays.", "Update: `test_arrow_dataset.py` ran fine in this branch on my Windows machine (Py 3.10), so I have no idea what's up with those tests", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008852 / 0.011353 (-0.002500) \| 0.004545 / 0.011008 (-0.006464) \| 0.099814 / 0.038508 (0.061306) \| 0.030314 / 0.023109 (0.007205) \| 0.310426 / 0.275898 (0.034528) \| 0.366893 / 0.323480 (0.043413) \| 0.007183 / 0.007986 (-0.000802) \| 0.003476 / 0.004328 (-0.000853) \| 0.077566 / 0.004250 (0.073315) \| 0.038269 / 0.037052 (0.001217) \| 0.319133 / 0.258489 (0.060644) \| 0.352399 / 0.293841 (0.058558) \| 0.033847 / 0.128546 (-0.094700) \| 0.011568 / 0.075646 (-0.064078) \| 0.321355 / 0.419271 (-0.097917) \| 0.040719 / 0.043533 (-0.002814) \| 0.304812 / 0.255139 (0.049673) \| 0.329512 / 0.283200 (0.046312) \| 0.088045 / 0.141683 (-0.053638) \| 1.514182 / 1.452155 (0.062027) \| 1.529459 / 1.492716 (0.036742) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216749 / 0.018006 (0.198743) \| 0.409909 / 0.000490 (0.409419) \| 0.002790 / 0.000200 (0.002590) \| 0.000081 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023390 / 0.037411 (-0.014021) \| 0.095955 / 0.014526 (0.081430) \| 0.104749 / 0.176557 (-0.071807) \| 0.143414 / 0.737135 (-0.593721) \| 0.109011 / 0.296338 (-0.187328) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420410 / 0.215209 (0.205201) \| 4.185745 / 2.077655 (2.108090) \| 1.910207 / 1.504120 (0.406087) \| 1.679330 / 1.541195 (0.138135) \| 1.727134 / 1.468490 (0.258644) \| 0.692379 / 4.584777 (-3.892398) \| 3.358731 / 3.745712 (-0.386982) \| 2.914657 / 5.269862 (-2.355205) \| 1.506083 / 4.565676 (-3.059594) \| 0.081922 / 0.424275 (-0.342353) \| 0.012691 / 0.007607 (0.005084) \| 0.530942 / 0.226044 (0.304897) \| 5.357642 / 2.268929 (3.088714) \| 2.387347 / 55.444624 (-53.057277) \| 2.030001 / 6.876477 (-4.846476) \| 2.026405 / 2.142072 (-0.115667) \| 0.809406 / 4.805227 (-3.995821) \| 0.149003 / 6.500664 (-6.351661) \| 0.066910 / 0.075469 (-0.008559) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.278160 / 1.841788 (-0.563627) \| 13.632742 / 8.074308 (5.558434) \| 13.995537 / 10.191392 (3.804145) \| 0.136507 / 0.680424 (-0.543917) \| 0.028817 / 0.534201 (-0.505384) \| 0.394842 / 0.579283 (-0.184441) \| 0.399526 / 0.434364 (-0.034838) \| 0.459174 / 0.540337 (-0.081163) \| 0.536877 / 1.386936 (-0.850059) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006814 / 0.011353 (-0.004539) \| 0.004456 / 0.011008 (-0.006552) \| 0.098386 / 0.038508 (0.059878) \| 0.028124 / 0.023109 (0.005015) \| 0.409004 / 0.275898 (0.133106) \| 0.446746 / 0.323480 (0.123266) \| 0.005108 / 0.007986 (-0.002877) \| 0.004807 / 0.004328 (0.000479) \| 0.075751 / 0.004250 (0.071500) \| 0.039297 / 0.037052 (0.002244) \| 0.413198 / 0.258489 (0.154709) \| 0.452124 / 0.293841 (0.158283) \| 0.032534 / 0.128546 (-0.096012) \| 0.011689 / 0.075646 (-0.063957) \| 0.325465 / 0.419271 (-0.093806) \| 0.041347 / 0.043533 (-0.002185) \| 0.411489 / 0.255139 (0.156350) \| 0.447120 / 0.283200 (0.163920) \| 0.093058 / 0.141683 (-0.048625) \| 1.489903 / 1.452155 (0.037748) \| 1.580771 / 1.492716 (0.088055) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192619 / 0.018006 (0.174613) \| 0.399201 / 0.000490 (0.398711) \| 0.002894 / 0.000200 (0.002694) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025120 / 0.037411 (-0.012292) \| 0.100126 / 0.014526 (0.085600) \| 0.108669 / 0.176557 (-0.067887) \| 0.148687 / 0.737135 (-0.588448) \| 0.112286 / 0.296338 (-0.184052) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438866 / 0.215209 (0.223657) \| 4.382418 / 2.077655 (2.304764) \| 2.106450 / 1.504120 (0.602330) \| 1.885105 / 1.541195 (0.343910) \| 1.922948 / 1.468490 (0.454458) \| 0.693145 / 4.584777 (-3.891632) \| 3.378206 / 3.745712 (-0.367506) \| 1.867295 / 5.269862 (-3.402566) \| 1.164999 / 4.565676 (-3.400678) \| 0.081918 / 0.424275 (-0.342357) \| 0.012225 / 0.007607 (0.004618) \| 0.547114 / 0.226044 (0.321069) \| 5.454208 / 2.268929 (3.185279) \| 2.532112 / 55.444624 (-52.912512) \| 2.192573 / 6.876477 (-4.683904) \| 2.225364 / 2.142072 (0.083291) \| 0.797165 / 4.805227 (-4.008062) \| 0.151185 / 6.500664 (-6.349480) \| 0.067512 / 0.075469 (-0.007957) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303905 / 1.841788 (-0.537883) \| 14.107678 / 8.074308 (6.033370) \| 14.147630 / 10.191392 (3.956238) \| 0.156597 / 0.680424 (-0.523827) \| 0.017037 / 0.534201 (-0.517164) \| 0.383202 / 0.579283 (-0.196081) \| 0.385340 / 0.434364 (-0.049024) \| 0.443338 / 0.540337 (-0.097000) \| 0.542345 / 1.386936 (-0.844591) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#38228533a03767aab713a3806aac0e8503668c68 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009982 / 0.011353 (-0.001371) \| 0.005327 / 0.011008 (-0.005681) \| 0.099092 / 0.038508 (0.060584) \| 0.035824 / 0.023109 (0.012715) \| 0.303258 / 0.275898 (0.027360) \| 0.335379 / 0.323480 (0.011899) \| 0.008192 / 0.007986 (0.000207) \| 0.004242 / 0.004328 (-0.000087) \| 0.076277 / 0.004250 (0.072026) \| 0.043851 / 0.037052 (0.006799) \| 0.307750 / 0.258489 (0.049261) \| 0.348459 / 0.293841 (0.054618) \| 0.038943 / 0.128546 (-0.089604) \| 0.012128 / 0.075646 (-0.063519) \| 0.334143 / 0.419271 (-0.085128) \| 0.047865 / 0.043533 (0.004332) \| 0.300909 / 0.255139 (0.045770) \| 0.320879 / 0.283200 (0.037680) \| 0.103812 / 0.141683 (-0.037871) \| 1.468646 / 1.452155 (0.016491) \| 1.557660 / 1.492716 (0.064944) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.244108 / 0.018006 (0.226102) \| 0.554895 / 0.000490 (0.554405) \| 0.005311 / 0.000200 (0.005111) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028771 / 0.037411 (-0.008640) \| 0.108133 / 0.014526 (0.093608) \| 0.120098 / 0.176557 (-0.056458) \| 0.159815 / 0.737135 (-0.577320) \| 0.125437 / 0.296338 (-0.170901) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397675 / 0.215209 (0.182466) \| 3.975839 / 2.077655 (1.898184) \| 1.797803 / 1.504120 (0.293683) \| 1.612517 / 1.541195 (0.071322) \| 1.659086 / 1.468490 (0.190596) \| 0.679822 / 4.584777 (-3.904955) \| 3.688321 / 3.745712 (-0.057391) \| 2.155285 / 5.269862 (-3.114576) \| 1.466453 / 4.565676 (-3.099223) \| 0.084102 / 0.424275 (-0.340173) \| 0.012074 / 0.007607 (0.004467) \| 0.503744 / 0.226044 (0.277699) \| 5.075599 / 2.268929 (2.806670) \| 2.312149 / 55.444624 (-53.132476) \| 1.975028 / 6.876477 (-4.901449) \| 2.069554 / 2.142072 (-0.072519) \| 0.828329 / 4.805227 (-3.976898) \| 0.162816 / 6.500664 (-6.337849) \| 0.063813 / 0.075469 (-0.011656) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.173327 / 1.841788 (-0.668461) \| 15.281584 / 8.074308 (7.207276) \| 14.450851 / 10.191392 (4.259459) \| 0.165621 / 0.680424 (-0.514802) \| 0.028779 / 0.534201 (-0.505422) \| 0.438483 / 0.579283 (-0.140800) \| 0.438477 / 0.434364 (0.004113) \| 0.517703 / 0.540337 (-0.022634) \| 0.615119 / 1.386936 (-0.771817) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007013 / 0.011353 (-0.004340) \| 0.005272 / 0.011008 (-0.005736) \| 0.097203 / 0.038508 (0.058695) \| 0.033103 / 0.023109 (0.009994) \| 0.380203 / 0.275898 (0.104305) \| 0.414868 / 0.323480 (0.091388) \| 0.006326 / 0.007986 (-0.001659) \| 0.005433 / 0.004328 (0.001104) \| 0.074299 / 0.004250 (0.070049) \| 0.049418 / 0.037052 (0.012366) \| 0.388771 / 0.258489 (0.130282) \| 0.435169 / 0.293841 (0.141328) \| 0.036170 / 0.128546 (-0.092377) \| 0.012452 / 0.075646 (-0.063195) \| 0.331215 / 0.419271 (-0.088056) \| 0.048577 / 0.043533 (0.005044) \| 0.381491 / 0.255139 (0.126352) \| 0.396731 / 0.283200 (0.113531) \| 0.106435 / 0.141683 (-0.035248) \| 1.446437 / 1.452155 (-0.005718) \| 1.542337 / 1.492716 (0.049621) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216714 / 0.018006 (0.198707) \| 0.562460 / 0.000490 (0.561970) \| 0.003636 / 0.000200 (0.003436) \| 0.000100 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028726 / 0.037411 (-0.008686) \| 0.111993 / 0.014526 (0.097467) \| 0.125325 / 0.176557 (-0.051232) \| 0.157779 / 0.737135 (-0.579356) \| 0.130633 / 0.296338 (-0.165705) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440520 / 0.215209 (0.225311) \| 4.396283 / 2.077655 (2.318628) \| 2.204714 / 1.504120 (0.700594) \| 2.011667 / 1.541195 (0.470473) \| 2.050518 / 1.468490 (0.582028) \| 0.695204 / 4.584777 (-3.889573) \| 3.779699 / 3.745712 (0.033987) \| 2.096064 / 5.269862 (-3.173798) \| 1.325446 / 4.565676 (-3.240230) \| 0.085315 / 0.424275 (-0.338960) \| 0.012178 / 0.007607 (0.004570) \| 0.550478 / 0.226044 (0.324434) \| 5.471872 / 2.268929 (3.202943) \| 2.687147 / 55.444624 (-52.757478) \| 2.348465 / 6.876477 (-4.528011) \| 2.409700 / 2.142072 (0.267628) \| 0.839468 / 4.805227 (-3.965760) \| 0.167030 / 6.500664 (-6.333635) \| 0.063243 / 0.075469 (-0.012226) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.257347 / 1.841788 (-0.584441) \| 15.157821 / 8.074308 (7.083512) \| 14.646381 / 10.191392 (4.454989) \| 0.185550 / 0.680424 (-0.494874) \| 0.018441 / 0.534201 (-0.515760) \| 0.423330 / 0.579283 (-0.155954) \| 0.426204 / 0.434364 (-0.008160) \| 0.498985 / 0.540337 (-0.041352) \| 0.608432 / 1.386936 (-0.778504) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f96e349ec5665e1e4135b5a108ba5db227bd3b1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010856 / 0.011353 (-0.000497) \| 0.005897 / 0.011008 (-0.005111) \| 0.117826 / 0.038508 (0.079317) \| 0.041899 / 0.023109 (0.018790) \| 0.353804 / 0.275898 (0.077906) \| 0.431021 / 0.323480 (0.107541) \| 0.009288 / 0.007986 (0.001303) \| 0.004556 / 0.004328 (0.000227) \| 0.089344 / 0.004250 (0.085094) \| 0.052224 / 0.037052 (0.015172) \| 0.373242 / 0.258489 (0.114753) \| 0.420667 / 0.293841 (0.126826) \| 0.044191 / 0.128546 (-0.084355) \| 0.014083 / 0.075646 (-0.061564) \| 0.400373 / 0.419271 (-0.018898) \| 0.056119 / 0.043533 (0.012586) \| 0.363302 / 0.255139 (0.108163) \| 0.382073 / 0.283200 (0.098873) \| 0.118646 / 0.141683 (-0.023037) \| 1.696576 / 1.452155 (0.244422) \| 1.756518 / 1.492716 (0.263802) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216388 / 0.018006 (0.198382) \| 0.485732 / 0.000490 (0.485242) \| 0.004012 / 0.000200 (0.003812) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032095 / 0.037411 (-0.005316) \| 0.128954 / 0.014526 (0.114429) \| 0.137564 / 0.176557 (-0.038993) \| 0.184315 / 0.737135 (-0.552820) \| 0.144707 / 0.296338 (-0.151631) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472792 / 0.215209 (0.257583) \| 4.723044 / 2.077655 (2.645390) \| 2.115075 / 1.504120 (0.610955) \| 1.898993 / 1.541195 (0.357798) \| 1.972894 / 1.468490 (0.504404) \| 0.807210 / 4.584777 (-3.777567) \| 4.493139 / 3.745712 (0.747427) \| 2.501053 / 5.269862 (-2.768808) \| 1.686121 / 4.565676 (-2.879556) \| 0.099545 / 0.424275 (-0.324730) \| 0.014360 / 0.007607 (0.006753) \| 0.596235 / 0.226044 (0.370191) \| 5.944285 / 2.268929 (3.675357) \| 2.654944 / 55.444624 (-52.789681) \| 2.281451 / 6.876477 (-4.595026) \| 2.448407 / 2.142072 (0.306334) \| 1.000512 / 4.805227 (-3.804716) \| 0.196413 / 6.500664 (-6.304251) \| 0.075810 / 0.075469 (0.000341) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.435707 / 1.841788 (-0.406081) \| 17.931070 / 8.074308 (9.856762) \| 16.635522 / 10.191392 (6.444130) \| 0.189119 / 0.680424 (-0.491304) \| 0.034392 / 0.534201 (-0.499809) \| 0.519041 / 0.579283 (-0.060242) \| 0.516159 / 0.434364 (0.081795) \| 0.601180 / 0.540337 (0.060843) \| 0.713180 / 1.386936 (-0.673756) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008741 / 0.011353 (-0.002612) \| 0.006102 / 0.011008 (-0.004906) \| 0.114787 / 0.038508 (0.076279) \| 0.039610 / 0.023109 (0.016501) \| 0.451730 / 0.275898 (0.175832) \| 0.488820 / 0.323480 (0.165340) \| 0.006979 / 0.007986 (-0.001006) \| 0.006458 / 0.004328 (0.002130) \| 0.086505 / 0.004250 (0.082254) \| 0.057684 / 0.037052 (0.020632) \| 0.451354 / 0.258489 (0.192865) \| 0.523143 / 0.293841 (0.229302) \| 0.043224 / 0.128546 (-0.085323) \| 0.014671 / 0.075646 (-0.060975) \| 0.398030 / 0.419271 (-0.021241) \| 0.063650 / 0.043533 (0.020117) \| 0.448324 / 0.255139 (0.193185) \| 0.476560 / 0.283200 (0.193361) \| 0.125772 / 0.141683 (-0.015911) \| 1.801051 / 1.452155 (0.348896) \| 1.872736 / 1.492716 (0.380020) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.256146 / 0.018006 (0.238139) \| 0.486915 / 0.000490 (0.486425) \| 0.000513 / 0.000200 (0.000313) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035242 / 0.037411 (-0.002170) \| 0.134322 / 0.014526 (0.119797) \| 0.144786 / 0.176557 (-0.031770) \| 0.188786 / 0.737135 (-0.548349) \| 0.151737 / 0.296338 (-0.144602) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.506047 / 0.215209 (0.290838) \| 5.028253 / 2.077655 (2.950598) \| 2.393070 / 1.504120 (0.888950) \| 2.157847 / 1.541195 (0.616652) \| 2.229412 / 1.468490 (0.760922) \| 0.828973 / 4.584777 (-3.755804) \| 4.741470 / 3.745712 (0.995758) \| 4.048118 / 5.269862 (-1.221744) \| 2.573818 / 4.565676 (-1.991859) \| 0.101019 / 0.424275 (-0.323256) \| 0.014640 / 0.007607 (0.007033) \| 0.632591 / 0.226044 (0.406546) \| 6.289153 / 2.268929 (4.020224) \| 2.977261 / 55.444624 (-52.467363) \| 2.554396 / 6.876477 (-4.322081) \| 2.619446 / 2.142072 (0.477374) \| 0.988376 / 4.805227 (-3.816851) \| 0.196895 / 6.500664 (-6.303769) \| 0.076355 / 0.075469 (0.000886) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.493570 / 1.841788 (-0.348218) \| 18.422758 / 8.074308 (10.348449) \| 17.007352 / 10.191392 (6.815960) \| 0.191903 / 0.680424 (-0.488521) \| 0.020974 / 0.534201 (-0.513227) \| 0.500573 / 0.579283 (-0.078710) \| 0.489381 / 0.434364 (0.055017) \| 0.580765 / 0.540337 (0.040428) \| 0.698907 / 1.386936 (-0.688029) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa9baa268a6d285ab0a61cc37413392c94cfe2e8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008979 / 0.011353 (-0.002374) \| 0.004497 / 0.011008 (-0.006511) \| 0.102227 / 0.038508 (0.063719) \| 0.031302 / 0.023109 (0.008193) \| 0.298488 / 0.275898 (0.022590) \| 0.372589 / 0.323480 (0.049109) \| 0.007261 / 0.007986 (-0.000725) \| 0.003542 / 0.004328 (-0.000786) \| 0.078503 / 0.004250 (0.074253) \| 0.039474 / 0.037052 (0.002422) \| 0.310991 / 0.258489 (0.052502) \| 0.353245 / 0.293841 (0.059404) \| 0.033798 / 0.128546 (-0.094749) \| 0.011634 / 0.075646 (-0.064012) \| 0.321141 / 0.419271 (-0.098131) \| 0.041264 / 0.043533 (-0.002268) \| 0.300900 / 0.255139 (0.045761) \| 0.326255 / 0.283200 (0.043055) \| 0.092477 / 0.141683 (-0.049205) \| 1.478921 / 1.452155 (0.026766) \| 1.514915 / 1.492716 (0.022198) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.184415 / 0.018006 (0.166408) \| 0.428986 / 0.000490 (0.428497) \| 0.002590 / 0.000200 (0.002390) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023730 / 0.037411 (-0.013681) \| 0.099846 / 0.014526 (0.085320) \| 0.107075 / 0.176557 (-0.069482) \| 0.147475 / 0.737135 (-0.589661) \| 0.111802 / 0.296338 (-0.184537) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.413704 / 0.215209 (0.198494) \| 4.144498 / 2.077655 (2.066843) \| 1.855900 / 1.504120 (0.351780) \| 1.647958 / 1.541195 (0.106763) \| 1.712437 / 1.468490 (0.243947) \| 0.688382 / 4.584777 (-3.896395) \| 3.432136 / 3.745712 (-0.313576) \| 2.837211 / 5.269862 (-2.432651) \| 1.519004 / 4.565676 (-3.046672) \| 0.082429 / 0.424275 (-0.341846) \| 0.012610 / 0.007607 (0.005003) \| 0.525078 / 0.226044 (0.299034) \| 5.272932 / 2.268929 (3.004003) \| 2.340482 / 55.444624 (-53.104143) \| 2.007372 / 6.876477 (-4.869104) \| 2.060567 / 2.142072 (-0.081506) \| 0.806476 / 4.805227 (-3.998752) \| 0.149421 / 6.500664 (-6.351243) \| 0.066252 / 0.075469 (-0.009218) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235078 / 1.841788 (-0.606710) \| 13.870758 / 8.074308 (5.796450) \| 14.104582 / 10.191392 (3.913190) \| 0.159375 / 0.680424 (-0.521049) \| 0.029233 / 0.534201 (-0.504968) \| 0.392184 / 0.579283 (-0.187099) \| 0.407909 / 0.434364 (-0.026455) \| 0.458757 / 0.540337 (-0.081581) \| 0.547681 / 1.386936 (-0.839255) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007194 / 0.011353 (-0.004159) \| 0.004578 / 0.011008 (-0.006431) \| 0.098936 / 0.038508 (0.060428) \| 0.029639 / 0.023109 (0.006530) \| 0.347241 / 0.275898 (0.071343) \| 0.378838 / 0.323480 (0.055358) \| 0.005632 / 0.007986 (-0.002353) \| 0.003469 / 0.004328 (-0.000860) \| 0.075536 / 0.004250 (0.071285) \| 0.043301 / 0.037052 (0.006249) \| 0.348091 / 0.258489 (0.089602) \| 0.388595 / 0.293841 (0.094754) \| 0.033512 / 0.128546 (-0.095034) \| 0.011754 / 0.075646 (-0.063892) \| 0.321003 / 0.419271 (-0.098268) \| 0.044634 / 0.043533 (0.001101) \| 0.346688 / 0.255139 (0.091549) \| 0.366346 / 0.283200 (0.083147) \| 0.093650 / 0.141683 (-0.048033) \| 1.509913 / 1.452155 (0.057759) \| 1.596414 / 1.492716 (0.103698) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230466 / 0.018006 (0.212459) \| 0.417106 / 0.000490 (0.416617) \| 0.000959 / 0.000200 (0.000759) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025581 / 0.037411 (-0.011830) \| 0.105246 / 0.014526 (0.090720) \| 0.108997 / 0.176557 (-0.067560) \| 0.144342 / 0.737135 (-0.592794) \| 0.113911 / 0.296338 (-0.182427) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.479608 / 0.215209 (0.264399) \| 4.766081 / 2.077655 (2.688426) \| 2.446597 / 1.504120 (0.942477) \| 2.228278 / 1.541195 (0.687083) \| 2.289943 / 1.468490 (0.821453) \| 0.703146 / 4.584777 (-3.881631) \| 3.414150 / 3.745712 (-0.331562) \| 2.957730 / 5.269862 (-2.312132) \| 1.531524 / 4.565676 (-3.034152) \| 0.083449 / 0.424275 (-0.340826) \| 0.012684 / 0.007607 (0.005077) \| 0.587622 / 0.226044 (0.361578) \| 5.888791 / 2.268929 (3.619863) \| 2.884200 / 55.444624 (-52.560424) \| 2.543739 / 6.876477 (-4.332737) \| 2.596245 / 2.142072 (0.454173) \| 0.813070 / 4.805227 (-3.992157) \| 0.152706 / 6.500664 (-6.347958) \| 0.069257 / 0.075469 (-0.006212) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.302945 / 1.841788 (-0.538842) \| 14.484051 / 8.074308 (6.409743) \| 14.216143 / 10.191392 (4.024751) \| 0.154537 / 0.680424 (-0.525886) \| 0.016909 / 0.534201 (-0.517292) \| 0.389433 / 0.579283 (-0.189850) \| 0.393280 / 0.434364 (-0.041084) \| 0.446884 / 0.540337 (-0.093453) \| 0.534394 / 1.386936 (-0.852542) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bcdeb952c57c5f22643061d49d16014a7b6426a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008822 / 0.011353 (-0.002530) \| 0.004826 / 0.011008 (-0.006182) \| 0.102710 / 0.038508 (0.064202) \| 0.030353 / 0.023109 (0.007244) \| 0.297224 / 0.275898 (0.021326) \| 0.371861 / 0.323480 (0.048381) \| 0.007266 / 0.007986 (-0.000720) \| 0.003632 / 0.004328 (-0.000696) \| 0.079960 / 0.004250 (0.075710) \| 0.036908 / 0.037052 (-0.000144) \| 0.309582 / 0.258489 (0.051093) \| 0.350108 / 0.293841 (0.056267) \| 0.034280 / 0.128546 (-0.094266) \| 0.011739 / 0.075646 (-0.063907) \| 0.323217 / 0.419271 (-0.096054) \| 0.043491 / 0.043533 (-0.000042) \| 0.298454 / 0.255139 (0.043315) \| 0.326735 / 0.283200 (0.043535) \| 0.093955 / 0.141683 (-0.047728) \| 1.494313 / 1.452155 (0.042159) \| 1.562104 / 1.492716 (0.069388) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.182796 / 0.018006 (0.164790) \| 0.420133 / 0.000490 (0.419643) \| 0.002537 / 0.000200 (0.002337) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023143 / 0.037411 (-0.014269) \| 0.098560 / 0.014526 (0.084034) \| 0.105060 / 0.176557 (-0.071496) \| 0.140269 / 0.737135 (-0.596866) \| 0.109120 / 0.296338 (-0.187219) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419907 / 0.215209 (0.204698) \| 4.196179 / 2.077655 (2.118524) \| 1.887663 / 1.504120 (0.383543) \| 1.686232 / 1.541195 (0.145037) \| 1.741741 / 1.468490 (0.273251) \| 0.696222 / 4.584777 (-3.888555) \| 3.400250 / 3.745712 (-0.345462) \| 1.875058 / 5.269862 (-3.394803) \| 1.159466 / 4.565676 (-3.406211) \| 0.082520 / 0.424275 (-0.341755) \| 0.012408 / 0.007607 (0.004801) \| 0.525212 / 0.226044 (0.299168) \| 5.283691 / 2.268929 (3.014762) \| 2.314487 / 55.444624 (-53.130138) \| 1.966212 / 6.876477 (-4.910265) \| 2.023458 / 2.142072 (-0.118615) \| 0.808896 / 4.805227 (-3.996331) \| 0.148973 / 6.500664 (-6.351691) \| 0.065378 / 0.075469 (-0.010091) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.223833 / 1.841788 (-0.617955) \| 14.053651 / 8.074308 (5.979343) \| 14.072165 / 10.191392 (3.880773) \| 0.156006 / 0.680424 (-0.524418) \| 0.028665 / 0.534201 (-0.505536) \| 0.392099 / 0.579283 (-0.187184) \| 0.401460 / 0.434364 (-0.032904) \| 0.462184 / 0.540337 (-0.078153) \| 0.540459 / 1.386936 (-0.846477) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006907 / 0.011353 (-0.004446) \| 0.004585 / 0.011008 (-0.006423) \| 0.099027 / 0.038508 (0.060519) \| 0.028317 / 0.023109 (0.005208) \| 0.421068 / 0.275898 (0.145170) \| 0.450712 / 0.323480 (0.127233) \| 0.005229 / 0.007986 (-0.002756) \| 0.004873 / 0.004328 (0.000545) \| 0.077374 / 0.004250 (0.073124) \| 0.042530 / 0.037052 (0.005477) \| 0.417392 / 0.258489 (0.158903) \| 0.462605 / 0.293841 (0.168764) \| 0.032195 / 0.128546 (-0.096351) \| 0.011777 / 0.075646 (-0.063870) \| 0.321927 / 0.419271 (-0.097344) \| 0.041999 / 0.043533 (-0.001533) \| 0.419402 / 0.255139 (0.164263) \| 0.437179 / 0.283200 (0.153979) \| 0.089549 / 0.141683 (-0.052134) \| 1.469525 / 1.452155 (0.017370) \| 1.586407 / 1.492716 (0.093691) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209533 / 0.018006 (0.191526) \| 0.413886 / 0.000490 (0.413396) \| 0.003357 / 0.000200 (0.003157) \| 0.000121 / 0.000054 (0.000067) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026133 / 0.037411 (-0.011278) \| 0.103128 / 0.014526 (0.088602) \| 0.110604 / 0.176557 (-0.065952) \| 0.153055 / 0.737135 (-0.584080) \| 0.112257 / 0.296338 (-0.184081) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471281 / 0.215209 (0.256072) \| 4.708361 / 2.077655 (2.630706) \| 2.572681 / 1.504120 (1.068561) \| 2.370536 / 1.541195 (0.829341) \| 2.456010 / 1.468490 (0.987520) \| 0.694173 / 4.584777 (-3.890603) \| 3.434511 / 3.745712 (-0.311201) \| 1.877169 / 5.269862 (-3.392693) \| 1.158387 / 4.565676 (-3.407289) \| 0.081849 / 0.424275 (-0.342426) \| 0.012176 / 0.007607 (0.004569) \| 0.581736 / 0.226044 (0.355692) \| 5.803173 / 2.268929 (3.534245) \| 3.040003 / 55.444624 (-52.404621) \| 2.704698 / 6.876477 (-4.171779) \| 2.760138 / 2.142072 (0.618065) \| 0.802557 / 4.805227 (-4.002671) \| 0.151397 / 6.500664 (-6.349268) \| 0.068308 / 0.075469 (-0.007161) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.304062 / 1.841788 (-0.537725) \| 14.364809 / 8.074308 (6.290501) \| 14.192131 / 10.191392 (4.000739) \| 0.150025 / 0.680424 (-0.530399) \| 0.017020 / 0.534201 (-0.517181) \| 0.389235 / 0.579283 (-0.190048) \| 0.387557 / 0.434364 (-0.046807) \| 0.454636 / 0.540337 (-0.085702) \| 0.558182 / 1.386936 (-0.828754) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#663e5eddca188abbb37e2f803846f02fe4ca0d9b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008519 / 0.011353 (-0.002834) \| 0.004538 / 0.011008 (-0.006470) \| 0.102066 / 0.038508 (0.063558) \| 0.029700 / 0.023109 (0.006591) \| 0.304573 / 0.275898 (0.028675) \| 0.366232 / 0.323480 (0.042752) \| 0.007154 / 0.007986 (-0.000832) \| 0.003497 / 0.004328 (-0.000831) \| 0.079119 / 0.004250 (0.074868) \| 0.036088 / 