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https://github.com/huggingface/datasets/pull/6307
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PR_kwDODunzps5c9s0j
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|
Fix typo in code example in docs
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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) | 0.011548 / 0.011353 (0.000196) | 0.004630 / 0.011008 (-0.006378) | 0.105349 / 0.038508 (0.066841) | 0.110557 / 0.023109 (0.087448) | 0.395463 / 0.275898 (0.119565) | 0.448391 / 0.323480 (0.124912) | 0.005112 / 0.007986 (-0.002873) | 0.003854 / 0.004328 (-0.000474) | 0.088513 / 0.004250 (0.084263) | 0.073081 / 0.037052 (0.036028) | 0.391572 / 0.258489 (0.133083) | 0.459543 / 0.293841 (0.165702) | 0.040424 / 0.128546 (-0.088122) | 0.010306 / 0.075646 (-0.065340) | 0.365493 / 0.419271 (-0.053778) | 0.068154 / 0.043533 (0.024622) | 0.397675 / 0.255139 (0.142536) | 0.447147 / 0.283200 (0.163947) | 0.033482 / 0.141683 (-0.108201) | 1.857087 / 1.452155 (0.404932) | 1.973311 / 1.492716 (0.480595) |\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) | 0.257938 / 0.018006 (0.239932) | 0.569572 / 0.000490 (0.569083) | 0.012155 / 0.000200 (0.011955) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033094 / 0.037411 (-0.004318) | 0.102370 / 0.014526 (0.087844) | 0.122421 / 0.176557 (-0.054136) | 0.189983 / 0.737135 (-0.547152) | 0.117902 / 0.296338 (-0.178437) |\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) | 0.468419 / 0.215209 (0.253210) | 4.671410 / 2.077655 (2.593755) | 2.371136 / 1.504120 (0.867016) | 2.191877 / 1.541195 (0.650682) | 2.301894 / 1.468490 (0.833404) | 0.572260 / 4.584777 (-4.012517) | 4.302031 / 3.745712 (0.556319) | 4.128431 / 5.269862 (-1.141431) | 2.464543 / 4.565676 (-2.101133) | 0.067663 / 0.424275 (-0.356612) | 0.008947 / 0.007607 (0.001340) | 0.570063 / 0.226044 (0.344018) | 5.684460 / 2.268929 (3.415531) | 2.969708 / 55.444624 (-52.474916) | 2.573568 / 6.876477 (-4.302909) | 2.666074 / 2.142072 (0.524001) | 0.710098 / 4.805227 (-4.095129) | 0.158413 / 6.500664 (-6.342251) | 0.072776 / 0.075469 (-0.002693) |\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) | 1.564166 / 1.841788 (-0.277622) | 23.612774 / 8.074308 (15.538465) | 17.725070 / 10.191392 (7.533678) | 0.178982 / 0.680424 (-0.501442) | 0.021615 / 0.534201 (-0.512586) | 0.467090 / 0.579283 (-0.112193) | 0.472648 / 0.434364 (0.038284) | 0.578820 / 0.540337 (0.038483) | 0.783533 / 1.386936 (-0.603403) |\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) | 0.008895 / 0.011353 (-0.002458) | 0.004617 / 0.011008 (-0.006392) | 0.077677 / 0.038508 (0.039169) | 0.090283 / 0.023109 (0.067174) | 0.491115 / 0.275898 (0.215217) | 0.525189 / 0.323480 (0.201709) | 0.007845 / 0.007986 (-0.000141) | 0.003742 / 0.004328 (-0.000586) | 0.077856 / 0.004250 (0.073606) | 0.067447 / 0.037052 (0.030394) | 0.488423 / 0.258489 (0.229933) | 0.532938 / 0.293841 (0.239097) | 0.041035 / 0.128546 (-0.087511) | 0.009917 / 0.075646 (-0.065730) | 0.085313 / 0.419271 (-0.333958) | 0.063374 / 0.043533 (0.019841) | 0.472287 / 0.255139 (0.217148) | 0.509773 / 0.283200 (0.226573) | 0.028706 / 0.141683 (-0.112977) | 1.775558 / 1.452155 (0.323403) | 1.967778 / 1.492716 (0.475061) |\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) | 0.249834 / 0.018006 (0.231828) | 0.467266 / 0.000490 (0.466776) | 0.005837 / 0.000200 (0.005637) | 0.000128 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038759 / 0.037411 (0.001347) | 0.113156 / 0.014526 (0.098630) | 0.123936 / 0.176557 (-0.052621) | 0.186831 / 0.737135 (-0.550304) | 0.125195 / 0.296338 (-0.171143) |\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) | 0.545666 / 0.215209 (0.330457) | 5.465713 / 2.077655 (3.388058) | 2.941279 / 1.504120 (1.437159) | 2.688377 / 1.541195 (1.147182) | 2.619501 / 1.468490 (1.151010) | 0.577974 / 4.584777 (-4.006803) | 4.300966 / 3.745712 (0.555254) | 3.879552 / 5.269862 (-1.390310) | 2.454932 / 4.565676 (-2.110745) | 0.069233 / 0.424275 (-0.355043) | 0.009729 / 0.007607 (0.002122) | 0.595290 / 0.226044 (0.369245) | 5.945445 / 2.268929 (3.676516) | 3.314607 / 55.444624 (-52.130017) | 2.894474 / 6.876477 (-3.982002) | 3.140790 / 2.142072 (0.998718) | 0.695808 / 4.805227 (-4.109419) | 0.158087 / 6.500664 (-6.342577) | 0.071374 / 0.075469 (-0.004095) |\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) | 1.706482 / 1.841788 (-0.135306) | 24.022666 / 8.074308 (15.948358) | 17.658003 / 10.191392 (7.466611) | 0.196771 / 0.680424 (-0.483653) | 0.023928 / 0.534201 (-0.510273) | 0.471992 / 0.579283 (-0.107291) | 0.510463 / 0.434364 (0.076099) | 0.621250 / 0.540337 (0.080912) | 0.807670 / 1.386936 (-0.579266) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f77539cbd88d00ec1ab2b9d4edfd01d5a58ef88a \"CML watermark\")\n"
] | 2023-10-17T02:28:50Z
| 2023-10-17T12:59:26Z
| 2023-10-17T06:36:19Z
|
CONTRIBUTOR
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I_kwDODunzps5vS0I7
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|
Loading Meteor metric in HF evaluate module crashes due to datasets import issue
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"Our minimal Python version requirement is 3.8, so we dropped `importlib_metadata`. \r\n\r\nFeel free to open a PR in the `evaluate` repo to replace the problematic import with\r\n```python\r\nif PY_VERSION < version.parse(\"3.8\"):\r\n import importlib_metadata\r\nelse:\r\n import importlib.metadata as importlib_metadata\r\n```",
"Any idea when you guys will release the next version which deals with this problem?\r\nI'm still having the same issue with py 3.10 when I install the lib with pip.\r\nI'm assuming that it has not yet been updated since the merge was 3 days ago.",
"Yes, this requires a new `evaluate` release (cc @lvwerra for this). \r\n\r\nIn the meantime, you can get the fixed version by installing `evaluate` from `main`: `pip install git+https://github.com/huggingface/evaluate.git`",
"I'll aim for a release this week!"
] | 2023-08-25T14:54:06Z
| 2023-09-04T16:41:11Z
| 2023-08-31T14:38:23Z
|
NONE
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### Describe the bug
When using python3.9 and ```evaluate``` module loading Meteor metric crashes at a non-existent import from ```datasets.config``` in ```datasets v2.14```
### Steps to reproduce the bug
```
from evaluate import load
meteor = load("meteor")
```
produces the following error:
```
from datasets.config import importlib_metadata, version
ImportError: cannot import name 'importlib_metadata' from 'datasets.config' (<path_to_project>/venv/lib/python3.9/site-packages/datasets/config.py)
```
### Expected behavior
```datasets``` of v2.10 has the following workaround in ```config.py```:
```
if PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
```
However, it's absent in v2.14 which might be the cause of the issue.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-13.5-arm64-arm-64bit
- Python version: 3.9.6
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- Evaluate version: 0.4.0
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Deprecate num_proc parameter in DownloadManager.extract
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hey @mariosasko . Can you please help me with why the tests keep failing. I have reviewed the code changes multiple times but can't spot any mistakes. ",
"You can fix this failure by formatting your code with the `make style` command (run it from the root of the cloned repo).",
"hey @mariosasko ,i cant understand how to use the `make style` command .I searched for it on the internet but cant find any results. \r\nSo i formatted the code using vs-code document formatter. Hope this helps.",
"`make style` runs the \"style\" target defined here: https://github.com/huggingface/datasets/blob/f09f781be3278156ce3aa6ec90c1926b1846a78f/Makefile#L12\r\n\r\nThis seems to be a good tutorial on Makefiles: https://opensource.com/article/18/8/what-how-makefile",
"\r\n\r\n\r\n\r\n> `make style` runs the \"style\" target defined here:\r\n> \r\n> https://github.com/huggingface/datasets/blob/f09f781be3278156ce3aa6ec90c1926b1846a78f/Makefile#L12\r\n> \r\n> This seems to be a good tutorial on Makefiles: https://opensource.com/article/18/8/what-how-makefile\r\n\r\nThanks! I will look into this :relaxed: "
] | 2022-10-20T09:52:52Z
| 2022-10-25T18:06:56Z
| 2022-10-25T15:56:45Z
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CONTRIBUTOR
| null | null | null |
fixes #5132 : Deprecated the `num_proc` parameter in `DownloadManager.extract` by passing `num_proc` parameter to `map_nested` .
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PR_kwDODunzps6Nkhla
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Use pyupgrade --py39-plus for remaining files
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[] | 2025-03-06T02:12:25Z
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This work follows #7428. And "requires-python" is set in pyproject.toml
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PR_kwDODunzps5Yy2VO
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Fix import in `image_load` doc
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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) | 0.009072 / 0.011353 (-0.002281) | 0.006088 / 0.011008 (-0.004920) | 0.134520 / 0.038508 (0.096011) | 0.074935 / 0.023109 (0.051826) | 0.480364 / 0.275898 (0.204466) | 0.568943 / 0.323480 (0.245464) | 0.006821 / 0.007986 (-0.001164) | 0.004941 / 0.004328 (0.000612) | 0.083274 / 0.004250 (0.079023) | 0.061080 / 0.037052 (0.024028) | 0.478960 / 0.258489 (0.220471) | 0.542720 / 0.293841 (0.248879) | 0.058023 / 0.128546 (-0.070524) | 0.020120 / 0.075646 (-0.055526) | 0.492680 / 0.419271 (0.073409) | 0.079118 / 0.043533 (0.035585) | 0.425087 / 0.255139 (0.169948) | 0.603228 / 0.283200 (0.320028) | 0.044102 / 0.141683 (-0.097581) | 2.138848 / 1.452155 (0.686693) | 2.454418 / 1.492716 (0.961702) |\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) | 0.255745 / 0.018006 (0.237738) | 0.587559 / 0.000490 (0.587069) | 0.006872 / 0.000200 (0.006672) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038480 / 0.037411 (0.001069) | 0.115479 / 0.014526 (0.100953) | 0.138395 / 0.176557 (-0.038161) | 0.218007 / 0.737135 (-0.519129) | 0.128866 / 0.296338 (-0.167472) |\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) | 0.756089 / 0.215209 (0.540880) | 7.754631 / 2.077655 (5.676976) | 3.615716 / 1.504120 (2.111596) | 2.994327 / 1.541195 (1.453132) | 3.196169 / 1.468490 (1.727679) | 1.066937 / 4.584777 (-3.517840) | 6.079595 / 3.745712 (2.333883) | 5.455523 / 5.269862 (0.185661) | 3.559036 / 4.565676 (-1.006640) | 0.113044 / 0.424275 (-0.311231) | 0.011401 / 0.007607 (0.003794) | 0.961475 / 0.226044 (0.735430) | 8.664226 / 2.268929 (6.395298) | 4.203804 / 55.444624 (-51.240821) | 3.122437 / 6.876477 (-3.754039) | 3.549168 / 2.142072 (1.407095) | 1.213035 / 4.805227 (-3.592193) | 0.274725 / 6.500664 (-6.225939) | 0.094499 / 0.075469 (0.019030) |\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) | 1.770299 / 1.841788 (-0.071489) | 27.644591 / 8.074308 (19.570283) | 23.239529 / 10.191392 (13.048137) | 0.270185 / 0.680424 (-0.410238) | 0.033563 / 0.534201 (-0.500638) | 0.588301 / 0.579283 (0.009018) | 0.658746 / 0.434364 (0.224382) | 0.644476 / 0.540337 (0.104139) | 0.834314 / 1.386936 (-0.552622) |\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) | 0.011021 / 0.011353 (-0.000332) | 0.006719 / 0.011008 (-0.004289) | 0.087669 / 0.038508 (0.049161) | 0.088905 / 0.023109 (0.065796) | 0.594230 / 0.275898 (0.318332) | 0.620929 / 0.323480 (0.297449) | 0.006776 / 0.007986 (-0.001210) | 0.004725 / 0.004328 (0.000396) | 0.082006 / 0.004250 (0.077756) | 0.072164 / 0.037052 (0.035111) | 0.604489 / 0.258489 (0.346000) | 0.598520 / 0.293841 (0.304679) | 0.057534 / 0.128546 (-0.071013) | 0.016799 / 0.075646 (-0.058847) | 0.115029 / 0.419271 (-0.304243) | 0.070013 / 0.043533 (0.026481) | 0.561773 / 0.255139 (0.306634) | 0.624097 / 0.283200 (0.340897) | 0.043518 / 0.141683 (-0.098164) | 2.017089 / 1.452155 (0.564934) | 2.188159 / 1.492716 (0.695443) |\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) | 0.386476 / 0.018006 (0.368469) | 0.633195 / 0.000490 (0.632705) | 0.028469 / 0.000200 (0.028269) | 0.000159 / 0.000054 (0.000104) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.040020 / 0.037411 (0.002609) | 0.112927 / 0.014526 (0.098402) | 0.143663 / 0.176557 (-0.032894) | 0.205931 / 0.737135 (-0.531204) | 0.177814 / 0.296338 (-0.118524) |\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) | 0.711542 / 0.215209 (0.496333) | 7.518535 / 2.077655 (5.440880) | 3.714930 / 1.504120 (2.210810) | 3.031999 / 1.541195 (1.490804) | 3.328497 / 1.468490 (1.860006) | 0.858912 / 4.584777 (-3.725865) | 6.108384 / 3.745712 (2.362672) | 5.184329 / 5.269862 (-0.085532) | 3.622589 / 4.565676 (-0.943087) | 0.096933 / 0.424275 (-0.327342) | 0.008727 / 0.007607 (0.001120) | 0.830102 / 0.226044 (0.604057) | 8.331959 / 2.268929 (6.063030) | 4.165106 / 55.444624 (-51.279519) | 3.477003 / 6.876477 (-3.399474) | 3.794225 / 2.142072 (1.652153) | 1.237667 / 4.805227 (-3.567561) | 0.233731 / 6.500664 (-6.266933) | 0.076682 / 0.075469 (0.001213) |\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) | 1.944813 / 1.841788 (0.103026) | 27.666997 / 8.074308 (19.592689) | 24.562677 / 10.191392 (14.371285) | 0.279320 / 0.680424 (-0.401104) | 0.037802 / 0.534201 (-0.496399) | 0.553579 / 0.579283 (-0.025704) | 0.718229 / 0.434364 (0.283865) | 0.623456 / 0.540337 (0.083118) | 0.856777 / 1.386936 (-0.530159) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c2a9d31d5e720e85976af8b457d45755a7e6911 \"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) | 0.007716 / 0.011353 (-0.003637) | 0.004624 / 0.011008 (-0.006384) | 0.099987 / 0.038508 (0.061479) | 0.082651 / 0.023109 (0.059542) | 0.376277 / 0.275898 (0.100379) | 0.401210 / 0.323480 (0.077730) | 0.004528 / 0.007986 (-0.003458) | 0.003763 / 0.004328 (-0.000566) | 0.076274 / 0.004250 (0.072024) | 0.062933 / 0.037052 (0.025881) | 0.393881 / 0.258489 (0.135392) | 0.431695 / 0.293841 (0.137854) | 0.036795 / 0.128546 (-0.091752) | 0.009935 / 0.075646 (-0.065712) | 0.343638 / 0.419271 (-0.075634) | 0.061456 / 0.043533 (0.017923) | 0.372235 / 0.255139 (0.117096) | 0.412994 / 0.283200 (0.129794) | 0.027993 / 0.141683 (-0.113690) | 1.798018 / 1.452155 (0.345863) | 1.898502 / 1.492716 (0.405786) |\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) | 0.237330 / 0.018006 (0.219324) | 0.494956 / 0.000490 (0.494467) | 0.003543 / 0.000200 (0.003343) | 0.000113 / 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) | 0.034084 / 0.037411 (-0.003327) | 0.093407 / 0.014526 (0.078881) | 0.108378 / 0.176557 (-0.068179) | 0.177016 / 0.737135 (-0.560119) | 0.108622 / 0.296338 (-0.187716) |\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) | 0.456449 / 0.215209 (0.241240) | 4.522405 / 2.077655 (2.444750) | 2.206564 / 1.504120 (0.702444) | 1.994185 / 1.541195 (0.452990) | 2.083785 / 1.468490 (0.615295) | 0.563352 / 4.584777 (-4.021425) | 4.207295 / 3.745712 (0.461583) | 3.783061 / 5.269862 (-1.486800) | 2.372874 / 4.565676 (-2.192802) | 0.066907 / 0.424275 (-0.357368) | 0.009013 / 0.007607 (0.001406) | 0.537852 / 0.226044 (0.311808) | 5.349928 / 2.268929 (3.081000) | 2.759409 / 55.444624 (-52.685215) | 2.345972 / 6.876477 (-4.530505) | 2.630559 / 2.142072 (0.488486) | 0.681134 / 4.805227 (-4.124093) | 0.157898 / 6.500664 (-6.342766) | 0.071638 / 0.075469 (-0.003831) |\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) | 1.470730 / 1.841788 (-0.371058) | 22.479252 / 8.074308 (14.404944) | 16.543080 / 10.191392 (6.351688) | 0.191943 / 0.680424 (-0.488481) | 0.021641 / 0.534201 (-0.512560) | 0.467571 / 0.579283 (-0.111712) | 0.486728 / 0.434364 (0.052364) | 0.543359 / 0.540337 (0.003021) | 0.733968 / 1.386936 (-0.652968) |\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) | 0.008135 / 0.011353 (-0.003218) | 0.004662 / 0.011008 (-0.006347) | 0.077218 / 0.038508 (0.038710) | 0.092220 / 0.023109 (0.069111) | 0.481219 / 0.275898 (0.205321) | 0.530373 / 0.323480 (0.206893) | 0.006418 / 0.007986 (-0.001568) | 0.003924 / 0.004328 (-0.000404) | 0.076681 / 0.004250 (0.072431) | 0.068693 / 0.037052 (0.031641) | 0.491938 / 0.258489 (0.233449) | 0.540501 / 0.293841 (0.246660) | 0.038106 / 0.128546 (-0.090441) | 0.010035 / 0.075646 (-0.065611) | 0.084502 / 0.419271 (-0.334769) | 0.057234 / 0.043533 (0.013701) | 0.483239 / 0.255139 (0.228100) | 0.510026 / 0.283200 (0.226826) | 0.028770 / 0.141683 (-0.112913) | 1.854937 / 1.452155 (0.402783) | 1.948268 / 1.492716 (0.455552) |\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) | 0.380192 / 0.018006 (0.362186) | 0.523318 / 0.000490 (0.522828) | 0.051153 / 0.000200 (0.050953) | 0.000691 / 0.000054 (0.000637) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036838 / 0.037411 (-0.000573) | 0.109202 / 0.014526 (0.094676) | 0.124110 / 0.176557 (-0.052446) | 0.186717 / 0.737135 (-0.550419) | 0.124088 / 0.296338 (-0.172250) |\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) | 0.506411 / 0.215209 (0.291202) | 5.045421 / 2.077655 (2.967766) | 2.711911 / 1.504120 (1.207791) | 2.531668 / 1.541195 (0.990474) | 2.635680 / 1.468490 (1.167190) | 0.578395 / 4.584777 (-4.006382) | 4.206891 / 3.745712 (0.461178) | 3.851063 / 5.269862 (-1.418799) | 2.388327 / 4.565676 (-2.177350) | 0.068041 / 0.424275 (-0.356234) | 0.008769 / 0.007607 (0.001162) | 0.594170 / 0.226044 (0.368125) | 5.953138 / 2.268929 (3.684210) | 3.290586 / 55.444624 (-52.154038) | 2.877086 / 6.876477 (-3.999390) | 3.138600 / 2.142072 (0.996528) | 0.686393 / 4.805227 (-4.118834) | 0.156541 / 6.500664 (-6.344123) | 0.071514 / 0.075469 (-0.003955) |\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) | 1.613514 / 1.841788 (-0.228274) | 23.593185 / 8.074308 (15.518877) | 17.146647 / 10.191392 (6.955255) | 0.177230 / 0.680424 (-0.503193) | 0.023661 / 0.534201 (-0.510540) | 0.472367 / 0.579283 (-0.106916) | 0.484614 / 0.434364 (0.050250) | 0.547150 / 0.540337 (0.006813) | 0.843726 / 1.386936 (-0.543210) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dba64cd381bfe384cb64ab9826f6054a0f1df1ff \"CML watermark\")\n"
] | 2023-08-25T13:12:19Z
| 2023-08-25T16:12:46Z
| 2023-08-25T16:02:24Z
|
COLLABORATOR
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Reported on [Discord](https://discord.com/channels/879548962464493619/1144295822209581168/1144295822209581168)
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Enable setting config as default when push_to_hub
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6500). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"This is ready for review @huggingface/datasets. ",
"Also what if the config is being overwritten and it was the default config and the user doesn't pass `set_default` ?\r\nI'd expect the config to keep being the default one but lmk what you think",
"How can you unset a config as the default one? In the case you mentioned, I would expect the config not being the default one.",
"Maybe by passing `set_default=False` ? (set_default can be None by default)",
"I think that way we are unnecessarily complicating the logic of `push_to_hub` and as I told you, I would expect the contrary: the result of calling `push_to_hub` with a determined set of arguments should always be the same, independently of previous calls and the current state of the config on the Hub. Push to hub should be somehow stateless in that sense, and IMO the user expects that the push overwrites previous config if already present on the Hub. I find very confusing making it to partially update the config on the Hub.",
"That makes sense, having it stateless is simpler and no need to do something too fancy indeed",
"<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) | 0.005329 / 0.011353 (-0.006024) | 0.002998 / 0.011008 (-0.008010) | 0.063756 / 0.038508 (0.025248) | 0.051713 / 0.023109 (0.028603) | 0.248135 / 0.275898 (-0.027763) | 0.269136 / 0.323480 (-0.054344) | 0.002970 / 0.007986 (-0.005015) | 0.002566 / 0.004328 (-0.001763) | 0.048110 / 0.004250 (0.043859) | 0.038415 / 0.037052 (0.001363) | 0.254012 / 0.258489 (-0.004477) | 0.281915 / 0.293841 (-0.011926) | 0.027503 / 0.128546 (-0.101043) | 0.010370 / 0.075646 (-0.065276) | 0.208965 / 0.419271 (-0.210306) | 0.035508 / 0.043533 (-0.008024) | 0.249116 / 0.255139 (-0.006023) | 0.266350 / 0.283200 (-0.016850) | 0.018440 / 0.141683 (-0.123243) | 1.101089 / 1.452155 (-0.351066) | 1.164870 / 1.492716 (-0.327847) |\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) | 0.090909 / 0.018006 (0.072903) | 0.298041 / 0.000490 (0.297551) | 0.000211 / 0.000200 (0.000012) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018137 / 0.037411 (-0.019275) | 0.059574 / 0.014526 (0.045048) | 0.071754 / 0.176557 (-0.104803) | 0.117980 / 0.737135 (-0.619155) | 0.072903 / 0.296338 (-0.223435) |\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) | 0.282844 / 0.215209 (0.067635) | 2.740916 / 2.077655 (0.663261) | 1.444546 / 1.504120 (-0.059574) | 1.321904 / 1.541195 (-0.219291) | 1.356957 / 1.468490 (-0.111533) | 0.568389 / 4.584777 (-4.016388) | 2.354042 / 3.745712 (-1.391671) | 2.719427 / 5.269862 (-2.550435) | 1.719616 / 4.565676 (-2.846061) | 0.062537 / 0.424275 (-0.361738) | 0.004915 / 0.007607 (-0.002692) | 0.334716 / 0.226044 (0.108672) | 3.299499 / 2.268929 (1.030571) | 1.814629 / 55.444624 (-53.629996) | 1.515245 / 6.876477 (-5.361232) | 1.553085 / 2.142072 (-0.588987) | 0.643859 / 4.805227 (-4.161368) | 0.116650 / 6.500664 (-6.384014) | 0.041432 / 0.075469 (-0.034037) |\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) | 0.948227 / 1.841788 (-0.893561) | 11.331103 / 8.074308 (3.256795) | 10.209658 / 10.191392 (0.018266) | 0.126721 / 0.680424 (-0.553703) | 0.013638 / 0.534201 (-0.520563) | 0.282540 / 0.579283 (-0.296743) | 0.262635 / 0.434364 (-0.171729) | 0.335357 / 0.540337 (-0.204981) | 0.441798 / 1.386936 (-0.945138) |\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) | 0.005200 / 0.011353 (-0.006153) | 0.003012 / 0.011008 (-0.007996) | 0.047571 / 0.038508 (0.009063) | 0.055069 / 0.023109 (0.031959) | 0.271150 / 0.275898 (-0.004748) | 0.294957 / 0.323480 (-0.028523) | 0.003922 / 0.007986 (-0.004064) | 0.002627 / 0.004328 (-0.001702) | 0.047777 / 0.004250 (0.043527) | 0.039507 / 0.037052 (0.002454) | 0.276314 / 0.258489 (0.017825) | 0.300436 / 0.293841 (0.006595) | 0.028951 / 0.128546 (-0.099595) | 0.010583 / 0.075646 (-0.065063) | 0.056535 / 0.419271 (-0.362737) | 0.032654 / 0.043533 (-0.010879) | 0.272945 / 0.255139 (0.017806) | 0.291909 / 0.283200 (0.008709) | 0.017545 / 0.141683 (-0.124138) | 1.195897 / 1.452155 (-0.256258) | 1.171855 / 1.492716 (-0.320861) |\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) | 0.091919 / 0.018006 (0.073913) | 0.299297 / 0.000490 (0.298807) | 0.000225 / 0.000200 (0.000025) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022271 / 0.037411 (-0.015140) | 0.068903 / 0.014526 (0.054377) | 0.083767 / 0.176557 (-0.092790) | 0.120239 / 0.737135 (-0.616896) | 0.083448 / 0.296338 (-0.212891) |\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) | 0.295353 / 0.215209 (0.080144) | 2.911452 / 2.077655 (0.833798) | 1.577941 / 1.504120 (0.073821) | 1.454514 / 1.541195 (-0.086681) | 1.459575 / 1.468490 (-0.008915) | 0.572475 / 4.584777 (-4.012302) | 2.443634 / 3.745712 (-1.302078) | 2.801171 / 5.269862 (-2.468691) | 1.724214 / 4.565676 (-2.841462) | 0.063539 / 0.424275 (-0.360736) | 0.004939 / 0.007607 (-0.002668) | 0.347705 / 0.226044 (0.121660) | 3.489591 / 2.268929 (1.220663) | 1.944952 / 55.444624 (-53.499672) | 1.652810 / 6.876477 (-5.223667) | 1.656361 / 2.142072 (-0.485712) | 0.647052 / 4.805227 (-4.158176) | 0.117286 / 6.500664 (-6.383379) | 0.040979 / 0.075469 (-0.034490) |\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) | 0.971761 / 1.841788 (-0.870027) | 11.770547 / 8.074308 (3.696239) | 10.402502 / 10.191392 (0.211110) | 0.128280 / 0.680424 (-0.552144) | 0.015160 / 0.534201 (-0.519041) | 0.286706 / 0.579283 (-0.292578) | 0.274539 / 0.434364 (-0.159825) | 0.324591 / 0.540337 (-0.215747) | 0.573846 / 1.386936 (-0.813090) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3329be80b9abfe83285ef940a590a4e9f68835a3 \"CML watermark\")\n"
] | 2023-12-15T09:17:41Z
| 2023-12-18T11:56:11Z
| 2023-12-18T11:50:03Z
|
MEMBER
| null | null | null |
Fix #6497.
|
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| 2,994,678,437
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I_kwDODunzps6yfyql
| 7,513
|
MemoryError while creating dataset from generator
|
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[
"Upd: created a PR that can probably solve the problem: #7514",
"Hi ! We need to take the generator into account for the cache. The generator is hashed to make the dataset fingerprint used by the cache. This way you can reload the Dataset from the cache without regenerating in subsequent `from_generator` calls.\n\nMaybe instead of removing generator from the hasher input, we can let users pass their own Dataset fingerprint to `from_generator`, and if it's specified we don't need to hash anything",
"Upd: I successfully generated a dataset from my large geospatial data with `generator` excluded from hashing and saved it to disk without running into memory errors. So, it looks like there are no other bottlenecks in dataset generation in my case\n\nMaybe letting users pass their own fingerprint to skip hashing can be a great solution to that issue!",
"@lhoestq I tried to implement user-defined dataset fingerprint in #7533 . Am I doing it right?"
