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https://api.github.com/repos/huggingface/datasets/issues/6178 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6178/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6178/comments | https://api.github.com/repos/huggingface/datasets/issues/6178/events | https://github.com/huggingface/datasets/issues/6178 | 1,866,610,102 | I_kwDODunzps5vQjW2 | 6,178 | 'import datasets' throws "invalid syntax error" | {
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} | [] | open | false | null | [] | null | [] | "2023-08-25T08:35:14" | "2023-08-25T08:36:15" | null | NONE | null | ### Describe the bug
Hi,
I have been trying to import the datasets library but I keep gtting this error.
`Traceback (most recent call last):
File /opt/local/jupyterhub/lib64/python3.9/site-packages/IPython/core/interactiveshell.py:3508 in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
Cell In[2], line 1
import datasets
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/__init__.py:22
from .arrow_dataset import Dataset
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_dataset.py:67
from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_writer.py:27
from .features import Features, Image, Value
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/__init__.py:17
from .audio import Audio
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/audio.py:11
from ..download.streaming_download_manager import xopen, xsplitext
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/__init__.py:10
from .streaming_download_manager import StreamingDownloadManager
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/streaming_download_manager.py:18
from aiohttp.client_exceptions import ClientError
File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/__init__.py:7
from .connector import * # noqa
File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/connector.py:12
from .client import ClientRequest
File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/client.py:144
yield from asyncio.async(resp.release(), loop=loop)
^
SyntaxError: invalid syntax`
I have simply used these commands:
`import datasets`
and
`from datasets import load_dataset`
### Environment info
The library has been installed a virtual machine on JupyterHub. Although I have used this library multiple times (on the same VM) before, to train/test an ASR or other ML models, I had never encountered this error. | {
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https://api.github.com/repos/huggingface/datasets/issues/6177 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6177/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6177/comments | https://api.github.com/repos/huggingface/datasets/issues/6177/events | https://github.com/huggingface/datasets/pull/6177 | 1,865,490,962 | PR_kwDODunzps5Ytky- | 6,177 | Use object detection images from `huggingface/documentation-images` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6177). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.005847 / 0.011353 (-0.005506) | 0.003488 / 0.011008 (-0.007521) | 0.079545 / 0.038508 (0.041037) | 0.055114 / 0.023109 (0.032005) | 0.312694 / 0.275898 (0.036796) | 0.338808 / 0.323480 (0.015329) | 0.004573 / 0.007986 (-0.003413) | 0.002818 / 0.004328 (-0.001510) | 0.062102 / 0.004250 (0.057852) | 0.044072 / 0.037052 (0.007019) | 0.317682 / 0.258489 (0.059192) | 0.354139 / 0.293841 (0.060298) | 0.026905 / 0.128546 (-0.101641) | 0.007990 / 0.075646 (-0.067656) | 0.260071 / 0.419271 (-0.159201) | 0.043658 / 0.043533 (0.000125) | 0.313828 / 0.255139 (0.058689) | 0.339678 / 0.283200 (0.056478) | 0.020076 / 0.141683 (-0.121607) | 1.446321 / 1.452155 (-0.005834) | 1.527046 / 1.492716 (0.034330) |\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) | 0.197801 / 0.018006 (0.179795) | 0.432874 / 0.000490 (0.432385) | 0.004093 / 0.000200 (0.003893) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023505 / 0.037411 (-0.013906) | 0.072377 / 0.014526 (0.057852) | 0.081058 / 0.176557 (-0.095498) | 0.141628 / 0.737135 (-0.595507) | 0.081622 / 0.296338 (-0.214716) |\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) | 0.395005 / 0.215209 (0.179795) | 3.949006 / 2.077655 (1.871352) | 1.934028 / 1.504120 (0.429908) | 1.756065 / 1.541195 (0.214871) | 1.778719 / 1.468490 (0.310229) | 0.501279 / 4.584777 (-4.083498) | 3.032120 / 3.745712 (-0.713592) | 2.859751 / 5.269862 (-2.410110) | 1.885924 / 4.565676 (-2.679753) | 0.057236 / 0.424275 (-0.367039) | 0.006704 / 0.007607 (-0.000903) | 0.465794 / 0.226044 (0.239750) | 4.648622 / 2.268929 (2.379694) | 2.345649 / 55.444624 (-53.098975) | 1.981122 / 6.876477 (-4.895355) | 2.148235 / 2.142072 (0.006163) | 0.591466 / 4.805227 (-4.213761) | 0.125262 / 6.500664 (-6.375402) | 0.061305 / 0.075469 (-0.014164) |\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) | 1.243932 / 1.841788 (-0.597856) | 17.912110 / 8.074308 (9.837802) | 13.662097 / 10.191392 (3.470705) | 0.148051 / 0.680424 (-0.532373) | 0.016778 / 0.534201 (-0.517423) | 0.340342 / 0.579283 (-0.238941) | 0.351720 / 0.434364 (-0.082644) | 0.377837 / 0.540337 (-0.162501) | 0.521163 / 1.386936 (-0.865774) |\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) | 0.006011 / 0.011353 (-0.005342) | 0.003549 / 0.011008 (-0.007459) | 0.063579 / 0.038508 (0.025071) | 0.056196 / 0.023109 (0.033087) | 0.448879 / 0.275898 (0.172981) | 0.491542 / 0.323480 (0.168062) | 0.004597 / 0.007986 (-0.003389) | 0.002790 / 0.004328 (-0.001539) | 0.063257 / 0.004250 (0.059006) | 0.045653 / 0.037052 (0.008600) | 0.459714 / 0.258489 (0.201225) | 0.491371 / 0.293841 (0.197530) | 0.028124 / 0.128546 (-0.100422) | 0.008016 / 0.075646 (-0.067630) | 0.069418 / 0.419271 (-0.349853) | 0.040393 / 0.043533 (-0.003140) | 0.450978 / 0.255139 (0.195839) | 0.472075 / 0.283200 (0.188875) | 0.020006 / 0.141683 (-0.121677) | 1.451946 / 1.452155 (-0.000209) | 1.513557 / 1.492716 (0.020840) |\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) | 0.225416 / 0.018006 (0.207410) | 0.412287 / 0.000490 (0.411797) | 0.004075 / 0.000200 (0.003875) | 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) | 0.025949 / 0.037411 (-0.011463) | 0.080633 / 0.014526 (0.066108) | 0.089960 / 0.176557 (-0.086597) | 0.144530 / 0.737135 (-0.592606) | 0.091427 / 0.296338 (-0.204911) |\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) | 0.462311 / 0.215209 (0.247102) | 4.605063 / 2.077655 (2.527408) | 2.541083 / 1.504120 (1.036963) | 2.356341 / 1.541195 (0.815147) | 2.389824 / 1.468490 (0.921334) | 0.507397 / 4.584777 (-4.077380) | 3.079023 / 3.745712 (-0.666689) | 2.792025 / 5.269862 (-2.477837) | 1.846931 / 4.565676 (-2.718746) | 0.058422 / 0.424275 (-0.365853) | 0.006409 / 0.007607 (-0.001199) | 0.530648 / 0.226044 (0.304604) | 5.321030 / 2.268929 (3.052101) | 2.978335 / 55.444624 (-52.466289) | 2.641188 / 6.876477 (-4.235288) | 2.780450 / 2.142072 (0.638378) | 0.593864 / 4.805227 (-4.211363) | 0.125394 / 6.500664 (-6.375270) | 0.061432 / 0.075469 (-0.014037) |\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) | 1.337142 / 1.841788 (-0.504646) | 18.841575 / 8.074308 (10.767267) | 14.678622 / 10.191392 (4.487230) | 0.144491 / 0.680424 (-0.535933) | 0.018145 / 0.534201 (-0.516056) | 0.339376 / 0.579283 (-0.239907) | 0.339129 / 0.434364 (-0.095235) | 0.394842 / 0.540337 (-0.145495) | 0.547924 / 1.386936 (-0.839012) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#57af0ab30796df59d28bf933e756ffbe5f34db1e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.006478 / 0.011353 (-0.004875) | 0.003845 / 0.011008 (-0.007163) | 0.084179 / 0.038508 (0.045671) | 0.071327 / 0.023109 (0.048217) | 0.315206 / 0.275898 (0.039308) | 0.353477 / 0.323480 (0.029997) | 0.005267 / 0.007986 (-0.002719) | 0.003282 / 0.004328 (-0.001046) | 0.064062 / 0.004250 (0.059811) | 0.051940 / 0.037052 (0.014888) | 0.332004 / 0.258489 (0.073515) | 0.363199 / 0.293841 (0.069358) | 0.030546 / 0.128546 (-0.098000) | 0.008453 / 0.075646 (-0.067193) | 0.287636 / 0.419271 (-0.131636) | 0.051999 / 0.043533 (0.008466) | 0.325220 / 0.255139 (0.070081) | 0.355324 / 0.283200 (0.072125) | 0.023417 / 0.141683 (-0.118266) | 1.473370 / 1.452155 (0.021215) | 1.596903 / 1.492716 (0.104186) |\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) | 0.212645 / 0.018006 (0.194638) | 0.463766 / 0.000490 (0.463276) | 0.002834 / 0.000200 (0.002634) | 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) | 0.028424 / 0.037411 (-0.008987) | 0.082188 / 0.014526 (0.067662) | 0.777186 / 0.176557 (0.600629) | 0.218290 / 0.737135 (-0.518845) | 0.099098 / 0.296338 (-0.197240) |\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) | 0.387138 / 0.215209 (0.171929) | 3.845655 / 2.077655 (1.768000) | 1.929812 / 1.504120 (0.425692) | 1.718263 / 1.541195 (0.177069) | 1.760933 / 1.468490 (0.292443) | 0.475171 / 4.584777 (-4.109606) | 3.523366 / 3.745712 (-0.222346) | 3.167322 / 5.269862 (-2.102540) | 1.975164 / 4.565676 (-2.590513) | 0.056106 / 0.424275 (-0.368169) | 0.007448 / 0.007607 (-0.000159) | 0.459824 / 0.226044 (0.233779) | 4.590566 / 2.268929 (2.321638) | 2.377968 / 55.444624 (-53.066656) | 2.034052 / 6.876477 (-4.842425) | 2.224976 / 2.142072 (0.082904) | 0.575901 / 4.805227 (-4.229326) | 0.131546 / 6.500664 (-6.369118) | 0.059266 / 0.075469 (-0.016203) |\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) | 1.254783 / 1.841788 (-0.587005) | 19.497795 / 8.074308 (11.423487) | 13.937672 / 10.191392 (3.746280) | 0.164092 / 0.680424 (-0.516332) | 0.017915 / 0.534201 (-0.516286) | 0.391430 / 0.579283 (-0.187853) | 0.403681 / 0.434364 (-0.030683) | 0.457711 / 0.540337 (-0.082626) | 0.620395 / 1.386936 (-0.766541) |\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) | 0.006793 / 0.011353 (-0.004560) | 0.004101 / 0.011008 (-0.006907) | 0.064780 / 0.038508 (0.026272) | 0.071087 / 0.023109 (0.047977) | 0.401963 / 0.275898 (0.126065) | 0.433085 / 0.323480 (0.109605) | 0.005348 / 0.007986 (-0.002638) | 0.003289 / 0.004328 (-0.001039) | 0.065209 / 0.004250 (0.060958) | 0.054202 / 0.037052 (0.017150) | 0.405629 / 0.258489 (0.147140) | 0.440326 / 0.293841 (0.146485) | 0.032283 / 0.128546 (-0.096263) | 0.008510 / 0.075646 (-0.067137) | 0.071144 / 0.419271 (-0.348127) | 0.047414 / 0.043533 (0.003881) | 0.402065 / 0.255139 (0.146926) | 0.421217 / 0.283200 (0.138017) | 0.021924 / 0.141683 (-0.119759) | 1.490067 / 1.452155 (0.037913) | 1.539134 / 1.492716 (0.046417) |\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) | 0.280072 / 0.018006 (0.262066) | 0.456130 / 0.000490 (0.455641) | 0.020926 / 0.000200 (0.020726) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032040 / 0.037411 (-0.005371) | 0.092343 / 0.014526 (0.077817) | 0.104866 / 0.176557 (-0.071690) | 0.156631 / 0.737135 (-0.580505) | 0.107203 / 0.296338 (-0.189136) |\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) | 0.426268 / 0.215209 (0.211059) | 4.255539 / 2.077655 (2.177884) | 2.285077 / 1.504120 (0.780957) | 2.114277 / 1.541195 (0.573083) | 2.159242 / 1.468490 (0.690752) | 0.489421 / 4.584777 (-4.095356) | 3.630797 / 3.745712 (-0.114915) | 3.205238 / 5.269862 (-2.064624) | 1.985846 / 4.565676 (-2.579830) | 0.057436 / 0.424275 (-0.366839) | 0.007154 / 0.007607 (-0.000454) | 0.507294 / 0.226044 (0.281250) | 5.050105 / 2.268929 (2.781176) | 2.750474 / 55.444624 (-52.694151) | 2.404116 / 6.876477 (-4.472360) | 2.576483 / 2.142072 (0.434411) | 0.584909 / 4.805227 (-4.220318) | 0.130695 / 6.500664 (-6.369969) | 0.059743 / 0.075469 (-0.015726) |\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) | 1.352702 / 1.841788 (-0.489086) | 19.687944 / 8.074308 (11.613636) | 14.991847 / 10.191392 (4.800455) | 0.185164 / 0.680424 (-0.495260) | 0.020314 / 0.534201 (-0.513887) | 0.395162 / 0.579283 (-0.184121) | 0.408917 / 0.434364 (-0.025447) | 0.467049 / 0.540337 (-0.073288) | 0.649209 / 1.386936 (-0.737727) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#885518608ceab83b7ed8ceba7a0b72bc68096026 \"CML watermark\")\n"
] | "2023-08-24T16:16:09" | "2023-08-24T16:25:25" | null | CONTRIBUTOR | null | null | {
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"Hi! Can you share the error this reproducer throws in your environment? `streaming=True` streams the dataset as it's iterated over without creating a memory-map file.",
"The trace of the error. Streaming works but is slower.\r\n```\r\nRoot Cause (first observed failure):\r\n[0]:\r\n time : 2023-08-24_06:06:01\r\n host : compute-126.cm.cluster\r\n rank : 0 (local_rank: 0)\r\n exitcode : 1 (pid: 48442)\r\n error_file: /tmp/torchelastic_4fqzcuuz/none_rx2470jl/attempt_0/0/error.json\r\n traceback : Traceback (most recent call last):\r\n File \"/users/yli7/.conda/envs/pytorch2.0/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py\", line 346, in wrapper\r\n return f(*args, **kwargs)\r\n File \"Pretrain.py\", line 214, in main\r\n pair_dataset, c4_dataset = create_dataset('pretrain', config)\r\n File \"/dcs05/qiao/data/william/project/DaVinci/dataset/__init__.py\", line 109, in create_dataset\r\n c4_dataset = load_dataset(\"c4\", \"en\", split=\"train\").to_iterable_dataset(num_shards=1024).map(pre_caption_huggingface)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/load.py\", line 1810, in load_dataset\r\n ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1145, in as_dataset\r\n datasets = map_nested(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 436, in map_nested\r\n return function(data_struct)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1175, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1246, in _as_dataset\r\n dataset_kwargs = ArrowReader(cache_dir, self.info).read(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 244, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 265, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 200, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 336, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 357, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 1059, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 65, in _memory_mapped_arrow_table_from_file\r\n opened_stream = _memory_mapped_record_batch_reader_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 50, in _memory_mapped_record_batch_reader_from_file\r\n memory_mapped_stream = pa.memory_map(filename)\r\n File \"pyarrow/io.pxi\", line 1009, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 956, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\n OSError: Memory mapping file failed: Cannot allocate memory\r\n```"
] | "2023-08-24T05:33:45" | "2023-08-24T17:32:00" | null | NONE | null | ### Describe the bug
Huggingface datasets use memory-mapped file to map large datasets in memory for fast access.
However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed.
So is there a way to explicitly limit the size of memory mapped file?
### Steps to reproduce the bug
python
>>> from datasets import load_dataset
>>> dataset = load_dataset("c4", "en", streaming=True)
### Expected behavior
In a normal environment, this will not have any problem.
However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed.
### Environment info
linux cluster with SGE（Sun Grid Engine） | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.006095 / 0.011353 (-0.005258) | 0.003580 / 0.011008 (-0.007429) | 0.080146 / 0.038508 (0.041638) | 0.063445 / 0.023109 (0.040336) | 0.321930 / 0.275898 (0.046032) | 0.397933 / 0.323480 (0.074453) | 0.003455 / 0.007986 (-0.004531) | 0.002856 / 0.004328 (-0.001472) | 0.062938 / 0.004250 (0.058687) | 0.048896 / 0.037052 (0.011843) | 0.333070 / 0.258489 (0.074581) | 0.404485 / 0.293841 (0.110644) | 0.027156 / 0.128546 (-0.101390) | 0.007974 / 0.075646 (-0.067672) | 0.261505 / 0.419271 (-0.157766) | 0.045328 / 0.043533 (0.001795) | 0.311203 / 0.255139 (0.056064) | 0.390006 / 0.283200 (0.106806) | 0.023650 / 0.141683 (-0.118033) | 1.468856 / 1.452155 (0.016701) | 1.503867 / 1.492716 (0.011151) |\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) | 0.202110 / 0.018006 (0.184103) | 0.436433 / 0.000490 (0.435944) | 0.002278 / 0.000200 (0.002078) | 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) | 0.024575 / 0.037411 (-0.012836) | 0.073005 / 0.014526 (0.058479) | 0.083609 / 0.176557 (-0.092947) | 0.144881 / 0.737135 (-0.592254) | 0.083495 / 0.296338 (-0.212844) |\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) | 0.398911 / 0.215209 (0.183702) | 3.994035 / 2.077655 (1.916381) | 2.056768 / 1.504120 (0.552649) | 1.913242 / 1.541195 (0.372047) | 1.932934 / 1.468490 (0.464444) | 0.498953 / 4.584777 (-4.085824) | 3.031107 / 3.745712 (-0.714605) | 2.817165 / 5.269862 (-2.452696) | 1.858886 / 4.565676 (-2.706790) | 0.056977 / 0.424275 (-0.367299) | 0.006634 / 0.007607 (-0.000973) | 0.472580 / 0.226044 (0.246536) | 4.738301 / 2.268929 (2.469372) | 2.373938 / 55.444624 (-53.070686) | 2.021057 / 6.876477 (-4.855420) | 2.195419 / 2.142072 (0.053346) | 0.585182 / 4.805227 (-4.220045) | 0.124260 / 6.500664 (-6.376405) | 0.060250 / 0.075469 (-0.015219) |\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) | 1.227350 / 1.841788 (-0.614438) | 18.496525 / 8.074308 (10.422216) | 13.946658 / 10.191392 (3.755266) | 0.140024 / 0.680424 (-0.540399) | 0.017077 / 0.534201 (-0.517124) | 0.334415 / 0.579283 (-0.244868) | 0.351118 / 0.434364 (-0.083246) | 0.379556 / 0.540337 (-0.160782) | 0.525064 / 1.386936 (-0.861872) |\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) | 0.006176 / 0.011353 (-0.005177) | 0.003648 / 0.011008 (-0.007360) | 0.063461 / 0.038508 (0.024953) | 0.062770 / 0.023109 (0.039660) | 0.448786 / 0.275898 (0.172888) | 0.486490 / 0.323480 (0.163010) | 0.005527 / 0.007986 (-0.002458) | 0.002860 / 0.004328 (-0.001469) | 0.063803 / 0.004250 (0.059553) | 0.049657 / 0.037052 (0.012604) | 0.449625 / 0.258489 (0.191136) | 0.489378 / 0.293841 (0.195537) | 0.028406 / 0.128546 (-0.100140) | 