0.037052 (-0.000964) \| 0.311076 / 0.258489 (0.052587) \| 0.352205 / 0.293841 (0.058364) \| 0.033706 / 0.128546 (-0.094840) \| 0.011657 / 0.075646 (-0.063990) \| 0.324024 / 0.419271 (-0.095247) \| 0.040777 / 0.043533 (-0.002756) \| 0.302661 / 0.255139 (0.047522) \| 0.329091 / 0.283200 (0.045891) \| 0.086774 / 0.141683 (-0.054909) \| 1.485874 / 1.452155 (0.033720) \| 1.535726 / 1.492716 (0.043009) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.194284 / 0.018006 (0.176277) \| 0.412875 / 0.000490 (0.412385) \| 0.003348 / 0.000200 (0.003148) \| 0.000074 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022432 / 0.037411 (-0.014979) \| 0.095008 / 0.014526 (0.080482) \| 0.103268 / 0.176557 (-0.073288) \| 0.140121 / 0.737135 (-0.597014) \| 0.106619 / 0.296338 (-0.189719) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414786 / 0.215209 (0.199577) \| 4.146345 / 2.077655 (2.068690) \| 1.873703 / 1.504120 (0.369583) \| 1.673498 / 1.541195 (0.132303) \| 1.716993 / 1.468490 (0.248502) \| 0.692098 / 4.584777 (-3.892679) \| 3.380991 / 3.745712 (-0.364721) \| 1.846811 / 5.269862 (-3.423050) \| 1.159617 / 4.565676 (-3.406059) \| 0.081867 / 0.424275 (-0.342408) \| 0.012371 / 0.007607 (0.004764) \| 0.526228 / 0.226044 (0.300184) \| 5.273139 / 2.268929 (3.004211) \| 2.327147 / 55.444624 (-53.117477) \| 1.968366 / 6.876477 (-4.908111) \| 2.018053 / 2.142072 (-0.124019) \| 0.816098 / 4.805227 (-3.989130) \| 0.149438 / 6.500664 (-6.351226) \| 0.065000 / 0.075469 (-0.010469) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244408 / 1.841788 (-0.597380) \| 13.774354 / 8.074308 (5.700046) \| 14.178923 / 10.191392 (3.987531) \| 0.150032 / 0.680424 (-0.530392) \| 0.029736 / 0.534201 (-0.504465) \| 0.399134 / 0.579283 (-0.180149) \| 0.404214 / 0.434364 (-0.030150) \| 0.462096 / 0.540337 (-0.078242) \| 0.542256 / 1.386936 (-0.844680) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006776 / 0.011353 (-0.004577) \| 0.004586 / 0.011008 (-0.006422) \| 0.097658 / 0.038508 (0.059150) \| 0.027627 / 0.023109 (0.004517) \| 0.423794 / 0.275898 (0.147896) \| 0.447443 / 0.323480 (0.123963) \| 0.005099 / 0.007986 (-0.002886) \| 0.004846 / 0.004328 (0.000517) \| 0.075135 / 0.004250 (0.070884) \| 0.038068 / 0.037052 (0.001016) \| 0.420999 / 0.258489 (0.162510) \| 0.460368 / 0.293841 (0.166527) \| 0.032107 / 0.128546 (-0.096439) \| 0.011775 / 0.075646 (-0.063871) \| 0.323854 / 0.419271 (-0.095418) \| 0.045538 / 0.043533 (0.002005) \| 0.420949 / 0.255139 (0.165810) \| 0.441906 / 0.283200 (0.158706) \| 0.091955 / 0.141683 (-0.049728) \| 1.523736 / 1.452155 (0.071581) \| 1.587865 / 1.492716 (0.095148) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.263297 / 0.018006 (0.245290) \| 0.416170 / 0.000490 (0.415680) \| 0.023161 / 0.000200 (0.022961) \| 0.000243 / 0.000054 (0.000188) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024000 / 0.037411 (-0.013412) \| 0.097787 / 0.014526 (0.083262) \| 0.106884 / 0.176557 (-0.069672) \| 0.140861 / 0.737135 (-0.596274) \| 0.108228 / 0.296338 (-0.188111) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.477222 / 0.215209 (0.262013) \| 4.774729 / 2.077655 (2.697074) \| 2.451575 / 1.504120 (0.947455) \| 2.251255 / 1.541195 (0.710060) \| 2.281154 / 1.468490 (0.812664) \| 0.699394 / 4.584777 (-3.885383) \| 3.421575 / 3.745712 (-0.324137) \| 2.704713 / 5.269862 (-2.565148) \| 1.508464 / 4.565676 (-3.057212) \| 0.082199 / 0.424275 (-0.342076) \| 0.012586 / 0.007607 (0.004979) \| 0.588783 / 0.226044 (0.362739) \| 5.878434 / 2.268929 (3.609505) \| 2.927422 / 55.444624 (-52.517202) \| 2.574357 / 6.876477 (-4.302120) \| 2.603626 / 2.142072 (0.461554) \| 0.804706 / 4.805227 (-4.000521) \| 0.152919 / 6.500664 (-6.347745) \| 0.069316 / 0.075469 (-0.006153) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.280025 / 1.841788 (-0.561763) \| 13.968407 / 8.074308 (5.894099) \| 13.874506 / 10.191392 (3.683114) \| 0.154711 / 0.680424 (-0.525713) \| 0.016827 / 0.534201 (-0.517374) \| 0.377775 / 0.579283 (-0.201508) \| 0.393035 / 0.434364 (-0.041329) \| 0.439405 / 0.540337 (-0.100932) \| 0.528135 / 1.386936 (-0.858801) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#00b27a59b8af9075967b800e3b0f1de8616aa0ce \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009035 / 0.011353 (-0.002318) \| 0.004518 / 0.011008 (-0.006490) \| 0.102077 / 0.038508 (0.063569) \| 0.030169 / 0.023109 (0.007060) \| 0.297713 / 0.275898 (0.021815) \| 0.364976 / 0.323480 (0.041496) \| 0.007079 / 0.007986 (-0.000906) \| 0.003438 / 0.004328 (-0.000890) \| 0.079667 / 0.004250 (0.075416) \| 0.035890 / 0.037052 (-0.001162) \| 0.306065 / 0.258489 (0.047576) \| 0.352133 / 0.293841 (0.058292) \| 0.033800 / 0.128546 (-0.094746) \| 0.011613 / 0.075646 (-0.064034) \| 0.322917 / 0.419271 (-0.096354) \| 0.040973 / 0.043533 (-0.002560) \| 0.300896 / 0.255139 (0.045757) \| 0.331540 / 0.283200 (0.048341) \| 0.089579 / 0.141683 (-0.052103) \| 1.466755 / 1.452155 (0.014600) \| 1.522120 / 1.492716 (0.029404) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193172 / 0.018006 (0.175166) \| 0.408878 / 0.000490 (0.408389) \| 0.001586 / 0.000200 (0.001386) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023496 / 0.037411 (-0.013915) \| 0.098046 / 0.014526 (0.083520) \| 0.104599 / 0.176557 (-0.071957) \| 0.139054 / 0.737135 (-0.598081) \| 0.111163 / 0.296338 (-0.185175) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417374 / 0.215209 (0.202165) \| 4.145808 / 2.077655 (2.068153) \| 1.847101 / 1.504120 (0.342981) \| 1.637207 / 1.541195 (0.096012) \| 1.676906 / 1.468490 (0.208416) \| 0.689851 / 4.584777 (-3.894926) \| 3.402099 / 3.745712 (-0.343614) \| 1.896808 / 5.269862 (-3.373054) \| 1.257876 / 4.565676 (-3.307801) \| 0.081744 / 0.424275 (-0.342531) \| 0.012206 / 0.007607 (0.004599) \| 0.524830 / 0.226044 (0.298786) \| 5.251344 / 2.268929 (2.982416) \| 2.277907 / 55.444624 (-53.166717) \| 1.933985 / 6.876477 (-4.942491) \| 2.038500 / 2.142072 (-0.103573) \| 0.808696 / 4.805227 (-3.996532) \| 0.149488 / 6.500664 (-6.351176) \| 0.065323 / 0.075469 (-0.010146) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.204294 / 1.841788 (-0.637493) \| 13.696526 / 8.074308 (5.622218) \| 13.947195 / 10.191392 (3.755802) \| 0.136812 / 0.680424 (-0.543611) \| 0.028625 / 0.534201 (-0.505576) \| 0.397662 / 0.579283 (-0.181621) \| 0.403423 / 0.434364 (-0.030941) \| 0.465288 / 0.540337 (-0.075049) \| 0.551919 / 1.386936 (-0.835017) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006467 / 0.011353 (-0.004886) \| 0.004562 / 0.011008 (-0.006447) \| 0.097514 / 0.038508 (0.059006) \| 0.027471 / 0.023109 (0.004362) \| 0.425504 / 0.275898 (0.149606) \| 0.458856 / 0.323480 (0.135376) \| 0.004816 / 0.007986 (-0.003169) \| 0.003264 / 0.004328 (-0.001065) \| 0.074947 / 0.004250 (0.070697) \| 0.037147 / 0.037052 (0.000095) \| 0.429513 / 0.258489 (0.171024) \| 0.463971 / 0.293841 (0.170130) \| 0.031638 / 0.128546 (-0.096908) \| 0.011545 / 0.075646 (-0.064101) \| 0.320261 / 0.419271 (-0.099010) \| 0.041570 / 0.043533 (-0.001963) \| 0.424809 / 0.255139 (0.169670) \| 0.447158 / 0.283200 (0.163959) \| 0.088418 / 0.141683 (-0.053265) \| 1.492242 / 1.452155 (0.040087) \| 1.545523 / 1.492716 (0.052807) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217865 / 0.018006 (0.199859) \| 0.399925 / 0.000490 (0.399436) \| 0.004853 / 0.000200 (0.004653) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024275 / 0.037411 (-0.013137) \| 0.098249 / 0.014526 (0.083723) \| 0.107110 / 0.176557 (-0.069446) \| 0.143870 / 0.737135 (-0.593265) \| 0.108796 / 0.296338 (-0.187542) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.470856 / 0.215209 (0.255647) \| 4.687921 / 2.077655 (2.610266) \| 2.448631 / 1.504120 (0.944511) \| 2.247748 / 1.541195 (0.706553) \| 2.287713 / 1.468490 (0.819223) \| 0.687534 / 4.584777 (-3.897243) \| 3.421099 / 3.745712 (-0.324613) \| 2.977280 / 5.269862 (-2.292582) \| 1.274837 / 4.565676 (-3.290839) \| 0.081611 / 0.424275 (-0.342664) \| 0.012603 / 0.007607 (0.004996) \| 0.574600 / 0.226044 (0.348556) \| 5.802826 / 2.268929 (3.533898) \| 2.913178 / 55.444624 (-52.531446) \| 2.589486 / 6.876477 (-4.286991) \| 2.630004 / 2.142072 (0.487932) \| 0.790087 / 4.805227 (-4.015140) \| 0.150019 / 6.500664 (-6.350645) \| 0.067346 / 0.075469 (-0.008123) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.266521 / 1.841788 (-0.575267) \| 13.818770 / 8.074308 (5.744462) \| 13.872277 / 10.191392 (3.680885) \| 0.147375 / 0.680424 (-0.533049) \| 0.016837 / 0.534201 (-0.517363) \| 0.376421 / 0.579283 (-0.202862) \| 0.400236 / 0.434364 (-0.034128) \| 0.436623 / 0.540337 (-0.103714) \| 0.527173 / 1.386936 (-0.859763) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f347cf8443aa35401ba6a4159600b92bc6a156b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009341 / 0.011353 (-0.002012) \| 0.005188 / 0.011008 (-0.005820) \| 0.101831 / 0.038508 (0.063323) \| 0.035141 / 0.023109 (0.012032) \| 0.299324 / 0.275898 (0.023426) \| 0.334749 / 0.323480 (0.011269) \| 0.007958 / 0.007986 (-0.000027) \| 0.005482 / 0.004328 (0.001153) \| 0.077070 / 0.004250 (0.072820) \| 0.044733 / 0.037052 (0.007680) \| 0.310398 / 0.258489 (0.051909) \| 0.347925 / 0.293841 (0.054084) \| 0.038141 / 0.128546 (-0.090405) \| 0.012135 / 0.075646 (-0.063512) \| 0.333799 / 0.419271 (-0.085472) \| 0.048881 / 0.043533 (0.005348) \| 0.301336 / 0.255139 (0.046197) \| 0.314592 / 0.283200 (0.031393) \| 0.103635 / 0.141683 (-0.038048) \| 1.437321 / 1.452155 (-0.014833) \| 1.598781 / 1.492716 (0.106065) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248911 / 0.018006 (0.230905) \| 0.528932 / 0.000490 (0.528442) \| 0.002495 / 0.000200 (0.002295) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027903 / 0.037411 (-0.009509) \| 0.106716 / 0.014526 (0.092190) \| 0.122650 / 0.176557 (-0.053907) \| 0.162481 / 0.737135 (-0.574654) \| 0.126402 / 0.296338 (-0.169937) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.352819 / 0.215209 (0.137610) \| 3.522761 / 2.077655 (1.445106) \| 1.576761 / 1.504120 (0.072641) \| 1.411631 / 1.541195 (-0.129563) \| 1.449689 / 1.468490 (-0.018801) \| 0.608987 / 4.584777 (-3.975790) \| 3.705121 / 3.745712 (-0.040592) \| 2.085071 / 5.269862 (-3.184790) \| 1.308653 / 4.565676 (-3.257024) \| 0.083763 / 0.424275 (-0.340512) \| 0.011957 / 0.007607 (0.004350) \| 0.502182 / 0.226044 (0.276137) \| 5.008829 / 2.268929 (2.739900) \| 2.244687 / 55.444624 (-53.199937) \| 1.891411 / 6.876477 (-4.985065) \| 1.940789 / 2.142072 (-0.201284) \| 0.825966 / 4.805227 (-3.979261) \| 0.165267 / 6.500664 (-6.335397) \| 0.063020 / 0.075469 (-0.012449) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.196707 / 1.841788 (-0.645081) \| 14.236877 / 8.074308 (6.162569) \| 14.872954 / 10.191392 (4.681562) \| 0.168560 / 0.680424 (-0.511864) \| 0.029038 / 0.534201 (-0.505163) \| 0.440192 / 0.579283 (-0.139091) \| 0.437021 / 0.434364 (0.002657) \| 0.519612 / 0.540337 (-0.020725) \| 0.612013 / 1.386936 (-0.774923) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007170 / 0.011353 (-0.004183) \| 0.005303 / 0.011008 (-0.005705) \| 0.098503 / 0.038508 (0.059995) \| 0.032573 / 0.023109 (0.009463) \| 0.398203 / 0.275898 (0.122305) \| 0.446075 / 0.323480 (0.122595) \| 0.005712 / 0.007986 (-0.002274) \| 0.004165 / 0.004328 (-0.000164) \| 0.074273 / 0.004250 (0.070023) \| 0.049587 / 0.037052 (0.012534) \| 0.399458 / 0.258489 (0.140969) \| 0.459167 / 0.293841 (0.165327) \| 0.036063 / 0.128546 (-0.092483) \| 0.012394 / 0.075646 (-0.063253) \| 0.332559 / 0.419271 (-0.086713) \| 0.048499 / 0.043533 (0.004967) \| 0.404044 / 0.255139 (0.148905) \| 0.410462 / 0.283200 (0.127262) \| 0.104104 / 0.141683 (-0.037579) \| 1.488141 / 1.452155 (0.035986) \| 1.535517 / 1.492716 (0.042801) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292976 / 0.018006 (0.274970) \| 0.569139 / 0.000490 (0.568649) \| 0.000553 / 0.000200 (0.000353) \| 0.000063 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030144 / 0.037411 (-0.007267) \| 0.098699 / 0.014526 (0.084173) \| 0.114437 / 0.176557 (-0.062120) \| 0.156657 / 0.737135 (-0.580478) \| 0.117449 / 0.296338 (-0.178890) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441921 / 0.215209 (0.226712) \| 4.413090 / 2.077655 (2.335435) \| 2.190458 / 1.504120 (0.686338) \| 2.008919 / 1.541195 (0.467724) \| 2.049657 / 1.468490 (0.581167) \| 0.691751 / 4.584777 (-3.893026) \| 3.767524 / 3.745712 (0.021812) \| 3.395564 / 5.269862 (-1.874297) \| 1.633480 / 4.565676 (-2.932196) \| 0.084880 / 0.424275 (-0.339395) \| 0.012133 / 0.007607 (0.004526) \| 0.555372 / 0.226044 (0.329327) \| 5.522820 / 2.268929 (3.253892) \| 2.723331 / 55.444624 (-52.721293) \| 2.337583 / 6.876477 (-4.538894) \| 2.368746 / 2.142072 (0.226674) \| 0.830127 / 4.805227 (-3.975100) \| 0.166239 / 6.500664 (-6.334425) \| 0.064279 / 0.075469 (-0.011190) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.123421 / 1.841788 (-0.718367) \| 14.413392 / 8.074308 (6.339084) \| 12.865143 / 10.191392 (2.673751) \| 0.132198 / 0.680424 (-0.548226) \| 0.016138 / 0.534201 (-0.518063) \| 0.380760 / 0.579283 (-0.198523) \| 0.387223 / 0.434364 (-0.047141) \| 0.445574 / 0.540337 (-0.094764) \| 0.535658 / 1.386936 (-0.851278) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a89564d3d17b5960db2435662cb9c49f8ad7488a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008316 / 0.011353 (-0.003037) \| 0.004503 / 0.011008 (-0.006505) \| 0.100565 / 0.038508 (0.062057) \| 0.030388 / 0.023109 (0.007279) \| 0.304417 / 0.275898 (0.028519) \| 0.369655 / 0.323480 (0.046175) \| 0.007796 / 0.007986 (-0.000190) \| 0.003450 / 0.004328 (-0.000878) \| 0.078694 / 0.004250 (0.074443) \| 0.038068 / 0.037052 (0.001016) \| 0.316353 / 0.258489 (0.057864) \| 0.352344 / 0.293841 (0.058503) \| 0.033271 / 0.128546 (-0.095276) \| 0.011427 / 0.075646 (-0.064220) \| 0.322367 / 0.419271 (-0.096904) \| 0.041497 / 0.043533 (-0.002036) \| 0.305876 / 0.255139 (0.050737) \| 0.332279 / 0.283200 (0.049079) \| 0.086719 / 0.141683 (-0.054964) \| 1.488367 / 1.452155 (0.036212) \| 1.528943 / 1.492716 (0.036227) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.171072 / 0.018006 (0.153066) \| 0.421048 / 0.000490 (0.420558) \| 0.003622 / 0.000200 (0.003422) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022632 / 0.037411 (-0.014779) \| 0.095304 / 0.014526 (0.080778) \| 0.106254 / 0.176557 (-0.070302) \| 0.138437 / 0.737135 (-0.598698) \| 0.107258 / 0.296338 (-0.189080) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.423201 / 0.215209 (0.207992) \| 4.208397 / 2.077655 (2.130742) \| 1.899800 / 1.504120 (0.395680) \| 1.682782 / 1.541195 (0.141587) \| 1.708840 / 1.468490 (0.240350) \| 0.694492 / 4.584777 (-3.890285) \| 3.380369 / 3.745712 (-0.365344) \| 1.851731 / 5.269862 (-3.418130) \| 1.151615 / 4.565676 (-3.414061) \| 0.082446 / 0.424275 (-0.341829) \| 0.012483 / 0.007607 (0.004876) \| 0.533688 / 0.226044 (0.307643) \| 5.373434 / 2.268929 (3.104505) \| 2.346403 / 55.444624 (-53.098221) \| 1.978505 / 6.876477 (-4.897971) \| 2.005875 / 2.142072 (-0.136198) \| 0.820785 / 4.805227 (-3.984442) \| 0.150728 / 6.500664 (-6.349936) \| 0.065761 / 0.075469 (-0.009708) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244550 / 1.841788 (-0.597237) \| 13.219096 / 8.074308 (5.144788) \| 13.960463 / 10.191392 (3.769071) \| 0.135572 / 0.680424 (-0.544852) \| 0.028746 / 0.534201 (-0.505455) \| 0.393082 / 0.579283 (-0.186201) \| 0.402852 / 0.434364 (-0.031512) \| 0.461191 / 0.540337 (-0.079147) \| 0.543500 / 1.386936 (-0.843436) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006316 / 0.011353 (-0.005037) \| 0.004394 / 0.011008 (-0.006615) \| 0.096478 / 0.038508 (0.057970) \| 0.026965 / 0.023109 (0.003855) \| 0.340371 / 0.275898 (0.064473) \| 0.368334 / 0.323480 (0.044854) \| 0.004744 / 0.007986 (-0.003242) \| 0.004652 / 0.004328 (0.000324) \| 0.074479 / 0.004250 (0.070228) \| 0.036358 / 0.037052 (-0.000694) \| 0.342968 / 0.258489 (0.084479) \| 0.383675 / 0.293841 (0.089834) \| 0.031439 / 0.128546 (-0.097107) \| 0.011529 / 0.075646 (-0.064117) \| 0.319560 / 0.419271 (-0.099711) \| 0.041370 / 0.043533 (-0.002163) \| 0.342594 / 0.255139 (0.087455) \| 0.363237 / 0.283200 (0.080038) \| 0.087316 / 0.141683 (-0.054367) \| 1.468690 / 1.452155 (0.016535) \| 1.553974 / 1.492716 (0.061257) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198366 / 0.018006 (0.180360) \| 0.401581 / 0.000490 (0.401091) \| 0.000400 / 0.000200 (0.000200) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023150 / 0.037411 (-0.014261) \| 0.097797 / 0.014526 (0.083271) \| 0.106198 / 0.176557 (-0.070359) \| 0.139599 / 0.737135 (-0.597536) \| 0.108361 / 0.296338 (-0.187978) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472962 / 0.215209 (0.257753) \| 4.702688 / 2.077655 (2.625033) \| 2.401002 / 1.504120 (0.896882) \| 2.193857 / 1.541195 (0.652663) \| 2.219188 / 1.468490 (0.750697) \| 0.689993 / 4.584777 (-3.894784) \| 3.369409 / 3.745712 (-0.376304) \| 1.824801 / 5.269862 (-3.445061) \| 1.150815 / 4.565676 (-3.414862) \| 0.082197 / 0.424275 (-0.342078) \| 0.012287 / 0.007607 (0.004679) \| 0.581963 / 0.226044 (0.355918) \| 5.786943 / 2.268929 (3.518015) \| 2.871235 / 55.444624 (-52.573389) \| 2.516009 / 6.876477 (-4.360468) \| 2.535669 / 2.142072 (0.393597) \| 0.804733 / 4.805227 (-4.000494) \| 0.150545 / 6.500664 (-6.350119) \| 0.066964 / 0.075469 (-0.008505) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.285431 / 1.841788 (-0.556356) \| 14.097108 / 8.074308 (6.022800) \| 13.821497 / 10.191392 (3.630105) \| 0.141922 / 0.680424 (-0.538502) \| 0.016964 / 0.534201 (-0.517237) \| 0.374784 / 0.579283 (-0.204500) \| 0.381034 / 0.434364 (-0.053330) \| 0.435487 / 0.540337 (-0.104850) \| 0.521894 / 1.386936 (-0.865042) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#462000c2b12a11f1fc26853e842d3f6e40287737 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009486 / 0.011353 (-0.001867) \| 0.005363 / 0.011008 (-0.005645) \| 0.101008 / 0.038508 (0.062500) \| 0.036355 / 0.023109 (0.013246) \| 0.290575 / 0.275898 (0.014677) \| 0.391634 / 0.323480 (0.068154) \| 0.009085 / 0.007986 (0.001099) \| 0.005780 / 0.004328 (0.001451) \| 0.077848 / 0.004250 (0.073598) \| 0.049062 / 0.037052 (0.012009) \| 0.310900 / 0.258489 (0.052411) \| 0.358224 / 0.293841 (0.064383) \| 0.038838 / 0.128546 (-0.089708) \| 0.012244 / 0.075646 (-0.063402) \| 0.333701 / 0.419271 (-0.085570) \| 0.048021 / 0.043533 (0.004488) \| 0.289584 / 0.255139 (0.034445) \| 0.317556 / 0.283200 (0.034356) \| 0.109807 / 0.141683 (-0.031876) \| 1.465966 / 1.452155 (0.013811) \| 1.526341 / 1.492716 (0.033625) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246221 / 0.018006 (0.228215) \| 0.580659 / 0.000490 (0.580169) \| 0.000627 / 0.000200 (0.000427) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028352 / 0.037411 (-0.009059) \| 0.110569 / 0.014526 (0.096043) \| 0.126456 / 0.176557 (-0.050100) \| 0.163633 / 0.737135 (-0.573503) \| 0.128252 / 0.296338 (-0.168087) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397271 / 0.215209 (0.182062) \| 3.975336 / 2.077655 (1.897682) \| 1.786957 / 1.504120 (0.282837) \| 1.598468 / 1.541195 (0.057273) \| 1.645299 / 1.468490 (0.176809) \| 0.686221 / 4.584777 (-3.898556) \| 3.753184 / 3.745712 (0.007472) \| 2.089505 / 5.269862 (-3.180356) \| 1.325799 / 4.565676 (-3.239878) \| 0.084608 / 0.424275 (-0.339667) \| 0.012343 / 0.007607 (0.004736) \| 0.509951 / 0.226044 (0.283907) \| 5.092102 / 2.268929 (2.823174) \| 2.297551 / 55.444624 (-53.147073) \| 1.938177 / 6.876477 (-4.938300) \| 2.012448 / 2.142072 (-0.129625) \| 0.835206 / 4.805227 (-3.970021) \| 0.166373 / 6.500664 (-6.334291) \| 0.063996 / 0.075469 (-0.011473) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.212936 / 1.841788 (-0.628851) \| 15.067370 / 8.074308 (6.993062) \| 14.165214 / 10.191392 (3.973822) \| 0.157041 / 0.680424 (-0.523383) \| 0.029612 / 0.534201 (-0.504589) \| 0.440006 / 0.579283 (-0.139277) \| 0.439165 / 0.434364 (0.004801) \| 0.524970 / 0.540337 (-0.015368) \| 0.608305 / 1.386936 (-0.778631) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007433 / 0.011353 (-0.003920) \| 0.005310 / 0.011008 (-0.005698) \| 0.097194 / 0.038508 (0.058686) \| 0.033265 / 0.023109 (0.010156) \| 0.369908 / 0.275898 (0.094010) \| 0.411508 / 0.323480 (0.088028) \| 0.006000 / 0.007986 (-0.001986) \| 0.005647 / 0.004328 (0.001319) \| 0.075597 / 0.004250 (0.071347) \| 0.051951 / 0.037052 (0.014899) \| 0.378469 / 0.258489 (0.119980) \| 0.424849 / 0.293841 (0.131008) \| 0.036700 / 0.128546 (-0.091846) \| 0.012535 / 0.075646 (-0.063111) \| 0.333197 / 0.419271 (-0.086074) \| 0.049046 / 0.043533 (0.005513) \| 0.381845 / 0.255139 (0.126706) \| 0.397846 / 0.283200 (0.114646) \| 0.109152 / 0.141683 (-0.032531) \| 1.432407 / 1.452155 (-0.019748) \| 1.555509 / 1.492716 (0.062793) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265433 / 0.018006 (0.247427) \| 0.559590 / 0.000490 (0.559100) \| 0.000492 / 0.000200 (0.000292) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029748 / 0.037411 (-0.007663) \| 0.110490 / 0.014526 (0.095964) \| 0.124125 / 0.176557 (-0.052431) \| 0.160089 / 0.737135 (-0.577046) \| 0.128755 / 0.296338 (-0.167583) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.443976 / 0.215209 (0.228767) \| 4.416960 / 2.077655 (2.339305) \| 2.239408 / 1.504120 (0.735288) \| 2.055341 / 1.541195 (0.514147) \| 2.093479 / 1.468490 (0.624988) \| 0.688846 / 4.584777 (-3.895930) \| 3.797526 / 3.745712 (0.051814) \| 3.578137 / 5.269862 (-1.691725) \| 2.015073 / 4.565676 (-2.550603) \| 0.084126 / 0.424275 (-0.340149) \| 0.012581 / 0.007607 (0.004974) \| 0.549774 / 0.226044 (0.323730) \| 5.492185 / 2.268929 (3.223256) \| 2.739851 / 55.444624 (-52.704773) \| 2.371091 / 6.876477 (-4.505386) \| 2.400178 / 2.142072 (0.258105) \| 0.831227 / 4.805227 (-3.974001) \| 0.166156 / 6.500664 (-6.334508) \| 0.063901 / 0.075469 (-0.011568) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.236127 / 1.841788 (-0.605660) \| 15.236884 / 8.074308 (7.162576) \| 14.434351 / 10.191392 (4.242959) \| 0.163725 / 0.680424 (-0.516699) \| 0.018009 / 0.534201 (-0.516192) \| 0.430612 / 0.579283 (-0.148671) \| 0.420426 / 0.434364 (-0.013938) \| 0.497062 / 0.540337 (-0.043275) \| 0.590924 / 1.386936 (-0.796012) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#63377dc53fc94f19bc2b0bbfb118a90d01a1d020 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010862 / 0.011353 (-0.000491) \| 0.005741 / 0.011008 (-0.005267) \| 0.111911 / 0.038508 (0.073403) \| 0.042316 / 0.023109 (0.019207) \| 0.347665 / 0.275898 (0.071767) \| 0.377335 / 0.323480 (0.053855) \| 0.009400 / 0.007986 (0.001414) \| 0.006814 / 0.004328 (0.002486) \| 0.087194 / 0.004250 (0.082943) \| 0.046878 / 0.037052 (0.009826) \| 0.348920 / 0.258489 (0.090430) \| 0.393347 / 0.293841 (0.099507) \| 0.044212 / 0.128546 (-0.084334) \| 0.013925 / 0.075646 (-0.061722) \| 0.386076 / 0.419271 (-0.033195) \| 0.054195 / 0.043533 (0.010662) \| 0.358486 / 0.255139 (0.103347) \| 0.360132 / 0.283200 (0.076932) \| 0.109783 / 0.141683 (-0.031900) \| 1.679875 / 1.452155 (0.227720) \| 1.794379 / 1.492716 (0.301663) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221927 / 0.018006 (0.203921) \| 0.487352 / 0.000490 (0.486863) \| 0.003494 / 0.000200 (0.003294) \| 0.000091 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032201 / 0.037411 (-0.005210) \| 0.125861 / 0.014526 (0.111335) \| 0.133905 / 0.176557 (-0.042652) \| 0.183319 / 0.737135 (-0.553817) \| 0.142646 / 0.296338 (-0.153693) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442720 / 0.215209 (0.227511) \| 4.602619 / 2.077655 (2.524964) \| 2.050214 / 1.504120 (0.546094) \| 1.837968 / 1.541195 (0.296773) \| 1.961199 / 1.468490 (0.492709) \| 0.793426 / 4.584777 (-3.791351) \| 4.472078 / 3.745712 (0.726366) \| 2.364903 / 5.269862 (-2.904959) \| 1.515076 / 4.565676 (-3.050600) \| 0.103087 / 0.424275 (-0.321188) \| 0.014676 / 0.007607 (0.007068) \| 0.576887 / 0.226044 (0.350843) \| 5.785525 / 2.268929 (3.516596) \| 2.765231 / 55.444624 (-52.679393) \| 2.365364 / 6.876477 (-4.511113) \| 2.448335 / 2.142072 (0.306262) \| 0.978726 / 4.805227 (-3.826501) \| 0.191417 / 6.500664 (-6.309247) \| 0.073295 / 0.075469 (-0.002174) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.378995 / 1.841788 (-0.462792) \| 16.583655 / 8.074308 (8.509347) \| 14.944731 / 10.191392 (4.753339) \| 0.168916 / 0.680424 (-0.511508) \| 0.035272 / 0.534201 (-0.498928) \| 0.489729 / 0.579283 (-0.089554) \| 0.496231 / 0.434364 (0.061867) \| 0.576218 / 0.540337 (0.035880) \| 0.673558 / 1.386936 (-0.713378) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008104 / 0.011353 (-0.003249) \| 0.005179 / 0.011008 (-0.005829) \| 0.103908 / 0.038508 (0.065400) \| 0.034661 / 0.023109 (0.011552) \| 0.398119 / 0.275898 (0.122221) \| 0.411765 / 0.323480 (0.088286) \| 0.006016 / 0.007986 (-0.001970) \| 0.005637 / 0.004328 (0.001308) \| 0.073662 / 0.004250 (0.069412) \| 0.052411 / 0.037052 (0.015359) \| 0.391826 / 0.258489 (0.133337) \| 0.455217 / 0.293841 (0.161376) \| 0.039924 / 0.128546 (-0.088622) \| 0.013390 / 0.075646 (-0.062256) \| 0.390319 / 0.419271 (-0.028953) \| 0.054312 / 0.043533 (0.010779) \| 0.395492 / 0.255139 (0.140353) \| 0.446324 / 0.283200 (0.163124) \| 0.116461 / 0.141683 (-0.025222) \| 1.502163 / 1.452155 (0.050008) \| 1.731541 / 1.492716 (0.238825) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282612 / 0.018006 (0.264606) \| 0.503170 / 0.000490 (0.502680) \| 0.005307 / 0.000200 (0.005107) \| 0.000100 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029071 / 0.037411 (-0.008340) \| 0.123831 / 0.014526 (0.109306) \| 0.133284 / 0.176557 (-0.043272) \| 0.172029 / 0.737135 (-0.565106) \| 0.140639 / 0.296338 (-0.155700) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.496812 / 0.215209 (0.281603) \| 4.958915 / 2.077655 (2.881260) \| 2.559188 / 1.504120 (1.055068) \| 2.262434 / 1.541195 (0.721240) \| 2.371126 / 1.468490 (0.902636) \| 0.780150 / 4.584777 (-3.804627) \| 4.417060 / 3.745712 (0.671348) \| 2.401909 / 5.269862 (-2.867953) \| 1.527943 / 4.565676 (-3.037733) \| 0.100074 / 0.424275 (-0.324201) \| 0.014853 / 0.007607 (0.007246) \| 0.630192 / 0.226044 (0.404147) \| 6.409685 / 2.268929 (4.140757) \| 3.224718 / 55.444624 (-52.219906) \| 2.795301 / 6.876477 (-4.081176) \| 2.927205 / 2.142072 (0.785132) \| 0.989537 / 4.805227 (-3.815690) \| 0.199775 / 6.500664 (-6.300889) \| 0.076725 / 0.075469 (0.001256) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.433504 / 1.841788 (-0.408284) \| 17.117134 / 8.074308 (9.042825) \| 16.606367 / 10.191392 (6.414975) \| 0.165653 / 0.680424 (-0.514771) \| 0.020818 / 0.534201 (-0.513383) \| 0.496782 / 0.579283 (-0.082501) \| 0.473895 / 0.434364 (0.039531) \| 0.576796 / 0.540337 (0.036459) \| 0.703272 / 1.386936 (-0.683664) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6627fb6f2639ac3b1435b3386545612db038a42e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012501 / 0.011353 (0.001148) \| 0.006437 / 0.011008 (-0.004571) \| 0.129387 / 0.038508 (0.090878) \| 0.035847 / 0.023109 (0.012737) \| 0.339243 / 0.275898 (0.063345) \| 0.423274 / 0.323480 (0.099794) \| 0.008489 / 0.007986 (0.000503) \| 0.004596 / 0.004328 (0.000268) \| 0.103322 / 0.004250 (0.099071) \| 0.043570 / 0.037052 (0.006517) \| 0.357004 / 0.258489 (0.098515) \| 0.426511 / 0.293841 (0.132670) \| 0.062923 / 0.128546 (-0.065623) \| 0.021168 / 0.075646 (-0.054478) \| 0.387485 / 0.419271 (-0.031787) \| 0.059745 / 0.043533 (0.016213) \| 0.341101 / 0.255139 (0.085962) \| 0.365530 / 0.283200 (0.082331) \| 0.102110 / 0.141683 (-0.039573) \| 1.729408 / 1.452155 (0.277253) \| 1.759510 / 1.492716 (0.266794) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.187065 / 0.018006 (0.169059) \| 0.499685 / 0.000490 (0.499196) \| 0.004677 / 0.000200 (0.004478) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025827 / 0.037411 (-0.011584) \| 0.113780 / 0.014526 (0.099255) \| 0.146060 / 0.176557 (-0.030496) \| 0.158169 / 0.737135 (-0.578966) \| 0.136133 / 0.296338 (-0.160206) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.608421 / 0.215209 (0.393211) \| 5.907395 / 2.077655 (3.829741) \| 2.193140 / 1.504120 (0.689021) \| 1.870315 / 1.541195 (0.329120) \| 1.885660 / 1.468490 (0.417170) \| 1.227637 / 4.584777 (-3.357140) \| 5.319242 / 3.745712 (1.573530) \| 2.991595 / 5.269862 (-2.278267) \| 2.043906 / 4.565676 (-2.521771) \| 0.151829 / 0.424275 (-0.272447) \| 0.018974 / 0.007607 (0.011367) \| 0.778035 / 0.226044 (0.551991) \| 7.705796 / 2.268929 (5.436868) \| 2.990156 / 55.444624 (-52.454468) \| 2.372643 / 6.876477 (-4.503834) \| 2.240847 / 2.142072 (0.098775) \| 1.407209 / 4.805227 (-3.398018) \| 0.242336 / 6.500664 (-6.258328) \| 0.069847 / 0.075469 (-0.005622) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.445817 / 1.841788 (-0.395970) \| 16.059632 / 8.074308 (7.985324) \| 18.541971 / 10.191392 (8.350579) \| 0.237830 / 0.680424 (-0.442594) \| 0.041060 / 0.534201 (-0.493141) \| 0.496765 / 0.579283 (-0.082518) \| 0.609666 / 0.434364 (0.175302) \| 0.584614 / 0.540337 (0.044277) \| 0.680858 / 1.386936 (-0.706078) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009037 / 0.011353 (-0.002315) \| 0.005961 / 0.011008 (-0.005047) \| 0.127204 / 0.038508 (0.088696) \| 0.030664 / 0.023109 (0.007555) \| 0.417968 / 0.275898 (0.142070) \| 0.515316 / 0.323480 (0.191836) \| 0.006549 / 0.007986 (-0.001436) \| 0.004456 / 0.004328 (0.000128) \| 0.083715 / 0.004250 (0.079464) \| 0.043701 / 0.037052 (0.006648) \| 0.521153 / 0.258489 (0.262664) \| 0.565456 / 0.293841 (0.271615) \| 0.055298 / 0.128546 (-0.073248) \| 0.018103 / 0.075646 (-0.057544) \| 0.403990 / 0.419271 (-0.015282) \| 0.060162 / 0.043533 (0.016629) \| 0.486383 / 0.255139 (0.231244) \| 0.470342 / 0.283200 (0.187142) \| 0.102269 / 0.141683 (-0.039414) \| 1.643241 / 1.452155 (0.191086) \| 1.763850 / 1.492716 (0.271133) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185602 / 0.018006 (0.167596) \| 0.489163 / 0.000490 (0.488674) \| 0.000426 / 0.000200 (0.000226) \| 0.000086 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026689 / 0.037411 (-0.010722) \| 0.111520 / 0.014526 (0.096994) \| 0.119838 / 0.176557 (-0.056719) \| 0.153698 / 0.737135 (-0.583437) \| 0.130969 / 0.296338 (-0.165370) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616170 / 0.215209 (0.400961) \| 6.219702 / 2.077655 (4.142048) \| 2.533554 / 1.504120 (1.029434) \| 2.256009 / 1.541195 (0.714815) \| 2.217617 / 1.468490 (0.749127) \| 1.156920 / 4.584777 (-3.427857) \| 5.175759 / 3.745712 (1.430046) \| 2.848419 / 5.269862 (-2.421442) \| 1.943864 / 4.565676 (-2.621813) \| 0.138342 / 0.424275 (-0.285933) \| 0.013140 / 0.007607 (0.005533) \| 0.782105 / 0.226044 (0.556060) \| 7.602003 / 2.268929 (5.333075) \| 3.629577 / 55.444624 (-51.815047) \| 2.713849 / 6.876477 (-4.162628) \| 2.663888 / 2.142072 (0.521816) \| 1.418381 / 4.805227 (-3.386847) \| 0.250649 / 6.500664 (-6.250015) \| 0.073564 / 0.075469 (-0.001905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.483739 / 1.841788 (-0.358049) \| 16.386204 / 8.074308 (8.311896) \| 20.685262 / 10.191392 (10.493870) \| 0.237084 / 0.680424 (-0.443340) \| 0.039097 / 0.534201 (-0.495104) \| 0.525399 / 0.579283 (-0.053884) \| 0.587541 / 0.434364 (0.153177) \| 0.566605 / 0.540337 (0.026268) \| 0.677384 / 1.386936 (-0.709552) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3b67d42733dabb15ce4997c8324f8e047ce12bd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.014050 / 0.011353 (0.002697) \| 0.005981 / 0.011008 (-0.005028) \| 0.126307 / 0.038508 (0.087799) \| 0.035400 / 0.023109 (0.012290) \| 0.387821 / 0.275898 (0.111923) \| 0.462785 / 0.323480 (0.139305) \| 0.009427 / 0.007986 (0.001441) \| 0.005081 / 0.004328 (0.000753) \| 0.097273 / 0.004250 (0.093023) \| 0.044699 / 0.037052 (0.007647) \| 0.396025 / 0.258489 (0.137536) \| 0.450137 / 0.293841 (0.156296) \| 0.055660 / 0.128546 (-0.072886) \| 0.022710 / 0.075646 (-0.052936) \| 0.443784 / 0.419271 (0.024513) \| 0.065756 / 0.043533 (0.022223) \| 0.379350 / 0.255139 (0.124211) \| 0.396783 / 0.283200 (0.113583) \| 0.114088 / 0.141683 (-0.027594) \| 1.856834 / 1.452155 (0.404679) \| 1.839292 / 1.492716 (0.346576) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206748 / 0.018006 (0.188742) \| 0.517711 / 0.000490 (0.517222) \| 0.008302 / 0.000200 (0.008102) \| 0.000494 / 0.000054 (0.000440) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033987 / 0.037411 (-0.003424) \| 0.131067 / 0.014526 (0.116542) \| 0.155539 / 0.176557 (-0.021018) \| 0.188598 / 0.737135 (-0.548537) \| 0.156000 / 0.296338 (-0.140338) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.641413 / 0.215209 (0.426204) \| 6.156680 / 2.077655 (4.079025) \| 2.428858 / 1.504120 (0.924738) \| 2.086195 / 1.541195 (0.545000) \| 2.109604 / 1.468490 (0.641114) \| 1.209426 / 4.584777 (-3.375351) \| 5.139398 / 3.745712 (1.393686) \| 3.041337 / 5.269862 (-2.228524) \| 2.294809 / 4.565676 (-2.270868) \| 0.142206 / 0.424275 (-0.282069) \| 0.015167 / 0.007607 (0.007560) \| 0.816269 / 0.226044 (0.590224) \| 7.953931 / 2.268929 (5.685002) \| 3.201793 / 55.444624 (-52.242832) \| 2.448620 / 6.876477 (-4.427857) \| 2.521670 / 2.142072 (0.379597) \| 1.484094 / 4.805227 (-3.321133) \| 0.255069 / 6.500664 (-6.245595) \| 0.076031 / 0.075469 (0.000561) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.590951 / 1.841788 (-0.250836) \| 17.661353 / 8.074308 (9.587045) \| 21.097837 / 10.191392 (10.906445) \| 0.229265 / 0.680424 (-0.451159) \| 0.042618 / 0.534201 (-0.491583) \| 0.535942 / 0.579283 (-0.043342) \| 0.590195 / 0.434364 (0.155831) \| 0.623985 / 0.540337 (0.083648) \| 0.742637 / 1.386936 (-0.644299) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009264 / 0.011353 (-0.002088) \| 0.008798 / 0.011008 (-0.002210) \| 0.122208 / 0.038508 (0.083700) \| 0.034835 / 0.023109 (0.011726) \| 0.462618 / 0.275898 (0.186720) \| 0.505632 / 0.323480 (0.182152) \| 0.006320 / 0.007986 (-0.001665) \| 0.005383 / 0.004328 (0.001054) \| 0.091229 / 0.004250 (0.086979) \| 0.045828 / 0.037052 (0.008775) \| 0.477507 / 0.258489 (0.219018) \| 0.539616 / 0.293841 (0.245775) \| 0.061913 / 0.128546 (-0.066633) \| 0.019390 / 0.075646 (-0.056257) \| 0.420016 / 0.419271 (0.000745) \| 0.065958 / 0.043533 (0.022425) \| 0.468603 / 0.255139 (0.213464) \| 0.486246 / 0.283200 (0.203046) \| 0.107924 / 0.141683 (-0.033759) \| 1.843614 / 1.452155 (0.391459) \| 1.988159 / 1.492716 (0.495442) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.247043 / 0.018006 (0.229037) \| 0.515580 / 0.000490 (0.515090) \| 0.005630 / 0.000200 (0.005430) \| 0.000115 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030674 / 0.037411 (-0.006737) \| 0.130783 / 0.014526 (0.116258) \| 0.147669 / 0.176557 (-0.028888) \| 0.175656 / 0.737135 (-0.561479) \| 0.138317 / 0.296338 (-0.158022) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.727119 / 0.215209 (0.511909) \| 6.848208 / 2.077655 (4.770553) \| 3.121418 / 1.504120 (1.617298) \| 2.701799 / 1.541195 (1.160604) \| 2.749179 / 1.468490 (1.280689) \| 1.312058 / 4.584777 (-3.272719) \| 5.400562 / 3.745712 (1.654850) \| 3.058142 / 5.269862 (-2.211719) \| 2.076361 / 4.565676 (-2.489316) \| 0.142169 / 0.424275 (-0.282106) \| 0.014340 / 0.007607 (0.006733) \| 0.853534 / 0.226044 (0.627490) \| 8.734484 / 2.268929 (6.465556) \| 3.968130 / 55.444624 (-51.476495) \| 3.118032 / 6.876477 (-3.758444) \| 3.078757 / 2.142072 (0.936684) \| 1.460694 / 4.805227 (-3.344533) \| 0.261858 / 6.500664 (-6.238806) \| 0.081089 / 0.075469 (0.005620) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.611473 / 1.841788 (-0.230315) \| 17.660545 / 8.074308 (9.586237) \| 20.526023 / 10.191392 (10.334631) \| 0.223320 / 0.680424 (-0.457103) \| 0.027939 / 0.534201 (-0.506261) \| 0.542704 / 0.579283 (-0.036579) \| 0.563826 / 0.434364 (0.129462) \| 0.639936 / 0.540337 (0.099599) \| 0.755974 / 1.386936 (-0.630962) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#942141e13ba2be853e2231d9edbfa38044e2632d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008776 / 0.011353 (-0.002577) \| 0.004532 / 0.011008 (-0.006476) \| 0.100373 / 0.038508 (0.061865) \| 0.029706 / 0.023109 (0.006597) \| 0.304374 / 0.275898 (0.028476) \| 0.337223 / 0.323480 (0.013743) \| 0.007021 / 0.007986 (-0.000965) \| 0.003420 / 0.004328 (-0.000908) \| 0.077754 / 0.004250 (0.073504) \| 0.034411 / 0.037052 (-0.002642) \| 0.302926 / 0.258489 (0.044437) \| 0.342654 / 0.293841 (0.048813) \| 0.034528 / 0.128546 (-0.094018) \| 0.011926 / 0.075646 (-0.063721) \| 0.322971 / 0.419271 (-0.096301) \| 0.041384 / 0.043533 (-0.002149) \| 0.306433 / 0.255139 (0.051294) \| 0.332293 / 0.283200 (0.049093) \| 0.084972 / 0.141683 (-0.056711) \| 1.493426 / 1.452155 (0.041271) \| 1.570446 / 1.492716 (0.077729) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.189090 / 0.018006 (0.171084) \| 0.433904 / 0.000490 (0.433414) \| 0.001323 / 0.000200 (0.001124) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023531 / 0.037411 (-0.013880) \| 0.097774 / 0.014526 (0.083248) \| 0.106383 / 0.176557 (-0.070174) \| 0.139158 / 0.737135 (-0.597977) \| 0.109443 / 0.296338 (-0.186896) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419078 / 0.215209 (0.203869) \| 4.182657 / 2.077655 (2.105002) \| 1.887276 / 1.504120 (0.383156) \| 1.679542 / 1.541195 (0.138347) \| 1.718035 / 1.468490 (0.249545) \| 0.692628 / 4.584777 (-3.892149) \| 3.361354 / 3.745712 (-0.384358) \| 1.928583 / 5.269862 (-3.341278) \| 1.317291 / 4.565676 (-3.248386) \| 0.081799 / 0.424275 (-0.342476) \| 0.012318 / 0.007607 (0.004711) \| 0.525927 / 0.226044 (0.299883) \| 5.285905 / 2.268929 (3.016977) \| 2.317524 / 55.444624 (-53.127100) \| 1.966478 / 6.876477 (-4.909998) \| 2.054869 / 2.142072 (-0.087204) \| 0.807579 / 4.805227 (-3.997649) \| 0.149854 / 6.500664 (-6.350810) \| 0.065285 / 0.075469 (-0.010184) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.180516 / 1.841788 (-0.661271) \| 13.889734 / 8.074308 (5.815426) \| 14.076163 / 10.191392 (3.884771) \| 0.156276 / 0.680424 (-0.524148) \| 0.029187 / 0.534201 (-0.505013) \| 0.403859 / 0.579283 (-0.175424) \| 0.404998 / 0.434364 (-0.029366) \| 0.471467 / 0.540337 (-0.068871) \| 0.564526 / 1.386936 (-0.822410) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006739 / 0.011353 (-0.004614) \| 0.004644 / 0.011008 (-0.006364) \| 0.097326 / 0.038508 (0.058818) \| 0.027728 / 0.023109 (0.004619) \| 0.413537 / 0.275898 (0.137639) \| 0.452012 / 0.323480 (0.128532) \| 0.005346 / 0.007986 (-0.002639) \| 0.003338 / 0.004328 (-0.000991) \| 0.075670 / 0.004250 (0.071420) \| 0.038825 / 0.037052 (0.001772) \| 0.415612 / 0.258489 (0.157123) \| 0.454680 / 0.293841 (0.160839) \| 0.031866 / 0.128546 (-0.096680) \| 0.011616 / 0.075646 (-0.064031) \| 0.319527 / 0.419271 (-0.099745) \| 0.041283 / 0.043533 (-0.002250) \| 0.412046 / 0.255139 (0.156907) \| 0.435244 / 0.283200 (0.152044) \| 0.088400 / 0.141683 (-0.053283) \| 1.478125 / 1.452155 (0.025970) \| 1.553677 / 1.492716 (0.060960) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229919 / 0.018006 (0.211913) \| 0.415446 / 0.000490 (0.414956) \| 0.000386 / 0.000200 (0.000186) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024365 / 0.037411 (-0.013046) \| 0.098225 / 0.014526 (0.083699) \| 0.106674 / 0.176557 (-0.069883) \| 0.144755 / 0.737135 (-0.592380) \| 0.109221 / 0.296338 (-0.187117) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457665 / 0.215209 (0.242456) \| 4.597849 / 2.077655 (2.520195) \| 2.171275 / 1.504120 (0.667155) \| 1.945547 / 1.541195 (0.404352) \| 2.014043 / 1.468490 (0.545553) \| 0.699732 / 4.584777 (-3.885045) \| 3.420711 / 3.745712 (-0.325001) \| 3.298702 / 5.269862 (-1.971159) \| 1.390324 / 4.565676 (-3.175353) \| 0.082668 / 0.424275 (-0.341607) \| 0.012556 / 0.007607 (0.004949) \| 0.550406 / 0.226044 (0.324361) \| 5.501060 / 2.268929 (3.232132) \| 2.659841 / 55.444624 (-52.784783) \| 2.243443 / 6.876477 (-4.633034) \| 2.266006 / 2.142072 (0.123934) \| 0.806295 / 4.805227 (-3.998933) \| 0.151399 / 6.500664 (-6.349265) \| 0.067048 / 0.075469 (-0.008421) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291404 / 1.841788 (-0.550384) \| 14.164728 / 8.074308 (6.090419) \| 13.980219 / 10.191392 (3.788827) \| 0.140599 / 0.680424 (-0.539824) \| 0.016880 / 0.534201 (-0.517321) \| 0.379073 / 0.579283 (-0.200210) \| 0.385770 / 0.434364 (-0.048594) \| 0.442516 / 0.540337 (-0.097822) \| 0.533569 / 1.386936 (-0.853367) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#29fa15df972353f51fc434cf8eceb574b60a415f \"CML watermark\")\n", "Tests seem to be failing for unrelated reasons.", "Tests are failing because of a bug on the Hub side - this is being fixed :)\r\n\r\nlmk once the TF documentation page is updated and we can merge !", "@lhoestq Docs updated!" ]	2022-12-19T19:40:27	2023-01-25T16:28:44	2023-01-25T16:21:40	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5377", "html_url": "https://github.com/huggingface/datasets/pull/5377", "diff_url": "https://github.com/huggingface/datasets/pull/5377.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5377.patch", "merged_at": "2023-01-25T16:21:40" }	Hey all! Here's a first draft of the PR to add a multiprocessing implementation for `to_tf_dataset()`. It worked in some quick testing for me, but obviously I need to do some much more rigorous testing/benchmarking, and add some proper library tests. The core idea is that we do everything using `multiprocessing` and `numpy`, and just wrap a `tf.data.Dataset` around the output. We could also rewrite the existing single-threaded implementation based on this code, which might simplify it a bit. Checklist: - [X] Add initial draft - [x] Check that it works regardless of whether the `collate_fn` or dataset returns `tf` or `np` arrays - [x] Check that it works with `tf.string` return data - [x] Check indices are correctly reshuffled each epoch - [x] Make sure workers don't try to initialize a GPU device!! - [x] Check `fit()` with multiple epochs works fine and that the progress bar is correct - [x] Check there are no memory leaks or zombie processes - [x] Benchmark performance - [x] Tweak params for dataset inference - can we speed things up there a bit? - [x] Add tests to the library - [x] Add a PR to `transformers` to expose the `num_workers` argument via `prepare_tf_dataset` (will merge after this one is released) - [x] Stop TF console spam!! (almost) - [x] Add a method for creating SHM that doesn't crash if it was left and still linked - [x] Add a barrier for Py <= 3.7 because it doesn't support SharedMemory - [x] Support string dtypes by converting them into fixed-width character arrays	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5377/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5377/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5376	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5376/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5376/comments	https://api.github.com/repos/huggingface/datasets/issues/5376/events	https://github.com/huggingface/datasets/pull/5376	1,502,730,559	PR_kwDODunzps5FxWkM	5,376	set dev version	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5376). All of your documentation changes will be reflected on that endpoint." ]	2022-12-19T10:56:56	2022-12-19T11:01:55	2022-12-19T10:57:16	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5376", "html_url": "https://github.com/huggingface/datasets/pull/5376", "diff_url": "https://github.com/huggingface/datasets/pull/5376.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5376.patch", "merged_at": "2022-12-19T10:57:16" }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5376/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5376/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5375	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5375/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5375/comments	https://api.github.com/repos/huggingface/datasets/issues/5375/events	https://github.com/huggingface/datasets/pull/5375	1,502,720,404	PR_kwDODunzps5FxUbG	5,375	Release: 2.8.0	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-12-19T10:48:26	2022-12-19T10:55:43	2022-12-19T10:53:15	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5375", "html_url": "https://github.com/huggingface/datasets/pull/5375", "diff_url": "https://github.com/huggingface/datasets/pull/5375.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5375.patch", "merged_at": "2022-12-19T10:53:15" }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5375/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5375/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5374	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5374/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5374/comments	https://api.github.com/repos/huggingface/datasets/issues/5374/events	https://github.com/huggingface/datasets/issues/5374	1,501,872,945	I_kwDODunzps5ZhMMx	5,374	Using too many threads results in: Got disconnected from remote data host. Retrying in 5sec	{ "login": "Muennighoff", "id": 62820084, "node_id": "MDQ6VXNlcjYyODIwMDg0", "avatar_url": "https://avatars.githubusercontent.com/u/62820084?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Muennighoff", "html_url": "https://github.com/Muennighoff", "followers_url": "https://api.github.com/users/Muennighoff/followers", "following_url": "https://api.github.com/users/Muennighoff/following{/other_user}", "gists_url": "https://api.github.com/users/Muennighoff/gists{/gist_id}", "starred_url": "https://api.github.com/users/Muennighoff/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Muennighoff/subscriptions", "organizations_url": "https://api.github.com/users/Muennighoff/orgs", "repos_url": "https://api.github.com/users/Muennighoff/repos", "events_url": "https://api.github.com/users/Muennighoff/events{/privacy}", "received_events_url": "https://api.github.com/users/Muennighoff/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The data files are hosted on HF at https://huggingface.co/datasets/allenai/c4/tree/main\r\n\r\nYou have 200 runs streaming the same files in parallel. So this is probably a Hub limitation. Maybe rate limiting ? cc @julien-c \r\n\r\nMaybe you can also try to reduce the number of HTTP requests by increasing the block size of each request. This can be done by increasing `DEFAULT_BLOCK_SIZE` in `fsspec.implementations.http`. Default is `5 * 220` (5MiB)\r\n\r\nAnyway maybe it's just better to save the dataset locally in that case ?", "you don't get an HTTP error code or something in your stack trace? Kinda hard to debug with this info", "You could try to re-run using this `datasets` branch: [raise-err-when-disconnect](https://github.com/huggingface/datasets/compare/raise-err-when-disconnect?expand=1)\r\nIt should raise the fsspec error", "The weird thing is that I already have it saved locally & it seems to indeed be using the cached one 🧐 ; I'm also using offline mode, so I don't think it has something to do with the Hub.