] | 2025-04-15T01:02:02Z
| 2025-04-23T19:37:08Z
| null |
NONE
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### Describe the bug
# TL:DR
`Dataset.from_generator` function passes all of its arguments to `BuilderConfig.create_config_id`, including `generator` function itself. `BuilderConfig.create_config_id` function tries to hash all the args, which can take a large amount of time or even cause MemoryError if the dataset processed in a generator function is large enough.
Maybe we should pop `generator` from `config_kwargs_to_add_to_suffix` before hashing to avoid it.
# Full description
I have a pretty large spatial imagery dataset that is generated from two xbatcher.BatchGenerators via custom `dataset_generator` function that looks like this if simplified:
```
def dataset_generator():
for index in samples:
data_dict = {
"key": index,
"x": x_batches[index].data,
"y": y_batches[index].data,
}
yield data_dict
```
Then I use `datasets.Dataset.from_generator` to generate the dataset itself.
```
# Create dataset
ds = datasets.Dataset.from_generator(
dataset_generator,
features=feat,
cache_dir=(output / ".cache"),
)
```
It works nicely with pretty small data, but if the dataset is huge and barely fits in memory, it crashes with memory error:
<details>
<summary>Full stack trace</summary>
```
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\remote_sensing_processor\segmentation\semantic\tiles.py:248](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/remote_sensing_processor/segmentation/semantic/tiles.py#line=247), in generate_tiles(x, y, output, tile_size, shuffle, split, x_dtype, y_dtype, x_nodata, y_nodata)
245 yield data_dict
247 # Create dataset
--> 248 ds = datasets.Dataset.from_generator(
249 dataset_generator,
250 features=feat,
251 cache_dir=(output / ".cache"),
252 )
254 # Save dataset
255 ds.save_to_disk(output / name)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\arrow_dataset.py:1105](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/arrow_dataset.py#line=1104), in Dataset.from_generator(generator, features, cache_dir, keep_in_memory, gen_kwargs, num_proc, split, **kwargs)
1052 """Create a Dataset from a generator.
1053
1054 Args:
(...) 1101 ```
1102 """
1103 from .io.generator import GeneratorDatasetInputStream
-> 1105 return GeneratorDatasetInputStream(
1106 generator=generator,
1107 features=features,
1108 cache_dir=cache_dir,
1109 keep_in_memory=keep_in_memory,
1110 gen_kwargs=gen_kwargs,
1111 num_proc=num_proc,
1112 split=split,
1113 **kwargs,
1114 ).read()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\io\generator.py:29](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/io/generator.py#line=28), in GeneratorDatasetInputStream.__init__(self, generator, features, cache_dir, keep_in_memory, streaming, gen_kwargs, num_proc, split, **kwargs)
9 def __init__(
10 self,
11 generator: Callable,
(...) 19 **kwargs,
20 ):
21 super().__init__(
22 features=features,
23 cache_dir=cache_dir,
(...) 27 **kwargs,
28 )
---> 29 self.builder = Generator(
30 cache_dir=cache_dir,
31 features=features,
32 generator=generator,
33 gen_kwargs=gen_kwargs,
34 split=split,
35 **kwargs,
36 )
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\builder.py:343](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/builder.py#line=342), in DatasetBuilder.__init__(self, cache_dir, dataset_name, config_name, hash, base_path, info, features, token, repo_id, data_files, data_dir, storage_options, writer_batch_size, **config_kwargs)
341 config_kwargs["data_dir"] = data_dir
342 self.config_kwargs = config_kwargs
--> 343 self.config, self.config_id = self._create_builder_config(
344 config_name=config_name,
345 custom_features=features,
346 **config_kwargs,
347 )
349 # prepare info: DatasetInfo are a standardized dataclass across all datasets
350 # Prefill datasetinfo
351 if info is None:
352 # TODO FOR PACKAGED MODULES IT IMPORTS DATA FROM src/packaged_modules which doesn't make sense
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\builder.py:604](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/builder.py#line=603), in DatasetBuilder._create_builder_config(self, config_name, custom_features, **config_kwargs)
598 builder_config._resolve_data_files(
599 base_path=self.base_path,
600 download_config=DownloadConfig(token=self.token, storage_options=self.storage_options),
601 )
603 # compute the config id that is going to be used for caching
--> 604 config_id = builder_config.create_config_id(
605 config_kwargs,
606 custom_features=custom_features,
607 )
608 is_custom = (config_id not in self.builder_configs) and config_id != "default"
609 if is_custom:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\builder.py:187](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/builder.py#line=186), in BuilderConfig.create_config_id(self, config_kwargs, custom_features)
185 suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
186 else:
--> 187 suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
189 if custom_features is not None:
190 m = Hasher()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\fingerprint.py:188](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/fingerprint.py#line=187), in Hasher.hash(cls, value)
186 @classmethod
187 def hash(cls, value: Any) -> str:
--> 188 return cls.hash_bytes(dumps(value))
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:109](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=108), in dumps(obj)
107 """Pickle an object to a string."""
108 file = BytesIO()
--> 109 dump(obj, file)
110 return file.getvalue()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:103](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=102), in dump(obj, file)
101 def dump(obj, file):
102 """Pickle an object to a file."""
--> 103 Pickler(file, recurse=True).dump(obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:420](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=419), in Pickler.dump(self, obj)
418 def dump(self, obj): #NOTE: if settings change, need to update attributes
419 logger.trace_setup(self)
--> 420 StockPickler.dump(self, obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:484](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=483), in _Pickler.dump(self, obj)
482 if self.proto >= 4:
483 self.framer.start_framing()
--> 484 self.save(obj)
485 self.write(STOP)
486 self.framer.end_framing()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1985](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1984), in save_function(pickler, obj)
1982 if state_dict:
1983 state = state, state_dict
-> 1985 _save_with_postproc(pickler, (_create_function, (
1986 obj.__code__, globs, obj.__name__, obj.__defaults__,
1987 closure
1988 ), state), obj=obj, postproc_list=postproc_list)
1990 # Lift closure cell update to earliest function (#458)
1991 if _postproc:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1117](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1116), in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list)
1115 continue
1116 else:
-> 1117 pickler.save_reduce(*reduction)
1118 # pop None created by calling preprocessing step off stack
1119 pickler.write(POP)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:690](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=689), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
688 else:
689 save(func)
--> 690 save(args)
691 write(REDUCE)
693 if obj is not None:
694 # If the object is already in the memo, this means it is
695 # recursive. In this case, throw away everything we put on the
696 # stack, and fetch the object back from the memo.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:905](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=904), in _Pickler.save_tuple(self, obj)
903 if n <= 3 and self.proto >= 2:
904 for element in obj:
--> 905 save(element)
906 # Subtle. Same as in the big comment below.
907 if id(obj) in memo:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
[... skipping similar frames: Pickler.save at line 70 (1 times), Pickler.save at line 414 (1 times)]
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:905](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=904), in _Pickler.save_tuple(self, obj)
903 if n <= 3 and self.proto >= 2:
904 for element in obj:
--> 905 save(element)
906 # Subtle. Same as in the big comment below.
907 if id(obj) in memo:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:905](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=904), in _Pickler.save_tuple(self, obj)
903 if n <= 3 and self.proto >= 2:
904 for element in obj:
--> 905 save(element)
906 # Subtle. Same as in the big comment below.
907 if id(obj) in memo:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:690](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=689), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
688 else:
689 save(func)
--> 690 save(args)
691 write(REDUCE)
693 if obj is not None:
694 # If the object is already in the memo, this means it is
695 # recursive. In this case, throw away everything we put on the
696 # stack, and fetch the object back from the memo.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:920](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=919), in _Pickler.save_tuple(self, obj)
918 write(MARK)
919 for element in obj:
--> 920 save(element)
922 if id(obj) in memo:
923 # Subtle. d was not in memo when we entered save_tuple(), so
924 # the process of saving the tuple's elements must have saved
(...) 928 # could have been done in the "for element" loop instead, but
929 # recursive tuples are a rare thing.
930 get = self.get(memo[id(obj)][0])
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1019](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1018), in _Pickler._batch_setitems(self, items)
1017 k, v = tmp[0]
1018 save(k)
-> 1019 save(v)
1020 write(SETITEM)
1021 # else tmp is empty, and we're done
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
[... skipping similar frames: Pickler.save at line 70 (1 times), Pickler.save at line 414 (1 times)]
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
[... skipping similar frames: Pickler.save at line 70 (1 times), Pickler.save at line 414 (1 times)]
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:920](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=919), in _Pickler.save_tuple(self, obj)
918 write(MARK)
919 for element in obj:
--> 920 save(element)
922 if id(obj) in memo:
923 # Subtle. d was not in memo when we entered save_tuple(), so
924 # the process of saving the tuple's elements must have saved
(...) 928 # could have been done in the "for element" loop instead, but
929 # recursive tuples are a rare thing.
930 get = self.get(memo[id(obj)][0])
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:809](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=808), in _Pickler.save_bytes(self, obj)
806 self.save_reduce(codecs.encode,
807 (str(obj, 'latin1'), 'latin1'), obj=obj)
808 return
--> 809 self._save_bytes_no_memo(obj)
810 self.memoize(obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:797](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=796), in _Pickler._save_bytes_no_memo(self, obj)
795 self._write_large_bytes(BINBYTES8 + pack("<Q", n), obj)
796 elif n >= self.framer._FRAME_SIZE_TARGET:
--> 797 self._write_large_bytes(BINBYTES + pack("<I", n), obj)
798 else:
799 self.write(BINBYTES + pack("<I", n) + obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:254](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=253), in _Framer.write_large_bytes(self, header, payload)
247 # Perform direct write of the header and payload of the large binary
248 # object. Be careful not to concatenate the header and the payload
249 # prior to calling 'write' as we do not want to allocate a large
250 # temporary bytes object.
251 # We intentionally do not insert a protocol 4 frame opcode to make
252 # it possible to optimize file.read calls in the loader.
253 write(header)
--> 254 write(payload)
MemoryError:
```
</details>
Memory error is an expected type of error in such case, but when I started digging down, I found out that it occurs in a kinda unexpected place - in `create_config_id` function. It tries to hash `config_kwargs_to_add_to_suffix`, including generator function itself.
I modified the `BuilderConfig.create_config_id` code like this to check which values are hashed and how much time it takes to hash them and ran it on a toy dataset:
```
print(config_kwargs_to_add_to_suffix)
start_time = time.time()
if all(isinstance(v, (str, bool, int, float)) for v in config_kwargs_to_add_to_suffix.values()):
suffix = ",".join(
str(k) + "=" + urllib.parse.quote_plus(str(v)) for k, v in config_kwargs_to_add_to_suffix.items()
)
if len(suffix) > 32: # hash if too long
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
else:
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
end_time = time.time()
print(f"Execution time: {end_time - start_time:.4f} seconds")
print(suffix)
```
In my case the content of `config_kwargs_to_add_to_suffix` was like this:
```
{'features': {'key': Value(dtype='int64', id=None), 'x': Array3D(shape=(44, 128, 128), dtype='float32', id=None), 'y_class': Array2D(shape=(128, 128), dtype='int32', id=None)}, 'gen_kwargs': None, 'generator': <function generate_tiles.<locals>.dataset_generator at 0x00000139D10D7920>, 'split': NamedSplit('train')}
```
Also I noticed that hashing took a significant amount of time - 43.1482 seconds, while the overall function execution (with data loading, batching and saving dataset) took 2min 45s. The output of `create_config_id` is just a dataset id, so, it is inappropirately large amount of time.
But when I added `config_kwargs_to_add_to_suffix.pop("generator", None)`, the hashing took only 0.0060 seconds.
Maybe we shouldn't hash the generator function, as it can be really computationally and memory expensive.
### Steps to reproduce the bug
This is a simplified example of a workflow I used to generate dataset. But I think that you can use almost any workflow to reproduce that bug.
```
import pystac
import pystac_client
import planetary_computer
import numpy as np
import xarray as xr
import rioxarray as rxr
import dask
import xbatcher
import datasets
# Loading a dataset, in our case - single Landsat image
catalog = pystac_client.Client.open(
"https://planetarycomputer.microsoft.com/api/stac/v1",
modifier=planetary_computer.sign_inplace,
)
brazil = [-60.2, -3.31]
time_of_interest = "2021-06-01/2021-08-31"
search = catalog.search(collections=["landsat-c2-l2"], intersects={"type": "Point", "coordinates": brazil}, datetime=time_of_interest)
items = search.item_collection()
item = min(items, key=lambda item: pystac.extensions.eo.EOExtension.ext(item).cloud_cover)
# Getting x data
bands = []
for band in ["red", "green", "blue", "nir08", "coastal", "swir16", "swir22", "lwir11"]:
with rxr.open_rasterio(item.assets[band].href, chunks=True, lock=True) as raster:
raster = raster.to_dataset('band')
#print(raster)
raster = raster.rename({1: band})
bands.append(raster)
x = xr.merge(bands).squeeze().to_array("band").persist()
# Getting y data
with rxr.open_rasterio(item.assets['qa_pixel'].href, chunks=True, lock=True) as raster:
y = raster.squeeze().persist()
# Setting up batches generators
x_batches = xbatcher.BatchGenerator(ds=x, input_dims={"x": 256, "y": 256})
y_batches = xbatcher.BatchGenerator(ds=y, input_dims={"x": 256, "y": 256})
# Filtering samples that contain only nodata
samples = list(range(len(x_batches)))
samples_filtered = []
for i in samples:
if not np.array_equal(np.unique(x_batches[i]), np.array([0.])) and not np.array_equal(np.unique(y_batches[i]), np.array([0])):
samples_filtered.append(i)
samples = samples_filtered
np.random.shuffle(samples)
# Setting up features
feat = {
"key": datasets.Value(dtype="int64"),
"x": datasets.Array3D(dtype="float32", shape=(4, 256, 256)),
"y": datasets.Array2D(dtype="int32", shape=(256, 256))
}
feat = datasets.Features(feat)
# Setting up a generator
def dataset_generator():
for index in samples:
data_dict = {
"key": index,
"x": x_batches[index].data,
"y": y_batches[index].data,
}
yield data_dict
# Create dataset
ds = datasets.Dataset.from_generator(
dataset_generator,
features=feat,
cache_dir="temp/cache",
)
```
Please, try adding `config_kwargs_to_add_to_suffix.pop("generator", None)` to `BuilderConfig.create_config_id` and then measuring how much time it takes to run
```
if all(isinstance(v, (str, bool, int, float)) for v in config_kwargs_to_add_to_suffix.values()):
suffix = ",".join(
str(k) + "=" + urllib.parse.quote_plus(str(v)) for k, v in config_kwargs_to_add_to_suffix.items()
)
if len(suffix) > 32: # hash if too long
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
else:
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
```
code block with and without `config_kwargs_to_add_to_suffix.pop("generator", None)`
In my case the difference was 3.3828 seconds without popping generator function and 0.0010 seconds with popping.
### Expected behavior
Much faster hashing and no MemoryErrors
### Environment info
- `datasets` version: 3.5.0
- Platform: Windows-11-10.0.26100-SP0
- Python version: 3.12.9
- `huggingface_hub` version: 0.30.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.12.0
| null |
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|
https://api.github.com/repos/huggingface/datasets/issues/7513/timeline
| null | null | null | null |
https://api.github.com/repos/huggingface/datasets/issues/6631
|
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https://github.com/huggingface/datasets/pull/6631
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PR_kwDODunzps5lcu9A
| 6,631
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Fix filelock: use current umask for filelock >= 3.10
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6631). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005076 / 0.011353 (-0.006277) | 0.003665 / 0.011008 (-0.007343) | 0.063602 / 0.038508 (0.025094) | 0.029103 / 0.023109 (0.005993) | 0.233133 / 0.275898 (-0.042765) | 0.257000 / 0.323480 (-0.066480) | 0.003059 / 0.007986 (-0.004926) | 0.004007 / 0.004328 (-0.000321) | 0.049804 / 0.004250 (0.045553) | 0.039946 / 0.037052 (0.002893) | 0.248003 / 0.258489 (-0.010486) | 0.272729 / 0.293841 (-0.021112) | 0.027542 / 0.128546 (-0.101004) | 0.010745 / 0.075646 (-0.064901) | 0.207686 / 0.419271 (-0.211586) | 0.035438 / 0.043533 (-0.008095) | 0.236864 / 0.255139 (-0.018275) | 0.258610 / 0.283200 (-0.024590) | 0.017225 / 0.141683 (-0.124458) | 1.130894 / 1.452155 (-0.321261) | 1.171266 / 1.492716 (-0.321450) |\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) | 0.092532 / 0.018006 (0.074525) | 0.301650 / 0.000490 (0.301161) | 0.000216 / 0.000200 (0.000016) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018175 / 0.037411 (-0.019237) | 0.061538 / 0.014526 (0.047012) | 0.073673 / 0.176557 (-0.102884) | 0.120676 / 0.737135 (-0.616460) | 0.074753 / 0.296338 (-0.221586) |\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) | 0.283625 / 0.215209 (0.068416) | 2.794903 / 2.077655 (0.717248) | 1.485149 / 1.504120 (-0.018970) | 1.361154 / 1.541195 (-0.180041) | 1.371436 / 1.468490 (-0.097054) | 0.580401 / 4.584777 (-4.004376) | 2.457068 / 3.745712 (-1.288644) | 2.760878 / 5.269862 (-2.508984) | 1.725507 / 4.565676 (-2.840169) | 0.063632 / 0.424275 (-0.360644) | 0.005036 / 0.007607 (-0.002572) | 0.337167 / 0.226044 (0.111122) | 3.314508 / 2.268929 (1.045579) | 1.863412 / 55.444624 (-53.581213) | 1.621966 / 6.876477 (-5.254511) | 1.600422 / 2.142072 (-0.541651) | 0.647753 / 4.805227 (-4.157475) | 0.117169 / 6.500664 (-6.383495) | 0.042338 / 0.075469 (-0.033131) |\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) | 0.981818 / 1.841788 (-0.859969) | 12.044657 / 8.074308 (3.970349) | 10.654091 / 10.191392 (0.462699) | 0.130693 / 0.680424 (-0.549731) | 0.014733 / 0.534201 (-0.519468) | 0.317432 / 0.579283 (-0.261851) | 0.267196 / 0.434364 (-0.167168) | 0.329310 / 0.540337 (-0.211028) | 0.433379 / 1.386936 (-0.953557) |\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) | 0.005502 / 0.011353 (-0.005851) | 0.003951 / 0.011008 (-0.007057) | 0.050651 / 0.038508 (0.012143) | 0.031802 / 0.023109 (0.008693) | 0.281384 / 0.275898 (0.005485) | 0.303900 / 0.323480 (-0.019580) | 0.004451 / 0.007986 (-0.003534) | 0.002801 / 0.004328 (-0.001527) | 0.048688 / 0.004250 (0.044438) | 0.044717 / 0.037052 (0.007664) | 0.295017 / 0.258489 (0.036528) | 0.328003 / 0.293841 (0.034162) | 0.048421 / 0.128546 (-0.080125) | 0.011254 / 0.075646 (-0.064392) | 0.058223 / 0.419271 (-0.361048) | 0.033915 / 0.043533 (-0.009618) | 0.279893 / 0.255139 (0.024754) | 0.297605 / 0.283200 (0.014405) | 0.017115 / 0.141683 (-0.124568) | 1.146966 / 1.452155 (-0.305189) | 1.191650 / 1.492716 (-0.301066) |\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) | 0.092524 / 0.018006 (0.074518) | 0.309332 / 0.000490 (0.308842) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022265 / 0.037411 (-0.015146) | 0.075732 / 0.014526 (0.061206) | 0.087340 / 0.176557 (-0.089217) | 0.126079 / 0.737135 (-0.611056) | 0.090349 / 0.296338 (-0.205990) |\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) | 0.288882 / 0.215209 (0.073673) | 2.833046 / 2.077655 (0.755392) | 1.602905 / 1.504120 (0.098785) | 1.473110 / 1.541195 (-0.068085) | 1.491300 / 1.468490 (0.022810) | 0.557799 / 4.584777 (-4.026978) | 2.439526 / 3.745712 (-1.306186) | 2.669336 / 5.269862 (-2.600526) | 1.719472 / 4.565676 (-2.846204) | 0.062456 / 0.424275 (-0.361819) | 0.005058 / 0.007607 (-0.002549) | 0.343706 / 0.226044 (0.117662) | 3.422397 / 2.268929 (1.153469) | 1.983679 / 55.444624 (-53.460946) | 1.673784 / 6.876477 (-5.202693) | 1.785144 / 2.142072 (-0.356928) | 0.643127 / 4.805227 (-4.162100) | 0.115254 / 6.500664 (-6.385410) | 0.041235 / 0.075469 (-0.034235) |\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) | 1.005448 / 1.841788 (-0.836340) | 12.240100 / 8.074308 (4.165792) | 11.051965 / 10.191392 (0.860573) | 0.130438 / 0.680424 (-0.549986) | 0.015918 / 0.534201 (-0.518283) | 0.287468 / 0.579283 (-0.291815) | 0.287699 / 0.434364 (-0.146665) | 0.324561 / 0.540337 (-0.215777) | 0.418820 / 1.386936 (-0.968116) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#237a2a688155e23cfbcdfadd2d491ce1667fa494 \"CML watermark\")\n"
] | 2024-01-30T12:56:01Z
| 2024-01-30T15:34:49Z
| 2024-01-30T15:28:37Z
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MEMBER
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reported in https://github.com/huggingface/evaluate/issues/542
cc @stas00 @williamberrios
close https://github.com/huggingface/datasets/issues/6589
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PR_kwDODunzps5YkJOV
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feat: Return the name of the currently loaded file
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"Your change adds a new element in the key used to avoid duplicates when generating the examples of a dataset. I don't think it fixes the issue you're trying to solve."
] | 2023-08-23T07:08:17Z
| 2023-08-29T12:41:05Z
| null |
NONE
| null | null | 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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I_kwDODunzps6TbJko
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ConnectionError for gated datasets and unauthenticated users
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[] | 2024-08-19T13:27:45Z
| 2024-08-20T09:14:36Z
| 2024-08-20T09:14:35Z
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MEMBER
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Since the Hub returns dataset info for gated datasets and unauthenticated users, there is dead code: https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/load.py#L1846-L1852
We should remove the dead code and properly handle this case: currently we are raising a `ConnectionError` instead of a `DatasetNotFoundError` (as before).
See:
- https://github.com/huggingface/dataset-viewer/issues/3025
- https://github.com/huggingface/huggingface_hub/issues/2457
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Getting a 409 error while loading xglue dataset
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"Thanks for reporting, @Praful932.\r\n\r\nLet's continue the conversation on the Hub: https://huggingface.co/datasets/xglue/discussions/5"
] | 2023-06-30T04:13:54Z
| 2023-06-30T05:57:23Z
| 2023-06-30T05:57:22Z
|
NONE
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### Describe the bug
Unable to load xglue dataset
### Steps to reproduce the bug
```python
import datasets
dataset = datasets.load_dataset("xglue", "ntg")
```
> ConnectionError: Couldn't reach https://xglue.blob.core.windows.net/xglue/xglue_full_dataset.tar.gz (error 409)
### Expected behavior
Expected the dataset to load
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.15.107+-x86_64-with-glibc2.31
- Python version: 3.10.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
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I_kwDODunzps5ielI7
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Add Qdrant as another search index
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"@mariosasko I'd appreciate your feedback on this. "
] | 2023-04-03T14:25:19Z
| 2023-04-11T10:28:40Z
| null |
CONTRIBUTOR
| null | null |
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### Feature request
I'd suggest adding Qdrant (https://qdrant.tech) as another search index available, so users can directly build an index from a dataset. Currently, FAISS and ElasticSearch are only supported: https://huggingface.co/docs/datasets/faiss_es
### Motivation
ElasticSearch is a keyword-based search system, while FAISS is a vector search library. Vector database, such as Qdrant, is a different tool based on similarity (like FAISS) but is not limited to a single machine. It makes the vector database well-suited for bigger datasets and collaboration if several people want to access a particular dataset.
### Your contribution
I can provide a PR implementing that functionality on my own.