0.008062 / 0.075646 (-0.067584) | 0.068417 / 0.419271 (-0.350854) | 0.040854 / 0.043533 (-0.002678) | 0.461670 / 0.255139 (0.206531) | 0.481622 / 0.283200 (0.198423) | 0.021018 / 0.141683 (-0.120665) | 1.450328 / 1.452155 (-0.001826) | 1.501283 / 1.492716 (0.008567) |\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) | 0.269824 / 0.018006 (0.251817) | 0.412296 / 0.000490 (0.411807) | 0.039582 / 0.000200 (0.039382) | 0.000266 / 0.000054 (0.000211) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026436 / 0.037411 (-0.010976) | 0.080633 / 0.014526 (0.066107) | 0.089786 / 0.176557 (-0.086770) | 0.145020 / 0.737135 (-0.592115) | 0.092327 / 0.296338 (-0.204012) |\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) | 0.464349 / 0.215209 (0.249140) | 4.630631 / 2.077655 (2.552976) | 2.560527 / 1.504120 (1.056407) | 2.374195 / 1.541195 (0.833000) | 2.424774 / 1.468490 (0.956284) | 0.510428 / 4.584777 (-4.074349) | 3.099805 / 3.745712 (-0.645907) | 2.781096 / 5.269862 (-2.488765) | 1.854276 / 4.565676 (-2.711400) | 0.058102 / 0.424275 (-0.366173) | 0.006365 / 0.007607 (-0.001242) | 0.534082 / 0.226044 (0.308038) | 5.355003 / 2.268929 (3.086074) | 3.012546 / 55.444624 (-52.432078) | 2.665222 / 6.876477 (-4.211255) | 2.821014 / 2.142072 (0.678942) | 0.597733 / 4.805227 (-4.207494) | 0.125433 / 6.500664 (-6.375231) | 0.060802 / 0.075469 (-0.014667) |\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) | 1.345699 / 1.841788 (-0.496088) | 18.836083 / 8.074308 (10.761774) | 14.895458 / 10.191392 (4.704066) | 0.146843 / 0.680424 (-0.533581) | 0.018082 / 0.534201 (-0.516119) | 0.335729 / 0.579283 (-0.243554) | 0.351013 / 0.434364 (-0.083351) | 0.388435 / 0.540337 (-0.151902) | 0.543826 / 1.386936 (-0.843110) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d0c7e8c4808a1fb6ee7234b4caa25aa9fcfdc88f \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.006593 / 0.011353 (-0.004760) | 0.004089 / 0.011008 (-0.006919) | 0.084753 / 0.038508 (0.046245) | 0.079899 / 0.023109 (0.056790) | 0.311528 / 0.275898 (0.035630) | 0.349722 / 0.323480 (0.026243) | 0.004288 / 0.007986 (-0.003698) | 0.004552 / 0.004328 (0.000224) | 0.065896 / 0.004250 (0.061646) | 0.053813 / 0.037052 (0.016760) | 0.316958 / 0.258489 (0.058469) | 0.367011 / 0.293841 (0.073170) | 0.031082 / 0.128546 (-0.097464) | 0.008684 / 0.075646 (-0.066963) | 0.288003 / 0.419271 (-0.131268) | 0.052560 / 0.043533 (0.009027) | 0.305589 / 0.255139 (0.050450) | 0.349656 / 0.283200 (0.066457) | 0.023857 / 0.141683 (-0.117826) | 1.462360 / 1.452155 (0.010205) | 1.568170 / 1.492716 (0.075454) |\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) | 0.272342 / 0.018006 (0.254336) | 0.585108 / 0.000490 (0.584618) | 0.003427 / 0.000200 (0.003227) | 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) | 0.030347 / 0.037411 (-0.007064) | 0.086325 / 0.014526 (0.071799) | 0.100958 / 0.176557 (-0.075598) | 0.156534 / 0.737135 (-0.580601) | 0.102506 / 0.296338 (-0.193832) |\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) | 0.406625 / 0.215209 (0.191416) | 4.065957 / 2.077655 (1.988302) | 2.075867 / 1.504120 (0.571747) | 1.914390 / 1.541195 (0.373196) | 2.013321 / 1.468490 (0.544831) | 0.486832 / 4.584777 (-4.097945) | 3.545940 / 3.745712 (-0.199772) | 3.323226 / 5.269862 (-1.946635) | 2.067742 / 4.565676 (-2.497934) | 0.057884 / 0.424275 (-0.366391) | 0.007751 / 0.007607 (0.000144) | 0.484923 / 0.226044 (0.258878) | 4.844885 / 2.268929 (2.575956) | 2.569828 / 55.444624 (-52.874796) | 2.224058 / 6.876477 (-4.652419) | 2.485587 / 2.142072 (0.343515) | 0.584311 / 4.805227 (-4.220916) | 0.134984 / 6.500664 (-6.365680) | 0.062164 / 0.075469 (-0.013305) |\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) | 1.247182 / 1.841788 (-0.594605) | 20.107500 / 8.074308 (12.033192) | 14.194444 / 10.191392 (4.003052) | 0.147134 / 0.680424 (-0.533290) | 0.018062 / 0.534201 (-0.516138) | 0.392029 / 0.579283 (-0.187254) | 0.402991 / 0.434364 (-0.031373) | 0.457600 / 0.540337 (-0.082737) | 0.632553 / 1.386936 (-0.754383) |\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) | 0.006920 / 0.011353 (-0.004433) | 0.004257 / 0.011008 (-0.006751) | 0.065233 / 0.038508 (0.026725) | 0.078151 / 0.023109 (0.055042) | 0.389141 / 0.275898 (0.113243) | 0.431518 / 0.323480 (0.108038) | 0.005752 / 0.007986 (-0.002234) | 0.003584 / 0.004328 (-0.000745) | 0.065173 / 0.004250 (0.060922) | 0.059113 / 0.037052 (0.022060) | 0.398225 / 0.258489 (0.139736) | 0.430980 / 0.293841 (0.137139) | 0.032802 / 0.128546 (-0.095744) | 0.008702 / 0.075646 (-0.066945) | 0.071345 / 0.419271 (-0.347926) | 0.048269 / 0.043533 (0.004736) | 0.389264 / 0.255139 (0.134125) | 0.416008 / 0.283200 (0.132809) | 0.024845 / 0.141683 (-0.116838) | 1.499100 / 1.452155 (0.046945) | 1.576397 / 1.492716 (0.083681) |\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) | 0.296674 / 0.018006 (0.278668) | 0.540108 / 0.000490 (0.539619) | 0.004293 / 0.000200 (0.004093) | 0.000151 / 0.000054 (0.000096) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034108 / 0.037411 (-0.003303) | 0.092747 / 0.014526 (0.078221) | 0.112203 / 0.176557 (-0.064354) | 0.162728 / 0.737135 (-0.574407) | 0.109955 / 0.296338 (-0.186383) |\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) | 0.432006 / 0.215209 (0.216797) | 4.297591 / 2.077655 (2.219937) | 2.379645 / 1.504120 (0.875525) | 2.218680 / 1.541195 (0.677485) | 2.314608 / 1.468490 (0.846117) | 0.495562 / 4.584777 (-4.089215) | 3.589787 / 3.745712 (-0.155925) | 3.349593 / 5.269862 (-1.920268) | 2.119893 / 4.565676 (-2.445783) | 0.057976 / 0.424275 (-0.366299) | 0.007612 / 0.007607 (0.000005) | 0.509422 / 0.226044 (0.283378) | 5.101444 / 2.268929 (2.832515) | 2.794532 / 55.444624 (-52.650092) | 2.459033 / 6.876477 (-4.417444) | 2.714424 / 2.142072 (0.572352) | 0.588444 / 4.805227 (-4.216784) | 0.135763 / 6.500664 (-6.364901) | 0.062593 / 0.075469 (-0.012876) |\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) | 1.361415 / 1.841788 (-0.480372) | 20.940684 / 8.074308 (12.866376) | 15.161364 / 10.191392 (4.969972) | 0.154243 / 0.680424 (-0.526181) | 0.020305 / 0.534201 (-0.513896) | 0.397438 / 0.579283 (-0.181845) | 0.415047 / 0.434364 (-0.019317) | 0.473250 / 0.540337 (-0.067088) | 0.740681 / 1.386936 (-0.646255) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e84937af4f24194bf61f09244ebef6528fb7c4c \"CML watermark\")\n"
] | "2023-08-23T15:45:53" | "2023-08-24T12:37:41" | null | CONTRIBUTOR | null | Fixes:
* bumps the PyArrow version check in the `cast_array_to_feature` to avoid the offset bug (still not fixed)
* aligns the Pandas formatting tests with the Numpy ones (the current test fails due to https://github.com/apache/arrow/pull/35656, which requires `.to_pandas(coerce_temporal_nanoseconds=True)` to always return `datetime [ns]` objects)
Fix #6173
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} | [] | open | false | null | [] | null | [] | "2023-08-23T14:11:20" | "2023-08-23T14:45:42" | null | MEMBER | null | pyarrow 13.0.0 just came out
```
FAILED tests/test_formatting.py::ArrowExtractorTest::test_pandas_extractor - AssertionError: Attributes of Series are different
Attribute "dtype" are different
[left]: datetime64[us, UTC]
[right]: datetime64[ns, UTC]
```
```
FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type
fixed_size_list<item: int32>[3]
to
Sequence(feature=Value(dtype='int64', id=None), length=3, id=None)
```
e.g. in https://github.com/huggingface/datasets/actions/runs/5952253963/job/16143847230
first error may be related to https://github.com/apache/arrow/issues/33321
second one maybe because `feature.length * len(array) == len(array_values)` is not satisfied anymore somehow ? | {
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"Hi! The streaming mode also retries requests - `datasets.config.STREAMING_READ_MAX_RETRIES` (20 sec by default) controls the number of retries and `datasets.config.STREAMING_READ_RETRY_INTERVAL` (5 sec) the sleep time between retries.\r\n\r\n> At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch dataloader\r\n\r\nA minor Hub outage that we experienced yesterday could be the cause."
] | "2023-08-23T13:15:38" | "2023-08-24T14:29:27" | null | NONE | null | ### Feature request
Streaming datasets, as intended, do not load the entire dataset in memory or disk. However, while querying the next data chunk from the remote, sometimes it is possible that the service is down or there might be other issues that may cause the query to fail. In such a scenario, it would be nice to make these queries retryable (perhaps with a backoff strategy).
### Motivation
I was working on a model and the model checkpoints after every 1000 steps. At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch `dataloader`. Given the size of my model and data, it took around 2 hours to reach 1800 steps and now it will take about an hour to recover the lost 800. It would be better to get a retryable querying strategy.