\r\n```\r\nWARNING:datasets.load:Using the latest cached version of the module from /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01 (last modified on Mon Dec 12 10:45:02 2022) since it couldn't be found locally at c4.\r\n```\r\n\r\n", "No, you passed `streaming=True` so it streams the data from the Hub.\r\nThis message just shows that you use the cached version of the `c4` module**, aka the python script that is run to generate the examples from the raw data files.\r\n\r\nMaybe the offline mode should also disable `fsspec`/`aiohttp` HTTP calls in `datasets` and not just the `requests` ones.", "> This message just shows that you use the cached version of the c4 module\r\n\r\nAh my bad you're right about the module, but it's also using the downloaded & cached c4 dataset. There's no internet during the runs so it wouldn't work otherwise", "You don't have internet, therefore you get an error while trying to stream ;)" ]	2022-12-18T11:38:58	2023-07-24T15:23:07	2023-07-24T15:23:07	CONTRIBUTOR	null	null	null	### Describe the bug `streaming_download_manager` seems to disconnect if too many runs access the same underlying dataset 🧐 The code works fine for me if I have ~100 runs in parallel, but disconnects once scaling to 200. Possibly related: - https://github.com/huggingface/datasets/pull/3100 - https://github.com/huggingface/datasets/pull/3050 ### Steps to reproduce the bug Running ```python c4 = datasets.load_dataset("c4", "en", split="train", streaming=True).skip(args.start).take(args.end-args.start) df = pd.DataFrame(c4, index=None) ``` with different start & end arguments on 200 CPUs in parallel yields: ``` WARNING:datasets.load:Using the latest cached version of the module from /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01 (last modified on Mon Dec 12 10:45:02 2022) since it couldn't be found locally at c4. WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [1/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [2/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [3/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [4/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [5/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [6/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [7/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [8/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [9/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [10/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [11/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [12/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [13/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [14/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [15/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [16/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [17/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [18/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [19/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [20/20] ╭───────────────────── Traceback (most recent call last) ──────────────────────╮ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/dec-2022-tasky/inference │ │ _c4.py:68 in <module> │ │ │ │ 65 │ model.eval() │ │ 66 │ │ │ 67 │ c4 = datasets.load_dataset("c4", "en", split="train", streaming=Tru │ │ ❱ 68 │ df = pd.DataFrame(c4, index=None) │ │ 69 │ texts = df["text"].to_list() │ │ 70 │ preds = batch_inference(texts, batch_size=args.batch_size) │ │ 71 │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/site-packages/pandas/core/frame.p │ │ y:684 in __init__ │ │ │ │ 681 │ │ # For data is list-like, or Iterable (will consume into list │ │ 682 │ │ elif is_list_like(data): │ │ 683 │ │ │ if not isinstance(data, (abc.Sequence, ExtensionArray)): │ │ ❱ 684 │ │ │ │ data = list(data) │ │ 685 │ │ │ if len(data) > 0: │ │ 686 │ │ │ │ if is_dataclass(data[0]): │ │ 687 │ │ │ │ │ data = dataclasses_to_dicts(data) │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:751 in __iter__ │ │ │ │ 748 │ │ yield from ex_iterable.shard_data_sources(shard_idx) │ │ 749 │ │ │ 750 │ def __iter__(self): │ │ ❱ 751 │ │ for key, example in self._iter(): │ │ 752 │ │ │ if self.features: │ │ 753 │ │ │ │ # `IterableDataset` automatically fills missing colum │ │ 754 │ │ │ │ # This is done with `_apply_feature_types`. │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:741 in _iter │ │ │ │ 738 │ │ │ ex_iterable = self._ex_iterable.shuffle_data_sources(self │ │ 739 │ │ else: │ │ 740 │ │ │ ex_iterable = self._ex_iterable │ │ ❱ 741 │ │ yield from ex_iterable │ │ 742 │ │ │ 743 │ def _iter_shard(self, shard_idx: int): │ │ 744 │ │ if self._shuffling: │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:617 in __iter__ │ │ │ │ 614 │ │ self.n = n │ │ 615 │ │ │ 616 │ def __iter__(self): │ │ ❱ 617 │ │ yield from islice(self.ex_iterable, self.n) │ │ 618 │ │ │ 619 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │ │ 620 │ │ """Doesn't shuffle the wrapped examples iterable since it wou │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:594 in __iter__ │ │ │ │ 591 │ │ │ 592 │ def __iter__(self): │ │ 593 │ │ #ex_iterator = iter(self.ex_iterable) │ │ ❱ 594 │ │ yield from islice(self.ex_iterable, self.n, None) │ │ 595 │ │ #for _ in range(self.n): │ │ 596 │ │ # next(ex_iterator) │ │ 597 │ │ #yield from islice(ex_iterator, self.n, None) │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:106 in __iter__ │ │ │ │ 103 │ │ self.kwargs = kwargs │ │ 104 │ │ │ 105 │ def __iter__(self): │ │ ❱ 106 │ │ yield from self.generate_examples_fn(**self.kwargs) │ │ 107 │ │ │ 108 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │ │ 109 │ │ return ShardShuffledExamplesIterable(self.generate_examples_f │ │ │ │ /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/d │ │ f532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01/c4.py:89 in │ │ _generate_examples │ │ │ │ 86 │ │ for filepath in filepaths: │ │ 87 │ │ │ logger.info("generating examples from = %s", filepath) │ │ 88 │ │ │ with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8" │ │ ❱ 89 │ │ │ │ for line in f: │ │ 90 │ │ │ │ │ if line: │ │ 91 │ │ │ │ │ │ example = json.loads(line) │ │ 92 │ │ │ │ │ │ yield id_, example │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:313 in read1 │ │ │ │ 310 │ │ │ │ 311 │ │ if size < 0: │ │ 312 │ │ │ size = io.DEFAULT_BUFFER_SIZE │ │ ❱ 313 │ │ return self._buffer.read1(size) │ │ 314 │ │ │ 315 │ def peek(self, n): │ │ 316 │ │ self._check_not_closed() │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/_compression.py:68 in readinto │ │ │ │ 65 │ │ │ 66 │ def readinto(self, b): │ │ 67 │ │ with memoryview(b) as view, view.cast("B") as byte_view: │ │ ❱ 68 │ │ │ data = self.read(len(byte_view)) │ │ 69 │ │ │ byte_view[:len(data)] = data │ │ 70 │ │ return len(data) │ │ 71 │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:493 in read │ │ │ │ 490 │ │ │ │ self._new_member = False │ │ 491 │ │ │ │ │ 492 │ │ │ # Read a chunk of data from the file │ │ ❱ 493 │ │ │ buf = self._fp.read(io.DEFAULT_BUFFER_SIZE) │ │ 494 │ │ │ │ │ 495 │ │ │ uncompress = self._decompressor.decompress(buf, size) │ │ 496 │ │ │ if self._decompressor.unconsumed_tail != b"": │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:96 in read │ │ │ │ 93 │ │ │ read = self._read │ │ 94 │ │ │ self._read = None │ │ 95 │ │ │ return self._buffer[read:] + \ │ │ ❱ 96 │ │ │ │ self.file.read(size-self._length+read) │ │ 97 │ │ │ 98 │ def prepend(self, prepend=b''): │ │ 99 │ │ if self._read is None: │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/download/streaming_download_manager.py: │ │ 365 in read_with_retries │ │ │ │ 362 │ │ │ │ ) │ │ 363 │ │ │ │ time.sleep(config.STREAMING_READ_RETRY_INTERVAL) │ │ 364 │ │ else: │ │ ❱ 365 │ │ │ raise ConnectionError("Server Disconnected") │ │ 366 │ │ return out │ │ 367 │ │ │ 368 │ file_obj.read = read_with_retries │ ╰──────────────────────────────────────────────────────────────────────────────╯ ConnectionError: Server Disconnected ``` ### Expected behavior There should be no disconnect I think. ### Environment info ``` datasets=2.7.0 Python 3.9.12 ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5374/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5374/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5373	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5373/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5373/comments	https://api.github.com/repos/huggingface/datasets/issues/5373/events	https://github.com/huggingface/datasets/pull/5373	1,501,484,197	PR_kwDODunzps5FtRU4	5,373	Simplify skipping	{ "login": "Muennighoff", "id": 62820084, "node_id": "MDQ6VXNlcjYyODIwMDg0", "avatar_url": "https://avatars.githubusercontent.com/u/62820084?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Muennighoff", "html_url": "https://github.com/Muennighoff", "followers_url": "https://api.github.com/users/Muennighoff/followers", "following_url": "https://api.github.com/users/Muennighoff/following{/other_user}", "gists_url": "https://api.github.com/users/Muennighoff/gists{/gist_id}", "starred_url": "https://api.github.com/users/Muennighoff/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Muennighoff/subscriptions", "organizations_url": "https://api.github.com/users/Muennighoff/orgs", "repos_url": "https://api.github.com/users/Muennighoff/repos", "events_url": "https://api.github.com/users/Muennighoff/events{/privacy}", "received_events_url": "https://api.github.com/users/Muennighoff/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-12-17T17:23:52	2022-12-18T21:43:31	2022-12-18T21:40:21	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5373", "html_url": "https://github.com/huggingface/datasets/pull/5373", "diff_url": "https://github.com/huggingface/datasets/pull/5373.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5373.patch", "merged_at": "2022-12-18T21:40:21" }	Was hoping to find a way to speed up the skipping as I'm running into bottlenecks skipping 100M examples on C4 (it takes 12 hours to skip), but didn't find anything better than this small change :( Maybe there's a way to directly skip whole shards to speed it up? 🧐	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5373/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5373/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5372	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5372/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5372/comments	https://api.github.com/repos/huggingface/datasets/issues/5372/events	https://github.com/huggingface/datasets/pull/5372	1,501,377,802	PR_kwDODunzps5Fs9w5	5,372	Fix streaming pandas.read_excel	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009517 / 0.011353 (-0.001835) \| 0.005210 / 0.011008 (-0.005798) \| 0.098916 / 0.038508 (0.060408) \| 0.036123 / 0.023109 (0.013014) \| 0.301564 / 0.275898 (0.025666) \| 0.358086 / 0.323480 (0.034606) \| 0.008159 / 0.007986 (0.000174) \| 0.004122 / 0.004328 (-0.000206) \| 0.075899 / 0.004250 (0.071648) \| 0.046082 / 0.037052 (0.009030) \| 0.302871 / 0.258489 (0.044382) \| 0.351162 / 0.293841 (0.057321) \| 0.038215 / 0.128546 (-0.090331) \| 0.012026 / 0.075646 (-0.063620) \| 0.330988 / 0.419271 (-0.088284) \| 0.048351 / 0.043533 (0.004818) \| 0.291840 / 0.255139 (0.036701) \| 0.320387 / 0.283200 (0.037187) \| 0.105018 / 0.141683 (-0.036665) \| 1.447158 / 1.452155 (-0.004997) \| 1.491205 / 1.492716 (-0.001511) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250870 / 0.018006 (0.232863) \| 0.562974 / 0.000490 (0.562484) \| 0.001789 / 0.000200 (0.001589) \| 0.000252 / 0.000054 (0.000197) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028208 / 0.037411 (-0.009203) \| 0.110897 / 0.014526 (0.096371) \| 0.120394 / 0.176557 (-0.056163) \| 0.164980 / 0.737135 (-0.572156) \| 0.126283 / 0.296338 (-0.170056) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397922 / 0.215209 (0.182713) \| 3.969233 / 2.077655 (1.891578) \| 1.766422 / 1.504120 (0.262302) \| 1.577503 / 1.541195 (0.036308) \| 1.672344 / 1.468490 (0.203854) \| 0.695708 / 4.584777 (-3.889069) \| 3.770763 / 3.745712 (0.025051) \| 3.369592 / 5.269862 (-1.900269) \| 1.851122 / 4.565676 (-2.714554) \| 0.084063 / 0.424275 (-0.340212) \| 0.012156 / 0.007607 (0.004549) \| 0.534639 / 0.226044 (0.308594) \| 5.021955 / 2.268929 (2.753027) \| 2.215438 / 55.444624 (-53.229186) \| 1.890459 / 6.876477 (-4.986018) \| 2.071361 / 2.142072 (-0.070712) \| 0.834623 / 4.805227 (-3.970604) \| 0.165588 / 6.500664 (-6.335076) \| 0.064336 / 0.075469 (-0.011133) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.205651 / 1.841788 (-0.636136) \| 14.916871 / 8.074308 (6.842563) \| 14.559495 / 10.191392 (4.368103) \| 0.166889 / 0.680424 (-0.513535) \| 0.028645 / 0.534201 (-0.505556) \| 0.433634 / 0.579283 (-0.145649) \| 0.429849 / 0.434364 (-0.004515) \| 0.508617 / 0.540337 (-0.031720) \| 0.595261 / 1.386936 (-0.791675) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007696 / 0.011353 (-0.003657) \| 0.005434 / 0.011008 (-0.005574) \| 0.099234 / 0.038508 (0.060725) \| 0.033904 / 0.023109 (0.010795) \| 0.379181 / 0.275898 (0.103283) \| 0.401858 / 0.323480 (0.078379) \| 0.006257 / 0.007986 (-0.001729) \| 0.004406 / 0.004328 (0.000077) \| 0.073174 / 0.004250 (0.068923) \| 0.056033 / 0.037052 (0.018981) \| 0.379375 / 0.258489 (0.120886) \| 0.425928 / 0.293841 (0.132087) \| 0.037476 / 0.128546 (-0.091071) \| 0.012520 / 0.075646 (-0.063127) \| 0.364975 / 0.419271 (-0.054297) \| 0.049341 / 0.043533 (0.005808) \| 0.370519 / 0.255139 (0.115380) \| 0.390585 / 0.283200 (0.107385) \| 0.113339 / 0.141683 (-0.028344) \| 1.460575 / 1.452155 (0.008421) \| 1.564951 / 1.492716 (0.072235) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246217 / 0.018006 (0.228210) \| 0.554358 / 0.000490 (0.553869) \| 0.000451 / 0.000200 (0.000251) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029557 / 0.037411 (-0.007855) \| 0.110472 / 0.014526 (0.095946) \| 0.122652 / 0.176557 (-0.053904) \| 0.159396 / 0.737135 (-0.577739) \| 0.128852 / 0.296338 (-0.167486) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.447927 / 0.215209 (0.232718) \| 4.448292 / 2.077655 (2.370637) \| 2.228874 / 1.504120 (0.724754) \| 2.030231 / 1.541195 (0.489036) \| 2.116417 / 1.468490 (0.647927) \| 0.702713 / 4.584777 (-3.882064) \| 3.774063 / 3.745712 (0.028351) \| 3.521662 / 5.269862 (-1.748200) \| 1.476700 / 4.565676 (-3.088976) \| 0.084921 / 0.424275 (-0.339354) \| 0.012862 / 0.007607 (0.005255) \| 0.559142 / 0.226044 (0.333098) \| 5.512233 / 2.268929 (3.243305) \| 2.750024 / 55.444624 (-52.694600) \| 2.388845 / 6.876477 (-4.487632) \| 2.541786 / 2.142072 (0.399714) \| 0.842256 / 4.805227 (-3.962971) \| 0.168088 / 6.500664 (-6.332576) \| 0.064211 / 0.075469 (-0.011258) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.239001 / 1.841788 (-0.602787) \| 15.286345 / 8.074308 (7.212036) \| 13.883981 / 10.191392 (3.692589) \| 0.186212 / 0.680424 (-0.494212) \| 0.018305 / 0.534201 (-0.515896) \| 0.420459 / 0.579283 (-0.158824) \| 0.421039 / 0.434364 (-0.013325) \| 0.487348 / 0.540337 (-0.052989) \| 0.587730 / 1.386936 (-0.799206) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]	2022-12-17T12:58:52	2023-01-06T11:50:58	2023-01-06T11:43:37	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5372", "html_url": "https://github.com/huggingface/datasets/pull/5372", "diff_url": "https://github.com/huggingface/datasets/pull/5372.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5372.patch", "merged_at": "2023-01-06T11:43:37" }	This PR fixes `xpandas_read_excel`: - Support passing a path string, besides a file-like object - Support passing `use_auth_token` - First assumes the host server supports HTTP range requests; only if a ValueError is thrown (Cannot seek streaming HTTP file), then it preserves previous behavior (see [#3355](https://github.com/huggingface/datasets/pull/3355)). Fix https://huggingface.co/datasets/bigbio/meqsum/discussions/1 Fix: - https://github.com/bigscience-workshop/biomedical/issues/801 Related to: - #3355	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5372/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5372/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5371	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5371/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5371/comments	https://api.github.com/repos/huggingface/datasets/issues/5371/events	https://github.com/huggingface/datasets/issues/5371	1,501,369,036	I_kwDODunzps5ZfRLM	5,371	Add a robustness benchmark dataset for vision	{ "login": "sayakpaul", "id": 22957388, "node_id": "MDQ6VXNlcjIyOTU3Mzg4", "avatar_url": "https://avatars.githubusercontent.com/u/22957388?v=4", "gravatar_id": "", "url": "https://api.github.com/users/sayakpaul", "html_url": "https://github.com/sayakpaul", "followers_url": "https://api.github.com/users/sayakpaul/followers", "following_url": "https://api.github.com/users/sayakpaul/following{/other_user}", "gists_url": "https://api.github.com/users/sayakpaul/gists{/gist_id}", "starred_url": "https://api.github.com/users/sayakpaul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sayakpaul/subscriptions", "organizations_url": "https://api.github.com/users/sayakpaul/orgs", "repos_url": "https://api.github.com/users/sayakpaul/repos", "events_url": "https://api.github.com/users/sayakpaul/events{/privacy}", "received_events_url": "https://api.github.com/users/sayakpaul/received_events", "type": "User", "site_admin": false }	[ { "id": 2067376369, "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request", "name": "dataset request", "color": "e99695", "default": false, "description": "Requesting to add a new dataset" } ]	open	false	{ "login": "sayakpaul", "id": 22957388, "node_id": "MDQ6VXNlcjIyOTU3Mzg4", "avatar_url": "https://avatars.githubusercontent.com/u/22957388?v=4", "gravatar_id": "", "url": "https://api.github.com/users/sayakpaul", "html_url": "https://github.com/sayakpaul", "followers_url": "https://api.github.com/users/sayakpaul/followers", "following_url": "https://api.github.com/users/sayakpaul/following{/other_user}", "gists_url": "https://api.github.com/users/sayakpaul/gists{/gist_id}", "starred_url": "https://api.github.com/users/sayakpaul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sayakpaul/subscriptions", "organizations_url": "https://api.github.com/users/sayakpaul/orgs", "repos_url": "https://api.github.com/users/sayakpaul/repos", "events_url": "https://api.github.com/users/sayakpaul/events{/privacy}", "received_events_url": "https://api.github.com/users/sayakpaul/received_events", "type": "User", "site_admin": false }	[ { "login": "sayakpaul", "id": 22957388, "node_id": "MDQ6VXNlcjIyOTU3Mzg4", "avatar_url": "https://avatars.githubusercontent.com/u/22957388?v=4", "gravatar_id": "", "url": "https://api.github.com/users/sayakpaul", "html_url": "https://github.com/sayakpaul", "followers_url": "https://api.github.com/users/sayakpaul/followers", "following_url": "https://api.github.com/users/sayakpaul/following{/other_user}", "gists_url": "https://api.github.com/users/sayakpaul/gists{/gist_id}", "starred_url": "https://api.github.com/users/sayakpaul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sayakpaul/subscriptions", "organizations_url": "https://api.github.com/users/sayakpaul/orgs", "repos_url": "https://api.github.com/users/sayakpaul/repos", "events_url": "https://api.github.com/users/sayakpaul/events{/privacy}", "received_events_url": "https://api.github.com/users/sayakpaul/received_events", "type": "User", "site_admin": false } ]	null	[ "Ccing @nazneenrajani @lvwerra @osanseviero " ]	2022-12-17T12:35:13	2022-12-20T06:21:41	null	MEMBER	null	null	null	### Name ImageNet-C ### Paper Benchmarking Neural Network Robustness to Common Corruptions and Perturbations ### Data https://github.com/hendrycks/robustness ### Motivation It's a known fact that vision models are brittle when they meet with slightly corrupted and perturbed data. This is also correlated to the robustness aspects of vision models. Researchers use different benchmark datasets to evaluate the robustness aspects of vision models. ImageNet-C is one of them. Having this dataset in 🤗 Datasets would allow researchers to evaluate and study the robustness aspects of vision models. Since the metric associated with these evaluations is top-1 accuracy, researchers should be able to easily take advantage of the evaluation benchmarks on the Hub and perform comprehensive reporting. ImageNet-C is a large dataset. Once it's in, it can act as a reference and we can also reach out to the authors of the other robustness benchmark datasets in vision, such as ObjectNet, WILDS, Metashift, etc. These datasets cater to different aspects. For example, ObjectNet is related to assessing how well a model performs under sub-population shifts. Related thread: https://huggingface.slack.com/archives/C036H4A5U8Z/p1669994598060499	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5371/reactions", "total_count": 2, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 2, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5371/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5369	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5369/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5369/comments	https://api.github.com/repos/huggingface/datasets/issues/5369/events	https://github.com/huggingface/datasets/pull/5369	1,500,622,276	PR_kwDODunzps5Fqaj-	5,369	Distributed support	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Alright all the tests are passing - this is ready for review", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.015146 / 0.011353 (0.003793) \| 0.006683 / 0.011008 (-0.004326) \| 0.125994 / 0.038508 (0.087486) \| 0.041345 / 0.023109 (0.018235) \| 0.378609 / 0.275898 (0.102711) \| 0.483139 / 0.323480 (0.159659) \| 0.009669 / 0.007986 (0.001684) \| 0.005143 / 0.004328 (0.000814) \| 0.092015 / 0.004250 (0.087765) \| 0.052728 / 0.037052 (0.015676) \| 0.397166 / 0.258489 (0.138677) \| 0.465820 / 0.293841 (0.171979) \| 0.051025 / 0.128546 (-0.077521) \| 0.018451 / 0.075646 (-0.057196) \| 0.397311 / 0.419271 (-0.021960) \| 0.054842 / 0.043533 (0.011309) \| 0.391203 / 0.255139 (0.136064) \| 0.412743 / 0.283200 (0.129543) \| 0.111356 / 0.141683 (-0.030327) \| 1.697526 / 1.452155 (0.245372) \| 1.795017 / 1.492716 (0.302301) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.253737 / 0.018006 (0.235731) \| 0.583071 / 0.000490 (0.582581) \| 0.005958 / 0.000200 (0.005758) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030397 / 0.037411 (-0.007014) \| 0.112242 / 0.014526 (0.097716) \| 0.138807 / 0.176557 (-0.037749) \| 0.209820 / 0.737135 (-0.527316) \| 0.139530 / 0.296338 (-0.156808) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.574111 / 0.215209 (0.358902) \| 5.623713 / 2.077655 (3.546058) \| 2.416880 / 1.504120 (0.912760) \| 1.951013 / 1.541195 (0.409819) \| 2.124565 / 1.468490 (0.656075) \| 1.268854 / 4.584777 (-3.315923) \| 5.942368 / 3.745712 (2.196656) \| 5.413814 / 5.269862 (0.143952) \| 2.931638 / 4.565676 (-1.634038) \| 0.135070 / 0.424275 (-0.289205) \| 0.014290 / 0.007607 (0.006683) \| 0.708384 / 0.226044 (0.482340) \| 7.487994 / 2.268929 (5.219065) \| 3.074210 / 55.444624 (-52.370414) \| 2.380583 / 6.876477 (-4.495893) \| 2.522298 / 2.142072 (0.380226) \| 1.336741 / 4.805227 (-3.468486) \| 0.236761 / 6.500664 (-6.263903) \| 0.076592 / 0.075469 (0.001123) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.629415 / 1.841788 (-0.212373) \| 19.000640 / 8.074308 (10.926332) \| 21.474058 / 10.191392 (11.282666) \| 0.231227 / 0.680424 (-0.449197) \| 0.046213 / 0.534201 (-0.487988) \| 0.565703 / 0.579283 (-0.013580) \| 0.662956 / 0.434364 (0.228592) \| 0.656475 / 0.540337 (0.116137) \| 0.762534 / 1.386936 (-0.624402) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010952 / 0.011353 (-0.000400) \| 0.006259 / 0.011008 (-0.004749) \| 0.132430 / 0.038508 (0.093922) \| 0.037920 / 0.023109 (0.014811) \| 0.483565 / 0.275898 (0.207667) \| 0.528190 / 0.323480 (0.204710) \| 0.008116 / 0.007986 (0.000130) \| 0.006768 / 0.004328 (0.002440) \| 0.100520 / 0.004250 (0.096270) \| 0.055208 / 0.037052 (0.018155) \| 0.484672 / 0.258489 (0.226183) \| 0.556937 / 0.293841 (0.263096) \| 0.057938 / 0.128546 (-0.070609) \| 0.020821 / 0.075646 (-0.054826) \| 0.430735 / 0.419271 (0.011464) \| 0.066317 / 0.043533 (0.022785) \| 0.496652 / 0.255139 (0.241513) \| 0.502004 / 0.283200 (0.218804) \| 0.125403 / 0.141683 (-0.016280) \| 1.833396 / 1.452155 (0.381241) \| 1.974517 / 1.492716 (0.481800) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269198 / 0.018006 (0.251191) \| 0.620314 / 0.000490 (0.619824) \| 0.000535 / 0.000200 (0.000335) \| 0.000083 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032373 / 0.037411 (-0.005039) \| 0.130043 / 0.014526 (0.115517) \| 0.146217 / 0.176557 (-0.030339) \| 0.200187 / 0.737135 (-0.536948) \| 0.152839 / 0.296338 (-0.143499) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.677478 / 0.215209 (0.462268) \| 6.678856 / 2.077655 (4.601201) \| 3.025870 / 1.504120 (1.521750) \| 2.678196 / 1.541195 (1.137001) \| 2.740640 / 1.468490 (1.272150) \| 1.237163 / 4.584777 (-3.347614) \| 5.752621 / 3.745712 (2.006908) \| 3.170435 / 5.269862 (-2.099427) \| 2.049174 / 4.565676 (-2.516502) \| 0.147663 / 0.424275 (-0.276612) \| 0.016107 / 0.007607 (0.008500) \| 0.849666 / 0.226044 (0.623621) \| 8.395212 / 2.268929 (6.126283) \| 3.741120 / 55.444624 (-51.703505) \| 3.102926 / 6.876477 (-3.773550) \| 3.233655 / 2.142072 (1.091583) \| 1.520349 / 4.805227 (-3.284878) \| 0.267159 / 6.500664 (-6.233505) \| 0.083646 / 0.075469 (0.008177) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.640458 / 1.841788 (-0.201330) \| 19.043169 / 8.074308 (10.968861) \| 22.786126 / 10.191392 (12.594734) \| 0.218040 / 0.680424 (-0.462384) \| 0.032948 / 0.534201 (-0.501253) \| 0.569574 / 0.579283 (-0.009710) \| 0.658746 / 0.434364 (0.224382) \| 0.650501 / 0.540337 (0.110164) \| 0.730588 / 1.386936 (-0.656348) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "just added a note :)", "Hi @lhoestq ,\r\nCan you please throw some light on the following statement\r\n`If the dataset has a number of shards that is a factor of world_size (i.e. if dataset.n_shards % world_size == 0), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of world_size, skipping the other examples.`\r\n\r\nLet's assume I have 127 parquet files and world_size is 4. I was not able to fully comprehend the above statement\r\nWhat does this statement mean?\r\n`each node keeps 1 example out of world_size, skipping the other examples.`\r\nThank you!", "If you have 128 parquet files, then `dataset.n_shards % world_size == 0`. In this case each worker can take care of 32 parquet files.\r\n\r\nOn the other hand if you have `dataset.n_shards % world_size != 0` (in your case 127 files), then we can't assign the same number of files to each worker. This is an issue because it may under-utilize your GPU at the end of your training since some workers will take longer to iterate on the dataset than others.\r\n\r\nTherefore in this case, all the workers take care of the 127 parquet files but workers will skip examples to not end up with duplicates. That's what \"each node keeps 1 example out of world_size, skipping the other examples\" means, and in your case it implies:\r\n- rank=0 will read the samples with idx=0, 4, 8 etc.\r\n- rank=1 will read the samples with idx=1, 5, 9 etc.\r\n- rank=2 will read the samples with idx=2, 6, 10 etc.\r\n- rank=3 will read the samples with idx=3, 7, 11 etc.", "Thanks a lot @lhoestq , this helps!", "Hi, in the case above, if we use `keep_in_memory=True` for `Dataset`, then we still need to read in n times the dataset if we use DDP on n GPUs (1 node), right? That means we need n times the memory. Is there any way to only load the data once, to save memory?", "`Dataset` objects are memory mapped from disk so they use almost no RAM (only the current batch)\r\n\r\nAlso they are perfectly sharded using `split_dataset_by_node` so it's going to be read exactly once in total using DDP.\r\nYou can also achieve the same thing using a DistributedSampler in pytorch for DDP instead of using `split_dataset_by_node`.", "Hi, please correct if I mistake anything: \r\n1. `Dataset` with `keep_in_memory=True` would explicitly pre-load the data into memory, instead of reading from disk via the memory map for every batch. The former way should be faster than the latter.\r\n2. When using DDP, before sending the `Dataset` object into `split_dataset_by_node` or incorporate it with `DistributedSampler`, every process still needs to pre-load the entire data into memory (when `keep_in_memory=True`) and then select the chunked indices from the loaded data. \r\n\r\nGenerally, the dilemma I'm facing is:\r\nSuppose we have a data around 120GB, and we want to use `DistributedLengthGroupedSampler` to optimize batching. When using DDP and `keep_in_memory=True`, every process loads 120GB which is not acceptable. For now, I turned off `keep_in_memory` and try to increase the number of workers for `DataLoader` to get better pipelining. \r\n\r\n*But is it possible to load 120GB once into 4 A100 (which has around 4120GB memory) and make each process read from this shared data from memory? Theoretically, maybe it should be faster?* ", "Feel free to ask your questions on the [forum](https://discuss.huggingface.co/c/datasets/10) if you don't mind, this way the discussions may be useful to other people ;) " ]	2022-12-16T17:43:47	2023-07-25T12:00:31	2023-01-16T13:33:32	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5369", "html_url": "https://github.com/huggingface/datasets/pull/5369", "diff_url": "https://github.com/huggingface/datasets/pull/5369.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5369.patch", "merged_at": "2023-01-16T13:33:32" }	To split your dataset across your training nodes, you can use the new [`datasets.distributed.split_dataset_by_node`]: ```python import os from datasets.distributed import split_dataset_by_node ds = split_dataset_by_node(ds, rank=int(os.environ["RANK"]), world_size=int(os.environ["WORLD_SIZE"])) ``` This works for both map-style datasets and iterable datasets. The dataset is split for the node at rank `rank` in a pool of nodes of size `world_size`. For map-style datasets: Each node is assigned a chunk of data, e.g. rank 0 is given the first chunk of the dataset. For iterable datasets: If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples. This can also be combined with a `torch.utils.data.DataLoader` if you want each node to use multiple workers to load the data. This also supports shuffling. At each epoch, the iterable dataset shards are reshuffled across all the nodes - you just have to call `iterable_ds.set_epoch(epoch_number)`. TODO: - [x] docs for usage in PyTorch - [x] unit tests - [x] integration tests with torch.distributed.launch Related to https://github.com/huggingface/transformers/issues/20770 Close https://github.com/huggingface/datasets/issues/5360	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5369/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5369/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5368	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5368/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5368/comments	https://api.github.com/repos/huggingface/datasets/issues/5368/events	https://github.com/huggingface/datasets/pull/5368	1,500,322,973	PR_kwDODunzps5FpZyx	5,368	Align remove columns behavior and input dict mutation in `map` with previous behavior	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-12-16T14:28:47	2022-12-16T16:28:08	2022-12-16T16:25:12	COLLABORATOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5368", "html_url": "https://github.com/huggingface/datasets/pull/5368", "diff_url": "https://github.com/huggingface/datasets/pull/5368.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5368.patch", "merged_at": "2022-12-16T16:25:12" }	Align the `remove_columns` behavior and input dict mutation in `map` with the behavior before https://github.com/huggingface/datasets/pull/5252.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5368/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5368/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5367	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5367/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5367/comments	https://api.github.com/repos/huggingface/datasets/issues/5367/events	https://github.com/huggingface/datasets/pull/5367	1,499,174,749	PR_kwDODunzps5FlevK	5,367	Fix remove columns from lazy dict	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-12-15T22:04:12	2022-12-15T22:27:53	2022-12-15T22:24:50	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5367", "html_url": "https://github.com/huggingface/datasets/pull/5367", "diff_url": "https://github.com/huggingface/datasets/pull/5367.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5367.patch", "merged_at": "2022-12-15T22:24:50" }	This was introduced in https://github.com/huggingface/datasets/pull/5252 and causing the transformers CI to break: https://app.circleci.com/pipelines/github/huggingface/transformers/53886/workflows/522faf2e-a053-454c-94f8-a617fde33393/jobs/648597 Basically this code should return a dataset with only one column: ```python from datasets import * ds = Dataset.from_dict({"a": range(5)}) def f(x): x["b"] = x["a"] return x ds = ds.map(f, remove_columns=["a"]) assert ds.column_names == ["b"] ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5367/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5367/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5366	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5366/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5366/comments	https://api.github.com/repos/huggingface/datasets/issues/5366/events	https://github.com/huggingface/datasets/pull/5366	1,498,530,851	PR_kwDODunzps5FjSFl	5,366	ExamplesIterable fixes	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-12-15T14:23:05	2022-12-15T14:44:47	2022-12-15T14:41:45	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5366", "html_url": "https://github.com/huggingface/datasets/pull/5366", "diff_url": "https://github.com/huggingface/datasets/pull/5366.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5366.patch", "merged_at": "2022-12-15T14:41:45" }	fix typing and ExamplesIterable.shard_data_sources	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5366/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5366/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5365	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5365/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5365/comments	https://api.github.com/repos/huggingface/datasets/issues/5365/events	https://github.com/huggingface/datasets/pull/5365	1,498,422,466	PR_kwDODunzps5Fi6ZD	5,365	fix: image array should support other formats than uint8	{ "login": "vigsterkr", "id": 30353, "node_id": "MDQ6VXNlcjMwMzUz", "avatar_url": "https://avatars.githubusercontent.com/u/30353?v=4", "gravatar_id": "", "url": "https://api.github.com/users/vigsterkr", "html_url": "https://github.com/vigsterkr", "followers_url": "https://api.github.com/users/vigsterkr/followers", "following_url": "https://api.github.com/users/vigsterkr/following{/other_user}", "gists_url": "https://api.github.com/users/vigsterkr/gists{/gist_id}", "starred_url": "https://api.github.com/users/vigsterkr/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vigsterkr/subscriptions", "organizations_url": "https://api.github.com/users/vigsterkr/orgs", "repos_url": "https://api.github.com/users/vigsterkr/repos", "events_url": "https://api.github.com/users/vigsterkr/events{/privacy}", "received_events_url": "https://api.github.com/users/vigsterkr/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi, thanks for working on this! \r\n\r\nI agree that the current type-casting (always cast to `np.uint8` as Tensorflow Datasets does) is a bit too harsh. However, not all dtypes are supported in `Image.fromarray` (e.g. np.int64), so we need to treat these with special care (e.g. downcast to the closest supported dtype, maybe with warnings to let the user know what's happening).\r\n\r\nPS: To avoid the CI failures, we need to handle two more instances of the cast to `np.uint8` (both are in the `image.py` file).", "I've made some changes to the PR.\r\n\r\nNow the encoding procedure behaves as follows:\r\n* for multi-channel arrays: if their dtype is `int`/`uint`, cast to np.uint8 (the only supported dtype for multi-channel arrays), throw an error otherwise\r\n* if the array dtype is of valid kind (\"u\", \"i\", \"f\", ...):\r\n * don't do anything if Pillow natively supports it\r\n * otherwise, downcast until it becomes compatible with Pillow\r\n* raise an error if nothing from above is true", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009537 / 0.011353 (-0.001816) \| 0.004946 / 0.011008 (-0.006062) \| 0.100552 / 0.038508 (0.062043) \| 0.035119 / 0.023109 (0.012009) \| 0.295989 / 0.275898 (0.020091) \| 0.361326 / 0.323480 (0.037846) \| 0.007608 / 0.007986 (-0.000378) \| 0.004151 / 0.004328 (-0.000177) \| 0.077301 / 0.004250 (0.073050) \| 0.042921 / 0.037052 (0.005869) \| 0.304804 / 0.258489 (0.046315) \| 0.345934 / 0.293841 (0.052093) \| 0.038987 / 0.128546 (-0.089559) \| 0.012055 / 0.075646 (-0.063591) \| 0.334035 / 0.419271 (-0.085236) \| 0.052679 / 0.043533 (0.009146) \| 0.291700 / 0.255139 (0.036561) \| 0.335423 / 0.283200 (0.052223) \| 0.107002 / 0.141683 (-0.034680) \| 1.516780 / 1.452155 (0.064625) \| 1.514137 / 1.492716 (0.021420) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.014719 / 0.018006 (-0.003287) \| 0.545251 / 0.000490 (0.544761) \| 0.004719 / 0.000200 (0.004519) \| 0.000275 / 0.000054 (0.000220) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026633 / 0.037411 (-0.010779) \| 0.106911 / 0.014526 (0.092385) \| 0.120258 / 0.176557 (-0.056299) \| 0.156196 / 0.737135 (-0.580940) \| 0.123132 / 0.296338 (-0.173207) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398018 / 0.215209 (0.182809) \| 3.973992 / 2.077655 (1.896337) \| 1.776436 / 1.504120 (0.272316) \| 1.579036 / 1.541195 (0.037841) \| 1.643345 / 1.468490 (0.174855) \| 0.692408 / 4.584777 (-3.892369) \| 3.757243 / 3.745712 (0.011531) \| 3.226212 / 5.269862 (-2.043649) \| 1.797845 / 4.565676 (-2.767831) \| 0.085878 / 0.424275 (-0.338398) \| 0.012451 / 0.007607 (0.004844) \| 0.509755 / 0.226044 (0.283711) \| 5.029035 / 2.268929 (2.760107) \| 2.255507 / 55.444624 (-53.189117) \| 1.892868 / 6.876477 (-4.983609) \| 1.900017 / 2.142072 (-0.242055) \| 0.853965 / 4.805227 (-3.951263) \| 0.167268 / 6.500664 (-6.333396) \| 0.062796 / 0.075469 (-0.012673) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.183361 / 1.841788 (-0.658427) \| 15.103797 / 8.074308 (7.029489) \| 14.112931 / 10.191392 (3.921539) \| 0.167234 / 0.680424 (-0.513190) \| 0.029487 / 0.534201 (-0.504713) \| 0.444121 / 0.579283 (-0.135162) \| 0.437821 / 0.434364 (0.003457) \| 0.544900 / 0.540337 (0.004562) \| 0.642142 / 1.386936 (-0.744794) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007078 / 0.011353 (-0.004275) \| 0.004983 / 0.011008 (-0.006026) \| 0.097106 / 0.038508 (0.058598) \| 0.033747 / 0.023109 (0.010637) \| 0.382030 / 0.275898 (0.106132) \| 0.410193 / 0.323480 (0.086713) \| 0.006658 / 0.007986 (-0.001327) \| 0.005358 / 0.004328 (0.001029) \| 0.073878 / 0.004250 (0.069628) \| 0.049292 / 0.037052 (0.012240) \| 0.384053 / 0.258489 (0.125564) \| 0.427826 / 0.293841 (0.133985) \| 0.036780 / 0.128546 (-0.091766) \| 0.012469 / 0.075646 (-0.063178) \| 0.332989 / 0.419271 (-0.086283) \| 0.059531 / 0.043533 (0.015998) \| 0.378431 / 0.255139 (0.123292) \| 0.402672 / 0.283200 (0.119473) \| 0.110782 / 0.141683 (-0.030901) \| 1.484570 / 1.452155 (0.032416) \| 1.608081 / 1.492716 (0.115365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232356 / 0.018006 (0.214350) \| 0.545648 / 0.000490 (0.545158) \| 0.003113 / 0.000200 (0.002913) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028138 / 0.037411 (-0.009273) \| 0.110786 / 0.014526 (0.096260) \| 0.123615 / 0.176557 (-0.052941) \| 0.165773 / 0.737135 (-0.571362) \| 0.126401 / 0.296338 (-0.169937) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440518 / 0.215209 (0.225309) \| 4.393821 / 2.077655 (2.316166) \| 2.295479 / 1.504120 (0.791359) \| 2.116679 / 1.541195 (0.575485) \| 2.215561 / 1.468490 (0.747071) \| 0.722343 / 4.584777 (-3.862434) \| 3.783360 / 3.745712 (0.037647) \| 3.302242 / 5.269862 (-1.967620) \| 1.681535 / 4.565676 (-2.884142) \| 0.085738 / 0.424275 (-0.338537) \| 0.012373 / 0.007607 (0.004766) \| 0.540499 / 0.226044 (0.314455) \| 5.384915 / 2.268929 (3.115986) \| 2.766346 / 55.444624 (-52.678279) \| 2.451994 / 6.876477 (-4.424483) \| 2.505720 / 2.142072 (0.363647) \| 0.833006 / 4.805227 (-3.972221) \| 0.168206 / 6.500664 (-6.332458) \| 0.064971 / 0.075469 (-0.010498) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253499 / 1.841788 (-0.588289) \| 15.381840 / 8.074308 (7.307532) \| 13.519493 / 10.191392 (3.328101) \| 0.165559 / 0.680424 (-0.514865) \| 0.017682 / 0.534201 (-0.516519) \| 0.422248 / 0.579283 (-0.157035) \| 0.422750 / 0.434364 (-0.011614) \| 0.524546 / 0.540337 (-0.015792) \| 0.626956 / 1.386936 (-0.759980) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d9a8d8af0961c473103516dd018e2d34d23cea02 \"CML watermark\")\n" ]	2022-12-15T13:17:50	2023-01-26T18:46:45	2023-01-26T18:39:36	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5365", "html_url": "https://github.com/huggingface/datasets/pull/5365", "diff_url": "https://github.com/huggingface/datasets/pull/5365.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5365.patch", "merged_at": "2023-01-26T18:39:36" }	Currently images that are provided as ndarrays, but not in `uint8` format are going to loose data. Namely, for example in a depth image where the data is in float32 format, the type-casting to uint8 will basically make the whole image blank. `PIL.Image.fromarray` [does support mode `F`](https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes). although maybe some further metadata could be supplied via the [Image](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/main_classes#datasets.Image) object.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5365/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5365/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5364	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5364/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5364/comments	https://api.github.com/repos/huggingface/datasets/issues/5364/events	https://github.com/huggingface/datasets/pull/5364	1,498,360,628	PR_kwDODunzps5Fiss1	5,364	Support for writing arrow files directly with BeamWriter	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5364). All of your documentation changes will be reflected on that endpoint.", "Deleting `BeamPipeline` and `upload_local_to_remote` would break the existing Beam scripts, so I reverted this change.\r\n\r\nFrom what I understand, we need these components in our scripts for the pattern:\r\n```python\r\nif not pipeline.is_local():\r\n dl_manager.ship_files_with_pipeline()\r\n```\r\n\r\nI plan to address this in a subsequent PR by (implicitly) downloading the files directly to the remote storage of the non-local runners.", "I got `AttributeError: 'Pipeline' object has no attribute 'is_local'` when running\r\n```python\r\nload_dataset(\"wikipedia\", language=\"af\", date=\"20230101\", beam_runner=\"DirectRunner\")\r\n```\r\n```python\r\n~/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py in _split_generators(self, dl_manager, pipeline)\r\n 965 # Use dictionary since testing mock always returns the same result.