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load_dataset('emotion') UnicodeDecodeError
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"I met the same problem, here is my code:\r\n```\r\nfrom datasets import load_dataset\r\n\r\nds_name = \"togethercomputer/RedPajama-Data-1T\"\r\nds = load_dataset(ds_name, download_mode=DownloadMode.FORCE_REDOWNLOAD)\r\n```\r\nAnd output error is:\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/yatorho/doc/projs/TransformerEngine/local/download_redpajama.py\", line 10, in <module>\r\n ds = load_dataset(ds_name, download_mode=DownloadMode.FORCE_REDOWNLOAD)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 2606, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 2277, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 1923, in dataset_module_factory\r\n raise e1 from None\r\n File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 1875, in dataset_module_factory\r\n can_load_config_from_parquet_export = \"DEFAULT_CONFIG_NAME\" not in f.read()\r\n ^^^^^^^^\r\n File \"<frozen codecs>\", line 322, in decode\r\nUnicodeDecodeError: 'utf-8' codec can't decode byte 0xb5 in position 1: invalid start byte\r\n```\r\nMy `datasets` version is 2.21.0. Any help here would be appreciated!\r\n",
"> I met the same problem, here is my code:\r\n> \r\n> ```\r\n> from datasets import load_dataset\r\n> \r\n> ds_name = \"togethercomputer/RedPajama-Data-1T\"\r\n> ds = load_dataset(ds_name, download_mode=DownloadMode.FORCE_REDOWNLOAD)\r\n> ```\r\n> \r\n> And output error is:\r\n> \r\n> ```\r\n> Traceback (most recent call last):\r\n> File \"/home/yatorho/doc/projs/TransformerEngine/local/download_redpajama.py\", line 10, in <module>\r\n> ds = load_dataset(ds_name, download_mode=DownloadMode.FORCE_REDOWNLOAD)\r\n> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n> File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 2606, in load_dataset\r\n> builder_instance = load_dataset_builder(\r\n> ^^^^^^^^^^^^^^^^^^^^^\r\n> File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 2277, in load_dataset_builder\r\n> dataset_module = dataset_module_factory(\r\n> ^^^^^^^^^^^^^^^^^^^^^^^\r\n> File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 1923, in dataset_module_factory\r\n> raise e1 from None\r\n> File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 1875, in dataset_module_factory\r\n> can_load_config_from_parquet_export = \"DEFAULT_CONFIG_NAME\" not in f.read()\r\n> ^^^^^^^^\r\n> File \"<frozen codecs>\", line 322, in decode\r\n> UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb5 in position 1: invalid start byte\r\n> ```\r\n> \r\n> My `datasets` version is 2.21.0. Any help here would be appreciated!\r\n\r\nI passed encoding=\"utf-16\" to the `load_dataset` call and now it works for me.\r\n```\r\nds = load_dataset(ds_name, download_mode=DownloadMode.FORCE_REDOWNLOAD, encoding=\"utf-16\")\r\n```"
] | 2024-04-30T09:25:01Z
| 2024-09-05T03:11:04Z
| null |
NONE
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### Describe the bug
**emotions = load_dataset('emotion')**
_UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte_
### Steps to reproduce the bug
load_dataset('emotion')
### Expected behavior
succese
### Environment info
py3.10
transformers 4.41.0.dev0
datasets 2.19.0
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[
"Assigned to myself and will get to it in the next week, but if someone finds this issue annoying and wants to submit a PR before I do, just ping me here and I'll reassign :). ",
"I would like to be assigned to this issue, @nateraw . #self-assign"
] | 2023-02-01T18:32:14Z
| 2023-10-16T13:30:48Z
| 2023-10-16T13:30:48Z
|
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Right now `ds.push_to_hub()` can push a dataset on `main` or on a new branch with `branch=`, but there is no way to open a pull request. Even passing `branch=refs/pr/x` doesn't seem to work: it tries to create a branch with that name
cc @nateraw
It should be possible to tweak the use of `huggingface_hub` in `push_to_hub` to make it open a PR or push to an existing PR
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PR_kwDODunzps5YM0g7
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Fix protocol prefix for Beam
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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) | 0.006736 / 0.011353 (-0.004617) | 0.004099 / 0.011008 (-0.006909) | 0.084339 / 0.038508 (0.045831) | 0.073715 / 0.023109 (0.050605) | 0.311962 / 0.275898 (0.036064) | 0.356108 / 0.323480 (0.032628) | 0.005321 / 0.007986 (-0.002665) | 0.003390 / 0.004328 (-0.000939) | 0.064622 / 0.004250 (0.060372) | 0.053978 / 0.037052 (0.016926) | 0.328967 / 0.258489 (0.070478) | 0.370506 / 0.293841 (0.076665) | 0.031123 / 0.128546 (-0.097423) | 0.008465 / 0.075646 (-0.067181) | 0.288136 / 0.419271 (-0.131136) | 0.052909 / 0.043533 (0.009376) | 0.325189 / 0.255139 (0.070050) | 0.360112 / 0.283200 (0.076912) | 0.023389 / 0.141683 (-0.118294) | 1.492899 / 1.452155 (0.040744) | 1.586449 / 1.492716 (0.093733) |\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) | 0.219708 / 0.018006 (0.201702) | 0.469550 / 0.000490 (0.469060) | 0.002776 / 0.000200 (0.002576) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028985 / 0.037411 (-0.008427) | 0.083487 / 0.014526 (0.068961) | 0.096938 / 0.176557 (-0.079619) | 0.152886 / 0.737135 (-0.584249) | 0.096242 / 0.296338 (-0.200096) |\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) | 0.381959 / 0.215209 (0.166750) | 3.800033 / 2.077655 (1.722378) | 1.831903 / 1.504120 (0.327783) | 1.663207 / 1.541195 (0.122012) | 1.747282 / 1.468490 (0.278792) | 0.481671 / 4.584777 (-4.103106) | 3.653725 / 3.745712 (-0.091987) | 3.253058 / 5.269862 (-2.016804) | 2.022014 / 4.565676 (-2.543663) | 0.056651 / 0.424275 (-0.367624) | 0.007640 / 0.007607 (0.000033) | 0.461795 / 0.226044 (0.235750) | 4.625535 / 2.268929 (2.356606) | 2.356341 / 55.444624 (-53.088283) | 1.977437 / 6.876477 (-4.899040) | 2.179672 / 2.142072 (0.037599) | 0.582875 / 4.805227 (-4.222353) | 0.132964 / 6.500664 (-6.367700) | 0.060398 / 0.075469 (-0.015071) |\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) | 1.309567 / 1.841788 (-0.532220) | 19.856306 / 8.074308 (11.781997) | 14.074350 / 10.191392 (3.882958) | 0.149615 / 0.680424 (-0.530809) | 0.018487 / 0.534201 (-0.515714) | 0.393995 / 0.579283 (-0.185288) | 0.409057 / 0.434364 (-0.025307) | 0.459551 / 0.540337 (-0.080787) | 0.644594 / 1.386936 (-0.742342) |\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) | 0.006824 / 0.011353 (-0.004529) | 0.004099 / 0.011008 (-0.006909) | 0.064415 / 0.038508 (0.025907) | 0.077983 / 0.023109 (0.054874) | 0.359351 / 0.275898 (0.083453) | 0.395168 / 0.323480 (0.071688) | 0.005384 / 0.007986 (-0.002602) | 0.003298 / 0.004328 (-0.001030) | 0.065041 / 0.004250 (0.060791) | 0.056717 / 0.037052 (0.019664) | 0.366882 / 0.258489 (0.108393) | 0.401337 / 0.293841 (0.107496) | 0.032273 / 0.128546 (-0.096273) | 0.008666 / 0.075646 (-0.066981) | 0.071442 / 0.419271 (-0.347829) | 0.049999 / 0.043533 (0.006466) | 0.365001 / 0.255139 (0.109862) | 0.379579 / 0.283200 (0.096379) | 0.023357 / 0.141683 (-0.118326) | 1.476839 / 1.452155 (0.024684) | 1.541703 / 1.492716 (0.048987) |\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) | 0.239014 / 0.018006 (0.221008) | 0.460678 / 0.000490 (0.460188) | 0.003368 / 0.000200 (0.003168) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030981 / 0.037411 (-0.006430) | 0.088287 / 0.014526 (0.073761) | 0.102459 / 0.176557 (-0.074098) | 0.154695 / 0.737135 (-0.582441) | 0.103479 / 0.296338 (-0.192860) |\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) | 0.416084 / 0.215209 (0.200874) | 4.128365 / 2.077655 (2.050710) | 2.113053 / 1.504120 (0.608934) | 1.948993 / 1.541195 (0.407798) | 2.035609 / 1.468490 (0.567119) | 0.481705 / 4.584777 (-4.103072) | 3.630366 / 3.745712 (-0.115346) | 3.340837 / 5.269862 (-1.929024) | 2.052573 / 4.565676 (-2.513104) | 0.056805 / 0.424275 (-0.367470) | 0.007294 / 0.007607 (-0.000313) | 0.489597 / 0.226044 (0.263553) | 4.892728 / 2.268929 (2.623799) | 2.564692 / 55.444624 (-52.879932) | 2.251964 / 6.876477 (-4.624513) | 2.457912 / 2.142072 (0.315839) | 0.588433 / 4.805227 (-4.216794) | 0.133588 / 6.500664 (-6.367076) | 0.062298 / 0.075469 (-0.013171) |\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) | 1.328566 / 1.841788 (-0.513222) | 20.145568 / 8.074308 (12.071260) | 14.231306 / 10.191392 (4.039914) | 0.168356 / 0.680424 (-0.512067) | 0.018333 / 0.534201 (-0.515868) | 0.390901 / 0.579283 (-0.188382) | 0.415005 / 0.434364 (-0.019359) | 0.477282 / 0.540337 (-0.063055) | 0.652085 / 1.386936 (-0.734851) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#341a41880a70b29f030caa0d36f1e297535ba5f9 \"CML watermark\")\n",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6161). 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) | 0.006388 / 0.011353 (-0.004965) | 0.003917 / 0.011008 (-0.007092) | 0.087397 / 0.038508 (0.048889) | 0.068522 / 0.023109 (0.045412) | 0.313299 / 0.275898 (0.037401) | 0.342884 / 0.323480 (0.019405) | 0.005216 / 0.007986 (-0.002770) | 0.003293 / 0.004328 (-0.001035) | 0.067474 / 0.004250 (0.063224) | 0.051122 / 0.037052 (0.014070) | 0.326443 / 0.258489 (0.067954) | 0.355744 / 0.293841 (0.061903) | 0.031130 / 0.128546 (-0.097416) | 0.008617 / 0.075646 (-0.067029) | 0.291201 / 0.419271 (-0.128070) | 0.052050 / 0.043533 (0.008517) | 0.312135 / 0.255139 (0.056996) | 0.347233 / 0.283200 (0.064034) | 0.023775 / 0.141683 (-0.117907) | 1.478807 / 1.452155 (0.026652) | 1.581239 / 1.492716 (0.088522) |\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) | 0.208252 / 0.018006 (0.190246) | 0.466314 / 0.000490 (0.465824) | 0.004439 / 0.000200 (0.004239) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027918 / 0.037411 (-0.009494) | 0.082410 / 0.014526 (0.067884) | 0.094231 / 0.176557 (-0.082326) | 0.150189 / 0.737135 (-0.586946) | 0.095404 / 0.296338 (-0.200935) |\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) | 0.382026 / 0.215209 (0.166817) | 3.822213 / 2.077655 (1.744559) | 1.833716 / 1.504120 (0.329596) | 1.666250 / 1.541195 (0.125055) | 1.703350 / 1.468490 (0.234860) | 0.477918 / 4.584777 (-4.106859) | 3.629304 / 3.745712 (-0.116408) | 3.199672 / 5.269862 (-2.070190) | 1.977855 / 4.565676 (-2.587821) | 0.056275 / 0.424275 (-0.368000) | 0.007538 / 0.007607 (-0.000070) | 0.455995 / 0.226044 (0.229950) | 4.559234 / 2.268929 (2.290305) | 2.333819 / 55.444624 (-53.110805) | 2.006851 / 6.876477 (-4.869625) | 2.150683 / 2.142072 (0.008611) | 0.576786 / 4.805227 (-4.228441) | 0.132352 / 6.500664 (-6.368312) | 0.059359 / 0.075469 (-0.016110) |\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) | 1.261525 / 1.841788 (-0.580262) | 19.174957 / 8.074308 (11.100649) | 14.286796 / 10.191392 (4.095404) | 0.144610 / 0.680424 (-0.535813) | 0.018213 / 0.534201 (-0.515988) | 0.390404 / 0.579283 (-0.188879) | 0.404678 / 0.434364 (-0.029686) | 0.455636 / 0.540337 (-0.084701) | 0.620801 / 1.386936 (-0.766135) |\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) | 0.006383 / 0.011353 (-0.004970) | 0.003852 / 0.011008 (-0.007156) | 0.064116 / 0.038508 (0.025607) | 0.068920 / 0.023109 (0.045810) | 0.359439 / 0.275898 (0.083541) | 0.388904 / 0.323480 (0.065425) | 0.005192 / 0.007986 (-0.002794) | 0.003233 / 0.004328 (-0.001095) | 0.064589 / 0.004250 (0.060339) | 0.054496 / 0.037052 (0.017444) | 0.368699 / 0.258489 (0.110210) | 0.400420 / 0.293841 (0.106579) | 0.030869 / 0.128546 (-0.097677) | 0.008424 / 0.075646 (-0.067222) | 0.071015 / 0.419271 (-0.348257) | 0.048333 / 0.043533 (0.004801) | 0.360652 / 0.255139 (0.105513) | 0.393534 / 0.283200 (0.110334) | 0.022685 / 0.141683 (-0.118998) | 1.495565 / 1.452155 (0.043410) | 1.537947 / 1.492716 (0.045230) |\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) | 0.232911 / 0.018006 (0.214905) | 0.454191 / 0.000490 (0.453702) | 0.005711 / 0.000200 (0.005511) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029486 / 0.037411 (-0.007925) | 0.087249 / 0.014526 (0.072724) | 0.100104 / 0.176557 (-0.076453) | 0.151556 / 0.737135 (-0.585580) | 0.100853 / 0.296338 (-0.195485) |\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) | 0.415134 / 0.215209 (0.199925) | 4.139068 / 2.077655 (2.061413) | 2.121079 / 1.504120 (0.616959) | 1.945616 / 1.541195 (0.404421) | 1.988188 / 1.468490 (0.519698) | 0.483994 / 4.584777 (-4.100783) | 3.640366 / 3.745712 (-0.105347) | 3.218896 / 5.269862 (-2.050966) | 2.015527 / 4.565676 (-2.550149) | 0.056946 / 0.424275 (-0.367329) | 0.007262 / 0.007607 (-0.000345) | 0.486075 / 0.226044 (0.260031) | 4.864191 / 2.268929 (2.595262) | 2.590853 / 55.444624 (-52.853772) | 2.315359 / 6.876477 (-4.561118) | 2.418733 / 2.142072 (0.276661) | 0.582378 / 4.805227 (-4.222849) | 0.134097 / 6.500664 (-6.366568) | 0.060797 / 0.075469 (-0.014672) |\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) | 1.337021 / 1.841788 (-0.504766) | 19.468907 / 8.074308 (11.394599) | 14.348874 / 10.191392 (4.157482) | 0.170408 / 0.680424 (-0.510016) | 0.018414 / 0.534201 (-0.515787) | 0.394551 / 0.579283 (-0.184732) | 0.404750 / 0.434364 (-0.029613) | 0.471972 / 0.540337 (-0.068365) | 0.650607 / 1.386936 (-0.736329) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ab4d978e2d5c246dc91e2fed041b06a38190be3b \"CML watermark\")\n",
"The CI errors are unrelated to the changes",
"The Beam API is now deprecated, so I think closing this PR is also fine :)"
] | 2023-08-17T22:40:37Z
| 2024-03-18T17:01:21Z
| 2024-03-18T17:01:21Z
|
COLLABORATOR
| null | null | null |
Fix #6147
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PR_kwDODunzps5kk3KB
| 6,606
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Dedicated RNG object for fingerprinting
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6606). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005625 / 0.011353 (-0.005728) | 0.003313 / 0.011008 (-0.007695) | 0.063997 / 0.038508 (0.025489) | 0.028949 / 0.023109 (0.005839) | 0.250069 / 0.275898 (-0.025829) | 0.271412 / 0.323480 (-0.052068) | 0.003837 / 0.007986 (-0.004148) | 0.002632 / 0.004328 (-0.001697) | 0.048351 / 0.004250 (0.044100) | 0.040664 / 0.037052 (0.003612) | 0.267540 / 0.258489 (0.009051) | 0.285237 / 0.293841 (-0.008604) | 0.026962 / 0.128546 (-0.101584) | 0.010417 / 0.075646 (-0.065229) | 0.211430 / 0.419271 (-0.207842) | 0.035411 / 0.043533 (-0.008122) | 0.258867 / 0.255139 (0.003728) | 0.278562 / 0.283200 (-0.004638) | 0.017690 / 0.141683 (-0.123993) | 1.128813 / 1.452155 (-0.323342) | 1.169384 / 1.492716 (-0.323333) |\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) | 0.091322 / 0.018006 (0.073316) | 0.303272 / 0.000490 (0.302782) | 0.000202 / 0.000200 (0.000002) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017551 / 0.037411 (-0.019861) | 0.060027 / 0.014526 (0.045502) | 0.073431 / 0.176557 (-0.103125) | 0.120550 / 0.737135 (-0.616585) | 0.073107 / 0.296338 (-0.223231) |\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) | 0.283064 / 0.215209 (0.067855) | 2.754593 / 2.077655 (0.676938) | 1.477303 / 1.504120 (-0.026817) | 1.341072 / 1.541195 (-0.200123) | 1.366625 / 1.468490 (-0.101865) | 0.573467 / 4.584777 (-4.011310) | 2.395225 / 3.745712 (-1.350487) | 2.777021 / 5.269862 (-2.492841) | 1.720733 / 4.565676 (-2.844944) | 0.063339 / 0.424275 (-0.360936) | 0.004954 / 0.007607 (-0.002653) | 0.350359 / 0.226044 (0.124315) | 3.376221 / 2.268929 (1.107293) | 1.835539 / 55.444624 (-53.609086) | 1.558064 / 6.876477 (-5.318413) | 1.582778 / 2.142072 (-0.559294) | 0.649918 / 4.805227 (-4.155309) | 0.117761 / 6.500664 (-6.382903) | 0.041771 / 0.075469 (-0.033698) |\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) | 0.950202 / 1.841788 (-0.891586) | 11.476160 / 8.074308 (3.401852) | 10.290618 / 10.191392 (0.099226) | 0.140659 / 0.680424 (-0.539765) | 0.014525 / 0.534201 (-0.519676) | 0.287253 / 0.579283 (-0.292030) | 0.266204 / 0.434364 (-0.168160) | 0.327818 / 0.540337 (-0.212519) | 0.431680 / 1.386936 (-0.955256) |\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) | 0.005096 / 0.011353 (-0.006257) | 0.003460 / 0.011008 (-0.007548) | 0.049474 / 0.038508 (0.010966) | 0.031063 / 0.023109 (0.007954) | 0.272899 / 0.275898 (-0.002999) | 0.291859 / 0.323480 (-0.031621) | 0.004858 / 0.007986 (-0.003128) | 0.002598 / 0.004328 (-0.001731) | 0.049074 / 0.004250 (0.044824) | 0.044722 / 0.037052 (0.007669) | 0.285262 / 0.258489 (0.026772) | 0.314168 / 0.293841 (0.020327) | 0.046346 / 0.128546 (-0.082200) | 0.010384 / 0.075646 (-0.065262) | 0.058331 / 0.419271 (-0.360940) | 0.033728 / 0.043533 (-0.009805) | 0.276217 / 0.255139 (0.021078) | 0.295465 / 0.283200 (0.012265) | 0.018215 / 0.141683 (-0.123467) | 1.163847 / 1.452155 (-0.288308) | 1.213901 / 1.492716 (-0.278816) |\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) | 0.091953 / 0.018006 (0.073947) | 0.299977 / 0.000490 (0.299487) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022031 / 0.037411 (-0.015381) | 0.075067 / 0.014526 (0.060541) | 0.087305 / 0.176557 (-0.089251) | 0.125530 / 0.737135 (-0.611605) | 0.088761 / 0.296338 (-0.207578) |\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) | 0.302682 / 0.215209 (0.087473) | 2.941509 / 2.077655 (0.863854) | 1.643399 / 1.504120 (0.139280) | 1.530148 / 1.541195 (-0.011046) | 1.542067 / 1.468490 (0.073577) | 0.575883 / 4.584777 (-4.008894) | 2.434320 / 3.745712 (-1.311392) | 2.761683 / 5.269862 (-2.508179) | 1.732068 / 4.565676 (-2.833609) | 0.063543 / 0.424275 (-0.360732) | 0.005089 / 0.007607 (-0.002518) | 0.351314 / 0.226044 (0.125269) | 3.494572 / 2.268929 (1.225643) | 2.032503 / 55.444624 (-53.412121) | 1.697949 / 6.876477 (-5.178528) | 1.700392 / 2.142072 (-0.441680) | 0.650757 / 4.805227 (-4.154471) | 0.116719 / 6.500664 (-6.383945) | 0.040559 / 0.075469 (-0.034910) |\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) | 0.978218 / 1.841788 (-0.863570) | 11.972379 / 8.074308 (3.898071) | 10.725735 / 10.191392 (0.534343) | 0.130564 / 0.680424 (-0.549860) | 0.015396 / 0.534201 (-0.518805) | 0.286900 / 0.579283 (-0.292383) | 0.279633 / 0.434364 (-0.154730) | 0.327483 / 0.540337 (-0.212854) | 0.417848 / 1.386936 (-0.969088) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#adfe8f8fa37b9f220c152f5b8b2473ba2cef0307 \"CML watermark\")\n"
] | 2024-01-19T18:34:47Z
| 2024-01-26T15:11:38Z
| 2024-01-26T15:05:34Z
|
COLLABORATOR
| null | null | null |
Closes https://github.com/huggingface/datasets/issues/6604, closes https://github.com/huggingface/datasets/issues/2775
|
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PR_kwDODunzps5Vd0ZG
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fixed typo in comment
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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) | 0.005855 / 0.011353 (-0.005498) | 0.003556 / 0.011008 (-0.007452) | 0.079430 / 0.038508 (0.040922) | 0.056754 / 0.023109 (0.033645) | 0.311718 / 0.275898 (0.035820) | 0.346731 / 0.323480 (0.023251) | 0.004414 / 0.007986 (-0.003571) | 0.002835 / 0.004328 (-0.001493) | 0.062138 / 0.004250 (0.057888) | 0.044259 / 0.037052 (0.007206) | 0.314681 / 0.258489 (0.056192) | 0.359802 / 0.293841 (0.065961) | 0.026684 / 0.128546 (-0.101862) | 0.008023 / 0.075646 (-0.067623) | 0.260148 / 0.419271 (-0.159123) | 0.043734 / 0.043533 (0.000202) | 0.312081 / 0.255139 (0.056942) | 0.340004 / 0.283200 (0.056805) | 0.019559 / 0.141683 (-0.122124) | 1.488758 / 1.452155 (0.036604) | 1.510828 / 1.492716 (0.018111) |\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) | 0.181376 / 0.018006 (0.163370) | 0.441726 / 0.000490 (0.441236) | 0.001722 / 0.000200 (0.001522) | 0.000066 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023760 / 0.037411 (-0.013651) | 0.071847 / 0.014526 (0.057321) | 0.082642 / 0.176557 (-0.093915) | 0.145555 / 0.737135 (-0.591580) | 0.084554 / 0.296338 (-0.211784) |\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) | 0.401688 / 0.215209 (0.186479) | 4.000994 / 2.077655 (1.923339) | 2.047109 / 1.504120 (0.542989) | 1.891874 / 1.541195 (0.350679) | 1.970599 / 1.468490 (0.502109) | 0.500646 / 4.584777 (-4.084131) | 3.006623 / 3.745712 (-0.739089) | 4.248359 / 5.269862 (-1.021503) | 2.613946 / 4.565676 (-1.951730) | 0.057921 / 0.424275 (-0.366354) | 0.006407 / 0.007607 (-0.001200) | 0.470676 / 0.226044 (0.244631) | 4.722280 / 2.268929 (2.453352) | 2.448530 / 55.444624 (-52.996095) | 2.175841 / 6.876477 (-4.700635) | 2.352287 / 2.142072 (0.210214) | 0.589049 / 4.805227 (-4.216179) | 0.125145 / 6.500664 (-6.375519) | 0.060829 / 0.075469 (-0.014640) |\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) | 1.189225 / 1.841788 (-0.652563) | 16.753085 / 8.074308 (8.678777) | 13.086512 / 10.191392 (2.895120) | 0.132371 / 0.680424 (-0.548052) | 0.016933 / 0.534201 (-0.517268) | 0.328258 / 0.579283 (-0.251025) | 0.344074 / 0.434364 (-0.090290) | 0.374042 / 0.540337 (-0.166296) | 0.515307 / 1.386936 (-0.871629) |\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) | 0.005963 / 0.011353 (-0.005390) | 0.003484 / 0.011008 (-0.007525) | 0.062618 / 0.038508 (0.024110) | 0.057217 / 0.023109 (0.034108) | 0.426760 / 0.275898 (0.150862) | 0.464422 / 0.323480 (0.140942) | 0.005276 / 0.007986 (-0.002709) | 0.002872 / 0.004328 (-0.001456) | 0.062636 / 0.004250 (0.058385) | 0.045953 / 0.037052 (0.008900) | 0.433221 / 0.258489 (0.174732) | 0.475087 / 0.293841 (0.181246) | 0.027217 / 0.128546 (-0.101329) | 0.007965 / 0.075646 (-0.067681) | 0.067749 / 0.419271 (-0.351522) | 0.041235 / 0.043533 (-0.002298) | 0.425424 / 0.255139 (0.170285) | 0.453390 / 0.283200 (0.170190) | 0.020217 / 0.141683 (-0.121466) | 1.436354 / 1.452155 (-0.015801) | 1.492372 / 1.492716 (-0.000345) |\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) | 0.226896 / 0.018006 (0.208889) | 0.411935 / 0.000490 (0.411445) | 0.000356 / 0.000200 (0.000156) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024705 / 0.037411 (-0.012706) | 0.076232 / 0.014526 (0.061706) | 0.086949 / 0.176557 (-0.089608) | 0.141867 / 0.737135 (-0.595269) | 0.088199 / 0.296338 (-0.208140) |\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) | 0.419748 / 0.215209 (0.204539) | 4.198597 / 2.077655 (2.120942) | 2.338477 / 1.504120 (0.834357) | 2.195741 / 1.541195 (0.654547) | 2.278145 / 1.468490 (0.809655) | 0.502365 / 4.584777 (-4.082412) | 2.987773 / 3.745712 (-0.757939) | 2.896526 / 5.269862 (-2.373336) | 1.841610 / 4.565676 (-2.724067) | 0.058032 / 0.424275 (-0.366243) | 0.006470 / 0.007607 (-0.001137) | 0.496969 / 0.226044 (0.270925) | 4.960984 / 2.268929 (2.692056) | 2.648615 / 55.444624 (-52.796009) | 2.286846 / 6.876477 (-4.589631) | 2.320176 / 2.142072 (0.178104) | 0.600550 / 4.805227 (-4.204678) | 0.125652 / 6.500664 (-6.375012) | 0.062177 / 0.075469 (-0.013292) |\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) | 1.293063 / 1.841788 (-0.548725) | 18.294204 / 8.074308 (10.219896) | 13.720502 / 10.191392 (3.529110) | 0.146480 / 0.680424 (-0.533944) | 0.016965 / 0.534201 (-0.517236) | 0.330137 / 0.579283 (-0.249146) | 0.352051 / 0.434364 (-0.082313) | 0.381754 / 0.540337 (-0.158584) | 0.517935 / 1.386936 (-0.869001) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#269fcd31a2e759c65ffd5952ecef13e6a0d92574 \"CML watermark\")\n"
] | 2023-07-13T22:49:57Z
| 2023-07-14T14:21:58Z
| 2023-07-14T14:13:38Z
|
CONTRIBUTOR
| null | null | null |
This mistake was a bit confusing, so I thought it was worth sending a PR over.
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PR_kwDODunzps5gsWlz
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Retrieve cached datasets that were pushed to hub when offline
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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) | 0.005292 / 0.011353 (-0.006061) | 0.003811 / 0.011008 (-0.007197) | 0.064912 / 0.038508 (0.026404) | 0.061199 / 0.023109 (0.038090) | 0.242953 / 0.275898 (-0.032945) | 0.271789 / 0.323480 (-0.051691) | 0.003994 / 0.007986 (-0.003991) | 0.002723 / 0.004328 (-0.001606) | 0.049952 / 0.004250 (0.045701) | 0.039489 / 0.037052 (0.002437) | 0.261143 / 0.258489 (0.002654) | 0.288800 / 0.293841 (-0.005041) | 0.028130 / 0.128546 (-0.100416) | 0.010724 / 0.075646 (-0.064922) | 0.208218 / 0.419271 (-0.211054) | 0.036224 / 0.043533 (-0.007309) | 0.247189 / 0.255139 (-0.007950) | 0.274702 / 0.283200 (-0.008498) | 0.019714 / 0.141683 (-0.121969) | 1.134853 / 1.452155 (-0.317301) | 1.192655 / 1.492716 (-0.300062) |\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) | 0.096391 / 0.018006 (0.078385) | 0.303802 / 0.000490 (0.303312) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019530 / 0.037411 (-0.017881) | 0.061588 / 0.014526 (0.047062) | 0.075122 / 0.176557 (-0.101434) | 0.120980 / 0.737135 (-0.616155) | 0.075807 / 0.296338 (-0.220532) |\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) | 0.281672 / 0.215209 (0.066463) | 2.779884 / 2.077655 (0.702229) | 1.502026 / 1.504120 (-0.002094) | 1.369474 / 1.541195 (-0.171721) | 1.402694 / 1.468490 (-0.065796) | 0.559120 / 4.584777 (-4.025657) | 2.355320 / 3.745712 (-1.390393) | 2.823987 / 5.269862 (-2.445875) | 1.763888 / 4.565676 (-2.801788) | 0.061715 / 0.424275 (-0.362560) | 0.005015 / 0.007607 (-0.002592) | 0.342669 / 0.226044 (0.116625) | 3.360651 / 2.268929 (1.091722) | 1.887277 / 55.444624 (-53.557348) | 1.555613 / 6.876477 (-5.320864) | 1.614126 / 2.142072 (-0.527946) | 0.643797 / 4.805227 (-4.161430) | 0.118365 / 6.500664 (-6.382299) | 0.042596 / 0.075469 (-0.032873) |\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) | 0.951383 / 1.841788 (-0.890405) | 13.169812 / 8.074308 (5.095504) | 10.772460 / 10.191392 (0.581068) | 0.133248 / 0.680424 (-0.547176) | 0.014597 / 0.534201 (-0.519604) | 0.289758 / 0.579283 (-0.289525) | 0.266324 / 0.434364 (-0.168040) | 0.334811 / 0.540337 (-0.205526) | 0.445566 / 1.386936 (-0.941370) |\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) | 0.005668 / 0.011353 (-0.005684) | 0.003583 / 0.011008 (-0.007425) | 0.050681 / 0.038508 (0.012173) | 0.063244 / 0.023109 (0.040135) | 0.279624 / 0.275898 (0.003726) | 0.308030 / 0.323480 (-0.015450) | 0.004160 / 0.007986 (-0.003826) | 0.002633 / 0.004328 (-0.001696) | 0.048475 / 0.004250 (0.044225) | 0.043106 / 0.037052 (0.006054) | 0.283678 / 0.258489 (0.025189) | 0.309730 / 0.293841 (0.015889) | 0.030290 / 0.128546 (-0.098256) | 0.011112 / 0.075646 (-0.064534) | 0.058234 / 0.419271 (-0.361038) | 0.033553 / 0.043533 (-0.009979) | 0.279902 / 0.255139 (0.024763) | 0.298041 / 0.283200 (0.014841) | 0.019367 / 0.141683 (-0.122316) | 1.142438 / 1.452155 (-0.309717) | 1.197305 / 1.492716 (-0.295411) |\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) | 0.090875 / 0.018006 (0.072869) | 0.301174 / 0.000490 (0.300685) | 0.000216 / 0.000200 (0.000016) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021544 / 0.037411 (-0.015867) | 0.071371 / 0.014526 (0.056846) | 0.080821 / 0.176557 (-0.095736) | 0.120054 / 0.737135 (-0.617082) | 0.082611 / 0.296338 (-0.213728) |\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) | 0.293787 / 0.215209 (0.078578) | 2.862610 / 2.077655 (0.784955) | 1.597282 / 1.504120 (0.093162) | 1.485094 / 1.541195 (-0.056101) | 1.507384 / 1.468490 (0.038893) | 0.558470 / 4.584777 (-4.026307) | 2.414137 / 3.745712 (-1.331575) | 2.863342 / 5.269862 (-2.406520) | 1.776973 / 4.565676 (-2.788704) | 0.062296 / 0.424275 (-0.361979) | 0.004954 / 0.007607 (-0.002653) | 0.346037 / 0.226044 (0.119993) | 3.441864 / 2.268929 (1.172935) | 1.969842 / 55.444624 (-53.474783) | 1.714878 / 6.876477 (-5.161599) | 1.738141 / 2.142072 (-0.403931) | 0.645929 / 4.805227 (-4.159298) | 0.117332 / 6.500664 (-6.383332) | 0.041963 / 0.075469 (-0.033507) |\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) | 0.983229 / 1.841788 (-0.858559) | 13.186932 / 8.074308 (5.112624) | 11.220549 / 10.191392 (1.029157) | 0.142105 / 0.680424 (-0.538319) | 0.015210 / 0.534201 (-0.518991) | 0.290055 / 0.579283 (-0.289228) | 0.274513 / 0.434364 (-0.159851) | 0.346834 / 0.540337 (-0.193504) | 0.575897 / 1.386936 (-0.811039) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d3c0694d0c47a64a3cab5d468b4d9575ad7b1d96 \"CML watermark\")\n",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6459). 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) | 0.005308 / 0.011353 (-0.006045) | 0.003135 / 0.011008 (-0.007873) | 0.061820 / 0.038508 (0.023312) | 0.052005 / 0.023109 (0.028895) | 0.233507 / 0.275898 (-0.042391) | 0.257790 / 0.323480 (-0.065690) | 0.002848 / 0.007986 (-0.005138) | 0.002645 / 0.004328 (-0.001683) | 0.048379 / 0.004250 (0.044128) | 0.038320 / 0.037052 (0.001268) | 0.245470 / 0.258489 (-0.013019) | 0.274854 / 0.293841 (-0.018987) | 0.027335 / 0.128546 (-0.101211) | 0.010349 / 0.075646 (-0.065297) | 0.205872 / 0.419271 (-0.213400) | 0.035896 / 0.043533 (-0.007637) | 0.241645 / 0.255139 (-0.013494) | 0.260033 / 0.283200 (-0.023167) | 0.020325 / 0.141683 (-0.121358) | 1.116768 / 1.452155 (-0.335387) | 1.188067 / 1.492716 (-0.304649) |\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) | 0.092622 / 0.018006 (0.074616) | 0.302663 / 0.000490 (0.302173) | 0.000227 / 0.000200 (0.000027) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018633 / 0.037411 (-0.018778) | 0.060117 / 0.014526 (0.045592) | 0.072713 / 0.176557 (-0.103844) | 0.119955 / 0.737135 (-0.617180) | 0.074698 / 0.296338 (-0.221640) |\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) | 0.277157 / 0.215209 (0.061948) | 2.699650 / 2.077655 (0.621995) | 1.413625 / 1.504120 (-0.090494) | 1.295900 / 1.541195 (-0.245295) | 1.306280 / 1.468490 (-0.162210) | 0.555354 / 4.584777 (-4.029423) | 2.386866 / 3.745712 (-1.358847) | 2.794069 / 5.269862 (-2.475793) | 1.736275 / 4.565676 (-2.829401) | 0.061812 / 0.424275 (-0.362464) | 0.004957 / 0.007607 (-0.002650) | 0.334533 / 0.226044 (0.108488) | 3.251096 / 2.268929 (0.982168) | 1.768193 / 55.444624 (-53.676431) | 1.473752 / 6.876477 (-5.402724) | 1.476320 / 2.142072 (-0.665753) | 0.642485 / 4.805227 (-4.162742) | 0.116986 / 6.500664 (-6.383678) | 0.042083 / 0.075469 (-0.033386) |\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) | 0.941364 / 1.841788 (-0.900424) | 11.587408 / 8.074308 (3.513100) | 10.500198 / 10.191392 (0.308806) | 0.129126 / 0.680424 (-0.551298) | 0.015206 / 0.534201 (-0.518995) | 0.286580 / 0.579283 (-0.292703) | 0.263566 / 0.434364 (-0.170798) | 0.331662 / 0.540337 (-0.208676) | 0.431423 / 1.386936 (-0.955513) |\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) | 0.005151 / 0.011353 (-0.006202) | 0.003425 / 0.011008 (-0.007583) | 0.049301 / 0.038508 (0.010793) | 0.052005 / 0.023109 (0.028895) | 0.289594 / 0.275898 (0.013696) | 0.312630 / 0.323480 (-0.010849) | 0.003988 / 0.007986 (-0.003998) | 0.002705 / 0.004328 (-0.001624) | 0.048529 / 0.004250 (0.044279) | 0.039645 / 0.037052 (0.002592) | 0.293430 / 0.258489 (0.034941) | 0.311697 / 0.293841 (0.017856) | 0.029044 / 0.128546 (-0.099502) | 0.010282 / 0.075646 (-0.065364) | 0.057641 / 0.419271 (-0.361630) | 0.032733 / 0.043533 (-0.010800) | 0.293553 / 0.255139 (0.038414) | 0.308850 / 0.283200 (0.025651) | 0.018452 / 0.141683 (-0.123231) | 1.147931 / 1.452155 (-0.304224) | 1.173093 / 1.492716 (-0.319623) |\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) | 0.100862 / 0.018006 (0.082856) | 0.309286 / 0.000490 (0.308796) | 0.000223 / 0.000200 (0.000023) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021365 / 0.037411 (-0.016046) | 0.068987 / 0.014526 (0.054461) | 0.081092 / 0.176557 (-0.095465) | 0.119852 / 0.737135 (-0.617283) | 0.082850 / 0.296338 (-0.213489) |\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) | 0.288477 / 0.215209 (0.073268) | 2.833766 / 2.077655 (0.756111) | 1.576670 / 1.504120 (0.072550) | 1.431643 / 1.541195 (-0.109552) | 1.442132 / 1.468490 (-0.026358) | 0.556079 / 4.584777 (-4.028698) | 2.465042 / 3.745712 (-1.280670) | 2.786329 / 5.269862 (-2.483532) | 1.779428 / 4.565676 (-2.786249) | 0.062278 / 0.424275 (-0.361997) | 0.004867 / 0.007607 (-0.002740) | 0.348444 / 0.226044 (0.122399) | 3.389824 / 2.268929 (1.120896) | 1.919141 / 55.444624 (-53.525484) | 1.635411 / 6.876477 (-5.241066) | 1.654869 / 2.142072 (-0.487204) | 0.634467 / 4.805227 (-4.170761) | 0.114330 / 6.500664 (-6.386334) | 0.039900 / 0.075469 (-0.035569) |\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) | 0.970851 / 1.841788 (-0.870937) | 11.951660 / 8.074308 (3.877352) | 10.571115 / 10.191392 (0.379723) | 0.131040 / 0.680424 (-0.549384) | 0.015299 / 0.534201 (-0.518902) | 0.287851 / 0.579283 (-0.291432) | 0.278366 / 0.434364 (-0.155998) | 0.326468 / 0.540337 (-0.213870) | 0.552288 / 1.386936 (-0.834648) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8214ff2a9f706427669a6c2a01ccabffa5bf0d2b \"CML watermark\")\n"
] | 2023-11-29T16:56:15Z
| 2024-03-25T13:55:42Z
| 2024-03-25T13:55:42Z
|
MEMBER
| null | null | null |
I drafted the logic to retrieve a no-script dataset in the cache.