### Your contribution
It would be better if someone having experience in this area takes this up as this would require some testing. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6171). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.009315 / 0.011353 (-0.002038) | 0.004931 / 0.011008 (-0.006077) | 0.100534 / 0.038508 (0.062026) | 0.089270 / 0.023109 (0.066161) | 0.394995 / 0.275898 (0.119097) | 0.440244 / 0.323480 (0.116764) | 0.006026 / 0.007986 (-0.001959) | 0.004252 / 0.004328 (-0.000077) | 0.078828 / 0.004250 (0.074577) | 0.066770 / 0.037052 (0.029718) | 0.411152 / 0.258489 (0.152663) | 0.445616 / 0.293841 (0.151775) | 0.048344 / 0.128546 (-0.080203) | 0.013700 / 0.075646 (-0.061946) | 0.361205 / 0.419271 (-0.058066) | 0.072085 / 0.043533 (0.028552) | 0.399173 / 0.255139 (0.144034) | 0.439334 / 0.283200 (0.156134) | 0.035815 / 0.141683 (-0.105868) | 1.779023 / 1.452155 (0.326868) | 1.865099 / 1.492716 (0.372383) |\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) | 0.275978 / 0.018006 (0.257972) | 0.588850 / 0.000490 (0.588360) | 0.004953 / 0.000200 (0.004754) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031329 / 0.037411 (-0.006082) | 0.095435 / 0.014526 (0.080910) | 0.111182 / 0.176557 (-0.065375) | 0.177692 / 0.737135 (-0.559444) | 0.113345 / 0.296338 (-0.182993) |\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) | 0.577882 / 0.215209 (0.362673) | 5.865872 / 2.077655 (3.788217) | 2.664218 / 1.504120 (1.160098) | 2.383354 / 1.541195 (0.842159) | 2.336821 / 1.468490 (0.868331) | 0.834585 / 4.584777 (-3.750192) | 5.418720 / 3.745712 (1.673008) | 4.551790 / 5.269862 (-0.718072) | 2.921874 / 4.565676 (-1.643803) | 0.095738 / 0.424275 (-0.328537) | 0.009625 / 0.007607 (0.002018) | 0.688317 / 0.226044 (0.462273) | 6.831826 / 2.268929 (4.562897) | 3.482607 / 55.444624 (-51.962017) | 2.633482 / 6.876477 (-4.242995) | 2.878786 / 2.142072 (0.736714) | 0.971615 / 4.805227 (-3.833613) | 0.208661 / 6.500664 (-6.292003) | 0.080271 / 0.075469 (0.004802) |\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) | 1.661193 / 1.841788 (-0.180594) | 24.223041 / 8.074308 (16.148733) | 21.621791 / 10.191392 (11.430399) | 0.243809 / 0.680424 (-0.436614) | 0.031630 / 0.534201 (-0.502571) | 0.501408 / 0.579283 (-0.077875) | 0.600002 / 0.434364 (0.165638) | 0.572066 / 0.540337 (0.031728) | 0.791992 / 1.386936 (-0.594944) |\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) | 0.009410 / 0.011353 (-0.001943) | 0.005255 / 0.011008 (-0.005753) | 0.079202 / 0.038508 (0.040693) | 0.078973 / 0.023109 (0.055863) | 0.557416 / 0.275898 (0.281518) | 0.560417 / 0.323480 (0.236937) | 0.007066 / 0.007986 (-0.000920) | 0.004560 / 0.004328 (0.000232) | 0.080359 / 0.004250 (0.076109) | 0.060071 / 0.037052 (0.023019) | 0.538441 / 0.258489 (0.279952) | 0.592486 / 0.293841 (0.298645) | 0.053221 / 0.128546 (-0.075325) | 0.014056 / 0.075646 (-0.061591) | 0.094084 / 0.419271 (-0.325188) | 0.066721 / 0.043533 (0.023188) | 0.521873 / 0.255139 (0.266734) | 0.579637 / 0.283200 (0.296437) | 0.041476 / 0.141683 (-0.100206) | 1.829681 / 1.452155 (0.377527) | 1.948418 / 1.492716 (0.455702) |\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) | 0.347594 / 0.018006 (0.329588) | 0.606906 / 0.000490 (0.606417) | 0.035413 / 0.000200 (0.035213) | 0.000371 / 0.000054 (0.000317) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031987 / 0.037411 (-0.005425) | 0.096985 / 0.014526 (0.082459) | 0.109275 / 0.176557 (-0.067282) | 0.175340 / 0.737135 (-0.561795) | 0.110763 / 0.296338 (-0.185575) |\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) | 0.634823 / 0.215209 (0.419614) | 6.527172 / 2.077655 (4.449517) | 3.135709 / 1.504120 (1.631589) | 2.634357 / 1.541195 (1.093162) | 2.670583 / 1.468490 (1.202093) | 0.888686 / 4.584777 (-3.696091) | 5.382289 / 3.745712 (1.636577) | 4.701189 / 5.269862 (-0.568673) | 3.161290 / 4.565676 (-1.404386) | 0.112414 / 0.424275 (-0.311861) | 0.009443 / 0.007607 (0.001836) | 0.774703 / 0.226044 (0.548658) | 7.905334 / 2.268929 (5.636405) | 3.689548 / 55.444624 (-51.755076) | 3.087263 / 6.876477 (-3.789214) | 3.366568 / 2.142072 (1.224496) | 1.185951 / 4.805227 (-3.619277) | 0.248638 / 6.500664 (-6.252026) | 0.104598 / 0.075469 (0.029129) |\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) | 1.820667 / 1.841788 (-0.021120) | 24.536703 / 8.074308 (16.462395) | 23.083964 / 10.191392 (12.892572) | 0.252897 / 0.680424 (-0.427527) | 0.032954 / 0.534201 (-0.501247) | 0.482467 / 0.579283 (-0.096816) | 0.602247 / 0.434364 (0.167883) | 0.600563 / 0.540337 (0.060225) | 0.824013 / 1.386936 (-0.562923) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c07a54ed4d570c5842d7bbe467025805be16ef51 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.009242 / 0.011353 (-0.002111) | 0.005244 / 0.011008 (-0.005764) | 0.112678 / 0.038508 (0.074170) | 0.089176 / 0.023109 (0.066067) | 0.405823 / 0.275898 (0.129925) | 0.465703 / 0.323480 (0.142223) | 0.005227 / 0.007986 (-0.002758) | 0.004296 / 0.004328 (-0.000032) | 0.082961 / 0.004250 (0.078711) | 0.063144 / 0.037052 (0.026092) | 0.422369 / 0.258489 (0.163880) | 0.478185 / 0.293841 (0.184344) | 0.049770 / 0.128546 (-0.078776) | 0.016561 / 0.075646 (-0.059086) | 0.380172 / 0.419271 (-0.039100) | 0.068698 / 0.043533 (0.025165) | 0.397773 / 0.255139 (0.142634) | 0.461284 / 0.283200 (0.178084) | 0.036907 / 0.141683 (-0.104775) | 1.828017 / 1.452155 (0.375862) | 2.028385 / 1.492716 (0.535669) |\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) | 0.291245 / 0.018006 (0.273239) | 0.605519 / 0.000490 (0.605030) | 0.003790 / 0.000200 (0.003590) | 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) | 0.029269 / 0.037411 (-0.008142) | 0.087014 / 0.014526 (0.072488) | 0.116984 / 0.176557 (-0.059573) | 0.170644 / 0.737135 (-0.566491) | 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) | 0.603045 / 0.215209 (0.387836) | 6.125308 / 2.077655 (4.047653) | 2.637127 / 1.504120 (1.133007) | 2.468636 / 1.541195 (0.927441) | 2.383773 / 1.468490 (0.915283) | 0.838139 / 4.584777 (-3.746638) | 5.355777 / 3.745712 (1.610065) | 4.753015 / 5.269862 (-0.516846) | 3.097486 / 4.565676 (-1.468191) | 0.094749 / 0.424275 (-0.329526) | 0.009040 / 0.007607 (0.001433) | 0.699987 / 0.226044 (0.473942) | 7.111671 / 2.268929 (4.842742) | 3.297798 / 55.444624 (-52.146827) | 2.614578 / 6.876477 (-4.261898) | 2.927717 / 2.142072 (0.785645) | 1.037292 / 4.805227 (-3.767935) | 0.218025 / 6.500664 (-6.282639) | 0.086306 / 0.075469 (0.010836) |\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) | 1.645146 / 1.841788 (-0.196642) | 24.191875 / 8.074308 (16.117567) | 21.844371 / 10.191392 (11.652979) | 0.245369 / 0.680424 (-0.435055) | 0.031776 / 0.534201 (-0.502425) | 0.465634 / 0.579283 (-0.113649) | 0.565498 / 0.434364 (0.131134) | 0.497409 / 0.540337 (-0.042929) | 0.748048 / 1.386936 (-0.638889) |\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) | 0.009239 / 0.011353 (-0.002114) | 0.005345 / 0.011008 (-0.005663) | 0.072732 / 0.038508 (0.034224) | 0.099880 / 0.023109 (0.076770) | 0.466933 / 0.275898 (0.191035) | 0.471730 / 0.323480 (0.148250) | 0.006164 / 0.007986 (-0.001821) | 0.004486 / 0.004328 (0.000158) | 0.075475 / 0.004250 (0.071224) | 0.068291 / 0.037052 (0.031238) | 0.465925 / 0.258489 (0.207436) | 0.469198 / 0.293841 (0.175357) | 0.047304 / 0.128546 (-0.081242) | 0.013368 / 0.075646 (-0.062278) | 0.083563 / 0.419271 (-0.335708) | 0.063204 / 0.043533 (0.019671) | 0.457422 / 0.255139 (0.202283) | 0.478793 / 0.283200 (0.195593) | 0.036120 / 0.141683 (-0.105563) | 1.841209 / 1.452155 (0.389054) | 1.955984 / 1.492716 (0.463267) |\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) | 0.369160 / 0.018006 (0.351154) | 0.607140 / 0.000490 (0.606650) | 0.047253 / 0.000200 (0.047054) | 0.000475 / 0.000054 (0.000420) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.040226 / 0.037411 (0.002815) | 0.107361 / 0.014526 (0.092835) | 0.122424 / 0.176557 (-0.054133) | 0.186447 / 0.737135 (-0.550688) | 0.127060 / 0.296338 (-0.169279) |\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) | 0.706737 / 0.215209 (0.491528) | 6.791287 / 2.077655 (4.713632) | 3.194471 / 1.504120 (1.690352) | 2.928145 / 1.541195 (1.386950) | 2.829078 / 1.468490 (1.360588) | 0.929797 / 4.584777 (-3.654980) | 5.484638 / 3.745712 (1.738926) | 4.841570 / 5.269862 (-0.428292) | 2.995247 / 4.565676 (-1.570430) | 0.104709 / 0.424275 (-0.319566) | 0.009543 / 0.007607 (0.001936) | 0.817605 / 0.226044 (0.591561) | 7.879234 / 2.268929 (5.610305) | 3.838073 / 55.444624 (-51.606551) | 3.189728 / 6.876477 (-3.686749) | 3.483775 / 2.142072 (1.341703) | 1.092823 / 4.805227 (-3.712404) | 0.227660 / 6.500664 (-6.273004) | 0.082452 / 0.075469 (0.006983) |\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) | 1.750413 / 1.841788 (-0.091374) | 27.078082 / 8.074308 (19.003774) | 23.968038 / 10.191392 (13.776646) | 0.248065 / 0.680424 (-0.432359) | 0.029961 / 0.534201 (-0.504240) | 0.508630 / 0.579283 (-0.070653) | 0.608707 / 0.434364 (0.174343) | 0.611062 / 0.540337 (0.070725) | 0.830797 / 1.386936 (-0.556139) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d793220dd8cbaa099a3928c2132c94c9f7453bc \"CML watermark\")\n"
] | "2023-08-23T09:21:11" | "2023-08-23T09:32:59" | "2023-08-23T09:21:19" | MEMBER | null | null | {
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} | [] | open | false | null | [] | null | [] | "2023-08-23T07:08:17" | "2023-08-23T07:09:48" | null | NONE | null | Added an optional parameter return_file_name in the load_dataset function. When it is set to True, the function will include the name of the file corresponding to the current line as a feature in the returned output.
I added this here https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/json/json.py#L92.
fixes #5806 | {
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"Unfortunately, I cannot reproduce this behavior on my machine or Colab - the reproducer returns `['main_data', 'additional_data']` as expected.",
"Thank you for looking into this, Mario. Is this on [my repository](https://huggingface.co/datasets/tsor13/test), or on another one that you have reproduced? Would you mind pointing me to it if so?",
"Whoa, in colab I received the correct behavior using my dataset. It must have something to do with my local copy of `datasets` (which again just failed).\r\n\r\nI've tried uninstalling/reinstnalling to no avail",
"hi @tsor13 , I haven't been able to reproduce your issue on `tsor13/test` dataset locally either. reinstalling doesn't help?"