\r\n 966 downloaded_files = dl_manager.download({\"xml\": xml_urls})\r\n--> 967 if not pipeline.is_local():\r\n 968 downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline)\r\n 969 \r\n\r\nAttributeError: 'Pipeline' object has no attribute 'is_local'\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010649 / 0.011353 (-0.000704) \| 0.006116 / 0.011008 (-0.004892) \| 0.115568 / 0.038508 (0.077060) \| 0.041704 / 0.023109 (0.018595) \| 0.360459 / 0.275898 (0.084561) \| 0.425679 / 0.323480 (0.102200) \| 0.008992 / 0.007986 (0.001006) \| 0.006321 / 0.004328 (0.001993) \| 0.090223 / 0.004250 (0.085973) \| 0.049877 / 0.037052 (0.012824) \| 0.382447 / 0.258489 (0.123958) \| 0.406567 / 0.293841 (0.112726) \| 0.045138 / 0.128546 (-0.083409) \| 0.014203 / 0.075646 (-0.061444) \| 0.388897 / 0.419271 (-0.030375) \| 0.057176 / 0.043533 (0.013644) \| 0.358729 / 0.255139 (0.103590) \| 0.386086 / 0.283200 (0.102887) \| 0.119221 / 0.141683 (-0.022462) \| 1.731574 / 1.452155 (0.279419) \| 1.744103 / 1.492716 (0.251386) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230380 / 0.018006 (0.212373) \| 0.493690 / 0.000490 (0.493201) \| 0.005150 / 0.000200 (0.004950) \| 0.000097 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030771 / 0.037411 (-0.006641) \| 0.123196 / 0.014526 (0.108671) \| 0.134097 / 0.176557 (-0.042459) \| 0.190442 / 0.737135 (-0.546693) \| 0.138416 / 0.296338 (-0.157923) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.469763 / 0.215209 (0.254554) \| 4.682847 / 2.077655 (2.605192) \| 2.076717 / 1.504120 (0.572597) \| 1.843721 / 1.541195 (0.302527) \| 1.923486 / 1.468490 (0.454996) \| 0.817680 / 4.584777 (-3.767097) \| 4.482409 / 3.745712 (0.736697) \| 3.898695 / 5.269862 (-1.371167) \| 2.078291 / 4.565676 (-2.487386) \| 0.100285 / 0.424275 (-0.323990) \| 0.014761 / 0.007607 (0.007154) \| 0.611261 / 0.226044 (0.385217) \| 5.926919 / 2.268929 (3.657990) \| 2.685080 / 55.444624 (-52.759544) \| 2.232179 / 6.876477 (-4.644298) \| 2.305576 / 2.142072 (0.163504) \| 0.993729 / 4.805227 (-3.811498) \| 0.194491 / 6.500664 (-6.306173) \| 0.074176 / 0.075469 (-0.001293) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.388592 / 1.841788 (-0.453196) \| 17.146945 / 8.074308 (9.072636) \| 15.989570 / 10.191392 (5.798178) \| 0.200147 / 0.680424 (-0.480277) \| 0.034009 / 0.534201 (-0.500192) \| 0.517531 / 0.579283 (-0.061753) \| 0.533966 / 0.434364 (0.099602) \| 0.637024 / 0.540337 (0.096687) \| 0.749166 / 1.386936 (-0.637770) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008240 / 0.011353 (-0.003113) \| 0.006139 / 0.011008 (-0.004869) \| 0.112258 / 0.038508 (0.073750) \| 0.039001 / 0.023109 (0.015891) \| 0.449467 / 0.275898 (0.173569) \| 0.483422 / 0.323480 (0.159942) \| 0.006176 / 0.007986 (-0.001810) \| 0.006340 / 0.004328 (0.002012) \| 0.083105 / 0.004250 (0.078855) \| 0.047002 / 0.037052 (0.009950) \| 0.458564 / 0.258489 (0.200075) \| 0.513704 / 0.293841 (0.219863) \| 0.041359 / 0.128546 (-0.087188) \| 0.014515 / 0.075646 (-0.061131) \| 0.392599 / 0.419271 (-0.026673) \| 0.055222 / 0.043533 (0.011690) \| 0.446956 / 0.255139 (0.191817) \| 0.469194 / 0.283200 (0.185994) \| 0.118212 / 0.141683 (-0.023471) \| 1.682647 / 1.452155 (0.230492) \| 1.780076 / 1.492716 (0.287360) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.259124 / 0.018006 (0.241117) \| 0.507559 / 0.000490 (0.507069) \| 0.001080 / 0.000200 (0.000880) \| 0.000081 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031969 / 0.037411 (-0.005442) \| 0.126997 / 0.014526 (0.112471) \| 0.139593 / 0.176557 (-0.036963) \| 0.182735 / 0.737135 (-0.554400) \| 0.145871 / 0.296338 (-0.150468) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.530894 / 0.215209 (0.315685) \| 5.284979 / 2.077655 (3.207324) \| 2.592886 / 1.504120 (1.088766) \| 2.407202 / 1.541195 (0.866007) \| 2.434079 / 1.468490 (0.965589) \| 0.829382 / 4.584777 (-3.755395) \| 4.481710 / 3.745712 (0.735998) \| 3.912280 / 5.269862 (-1.357581) \| 1.962291 / 4.565676 (-2.603386) \| 0.101840 / 0.424275 (-0.322435) \| 0.014528 / 0.007607 (0.006921) \| 0.639956 / 0.226044 (0.413911) \| 6.414685 / 2.268929 (4.145756) \| 3.240290 / 55.444624 (-52.204334) \| 2.795208 / 6.876477 (-4.081269) \| 2.912122 / 2.142072 (0.770050) \| 0.992188 / 4.805227 (-3.813039) \| 0.200701 / 6.500664 (-6.299964) \| 0.074235 / 0.075469 (-0.001234) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.455075 / 1.841788 (-0.386712) \| 17.186669 / 8.074308 (9.112361) \| 15.404357 / 10.191392 (5.212965) \| 0.168267 / 0.680424 (-0.512157) \| 0.020774 / 0.534201 (-0.513427) \| 0.502603 / 0.579283 (-0.076680) \| 0.506500 / 0.434364 (0.072136) \| 0.624245 / 0.540337 (0.083907) \| 0.735529 / 1.386936 (-0.651407) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "I think we could close this PR.", "indeed, since Beam stuff are deprecated" ]	2022-12-15T12:38:05	2024-01-11T14:52:33	2024-01-11T14:45:15	COLLABORATOR	null	true	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5364", "html_url": "https://github.com/huggingface/datasets/pull/5364", "diff_url": "https://github.com/huggingface/datasets/pull/5364.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5364.patch", "merged_at": null }	Make it possible to write Arrow files directly with `BeamWriter` rather than converting from Parquet to Arrow, which is sub-optimal, especially for big datasets for which Beam is primarily used.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5364/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5364/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5363	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5363/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5363/comments	https://api.github.com/repos/huggingface/datasets/issues/5363/events	https://github.com/huggingface/datasets/issues/5363	1,498,171,317	I_kwDODunzps5ZTEe1	5,363	Dataset.from_generator() crashes on simple example	{ "login": "villmow", "id": 2743060, "node_id": "MDQ6VXNlcjI3NDMwNjA=", "avatar_url": "https://avatars.githubusercontent.com/u/2743060?v=4", "gravatar_id": "", "url": "https://api.github.com/users/villmow", "html_url": "https://github.com/villmow", "followers_url": "https://api.github.com/users/villmow/followers", "following_url": "https://api.github.com/users/villmow/following{/other_user}", "gists_url": "https://api.github.com/users/villmow/gists{/gist_id}", "starred_url": "https://api.github.com/users/villmow/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/villmow/subscriptions", "organizations_url": "https://api.github.com/users/villmow/orgs", "repos_url": "https://api.github.com/users/villmow/repos", "events_url": "https://api.github.com/users/villmow/events{/privacy}", "received_events_url": "https://api.github.com/users/villmow/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	2022-12-15T10:21:28	2022-12-15T11:51:33	2022-12-15T11:51:33	NONE	null	null	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5363/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5363/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5362	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5362/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5362/comments	https://api.github.com/repos/huggingface/datasets/issues/5362/events	https://github.com/huggingface/datasets/issues/5362	1,497,643,744	I_kwDODunzps5ZRDrg	5,362	Run 'GPT-J' failure due to download dataset fail (' ConnectionError: Couldn't reach http://eaidata.bmk.sh/data/enron_emails.jsonl.zst ' )	{ "login": "shaoyuta", "id": 52023469, "node_id": "MDQ6VXNlcjUyMDIzNDY5", "avatar_url": "https://avatars.githubusercontent.com/u/52023469?v=4", "gravatar_id": "", "url": "https://api.github.com/users/shaoyuta", "html_url": "https://github.com/shaoyuta", "followers_url": "https://api.github.com/users/shaoyuta/followers", "following_url": "https://api.github.com/users/shaoyuta/following{/other_user}", "gists_url": "https://api.github.com/users/shaoyuta/gists{/gist_id}", "starred_url": "https://api.github.com/users/shaoyuta/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/shaoyuta/subscriptions", "organizations_url": "https://api.github.com/users/shaoyuta/orgs", "repos_url": "https://api.github.com/users/shaoyuta/repos", "events_url": "https://api.github.com/users/shaoyuta/events{/privacy}", "received_events_url": "https://api.github.com/users/shaoyuta/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	null	[ "Thanks for reporting, @shaoyuta.\r\n\r\nWe have checked and yes, apparently there is an issue with the server hosting the data of the \"enron_emails\" subset of \"the_pile\" dataset: http://eaidata.bmk.sh/data/enron_emails.jsonl.zst\r\nIt seems to be down: The connection has timed out.\r\n\r\nPlease note that at the Hugging Face Hub, we are not hosting their data for this dataset, but only a script that downloads the data from their servers. We are updating the data URL to one in another server.\r\n\r\nIn the meantime, please note that you can train your model in the entire \"the_pile\" dataset, by passing the \"all\" config (instead of the \"enron_emails\" one).", "We have transferred this issue to the corresponding dataset Community tab: https://huggingface.co/datasets/the_pile/discussions/2\r\n\r\nPlease, follow the updates there." ]	2022-12-15T01:23:03	2022-12-15T07:45:54	2022-12-15T07:45:53	NONE	null	null	null	### Describe the bug Run model "GPT-J" with dataset "the_pile" fail. The fail out is as below: ![image](https://user-images.githubusercontent.com/52023469/207750127-118d9896-35f4-4ee9-90d4-d0ab9aae9c74.png) Looks like which is due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst" unreachable . ### Steps to reproduce the bug Steps to reproduce this issue: git clone https://github.com/huggingface/transformers cd transformers python examples/pytorch/language-modeling/run_clm.py --model_name_or_path EleutherAI/gpt-j-6B --dataset_name the_pile --dataset_config_name enron_emails --do_eval --output_dir /tmp/output --overwrite_output_dir ### Expected behavior This issue looks like due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst " couldn't be reached. Is there another way to download the dataset "the_pile" ? Is there another way to cache the dataset "the_pile" but not let the hg to download it when runtime ? ### Environment info huggingface_hub version: 0.11.1 Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35 Python version: 3.9.12 Running in iPython ?: No Running in notebook ?: No Running in Google Colab ?: No Token path ?: /home/taosy/.huggingface/token Has saved token ?: False Configured git credential helpers: FastAI: N/A Tensorflow: N/A Torch: N/A Jinja2: N/A Graphviz: N/A Pydot: N/A	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5362/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5362/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5361	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5361/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5361/comments	https://api.github.com/repos/huggingface/datasets/issues/5361/events	https://github.com/huggingface/datasets/issues/5361	1,497,153,889	I_kwDODunzps5ZPMFh	5,361	How concatenate `Audio` elements using batch mapping	{ "login": "bayartsogt-ya", "id": 43239645, "node_id": "MDQ6VXNlcjQzMjM5NjQ1", "avatar_url": "https://avatars.githubusercontent.com/u/43239645?v=4", "gravatar_id": "", "url": "https://api.github.com/users/bayartsogt-ya", "html_url": "https://github.com/bayartsogt-ya", "followers_url": "https://api.github.com/users/bayartsogt-ya/followers", "following_url": "https://api.github.com/users/bayartsogt-ya/following{/other_user}", "gists_url": "https://api.github.com/users/bayartsogt-ya/gists{/gist_id}", "starred_url": "https://api.github.com/users/bayartsogt-ya/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bayartsogt-ya/subscriptions", "organizations_url": "https://api.github.com/users/bayartsogt-ya/orgs", "repos_url": "https://api.github.com/users/bayartsogt-ya/repos", "events_url": "https://api.github.com/users/bayartsogt-ya/events{/privacy}", "received_events_url": "https://api.github.com/users/bayartsogt-ya/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "You can try something like this ?\r\n```python\r\ndef mapper_function(batch):\r\n return {\"concatenated_audio\": [np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]])]}\r\n\r\ndataset = dataset.map(\r\n mapper_function,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n```", "Thanks for the snippet!\r\n\r\nOne more question. I wonder why those two mappers are working so different that one taking 4 sec while other taking over 1 min :\r\n\r\n```python\r\n%%time\r\ndef mapper_function1(batch):\r\n # list_audio\r\n return {\r\n \"audio\": [\r\n {\r\n \"array\": np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]]),\r\n \"sampling_rate\": 16_000,\r\n }\r\n ]\r\n }\r\n\r\ndataset.map(\r\n mapper_function1,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n\r\n# 100%\r\n# 135/135 [01:13<00:00, 1.93ba/s]\r\n# CPU times: user 1min 10s, sys: 3.21 s, total: 1min 13s\r\n# Wall time: 1min 13s\r\n# Dataset({\r\n# features: ['audio'],\r\n# num_rows: 135\r\n# })\r\n\r\n# --------------------------------\r\n%%time\r\ndef mapper_function2(batch):\r\n # list_audio\r\n return {\"audio\": [np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]])]}\r\n\r\ndataset.map(\r\n mapper_function2,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n\r\n# 100%\r\n# 135/135 [00:03<00:00, 40.69ba/s]\r\n# CPU times: user 1.88 s, sys: 1.48 s, total: 3.36 s\r\n# Wall time: 4.8 s\r\n# Dataset({\r\n# features: ['audio'],\r\n# num_rows: 135\r\n# })\r\n```\r\n", "In the first one you get a dataset with an Audio type, and in the second one you get a dataset with a sequence of floats type.\r\n\r\nThe Audio type encodes the data as WAV to save disk space, so it takes more time to create.\r\nThe Audio type is automatically inferred because you modify the column \"audio\" which was already an Audio type. If you name it to something else, type inference will use a type struct with array and sampling rate fields." ]	2022-12-14T18:13:55	2023-07-21T14:30:51	2023-07-21T14:30:51	NONE	null	null	null	### Describe the bug I am trying to do concatenate audios in a dataset e.g. `google/fleurs`. ```python print(dataset) # Dataset({ # features: ['path', 'audio'], # num_rows: 24 # }) def mapper_function(batch): # to merge every 3 audio # np.concatnate(audios[i: i+3]) for i in range(i, len(batch), 3) dataset = dataset.map(mapper_function, batch=True, batch_size=24) print(dataset) # Expected output: # Dataset({ # features: ['path', 'audio'], # num_rows: 8 # }) ``` I tried to construct `result={}` dictionary inside the mapper function, I just found it will not work because it needs `byte` also needed :(( I'd appreciate if your share any use cases similar to my problem or any solutions really. Thanks! cc: @lhoestq ### Steps to reproduce the bug 1. load audio dataset 2. try to merge every k audios and return as one ### Expected behavior Merged dataset with a fewer rows. If we merge every 3 rows, then `n // 3` number of examples. ### Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 8.0.0 - Pandas version: 1.3.5	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5361/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5361/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5360	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5360/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5360/comments	https://api.github.com/repos/huggingface/datasets/issues/5360/events	https://github.com/huggingface/datasets/issues/5360	1,496,947,177	I_kwDODunzps5ZOZnp	5,360	IterableDataset returns duplicated data using PyTorch DDP	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false } ]	null	[ "If you use huggingface trainer, you will find the trainer has wrapped a `IterableDatasetShard` to avoid duplication.\r\nSee:\r\nhttps://github.com/huggingface/transformers/blob/dfd818420dcbad68e05a502495cf666d338b2bfb/src/transformers/trainer.py#L835\r\n", "If you want to support it by datasets natively, maybe we also need to change the code in `transformers` ?", "Opened https://github.com/huggingface/transformers/issues/20770 to discuss this :)", "Maybe something like this then ?\r\n```python\r\nfrom datasets.distributed import split_dataset_by_node\r\nds = split_dataset_by_node(ds, rank=rank, world_size=world_size)\r\n```\r\n\r\nFor map-style datasets the implementation is trivial (it can simply use `.shard()`).\r\n\r\nFor iterable datasets we would need to implement a new ExamplesIterable that would only iterate on a subset of the (possibly shuffled and re-shuffled after each epoch) list of shards, based on the rank and world size.", "My plan is to skip examples by default to not end up with duplicates.\r\n\r\nAnd if a dataset has a number of shards that is a factor of the world size, then I'd make it more optimized by distributing the shards evenly across nodes instead.", "Opened a PR here: https://github.com/huggingface/datasets/pull/5369\r\n\r\nfeel free to play with it and share your feedbacks :)", "@lhoestq I add shuffle after split_dataset_by_node, duplicated data still exist. \r\nFor example, we have a directory named `mock_pretraining_data`, which has three files, `part-00000`, `part-00002`,`part-00002`. \r\nText in `part-00000` is like this: \r\n{\"id\": 0}\r\n{\"id\": 1}\r\n{\"id\": 2}\r\n{\"id\": 3}\r\n{\"id\": 4}\r\n{\"id\": 5}\r\n{\"id\": 6}\r\n{\"id\": 7}\r\n{\"id\": 8}\r\n{\"id\": 9}\r\n\r\nand `part-00001`\r\n{\"id\": 10}\r\n{\"id\": 11}\r\n{\"id\": 12}\r\n{\"id\": 13}\r\n{\"id\": 14}\r\n{\"id\": 15}\r\n{\"id\": 16}\r\n{\"id\": 17}\r\n{\"id\": 18}\r\n{\"id\": 19}\r\n\r\nand `part-00002`\r\n{\"id\": 20}\r\n{\"id\": 21}\r\n{\"id\": 22}\r\n{\"id\": 23}\r\n{\"id\": 24}\r\n{\"id\": 25}\r\n{\"id\": 26}\r\n{\"id\": 27}\r\n{\"id\": 28}\r\n{\"id\": 29}\r\n\r\nAnd code in `test_dist.py` like this,\r\n```python\r\nimport torch\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom datasets import load_dataset\r\nimport os\r\nfrom transformers import AutoTokenizer, NezhaForPreTraining\r\nfrom transformers import AdamW, get_linear_schedule_with_warmup\r\nimport torch.nn.functional as F\r\nimport torch.nn as nn\r\nimport torch.distributed as dist\r\nfrom datasets.distributed import split_dataset_by_node\r\nfrom torch.nn.parallel import DistributedDataParallel as DDP\r\n\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = '5,6,7'\r\n\r\ndist.init_process_group(\"nccl\")\r\nlocal_rank = int(os.environ['LOCAL_RANK'])\r\nworld_size = torch.distributed.get_world_size()\r\ndevice = torch.device('cuda', local_rank)\r\ndata_dir = './'\r\n\r\ndef load_trainset(train_path):\r\n dataset = load_dataset('json', data_dir=os.path.join(data_dir, train_path), split='train', streaming=True)\r\n return dataset\r\n\r\ndef collate_fn(examples):\r\n input_ids = []\r\n for example in examples:\r\n input_ids.append(example['id'])\r\n return torch.LongTensor(input_ids).to(device)\r\n\r\n\r\ndataset = load_trainset('mock_pretraining_data')\r\ndataset = split_dataset_by_node(dataset, rank=local_rank, world_size=world_size).shuffle(buffer_size=512)\r\n# train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)\r\nbatch_size = 3\r\nprint('batch_size: {}'.format(batch_size))\r\ntrain_dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=collate_fn)\r\n\r\nfor x in train_dataloader:\r\n print({'rank': local_rank, 'id': x})\r\n```\r\nrun `python -m torch.distributed.launch --nproc_per_node=3 test_dist.py`\r\nThe output is\r\n```\r\n{'rank': 1, 'id': tensor([12, 15, 14], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([16, 10, 18], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([17, 13, 19], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([11], device='cuda:1')}\r\n{'rank': 0, 'id': tensor([0, 2, 9], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([4, 8, 1], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([5, 3, 6], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([7], device='cuda:0')}\r\n{'rank': 2, 'id': tensor([13, 15, 14], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([19, 17, 18], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([12, 16, 11], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([10], device='cuda:2')}\r\n```\r\n`part-00001` is loaded twice, `part-00002` isn't loaded.\r\n\r\nIf I run `python -m torch.distributed.launch --nproc_per_node=2 test_dist.py`\r\nThe output is weirder，many numbers appear twice\r\n```\r\n{'rank': 1, 'id': tensor([26, 8, 13], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([22, 19, 20], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([12, 28, 11], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([24, 2, 14], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([ 6, 27, 3], device='cuda:1')}\r\n{'rank': 0, 'id': tensor([ 8, 25, 1], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([20, 4, 12], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([14, 29, 5], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([ 7, 18, 23], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([19, 17, 11], device='cuda:0')}\r\n``` ", "Hi ! Thanks for reporting, you need to pass `seed=` to `shuffle()` or the processes won't use the same seed to shuffle the shards order before assigning each shard to a node.\r\n\r\nThe issue is that the workers are not using the same seed to shuffle the shards before splitting the shards list by node.", "Opened https://github.com/huggingface/datasets/issues/5696", "I have the same issue\r\n```\r\nds['train'] = load_dataset(streaming=True)\r\nds['train'] = split_dataset_by_node(ds['train'], rank=int(os.environ[\"RANK\"]), world_size=int(os.environ[\"WORLD_SIZE\"]))\r\nvectorized_datasets = ds.map(\r\n prepare_dataset,\r\n remove_columns=raw_datasets_features,\r\n).with_format(\"torch\")\r\n\r\nvectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].shuffle(\r\n buffer_size=500,\r\n seed=42,\r\n)\r\n\r\ndef prepare_dataset(batch):\r\n ....\r\n print(f\"sentence: {batch['sentence']}, target_text: {batch['target_text']}\")\r\n return batch\r\n```\r\nWhen using split_dataset_by_node(), the data being read is indeed different for each GPU ID.\r\n\r\n```\r\ntrainer = Trainer(\r\n model=model,\r\n data_collator=data_collator,\r\n args=training_args,\r\n compute_metrics=compute_metrics,\r\n train_dataset=vectorized_datasets[\"train\"] if training_args.do_train else None,\r\n eval_dataset=vectorized_datasets[\"eval\"] if training_args.do_eval else None,\r\n tokenizer=processor,\r\n callbacks=[ShuffleCallback()],\r\n )\r\n...\r\ntrain_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n```\r\nHowever, when I execute trainer.train(), the data being read is different from what I expected.\r\nBecause I print the batch value in prepare_dataset() , I observe that the data is the same for each GPU ID.\r\n\r\nHow should I handle this issue?\r\n\r\n\r\n", "There are two ways an iterable dataset can be split by node:\r\n1. if the number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU\r\n2. otherwise, each GPU iterate on the data and at the end keeps 1 sample out of n(GPUs) - skipping the others.\r\n\r\nIn case 2. it's therefore possible to have the same examples passed to `prepare_dataset` for each GPU.\r\n\r\nThis doesn't sound optimized though, because it runs the preprocessing on samples that won't be used in the end.\r\n\r\nCould you open a new issue so that we can discuss about this and find a solution ?" ]	2022-12-14T16:06:19	2023-06-15T09:51:13	2023-01-16T13:33:33	MEMBER	null	null	null	As mentioned in https://github.com/huggingface/datasets/issues/3423, when using PyTorch DDP the dataset ends up with duplicated data. We already check for the PyTorch `worker_info` for single node, but we should also check for `torch.distributed.get_world_size()` and `torch.distributed.get_rank()`	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5360/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5360/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5359	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5359/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5359/comments	https://api.github.com/repos/huggingface/datasets/issues/5359/events	https://github.com/huggingface/datasets/pull/5359	1,495,297,857	PR_kwDODunzps5FYHWm	5,359	Raise error if ClassLabel names is not python list	{ "login": "freddyheppell", "id": 1475568, "node_id": "MDQ6VXNlcjE0NzU1Njg=", "avatar_url": "https://avatars.githubusercontent.com/u/1475568?v=4", "gravatar_id": "", "url": "https://api.github.com/users/freddyheppell", "html_url": "https://github.com/freddyheppell", "followers_url": "https://api.github.com/users/freddyheppell/followers", "following_url": "https://api.github.com/users/freddyheppell/following{/other_user}", "gists_url": "https://api.github.com/users/freddyheppell/gists{/gist_id}", "starred_url": "https://api.github.com/users/freddyheppell/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/freddyheppell/subscriptions", "organizations_url": "https://api.github.com/users/freddyheppell/orgs", "repos_url": "https://api.github.com/users/freddyheppell/repos", "events_url": "https://api.github.com/users/freddyheppell/events{/privacy}", "received_events_url": "https://api.github.com/users/freddyheppell/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for your proposed fix, @freddyheppell.