For example it can reload datasets that were pushed to hub if they exist in the cache.
example:
```python
>>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp")
>>> load_dataset("lhoestq/tmp")
DatasetDict({
train: Dataset({
features: ['a'],
num_rows: 2
})
})
```
and later, without connection:
```python
>>> load_dataset("lhoestq/tmp")
Using the latest cached version of the dataset from /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/*/*/0b3caccda1725efb(last modified on Wed Nov 29 16:50:27 2023) since it couldn't be found locally at lhoestq/tmp.
DatasetDict({
train: Dataset({
features: ['a'],
num_rows: 2
})
})
```
fix https://github.com/huggingface/datasets/issues/3547
## Implementation details (EDITED)
I continued in https://github.com/huggingface/datasets/pull/6493, see the changes there
TODO:
- [x] tests
- [ ] compatible with https://github.com/huggingface/datasets/pull/6458
|
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| 2,226,040,636
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I_kwDODunzps6Erq88
| 6,778
|
Dataset.to_csv() missing commas in columns with lists
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[
"Hello!\r\n\r\nThis is due to how pandas write numpy arrays to csv. [Source](https://stackoverflow.com/questions/54753179/to-csv-saves-np-array-as-string-instead-of-as-a-list)\r\nTo fix this, you can convert them to list yourselves.\r\n\r\n```python\r\ndf = ds.to_pandas()\r\ndf['int'] = df['int'].apply(lambda arr: list(arr))\r\ndf.to_csv(index=False, '../output/temp.csv')\r\n```\r\n\r\nI think it would be good if `datasets` would do the conversion itself, but it's a breaking change and I would wait for the greenlight from someone from HF."
] | 2024-04-04T16:46:13Z
| 2024-04-08T15:24:41Z
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### Describe the bug
The `to_csv()` method does not output commas in lists. So when the Dataset is loaded back in the data structure of the column with a list is not correct.
Here's an example:
Obviously, it's not as trivial as inserting commas in the list, since its a comma-separated file. But hopefully there's a way to export the list in a way that it'll be imported by `load_dataset()` correctly.
### Steps to reproduce the bug
Here's some code to reproduce the bug:
```python
from datasets import Dataset
ds = Dataset.from_dict(
{
"pokemon": ["bulbasaur", "squirtle"],
"type": ["grass", "water"]
}
)
def ascii_to_hex(text):
return [ord(c) for c in text]
ds = ds.map(lambda x: {"int": ascii_to_hex(x['pokemon'])})
ds.to_csv('../output/temp.csv')
```
temp.csv then contains:
```
### Expected behavior
ACTUAL OUTPUT:
```
pokemon,type,int
bulbasaur,grass,[ 98 117 108 98 97 115 97 117 114]
squirtle,water,[115 113 117 105 114 116 108 101]
```
EXPECTED OUTPUT:
```
pokemon,type,int
bulbasaur,grass,[98, 117, 108, 98, 97, 115, 97, 117, 114]
squirtle,water,[115, 113, 117, 105, 114, 116, 108, 101]
```
or probably something more like this since it's a CSV file:
```
pokemon,type,int
bulbasaur,grass,"[98, 117, 108, 98, 97, 115, 97, 117, 114]"
squirtle,water,"[115, 113, 117, 105, 114, 116, 108, 101]"
```
### Environment info
### Package Version
Name: datasets
Version: 2.16.1
### Python
version: 3.10.12
### OS Info
PRETTY_NAME="Ubuntu 22.04.4 LTS"
NAME="Ubuntu"
VERSION_ID="22.04"
VERSION="22.04.4 LTS (Jammy Jellyfish)"
VERSION_CODENAME=jammy
ID=ubuntu
ID_LIKE=debian
...
UBUNTU_CODENAME=jammy
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AttributeError: module 'numpy' has no attribute 'object'. in Kaggle Notebook
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[
"Hi! You can fix this by updating the `datasets` package with `pip install -U datasets` and restarting the notebook.\r\n",
"Kaggle removed the problematic `datasets==2.1.0` pin last week, so I'm closing this issue (now it pre-installs the latest version)."
] | 2024-04-05T14:31:48Z
| 2024-04-11T17:18:53Z
| 2024-04-11T17:18:53Z
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### Describe the bug
# problem
I can't resample audio dataset in Kaggle Notebook. It looks like some code in `datasets` library use aliases that were deprecated in NumPy 1.20.
## code for resampling
```
from datasets import load_dataset, Audio
from transformers import AutoFeatureExtractor
from transformers import AutoModelForAudioClassification, TrainingArguments, Trainer
minds = load_dataset("PolyAI/minds14", name="en-US", split="train")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
def preprocess_function(examples):
audio_arrays = [x["array"] for x in examples["audio"]]
inputs = feature_extractor(
audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=16000, truncation=True
)
return inputs
dataset = dataset.map(preprocess_function, remove_columns="audio", batched=True, batch_size=100)
```
## the error I got
<details>
<summary>Click to expand</summary>
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[20], line 1
----> 1 dataset = dataset.map(preprocess_function, remove_columns="audio", batched=True, batch_size=100)
2 dataset
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1955, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1952 disable_tqdm = not logging.is_progress_bar_enabled()
1954 if num_proc is None or num_proc == 1:
-> 1955 return self._map_single(
1956 function=function,
1957 with_indices=with_indices,
1958 with_rank=with_rank,
1959 input_columns=input_columns,
1960 batched=batched,
1961 batch_size=batch_size,
1962 drop_last_batch=drop_last_batch,
1963 remove_columns=remove_columns,
1964 keep_in_memory=keep_in_memory,
1965 load_from_cache_file=load_from_cache_file,
1966 cache_file_name=cache_file_name,
1967 writer_batch_size=writer_batch_size,
1968 features=features,
1969 disable_nullable=disable_nullable,
1970 fn_kwargs=fn_kwargs,
1971 new_fingerprint=new_fingerprint,
1972 disable_tqdm=disable_tqdm,
1973 desc=desc,
1974 )
1975 else:
1977 def format_cache_file_name(cache_file_name, rank):
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:520, in transmit_tasks.<locals>.wrapper(*args, **kwargs)
518 self: "Dataset" = kwargs.pop("self")
519 # apply actual function
--> 520 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
521 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
522 for dataset in datasets:
523 # Remove task templates if a column mapping of the template is no longer valid
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:487, in transmit_format.<locals>.wrapper(*args, **kwargs)
480 self_format = {
481 "type": self._format_type,
482 "format_kwargs": self._format_kwargs,
483 "columns": self._format_columns,
484 "output_all_columns": self._output_all_columns,
485 }
486 # apply actual function
--> 487 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
488 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
489 # re-apply format to the output
File /opt/conda/lib/python3.10/site-packages/datasets/fingerprint.py:458, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
452 kwargs[fingerprint_name] = update_fingerprint(
453 self._fingerprint, transform, kwargs_for_fingerprint
454 )
456 # Call actual function
--> 458 out = func(self, *args, **kwargs)
460 # Update fingerprint of in-place transforms + update in-place history of transforms
462 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:2356, in Dataset._map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2354 writer.write_table(batch)
2355 else:
-> 2356 writer.write_batch(batch)
2357 if update_data and writer is not None:
2358 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_writer.py:507, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)
505 col_try_type = try_features[col] if try_features is not None and col in try_features else None
506 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 507 arrays.append(pa.array(typed_sequence))
508 inferred_features[col] = typed_sequence.get_inferred_type()
509 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
File /opt/conda/lib/python3.10/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()
File /opt/conda/lib/python3.10/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_writer.py:184, in TypedSequence.__arrow_array__(self, type)
182 out = numpy_to_pyarrow_listarray(data)
183 elif isinstance(data, list) and data and isinstance(first_non_null_value(data)[1], np.ndarray):
--> 184 out = list_of_np_array_to_pyarrow_listarray(data)
185 else:
186 trying_cast_to_python_objects = True
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1174, in list_of_np_array_to_pyarrow_listarray(l_arr, type)
1172 """Build a PyArrow ListArray from a possibly nested list of NumPy arrays"""
1173 if len(l_arr) > 0:
-> 1174 return list_of_pa_arrays_to_pyarrow_listarray(
1175 [numpy_to_pyarrow_listarray(arr, type=type) if arr is not None else None for arr in l_arr]
1176 )
1177 else:
1178 return pa.array([], type=type)
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1163, in list_of_pa_arrays_to_pyarrow_listarray(l_arr)
1160 null_indices = [i for i, arr in enumerate(l_arr) if arr is None]
1161 l_arr = [arr for arr in l_arr if arr is not None]
1162 offsets = np.cumsum(
-> 1163 [0] + [len(arr) for arr in l_arr], dtype=np.object
1164 ) # convert to dtype object to allow None insertion
1165 offsets = np.insert(offsets, null_indices, None)
1166 offsets = pa.array(offsets, type=pa.int32())
File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr)
319 warnings.warn(
320 f"In the future `np.{attr}` will be defined as the "
321 "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
323 if attr in __former_attrs__:
--> 324 raise AttributeError(__former_attrs__[attr])
326 if attr == 'testing':
327 import numpy.testing as testing
AttributeError: module 'numpy' has no attribute 'object'.
`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
```
</details>
### Steps to reproduce the bug
Run above code in Kaggle Notebook.
### Expected behavior
I can resample audio data without fail.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.133+-x86_64-with-glibc2.31
- Python version: 3.10.13
- PyArrow version: 11.0.0
- Pandas version: 2.2.1
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Return a split Dataset in load_dataset
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5301). All of your documentation changes will be reflected on that endpoint.",
"Just noticed that now we have to deal with indexed & split datasets. The remaining tests are failing because one should be able to get an indexed dataset when accessing the split of a dataset made of indexed splits (right now the index is just trashed)"
] | 2022-11-25T16:35:54Z
| 2023-09-24T10:06:15Z
| 2023-02-21T13:13:13Z
|
MEMBER
| null | null | null |
...instead of a DatasetDict.
```python
# now supported
ds = load_dataset("squad")
ds[0]
for example in ds:
pass
# still works
ds["train"]
ds["validation"]
# new
ds.splits # Dict[str, Dataset] | None
# soon to be supported (not in this PR)
ds = load_dataset("dataset_with_no_splits")
ds[0]
for example in ds:
pass
```
I implemented `Dataset.__getitem__` and `IterableDataset.__getitem__` to be able to get a split from a dataset.
The splits are defined by the `ds.info.splits` dictionary.
Therefore a dataset is a table that optionally has some splits defined in the dataset info. And a split dataset is the concatenation of all its splits.
I made as little breaking changes as possible. Notable breaking changes:
- `load_dataset("potato").keys() / .items() / .values() /` don't work anymore, since we don't return a dict
- same for `for split_name in load_dataset("potato")`, since we now iterate on the examples
- ..
TODO:
- [x] Update push_to_hub
- [x] Update save_to_disk/load_from_disk
- [ ] check for other breaking changes
- [ ] fix existing tests
- [ ] add new tests
- [ ] docs
This is related to https://github.com/huggingface/datasets/issues/5189, to extend `load_dataset` to return datasets without splits
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[
"Hi! I've merged a PR to use `int32` instead of `int8` for `reddit_scores`, so it should work now.\r\n\r\n"
] | 2023-02-24T13:01:48Z
| 2023-02-24T14:01:09Z
| 2023-02-24T14:01:09Z
|
NONE
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### Describe the bug
I met the same bug mentioned in #3053 which is never fixed. Because several `reddit_scores` are larger than `int8` even `int16`. https://huggingface.co/datasets/the_pile_openwebtext2/blob/main/the_pile_openwebtext2.py#L62
### Steps to reproduce the bug
```python3
from datasets import load_dataset
dataset = load_dataset("the_pile_openwebtext2")
```
### Expected behavior
load as normal.
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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PR_kwDODunzps5ok4VF
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fix CastError pickling
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6712). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005151 / 0.011353 (-0.006202) | 0.003813 / 0.011008 (-0.007196) | 0.062957 / 0.038508 (0.024449) | 0.028282 / 0.023109 (0.005173) | 0.246036 / 0.275898 (-0.029862) | 0.290024 / 0.323480 (-0.033456) | 0.004009 / 0.007986 (-0.003977) | 0.002749 / 0.004328 (-0.001580) | 0.049351 / 0.004250 (0.045101) | 0.041143 / 0.037052 (0.004090) | 0.264782 / 0.258489 (0.006293) | 0.290711 / 0.293841 (-0.003130) | 0.027248 / 0.128546 (-0.101298) | 0.010691 / 0.075646 (-0.064955) | 0.205926 / 0.419271 (-0.213345) | 0.035652 / 0.043533 (-0.007880) | 0.246357 / 0.255139 (-0.008782) | 0.267851 / 0.283200 (-0.015348) | 0.018498 / 0.141683 (-0.123185) | 1.135996 / 1.452155 (-0.316159) | 1.181841 / 1.492716 (-0.310875) |\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) | 0.094054 / 0.018006 (0.076048) | 0.305470 / 0.000490 (0.304980) | 0.000225 / 0.000200 (0.000025) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018842 / 0.037411 (-0.018569) | 0.061532 / 0.014526 (0.047006) | 0.073483 / 0.176557 (-0.103073) | 0.119426 / 0.737135 (-0.617709) | 0.075385 / 0.296338 (-0.220954) |\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) | 0.285544 / 0.215209 (0.070335) | 2.774256 / 2.077655 (0.696601) | 1.475719 / 1.504120 (-0.028401) | 1.353841 / 1.541195 (-0.187353) | 1.381891 / 1.468490 (-0.086599) | 0.570619 / 4.584777 (-4.014158) | 2.380300 / 3.745712 (-1.365412) | 2.788767 / 5.269862 (-2.481095) | 1.741790 / 4.565676 (-2.823886) | 0.061810 / 0.424275 (-0.362465) | 0.005004 / 0.007607 (-0.002603) | 0.334963 / 0.226044 (0.108918) | 3.286388 / 2.268929 (1.017459) | 1.831669 / 55.444624 (-53.612955) | 1.523372 / 6.876477 (-5.353105) | 1.581551 / 2.142072 (-0.560521) | 0.639642 / 4.805227 (-4.165585) | 0.117356 / 6.500664 (-6.383308) | 0.043277 / 0.075469 (-0.032192) |\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) | 0.973005 / 1.841788 (-0.868782) | 11.590148 / 8.074308 (3.515839) | 9.521262 / 10.191392 (-0.670130) | 0.143243 / 0.680424 (-0.537181) | 0.013529 / 0.534201 (-0.520672) | 0.285724 / 0.579283 (-0.293559) | 0.265642 / 0.434364 (-0.168721) | 0.366098 / 0.540337 (-0.174239) | 0.444410 / 1.386936 (-0.942526) |\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) | 0.005347 / 0.011353 (-0.006006) | 0.003797 / 0.011008 (-0.007212) | 0.050441 / 0.038508 (0.011933) | 0.032812 / 0.023109 (0.009703) | 0.281278 / 0.275898 (0.005379) | 0.304524 / 0.323480 (-0.018956) | 0.005039 / 0.007986 (-0.002946) | 0.002735 / 0.004328 (-0.001594) | 0.049184 / 0.004250 (0.044933) | 0.046751 / 0.037052 (0.009698) | 0.292093 / 0.258489 (0.033604) | 0.322087 / 0.293841 (0.028246) | 0.029775 / 0.128546 (-0.098771) | 0.010540 / 0.075646 (-0.065106) | 0.057927 / 0.419271 (-0.361345) | 0.054240 / 0.043533 (0.010707) | 0.281537 / 0.255139 (0.026398) | 0.298386 / 0.283200 (0.015186) | 0.019773 / 0.141683 (-0.121910) | 1.157161 / 1.452155 (-0.294994) | 1.210395 / 1.492716 (-0.282321) |\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) | 0.095098 / 0.018006 (0.077091) | 0.306952 / 0.000490 (0.306462) | 0.000211 / 0.000200 (0.000011) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022602 / 0.037411 (-0.014809) | 0.075242 / 0.014526 (0.060716) | 0.087134 / 0.176557 (-0.089422) | 0.127923 / 0.737135 (-0.609212) | 0.088645 / 0.296338 (-0.207693) |\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) | 0.304187 / 0.215209 (0.088978) | 2.977120 / 2.077655 (0.899465) | 1.663592 / 1.504120 (0.159473) | 1.527601 / 1.541195 (-0.013594) | 1.540121 / 1.468490 (0.071631) | 0.562492 / 4.584777 (-4.022285) | 2.473836 / 3.745712 (-1.271876) | 2.656782 / 5.269862 (-2.613080) | 1.754212 / 4.565676 (-2.811464) | 0.062330 / 0.424275 (-0.361945) | 0.005149 / 0.007607 (-0.002459) | 0.354905 / 0.226044 (0.128860) | 3.503587 / 2.268929 (1.234659) | 2.015682 / 55.444624 (-53.428943) | 1.744421 / 6.876477 (-5.132056) | 1.923120 / 2.142072 (-0.218952) | 0.652209 / 4.805227 (-4.153018) | 0.119406 / 6.500664 (-6.381258) | 0.042840 / 0.075469 (-0.032630) |\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) | 1.009164 / 1.841788 (-0.832624) | 12.379654 / 8.074308 (4.305346) | 10.408696 / 10.191392 (0.217304) | 0.141674 / 0.680424 (-0.538750) | 0.016815 / 0.534201 (-0.517386) | 0.292453 / 0.579283 (-0.286830) | 0.277577 / 0.434364 (-0.156787) | 0.325024 / 0.540337 (-0.215313) | 0.433181 / 1.386936 (-0.953755) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7a16a08c940e65397305aec5f1b484d91cee75a \"CML watermark\")\n"
] | 2024-03-04T11:14:18Z
| 2024-03-04T20:23:47Z
| 2024-03-04T20:17:17Z
|
MEMBER
| null | null | null |
reported in https://discuss.huggingface.co/t/datasetdict-save-to-disk-with-num-proc-1-seems-to-hang-with-error/75595
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I_kwDODunzps5V58sy
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Delta Tables usage using Datasets Library
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[] | 2022-11-09T02:42:18Z
| 2022-11-09T02:42:36Z
| 2022-11-09T02:42:36Z
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NONE
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### Feature request
Adding compatibility of Datasets library with Delta Format. Elevating the utilities of Datasets library from Machine Learning Scope to Data Engineering Scope as well.
### Motivation
We know datasets library can absorb csv, json, parquet, etc. file formats but it would be great if Datasets library could work with Delta Tables (with delta format) as it has different features such as time travelling, layout optimization, query performance, aids in Data Engineering.
This will help and enhance Datasets library from Machine Learning utility to Data Engineering utilities and expand horizons thereafter. I am totally using Datasets library in all my usecases and as my role expands so does the work, compatibility with Datasets library is something I don't want to lose.
### Your contribution
Would love to work on this feature, even if this has to picked up from scratch, including design paradigms and patterns.
I have basic idea about Delta Live Tables, would brush it easily for this feature.
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num_proc parallelization works only for first ~10s.
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"Hi, can you check if the processes are still alive ? It's a bit weird because `datasets` does check if processes crash and return an error in that case",
"Thank you for reverting quickly. I digged a bit, and realized my disk's IOPS is also limited - which is causing this. will check further and report if it's an issue of hf datasets' side or mine. "
] | 2025-04-15T11:44:03Z
| 2025-04-15T13:12:13Z
| null |
NONE
| null | null |
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### Describe the bug
When I try to load an already downloaded dataset with num_proc=64, the speed is very high for the first 10-20 seconds acheiving 30-40K samples / s, and 100% utilization for all cores but it soon drops to <= 1000 with almost 0% utilization for most cores.
### Steps to reproduce the bug
```
// download dataset with cli
!huggingface-cli download --repo-type dataset timm/imagenet-1k-wds --max-workers 32
from datasets import load_dataset
ds = load_dataset("timm/imagenet-1k-wds", num_proc=64)
```
### Expected behavior
100% core utilization throughout.