] | "2023-08-23T00:13:22" | "2023-08-23T15:35:31" | null | NONE | null | ### Dataset configurations cannot be created in YAML/README
Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118
I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test):
```
configs:
- config_name: main_data
data_files: "main_data.csv"
- config_name: additional_data
data_files: "additional_data.csv"
```
Yet, I'm unable to load different configurations:
```
from datasets import get_dataset_config_names
get_dataset_config_names('tsor13/test', use_auth_token=True)
```
returns a single split, `['tsor13--test']`
Does anyone have any insights?
@polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas?
### Steps to reproduce the bug
from datasets import get_dataset_config_names
get_dataset_config_names('tsor13/test')
### Expected behavior
I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-13.4-arm64-arm-64bit
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.1 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6168). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.009350 / 0.011353 (-0.002003) | 0.005658 / 0.011008 (-0.005350) | 0.123173 / 0.038508 (0.084664) | 0.096354 / 0.023109 (0.073244) | 0.464398 / 0.275898 (0.188500) | 0.544455 / 0.323480 (0.220975) | 0.007337 / 0.007986 (-0.000648) | 0.004424 / 0.004328 (0.000096) | 0.089715 / 0.004250 (0.085465) | 0.072462 / 0.037052 (0.035410) | 0.460601 / 0.258489 (0.202112) | 0.544384 / 0.293841 (0.250543) | 0.052994 / 0.128546 (-0.075552) | 0.014459 / 0.075646 (-0.061187) | 0.464368 / 0.419271 (0.045096) | 0.072889 / 0.043533 (0.029356) | 0.471387 / 0.255139 (0.216248) | 0.560982 / 0.283200 (0.277783) | 0.041398 / 0.141683 (-0.100285) | 1.964688 / 1.452155 (0.512533) | 2.240727 / 1.492716 (0.748011) |\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) | 0.308524 / 0.018006 (0.290518) | 0.669306 / 0.000490 (0.668816) | 0.006644 / 0.000200 (0.006444) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037395 / 0.037411 (-0.000016) | 0.111303 / 0.014526 (0.096777) | 0.158988 / 0.176557 (-0.017569) | 0.236155 / 0.737135 (-0.500980) | 0.134775 / 0.296338 (-0.161564) |\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) | 0.648830 / 0.215209 (0.433621) | 6.614794 / 2.077655 (4.537139) | 2.867526 / 1.504120 (1.363407) | 2.472967 / 1.541195 (0.931772) | 2.488419 / 1.468490 (1.019929) | 0.915785 / 4.584777 (-3.668992) | 6.010754 / 3.745712 (2.265042) | 5.468873 / 5.269862 (0.199011) | 3.446535 / 4.565676 (-1.119141) | 0.118592 / 0.424275 (-0.305684) | 0.012005 / 0.007607 (0.004398) | 0.808467 / 0.226044 (0.582423) | 8.152122 / 2.268929 (5.883193) | 3.751282 / 55.444624 (-51.693342) | 3.009569 / 6.876477 (-3.866908) | 3.282613 / 2.142072 (1.140540) | 1.152727 / 4.805227 (-3.652500) | 0.240224 / 6.500664 (-6.260440) | 0.097871 / 0.075469 (0.022402) |\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) | 1.824944 / 1.841788 (-0.016843) | 27.840842 / 8.074308 (19.766533) | 24.368669 / 10.191392 (14.177277) | 0.260621 / 0.680424 (-0.419803) | 0.033730 / 0.534201 (-0.500471) | 0.552494 / 0.579283 (-0.026789) | 0.666921 / 0.434364 (0.232557) | 0.648812 / 0.540337 (0.108475) | 0.912602 / 1.386936 (-0.474334) |\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) | 0.011688 / 0.011353 (0.000335) | 0.005794 / 0.011008 (-0.005215) | 0.093466 / 0.038508 (0.054958) | 0.102583 / 0.023109 (0.079474) | 0.593572 / 0.275898 (0.317674) | 0.614351 / 0.323480 (0.290871) | 0.007006 / 0.007986 (-0.000980) | 0.005557 / 0.004328 (0.001229) | 0.087779 / 0.004250 (0.083529) | 0.072639 / 0.037052 (0.035586) | 0.577464 / 0.258489 (0.318975) | 0.628240 / 0.293841 (0.334399) | 0.053876 / 0.128546 (-0.074670) | 0.015383 / 0.075646 (-0.060263) | 0.110633 / 0.419271 (-0.308639) | 0.067467 / 0.043533 (0.023934) | 0.613457 / 0.255139 (0.358318) | 0.604939 / 0.283200 (0.321739) | 0.041738 / 0.141683 (-0.099945) | 1.967167 / 1.452155 (0.515012) | 2.121009 / 1.492716 (0.628293) |\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) | 0.449937 / 0.018006 (0.431930) | 0.694410 / 0.000490 (0.693921) | 0.064051 / 0.000200 (0.063851) | 0.000810 / 0.000054 (0.000756) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.045138 / 0.037411 (0.007727) | 0.116831 / 0.014526 (0.102306) | 0.131906 / 0.176557 (-0.044651) | 0.202421 / 0.737135 (-0.534714) | 0.132568 / 0.296338 (-0.163770) |\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) | 0.698046 / 0.215209 (0.482837) | 7.112591 / 2.077655 (5.034936) | 3.332679 / 1.504120 (1.828559) | 2.946384 / 1.541195 (1.405189) | 3.074484 / 1.468490 (1.605994) | 0.970917 / 4.584777 (-3.613859) | 6.143506 / 3.745712 (2.397794) | 5.572496 / 5.269862 (0.302634) | 3.602673 / 4.565676 (-0.963004) | 0.115068 / 0.424275 (-0.309207) | 0.009971 / 0.007607 (0.002364) | 0.891090 / 0.226044 (0.665046) | 8.761788 / 2.268929 (6.492859) | 4.362685 / 55.444624 (-51.081939) | 3.612893 / 6.876477 (-3.263583) | 3.797948 / 2.142072 (1.655876) | 1.202890 / 4.805227 (-3.602337) | 0.238120 / 6.500664 (-6.262544) | 0.095612 / 0.075469 (0.020143) |\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) | 1.958880 / 1.841788 (0.117092) | 28.216454 / 8.074308 (20.142146) | 25.361424 / 10.191392 (15.170032) | 0.308203 / 0.680424 (-0.372221) | 0.032903 / 0.534201 (-0.501298) | 0.539714 / 0.579283 (-0.039569) | 0.688278 / 0.434364 (0.253914) | 0.644818 / 0.540337 (0.104481) | 0.905694 / 1.386936 (-0.481242) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5289345e5b23548fee680a0bbc047c0b9a5ee8c \"CML watermark\")\n"
] | "2023-08-22T17:02:54" | "2023-08-22T17:14:15" | null | CONTRIBUTOR | null | Replace the `shape` tuple with a list in the `ArrayXD` YAML conversion.
Fix #6112 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.007342 / 0.011353 (-0.004011) | 0.004586 / 0.011008 (-0.006422) | 0.100430 / 0.038508 (0.061922) | 0.081053 / 0.023109 (0.057944) | 0.368130 / 0.275898 (0.092232) | 0.402852 / 0.323480 (0.079372) | 0.004504 / 0.007986 (-0.003482) | 0.003824 / 0.004328 (-0.000505) | 0.075326 / 0.004250 (0.071076) | 0.063329 / 0.037052 (0.026277) | 0.372837 / 0.258489 (0.114348) | 0.437857 / 0.293841 (0.144017) | 0.035512 / 0.128546 (-0.093034) | 0.009756 / 0.075646 (-0.065890) | 0.341035 / 0.419271 (-0.078236) | 0.060503 / 0.043533 (0.016970) | 0.362555 / 0.255139 (0.107416) | 0.409216 / 0.283200 (0.126017) | 0.030093 / 0.141683 (-0.111590) | 1.751550 / 1.452155 (0.299395) | 1.848676 / 1.492716 (0.355959) |\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) | 0.229448 / 0.018006 (0.211442) | 0.500300 / 0.000490 (0.499811) | 0.005195 / 0.000200 (0.004995) | 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) | 0.031753 / 0.037411 (-0.005658) | 0.096075 / 0.014526 (0.081549) | 0.111476 / 0.176557 (-0.065081) | 0.179236 / 0.737135 (-0.557899) | 0.113599 / 0.296338 (-0.182739) |\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) | 0.472817 / 0.215209 (0.257608) | 4.715029 / 2.077655 (2.637374) | 2.417934 / 1.504120 (0.913814) | 2.235014 / 1.541195 (0.693819) | 2.323588 / 1.468490 (0.855098) | 0.553751 / 4.584777 (-4.031026) | 4.153467 / 3.745712 (0.407755) | 3.858836 / 5.269862 (-1.411025) | 2.377499 / 4.565676 (-2.188178) | 0.066528 / 0.424275 (-0.357747) | 0.008979 / 0.007607 (0.001372) | 0.561076 / 0.226044 (0.335032) | 5.609817 / 2.268929 (3.340888) | 3.011098 / 55.444624 (-52.433526) | 2.594162 / 6.876477 (-4.282314) | 2.863597 / 2.142072 (0.721525) | 0.681135 / 4.805227 (-4.124092) | 0.158863 / 6.500664 (-6.341801) | 0.072551 / 0.075469 (-0.002918) |\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) | 1.492230 / 1.841788 (-0.349558) | 23.028828 / 8.074308 (14.954519) | 16.663265 / 10.191392 (6.471873) | 0.173146 / 0.680424 (-0.507278) | 0.021635 / 0.534201 (-0.512566) | 0.478919 / 0.579283 (-0.100364) | 0.472908 / 0.434364 (0.038544) | 0.547248 / 0.540337 (0.006910) | 0.770288 / 1.386936 (-0.616648) |\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) | 0.007728 / 0.011353 (-0.003625) | 0.004477 / 0.011008 (-0.006531) | 0.074858 / 0.038508 (0.036350) | 0.084266 / 0.023109 (0.061157) | 0.420280 / 0.275898 (0.144382) | 0.466835 / 0.323480 (0.143356) | 0.005980 / 0.007986 (-0.002006) | 0.003600 / 0.004328 (-0.000729) | 0.074941 / 0.004250 (0.070691) | 0.066414 / 0.037052 (0.029361) | 0.425949 / 0.258489 (0.167460) | 0.473236 / 0.293841 (0.179395) | 0.037213 / 0.128546 (-0.091333) | 0.009743 / 0.075646 (-0.065903) | 0.083758 / 0.419271 (-0.335513) | 0.057916 / 0.043533 (0.014383) | 0.423031 / 0.255139 (0.167892) | 0.451107 / 0.283200 (0.167907) | 0.028577 / 0.141683 (-0.113106) | 1.810509 / 1.452155 (0.358354) | 1.875579 / 1.492716 (0.382863) |\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) | 0.296052 / 0.018006 (0.278046) | 0.496618 / 0.000490 (0.496128) | 0.028667 / 0.000200 (0.028467) | 0.000140 / 0.000054 (0.000086) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036694 / 0.037411 (-0.000717) | 0.110873 / 0.014526 (0.096347) | 0.126550 / 0.176557 (-0.050007) | 0.182924 / 0.737135 (-0.554212) | 0.123793 / 0.296338 (-0.172545) |\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) | 0.509881 / 0.215209 (0.294672) | 5.067402 / 2.077655 (2.989747) | 2.696028 / 1.504120 (1.191908) | 2.489861 / 1.541195 (0.948666) | 2.563400 / 1.468490 (1.094910) | 0.571184 / 4.584777 (-4.013593) | 4.154231 / 3.745712 (0.408519) | 3.891004 / 5.269862 (-1.378858) | 2.435290 / 4.565676 (-2.130387) | 0.065825 / 0.424275 (-0.358450) | 0.008460 / 0.007607 (0.000853) | 0.597579 / 0.226044 (0.371534) | 5.914954 / 2.268929 (3.646025) | 3.219305 / 55.444624 (-52.225319) | 2.843548 / 6.876477 (-4.032929) | 3.070300 / 2.142072 (0.928228) | 0.686018 / 4.805227 (-4.119209) | 0.160077 / 6.500664 (-6.340587) | 0.074058 / 0.075469 (-0.001411) |\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) | 1.598748 / 1.841788 (-0.243039) | 23.475685 / 8.074308 (15.401377) | 17.257831 / 10.191392 (7.066439) | 0.176539 / 0.680424 (-0.503885) | 0.021969 / 0.534201 (-0.512232) | 0.473565 / 0.579283 (-0.105718) | 0.465471 / 0.434364 (0.031107) | 0.567107 / 0.540337 (0.026769) | 0.783757 / 1.386936 (-0.603179) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2f6bb450b4a3065a7d5fc50ea67711082749a337 \"CML watermark\")\n",
"Note that the https://github.com/huggingface/datasets-server/ explicitly relies on the fact that a split cannot contain a hyphen. cc @lhoestq ",
"We can't enable this that easily unfortunately because it could make arrow file names ambiguous in the cache.\r\n\r\ne.g. dataset_name-train-0000-of-0008.arrow",
"Oh, this would indeed make the caching for the multi-proc case ambiguous. Implementing this is only worth it if we get more requests, so I'm closing this PR for now."