\r\n\r\nCurrently the CI fails because in a test we pass a `tuple` instead of a `list`. I would say we should accept `tuple` as a valid input type as well...\r\n\r\nWhat about checking for `Sequence` instead?", "Fixed that @albertvillanova, can you approve CI again please? Had some issues related to Pytorch .so files when running tests on my M1 mac, so wasn't able to test locally first. Have got them working on my desktop now though." ]	2022-12-13T23:04:06	2022-12-22T16:35:49	2022-12-22T16:32:49	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5359", "html_url": "https://github.com/huggingface/datasets/pull/5359", "diff_url": "https://github.com/huggingface/datasets/pull/5359.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5359.patch", "merged_at": "2022-12-22T16:32:49" }	Checks type of names provided to ClassLabel to avoid easy and hard to debug errors (closes #5332 - see for discussion)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5359/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5359/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5358	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5358/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5358/comments	https://api.github.com/repos/huggingface/datasets/issues/5358/events	https://github.com/huggingface/datasets/pull/5358	1,495,270,822	PR_kwDODunzps5FYBcq	5,358	Fix `fs.open` resource leaks	{ "login": "tkukurin", "id": 297847, "node_id": "MDQ6VXNlcjI5Nzg0Nw==", "avatar_url": "https://avatars.githubusercontent.com/u/297847?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tkukurin", "html_url": "https://github.com/tkukurin", "followers_url": "https://api.github.com/users/tkukurin/followers", "following_url": "https://api.github.com/users/tkukurin/following{/other_user}", "gists_url": "https://api.github.com/users/tkukurin/gists{/gist_id}", "starred_url": "https://api.github.com/users/tkukurin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/tkukurin/subscriptions", "organizations_url": "https://api.github.com/users/tkukurin/orgs", "repos_url": "https://api.github.com/users/tkukurin/repos", "events_url": "https://api.github.com/users/tkukurin/events{/privacy}", "received_events_url": "https://api.github.com/users/tkukurin/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "@mariosasko Sorry, I didn't check tests/style after doing a merge from the Git UI last week. Thx for fixing. \r\n\r\nFYI I'm getting \"Only those with [write access](https://docs.github.com/articles/what-are-the-different-access-permissions) to this repository can merge pull requests.\" so it seems somebody else needs to merge this.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008816 / 0.011353 (-0.002536) \| 0.004691 / 0.011008 (-0.006317) \| 0.100039 / 0.038508 (0.061531) \| 0.035422 / 0.023109 (0.012313) \| 0.312600 / 0.275898 (0.036702) \| 0.378684 / 0.323480 (0.055204) \| 0.007593 / 0.007986 (-0.000392) \| 0.005183 / 0.004328 (0.000855) \| 0.078040 / 0.004250 (0.073790) \| 0.041845 / 0.037052 (0.004793) \| 0.325251 / 0.258489 (0.066762) \| 0.363459 / 0.293841 (0.069618) \| 0.038006 / 0.128546 (-0.090540) \| 0.011911 / 0.075646 (-0.063735) \| 0.335020 / 0.419271 (-0.084251) \| 0.048765 / 0.043533 (0.005233) \| 0.305913 / 0.255139 (0.050774) \| 0.337620 / 0.283200 (0.054420) \| 0.101867 / 0.141683 (-0.039816) \| 1.450091 / 1.452155 (-0.002064) \| 1.437303 / 1.492716 (-0.055413) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225650 / 0.018006 (0.207644) \| 0.492480 / 0.000490 (0.491990) \| 0.002857 / 0.000200 (0.002658) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026231 / 0.037411 (-0.011180) \| 0.105479 / 0.014526 (0.090953) \| 0.118438 / 0.176557 (-0.058119) \| 0.167313 / 0.737135 (-0.569822) \| 0.119416 / 0.296338 (-0.176923) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396233 / 0.215209 (0.181024) \| 3.943325 / 2.077655 (1.865671) \| 1.778864 / 1.504120 (0.274744) \| 1.587957 / 1.541195 (0.046763) \| 1.615404 / 1.468490 (0.146914) \| 0.709427 / 4.584777 (-3.875350) \| 3.823310 / 3.745712 (0.077598) \| 3.461376 / 5.269862 (-1.808486) \| 1.888330 / 4.565676 (-2.677346) \| 0.086910 / 0.424275 (-0.337365) \| 0.012215 / 0.007607 (0.004608) \| 0.504877 / 0.226044 (0.278833) \| 5.051513 / 2.268929 (2.782584) \| 2.249389 / 55.444624 (-53.195235) \| 1.890949 / 6.876477 (-4.985528) \| 2.015584 / 2.142072 (-0.126489) \| 0.862313 / 4.805227 (-3.942914) \| 0.166295 / 6.500664 (-6.334369) \| 0.061131 / 0.075469 (-0.014338) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.201804 / 1.841788 (-0.639984) \| 14.589425 / 8.074308 (6.515117) \| 13.855522 / 10.191392 (3.664130) \| 0.193406 / 0.680424 (-0.487018) \| 0.028614 / 0.534201 (-0.505587) \| 0.439857 / 0.579283 (-0.139426) \| 0.443330 / 0.434364 (0.008966) \| 0.514078 / 0.540337 (-0.026259) \| 0.608245 / 1.386936 (-0.778691) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007087 / 0.011353 (-0.004265) \| 0.005024 / 0.011008 (-0.005985) \| 0.096852 / 0.038508 (0.058344) \| 0.032870 / 0.023109 (0.009761) \| 0.397790 / 0.275898 (0.121892) \| 0.420717 / 0.323480 (0.097237) \| 0.005552 / 0.007986 (-0.002434) \| 0.003742 / 0.004328 (-0.000586) \| 0.074788 / 0.004250 (0.070537) \| 0.048030 / 0.037052 (0.010977) \| 0.398520 / 0.258489 (0.140031) \| 0.460919 / 0.293841 (0.167078) \| 0.037652 / 0.128546 (-0.090894) \| 0.012249 / 0.075646 (-0.063397) \| 0.333077 / 0.419271 (-0.086194) \| 0.052364 / 0.043533 (0.008831) \| 0.394358 / 0.255139 (0.139219) \| 0.414193 / 0.283200 (0.130994) \| 0.103569 / 0.141683 (-0.038114) \| 1.499208 / 1.452155 (0.047053) \| 1.619481 / 1.492716 (0.126764) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229476 / 0.018006 (0.211470) \| 0.448670 / 0.000490 (0.448180) \| 0.000399 / 0.000200 (0.000199) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027550 / 0.037411 (-0.009862) \| 0.109180 / 0.014526 (0.094654) \| 0.118372 / 0.176557 (-0.058185) \| 0.153136 / 0.737135 (-0.583999) \| 0.122689 / 0.296338 (-0.173650) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445163 / 0.215209 (0.229954) \| 4.426350 / 2.077655 (2.348695) \| 2.194902 / 1.504120 (0.690782) \| 2.019049 / 1.541195 (0.477854) \| 2.032795 / 1.468490 (0.564305) \| 0.700752 / 4.584777 (-3.884025) \| 3.797616 / 3.745712 (0.051903) \| 2.046414 / 5.269862 (-3.223447) \| 1.345037 / 4.565676 (-3.220639) \| 0.085389 / 0.424275 (-0.338886) \| 0.012824 / 0.007607 (0.005217) \| 0.553875 / 0.226044 (0.327831) \| 5.550252 / 2.268929 (3.281323) \| 2.702822 / 55.444624 (-52.741803) \| 2.346257 / 6.876477 (-4.530220) \| 2.410772 / 2.142072 (0.268699) \| 0.848271 / 4.805227 (-3.956957) \| 0.170787 / 6.500664 (-6.329877) \| 0.064344 / 0.075469 (-0.011125) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.266222 / 1.841788 (-0.575566) \| 14.501194 / 8.074308 (6.426886) \| 13.413678 / 10.191392 (3.222286) \| 0.589048 / 0.680424 (-0.091375) \| 0.018246 / 0.534201 (-0.515955) \| 0.425221 / 0.579283 (-0.154062) \| 0.425900 / 0.434364 (-0.008464) \| 0.494023 / 0.540337 (-0.046314) \| 0.604324 / 1.386936 (-0.782612) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]	2022-12-13T22:35:51	2023-01-05T16:46:31	2023-01-05T15:59:51	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5358", "html_url": "https://github.com/huggingface/datasets/pull/5358", "diff_url": "https://github.com/huggingface/datasets/pull/5358.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5358.patch", "merged_at": "2023-01-05T15:59:51" }	Invoking `{load,save}_from_dict` results in resource leak warnings, this should fix. Introduces no significant logic changes.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5358/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5358/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5357	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5357/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5357/comments	https://api.github.com/repos/huggingface/datasets/issues/5357/events	https://github.com/huggingface/datasets/pull/5357	1,495,029,602	PR_kwDODunzps5FXNyR	5,357	Support torch dataloader without torch formatting	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Need some more time to fix the tests, especially with pickle", "> And I actually don't quite understand the idea - what's the motivation behind making only IterableDataset compatible with torch DataLoader without setting the format explicitly?\r\n\r\nSetting the format to pytorch = set the output types of the dataset to be pytorch tensors. However sometimes your dataset is not made of tensors but you still want to be able to use a pytorch DataLoader", "A bit more context. \r\n\r\nThe arrow-backed `Dataset` supports `DataLoader(ds)` (even if the format is not \"torch\"), and we want to be able to do the same with `IterableDataset` for consistency. However, this is when the PyTorch internals come into play - an iterable dataset needs to be an instance of `torch.utils.data.IterableDataset` due to [this](https://github.com/pytorch/pytorch/blob/abc54f93145830b502400faa92bec86e05422fbd/torch/utils/data/dataloader.py#L276) check (notice there is no check for the map-style version). Hence the explicit subclassing in this PR.", "Exactly :) Btw I just took your comments into account @polinaeterna , so feel free to review again", "@lhoestq just checking, does this change still preserve the fix to the \"data duplicate when setting num_works > 1 with streaming data\" issue from before?\r\n\r\nhttps://github.com/huggingface/datasets/issues/3423", "Yes :)" ]	2022-12-13T19:39:24	2023-01-04T12:45:40	2022-12-15T19:15:54	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5357", "html_url": "https://github.com/huggingface/datasets/pull/5357", "diff_url": "https://github.com/huggingface/datasets/pull/5357.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5357.patch", "merged_at": "2022-12-15T19:15:54" }	In https://github.com/huggingface/datasets/pull/5084 we make the torch formatting consistent with the map-style datasets formatting: a torch formatted iterable dataset will yield torch tensors. The previous behavior of the torch formatting for iterable dataset was simply to make the iterable dataset inherit from `torch.utils.data.Dataset` to make it work in a torch DataLoader. However ideally an unformatted dataset should also work with a DataLoader. To fix that, `datasets.IterableDataset` should inherit from `torch.utils.data.IterableDataset`. Since we don't want to import torch on startup, I created this PR to dynamically make the `datasets.IterableDataset` class inherit form the torch one when a `datasets.IterableDataset` is instantiated and if PyTorch is available. ```python >>> from datasets import load_dataset >>> ds = load_dataset("c4", "en", streaming=True, split="train") >>> import torch.utils.data >>> isinstance(ds, torch.utils.data.IterableDataset) True >>> dataloader = torch.utils.data.DataLoader(ds, batch_size=32, num_workers=4) >>> for example in dataloader: ...: ... ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5357/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5357/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5356	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5356/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5356/comments	https://api.github.com/repos/huggingface/datasets/issues/5356/events	https://github.com/huggingface/datasets/pull/5356	1,494,961,609	PR_kwDODunzps5FW-c9	5,356	Clean filesystem and logging docstrings	{ "login": "stevhliu", "id": 59462357, "node_id": "MDQ6VXNlcjU5NDYyMzU3", "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "gravatar_id": "", "url": "https://api.github.com/users/stevhliu", "html_url": "https://github.com/stevhliu", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "repos_url": "https://api.github.com/users/stevhliu/repos", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-12-13T18:54:09	2022-12-14T17:25:58	2022-12-14T17:22:16	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5356", "html_url": "https://github.com/huggingface/datasets/pull/5356", "diff_url": "https://github.com/huggingface/datasets/pull/5356.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5356.patch", "merged_at": "2022-12-14T17:22:16" }	This PR cleans the `Filesystems` and `Logging` docstrings.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5356/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5356/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5355	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5355/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5355/comments	https://api.github.com/repos/huggingface/datasets/issues/5355/events	https://github.com/huggingface/datasets/pull/5355	1,493,076,860	PR_kwDODunzps5FQcYG	5,355	Clean up Table class docstrings	{ "login": "stevhliu", "id": 59462357, "node_id": "MDQ6VXNlcjU5NDYyMzU3", "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "gravatar_id": "", "url": "https://api.github.com/users/stevhliu", "html_url": "https://github.com/stevhliu", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "repos_url": "https://api.github.com/users/stevhliu/repos", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-12-13T00:29:47	2022-12-13T18:17:56	2022-12-13T18:14:42	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5355", "html_url": "https://github.com/huggingface/datasets/pull/5355", "diff_url": "https://github.com/huggingface/datasets/pull/5355.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5355.patch", "merged_at": "2022-12-13T18:14:42" }	This PR cleans up the `Table` class docstrings :)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5355/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5355/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5354	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5354/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5354/comments	https://api.github.com/repos/huggingface/datasets/issues/5354/events	https://github.com/huggingface/datasets/issues/5354	1,492,174,125	I_kwDODunzps5Y8MUt	5,354	Consider using "Sequence" instead of "List"	{ "login": "tranhd95", "id": 15568078, "node_id": "MDQ6VXNlcjE1NTY4MDc4", "avatar_url": "https://avatars.githubusercontent.com/u/15568078?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tranhd95", "html_url": "https://github.com/tranhd95", "followers_url": "https://api.github.com/users/tranhd95/followers", "following_url": "https://api.github.com/users/tranhd95/following{/other_user}", "gists_url": "https://api.github.com/users/tranhd95/gists{/gist_id}", "starred_url": "https://api.github.com/users/tranhd95/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/tranhd95/subscriptions", "organizations_url": "https://api.github.com/users/tranhd95/orgs", "repos_url": "https://api.github.com/users/tranhd95/repos", "events_url": "https://api.github.com/users/tranhd95/events{/privacy}", "received_events_url": "https://api.github.com/users/tranhd95/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" }, { "id": 1935892877, "node_id": "MDU6TGFiZWwxOTM1ODkyODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue", "name": "good first issue", "color": "7057ff", "default": true, "description": "Good for newcomers" } ]	open	false	{ "login": "avinashsai", "id": 22453634, "node_id": "MDQ6VXNlcjIyNDUzNjM0", "avatar_url": "https://avatars.githubusercontent.com/u/22453634?v=4", "gravatar_id": "", "url": "https://api.github.com/users/avinashsai", "html_url": "https://github.com/avinashsai", "followers_url": "https://api.github.com/users/avinashsai/followers", "following_url": "https://api.github.com/users/avinashsai/following{/other_user}", "gists_url": "https://api.github.com/users/avinashsai/gists{/gist_id}", "starred_url": "https://api.github.com/users/avinashsai/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/avinashsai/subscriptions", "organizations_url": "https://api.github.com/users/avinashsai/orgs", "repos_url": "https://api.github.com/users/avinashsai/repos", "events_url": "https://api.github.com/users/avinashsai/events{/privacy}", "received_events_url": "https://api.github.com/users/avinashsai/received_events", "type": "User", "site_admin": false }	[ { "login": "avinashsai", "id": 22453634, "node_id": "MDQ6VXNlcjIyNDUzNjM0", "avatar_url": "https://avatars.githubusercontent.com/u/22453634?v=4", "gravatar_id": "", "url": "https://api.github.com/users/avinashsai", "html_url": "https://github.com/avinashsai", "followers_url": "https://api.github.com/users/avinashsai/followers", "following_url": "https://api.github.com/users/avinashsai/following{/other_user}", "gists_url": "https://api.github.com/users/avinashsai/gists{/gist_id}", "starred_url": "https://api.github.com/users/avinashsai/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/avinashsai/subscriptions", "organizations_url": "https://api.github.com/users/avinashsai/orgs", "repos_url": "https://api.github.com/users/avinashsai/repos", "events_url": "https://api.github.com/users/avinashsai/events{/privacy}", "received_events_url": "https://api.github.com/users/avinashsai/received_events", "type": "User", "site_admin": false } ]	null	[ "Hi! Linking a comment to provide more info on the issue: https://stackoverflow.com/a/39458225. This means we should replace all (most of) the occurrences of `List` with `Sequence` in function signatures.\r\n\r\n@tranhd95 Would you be interested in submitting a PR?", "Hi all! I tried to reproduce this issue and didn't work for me. Also in your example i noticed that the variables have different names: `list_of_filenames` and `list_of_files`, could this be related to that?\r\n```python\r\n#I found random data in parquet format:\r\n!wget \"https://github.com/Teradata/kylo/raw/master/samples/sample-data/parquet/userdata1.parquet\"\r\n!wget \"https://github.com/Teradata/kylo/raw/master/samples/sample-data/parquet/userdata2.parquet\"\r\n\r\n#Then i try reproduce\r\nlist_of_files = [\"userdata1.parquet\", \"userdata2.parquet\"]\r\nds = Dataset.from_parquet(list_of_files)\r\n```\r\nMy output:\r\n```python\r\nWARNING:datasets.builder:Using custom data configuration default-e287d097dc54e046\r\nDownloading and preparing dataset parquet/default to /root/.cache/huggingface/datasets/parquet/default-e287d097dc54e046/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\r\nDownloading data files: 100%\r\n1/1 [00:00<00:00, 40.38it/s]\r\nExtracting data files: 100%\r\n1/1 [00:00<00:00, 23.43it/s]\r\nDataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/parquet/default-e287d097dc54e046/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\r\n```\r\nP.S. This is my first experience with open source. So do not judge strictly if I do not understand something)", "@dantema There is indeed a typo in variable names. Nevertheless, I'm sorry if I was not clear but the output is from `mypy` type checker. You can run the code snippet without issues. The problem is with the type checking.", "However, I found out that the type annotation is actually misleading. The [`from_parquet`](https://github.com/huggingface/datasets/blob/5ef1ab1cc06c2b7a574bf2df454cd9fcb071ccb2/src/datasets/arrow_dataset.py#L1039) method should also accept list of [`PathLike`](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/typing.py#L8) objects which includes [`os.PathLike`](https://docs.python.org/3/library/os.html#os.PathLike). But if I would ran the code snippet below, an exception is thrown.\r\n\r\nCode\r\n```py\r\nfrom pathlib import Path\r\n\r\nlist_of_filenames = [Path(\"foo.parquet\"), Path(\"bar.parquet\")]\r\nds = Dataset.from_parquet(list_of_filenames)\r\n```\r\nOutput\r\n```py\r\n[/usr/local/lib/python3.8/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in from_parquet(path_or_paths, split, features, cache_dir, keep_in_memory, columns, kwargs)\r\n 1071 from .io.parquet import ParquetDatasetReader\r\n 1072 \r\n-> 1073 return ParquetDatasetReader(\r\n 1074 path_or_paths,\r\n 1075 split=split,\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/io/parquet.py](https://localhost:8080/#) in __init__(self, path_or_paths, split, features, cache_dir, keep_in_memory, streaming, kwargs)\r\n 35 path_or_paths = path_or_paths if isinstance(path_or_paths, dict) else {self.split: path_or_paths}\r\n 36 hash = _PACKAGED_DATASETS_MODULES[\"parquet\"][1]\r\n---> 37 self.builder = Parquet(\r\n 38 cache_dir=cache_dir,\r\n 39 data_files=path_or_paths,\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in __init__(self, cache_dir, config_name, hash, base_path, info, features, use_auth_token, repo_id, data_files, data_dir, name, *config_kwargs)\r\n 298 \r\n 299 if data_files is not None and not isinstance(data_files, DataFilesDict):\r\n--> 300 data_files = DataFilesDict.from_local_or_remote(\r\n 301 sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token\r\n 302 )\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/data_files.py](https://localhost:8080/#) in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n 794 for key, patterns_for_key in patterns.items():\r\n 795 out[key] = (\r\n--> 796 DataFilesList.from_local_or_remote(\r\n 797 patterns_for_key,\r\n 798 base_path=base_path,\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/data_files.py](https://localhost:8080/#) in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n 762 ) -> \"DataFilesList\":\r\n 763 base_path = base_path if base_path is not None else str(Path().resolve())\r\n--> 764 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n 765 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)\r\n 766 return cls(data_files, origin_metadata)\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/data_files.py](https://localhost:8080/#) in resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n 357 data_files = []\r\n 358 for pattern in patterns:\r\n--> 359 if is_remote_url(pattern):\r\n 360 data_files.append(Url(pattern))\r\n 361 else:\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py](https://localhost:8080/#) in is_remote_url(url_or_filename)\r\n 62 \r\n 63 def is_remote_url(url_or_filename: str) -> bool:\r\n---> 64 parsed = urlparse(url_or_filename)\r\n 65 return parsed.scheme in (\"http\", \"https\", \"s3\", \"gs\", \"hdfs\", \"ftp\")\r\n 66 \r\n\r\n[/usr/lib/python3.8/urllib/parse.py](https://localhost:8080/#) in urlparse(url, scheme, allow_fragments)\r\n 373 Note that we don't break the components up in smaller bits\r\n 374 (e.g. netloc is a single string) and we don't expand % escapes.