### Environment info
Azure A100-80GB, 16 cores VM
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PR_kwDODunzps4_3uzy
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Fix `flatten_indices` with empty indices mapping
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-29T16:17:28Z
| 2022-09-30T15:46:39Z
| 2022-09-30T15:44:25Z
|
COLLABORATOR
| null | null | null |
Fix #5038
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PR_kwDODunzps5ZsgPK
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Release: 2.14.5
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6219). 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) | 0.009523 / 0.011353 (-0.001830) | 0.005105 / 0.011008 (-0.005903) | 0.122664 / 0.038508 (0.084156) | 0.084688 / 0.023109 (0.061579) | 0.412057 / 0.275898 (0.136159) | 0.449690 / 0.323480 (0.126210) | 0.006627 / 0.007986 (-0.001358) | 0.004150 / 0.004328 (-0.000178) | 0.082079 / 0.004250 (0.077829) | 0.065289 / 0.037052 (0.028237) | 0.432934 / 0.258489 (0.174445) | 0.492068 / 0.293841 (0.198227) | 0.048317 / 0.128546 (-0.080229) | 0.015582 / 0.075646 (-0.060064) | 0.372050 / 0.419271 (-0.047222) | 0.070649 / 0.043533 (0.027116) | 0.431754 / 0.255139 (0.176615) | 0.473349 / 0.283200 (0.190149) | 0.037293 / 0.141683 (-0.104390) | 1.807537 / 1.452155 (0.355382) | 1.923073 / 1.492716 (0.430357) |\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) | 0.271214 / 0.018006 (0.253208) | 0.592961 / 0.000490 (0.592471) | 0.004062 / 0.000200 (0.003862) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034766 / 0.037411 (-0.002645) | 0.093014 / 0.014526 (0.078488) | 0.131332 / 0.176557 (-0.045225) | 0.188110 / 0.737135 (-0.549025) | 0.117617 / 0.296338 (-0.178722) |\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) | 0.668223 / 0.215209 (0.453013) | 6.707031 / 2.077655 (4.629376) | 3.040178 / 1.504120 (1.536058) | 2.641776 / 1.541195 (1.100581) | 2.524057 / 1.468490 (1.055567) | 0.893592 / 4.584777 (-3.691185) | 5.535848 / 3.745712 (1.790136) | 4.867067 / 5.269862 (-0.402794) | 2.999933 / 4.565676 (-1.565743) | 0.103602 / 0.424275 (-0.320673) | 0.008887 / 0.007607 (0.001280) | 0.822214 / 0.226044 (0.596169) | 8.028476 / 2.268929 (5.759547) | 3.708895 / 55.444624 (-51.735730) | 2.858314 / 6.876477 (-4.018163) | 3.101727 / 2.142072 (0.959655) | 1.083136 / 4.805227 (-3.722091) | 0.219588 / 6.500664 (-6.281076) | 0.080151 / 0.075469 (0.004682) |\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) | 1.645819 / 1.841788 (-0.195969) | 24.407887 / 8.074308 (16.333579) | 22.371901 / 10.191392 (12.180509) | 0.219557 / 0.680424 (-0.460867) | 0.037867 / 0.534201 (-0.496334) | 0.484136 / 0.579283 (-0.095147) | 0.620546 / 0.434364 (0.186182) | 0.562272 / 0.540337 (0.021934) | 0.774256 / 1.386936 (-0.612680) |\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) | 0.009381 / 0.011353 (-0.001972) | 0.005565 / 0.011008 (-0.005444) | 0.091057 / 0.038508 (0.052549) | 0.078085 / 0.023109 (0.054975) | 0.538929 / 0.275898 (0.263031) | 0.555155 / 0.323480 (0.231675) | 0.007007 / 0.007986 (-0.000978) | 0.004268 / 0.004328 (-0.000060) | 0.086618 / 0.004250 (0.082368) | 0.064117 / 0.037052 (0.027065) | 0.523788 / 0.258489 (0.265299) | 0.586451 / 0.293841 (0.292610) | 0.050804 / 0.128546 (-0.077742) | 0.013964 / 0.075646 (-0.061682) | 0.096008 / 0.419271 (-0.323263) | 0.062242 / 0.043533 (0.018709) | 0.530398 / 0.255139 (0.275259) | 0.568527 / 0.283200 (0.285327) | 0.032456 / 0.141683 (-0.109227) | 1.894975 / 1.452155 (0.442820) | 2.084172 / 1.492716 (0.591455) |\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) | 0.295539 / 0.018006 (0.277533) | 0.588804 / 0.000490 (0.588314) | 0.006445 / 0.000200 (0.006245) | 0.000113 / 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) | 0.033965 / 0.037411 (-0.003447) | 0.111743 / 0.014526 (0.097217) | 0.128805 / 0.176557 (-0.047752) | 0.185013 / 0.737135 (-0.552123) | 0.129400 / 0.296338 (-0.166938) |\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) | 0.749784 / 0.215209 (0.534575) | 7.091075 / 2.077655 (5.013420) | 3.424517 / 1.504120 (1.920397) | 3.069103 / 1.541195 (1.527908) | 3.122431 / 1.468490 (1.653941) | 0.949277 / 4.584777 (-3.635500) | 5.648731 / 3.745712 (1.903019) | 4.937684 / 5.269862 (-0.332178) | 3.198027 / 4.565676 (-1.367650) | 0.100289 / 0.424275 (-0.323987) | 0.009411 / 0.007607 (0.001803) | 0.862604 / 0.226044 (0.636559) | 8.615410 / 2.268929 (6.346482) | 4.306428 / 55.444624 (-51.138196) | 3.591404 / 6.876477 (-3.285073) | 3.823899 / 2.142072 (1.681827) | 1.108006 / 4.805227 (-3.697221) | 0.215330 / 6.500664 (-6.285334) | 0.080755 / 0.075469 (0.005286) |\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) | 1.774914 / 1.841788 (-0.066873) | 25.360983 / 8.074308 (17.286675) | 23.624044 / 10.191392 (13.432652) | 0.226887 / 0.680424 (-0.453537) | 0.032625 / 0.534201 (-0.501576) | 0.499730 / 0.579283 (-0.079553) | 0.647819 / 0.434364 (0.213455) | 0.592239 / 0.540337 (0.051901) | 0.805751 / 1.386936 (-0.581185) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0daa82428a0529478801574bcc68e1ed32051f3a \"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) | 0.008656 / 0.011353 (-0.002697) | 0.005545 / 0.011008 (-0.005463) | 0.107936 / 0.038508 (0.069428) | 0.077436 / 0.023109 (0.054327) | 0.391412 / 0.275898 (0.115514) | 0.452811 / 0.323480 (0.129331) | 0.004883 / 0.007986 (-0.003103) | 0.005125 / 0.004328 (0.000796) | 0.080006 / 0.004250 (0.075755) | 0.054425 / 0.037052 (0.017373) | 0.399667 / 0.258489 (0.141178) | 0.458099 / 0.293841 (0.164258) | 0.047302 / 0.128546 (-0.081244) | 0.014153 / 0.075646 (-0.061493) | 0.337281 / 0.419271 (-0.081991) | 0.062153 / 0.043533 (0.018620) | 0.399927 / 0.255139 (0.144788) | 0.407186 / 0.283200 (0.123987) | 0.036759 / 0.141683 (-0.104924) | 1.825935 / 1.452155 (0.373780) | 1.852238 / 1.492716 (0.359522) |\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) | 0.274163 / 0.018006 (0.256157) | 0.615624 / 0.000490 (0.615134) | 0.003782 / 0.000200 (0.003582) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026386 / 0.037411 (-0.011026) | 0.101151 / 0.014526 (0.086625) | 0.106115 / 0.176557 (-0.070442) | 0.161253 / 0.737135 (-0.575882) | 0.108861 / 0.296338 (-0.187478) |\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) | 0.587079 / 0.215209 (0.371870) | 6.141743 / 2.077655 (4.064089) | 2.727199 / 1.504120 (1.223079) | 2.526827 / 1.541195 (0.985632) | 2.598321 / 1.468490 (1.129831) | 0.904706 / 4.584777 (-3.680071) | 5.227742 / 3.745712 (1.482030) | 4.621627 / 5.269862 (-0.648234) | 2.931792 / 4.565676 (-1.633885) | 0.089538 / 0.424275 (-0.334737) | 0.008281 / 0.007607 (0.000674) | 0.675773 / 0.226044 (0.449729) | 7.212869 / 2.268929 (4.943941) | 3.541569 / 55.444624 (-51.903056) | 2.804034 / 6.876477 (-4.072443) | 3.080192 / 2.142072 (0.938120) | 1.034577 / 4.805227 (-3.770650) | 0.218727 / 6.500664 (-6.281937) | 0.084548 / 0.075469 (0.009079) |\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) | 1.528974 / 1.841788 (-0.312814) | 21.754329 / 8.074308 (13.680021) | 20.359808 / 10.191392 (10.168416) | 0.234719 / 0.680424 (-0.445705) | 0.026182 / 0.534201 (-0.508019) | 0.448956 / 0.579283 (-0.130327) | 0.577015 / 0.434364 (0.142651) | 0.513675 / 0.540337 (-0.026662) | 0.729780 / 1.386936 (-0.657156) |\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) | 0.010427 / 0.011353 (-0.000926) | 0.005126 / 0.011008 (-0.005882) | 0.082759 / 0.038508 (0.044251) | 0.084892 / 0.023109 (0.061783) | 0.543826 / 0.275898 (0.267927) | 0.603050 / 0.323480 (0.279570) | 0.006667 / 0.007986 (-0.001319) | 0.004036 / 0.004328 (-0.000292) | 0.079534 / 0.004250 (0.075283) | 0.067523 / 0.037052 (0.030471) | 0.544845 / 0.258489 (0.286356) | 0.578823 / 0.293841 (0.284982) | 0.054786 / 0.128546 (-0.073760) | 0.014888 / 0.075646 (-0.060759) | 0.095696 / 0.419271 (-0.323576) | 0.064908 / 0.043533 (0.021375) | 0.558087 / 0.255139 (0.302948) | 0.593919 / 0.283200 (0.310719) | 0.039190 / 0.141683 (-0.102493) | 1.828680 / 1.452155 (0.376526) | 1.908891 / 1.492716 (0.416174) |\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) | 0.298926 / 0.018006 (0.280920) | 0.589467 / 0.000490 (0.588977) | 0.005276 / 0.000200 (0.005076) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034300 / 0.037411 (-0.003111) | 0.096990 / 0.014526 (0.082464) | 0.109347 / 0.176557 (-0.067209) | 0.171312 / 0.737135 (-0.565823) | 0.121736 / 0.296338 (-0.174603) |\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) | 0.641619 / 0.215209 (0.426410) | 6.365556 / 2.077655 (4.287901) | 2.947989 / 1.504120 (1.443869) | 2.631680 / 1.541195 (1.090485) | 2.602762 / 1.468490 (1.134272) | 0.812767 / 4.584777 (-3.772010) | 5.185753 / 3.745712 (1.440041) | 4.589897 / 5.269862 (-0.679964) | 2.833020 / 4.565676 (-1.732656) | 0.097782 / 0.424275 (-0.326493) | 0.008625 / 0.007607 (0.001018) | 0.741613 / 0.226044 (0.515568) | 7.662905 / 2.268929 (5.393976) | 3.533753 / 55.444624 (-51.910871) | 2.898929 / 6.876477 (-3.977547) | 3.042616 / 2.142072 (0.900544) | 0.933932 / 4.805227 (-3.871296) | 0.195710 / 6.500664 (-6.304954) | 0.066954 / 0.075469 (-0.008515) |\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) | 1.745353 / 1.841788 (-0.096434) | 23.820840 / 8.074308 (15.746532) | 20.892645 / 10.191392 (10.701253) | 0.234853 / 0.680424 (-0.445571) | 0.029149 / 0.534201 (-0.505051) | 0.458953 / 0.579283 (-0.120330) | 0.594278 / 0.434364 (0.159914) | 0.522929 / 0.540337 (-0.017409) | 0.753731 / 1.386936 (-0.633205) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de6391d732ea0471ee5bdfb91b8cecc4503da96b \"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) | 0.005976 / 0.011353 (-0.005377) | 0.003636 / 0.011008 (-0.007372) | 0.079946 / 0.038508 (0.041437) | 0.060143 / 0.023109 (0.037034) | 0.314752 / 0.275898 (0.038854) | 0.353714 / 0.323480 (0.030234) | 0.004706 / 0.007986 (-0.003280) | 0.002862 / 0.004328 (-0.001466) | 0.061988 / 0.004250 (0.057737) | 0.045907 / 0.037052 (0.008855) | 0.316118 / 0.258489 (0.057629) | 0.358488 / 0.293841 (0.064647) | 0.027377 / 0.128546 (-0.101170) | 0.007970 / 0.075646 (-0.067677) | 0.261677 / 0.419271 (-0.157594) | 0.045289 / 0.043533 (0.001757) | 0.307931 / 0.255139 (0.052792) | 0.341364 / 0.283200 (0.058165) | 0.021021 / 0.141683 (-0.120662) | 1.440002 / 1.452155 (-0.012153) | 1.502904 / 1.492716 (0.010187) |\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) | 0.201746 / 0.018006 (0.183740) | 0.451114 / 0.000490 (0.450624) | 0.003351 / 0.000200 (0.003151) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024233 / 0.037411 (-0.013178) | 0.075042 / 0.014526 (0.060516) | 0.085636 / 0.176557 (-0.090920) | 0.144699 / 0.737135 (-0.592436) | 0.085222 / 0.296338 (-0.211117) |\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) | 0.389464 / 0.215209 (0.174255) | 3.889072 / 2.077655 (1.811417) | 1.908307 / 1.504120 (0.404187) | 1.738914 / 1.541195 (0.197719) | 1.866869 / 1.468490 (0.398379) | 0.500536 / 4.584777 (-4.084240) | 3.050155 / 3.745712 (-0.695557) | 2.832259 / 5.269862 (-2.437602) | 1.886657 / 4.565676 (-2.679020) | 0.059214 / 0.424275 (-0.365062) | 0.006711 / 0.007607 (-0.000896) | 0.467753 / 0.226044 (0.241709) | 4.666939 / 2.268929 (2.398011) | 2.471168 / 55.444624 (-52.973456) | 2.223508 / 6.876477 (-4.652968) | 2.176543 / 2.142072 (0.034470) | 0.593461 / 4.805227 (-4.211766) | 0.126216 / 6.500664 (-6.374448) | 0.061495 / 0.075469 (-0.013974) |\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) | 1.301279 / 1.841788 (-0.540509) | 18.317461 / 8.074308 (10.243153) | 13.877813 / 10.191392 (3.686421) | 0.143510 / 0.680424 (-0.536914) | 0.016826 / 0.534201 (-0.517375) | 0.328735 / 0.579283 (-0.250548) | 0.342272 / 0.434364 (-0.092092) | 0.375768 / 0.540337 (-0.164570) | 0.517600 / 1.386936 (-0.869336) |\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) | 0.006215 / 0.011353 (-0.005138) | 0.003587 / 0.011008 (-0.007422) | 0.062248 / 0.038508 (0.023740) | 0.059830 / 0.023109 (0.036721) | 0.443278 / 0.275898 (0.167380) | 0.481279 / 0.323480 (0.157799) | 0.004773 / 0.007986 (-0.003213) | 0.002870 / 0.004328 (-0.001459) | 0.062730 / 0.004250 (0.058480) | 0.049422 / 0.037052 (0.012369) | 0.444196 / 0.258489 (0.185707) | 0.498614 / 0.293841 (0.204773) | 0.028477 / 0.128546 (-0.100069) | 0.008009 / 0.075646 (-0.067638) | 0.067919 / 0.419271 (-0.351352) | 0.040416 / 0.043533 (-0.003117) | 0.439460 / 0.255139 (0.184321) | 0.470529 / 0.283200 (0.187329) | 0.020767 / 0.141683 (-0.120916) | 1.478223 / 1.452155 (0.026068) | 1.538580 / 1.492716 (0.045863) |\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) | 0.271321 / 0.018006 (0.253315) | 0.456436 / 0.000490 (0.455946) | 0.011817 / 0.000200 (0.011617) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026355 / 0.037411 (-0.011056) | 0.081681 / 0.014526 (0.067155) | 0.091699 / 0.176557 (-0.084858) | 0.146115 / 0.737135 (-0.591021) | 0.094376 / 0.296338 (-0.201963) |\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) | 0.471677 / 0.215209 (0.256468) | 4.702909 / 2.077655 (2.625254) | 2.664882 / 1.504120 (1.160762) | 2.504106 / 1.541195 (0.962911) | 2.573226 / 1.468490 (1.104736) | 0.509679 / 4.584777 (-4.075097) | 3.034970 / 3.745712 (-0.710742) | 2.894704 / 5.269862 (-2.375157) | 1.915148 / 4.565676 (-2.650528) | 0.058312 / 0.424275 (-0.365963) | 0.006615 / 0.007607 (-0.000993) | 0.545339 / 0.226044 (0.319295) | 5.462261 / 2.268929 (3.193332) | 3.101482 / 55.444624 (-52.343143) | 2.755417 / 6.876477 (-4.121060) | 2.931440 / 2.142072 (0.789368) | 0.597521 / 4.805227 (-4.207707) | 0.125676 / 6.500664 (-6.374988) | 0.061798 / 0.075469 (-0.013671) |\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) | 1.356208 / 1.841788 (-0.485579) | 18.912492 / 8.074308 (10.838184) | 14.830128 / 10.191392 (4.638736) | 0.145992 / 0.680424 (-0.534432) | 0.019121 / 0.534201 (-0.515080) | 0.331534 / 0.579283 (-0.247749) | 0.361712 / 0.434364 (-0.072652) | 0.387532 / 0.540337 (-0.152805) | 0.536075 / 1.386936 (-0.850861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de6391d732ea0471ee5bdfb91b8cecc4503da96b \"CML watermark\")\n"
] | 2023-09-06T15:17:10Z
| 2023-09-06T15:46:20Z
| 2023-09-06T15:18:51Z
|
MEMBER
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https://github.com/huggingface/datasets/issues/5659
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|
I_kwDODunzps5hevL0
| 5,659
|
[Audio] Soundfile/libsndfile requirements too stringent for decoding mp3 files
|
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[
"cc @polinaeterna @lhoestq ",
"@sanchit-gandhi can you please also post the logs of `pip install soundfile==0.12.1`? To check what wheel is being installed or if it's being built from source (I think it's the latter case). \r\nRequired `libsndfile` binary **should** be bundeled with `soundfile` wheel but I assume it **might not** be the case for some non standard Linux distributions. \r\nThe only solution for using `soundfile` here is to build [`libsndfile`](https://github.com/libsndfile/libsndfile) from source:\r\n\r\n```bash\r\ngit clone https://github.com/libsndfile/libsndfile.git\r\ncd libsndfile/\r\nautoreconf -vif\r\n./configure --enable-werror \r\nmake\r\nmake install\r\n```\r\nfor this, some building libraries should be installed, for Debian/Ubuntu it's like:\r\n```bash\r\napt install autoconf autogen automake build-essential libasound2-dev \\\r\n libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n libmpg123-dev pkg-config python\r\n```\r\nbut for other Linux distributions it might be different.\r\n\r\nWhen the binary is compiled, it should be put into location where `soundfile` would search for it (the directory is named `_soundfile_data`), it depends on where`libsdfile` (from the previous step) and `soundfile` were installed, might be something like this:\r\n\r\n```bash\r\ncp /usr/local/lib/libsndfile.so /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\ncp /usr/local/lib/libsndfile.la /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\n```\r\n\r\nAnother solution is to not use `soundfile` and apply custom processing function with `torchaudio` while setting `decode=False` in `Audio` feature and passing custom function to `.map`. ",
"Not sure if it may help, but you could also try updating `pip` before installing soundfile",
"@lhoestq @sanchit-gandhi. I encountered the same error (also on the TPU v4) when trying to run `datasets` from source.\r\n\r\nDowngrading soundfile with `pip install soundfile==0.12.0` seems to fix the issue for me.",
"Maybe let's open an issue at https://github.com/bastibe/python-soundfile/issues in case they might know why you get `OSError: cannot load library 'libsndfile.so'` ?",
"> @sanchit-gandhi can you please also post the logs of `pip install soundfile==0.12.1`? To check what wheel is being installed or if it's being built from source (I think it's the latter case). Required `libsndfile` binary **should** be bundeled with `soundfile` wheel but I assume it **might not** be the case for some non standard Linux distributions. The only solution for using `soundfile` here is to build [`libsndfile`](https://github.com/libsndfile/libsndfile) from source:\r\n> \r\n> ```shell\r\n> git clone https://github.com/libsndfile/libsndfile.git\r\n> cd libsndfile/\r\n> autoreconf -vif\r\n> ./configure --enable-werror \r\n> make\r\n> make install\r\n> ```\r\n\r\nThis fixed the issue for me. After installing libsndfile as described above, I had to uninstall soundfile and re-install it with this command. `pip install \"soundfile>=0.12.1\"`",
"Thank you so much for the comprehensive instructions @polinaeterna! Also confirming that they worked for me 🤗 In my case, I had to run several of these commands under \"sudo\" for privileges, but otherwise this workaround gave a successful `libsndfile` install:\r\n\r\n1. Grab source code:\r\n```\r\ngit clone https://github.com/libsndfile/libsndfile.git\r\n```\r\n\r\n2. Set up a build environment:\r\n```\r\nsudo apt install autoconf autogen automake build-essential libasound2-dev \\\r\n libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n libmpg123-dev pkg-config python\r\n```\r\n\r\n3. Build and test `libsndfile`:\r\n\r\n```\r\nautoreconf -vif\r\n./configure --enable-werror\r\nsudo make\r\nsudo make check\r\n```\r\n\r\n4. Create `_soundfile_data` submodule (if it does not exist already):\r\n```\r\nsudo mkdir /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n```\r\n\r\n5. Copy `libsndfile` files into submodule:\r\n```\r\nsudo cp /usr/local/lib/libsndfile.* /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n```",
"On a different machine, I also tried separately by first upgrading pip, then installing soundfile. This worked too! Thanks @lhoestq 🙌",
"> @sanchit-gandhi can you please also post the logs of `pip install soundfile==0.12.1`? To check what wheel is being installed or if it's being built from source (I think it's the latter case). Required `libsndfile` binary **should** be bundeled with `soundfile` wheel but I assume it **might not** be the case for some non standard Linux distributions. The only solution for using `soundfile` here is to build [`libsndfile`](https://github.com/libsndfile/libsndfile) from source:\r\n> \r\n> ```shell\r\n> git clone https://github.com/libsndfile/libsndfile.git\r\n> cd libsndfile/\r\n> autoreconf -vif\r\n> ./configure --enable-werror \r\n> make\r\n> make install\r\n> ```\r\n> \r\n> for this, some building libraries should be installed, for Debian/Ubuntu it's like:\r\n> \r\n> ```shell\r\n> apt install autoconf autogen automake build-essential libasound2-dev \\\r\n> libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n> libmpg123-dev pkg-config python\r\n> ```\r\n> \r\n> but for other Linux distributions it might be different.\r\n> \r\n> When the binary is compiled, it should be put into location where `soundfile` would search for it (the directory is named `_soundfile_data`), it depends on where`libsdfile` (from the previous step) and `soundfile` were installed, might be something like this:\r\n> \r\n> ```shell\r\n> cp /usr/local/lib/libsndfile.so /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\n> cp /usr/local/lib/libsndfile.la /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\n> ```\r\n> \r\n> Another solution is to not use `soundfile` and apply custom processing function with `torchaudio` while setting `decode=False` in `Audio` feature and passing custom function to `.map`.\r\n\r\nThanks, the solution solved my problem. \r\n\r\n1. Purge uninstall libsndfile, uninstall python-soundfile.\r\n2. Build libsndfile from source code and install.\r\n3. Build python-soundfile from source code and install\r\n4. Well done.",
"> Thank you so much for the comprehensive instructions @polinaeterna! Also confirming that they worked for me 🤗 In my case, I had to run several of these commands under \"sudo\" for privileges, but otherwise this workaround gave a successful `libsndfile` install:\r\n> \r\n> 1. Grab source code:\r\n> \r\n> ```\r\n> git clone https://github.com/libsndfile/libsndfile.git\r\n> ```\r\n> \r\n> 2. Set up a build environment:\r\n> \r\n> ```\r\n> sudo apt install autoconf autogen automake build-essential libasound2-dev \\\r\n> libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n> libmpg123-dev pkg-config python\r\n> ```\r\n> \r\n> 3. Build and test `libsndfile`:\r\n> \r\n> ```\r\n> autoreconf -vif\r\n> ./configure --enable-werror\r\n> sudo make\r\n> sudo make check\r\n> ```\r\n> \r\n> 4. Create `_soundfile_data` submodule (if it does not exist already):\r\n> \r\n> ```\r\n> sudo mkdir /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n> ```\r\n> \r\n> 5. Copy `libsndfile` files into submodule:\r\n> \r\n> ```\r\n> sudo cp /usr/local/lib/libsndfile.* /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n> ```\r\n\r\nI had to run 'make install' or the `/usr/local/lib/libsndfile.*` files didn't exist.\r\n\r\nIt's working though!",
"I had the same issue but it is working now! Thanks for all of your comments!",
"I had the same issue on SageMaker but not on Colab;\r\nThe `soundfile` versioning was fine.\r\n\r\n my approach to solve it was to match {\"numpy\", \"numba\"} exact versions\r\n\r\n```\r\n! pip install \"numpy==1.23.5\"\r\n! pip install \"numpy==0.58.1\"\r\n\r\n```\r\nthe numbers are from Colab where successfully I could do the job.\r\n\r\n",
"> Thank you so much for the comprehensive instructions @polinaeterna! Also confirming that they worked for me 🤗 In my case, I had to run several of these commands under \"sudo\" for privileges, but otherwise this workaround gave a successful `libsndfile` install:\r\n> \r\n> 1. Grab source code:\r\n> \r\n> ```\r\n> git clone https://github.com/libsndfile/libsndfile.git\r\n> ```\r\n> \r\n> 2. Set up a build environment:\r\n> \r\n> ```\r\n> sudo apt install autoconf autogen automake build-essential libasound2-dev \\\r\n> libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n> libmpg123-dev pkg-config python\r\n> ```\r\n> \r\n> 3. Build and test `libsndfile`:\r\n> \r\n> ```\r\n> autoreconf -vif\r\n> ./configure --enable-werror\r\n> sudo make\r\n> sudo make check\r\n> ```\r\n> \r\n> 4. Create `_soundfile_data` submodule (if it does not exist already):\r\n> \r\n> ```\r\n> sudo mkdir /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n> ```\r\n> \r\n> 5. Copy `libsndfile` files into submodule:\r\n> \r\n> ```\r\n> sudo cp /usr/local/lib/libsndfile.* /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n> ```\r\n\r\nIt works and don't forget to \"apt uninstall libsndfile1\" after installing it from source code."
] | 2023-03-22T10:07:33Z
| 2024-07-12T01:35:01Z
| 2023-04-07T08:51:28Z
|
CONTRIBUTOR
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### Describe the bug
I'm encountering several issues trying to load mp3 audio files using `datasets` on a TPU v4.
The PR https://github.com/huggingface/datasets/pull/5573 updated the audio loading logic to rely solely on the `soundfile`/`libsndfile` libraries for loading audio samples, regardless of their file type.
The installation guide suggests that `libsndfile` is bundled in when `soundfile` is pip installed:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/docs/source/installation.md?plain=1#L70-L71
However, just pip installing `soundfile==0.12.1` throws an error that `libsndfile` is missing:
```
pip install soundfile==0.12.1
```
Then:
```python
>>> soundfile
>>> soundfile.__libsndfile_version__
```
<details>
<summary> Traceback (most recent call last): </summary>
```
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 161, in <module>
import _soundfile_data # ImportError if this doesn't exist
ModuleNotFoundError: No module named '_soundfile_data'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 170, in <module>
raise OSError('sndfile library not found using ctypes.util.find_library')
OSError: sndfile library not found using ctypes.util.find_library
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 192, in <module>
_snd = _ffi.dlopen(_explicit_libname)
OSError: cannot load library 'libsndfile.so': libsndfile.so: cannot open shared object file: No such file or directory
```
</details>
Thus, I've followed the official instructions for installing the `soundfile` package from https://github.com/bastibe/python-soundfile#installation, which states that `libsndfile` needs to be installed separately as:
```
pip install --upgrade soundfile
sudo apt install libsndfile1
```
We can now import `soundfile`:
```python
>>> import soundfile
>>> soundfile.__version__
'0.12.1'
>>> soundfile.__libsndfile_version__
'1.0.28'
```
We see that we have `soundfile==0.12.1`, which matches the `datasets[audio]` package constraints:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/setup.py#L144-L147
But we have `libsndfile==1.0.28`, which is too low for decoding mp3 files:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/src/datasets/config.py#L136-L138
Updating/upgrading the `libsndfile` doesn't change this:
```
sudo apt-get update
sudo apt-get upgrade
```
Is there any other suggestion for how to get a compatible `libsndfile` version? Currently, the version bundled with Ubuntu `apt-get` is too low for decoding mp3 files.
Maybe we could add this under `setup.py` such that we install the correct `libsndfile` version when we do `pip install datasets[audio]`? IMO this would help circumvent such version issues.
### Steps to reproduce the bug
Environment described above. Loading mp3 files:
```python
from datasets import load_dataset
common_voice_es = load_dataset("common_voice", "es", split="validation", streaming=True)
print(next(iter(common_voice_es)))
```
```python
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[4], line 2
1 common_voice_es = load_dataset("common_voice", "es", split="validation", streaming=True)
----> 2 print(next(iter(common_voice_es)))
File ~/datasets/src/datasets/iterable_dataset.py:941, in IterableDataset.__iter__(self)
937 for key, example in ex_iterable:
938 if self.features:
939 # `IterableDataset` automatically fills missing columns with None.
940 # This is done with `_apply_feature_types_on_example`.
--> 941 yield _apply_feature_types_on_example(
942 example, self.features, token_per_repo_id=self._token_per_repo_id
943 )
944 else:
945 yield example
File ~/datasets/src/datasets/iterable_dataset.py:700, in _apply_feature_types_on_example(example, features, token_per_repo_id)
698 encoded_example = features.encode_example(example)
699 # Decode example for Audio feature, e.g.
--> 700 decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
701 return decoded_example
File ~/datasets/src/datasets/features/features.py:1864, in Features.decode_example(self, example, token_per_repo_id)
1850 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1851 """Decode example with custom feature decoding.
1852
1853 Args:
(...)
1861 `dict[str, Any]`
1862 """
-> 1864 return {
1865 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1866 if self._column_requires_decoding[column_name]
1867 else value
1868 for column_name, (feature, value) in zip_dict(
1869 {key: value for key, value in self.items() if key in example}, example
1870 )
1871 }
File ~/datasets/src/datasets/features/features.py:1865, in <dictcomp>(.0)
1850 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1851 """Decode example with custom feature decoding.
1852
1853 Args:
(...)
1861 `dict[str, Any]`
1862 """
1864 return {
-> 1865 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1866 if self._column_requires_decoding[column_name]
1867 else value
1868 for column_name, (feature, value) in zip_dict(
1869 {key: value for key, value in self.items() if key in example}, example
1870 )
1871 }
File ~/datasets/src/datasets/features/features.py:1308, in decode_nested_example(schema, obj, token_per_repo_id)
1305 elif isinstance(schema, (Audio, Image)):
1306 # we pass the token to read and decode files from private repositories in streaming mode
1307 if obj is not None and schema.decode:
-> 1308 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
1309 return obj
File ~/datasets/src/datasets/features/audio.py:167, in Audio.decode_example(self, value, token_per_repo_id)
162 raise RuntimeError(
163 "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
164 'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '
165 )
166 elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
--> 167 raise RuntimeError(
168 "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
169 'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '
170 )
172 if file is None:
173 token_per_repo_id = token_per_repo_id or {}
RuntimeError: Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`.
```
### Expected behavior
Load mp3 files!
### Environment info
- `datasets` version: 2.10.2.dev0
- Platform: Linux-5.13.0-1023-gcp-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.13.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- Soundfile version: 0.12.1
- Libsndfile version: 1.0.28
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Distributed data parallel training for streaming datasets
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"Hi ! According to https://huggingface.co/docs/datasets/use_with_pytorch#stream-data you can use the pytorch DataLoader with `num_workers>0` to distribute the shards across your workers (it uses `torch.utils.data.get_worker_info()` to get the worker ID and select the right subsets of shards to use)\r\n\r\n<s> EDIT: here is a code example </s>\r\n```python\r\n# ds = ds.with_format(\"torch\")\r\n# dataloader = DataLoader(ds, num_workers=num_workers)\r\n```\r\n\r\nEDIT: `with_format(\"torch\")` is not required, now you can just do\r\n```python\r\ndataloader = DataLoader(ds, num_workers=num_workers)\r\n```",
"@cyk1337 does streaming datasets with multi-gpu works for you? I am testing on one node with multiple gpus, but this is freezing, https://github.com/huggingface/datasets/issues/5123 \r\nIn case you could make this work, could you share with me your data-loading codes?\r\nthank you",
"+1",
"This has been implemented in `datasets` 2.8:\r\n```python\r\nfrom datasets.distributed import split_dataset_by_node\r\n\r\nds = split_dataset_by_node(ds, rank=rank, world_size=world_size)\r\n```\r\n\r\ndocs: https://huggingface.co/docs/datasets/use_with_pytorch#distributed",
"i'm having hanging issues with this when using DDP and allocating the datasets with `split_dataset_by_node` 🤔\r\n\r\n--- \r\n### edit\r\nI don't want to pollute this thread, but for the sake of following up, I observed hanging close to the final iteration of the dataloader. I think this was happening on the final shard. First, I removed the final shard and things worked. Then (including all shards), I reordered the list of shards: `load_dataset('json', data_files=reordered, streaming=True)` and no hang. \r\n\r\nI won't open an issue yet bc I am not quite sure about this observation.",
"@wconnell would you mind opening a different bug issue and giving more details?\r\nhttps://github.com/huggingface/datasets/issues/new?assignees=&labels=&template=bug-report.yml\r\n\r\nThanks."
] | 2022-07-17T01:29:43Z
| 2023-04-26T18:21:09Z
| null |
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### Feature request
Any documentations for the the `load_dataset(streaming=True)` for (multi-node multi-GPU) DDP training?