] | "2023-08-22T13:30:59" | "2023-08-22T15:39:24" | "2023-08-22T15:38:53" | CONTRIBUTOR | null | To fix https://discuss.huggingface.co/t/error-when-setting-up-the-dataset-viewer-streamingrowserror/51276.
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.009036 / 0.011353 (-0.002317) | 0.004564 / 0.011008 (-0.006444) | 0.114958 / 0.038508 (0.076449) | 0.087329 / 0.023109 (0.064220) | 0.440111 / 0.275898 (0.164213) | 0.486056 / 0.323480 (0.162576) | 0.006580 / 0.007986 (-0.001406) | 0.004257 / 0.004328 (-0.000072) | 0.093458 / 0.004250 (0.089208) | 0.063380 / 0.037052 (0.026328) | 0.469455 / 0.258489 (0.210966) | 0.521630 / 0.293841 (0.227790) | 0.053496 / 0.128546 (-0.075050) | 0.013466 / 0.075646 (-0.062181) | 0.361629 / 0.419271 (-0.057642) | 0.068095 / 0.043533 (0.024562) | 0.472440 / 0.255139 (0.217301) | 0.508682 / 0.283200 (0.225483) | 0.034648 / 0.141683 (-0.107035) | 1.820117 / 1.452155 (0.367962) | 1.933448 / 1.492716 (0.440732) |\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) | 0.276543 / 0.018006 (0.258537) | 0.563380 / 0.000490 (0.562890) | 0.005345 / 0.000200 (0.005146) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029230 / 0.037411 (-0.008181) | 0.095613 / 0.014526 (0.081087) | 0.106178 / 0.176557 (-0.070378) | 0.181095 / 0.737135 (-0.556040) | 0.107789 / 0.296338 (-0.188550) |\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) | 0.612051 / 0.215209 (0.396842) | 6.065008 / 2.077655 (3.987353) | 2.720911 / 1.504120 (1.216791) | 2.495218 / 1.541195 (0.954023) | 2.423351 / 1.468490 (0.954860) | 0.835571 / 4.584777 (-3.749205) | 5.438230 / 3.745712 (1.692518) | 4.550301 / 5.269862 (-0.719561) | 2.919889 / 4.565676 (-1.645788) | 0.097748 / 0.424275 (-0.326527) | 0.009285 / 0.007607 (0.001678) | 0.741968 / 0.226044 (0.515923) | 7.285394 / 2.268929 (5.016466) | 3.433634 / 55.444624 (-52.010991) | 2.680823 / 6.876477 (-4.195654) | 2.931149 / 2.142072 (0.789076) | 1.012852 / 4.805227 (-3.792375) | 0.224899 / 6.500664 (-6.275765) | 0.089411 / 0.075469 (0.013942) |\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) | 1.622759 / 1.841788 (-0.219029) | 23.690030 / 8.074308 (15.615721) | 21.034451 / 10.191392 (10.843059) | 0.241504 / 0.680424 (-0.438920) | 0.030109 / 0.534201 (-0.504092) | 0.472536 / 0.579283 (-0.106747) | 0.631396 / 0.434364 (0.197032) | 0.598997 / 0.540337 (0.058659) | 0.798680 / 1.386936 (-0.588256) |\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) | 0.008696 / 0.011353 (-0.002657) | 0.005032 / 0.011008 (-0.005977) | 0.087369 / 0.038508 (0.048861) | 0.078105 / 0.023109 (0.054996) | 0.464861 / 0.275898 (0.188963) | 0.509620 / 0.323480 (0.186140) | 0.006399 / 0.007986 (-0.001587) | 0.004276 / 0.004328 (-0.000052) | 0.081643 / 0.004250 (0.077393) | 0.062560 / 0.037052 (0.025508) | 0.495377 / 0.258489 (0.236888) | 0.484885 / 0.293841 (0.191044) | 0.054354 / 0.128546 (-0.074193) | 0.013851 / 0.075646 (-0.061795) | 0.089531 / 0.419271 (-0.329740) | 0.068732 / 0.043533 (0.025199) | 0.455842 / 0.255139 (0.200703) | 0.528775 / 0.283200 (0.245575) | 0.039646 / 0.141683 (-0.102037) | 1.733600 / 1.452155 (0.281445) | 1.879074 / 1.492716 (0.386358) |\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) | 0.369616 / 0.018006 (0.351610) | 0.607426 / 0.000490 (0.606936) | 0.055540 / 0.000200 (0.055341) | 0.000543 / 0.000054 (0.000488) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036026 / 0.037411 (-0.001385) | 0.103968 / 0.014526 (0.089442) | 0.114852 / 0.176557 (-0.061705) | 0.187313 / 0.737135 (-0.549822) | 0.116839 / 0.296338 (-0.179500) |\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) | 0.614018 / 0.215209 (0.398809) | 6.139914 / 2.077655 (4.062259) | 2.826246 / 1.504120 (1.322126) | 2.524133 / 1.541195 (0.982938) | 2.606981 / 1.468490 (1.138491) | 0.844604 / 4.584777 (-3.740173) | 5.537178 / 3.745712 (1.791465) | 4.594624 / 5.269862 (-0.675237) | 3.032145 / 4.565676 (-1.533532) | 0.094771 / 0.424275 (-0.329504) | 0.008132 / 0.007607 (0.000525) | 0.714287 / 0.226044 (0.488242) | 7.296733 / 2.268929 (5.027804) | 3.698066 / 55.444624 (-51.746558) | 2.862781 / 6.876477 (-4.013696) | 3.114502 / 2.142072 (0.972429) | 0.986612 / 4.805227 (-3.818616) | 0.214438 / 6.500664 (-6.286226) | 0.076201 / 0.075469 (0.000732) |\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) | 1.747728 / 1.841788 (-0.094060) | 24.159845 / 8.074308 (16.085537) | 23.553485 / 10.191392 (13.362093) | 0.248387 / 0.680424 (-0.432037) | 0.029850 / 0.534201 (-0.504351) | 0.526416 / 0.579283 (-0.052867) | 0.625681 / 0.434364 (0.191317) | 0.619690 / 0.540337 (0.079352) | 0.827485 / 1.386936 (-0.559451) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#75639f9064dab9549add79fd5ee7de2a4429992c \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.006728 / 0.011353 (-0.004625) | 0.003960 / 0.011008 (-0.007048) | 0.085569 / 0.038508 (0.047061) | 0.077463 / 0.023109 (0.054354) | 0.343112 / 0.275898 (0.067214) | 0.379128 / 0.323480 (0.055648) | 0.004087 / 0.007986 (-0.003899) | 0.003357 / 0.004328 (-0.000972) | 0.065570 / 0.004250 (0.061320) | 0.056259 / 0.037052 (0.019207) | 0.368595 / 0.258489 (0.110106) | 0.402672 / 0.293841 (0.108831) | 0.030946 / 0.128546 (-0.097600) | 0.008509 / 0.075646 (-0.067137) | 0.288552 / 0.419271 (-0.130719) | 0.052134 / 0.043533 (0.008601) | 0.344653 / 0.255139 (0.089514) | 0.374199 / 0.283200 (0.090999) | 0.026251 / 0.141683 (-0.115432) | 1.488258 / 1.452155 (0.036103) | 1.567119 / 1.492716 (0.074402) |\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) | 0.218740 / 0.018006 (0.200734) | 0.465483 / 0.000490 (0.464994) | 0.003959 / 0.000200 (0.003759) | 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) | 0.029860 / 0.037411 (-0.007551) | 0.087968 / 0.014526 (0.073442) | 0.098257 / 0.176557 (-0.078299) | 0.155478 / 0.737135 (-0.581657) | 0.100696 / 0.296338 (-0.195642) |\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) | 0.384642 / 0.215209 (0.169432) | 3.821692 / 2.077655 (1.744038) | 1.838012 / 1.504120 (0.333892) | 1.677554 / 1.541195 (0.136360) | 1.764284 / 1.468490 (0.295794) | 0.487512 / 4.584777 (-4.097265) | 3.614572 / 3.745712 (-0.131141) | 3.300740 / 5.269862 (-1.969122) | 2.079044 / 4.565676 (-2.486632) | 0.057392 / 0.424275 (-0.366883) | 0.007642 / 0.007607 (0.000035) | 0.456161 / 0.226044 (0.230117) | 4.554124 / 2.268929 (2.285196) | 2.319288 / 55.444624 (-53.125336) | 1.972024 / 6.876477 (-4.904452) | 2.210598 / 2.142072 (0.068526) | 0.588442 / 4.805227 (-4.216785) | 0.134474 / 6.500664 (-6.366191) | 0.062682 / 0.075469 (-0.012787) |\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) | 1.243548 / 1.841788 (-0.598239) | 20.267230 / 8.074308 (12.192922) | 14.872096 / 10.191392 (4.680704) | 0.165164 / 0.680424 (-0.515260) | 0.018985 / 0.534201 (-0.515216) | 0.394526 / 0.579283 (-0.184757) | 0.413918 / 0.434364 (-0.020446) | 0.467130 / 0.540337 (-0.073208) | 0.627055 / 1.386936 (-0.759881) |\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) | 0.006940 / 0.011353 (-0.004412) | 0.004203 / 0.011008 (-0.006805) | 0.065828 / 0.038508 (0.027320) | 0.076604 / 0.023109 (0.053495) | 0.401781 / 0.275898 (0.125883) | 0.434838 / 0.323480 (0.111358) | 0.005626 / 0.007986 (-0.002359) | 0.003409 / 0.004328 (-0.000920) | 0.064702 / 0.004250 (0.060452) | 0.057525 / 0.037052 (0.020473) | 0.405032 / 0.258489 (0.146543) | 0.440906 / 0.293841 (0.147065) | 0.032713 / 0.128546 (-0.095833) | 0.008723 / 0.075646 (-0.066923) | 0.071448 / 0.419271 (-0.347823) | 0.048186 / 0.043533 (0.004653) | 0.403950 / 0.255139 (0.148811) | 0.419506 / 0.283200 (0.136307) | 0.023532 / 0.141683 (-0.118150) | 1.496435 / 1.452155 (0.044280) | 1.567236 / 1.492716 (0.074519) |\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) | 0.229194 / 0.018006 (0.211188) | 0.451363 / 0.000490 (0.450873) | 0.003651 / 0.000200 (0.003451) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033674 / 0.037411 (-0.003737) | 0.097521 / 0.014526 (0.082995) | 0.108806 / 0.176557 (-0.067751) | 0.161002 / 0.737135 (-0.576133) | 0.108594 / 0.296338 (-0.187745) |\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) | 0.436638 / 0.215209 (0.221429) | 4.348844 / 2.077655 (2.271189) | 2.341737 / 1.504120 (0.837617) | 2.195850 / 1.541195 (0.654656) | 2.332147 / 1.468490 (0.863657) | 0.496180 / 4.584777 (-4.088597) | 3.680987 / 3.745712 (-0.064725) | 3.332203 / 5.269862 (-1.937659) | 2.099541 / 4.565676 (-2.466136) | 0.058629 / 0.424275 (-0.365646) | 0.007363 / 0.007607 (-0.000245) | 0.517658 / 0.226044 (0.291614) | 5.175321 / 2.268929 (2.906392) | 2.858660 / 55.444624 (-52.585964) | 2.540557 / 6.876477 (-4.335920) | 2.755360 / 2.142072 (0.613288) | 0.595488 / 4.805227 (-4.209739) | 0.134265 / 6.500664 (-6.366399) | 0.062033 / 0.075469 (-0.013436) |\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) | 1.389950 / 1.841788 (-0.451838) | 20.800274 / 8.074308 (12.725966) | 15.314531 / 10.191392 (5.123139) | 0.166822 / 0.680424 (-0.513602) | 0.021099 / 0.534201 (-0.513102) | 0.400388 / 0.579283 (-0.178895) | 0.419981 / 0.434364 (-0.014383) | 0.474259 / 0.540337 (-0.066078) | 0.731678 / 1.386936 (-0.655258) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4566827557acbeba0d4cb66449bb70367e341b05 \"CML watermark\")\n"