\"\"\"\r\n--> 375 url, scheme, _coerce_result = _coerce_args(url, scheme)\r\n 376 splitresult = urlsplit(url, scheme, allow_fragments)\r\n 377 scheme, netloc, url, query, fragment = splitresult\r\n\r\n[/usr/lib/python3.8/urllib/parse.py](https://localhost:8080/#) in _coerce_args(args)\r\n 125 if str_input:\r\n 126 return args + (_noop,)\r\n--> 127 return _decode_args(args) + (_encode_result,)\r\n 128 \r\n 129 # Result objects are more helpful than simple tuples\r\n\r\n[/usr/lib/python3.8/urllib/parse.py](https://localhost:8080/#) in _decode_args(args, encoding, errors)\r\n 109 def _decode_args(args, encoding=_implicit_encoding,\r\n 110 errors=_implicit_errors):\r\n--> 111 return tuple(x.decode(encoding, errors) if x else '' for x in args)\r\n 112 \r\n 113 def _coerce_args(args):\r\n\r\n[/usr/lib/python3.8/urllib/parse.py](https://localhost:8080/#) in <genexpr>(.0)\r\n 109 def _decode_args(args, encoding=_implicit_encoding,\r\n 110 errors=_implicit_errors):\r\n--> 111 return tuple(x.decode(encoding, errors) if x else '' for x in args)\r\n 112 \r\n 113 def _coerce_args(args):\r\n\r\nAttributeError: 'PosixPath' object has no attribute 'decode'\r\n```\r\n\r\n@mariosasko Should I create a new issue? ", "@mariosasko I would like to take this issue up. ", "@avinashsai Hi, I've assigned you the issue.\r\n\r\n@tranhd95 Yes, feel free to report this in a new issue.", "@avinashsai Are you still working on this? If not I would like to give it a try.", "@mariosasko I would like to take this issue up!", "Hi @tranhd95 @mariosasko ,I hope you all are doing well.\r\n\r\nI am interested in this issue, is this still open and unresolved ?\r\n\r\nThanks and Regards" ]	2022-12-12T15:39:45	2024-01-20T19:57:17	null	NONE	null	null	null	### Feature request Hi, please consider using `Sequence` type annotation instead of `List` in function arguments such as in [`Dataset.from_parquet()`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L1088). It leads to type checking errors, see below. How to reproduce ```py list_of_filenames = ["foo.parquet", "bar.parquet"] ds = Dataset.from_parquet(list_of_filenames) ``` Expected mypy output: ``` Success: no issues found ``` Actual mypy output: ```py test.py:19: error: Argument 1 to "from_parquet" of "Dataset" has incompatible type "List[str]"; expected "Union[Union[str, bytes, PathLike[Any]], List[Union[str, bytes, PathLike[Any]]]]" [arg-type] test.py:19: note: "List" is invariant -- see https://mypy.readthedocs.io/en/stable/common_issues.html#variance test.py:19: note: Consider using "Sequence" instead, which is covariant ``` Env: mypy 0.991, Python 3.10.0, datasets 2.7.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5354/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5354/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5353	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5353/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5353/comments	https://api.github.com/repos/huggingface/datasets/issues/5353/events	https://github.com/huggingface/datasets/issues/5353	1,491,880,500	I_kwDODunzps5Y7Eo0	5,353	Support remote file systems for `Audio`	{ "login": "OllieBroadhurst", "id": 46894149, "node_id": "MDQ6VXNlcjQ2ODk0MTQ5", "avatar_url": "https://avatars.githubusercontent.com/u/46894149?v=4", "gravatar_id": "", "url": "https://api.github.com/users/OllieBroadhurst", "html_url": "https://github.com/OllieBroadhurst", "followers_url": "https://api.github.com/users/OllieBroadhurst/followers", "following_url": "https://api.github.com/users/OllieBroadhurst/following{/other_user}", "gists_url": "https://api.github.com/users/OllieBroadhurst/gists{/gist_id}", "starred_url": "https://api.github.com/users/OllieBroadhurst/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/OllieBroadhurst/subscriptions", "organizations_url": "https://api.github.com/users/OllieBroadhurst/orgs", "repos_url": "https://api.github.com/users/OllieBroadhurst/repos", "events_url": "https://api.github.com/users/OllieBroadhurst/events{/privacy}", "received_events_url": "https://api.github.com/users/OllieBroadhurst/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	closed	false	null	[]	null	[ "Just seen https://github.com/huggingface/datasets/issues/5281" ]	2022-12-12T13:22:13	2022-12-12T13:37:14	2022-12-12T13:37:14	NONE	null	null	null	### Feature request Hi there! It would be super cool if `Audio()`, and potentially other features, could read files from a remote file system. ### Motivation Large amounts of data is often stored in buckets. `load_from_disk` is able to retrieve data from cloud storage but to my knowledge actually copies the datasets across first, so if you're working off a system with smaller disk specs (like a VM), you can run out of space very quickly. ### Your contribution Something like this (for Google Cloud Platform in this instance): ```python from datasets import Dataset, Audio import gcsfs fs = gcsfs.GCSFileSystem() list_of_audio_fp = {'audio': ['1', '2', '3']} ds = Dataset.from_dict(list_of_audio_fp) ds = ds.cast_column("audio", Audio(sampling_rate=16000, fs=fs)) ``` Under the hood: ```python import librosa from io import BytesIO def load_audio(fp, sampling_rate=None, fs=None): if fs is not None: with fs.open(fp, 'rb') as f: arr, sr = librosa.load(BytesIO(f), sr=sampling_rate) else: # Perform existing io operations ``` Written from memory so some things could be wrong.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5353/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5353/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5352	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5352/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5352/comments	https://api.github.com/repos/huggingface/datasets/issues/5352/events	https://github.com/huggingface/datasets/issues/5352	1,490,796,414	I_kwDODunzps5Y279-	5,352	__init__() got an unexpected keyword argument 'input_size'	{ "login": "J-shel", "id": 82662111, "node_id": "MDQ6VXNlcjgyNjYyMTEx", "avatar_url": "https://avatars.githubusercontent.com/u/82662111?v=4", "gravatar_id": "", "url": "https://api.github.com/users/J-shel", "html_url": "https://github.com/J-shel", "followers_url": "https://api.github.com/users/J-shel/followers", "following_url": "https://api.github.com/users/J-shel/following{/other_user}", "gists_url": "https://api.github.com/users/J-shel/gists{/gist_id}", "starred_url": "https://api.github.com/users/J-shel/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/J-shel/subscriptions", "organizations_url": "https://api.github.com/users/J-shel/orgs", "repos_url": "https://api.github.com/users/J-shel/repos", "events_url": "https://api.github.com/users/J-shel/events{/privacy}", "received_events_url": "https://api.github.com/users/J-shel/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Hi @J-shel, thanks for reporting.\r\n\r\nI think the issue comes from your call to `load_dataset`. As first argument, you should pass:\r\n- either the name of your dataset (\"mrf\") if this is already published on the Hub\r\n- or the path to the loading script of your dataset (\"path/to/your/local/mrf.py\").", "Hi, following your suggestion, I changed my call to load_dataset. Below is the latest:\r\nreader = load_dataset('data/mrf.py',\"default\", input_size=1024, split=split, streaming=True, keep_in_memory=None)\r\nHowever, I still got the same error.\r\nI have one question that is if I only define input_size=2048 in BUILDER_CONFIGS, may I specify input_size=1024 when loading the dataset? Cause I found that I could only specify name=\"default\" since I only define name=\"default\" in BUILDER_CONFIGS." ]	2022-12-12T02:52:03	2022-12-19T01:38:48	null	NONE	null	null	null	### Describe the bug I try to define a custom configuration with a input_size attribute following the instructions by "Specifying several dataset configurations" in https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html But when I load the dataset, I got an error "__init__() got an unexpected keyword argument 'input_size'" ### Steps to reproduce the bug Following is the code to define the dataset: class CsvConfig(datasets.BuilderConfig): """BuilderConfig for CSV.""" input_size: int = 2048 class MRF(datasets.ArrowBasedBuilder): """Archival MRF data""" BUILDER_CONFIG_CLASS = CsvConfig VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ CsvConfig(name="default", version=VERSION, description="MRF data", input_size=2048), ] ... def _generate_examples(self): input_size = self.config.input_size if input_size > 1000: numin = 10000 else: numin = 15000 Below is the code to load the dataset: reader = load_dataset("default", input_size=1024) ### Expected behavior I hope to pass the "input_size" parameter to MRF datasets, and change "input_size" to any value when loading the datasets. ### Environment info - `datasets` version: 2.5.1 - Platform: Linux-4.18.0-305.3.1.el8.x86_64-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 9.0.0 - Pandas version: 1.5.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5352/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5352/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5351	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5351/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5351/comments	https://api.github.com/repos/huggingface/datasets/issues/5351/events	https://github.com/huggingface/datasets/issues/5351	1,490,659,504	I_kwDODunzps5Y2aiw	5,351	Do we need to implement `_prepare_split`?	{ "login": "jmwoloso", "id": 7530947, "node_id": "MDQ6VXNlcjc1MzA5NDc=", "avatar_url": "https://avatars.githubusercontent.com/u/7530947?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jmwoloso", "html_url": "https://github.com/jmwoloso", "followers_url": "https://api.github.com/users/jmwoloso/followers", "following_url": "https://api.github.com/users/jmwoloso/following{/other_user}", "gists_url": "https://api.github.com/users/jmwoloso/gists{/gist_id}", "starred_url": "https://api.github.com/users/jmwoloso/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jmwoloso/subscriptions", "organizations_url": "https://api.github.com/users/jmwoloso/orgs", "repos_url": "https://api.github.com/users/jmwoloso/repos", "events_url": "https://api.github.com/users/jmwoloso/events{/privacy}", "received_events_url": "https://api.github.com/users/jmwoloso/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! `DatasetBuilder` is a parent class for concrete builders: `GeneratorBasedBuilder`, `ArrowBasedBuilder` and `BeamBasedBuilder`. When writing a builder script, these classes are the ones you should inherit from. And since all of them implement `_prepare_split`, you only have to implement the three methods mentioned above.", "Thanks so much @mariosasko for the fast response! I've been referencing [this page in the docs](https://huggingface.co/docs/datasets/v2.4.0/en/about_dataset_load) because it it pretty comprehensive in terms of what we have to do and I figured since we subclass the `BuilderConfig` the same pattern would hold, but I've also seen the page with those sub-classed builders as well, so that fills in a knowledge gap for me.", "cc @stevhliu who may have some ideas on how to improve this part of the docs.", "one more question for my understanding @mariosasko. the requirement of a loading script has always seemed counterintuitive to me. if i have to provide a script with every dataset, what is the point of using `datasets` if we're doing all the work of loading it, I can just do that in my code and skip the datasets integration (this of course discounts other potential benefits around metadata management, etc., my example is just simplest use case though for the sake of discussion).\r\n\r\nso i figured I would implement my own `BuilderConfig` and `DatasetBuilder` to handle that portion of it and not have to make a script. i _thought_ this would result in `datasets` (via `download_and_prepare`) then making me something that I could load using `load_dataset` moving forward.\r\n\r\nConcretely, i envisioned this pattern being possible:\r\n\r\n ```\r\nclass MyBuilderConfig(BuilderConfig):\r\n def __init__(self, name=\"my_named_dataset\", ...):\r\n super().__init__(name, ...)\r\n\r\nclass MyDatasetBuilder(GeneratorBasedBuilder):\r\n BUILDER_CONFIG_CLASS = MyBuilderConfig\r\n ....\r\n\r\nmy_builder = MyDatasetBuilder(...)\r\n\r\n# this doesn't exactly work like I thought; I don't get a dataset back, but NoneType instead\r\n# though I can see it loading the files and it generates the cache, etc.\r\nmy_dataset = my_builder.download_and_prepare()\r\n\r\n# load the dataset in the future by referencing it by name and loading from the cached arrow version\r\nnew_instance_of_my_dataset = load_dataset(\"my_named_dataset\")\r\n```\r\n\r\nI've seen references to the `save_to_disk` method which might be the next step I need in order to load it by name, in which case, that makes sense, then i just need to debug why `download_and_prepare` isn't returning me a dataset, but I feel like I still have a larger conceptual knowledge gap on how to use the library correctly.\r\n\r\nThanks again in advance!", "> the requirement of a loading script has always seemed counterintuitive to me\r\n\r\nThis is a requirement only for datasets not stored in standard formats such as CSV, JSON, SQL, Parquet, ImageFolder, etc. \r\n\r\n> if i have to provide a script with every dataset, what is the point of using datasets if we're doing all the work of loading it, I can just do that in my code and skip the datasets integration (this of course discounts other potential benefits around metadata management, etc., my example is just simplest use case though for the sake of discussion)\r\n\r\nOur README/documentation lists the main features... \r\n\r\nOne of the main ones is that our library makes it easy to work with datasets larger than RAM (thanks to Arrow and the caching mechanism), and this is not trivial to implement.\r\n\r\nRegarding the step-by-step builder, this is the pattern:\r\n```python\r\nfrom datasets import load_dataset_builder\r\nbuilder = load_dataset_builder(\"path/to/script\") # or direct instantiation with MyDatasetBuilder(...)\r\nbuilder.download_and_prepare()\r\ndset = builder.as_dataset()\r\n```", "ok, that makes sense. thank you @mariosasko. I realized i'd never looked on the hub at any of the files associated with any datasets. just did that now and it appears that i'll need to have a script regardless _but_ that will just contain my custom config and builder classes, so without realizing it I was already making my script, I just need to wrap that in a file that sits alongside my data (I looked at Glue and realized I was already doing what I thought didn't make sense to have to do, lol).\r\n\r\n`download_and_prepare` isn't returning me a dataset though, but I'll look into that and open another issue if I can't figure it out.", "`download_and_prepare` downloads and prepares the arrow files. You need to call `as_dataset` on the builder to get the dataset.", "ok, I think I was assigning the output of `builder.download_and_prepare` but it's an inplace op, so that explains the `NoneType` i was getting back. Now I'm getting:\r\n\r\n```\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-7-3ed50fb87c70> in <module>\r\n----> 1 ds = dataset_builder.as_dataset()\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/builder.py in as_dataset(self, split, run_post_process, ignore_verifications, in_memory)\r\n 1020 \r\n 1021 # Create a dataset for each of the given splits\r\n-> 1022 datasets = map_nested(\r\n 1023 partial(\r\n 1024 self._build_single_dataset,\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\r\n 442 num_proc = 1\r\n 443 if num_proc <= 1 or len(iterable) < parallel_min_length:\r\n--> 444 mapped = [\r\n 445 _single_map_nested((function, obj, types, None, True, None))\r\n 446 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)\r\n 443 if num_proc <= 1 or len(iterable) < parallel_min_length:\r\n 444 mapped = [\r\n--> 445 _single_map_nested((function, obj, types, None, True, None))\r\n 446 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 447 ]\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)\r\n 344 # Singleton first to spare some computation\r\n 345 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 346 return function(data_struct)\r\n 347 \r\n 348 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/builder.py in _build_single_dataset(self, split, run_post_process, ignore_verifications, in_memory)\r\n 1051 \r\n 1052 # Build base dataset\r\n-> 1053 ds = self._as_dataset(\r\n 1054 split=split,\r\n 1055 in_memory=in_memory,\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/builder.py in _as_dataset(self, split, in_memory)\r\n 1120 \"\"\"\r\n 1121 cache_dir = self._fs._strip_protocol(self._output_dir)\r\n-> 1122 dataset_kwargs = ArrowReader(cache_dir, self.info).read(\r\n 1123 name=self.name,\r\n 1124 instructions=split,\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/arrow_reader.py in read(self, name, instructions, split_infos, in_memory)\r\n 236 msg = f'Instruction \"{instructions}\" corresponds to no data!'\r\n 237 raise ValueError(msg)\r\n--> 238 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n 239 \r\n 240 def read_files(\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/arrow_reader.py in read_files(self, files, original_instructions, in_memory)\r\n 257 \"\"\"\r\n 258 # Prepend path to filename\r\n--> 259 pa_table = self._read_files(files, in_memory=in_memory)\r\n 260 # If original_instructions is not None, convert it to a human-readable NamedSplit\r\n 261 if original_instructions is not None:\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/arrow_reader.py in _read_files(self, files, in_memory)\r\n 192 f[\"filename\"] = os.path.join(self._path, f[\"filename\"])\r\n 193 for f_dict in files:\r\n--> 194 pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n 195 pa_tables.append(pa_table)\r\n 196 pa_tables = [t for t in pa_tables if len(t) > 0]\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/arrow_reader.py in _get_table_from_filename(self, filename_skip_take, in_memory)\r\n 327 filename_skip_take[\"take\"] if \"take\" in filename_skip_take else None,\r\n 328 )\r\n--> 329 table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n 330 if take == -1:\r\n 331 take = len(table) - skip\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/arrow_reader.py in read_table(filename, in_memory)\r\n 348 \"\"\"\r\n 349 table_cls = InMemoryTable if in_memory else MemoryMappedTable\r\n--> 350 return table_cls.from_file(filename)\r\n 351 \r\n 352 \r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/table.py in from_file(cls, filename, replays)\r\n 1034 @classmethod\r\n 1035 def from_file(cls, filename: str, replays=None):\r\n-> 1036 table = _memory_mapped_arrow_table_from_file(filename)\r\n 1037 table = cls._apply_replays(table, replays)\r\n 1038 return cls(table, filename, replays)\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/table.py in _memory_mapped_arrow_table_from_file(filename)\r\n 48 def _memory_mapped_arrow_table_from_file(filename: str) -> pa.Table:\r\n 49 memory_mapped_stream = pa.memory_map(filename)\r\n---> 50 opened_stream = pa.ipc.open_stream(memory_mapped_stream)\r\n 51 pa_table = opened_stream.read_all()\r\n 52 return pa_table\r\n\r\n/databricks/python/lib/python3.8/site-packages/pyarrow/ipc.py in open_stream(source)\r\n 152 reader : RecordBatchStreamReader\r\n 153 \"\"\"\r\n--> 154 return RecordBatchStreamReader(source)\r\n 155 \r\n 156 \r\n\r\n/databricks/python/lib/python3.8/site-packages/pyarrow/ipc.py in __init__(self, source)\r\n 43 \r\n 44 def __init__(self, source):\r\n---> 45 self._open(source)\r\n 46 \r\n 47 \r\n\r\n/databricks/python/lib/python3.8/site-packages/pyarrow/ipc.pxi in pyarrow.lib._RecordBatchStreamReader._open()\r\n\r\n/databricks/python/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n/databricks/python/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Tried reading schema message, was null or length 0\r\n```\r\n\r\n", "looks like my arrow files are all empty @mariosasko \r\n\r\n![image](https://user-images.githubusercontent.com/7530947/208179977-9ae62c9a-866c-472b-9a09-25d1191188fb.png)\r\n\r\n\r\ni also see the `incomplete_info.lock` file a level up too. seems like the data isn't being persisted to disk when I call `download_and_prepare`. is there something else i need to do before then, perhaps?", "quick update @mariosasko. i got it working! i had to downgrade to `datasets==2.4.0`. testing other versions now and will let you know the results.", "I've tested with every version of `datasets>2.4.0` and i get the same error with all of them." ]	2022-12-12T01:38:54	2022-12-20T18:20:57	2022-12-12T16:48:56	NONE	null	null	null	### Describe the bug I'm not sure this is a bug or if it's just missing in the documentation, or i'm not doing something correctly, but I'm subclassing `DatasetBuilder` and getting the following error because on the `DatasetBuilder` class the `_prepare_split` method is abstract (as are the others we are required to implement, hence the genesis of my question): ``` Traceback (most recent call last): File "/home/jason/source/python/prism_machine_learning/examples/create_hf_datasets.py", line 28, in <module> dataset_builder.download_and_prepare() File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 1124, in _prepare_split raise NotImplementedError() NotImplementedError ``` ### Steps to reproduce the bug I will share implementation if it turns out that everything should be working (i.e. we only need to implement those 3 methods the docs mention), but I don't want to distract from the original question. ### Expected behavior I just need to know if there are additional methods we need to implement when subclassing `DatasetBuilder` besides what the documentation specifies -> `_info`, `_split_generators` and `_generate_examples` ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.4.0-135-generic-x86_64-with-glibc2.2.5 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5351/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5351/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5350	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5350/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5350/comments	https://api.github.com/repos/huggingface/datasets/issues/5350/events	https://github.com/huggingface/datasets/pull/5350	1,487,559,904	PR_kwDODunzps5E8y2E	5,350	Clean up Loading methods docstrings	{ "login": "stevhliu", "id": 59462357, "node_id": "MDQ6VXNlcjU5NDYyMzU3", "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "gravatar_id": "", "url": "https://api.github.com/users/stevhliu", "html_url": "https://github.com/stevhliu", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "repos_url": "https://api.github.com/users/stevhliu/repos", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-12-09T22:25:30	2022-12-12T17:27:20	2022-12-12T17:24:01	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5350", "html_url": "https://github.com/huggingface/datasets/pull/5350", "diff_url": "https://github.com/huggingface/datasets/pull/5350.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5350.patch", "merged_at": "2022-12-12T17:24:01" }	Clean up for the docstrings in Loading methods!	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5350/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5350/timeline	null	null	true