### Motivation
Given a bunch of data files, it is expected to split them onto different GPUs. Is there a guide or documentation?
### Your contribution
Does it requires manually split on data files for each worker in `DatasetBuilder._split_generator()`? What is`IterableDatasetShard` expected to do?
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.map() fails if function uses pyvista
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"I found a similar (?) issue in https://github.com/huggingface/datasets/issues/6435, where someone had issues with forks and CUDA. According to https://huggingface.co/docs/datasets/main/en/process#multiprocessing we should do \n\n```\nfrom multiprocess import set_start_method\nset_start_method(\"spawn\")\n```\n\nto avoid the fork. The updated code\n\n```python\nimport numpy as np\nimport pyvista as pv\nimport datasets\nimport multiprocess\n\ndata = [{\"coords\": np.random.rand(5, 3)} for _ in range(3)]\n\ndef render_point(example):\n plotter = pv.Plotter(off_screen=True)\n cloud = pv.PolyData(example[\"coords\"])\n plotter.add_mesh(cloud)\n img = plotter.screenshot(return_img=True)\n return {\"image\": img}\n\n\n# breaks if num_proc>1\nmultiprocess.set_start_method(\"spawn\")\nds = datasets.Dataset.from_list(data).map(render_point, num_proc=2)\n```\n\ninstead fails with `TypeError: fork_exec() takes exactly 23 arguments (21 given)` which also seems like a bug to me."
] | 2025-04-14T19:43:02Z
| 2025-04-14T20:01:53Z
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### Describe the bug
Using PyVista inside a .map() produces a crash with `objc[78796]: +[NSResponder initialize] may have been in progress in another thread when fork() was called. We cannot safely call it or ignore it in the fork() child process. Crashing instead. Set a breakpoint on objc_initializeAfterForkError to debug.`
### Steps to reproduce the bug
Run
```python
import numpy as np
import pyvista as pv
import datasets
data = [{"coords": np.random.rand(5, 3)} for _ in range(3)]
def render_point(example):
plotter = pv.Plotter(off_screen=True)
cloud = pv.PolyData(example["coords"])
plotter.add_mesh(cloud)
img = plotter.screenshot(return_img=True)
return {"image": img}
# breaks if num_proc>1
ds = datasets.Dataset.from_list(data).map(render_point, num_proc=2)
```
### Expected behavior
It should work. Just like when I use a process pool to make it work.
```python
import numpy as np
import pyvista as pv
import multiprocessing
data = [{"coords": np.random.rand(5, 3)} for _ in range(3)]
def render_point(example):
plotter = pv.Plotter(off_screen=True)
cloud = pv.PolyData(example["coords"])
plotter.add_mesh(cloud)
img = plotter.screenshot(return_img=True)
return {"image": img}
if __name__ == "__main__":
with multiprocessing.Pool(processes=2) as pool:
results = pool.map(render_point, data)
print(results[0]["image"].shape)
```
### Environment info
- `datasets` version: 3.3.2
- Platform: macOS-15.3.2-arm64-arm-64bit
- Python version: 3.11.10
- `huggingface_hub` version: 0.28.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.10.0
And then I suppose pyvista info is good to have.
```python
import pyvista as pv
print(pv.Report())
```
gives
--------------------------------------------------------------------------------
Date: Mon Apr 14 21:42:08 2025 CEST
OS : Darwin (macOS 15.3.2)
CPU(s) : 10
Machine : arm64
Architecture : 64bit
RAM : 32.0 GiB
Environment : IPython
File system : apfs
GPU Vendor : Apple
GPU Renderer : Apple M1 Max
GPU Version : 4.1 Metal - 89.3
MathText Support : True
Python 3.11.10 (main, Oct 7 2024, 23:25:27) [Clang 18.1.8 ]
pyvista : 0.44.2
vtk : 9.4.0
numpy : 2.2.2
matplotlib : 3.10.0
scooby : 0.10.0
pooch : 1.8.2
pillow : 11.1.0
imageio : 2.36.1
PyQt5 : 5.15.11
IPython : 8.30.0
scipy : 1.14.1
tqdm : 4.67.1
jupyterlab : 4.3.5
nest_asyncio : 1.6.0
--------------------------------------------------------------------------------
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I_kwDODunzps6qQdV2
| 7,406
|
Adding Core Maintainer List to CONTRIBUTING.md
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[
"@lhoestq",
"there is no per-module maintainer and the list is me alone nowadays ^^'",
"@lhoestq \nOh... I feel for you. \nWhat are your criteria for choosing a core maintainer? \nIt seems like it's too much work for you to manage all this code by yourself.\n\nAlso, if you don't mind, can you check this PR for me?\n#7368 I'd like this to be added as soon as possible because I need it."
] | 2025-02-17T00:32:40Z
| 2025-03-24T10:57:54Z
| 2025-03-24T10:57:54Z
|
CONTRIBUTOR
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### Feature request
I propose adding a core maintainer list to the `CONTRIBUTING.md` file.
### Motivation
The Transformers and Liger-Kernel projects maintain lists of core maintainers for each module.
However, the Datasets project doesn't have such a list.
### Your contribution
I have nothing to add here.
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I_kwDODunzps5s1H1T
| 6,090
|
FilesIterable skips all the files after a hidden file
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[
"Thanks for reporting. We've merged a PR with a fix."
] | 2023-07-28T07:25:57Z
| 2023-07-28T10:51:14Z
| 2023-07-28T10:50:11Z
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NONE
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### Describe the bug
When initializing `FilesIterable` with a list of file paths using `FilesIterable.from_paths`, it will discard all the files after a hidden file.
The problem is in [this line](https://github.com/huggingface/datasets/blob/88896a7b28610ace95e444b94f9a4bc332cc1ee3/src/datasets/download/download_manager.py#L233C26-L233C26) where `return` should be replaced by `continue`.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1SQlxs4y_LSo1Q89KnFoYDSyyKEISun_J#scrollTo=93K4_blkW-8-
### Expected behavior
The script should print all the files except the hidden one.
### Environment info
- `datasets` version: 2.14.1
- Platform: Linux-5.15.109+-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.16.4
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
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PR_kwDODunzps5xP-KD
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Set dev version
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6944). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005150 / 0.011353 (-0.006203) | 0.003663 / 0.011008 (-0.007346) | 0.062832 / 0.038508 (0.024324) | 0.031928 / 0.023109 (0.008819) | 0.246455 / 0.275898 (-0.029443) | 0.272121 / 0.323480 (-0.051359) | 0.004220 / 0.007986 (-0.003765) | 0.002756 / 0.004328 (-0.001573) | 0.050071 / 0.004250 (0.045821) | 0.046074 / 0.037052 (0.009022) | 0.259676 / 0.258489 (0.001187) | 0.290674 / 0.293841 (-0.003167) | 0.027822 / 0.128546 (-0.100724) | 0.010791 / 0.075646 (-0.064855) | 0.202827 / 0.419271 (-0.216445) | 0.037057 / 0.043533 (-0.006476) | 0.256128 / 0.255139 (0.000989) | 0.269422 / 0.283200 (-0.013777) | 0.017395 / 0.141683 (-0.124288) | 1.125919 / 1.452155 (-0.326236) | 1.177708 / 1.492716 (-0.315008) |\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) | 0.098466 / 0.018006 (0.080460) | 0.305508 / 0.000490 (0.305018) | 0.000232 / 0.000200 (0.000032) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018866 / 0.037411 (-0.018545) | 0.062079 / 0.014526 (0.047553) | 0.074670 / 0.176557 (-0.101886) | 0.121025 / 0.737135 (-0.616111) | 0.075883 / 0.296338 (-0.220455) |\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) | 0.291880 / 0.215209 (0.076671) | 2.874064 / 2.077655 (0.796409) | 1.477040 / 1.504120 (-0.027080) | 1.356198 / 1.541195 (-0.184997) | 1.354676 / 1.468490 (-0.113814) | 0.559731 / 4.584777 (-4.025046) | 2.362746 / 3.745712 (-1.382966) | 2.678838 / 5.269862 (-2.591024) | 1.752633 / 4.565676 (-2.813044) | 0.064023 / 0.424275 (-0.360252) | 0.005035 / 0.007607 (-0.002572) | 0.354807 / 0.226044 (0.128762) | 3.424463 / 2.268929 (1.155534) | 1.810476 / 55.444624 (-53.634149) | 1.519031 / 6.876477 (-5.357446) | 1.693957 / 2.142072 (-0.448116) | 0.647987 / 4.805227 (-4.157240) | 0.118993 / 6.500664 (-6.381671) | 0.042186 / 0.075469 (-0.033283) |\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) | 0.982565 / 1.841788 (-0.859223) | 11.645075 / 8.074308 (3.570767) | 9.588360 / 10.191392 (-0.603032) | 0.142369 / 0.680424 (-0.538055) | 0.014025 / 0.534201 (-0.520176) | 0.285668 / 0.579283 (-0.293616) | 0.265825 / 0.434364 (-0.168539) | 0.323371 / 0.540337 (-0.216966) | 0.421227 / 1.386936 (-0.965709) |\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) | 0.005587 / 0.011353 (-0.005766) | 0.003664 / 0.011008 (-0.007345) | 0.050411 / 0.038508 (0.011903) | 0.033268 / 0.023109 (0.010159) | 0.266631 / 0.275898 (-0.009267) | 0.291135 / 0.323480 (-0.032345) | 0.004275 / 0.007986 (-0.003710) | 0.002822 / 0.004328 (-0.001506) | 0.049349 / 0.004250 (0.045099) | 0.040653 / 0.037052 (0.003601) | 0.282641 / 0.258489 (0.024152) | 0.315460 / 0.293841 (0.021619) | 0.029343 / 0.128546 (-0.099203) | 0.010606 / 0.075646 (-0.065040) | 0.058783 / 0.419271 (-0.360489) | 0.033205 / 0.043533 (-0.010327) | 0.266805 / 0.255139 (0.011666) | 0.288907 / 0.283200 (0.005707) | 0.017817 / 0.141683 (-0.123866) | 1.128132 / 1.452155 (-0.324023) | 1.175120 / 1.492716 (-0.317597) |\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) | 0.095653 / 0.018006 (0.077647) | 0.304825 / 0.000490 (0.304335) | 0.000212 / 0.000200 (0.000012) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022766 / 0.037411 (-0.014645) | 0.076598 / 0.014526 (0.062072) | 0.088314 / 0.176557 (-0.088242) | 0.127888 / 0.737135 (-0.609247) | 0.090391 / 0.296338 (-0.205947) |\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) | 0.293384 / 0.215209 (0.078175) | 2.883742 / 2.077655 (0.806087) | 1.533868 / 1.504120 (0.029748) | 1.391964 / 1.541195 (-0.149231) | 1.423732 / 1.468490 (-0.044759) | 0.575457 / 4.584777 (-4.009320) | 0.970860 / 3.745712 (-2.774852) | 2.711405 / 5.269862 (-2.558457) | 1.774468 / 4.565676 (-2.791208) | 0.064611 / 0.424275 (-0.359664) | 0.005120 / 0.007607 (-0.002487) | 0.343892 / 0.226044 (0.117847) | 3.362579 / 2.268929 (1.093650) | 1.880200 / 55.444624 (-53.564424) | 1.587435 / 6.876477 (-5.289042) | 1.756464 / 2.142072 (-0.385609) | 0.661469 / 4.805227 (-4.143759) | 0.119030 / 6.500664 (-6.381634) | 0.041704 / 0.075469 (-0.033765) |\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) | 1.025008 / 1.841788 (-0.816780) | 12.146244 / 8.074308 (4.071936) | 10.397267 / 10.191392 (0.205875) | 0.145917 / 0.680424 (-0.534507) | 0.015779 / 0.534201 (-0.518422) | 0.287122 / 0.579283 (-0.292161) | 0.125464 / 0.434364 (-0.308900) | 0.323315 / 0.540337 (-0.217023) | 0.416761 / 1.386936 (-0.970175) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e2d15a6b1871f3998986853298e4338d72891491 \"CML watermark\")\n"
] | 2024-06-03T05:29:59Z
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PR_kwDODunzps46YEEa
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Fix evaluation metadata for ncbi_disease
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"As discussed with @lewtun, we are closing this PR, because it requires first the task names to be aligned between AutoTrain and datasets."
] | 2022-06-26T20:29:32Z
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| 2022-09-23T09:38:02Z
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MEMBER
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This PR fixes the task in the evaluation metadata and removes the metrics info as we've decided this is not a great way to propagate this information downstream.
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PR_kwDODunzps5iNvAu
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Read GeoParquet files using parquet reader
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6508). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Cool ! Do you mind writing a test using a geoparquet file in `tests/io/test_parquet.py` ?\r\n\r\nI'm not too familiar with geoparquet, does it use e.g. pyarrow extension types ? or schema metadata ?",
"> Geometry columns MUST be stored using the BYTE_ARRAY parquet type. They MUST be encoded as [WKB](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry#Well-known_binary).\r\n\r\nhttps://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md#geometry-columns\r\n\r\nIt has metadata:\r\n\r\n> File metadata indicating things like the version of this specification used\r\n> Column metadata with additional metadata for each geometry column\r\n\r\nhttps://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md#metadata",
"The specification is very short by the way:\r\n\r\nhttps://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md",
"https://github.com/opengeospatial/geoparquet/blob/main/format-specs/compatible-parquet.md is also worth reading for this PR",
"> Cool ! Do you mind writing a test using a geoparquet file in `tests/io/test_parquet.py` ?\r\n\r\nYep, let me do that do that later today!\r\n\r\n> I'm not too familiar with geoparquet, does it use e.g. pyarrow extension types ? or schema metadata ?\r\n\r\nGeoParquet is a Parquet file with a `geometry` column that is encoded in a Binary format (technically WKB as @severo mentioned above). It is not a pyarrow extension type (though that would be cool). Regular `parquet` readers such as `pyarrow` would thus see the column as a binary column, while libraries such as `geopandas` which implement a GeoParquet reader would look at the schema metadata.\r\n\r\nE.g. taking this [file](https://huggingface.co/datasets/weiji14/clay_vector_embeddings/resolve/862b1602f326421adc99375912c08603a9f2cc5c/32VLM_v01.gpq) as an example, this is how the 'geo' schema looks like:\r\n\r\n```python\r\nimport pyarrow.parquet as pq\r\n\r\nschema = pq.read_schema(where=\"32VLM_v01.gpq\")\r\nprint(schema.metadata[b\"geo\"])\r\n```\r\n\r\n```\r\n{\r\n \"primary_column\": \"geometry\",\r\n \"columns\": {\r\n \"geometry\": {\r\n \"encoding\": \"WKB\",\r\n \"crs\": {\r\n \"$schema\": \"https://proj.org/schemas/v0.7/projjson.schema.json\",\r\n \"type\": \"GeographicCRS\",\r\n \"name\": \"WGS 84 (CRS84)\",\r\n \"datum_ensemble\": {\r\n \"name\": \"World Geodetic System 1984 ensemble\",\r\n \"members\": [\r\n {\"name\": \"World Geodetic System 1984 (Transit)\"},\r\n {\"name\": \"World Geodetic System 1984 (G730)\"},\r\n {\"name\": \"World Geodetic System 1984 (G873)\"},\r\n {\"name\": \"World Geodetic System 1984 (G1150)\"},\r\n {\"name\": \"World Geodetic System 1984 (G1674)\"},\r\n {\"name\": \"World Geodetic System 1984 (G1762)\"},\r\n {\"name\": \"World Geodetic System 1984 (G2139)\"},\r\n ],\r\n \"ellipsoid\": {\r\n \"name\": \"WGS 84\",\r\n \"semi_major_axis\": 6378137,\r\n \"inverse_flattening\": 298.257223563,\r\n },\r\n \"accuracy\": \"2.0\",\r\n \"id\": {\"authority\": \"EPSG\", \"code\": 6326},\r\n },\r\n \"coordinate_system\": {\r\n \"subtype\": \"ellipsoidal\",\r\n \"axis\": [\r\n {\r\n \"name\": \"Geodetic longitude\",\r\n \"abbreviation\": \"Lon\",\r\n \"direction\": \"east\",\r\n \"unit\": \"degree\",\r\n },\r\n {\r\n \"name\": \"Geodetic latitude\",\r\n \"abbreviation\": \"Lat\",\r\n \"direction\": \"north\",\r\n \"unit\": \"degree\",\r\n },\r\n ],\r\n },\r\n \"scope\": \"Not known.\",\r\n \"area\": \"World.\",\r\n \"bbox\": {\r\n \"south_latitude\": -90,\r\n \"west_longitude\": -180,\r\n \"north_latitude\": 90,\r\n \"east_longitude\": 180,\r\n },\r\n \"id\": {\"authority\": \"OGC\", \"code\": \"CRS84\"},\r\n },\r\n \"geometry_types\": [\"Polygon\"],\r\n \"bbox\": [\r\n 5.370542846111244,\r\n 59.42344573656881,\r\n 7.368267282586697,\r\n 60.42591328670696,\r\n ],\r\n }\r\n },\r\n \"version\": \"1.0.0\",\r\n \"creator\": {\"library\": \"geopandas\", \"version\": \"0.14.1\"},\r\n}\r\n```\r\n\r\nWe can continue the discussion on how to handle this extra 'geo' schema metadata in #6438. I'd like to keep this PR small by just piggy-backing off the default Parquet reader for now, which would just show the 'geometry' column as a binary column.",
"Thanks ! Also if you can make sure that doing `ds.to_parquet(\"path/to/output.geoparquet\")` also saves as a valid geoparquet files (including the schema metadata) that would be awesome.\r\n\r\nIt would also enable `push_to_hub` to save geoparquet files",
"> Thanks ! Also if you can make sure that doing `ds.to_parquet(\"path/to/output.geoparquet\")` also saves as a valid geoparquet files (including the schema metadata) that would be awesome.\r\n> \r\n> It would also enable `push_to_hub` to save geoparquet files\r\n\r\nHmm, it should be possible to let PyArrow save a Parquet file with a geometry WKB column, but saving the GeoParquet schema metadata won't be easy without introducing [`geopandas`](https://github.com/geopandas/geopandas) as a dependency. Does this need to be done in this PR, or can it be a separate one?",
"I see, then let's keep it like this for now.\r\nI just checked and it would require to add support for keeping the schema metadata in `Features` anyway.\r\n\r\nFeel free to fix your code formatting using\r\n\r\n```\r\nmake style\r\n```\r\n\r\nand we can merge this PR :)\r\n\r\n",
"Cool, linted to remove the extra blank line at 7088f585557807a63673cdc58900d7ce56146cf7. :rocket:",
"The previous CI failure at https://github.com/huggingface/datasets/actions/runs/7482863299/job/20668381959#step:6:5299 says `datasets.exceptions.DefunctDatasetError: Dataset 'eli5' is defunct and no longer accessible due to unavailability of the source data` which seems unrelated, might be to do with https://github.com/huggingface/datasets/issues/6605. I've updated the PR branch with changes from `main` again, could someone re-run the tests and merge if ok? Thanks!",
"sorry, it took me some time to fix the CI on the `main` branch\r\n\r\nwill merge once it's green :)",
"<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) | 0.005467 / 0.011353 (-0.005886) | 0.003696 / 0.011008 (-0.007313) | 0.063298 / 0.038508 (0.024790) | 0.032209 / 0.023109 (0.009100) | 0.246307 / 0.275898 (-0.029591) | 0.276864 / 0.323480 (-0.046616) | 0.003941 / 0.007986 (-0.004044) | 0.002616 / 0.004328 (-0.001713) | 0.049543 / 0.004250 (0.045292) | 0.044886 / 0.037052 (0.007833) | 0.266502 / 0.258489 (0.008013) | 0.288401 / 0.293841 (-0.005440) | 0.027911 / 0.128546 (-0.100635) | 0.011011 / 0.075646 (-0.064636) | 0.207972 / 0.419271 (-0.211299) | 0.036324 / 0.043533 (-0.007209) | 0.259450 / 0.255139 (0.004311) | 0.267317 / 0.283200 (-0.015883) | 0.018857 / 0.141683 (-0.122826) | 1.145350 / 1.452155 (-0.306805) | 1.204204 / 1.492716 (-0.288513) |\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) | 0.103864 / 0.018006 (0.085858) | 0.306941 / 0.000490 (0.306451) | 0.000218 / 0.000200 (0.000018) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018391 / 0.037411 (-0.019020) | 0.064600 / 0.014526 (0.050074) | 0.075454 / 0.176557 (-0.101102) | 0.120913 / 0.737135 (-0.616223) | 0.076998 / 0.296338 (-0.219341) |\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) | 0.279491 / 0.215209 (0.064282) | 2.742471 / 2.077655 (0.664816) | 1.447980 / 1.504120 (-0.056140) | 1.328202 / 1.541195 (-0.212992) | 1.397291 / 1.468490 (-0.071199) | 0.585726 / 4.584777 (-3.999051) | 2.385132 / 3.745712 (-1.360580) | 2.874888 / 5.269862 (-2.394974) | 1.820177 / 4.565676 (-2.745500) | 0.063876 / 0.424275 (-0.360399) | 0.004946 / 0.007607 (-0.002661) | 0.336445 / 0.226044 (0.110401) | 3.396813 / 2.268929 (1.127885) | 1.832644 / 55.444624 (-53.611981) | 1.581304 / 6.876477 (-5.295172) | 1.607243 / 2.142072 (-0.534829) | 0.662752 / 4.805227 (-4.142476) | 0.119494 / 6.500664 (-6.381170) | 0.042573 / 0.075469 (-0.032896) |\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) | 0.936784 / 1.841788 (-0.905003) | 12.154288 / 8.074308 (4.079980) | 10.944835 / 10.191392 (0.753443) | 0.132856 / 0.680424 (-0.547568) | 0.015197 / 0.534201 (-0.519004) | 0.290647 / 0.579283 (-0.288636) | 0.273498 / 0.434364 (-0.160866) | 0.324893 / 0.540337 (-0.215444) | 0.427905 / 1.386936 (-0.959032) |\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) | 0.005695 / 0.011353 (-0.005658) | 0.003562 / 0.011008 (-0.007446) | 0.050117 / 0.038508 (0.011608) | 0.033876 / 0.023109 (0.010767) | 0.275514 / 0.275898 (-0.000384) | 0.298460 / 0.323480 (-0.025020) | 0.004240 / 0.007986 (-0.003745) | 0.002738 / 0.004328 (-0.001591) | 0.048518 / 0.004250 (0.044268) | 0.049064 / 0.037052 (0.012012) | 0.287094 / 0.258489 (0.028605) | 0.314281 / 0.293841 (0.020440) | 0.057861 / 0.128546 (-0.070686) | 0.010893 / 0.075646 (-0.064753) | 0.062251 / 0.419271 (-0.357020) | 0.036788 / 0.043533 (-0.006745) | 0.272431 / 0.255139 (0.017292) | 0.292022 / 0.283200 (0.008822) | 0.019874 / 0.141683 (-0.121809) | 1.156939 / 1.452155 (-0.295216) | 1.237966 / 1.492716 (-0.254751) |\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) | 0.096156 / 0.018006 (0.078150) | 0.306652 / 0.000490 (0.306162) | 0.000230 / 0.000200 (0.000031) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022965 / 0.037411 (-0.014447) | 0.081349 / 0.014526 (0.066823) | 0.089035 / 0.176557 (-0.087521) | 0.128831 / 0.737135 (-0.608304) | 0.090321 / 0.296338 (-0.206017) |\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) | 0.293110 / 0.215209 (0.077901) | 2.884493 / 2.077655 (0.806839) | 1.582522 / 1.504120 (0.078402) | 1.518977 / 1.541195 (-0.022218) | 1.528449 / 1.468490 (0.059959) | 0.577369 / 4.584777 (-4.007408) | 2.473060 / 3.745712 (-1.272652) | 3.104363 / 5.269862 (-2.165499) | 1.916529 / 4.565676 (-2.649147) | 0.064594 / 0.424275 (-0.359682) | 0.005386 / 0.007607 (-0.002221) | 0.353336 / 0.226044 (0.127292) | 3.471914 / 2.268929 (1.202985) | 1.959222 / 55.444624 (-53.485402) | 1.677153 / 6.876477 (-5.199324) | 1.716961 / 2.142072 (-0.425112) | 0.658355 / 4.805227 (-4.146873) | 0.117296 / 6.500664 (-6.383368) | 0.041139 / 0.075469 (-0.034330) |\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) | 1.025220 / 1.841788 (-0.816567) | 14.510987 / 8.074308 (6.436679) | 11.851428 / 10.191392 (1.660036) | 0.143759 / 0.680424 (-0.536665) | 0.015644 / 0.534201 (-0.518557) | 0.296824 / 0.579283 (-0.282459) | 0.281566 / 0.434364 (-0.152798) | 0.335094 / 0.540337 (-0.205244) | 0.425199 / 1.386936 (-0.961737) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fabc2c8cee8822572115893715b76dfdabac1631 \"CML watermark\")\n"
] | 2023-12-18T04:50:37Z
| 2024-01-26T18:22:35Z
| 2024-01-26T16:18:41Z
|
CONTRIBUTOR
| null | null | null |
Let GeoParquet files with the file extension `*.geoparquet` or `*.gpq` be readable by the default parquet reader.
Those two file extensions are the ones most commonly used for GeoParquet files, and is included in the `gpq` validator tool at https://github.com/planetlabs/gpq/blob/e5576b4ee7306b4d2259d56c879465a9364dab90/cmd/gpq/command/convert.go#L73-L75
Addresses https://github.com/huggingface/datasets/issues/6438
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PR_kwDODunzps46tADt
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Fix `embed_storage` on features inside lists/sequences
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[
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-01T11:52:08Z
| 2022-07-08T12:13:10Z
| 2022-07-08T12:01:36Z
|
COLLABORATOR
| null | null | null |
Add a dedicated function for embed_storage to always preserve the embedded/casted arrays (and to have more control over `embed_storage` in general).
Fix #4591
~~(Waiting for #4608 to be merged to mark this PR as ready for review - required for fixing `xgetsize` in private repos)~~ Done!
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PR_kwDODunzps5CI0Ac
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[zstd] Use max window log size
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"@albertvillanova Please take a review.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5197). All of your documentation changes will be reflected on that endpoint."
] | 2022-11-03T13:35:58Z
| 2022-11-03T13:45:19Z
| null |
NONE
| null | null | null |
ZstdDecompressor has a parameter `max_window_size` to limit max memory usage when decompressing zstd files. The default `max_window_size` is not enough when files are compressed by `zstd --ultra` flags.
Change `max_window_size` to the zstd's max window size. NOTE, the `zstd.WINDOWLOG_MAX` is the log_2 value of the max window size.
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Cannot load data with different schemas from different parquet files
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"Hi ! `load_dataset` expects all the data_files to have the same schema.\n\nMaybe you can try enforcing certain `features` using:\n\n```python\nfeatures = Features({\"conversations\": {'content': Value('string'), 'role': Value('string',)}})\nds = load_dataset(..., features=features)\n```",
"Thanks! It works if I explicitly specify all nested fields of the data."
] | 2025-03-13T08:14:49Z
| 2025-03-17T07:27:48Z
| 2025-03-17T07:27:46Z
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### Describe the bug
Cannot load samples with optional fields from different files. The schema cannot be correctly derived.
### Steps to reproduce the bug
When I place two samples with an optional field `some_extra_field` within a single parquet file, it can be loaded via `load_dataset`.
```python
import pandas as pd
from datasets import load_dataset
data = [
{'conversations': {'role': 'user', 'content': 'hello'}},
{'conversations': {'role': 'user', 'content': 'hi', 'some_extra_field': 'some_value'}}
]
df = pd.DataFrame(data)
df.to_parquet('data.parquet')
dataset = load_dataset('parquet', data_files='data.parquet', split='train')
print(dataset.features)
```
The schema can be derived. `some_extra_field` is set to None for the first row where it is absent.
```
{'conversations': {'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None), 'some_extra_field': Value(dtype='string', id=None)}}
```
However, when I separate the samples into different files, it cannot be loaded.
```python
import pandas as pd
from datasets import load_dataset
data1 = [{'conversations': {'role': 'user', 'content': 'hello'}}]
pd.DataFrame(data1).to_parquet('data1.parquet')
data2 = [{'conversations': {'role': 'user', 'content': 'hi', 'some_extra_field': 'some_value'}}]
pd.DataFrame(data2).to_parquet('data2.parquet')
dataset = load_dataset('parquet', data_files=['data1.parquet', 'data2.parquet'], split='train')
print(dataset.features)
```
Traceback:
```
Traceback (most recent call last):
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/builder.py", line 1854, in _prepare_split_single
for _, table in generator:
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 106, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 73, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
return cast_table_to_schema(table, schema)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2245, in cast_table_to_schema
arrays = [
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2246, in <listcomp>
cast_array_to_feature(
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2108, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<content: string, role: string, some_extra_field: string>
to
{'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None)}
```
### Expected behavior
Correctly load data with optional fields from different parquet files.