] | "2023-08-22T11:27:41" | "2023-08-23T14:01:25" | "2023-08-23T13:52:36" | MEMBER | null | Related to https://github.com/huggingface/datasets/issues/6130 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6165). All of your documentation changes will be reflected on that endpoint.",
"@lhoestq \r\nA test is failing, but I don't think it is due to my changes",
"Good catch ! Could you add a test to make sure transformed IterableDataset objects are still picklable ?\r\n\r\nSomething like `test_pickle_after_many_transforms` in in `test_iterable_dataset.py` that does a bunch or rename, map, take on a dataset and checks that the dataset can be pickled at the end and the reloaded dataset returns the same elements",
"@lhoestq \r\nI added the test and fixed one last method"
] | "2023-08-22T10:07:23" | "2023-08-25T09:22:49" | null | CONTRIBUTOR | null | The "Spawn" method is preferred when multiprocessing on macOS or Windows systems, instead of the "Fork" method on linux systems.
This causes some methods of Iterable Datasets to break when using a dataloader with more than 0 workers.
I fixed the issue by replacing lambda and local methods which are not pickle-able.
See the example below:
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
if __name__ == "__main__":
dataset = load_dataset("lhoestq/demo1", split="train")
dataset = dataset.to_iterable_dataset(num_shards=3)
dataset = dataset.remove_columns(["package_name"])
dataset = dataset.rename_columns({
"review": "review1"
})
dataset = dataset.rename_column("date", "date1")
for sample in DataLoader(dataset, batch_size=None, num_workers=3):
print(sample)
```
To notice the fix on a linux system, adding these lines should do the trick:
```python
import multiprocessing
multiprocessing.set_start_method('spawn')
```
I also removed what looks like code duplication between rename_colums and rename_column
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.006874 / 0.011353 (-0.004479) | 0.004276 / 0.011008 (-0.006732) | 0.085198 / 0.038508 (0.046690) | 0.084281 / 0.023109 (0.061171) | 0.344767 / 0.275898 (0.068869) | 0.377798 / 0.323480 (0.054318) | 0.005656 / 0.007986 (-0.002330) | 0.003601 / 0.004328 (-0.000727) | 0.065486 / 0.004250 (0.061235) | 0.056191 / 0.037052 (0.019139) | 0.351412 / 0.258489 (0.092923) | 0.398591 / 0.293841 (0.104750) | 0.031662 / 0.128546 (-0.096884) | 0.008901 / 0.075646 (-0.066745) | 0.290423 / 0.419271 (-0.128849) | 0.053793 / 0.043533 (0.010260) | 0.347968 / 0.255139 (0.092829) | 0.376978 / 0.283200 (0.093778) | 0.026745 / 0.141683 (-0.114938) | 1.514119 / 1.452155 (0.061964) | 1.580920 / 1.492716 (0.088203) |\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) | 0.273648 / 0.018006 (0.255642) | 0.575176 / 0.000490 (0.574686) | 0.003557 / 0.000200 (0.003357) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031714 / 0.037411 (-0.005697) | 0.089166 / 0.014526 (0.074640) | 0.101525 / 0.176557 (-0.075032) | 0.161855 / 0.737135 (-0.575281) | 0.101391 / 0.296338 (-0.194947) |\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) | 0.380947 / 0.215209 (0.165738) | 3.800527 / 2.077655 (1.722873) | 1.820789 / 1.504120 (0.316669) | 1.657327 / 1.541195 (0.116132) | 1.776242 / 1.468490 (0.307752) | 0.486954 / 4.584777 (-4.097823) | 3.688340 / 3.745712 (-0.057372) | 3.354453 / 5.269862 (-1.915409) | 2.119995 / 4.565676 (-2.445682) | 0.057446 / 0.424275 (-0.366829) | 0.007752 / 0.007607 (0.000145) | 0.461907 / 0.226044 (0.235862) | 4.617870 / 2.268929 (2.348942) | 2.337025 / 55.444624 (-53.107599) | 1.964770 / 6.876477 (-4.911707) | 2.252066 / 2.142072 (0.109993) | 0.591585 / 4.805227 (-4.213642) | 0.134655 / 6.500664 (-6.366009) | 0.060646 / 0.075469 (-0.014823) |\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) | 1.263271 / 1.841788 (-0.578517) | 20.822286 / 8.074308 (12.747978) | 14.710256 / 10.191392 (4.518864) | 0.167285 / 0.680424 (-0.513139) | 0.018302 / 0.534201 (-0.515899) | 0.401023 / 0.579283 (-0.178260) | 0.428956 / 0.434364 (-0.005407) | 0.466120 / 0.540337 (-0.074218) | 0.637868 / 1.386936 (-0.749069) |\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) | 0.007174 / 0.011353 (-0.004179) | 0.004418 / 0.011008 (-0.006590) | 0.065731 / 0.038508 (0.027223) | 0.090457 / 0.023109 (0.067348) | 0.387306 / 0.275898 (0.111408) | 0.427178 / 0.323480 (0.103698) | 0.005699 / 0.007986 (-0.002286) | 0.003662 / 0.004328 (-0.000666) | 0.066190 / 0.004250 (0.061940) | 0.062860 / 0.037052 (0.025808) | 0.388855 / 0.258489 (0.130366) | 0.427853 / 0.293841 (0.134012) | 0.032770 / 0.128546 (-0.095776) | 0.008780 / 0.075646 (-0.066866) | 0.071156 / 0.419271 (-0.348116) | 0.050174 / 0.043533 (0.006641) | 0.385254 / 0.255139 (0.130115) | 0.405069 / 0.283200 (0.121869) | 0.025561 / 0.141683 (-0.116122) | 1.506907 / 1.452155 (0.054752) | 1.543270 / 1.492716 (0.050554) |\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) | 0.304651 / 0.018006 (0.286645) | 0.577269 / 0.000490 (0.576780) | 0.004479 / 0.000200 (0.004279) | 0.000127 / 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) | 0.034070 / 0.037411 (-0.003341) | 0.097664 / 0.014526 (0.083138) | 0.106969 / 0.176557 (-0.069588) | 0.163093 / 0.737135 (-0.574043) | 0.109384 / 0.296338 (-0.186955) |\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) | 0.414823 / 0.215209 (0.199614) | 4.148390 / 2.077655 (2.070735) | 2.114038 / 1.504120 (0.609918) | 1.959316 / 1.541195 (0.418121) | 2.098138 / 1.468490 (0.629648) | 0.486338 / 4.584777 (-4.098439) | 3.642850 / 3.745712 (-0.102863) | 3.458311 / 5.269862 (-1.811551) | 2.185662 / 4.565676 (-2.380014) | 0.057555 / 0.424275 (-0.366720) | 0.007522 / 0.007607 (-0.000085) | 0.497975 / 0.226044 (0.271931) | 4.971528 / 2.268929 (2.702600) | 2.614087 / 55.444624 (-52.830537) | 2.288406 / 6.876477 (-4.588070) | 2.564067 / 2.142072 (0.421995) | 0.582248 / 4.805227 (-4.222979) | 0.134931 / 6.500664 (-6.365733) | 0.062689 / 0.075469 (-0.012780) |\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) | 1.343331 / 1.841788 (-0.498457) | 21.398950 / 8.074308 (13.324642) | 14.620971 / 10.191392 (4.429579) | 0.169779 / 0.680424 (-0.510644) | 0.018683 / 0.534201 (-0.515518) | 0.396152 / 0.579283 (-0.183131) | 0.409596 / 0.434364 (-0.024768) | 0.482875 / 0.540337 (-0.057463) | 0.659977 / 1.386936 (-0.726959) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fd2234b8c802d47db5a5aa939148f98c9c49350 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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) | 0.006662 / 0.011353 (-0.004691) | 0.003959 / 0.011008 (-0.007049) | 0.084447 / 0.038508 (0.045939) | 0.070267 / 0.023109 (0.047158) | 0.310301 / 0.275898 (0.034403) | 0.339866 / 0.323480 (0.016386) | 0.004008 / 0.007986 (-0.003977) | 0.003270 / 0.004328 (-0.001058) | 0.064997 / 0.004250 (0.060746) | 0.053151 / 0.037052 (0.016099) | 0.327867 / 0.258489 (0.069378) | 0.368560 / 0.293841 (0.074719) | 0.031436 / 0.128546 (-0.097111) | 0.008547 / 0.075646 (-0.067099) | 0.288513 / 0.419271 (-0.130758) | 0.051833 / 0.043533 (0.008300) | 0.312660 / 0.255139 (0.057521) | 0.347180 / 0.283200 (0.063980) | 0.024982 / 0.141683 (-0.116701) | 1.472487 / 1.452155 (0.020333) | 1.550138 / 1.492716 (0.057422) |\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) | 0.208443 / 0.018006 (0.190437) | 0.451927 / 0.000490 (0.451437) | 0.004452 / 0.000200 (0.004252) | 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) | 0.029164 / 0.037411 (-0.008247) | 0.085801 / 0.014526 (0.071275) | 0.096229 / 0.176557 (-0.080327) | 0.153063 / 0.737135 (-0.584072) | 0.097712 / 0.296338 (-0.198626) |\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) | 0.383969 / 0.215209 (0.168760) | 3.829216 / 2.077655 (1.751561) | 1.854466 / 1.504120 (0.350346) | 1.684149 / 1.541195 (0.142954) | 1.759422 / 1.468490 (0.290932) | 0.480229 / 4.584777 (-4.104548) | 3.653363 / 3.745712 (-0.092349) | 3.264456 / 5.269862 (-2.005406) | 2.020579 / 4.565676 (-2.545097) | 0.056920 / 0.424275 (-0.367355) | 0.007625 / 0.007607 (0.000018) | 0.458559 / 0.226044 (0.232515) | 4.580288 / 2.268929 (2.311359) | 2.353783 / 55.444624 (-53.090841) | 1.967223 / 6.876477 (-4.909253) | 2.182707 / 2.142072 (0.040634) | 0.631341 / 4.805227 (-4.173886) | 0.141656 / 6.500664 (-6.359008) | 0.059918 / 0.075469 (-0.015551) |\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) | 1.279635 / 1.841788 (-0.562153) | 19.725763 / 8.074308 (11.651455) | 14.477946 / 10.191392 (4.286554) | 0.164360 / 0.680424 (-0.516064) | 0.018286 / 