### Environment info
- `datasets` version: 3.3.2
- Platform: Linux-5.10.135.bsk.4-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- `huggingface_hub` version: 0.28.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
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Discrepancy in `nyu_depth_v2` dataset
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[
"Ccing @dwofk (the author of `fast-depth`). \r\n\r\nThanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed. \r\n\r\nIf you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :) ",
"Good catch ! Ideally it would be nice to have the datasets in the raw form, this way users can choose whatever processing they want to apply",
"> Ccing @dwofk (the author of `fast-depth`).\r\n> \r\n> Thanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed.\r\n> \r\n> If you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :)\r\n\r\n@sayakpaul I would love to create a PR on this. As this will be my first PR here, some guidance would be helpful.\r\n\r\nNeed a bit of advice on the dataset, there are three publicly available datasets. Which one should I consider for PR?\r\n1. [BTS](https://github.com/cleinc/bts): Containst train/test: 36K/654 data, dtype = `uint16` hence more precise\r\n2. [DenseDepth](https://github.com/ialhashim/DenseDepth) It contains train/test: 50K/654 data, dtype = `uint8` hence less precise\r\n3. [Official](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html#raw_parts): Size is big 400GB+, requires **MatLab** code for fixing **projection** and **sync**, DataType: `pgm` and `dump` hence can't be used directly.\r\n\r\ncc: @lhoestq\r\n\r\n",
"I think BTS. Repositories like https://github.com/vinvino02/GLPDepth usually use BTS. Also, just for clarity, the PR will be to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2. Once we have worked it out, we can update the following things:\r\n\r\n* https://github.com/huggingface/blog/pull/718\r\n* https://huggingface.co/docs/datasets/main/en/depth_estimation\r\n\r\nDon't worry about it if it seems overwhelming. We will work it out together :) \r\n\r\n@lhoestq what do you think? ",
"@sayakpaul If I get this right I have to,\r\n1. Create a PR on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2\r\n2. Create a PR on https://github.com/huggingface/blog\r\n3. Create a PR on https://github.com/huggingface/datasets to update https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx",
"The last two are low-hanging fruits. Don't worry about them. ",
"Yup opening a PR to use BTS on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 sounds good :) Thanks for the help !",
"Finally, I have found the origin of the **discretized depth map**. When I first loaded the datasets from HF I noticed it was 30GB but in DenseDepth data is only 4GB with dtype=uint8. This means data from fast-depth (before loading to HF) must have high precision. So when I tried to dig deeper by directly loading depth_map from `h5py`, I found depth_map from `h5py` came with `float32`. But when the data is processed in HF with `datasets.Image()` it was directly converted to `uint8` from `float32` hence the **discretized** depth map.\r\nhttps://github.com/huggingface/datasets/blob/c78559cacbb0ca6e0bc8bfc313cc0359f8c23ead/src/datasets/features/image.py#L91-L93\r\n\r\n## Solutions:\r\n\r\n#### 1. Array2D\r\nUse `Array2D` feature with `float32` for depth_map \r\n\r\n* Code:\r\n```py\r\nFeatures({'depth_map': Array2D(shape=(480, 640), dtype='float32')})\r\n```\r\n* Pros:\r\nNo precision loss.\r\n\r\n* Cons:\r\nAs depth_map is saved as Array I think it can't be visuzlied in [hf.co/dataset](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) page like segmentation mask.\r\n\r\n#### 2. Uint16\r\nUse `uint16` as dtype for Image in `_h5_loader` for saving depth maps and accept `uint16` dtype in `datasets.Image()` feature.\r\n\r\n* Code\r\n```py\r\ndepth = np.array(h5f[\"depth\"])\r\ndepth /= 10.0 # [0, max_depth] -> [0, 1]\r\ndepth *= (2**16 -1) # transform from [0, 1] -> [0, 2^16 - 1]\r\ndepth = depth.astype('uint16')\r\n```\r\n* Pros:\r\n * We can visualize depth map in hf.co/datasets page like segmentation mask.\r\n * No need for post-processing.\r\n\r\n* Cons:\r\n * We need to make two change\r\n * Modify `_h5_loader` in https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 to convert depth_map from `float32` to `uint16`.\r\n * Make sure `datasets.Image()` converts `np.ndarray` to `uint16` checking max value\r\n * Precision loss due to `float32` to `uint16`\r\n * Post-processing required for depth_map to transform from `[0, 2^16 - 1]` to `[0, max_depth]` before feeding them to model.",
"Thanks so much for digging into this. \r\n\r\nSince the second solution entails changes to core datatypes in `datasets`, I think it's better to go with the first solution. \r\n\r\n@lhoestq WDYT?",
"@sayakpaul Yes, Solution 1 requires minimal change and provides no precision loss. But I think support for `uint16` image would be a great addition as many datasets come with `uint16` image. For example [UW-Madison GI Tract Image Segmentation](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation) dataset, here the image itself comes with `uint16` dtype rather than mask. So, saving `uint16` image with `uint8` will result in precision loss.\r\n\r\nPerhaps we can adapt solution 1 for this issue and Add support for `uint16` image separately?",
"Using Array2D makes it not practical to use to train a model - in `transformers` we expect an image type.\r\n\r\nThere is a pull request to support more precision than uint8 in Image() here: https://github.com/huggingface/datasets/pull/5365/files\r\n\r\nwe can probably merge it today and do a release right away",
"Fantastic, @lhoestq! \r\n\r\n@awsaf49 then let's wait for the PR to get merged and then take the next steps? ",
"Sure",
"The PR adds support for uint16 which is ok for BTS if I understand correctly, would it be ok for you ?",
"If the main issue with the current version of NYU we have on the Hub is related to the precision loss stemming from `Image()`, I'd prefer if `Image()` supported float32 as well. ",
"I also prefer `float32` as it offers more precision. But I'm not sure if we'll be able to visualize image with `float32` precision.",
"We could have a separate loading for the float32 one using Array2D, but I feel like it's less convenient to use due to the amount of disk space and because it's not an Image() type. That's why I think uint16 is a better solution for users",
"A bit confused here, If https://github.com/huggingface/datasets/pull/5365 gets merged won't this issue will be resolved automatically?",
"Yes in theory :)",
"actually float32 also seems to work in this PR (it just doesn't work for multi-channel)",
"In that case, a new PR isn't necessary, right?",
"Yep. I just tested from the PR and it works:\r\n```python\r\n>>> train_dataset = load_dataset(\"sayakpaul/nyu_depth_v2\", split=\"train\", streaming=True) \r\nDownloading readme: 100%|██████████████████| 8.71k/8.71k [00:00<00:00, 3.60MB/s]\r\n>>> next(iter(train_dataset))\r\n{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=640x480 at 0x1382ED7F0>,\r\n 'depth_map': <PIL.TiffImagePlugin.TiffImageFile image mode=F size=640x480 at 0x1382EDF28>}\r\n>>> x = next(iter(train_dataset))\r\n>>> np.asarray(x[\"depth_map\"]) \r\narray([[0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n [0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n [0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n ...,\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ],\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ],\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ]], dtype=float32)\r\n```",
"Great! the case is closed! This issue has been solved and I have to say, it was quite the thrill ride. I felt like Sherlock Holmes, solving a mystery and finding the bug🕵️♂️. But in all seriousness, it was a pleasure working on this issue and I'm glad we could get to the bottom of it.\r\n\r\nOn another note, should I consider closing the issue? I think we still need to make updates on https://github.com/huggingface/blog and https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx",
"Haha thanks Mr Holmes :p\r\n\r\nmaybe let's close this issue when we're done updating the blog post and the documentation",
"@awsaf49 thank you for your hard work! \r\n\r\nI am a little unsure why the other links need to be updated, though. They all rely on datasets internally. ",
"I think depth_map still shows discretized version. It would be nice to have corrected one.\r\n<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/depth_est_target_viz.png\" width = 300>",
"Also, I think we need to make some changes in the code to visualize depth_map as it is `float32` . `plot.imshow()` supports either [0, 1] + float32 or [0. 255] + uint8",
"Oh yes! Do you want to start with the fixes? Please feel free to say no but I wanted to make sure your contributions are reflected properly in our doc and the blog :)",
"Yes I think that would be nice :)",
"I'll make the changes tomorrow. I hope it's okay..."
] | 2023-01-24T19:15:46Z
| 2023-02-06T20:52:00Z
| null |
CONTRIBUTOR
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### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-side comparison,
I tried to find the origin of this issue but sadly as I mentioned in tensorflow/datasets/issues/4674, the download link from `fast-depth` doesn't work anymore hence couldn't verify if the error originated there or during porting data from there to HF.
Hi @sayakpaul, as you worked on huggingface/datasets/issues/5255, if you still have access to that data could you please share the data or perhaps checkout this issue?
### Steps to reproduce the bug
This [notebook](https://colab.research.google.com/drive/1K3ZU8XUPRDOYD38MQS9nreQXJYitlKSW?usp=sharing#scrollTo=UEW7QSh0jf0i) from @sayakpaul could be used to generate depth maps and actual ground truths could be checked from this [dataset](https://www.kaggle.com/datasets/awsaf49/nyuv2-bts-dataset) from BTS repo.
> Note: BTS dataset has only 36K data compared to the train-test 50K. They sampled the data as adjacent frames look quite the same
### Expected behavior
Expected depth maps should be smooth rather than discrete/clipped.
### Environment info
- `datasets` version: 2.8.1.dev0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
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Add a static `__all__` to `__init__.py` for typecheckers
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5509). All of your documentation changes will be reflected on that endpoint.",
"Hi! I've commented on the original issue to provide some context. Feel free to share your opinion there."
] | 2023-02-07T11:42:40Z
| 2023-02-08T17:48:24Z
| null |
NONE
| null | null | null |
This adds a static `__all__` field to `__init__.py`, allowing typecheckers to know which symbols are accessible from `datasets` at runtime. In particular [Pyright](https://github.com/microsoft/pylance-release/issues/2328#issuecomment-1029381258) seems to rely on this. At this point I have added all (modulo oversight) the symbols mentioned in the Reference part of [the docs](https://huggingface.co/docs/datasets), but that could be adjusted. As a side effect, only these symbols will be imported by `from datasets import *`, which may or may not be a good thing (and if it isn't, that's easy to fix).
Another option would be to add a pyi stub, but I think `__all__` should be the most pythonic solution.
This should fix #3841.
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Update csv.py
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"Removed it :)",
"Changed it :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008358 / 0.011353 (-0.002995) | 0.004555 / 0.011008 (-0.006453) | 0.100935 / 0.038508 (0.062427) | 0.029473 / 0.023109 (0.006364) | 0.336165 / 0.275898 (0.060266) | 0.420397 / 0.323480 (0.096917) | 0.006609 / 0.007986 (-0.001376) | 0.003338 / 0.004328 (-0.000991) | 0.078639 / 0.004250 (0.074388) | 0.034051 / 0.037052 (-0.003001) | 0.342820 / 0.258489 (0.084331) | 0.399392 / 0.293841 (0.105551) | 0.033935 / 0.128546 (-0.094611) | 0.011555 / 0.075646 (-0.064092) | 0.323467 / 0.419271 (-0.095804) | 0.040675 / 0.043533 (-0.002858) | 0.321247 / 0.255139 (0.066108) | 0.370967 / 0.283200 (0.087767) | 0.085766 / 0.141683 (-0.055917) | 1.461158 / 1.452155 (0.009003) | 1.504641 / 1.492716 (0.011925) |\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) | 0.180060 / 0.018006 (0.162053) | 0.403623 / 0.000490 (0.403134) | 0.002253 / 0.000200 (0.002053) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022793 / 0.037411 (-0.014618) | 0.098869 / 0.014526 (0.084343) | 0.104512 / 0.176557 (-0.072045) | 0.167721 / 0.737135 (-0.569414) | 0.107969 / 0.296338 (-0.188370) |\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) | 0.411179 / 0.215209 (0.195969) | 4.095345 / 2.077655 (2.017690) | 1.825992 / 1.504120 (0.321872) | 1.624386 / 1.541195 (0.083192) | 1.654903 / 1.468490 (0.186413) | 0.695041 / 4.584777 (-3.889736) | 3.319087 / 3.745712 (-0.426625) | 1.881945 / 5.269862 (-3.387917) | 1.250360 / 4.565676 (-3.315316) | 0.082405 / 0.424275 (-0.341870) | 0.012499 / 0.007607 (0.004892) | 0.522846 / 0.226044 (0.296801) | 5.241103 / 2.268929 (2.972175) | 2.293100 / 55.444624 (-53.151524) | 1.942937 / 6.876477 (-4.933540) | 1.957434 / 2.142072 (-0.184638) | 0.809782 / 4.805227 (-3.995445) | 0.148290 / 6.500664 (-6.352374) | 0.064157 / 0.075469 (-0.011312) |\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) | 1.185616 / 1.841788 (-0.656172) | 13.616791 / 8.074308 (5.542483) | 13.741806 / 10.191392 (3.550414) | 0.137396 / 0.680424 (-0.543028) | 0.028751 / 0.534201 (-0.505450) | 0.397636 / 0.579283 (-0.181647) | 0.403594 / 0.434364 (-0.030770) | 0.484039 / 0.540337 (-0.056299) | 0.568398 / 1.386936 (-0.818538) |\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) | 0.006712 / 0.011353 (-0.004640) | 0.004511 / 0.011008 (-0.006497) | 0.076946 / 0.038508 (0.038438) | 0.027219 / 0.023109 (0.004110) | 0.350769 / 0.275898 (0.074871) | 0.408539 / 0.323480 (0.085059) | 0.005014 / 0.007986 (-0.002971) | 0.003361 / 0.004328 (-0.000968) | 0.077106 / 0.004250 (0.072856) | 0.040105 / 0.037052 (0.003053) | 0.342041 / 0.258489 (0.083552) | 0.426355 / 0.293841 (0.132514) | 0.031684 / 0.128546 (-0.096863) | 0.011575 / 0.075646 (-0.064072) | 0.085797 / 0.419271 (-0.333474) | 0.041575 / 0.043533 (-0.001958) | 0.340837 / 0.255139 (0.085698) | 0.390461 / 0.283200 (0.107262) | 0.089531 / 0.141683 (-0.052152) | 1.504600 / 1.452155 (0.052445) | 1.538712 / 1.492716 (0.045996) |\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) | 0.236679 / 0.018006 (0.218673) | 0.396258 / 0.000490 (0.395768) | 0.006479 / 0.000200 (0.006279) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024682 / 0.037411 (-0.012729) | 0.100167 / 0.014526 (0.085641) | 0.106627 / 0.176557 (-0.069929) | 0.174592 / 0.737135 (-0.562543) | 0.109499 / 0.296338 (-0.186839) |\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) | 0.444702 / 0.215209 (0.229493) | 4.462779 / 2.077655 (2.385125) | 2.087711 / 1.504120 (0.583591) | 1.874900 / 1.541195 (0.333705) | 1.918609 / 1.468490 (0.450119) | 0.705867 / 4.584777 (-3.878910) | 3.355483 / 3.745712 (-0.390229) | 2.808348 / 5.269862 (-2.461514) | 1.253319 / 4.565676 (-3.312358) | 0.083747 / 0.424275 (-0.340528) | 0.012491 / 0.007607 (0.004884) | 0.542885 / 0.226044 (0.316841) | 5.453921 / 2.268929 (3.184993) | 2.545688 / 55.444624 (-52.898937) | 2.185022 / 6.876477 (-4.691455) | 2.215351 / 2.142072 (0.073279) | 0.808201 / 4.805227 (-3.997027) | 0.151754 / 6.500664 (-6.348910) | 0.066886 / 0.075469 (-0.008583) |\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) | 1.298583 / 1.841788 (-0.543205) | 14.014276 / 8.074308 (5.939968) | 13.505338 / 10.191392 (3.313946) | 0.142033 / 0.680424 (-0.538391) | 0.016863 / 0.534201 (-0.517338) | 0.381195 / 0.579283 (-0.198088) | 0.384455 / 0.434364 (-0.049909) | 0.465765 / 0.540337 (-0.074572) | 0.552571 / 1.386936 (-0.834366) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a29cca79ce64a5c64ad7047e57845b22154d7b8d \"CML watermark\")\n"
] | 2023-02-22T07:56:10Z
| 2023-02-23T11:07:49Z
| 2023-02-23T11:00:58Z
|
CONTRIBUTOR
| null | null | null |
Removed mangle_dup_cols=True from BuilderConfig.
It triggered following deprecation warning:
/usr/local/lib/python3.8/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs)
Further documentation of pandas: https://pandas.pydata.org/docs/whatsnew/v1.4.0.html#mangle-dupe-cols-in-read-csv-no-longer-renames-unique-columns-conflicting-with-target-names
At first sight it seems like this flag is resolved internally, it might need some more research.
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pinning `dill<0.3.9` without pinning `multiprocess`
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[] | 2024-10-01T22:29:32Z
| 2024-10-02T06:08:24Z
| 2024-10-02T06:08:24Z
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### Describe the bug
The [latest `multiprocess` release](https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17) requires `dill>=0.3.9` which causes issues when installing `datasets` without backtracking during package version resolution. Is it possible to add a pin for multiprocess so something like `multiprocess<=0.70.16` so that the `dill` version is compatible?
### Steps to reproduce the bug
NA
### Expected behavior
NA
### Environment info
NA
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Support push_to_hub canonical datasets
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6519). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"nice catch @albertvillanova ",
"@huggingface/datasets this PR is ready for review.",
"<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) | 0.005306 / 0.011353 (-0.006047) | 0.003454 / 0.011008 (-0.007555) | 0.062157 / 0.038508 (0.023649) | 0.051945 / 0.023109 (0.028835) | 0.241834 / 0.275898 (-0.034064) | 0.265590 / 0.323480 (-0.057890) | 0.003149 / 0.007986 (-0.004837) | 0.002695 / 0.004328 (-0.001633) | 0.049197 / 0.004250 (0.044947) | 0.045576 / 0.037052 (0.008524) | 0.242866 / 0.258489 (-0.015623) | 0.280963 / 0.293841 (-0.012878) | 0.028466 / 0.128546 (-0.100080) | 0.010670 / 0.075646 (-0.064976) | 0.206501 / 0.419271 (-0.212771) | 0.035314 / 0.043533 (-0.008219) | 0.240893 / 0.255139 (-0.014246) | 0.264762 / 0.283200 (-0.018438) | 0.019988 / 0.141683 (-0.121695) | 1.095222 / 1.452155 (-0.356933) | 1.144051 / 1.492716 (-0.348666) |\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) | 0.098034 / 0.018006 (0.080028) | 0.308541 / 0.000490 (0.308051) | 0.000261 / 0.000200 (0.000061) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018646 / 0.037411 (-0.018766) | 0.062881 / 0.014526 (0.048355) | 0.074062 / 0.176557 (-0.102494) | 0.120860 / 0.737135 (-0.616276) | 0.075388 / 0.296338 (-0.220951) |\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) | 0.282974 / 0.215209 (0.067765) | 2.755589 / 2.077655 (0.677934) | 1.459536 / 1.504120 (-0.044584) | 1.364543 / 1.541195 (-0.176652) | 1.429860 / 1.468490 (-0.038630) | 0.573277 / 4.584777 (-4.011500) | 2.422983 / 3.745712 (-1.322730) | 3.257258 / 5.269862 (-2.012603) | 1.930053 / 4.565676 (-2.635623) | 0.067476 / 0.424275 (-0.356799) | 0.005612 / 0.007607 (-0.001995) | 0.351538 / 0.226044 (0.125494) | 3.380356 / 2.268929 (1.111427) | 1.837887 / 55.444624 (-53.606738) | 1.537994 / 6.876477 (-5.338483) | 1.623630 / 2.142072 (-0.518442) | 0.662652 / 4.805227 (-4.142576) | 0.127074 / 6.500664 (-6.373590) | 0.049311 / 0.075469 (-0.026158) |\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) | 1.151273 / 1.841788 (-0.690515) | 12.766622 / 8.074308 (4.692314) | 10.967610 / 10.191392 (0.776218) | 0.131305 / 0.680424 (-0.549119) | 0.014227 / 0.534201 (-0.519974) | 0.292054 / 0.579283 (-0.287229) | 0.262737 / 0.434364 (-0.171627) | 0.334360 / 0.540337 (-0.205978) | 0.446711 / 1.386936 (-0.940225) |\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) | 0.005194 / 0.011353 (-0.006159) | 0.003508 / 0.011008 (-0.007500) | 0.049287 / 0.038508 (0.010779) | 0.052109 / 0.023109 (0.029000) | 0.271501 / 0.275898 (-0.004397) | 0.290959 / 0.323480 (-0.032521) | 0.004347 / 0.007986 (-0.003638) | 0.002659 / 0.004328 (-0.001669) | 0.048769 / 0.004250 (0.044518) | 0.039388 / 0.037052 (0.002336) | 0.272811 / 0.258489 (0.014322) | 0.305632 / 0.293841 (0.011791) | 0.028419 / 0.128546 (-0.100127) | 0.010617 / 0.075646 (-0.065029) | 0.057433 / 0.419271 (-0.361838) | 0.032383 / 0.043533 (-0.011149) | 0.266566 / 0.255139 (0.011427) | 0.290993 / 0.283200 (0.007794) | 0.019939 / 0.141683 (-0.121743) | 1.157623 / 1.452155 (-0.294532) | 1.183298 / 1.492716 (-0.309419) |\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) | 0.099074 / 0.018006 (0.081068) | 0.315282 / 0.000490 (0.314792) | 0.000235 / 0.000200 (0.000035) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022692 / 0.037411 (-0.014719) | 0.076455 / 0.014526 (0.061929) | 0.089094 / 0.176557 (-0.087462) | 0.126407 / 0.737135 (-0.610728) | 0.089588 / 0.296338 (-0.206750) |\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) | 0.338853 / 0.215209 (0.123644) | 2.809843 / 2.077655 (0.732188) | 1.538262 / 1.504120 (0.034143) | 1.418290 / 1.541195 (-0.122905) | 1.435145 / 1.468490 (-0.033345) | 0.565763 / 4.584777 (-4.019014) | 2.491525 / 3.745712 (-1.254187) | 2.944879 / 5.269862 (-2.324983) | 1.835840 / 4.565676 (-2.729837) | 0.065101 / 0.424275 (-0.359174) | 0.005196 / 0.007607 (-0.002412) | 0.345291 / 0.226044 (0.119247) | 3.399658 / 2.268929 (1.130729) | 1.892321 / 55.444624 (-53.552303) | 1.608293 / 6.876477 (-5.268184) | 1.651188 / 2.142072 (-0.490884) | 0.647806 / 4.805227 (-4.157421) | 0.119318 / 6.500664 (-6.381346) | 0.043058 / 0.075469 (-0.032412) |\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) | 0.983956 / 1.841788 (-0.857831) | 13.516125 / 8.074308 (5.441817) | 11.712571 / 10.191392 (1.521179) | 0.134253 / 0.680424 (-0.546171) | 0.015844 / 0.534201 (-0.518357) | 0.292444 / 0.579283 (-0.286839) | 0.282182 / 0.434364 (-0.152182) | 0.329327 / 0.540337 (-0.211010) | 0.419960 / 1.386936 (-0.966976) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a887ee78835573f5d80f9e414e8443b4caff3541 \"CML watermark\")\n"
] | 2023-12-20T15:16:45Z
| 2023-12-21T14:48:20Z
| 2023-12-21T14:40:57Z
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MEMBER
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Support `push_to_hub` canonical datasets.
This is necessary in the Space to convert script-datasets to Parquet: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet
Note that before this PR, the `repo_id` "dataset_name" was transformed to "user/dataset_name". This behavior was introduced by:
- #6269
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"I'd be happy to have a look, if nobody else has started working on this yet @lhoestq. \r\n\r\nIt seems to me that for the `arrow` format features are currently attached as metadata [in `datasets.arrow_writer`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/arrow_writer.py#L412) and retrieved from the metadata at `load_dataset` time using [`datasets.features.features.from_arrow_schema`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/features/features.py#L1602). \r\n\r\nThis will need to be replicated for `parquet` via calls to [this api](https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_metadata.html) from `io.parquet.ParquetWriter` and `io.parquet.ParquetReader` [respectively](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/io/parquet.py#L104).\r\n\r\nAny other important considerations?\r\n",
"Thanks @MFreidank ! That's correct :)\r\n\r\nReading the metadata to infer the features can be ideally done in the `parquet.py` file in `packaged_builder` when a parquet file is read. You can cast the arrow table to the schema you get from the features.arrow_schema",
"#self-assign"
] | 2023-01-28T13:12:31Z
| 2023-02-12T15:57:02Z
| 2023-02-12T15:57:02Z
|
MEMBER
| null | null |
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The idea would be to allow this :
```python
ds.to_parquet("my_dataset/ds.parquet")
reloaded = load_dataset("my_dataset")
assert ds.features == reloaded.features
```
And it should also work with Image and Audio types (right now they're reloaded as a dict type)
This can be implemented by storing and reading the feature types in the parquet metadata, as we do for arrow files.
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PR_kwDODunzps5KcF5E
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Remove instructions for `ffmpeg` system package installation on Colab
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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==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.014525 / 0.011353 (0.003172) | 0.006871 / 0.011008 (-0.004137) | 0.135577 / 0.038508 (0.097069) | 0.039620 / 0.023109 (0.016511) | 0.499829 / 0.275898 (0.223931) | 0.571000 / 0.323480 (0.247520) | 0.009726 / 0.007986 (0.001740) | 0.005654 / 0.004328 (0.001325) | 0.104732 / 0.004250 (0.100482) | 0.046849 / 0.037052 (0.009796) | 0.486667 / 0.258489 (0.228178) | 0.543611 / 0.293841 (0.249770) | 0.056414 / 0.128546 (-0.072133) | 0.019974 / 0.075646 (-0.055672) | 0.484878 / 0.419271 (0.065606) | 0.059244 / 0.043533 (0.015711) | 0.490046 / 0.255139 (0.234907) | 0.517427 / 0.283200 (0.234227) | 0.114692 / 0.141683 (-0.026991) | 1.935935 / 1.452155 (0.483780) | 1.990253 / 1.492716 (0.497537) |\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) | 0.271008 / 0.018006 (0.253002) | 0.610964 / 0.000490 (0.610474) | 0.013423 / 0.000200 (0.013223) | 0.000523 / 0.000054 (0.000468) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031940 / 0.037411 (-0.005472) | 0.130755 / 0.014526 (0.116229) | 0.146616 / 0.176557 (-0.029941) | 0.239386 / 0.737135 (-0.497749) | 0.146612 / 0.296338 (-0.149726) |\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) | 0.675383 / 0.215209 (0.460174) | 6.656828 / 2.077655 (4.579174) | 2.741231 / 1.504120 (1.237111) | 2.232921 / 1.541195 (0.691726) | 2.172116 / 1.468490 (0.703626) | 1.221623 / 4.584777 (-3.363154) | 5.683653 / 3.745712 (1.937941) | 5.344137 / 5.269862 (0.074275) | 2.969670 / 4.565676 (-1.596006) | 0.142107 / 0.424275 (-0.282168) | 0.015808 / 0.007607 (0.008201) | 0.767366 / 0.226044 (0.541321) | 8.059605 / 2.268929 (5.790676) | 3.333535 / 55.444624 (-52.111089) | 2.669619 / 6.876477 (-4.206857) | 2.652989 / 2.142072 (0.510917) | 1.526397 / 4.805227 (-3.278830) | 0.265609 / 6.500664 (-6.235055) | 0.082759 / 0.075469 (0.007290) |\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) | 1.631086 / 1.841788 (-0.210701) | 18.701351 / 8.074308 (10.627043) | 22.843802 / 10.191392 (12.652410) | 0.240134 / 0.680424 (-0.440290) | 0.046683 / 0.534201 (-0.487518) | 0.576488 / 0.579283 (-0.002795) | 0.650123 / 0.434364 (0.215759) | 0.661190 / 0.540337 (0.120853) | 0.759563 / 1.386936 (-0.627373) |\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) | 0.009883 / 0.011353 (-0.001470) | 0.006692 / 0.011008 (-0.004316) | 0.098550 / 0.038508 (0.060042) | 0.035188 / 0.023109 (0.012078) | 0.463535 / 0.275898 (0.187637) | 0.472762 / 0.323480 (0.149282) | 0.007199 / 0.007986 (-0.000787) | 0.007961 / 0.004328 (0.003632) | 0.093140 / 0.004250 (0.088890) | 0.051752 / 0.037052 (0.014700) | 0.453412 / 0.258489 (0.194922) | 0.502741 / 0.293841 (0.208900) | 0.056006 / 0.128546 (-0.072540) | 0.020164 / 0.075646 (-0.055482) | 0.116828 / 0.419271 (-0.302444) | 0.067205 / 0.043533 (0.023672) | 0.442715 / 0.255139 (0.187576) | 0.472525 / 0.283200 (0.189326) | 0.122767 / 0.141683 (-0.018915) | 1.881366 / 1.452155 (0.429212) | 1.978786 / 1.492716 (0.486069) |\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) | 0.284180 / 0.018006 (0.266174) | 0.601556 / 0.000490 (0.601067) | 0.008455 / 0.000200 (0.008255) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033515 / 0.037411 (-0.003896) | 0.136407 / 0.014526 (0.121881) | 0.143341 / 0.176557 (-0.033215) | 0.225394 / 0.737135 (-0.511741) | 0.153343 / 0.296338 (-0.142995) |\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) | 0.688202 / 0.215209 (0.472993) | 6.576502 / 2.077655 (4.498847) | 2.839175 / 1.504120 (1.335055) | 2.481152 / 1.541195 (0.939957) | 2.617227 / 1.468490 (1.148736) | 1.314854 / 4.584777 (-3.269922) | 5.805950 / 3.745712 (2.060238) | 3.188930 / 5.269862 (-2.080932) | 2.141719 / 4.565676 (-2.423957) | 0.145069 / 0.424275 (-0.279206) | 0.014567 / 0.007607 (0.006960) | 0.780000 / 0.226044 (0.553955) | 7.898016 / 2.268929 (5.629088) | 3.549060 / 55.444624 (-51.895564) | 2.856569 / 6.876477 (-4.019907) | 3.117719 / 2.142072 (0.975647) | 1.512560 / 4.805227 (-3.292668) | 0.262689 / 6.500664 (-6.237975) | 0.085979 / 0.075469 (0.010509) |\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) | 1.623550 / 1.841788 (-0.218238) | 19.597063 / 8.074308 (11.522755) | 21.293369 / 10.191392 (11.101977) | 0.263780 / 0.680424 (-0.416643) | 0.027289 / 0.534201 (-0.506912) | 0.560361 / 0.579283 (-0.018922) | 0.646288 / 0.434364 (0.211924) | 0.712699 / 0.540337 (0.172361) | 0.818332 / 1.386936 (-0.568604) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b304de5dde30c945ec1397d3b4fe86f3b323ca8b \"CML watermark\")\n"
] | 2023-02-21T15:13:36Z
| 2023-03-01T13:46:04Z
| 2023-02-23T13:50:27Z
|
CONTRIBUTOR
| null | null | null |
Colab now has Ubuntu 20.04 which already has `ffmpeg` of required (>4) version.