0.534201 (-0.515915) | 0.394935 / 0.579283 (-0.184348) | 0.419638 / 0.434364 (-0.014726) | 0.460366 / 0.540337 (-0.079972) | 0.636876 / 1.386936 (-0.750060) |\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) | 0.006568 / 0.011353 (-0.004785) | 0.004270 / 0.011008 (-0.006738) | 0.065522 / 0.038508 (0.027014) | 0.071597 / 0.023109 (0.048487) | 0.394929 / 0.275898 (0.119031) | 0.427548 / 0.323480 (0.104068) | 0.005320 / 0.007986 (-0.002665) | 0.003366 / 0.004328 (-0.000962) | 0.065780 / 0.004250 (0.061530) | 0.055390 / 0.037052 (0.018338) | 0.397950 / 0.258489 (0.139461) | 0.435800 / 0.293841 (0.141959) | 0.031816 / 0.128546 (-0.096730) | 0.008555 / 0.075646 (-0.067091) | 0.072110 / 0.419271 (-0.347161) | 0.049077 / 0.043533 (0.005544) | 0.390065 / 0.255139 (0.134926) | 0.410294 / 0.283200 (0.127094) | 0.023389 / 0.141683 (-0.118294) | 1.491491 / 1.452155 (0.039336) | 1.551057 / 1.492716 (0.058341) |\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) | 0.243869 / 0.018006 (0.225862) | 0.451961 / 0.000490 (0.451471) | 0.019834 / 0.000200 (0.019634) | 0.000114 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031031 / 0.037411 (-0.006380) | 0.088189 / 0.014526 (0.073663) | 0.101743 / 0.176557 (-0.074814) | 0.155236 / 0.737135 (-0.581899) | 0.101245 / 0.296338 (-0.195094) |\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) | 0.422178 / 0.215209 (0.206969) | 4.199989 / 2.077655 (2.122334) | 2.228816 / 1.504120 (0.724696) | 2.057172 / 1.541195 (0.515978) | 2.162651 / 1.468490 (0.694161) | 0.491186 / 4.584777 (-4.093591) | 3.666221 / 3.745712 (-0.079491) | 3.289531 / 5.269862 (-1.980331) | 2.050027 / 4.565676 (-2.515650) | 0.057464 / 0.424275 (-0.366811) | 0.007379 / 0.007607 (-0.000228) | 0.506532 / 0.226044 (0.280487) | 5.066385 / 2.268929 (2.797456) | 2.694405 / 55.444624 (-52.750219) | 2.372200 / 6.876477 (-4.504277) | 2.562724 / 2.142072 (0.420652) | 0.615474 / 4.805227 (-4.189753) | 0.148284 / 6.500664 (-6.352380) | 0.061380 / 0.075469 (-0.014089) |\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) | 1.332649 / 1.841788 (-0.509139) | 20.591063 / 8.074308 (12.516755) | 14.105253 / 10.191392 (3.913861) | 0.151886 / 0.680424 (-0.528537) | 0.018200 / 0.534201 (-0.516001) | 0.395278 / 0.579283 (-0.184005) | 0.407113 / 0.434364 (-0.027251) | 0.473168 / 0.540337 (-0.067170) | 0.660766 / 1.386936 (-0.726170) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8 \"CML watermark\")\n"
] | "2023-08-21T14:57:54" | "2023-08-21T16:27:05" | "2023-08-21T16:18:26" | CONTRIBUTOR | null | When I try to push to an arrow repo (can provide the link on Slack), it uploads the files but fails to update the metadata, with
```
File "app.py", line 123, in add_new_eval
eval_results[level].push_to_hub(my_repo, token=TOKEN, split=SPLIT)
File "blabla_my_env_path/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5501, in push_to_hub
if not metadata_configs:
UnboundLocalError: local variable 'metadata_configs' referenced before assignment
```
This fixes it. | {
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https://api.github.com/repos/huggingface/datasets/issues/6163 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6163/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6163/comments | https://api.github.com/repos/huggingface/datasets/issues/6163/events | https://github.com/huggingface/datasets/issues/6163 | 1,857,682,241 | I_kwDODunzps5uuftB | 6,163 | Error type: ArrowInvalid Details: Failed to parse string: '[254,254]' as a scalar of type int32 | {
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"Answered on the forum [here](https://discuss.huggingface.co/t/error-type-arrowinvalid-details-failed-to-parse-string-254-254-as-a-scalar-of-type-int32/51323)."
] | "2023-08-19T11:34:40" | "2023-08-21T13:28:16" | null | NONE | null | ### Describe the bug
I am getting the following error while I am trying to upload the CSV sheet to train a model. My CSV sheet content is exactly same as shown in the example CSV file in the Auto Train page. Attaching screenshot of error for reference. I have also tried converting the index of the answer that are integer into string by placing inverted commas and also without inverted commas.
Can anyone please help me out?
FYI : I am using Chrome browser.
Error type: ArrowInvalid
Details: Failed to parse string: '[254,254]' as a scalar of type int32
### Steps to reproduce the bug
Kindly let me know how to fix this?
### Expected behavior
Kindly let me know how to fix this?
### Environment info
Kindly let me know how to fix this? | {
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https://api.github.com/repos/huggingface/datasets/issues/6162 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6162/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6162/comments | https://api.github.com/repos/huggingface/datasets/issues/6162/events | https://github.com/huggingface/datasets/issues/6162 | 1,856,198,342 | I_kwDODunzps5uo1bG | 6,162 | load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields | {
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} | [] | open | false | null | [] | null | [
"Hi ! Feel free to open a discussion at https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T/discussions to ask the file to be fixed (or directly open a PR with the fixed file)\r\n\r\n`datasets` expects all the examples to have the same fields",
"@lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570). Maybe setting `streaming=True` can workaround this problem. Would you agree with my statement? ",
"> @lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570).\r\n\r\nCorrect. Therefore any example that doesn't follow the inferred schema will make the code fail.\r\n\r\n> Maybe setting streaming=True can workaround this problem. Would you agree with my statement?\r\n\r\nYou'll meet the same problem but later - when streaming and arriving at the problematic example",
"@lhoestq I just run below test with streaming=True and is not failing at the problematic example\r\n```python\r\nds = load_dataset('json', data_files='/path_to_local_RedPajamaData/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl', streaming=True)\r\ncount = 0\r\nfor i in ds['train']:\r\n count += 1\r\n print(count)\r\n```\r\n\r\nand completes the 262241 samples successfully. It does error our when streaming is not used "
] | "2023-08-18T07:19:39" | "2023-08-18T17:00:35" | null | NONE | null | ### Describe the bug
When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**.
When deleting that line the loading is successful.
We also tried loading this file with the discrepancy using this function and it is successful
```python
os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T"
ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train']
```
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. Load one jsonl from the redpajama-data-1T
```bash
wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl
```
2.Load dataset will give error:
```python
from datasets import load_dataset
ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl')
```
_TypeError: Couldn't cast array of type
Struct
<content_hash: string,
timestamp: string,
source: string,
line_count: int64,
max_line_length: int64,
avg_line_length: double,
alnum_prop: double,
repo_name: string,
id: string,
size: string,
binary: bool,
copies: string,
ref: string,
path: string,
mode: string,
license: string,
language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>**
to
{'content_hash': Value(dtype='string', id=None),
'timestamp': Value(dtype='string', id=None),
'source': Value(dtype='string', id=None),
'line_count': Value(dtype='int64', id=None),
'max_line_length': Value(dtype='int64', id=None),
'avg_line_length': Value(dtype='float64', id=None),
'alnum_prop': Value(dtype='float64', id=None),
'repo_name': Value(dtype='string', id=None),
'id': Value(dtype='string', id=None),
'size': Value(dtype='string', id=None),
'binary': Value(dtype='bool', id=None),
'copies': Value(dtype='string', id=None),
'ref': Value(dtype='string', id=None),
'path': Value(dtype='string', id=None),
'mode': Value(dtype='string', id=None),
'license': Value(dtype='string', id=None),
'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_
3. To remove the line causing the problem that includes the **symlink_target: string>** do:
```bash
sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl
```
4. Rerun the loading function now is succesful:
```python
from datasets import load_dataset
ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl')
```
### Expected behavior
Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out.
### Environment info
- `datasets` version: 2.14.1
- Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.17
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | {
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