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PR_kwDODunzps5ip9JH
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set dev version
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6528). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.004875 / 0.011353 (-0.006478) | 0.003501 / 0.011008 (-0.007507) | 0.062604 / 0.038508 (0.024096) | 0.031916 / 0.023109 (0.008806) | 0.256138 / 0.275898 (-0.019760) | 0.278514 / 0.323480 (-0.044966) | 0.002917 / 0.007986 (-0.005069) | 0.002636 / 0.004328 (-0.001693) | 0.049154 / 0.004250 (0.044904) | 0.041985 / 0.037052 (0.004933) | 0.256857 / 0.258489 (-0.001632) | 0.282628 / 0.293841 (-0.011213) | 0.027506 / 0.128546 (-0.101041) | 0.010736 / 0.075646 (-0.064910) | 0.207268 / 0.419271 (-0.212003) | 0.035312 / 0.043533 (-0.008221) | 0.259274 / 0.255139 (0.004135) | 0.281463 / 0.283200 (-0.001737) | 0.019905 / 0.141683 (-0.121778) | 1.108719 / 1.452155 (-0.343435) | 1.177871 / 1.492716 (-0.314845) |\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) | 0.004435 / 0.018006 (-0.013571) | 0.310643 / 0.000490 (0.310153) | 0.000243 / 0.000200 (0.000043) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018013 / 0.037411 (-0.019398) | 0.060702 / 0.014526 (0.046176) | 0.073243 / 0.176557 (-0.103314) | 0.119523 / 0.737135 (-0.617613) | 0.074204 / 0.296338 (-0.222134) |\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) | 0.281075 / 0.215209 (0.065866) | 2.722154 / 2.077655 (0.644499) | 1.441052 / 1.504120 (-0.063068) | 1.305940 / 1.541195 (-0.235255) | 1.356752 / 1.468490 (-0.111738) | 0.570399 / 4.584777 (-4.014378) | 2.329158 / 3.745712 (-1.416554) | 2.749093 / 5.269862 (-2.520768) | 1.717752 / 4.565676 (-2.847925) | 0.063228 / 0.424275 (-0.361047) | 0.004981 / 0.007607 (-0.002626) | 0.330601 / 0.226044 (0.104557) | 3.300987 / 2.268929 (1.032059) | 1.778673 / 55.444624 (-53.665951) | 1.507841 / 6.876477 (-5.368636) | 1.520454 / 2.142072 (-0.621619) | 0.650816 / 4.805227 (-4.154412) | 0.118606 / 6.500664 (-6.382058) | 0.042199 / 0.075469 (-0.033271) |\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) | 0.919668 / 1.841788 (-0.922119) | 11.293437 / 8.074308 (3.219129) | 9.928525 / 10.191392 (-0.262867) | 0.127142 / 0.680424 (-0.553282) | 0.013470 / 0.534201 (-0.520731) | 0.284648 / 0.579283 (-0.294636) | 0.264942 / 0.434364 (-0.169422) | 0.321866 / 0.540337 (-0.218471) | 0.414513 / 1.386936 (-0.972423) |\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) | 0.005052 / 0.011353 (-0.006301) | 0.003204 / 0.011008 (-0.007804) | 0.051102 / 0.038508 (0.012594) | 0.032105 / 0.023109 (0.008996) | 0.273923 / 0.275898 (-0.001976) | 0.297031 / 0.323480 (-0.026449) | 0.004002 / 0.007986 (-0.003984) | 0.002636 / 0.004328 (-0.001693) | 0.047696 / 0.004250 (0.043445) | 0.044086 / 0.037052 (0.007034) | 0.277779 / 0.258489 (0.019289) | 0.306678 / 0.293841 (0.012837) | 0.028557 / 0.128546 (-0.099989) | 0.010631 / 0.075646 (-0.065015) | 0.056419 / 0.419271 (-0.362852) | 0.054285 / 0.043533 (0.010752) | 0.276506 / 0.255139 (0.021367) | 0.296315 / 0.283200 (0.013116) | 0.018642 / 0.141683 (-0.123040) | 1.146926 / 1.452155 (-0.305229) | 1.257625 / 1.492716 (-0.235092) |\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) | 0.094231 / 0.018006 (0.076225) | 0.302805 / 0.000490 (0.302315) | 0.000229 / 0.000200 (0.000029) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022510 / 0.037411 (-0.014901) | 0.076092 / 0.014526 (0.061566) | 0.090642 / 0.176557 (-0.085915) | 0.127016 / 0.737135 (-0.610120) | 0.089169 / 0.296338 (-0.207169) |\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) | 0.290812 / 0.215209 (0.075603) | 2.858528 / 2.077655 (0.780873) | 1.577555 / 1.504120 (0.073436) | 1.447810 / 1.541195 (-0.093384) | 1.447546 / 1.468490 (-0.020944) | 0.559118 / 4.584777 (-4.025659) | 2.408930 / 3.745712 (-1.336782) | 2.733761 / 5.269862 (-2.536101) | 1.700107 / 4.565676 (-2.865570) | 0.062447 / 0.424275 (-0.361828) | 0.004999 / 0.007607 (-0.002608) | 0.340207 / 0.226044 (0.114162) | 3.344131 / 2.268929 (1.075203) | 1.902289 / 55.444624 (-53.542335) | 1.628226 / 6.876477 (-5.248251) | 1.629435 / 2.142072 (-0.512637) | 0.625011 / 4.805227 (-4.180216) | 0.119929 / 6.500664 (-6.380735) | 0.041097 / 0.075469 (-0.034372) |\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) | 0.977461 / 1.841788 (-0.864327) | 12.303189 / 8.074308 (4.228881) | 11.008743 / 10.191392 (0.817351) | 0.128578 / 0.680424 (-0.551845) | 0.015305 / 0.534201 (-0.518896) | 0.286468 / 0.579283 (-0.292816) | 0.275824 / 0.434364 (-0.158540) | 0.321487 / 0.540337 (-0.218851) | 0.420591 / 1.386936 (-0.966345) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ff3670c18ed34fa8ddfa70a9aa403ae6cc9ad54 \"CML watermark\")\n"
] | 2023-12-22T14:23:18Z
| 2023-12-22T14:31:42Z
| 2023-12-22T14:25:34Z
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MEMBER
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unsloth/DeepSeek-R1-Distill-Qwen-32B server error
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[] | 2025-04-15T09:26:53Z
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### Describe the bug
hfhubhttperror: 500 server error: internal server error for url: https://huggingface.co/api/models/unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit/commits/main (request id: root=1-67fe23fa-3a2150eb444c2a823c388579;de3aed68-c397-4da5-94d4-6565efd3b919) internal error - we're working hard to fix this as soon as possible!
### Steps to reproduce the bug
unsloth/DeepSeek-R1-Distill-Qwen-32B server error
### Expected behavior
Network repair
### Environment info
The web side is also unavailable
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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) | 0.006458 / 0.011353 (-0.004894) | 0.003895 / 0.011008 (-0.007114) | 0.084280 / 0.038508 (0.045772) | 0.071304 / 0.023109 (0.048195) | 0.313910 / 0.275898 (0.038012) | 0.344070 / 0.323480 (0.020590) | 0.005413 / 0.007986 (-0.002573) | 0.003308 / 0.004328 (-0.001021) | 0.064570 / 0.004250 (0.060320) | 0.056824 / 0.037052 (0.019771) | 0.321102 / 0.258489 (0.062613) | 0.355834 / 0.293841 (0.061993) | 0.031252 / 0.128546 (-0.097294) | 0.008427 / 0.075646 (-0.067219) | 0.287348 / 0.419271 (-0.131924) | 0.053261 / 0.043533 (0.009728) | 0.324892 / 0.255139 (0.069753) | 0.335847 / 0.283200 (0.052647) | 0.023453 / 0.141683 (-0.118230) | 1.485456 / 1.452155 (0.033301) | 1.531329 / 1.492716 (0.038612) |\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) | 0.201924 / 0.018006 (0.183918) | 0.447188 / 0.000490 (0.446698) | 0.005543 / 0.000200 (0.005343) | 0.000086 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027586 / 0.037411 (-0.009825) | 0.082412 / 0.014526 (0.067886) | 0.094851 / 0.176557 (-0.081706) | 0.151331 / 0.737135 (-0.585804) | 0.094475 / 0.296338 (-0.201863) |\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) | 0.399004 / 0.215209 (0.183795) | 3.974652 / 2.077655 (1.896997) | 1.991909 / 1.504120 (0.487789) | 1.811684 / 1.541195 (0.270489) | 1.869774 / 1.468490 (0.401283) | 0.487745 / 4.584777 (-4.097032) | 3.558945 / 3.745712 (-0.186768) | 5.530468 / 5.269862 (0.260606) | 3.293147 / 4.565676 (-1.272529) | 0.057531 / 0.424275 (-0.366744) | 0.007212 / 0.007607 (-0.000395) | 0.470325 / 0.226044 (0.244281) | 4.701652 / 2.268929 (2.432723) | 2.453020 / 55.444624 (-52.991605) | 2.110152 / 6.876477 (-4.766325) | 2.314669 / 2.142072 (0.172597) | 0.615039 / 4.805227 (-4.190189) | 0.133229 / 6.500664 (-6.367435) | 0.060821 / 0.075469 (-0.014648) |\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) | 1.296708 / 1.841788 (-0.545079) | 18.717251 / 8.074308 (10.642943) | 14.325305 / 10.191392 (4.133913) | 0.147680 / 0.680424 (-0.532744) | 0.018312 / 0.534201 (-0.515889) | 0.392766 / 0.579283 (-0.186517) | 0.403319 / 0.434364 (-0.031045) | 0.453696 / 0.540337 (-0.086641) | 0.622564 / 1.386936 (-0.764372) |\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) | 0.006483 / 0.011353 (-0.004870) | 0.004018 / 0.011008 (-0.006991) | 0.064436 / 0.038508 (0.025928) | 0.072365 / 0.023109 (0.049256) | 0.387532 / 0.275898 (0.111634) | 0.418175 / 0.323480 (0.094695) | 0.005453 / 0.007986 (-0.002533) | 0.003368 / 0.004328 (-0.000961) | 0.064896 / 0.004250 (0.060645) | 0.057018 / 0.037052 (0.019966) | 0.406596 / 0.258489 (0.148107) | 0.431194 / 0.293841 (0.137353) | 0.031788 / 0.128546 (-0.096759) | 0.008532 / 0.075646 (-0.067114) | 0.070605 / 0.419271 (-0.348666) | 0.053317 / 0.043533 (0.009785) | 0.391930 / 0.255139 (0.136791) | 0.406071 / 0.283200 (0.122872) | 0.028652 / 0.141683 (-0.113030) | 1.487677 / 1.452155 (0.035522) | 1.546071 / 1.492716 (0.053355) |\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) | 0.220063 / 0.018006 (0.202056) | 0.441111 / 0.000490 (0.440621) | 0.006066 / 0.000200 (0.005867) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035179 / 0.037411 (-0.002232) | 0.096745 / 0.014526 (0.082219) | 0.108171 / 0.176557 (-0.068386) | 0.164590 / 0.737135 (-0.572545) | 0.109425 / 0.296338 (-0.186913) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408101 / 0.215209 (0.192892) | 4.062961 / 2.077655 (1.985306) | 2.101849 / 1.504120 (0.597730) | 1.935919 / 1.541195 (0.394724) | 1.993749 / 1.468490 (0.525259) | 0.487788 / 4.584777 (-4.096989) | 3.533972 / 3.745712 (-0.211740) | 3.218448 / 5.269862 (-2.051414) | 2.002322 / 4.565676 (-2.563355) | 0.057371 / 0.424275 (-0.366904) | 0.007704 / 0.007607 (0.000097) | 0.491695 / 0.226044 (0.265650) | 4.905009 / 2.268929 (2.636080) | 2.597879 / 55.444624 (-52.846745) | 2.252086 / 6.876477 (-4.624391) | 2.434439 / 2.142072 (0.292367) | 0.583071 / 4.805227 (-4.222156) | 0.133765 / 6.500664 (-6.366899) | 0.061276 / 0.075469 (-0.014193) |\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) | 1.403111 / 1.841788 (-0.438676) | 19.218886 / 8.074308 (11.144578) | 13.981775 / 10.191392 (3.790383) | 0.167784 / 0.680424 (-0.512640) | 0.018401 / 0.534201 (-0.515800) | 0.392038 / 0.579283 (-0.187245) | 0.414776 / 0.434364 (-0.019587) | 0.476221 / 0.540337 (-0.064117) | 0.632724 / 1.386936 (-0.754212) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#420dbd92c42840d6c91ecf5d3560c6799ee0cca1 \"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) | 0.007595 / 0.011353 (-0.003758) | 0.004540 / 0.011008 (-0.006468) | 0.099350 / 0.038508 (0.060842) | 0.087062 / 0.023109 (0.063953) | 0.415980 / 0.275898 (0.140082) | 0.466390 / 0.323480 (0.142910) | 0.005958 / 0.007986 (-0.002027) | 0.003671 / 0.004328 (-0.000657) | 0.075714 / 0.004250 (0.071463) | 0.066062 / 0.037052 (0.029010) | 0.426527 / 0.258489 (0.168038) | 0.473282 / 0.293841 (0.179441) | 0.035669 / 0.128546 (-0.092878) | 0.009729 / 0.075646 (-0.065918) | 0.344035 / 0.419271 (-0.075237) | 0.061153 / 0.043533 (0.017620) | 0.428607 / 0.255139 (0.173468) | 0.445951 / 0.283200 (0.162752) | 0.026373 / 0.141683 (-0.115310) | 1.788725 / 1.452155 (0.336570) | 1.871055 / 1.492716 (0.378339) |\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) | 0.230606 / 0.018006 (0.212600) | 0.489835 / 0.000490 (0.489345) | 0.005669 / 0.000200 (0.005469) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032197 / 0.037411 (-0.005214) | 0.099571 / 0.014526 (0.085045) | 0.112686 / 0.176557 (-0.063871) | 0.179478 / 0.737135 (-0.557658) | 0.112670 / 0.296338 (-0.183668) |\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) | 0.449606 / 0.215209 (0.234397) | 4.503356 / 2.077655 (2.425701) | 2.190480 / 1.504120 (0.686361) | 1.986054 / 1.541195 (0.444860) | 2.071594 / 1.468490 (0.603104) | 0.566301 / 4.584777 (-4.018475) | 4.088460 / 3.745712 (0.342748) | 4.840100 / 5.269862 (-0.429761) | 2.857697 / 4.565676 (-1.707980) | 0.066718 / 0.424275 (-0.357557) | 0.008642 / 0.007607 (0.001034) | 0.539785 / 0.226044 (0.313740) | 5.383252 / 2.268929 (3.114323) | 2.878177 / 55.444624 (-52.566447) | 2.374577 / 6.876477 (-4.501899) | 2.590500 / 2.142072 (0.448428) | 0.675196 / 4.805227 (-4.130031) | 0.153544 / 6.500664 (-6.347120) | 0.070958 / 0.075469 (-0.004511) |\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) | 1.490403 / 1.841788 (-0.351385) | 22.085740 / 8.074308 (14.011432) | 16.588093 / 10.191392 (6.396701) | 0.188598 / 0.680424 (-0.491826) | 0.021567 / 0.534201 (-0.512634) | 0.472594 / 0.579283 (-0.106689) | 0.472903 / 0.434364 (0.038539) | 0.545305 / 0.540337 (0.004968) | 0.736399 / 1.386936 (-0.650537) |\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) | 0.007635 / 0.011353 (-0.003718) | 0.004731 / 0.011008 (-0.006277) | 0.076482 / 0.038508 (0.037974) | 0.083666 / 0.023109 (0.060557) | 0.469596 / 0.275898 (0.193698) | 0.493068 / 0.323480 (0.169588) | 0.006014 / 0.007986 (-0.001971) | 0.003902 / 0.004328 (-0.000426) | 0.077142 / 0.004250 (0.072891) | 0.064355 / 0.037052 (0.027303) | 0.468859 / 0.258489 (0.210370) | 0.504002 / 0.293841 (0.210161) | 0.037606 / 0.128546 (-0.090940) | 0.010141 / 0.075646 (-0.065505) | 0.083790 / 0.419271 (-0.335482) | 0.060923 / 0.043533 (0.017390) | 0.464752 / 0.255139 (0.209613) | 0.500464 / 0.283200 (0.217264) | 0.031183 / 0.141683 (-0.110499) | 1.779294 / 1.452155 (0.327139) | 1.870848 / 1.492716 (0.378131) |\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) | 0.246567 / 0.018006 (0.228560) | 0.477182 / 0.000490 (0.476693) | 0.000426 / 0.000200 (0.000226) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035377 / 0.037411 (-0.002034) | 0.106042 / 0.014526 (0.091516) | 0.119237 / 0.176557 (-0.057320) | 0.182145 / 0.737135 (-0.554991) | 0.119537 / 0.296338 (-0.176801) |\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) | 0.491352 / 0.215209 (0.276143) | 4.824220 / 2.077655 (2.746565) | 2.652039 / 1.504120 (1.147919) | 2.535310 / 1.541195 (0.994116) | 2.620009 / 1.468490 (1.151519) | 0.567865 / 4.584777 (-4.016912) | 4.158795 / 3.745712 (0.413082) | 6.042582 / 5.269862 (0.772721) | 3.957193 / 4.565676 (-0.608484) | 0.066647 / 0.424275 (-0.357628) | 0.008893 / 0.007607 (0.001285) | 0.570137 / 0.226044 (0.344093) | 5.687126 / 2.268929 (3.418198) | 3.137605 / 55.444624 (-52.307019) | 2.655979 / 6.876477 (-4.220498) | 2.893338 / 2.142072 (0.751265) | 0.698388 / 4.805227 (-4.106840) | 0.154897 / 6.500664 (-6.345767) | 0.071208 / 0.075469 (-0.004261) |\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) | 1.619346 / 1.841788 (-0.222441) | 22.782510 / 8.074308 (14.708202) | 16.317395 / 10.191392 (6.126003) | 0.197630 / 0.680424 (-0.482794) | 0.021795 / 0.534201 (-0.512406) | 0.466982 / 0.579283 (-0.112302) | 0.468609 / 0.434364 (0.034245) | 0.574380 / 0.540337 (0.034043) | 0.759827 / 1.386936 (-0.627109) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c1c5d8268ae59a0dcaea47da825e87c3f9528b4 \"CML watermark\")\n"
] | 2023-07-26T15:27:49Z
| 2023-07-26T16:24:43Z
| 2023-07-26T16:14:34Z
|
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PR_kwDODunzps5rENat
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|
Fix issue with case sensitivity when loading dataset from local cache
|
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"I also need this feature for [\"Cnam-LMSSC/vibravox \"](https://huggingface.co/datasets/Cnam-LMSSC/vibravox)\r\n\r\n\r\nEDIT: Upgrading to `2.19.0` fixed my problem thanks to [this PR](https://github.com/huggingface/datasets/pull/6754)"
] | 2024-03-28T14:52:35Z
| 2024-04-20T12:16:45Z
| null |
NONE
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When a dataset with upper-cases in its name is first loaded using `load_dataset()`, the local cache directory is created with all lowercase letters.
However, upon subsequent loads, the current version attempts to locate the cache directory using the dataset's original name, which includes uppercase letters. This discrepancy can lead to confusion and, particularly in offline mode, results in errors.
### Reproduce
```bash
~$ python
Python 3.9.19 (main, Mar 21 2024, 17:11:28)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> dataset = load_dataset("locuslab/TOFU", "full")
>>> quit()
~$ export HF_DATASETS_OFFLINE=1
~$ python
Python 3.9.19 (main, Mar 21 2024, 17:11:28)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> dataset = load_dataset("locuslab/TOFU", "full")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "xxxxxx/anaconda3/envs/llm/lib/python3.9/site-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "xxxxxx/anaconda3/envs/llm/lib/python3.9/site-packages/datasets/load.py", line 2228, in load_dataset_builder
dataset_module = dataset_module_factory(
File "xxxxxx/anaconda3/envs/llm/lib/python3.9/site-packages/datasets/load.py", line 1871, in dataset_module_factory
raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couldn't reach the Hugging Face Hub for dataset 'locuslab/TOFU': Offline mode is enabled.
>>>
```
I fix this issue by lowering the dataset name (`.lower()`) when generating cache_dir.
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I_kwDODunzps5glf7m
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Custom split names
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"Hi!\r\n\r\nYou can also use names other than \"train\", \"validation\" and \"test\". As an example, check the [script](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/blob/e095840f23f3dffc1056c078c2f9320dad9ca74d/common_voice_11_0.py#L139) of the Common Voice 11 dataset. "
] | 2023-03-12T17:21:43Z
| 2023-03-24T14:13:00Z
| 2023-03-24T14:13:00Z
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### Feature request
Hi,
I participated in multiple NLP tasks where there are more than just train, test, validation splits, there could be multiple validation sets or test sets. But it seems currently only those mentioned three splits supported. It would be nice to have the support for more splits on the hub. (currently i can have more splits when I am loading datasets from urls, but not hub)
### Motivation
Easier access to more splits
### Your contribution
No
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I_kwDODunzps6bV-oN
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Adding column with dict struction when mapping lead to wrong order
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### Describe the bug
in `map()` function, I want to add a new column with a dict structure.
```
def map_fn(example):
example['text'] = {'user': ..., 'assistant': ...}
return example
```
However this leads to a wrong order `{'assistant':..., 'user':...}` in the dataset.
Thus I can't concatenate two datasets due to the different feature structures.
[Here](https://colab.research.google.com/drive/1zeaWq9Ith4DKWP_EiBNyLfc8S8I68LyY?usp=sharing) is a minimal reproducible example
This seems an issue in low level pyarrow library instead of datasets, however, I think datasets should allow concatenate two datasets actually in the same structure.
### Steps to reproduce the bug
[Here](https://colab.research.google.com/drive/1zeaWq9Ith4DKWP_EiBNyLfc8S8I68LyY?usp=sharing) is a minimal reproducible example
### Expected behavior
two datasets could be concatenated.
### Environment info
N/A
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I_kwDODunzps5sxbb5
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|
Changing pixel values of images in the Winoground dataset
|
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[] | 2023-07-27T17:55:35Z
| 2023-07-27T17:55:35Z
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Hi, as I followed the instructions, with lasted "datasets" version:
"
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
"
I got slightly different datasets in colab and in my hpc environment. Specifically, the pixel values of images are slightly different.
I thought it was due to the package version difference, but today's morning I found out that my winoground dataset in colab became the same with the one in my hpc environment. The dataset in colab can produce the correct result but now it is gone as well.
Can you help me with this? What causes the datasets to have the wrong pixel values?
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Easy way to create a Kaggle dataset from a Huggingface dataset?
|
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[
"Hi @hrbigelow , I'm no expert for such a question so I'll ping @lhoestq from the `datasets` library (also this issue could be moved there if someone with permission can do it :) )",
"Hi ! Many datasets are made of several files, and how they are parsed often requires a python script. Because of that, datasets like wmt14 are not available as a single file on HF. Though you can create this file using `datasets`:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"wmt14\", \"de-en\", split=\"train\")\r\n\r\nds.to_json(\"wmt14-train.json\")\r\n# OR to parquet, which is compressed:\r\n# ds.to_parquet(\"wmt14-train.parquet\")\r\n```\r\n\r\nWe are also working on providing parquet exports for all datasets, but wmt14 is not supported yet (we're rolling it out for datasets <1GB first). They're usually available in the `refs/convert/parquet` branch (empty for wmt14):\r\n\r\n<img width=\"267\" alt=\"image\" src=\"https://user-images.githubusercontent.com/42851186/235878909-7339f5a4-be19-4ada-85d8-8a50d23acf35.png\">\r\n",
"also cc @nateraw for visibility on this (and cc @osanseviero too)",
"I've requested support for creating a Kaggle dataset from an imported HF dataset repo on their \"forum\" here: https://www.kaggle.com/discussions/product-feedback/427142 (upvotes appreciated 🙂)"
] | 2023-05-02T21:43:33Z
| 2023-07-26T16:13:31Z
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I'm not sure whether this is more appropriately addressed with HuggingFace or Kaggle. I would like to somehow directly create a Kaggle dataset from a HuggingFace Dataset.
While Kaggle does provide the option to create a dataset from a URI, that URI must point to a single file. For example:
Is there some mechanism from huggingface to represent a dataset (such as that from `load_dataset('wmt14', 'de-en', split='train')` as a single file? Or, some other way to get that into a Kaggle dataset so that I can use the huggingface `datasets` module to process and consume it inside of a Kaggle notebook?
Thanks in advance!
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|
Performance regression in `dataset.filter`
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[
"Thanks for reporting, I'll fix the regression today",
"I just released `datasets` 3.3.1 with a fix, let me know if it's good now :)",
"@lhoestq it fixed the issue.\n\nThis was (very) fast, thank you very much!"
] | 2025-02-16T22:19:14Z
| 2025-02-17T17:46:06Z
| 2025-02-17T14:28:48Z
|
NONE
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### Describe the bug
We're filtering dataset of ~1M (small-ish) records. At some point in the code we do `dataset.filter`, before (including 3.2.0) it was taking couple of seconds, and now it takes 4 hours.
We use 16 threads/workers, and stack trace at them look as follows:
```
Traceback (most recent call last):
File "/python/lib/python3.12/site-packages/multiprocess/process.py", line 314, in _bootstrap
self.run()
File "/python/lib/python3.12/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/python/lib/python3.12/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 678, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3511, in _map_single
for i, batch in iter_outputs(shard_iterable):
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3461, in iter_outputs
yield i, apply_function(example, i, offset=offset)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3390, in apply_function
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 6416, in get_indices_from_mask_function
indices_array = indices_mapping.column(0).take(indices_array)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 1079, in pyarrow.lib.ChunkedArray.take
File "/python/lib/python3.12/site-packages/pyarrow/compute.py", line 458, in take
def take(data, indices, *, boundscheck=True, memory_pool=None):
```
### Steps to reproduce the bug
1. Save dataset of 1M records in arrow
2. Filter it with 16 threads
3. Watch it take too long
### Expected behavior
Filtering done fast
### Environment info
datasets 3.3.0, python 3.12
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TensorDataset format does not work with Trainer from transformers
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[
"I figured it out. I found that `Trainer` does not work with TensorDataset even though the document says it uses it. Instead, I ended up creating a dictionary and converting it to a dataset using `dataset.Dataset.from_dict()`.\r\n\r\nI will leave this post open for a while. If someone knows a better approach, please leave a comment.",
"Only issues directly related to the HF datasets library should be reported here. ~So, I'm transferring this issue to the `transformers` repo.~ I'm not a `transformers` maintainer, so GitHub doesn't let me transfer it there :(. This means you need to do it manually."
] | 2023-11-01T10:09:54Z
| 2023-11-29T16:31:08Z
| 2023-11-29T16:31:08Z
|
NONE
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### Describe the bug
The model was built to do fine tunning on BERT model for relation extraction.
trainer.train() returns an error message ```TypeError: vars() argument must have __dict__ attribute``` when it has `train_dataset` generated from `torch.utils.data.TensorDataset`
However, in the document, the required data format is `torch.utils.data.TensorDataset`.
Transformers trainer is supposed to accept the train_dataset in the format of torch.utils.data.TensorDataset, but it returns error message *"TypeError: vars() argument must have __dict__ attribute"*
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-30-5df728c929a2> in <cell line: 1>()
----> 1 trainer.train()
2 trainer.evaluate(test_dataset)
9 frames
/usr/local/lib/python3.10/dist-packages/transformers/data/data_collator.py in <listcomp>(.0)
107
108 if not isinstance(features[0], Mapping):
--> 109 features = [vars(f) for f in features]
110 first = features[0]
111 batch = {}
TypeError: vars() argument must have __dict__ attribute
```
### Steps to reproduce the bug
Create train_dataset using `torch.utils.data.TensorDataset`, for instance,
```train_dataset = torch.utils.data.TensorDataset(train_input_ids, train_attention_masks, train_labels)```
Feed this `train_dataset` to your trainer and run trainer.train
```
trainer = Trainer(model,
training_args,
train_dataset=train_dataset,
eval_dataset=dev_dataset,
compute_metrics=compute_metrics,
)
```
### Expected behavior
Trainer should start training
### Environment info
It is running on Google Colab
- `datasets` version: 2.14.6
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.17.3
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
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[
"Are you able to reproduce this? My example is small enough that it should be easy to try.",
"Hi! Thanks for reporting and providing a reproducible example. Indeed, by default, `datasets` performs an expensive cast on the values returned by `map` to convert them to one of the types supported by PyArrow (the underlying storage format used by `datasets`). This cast is not needed on NumPy arrays as PyArrow supports them natively, so one way to make this transform faster is to add `return_tensors=\"np\"` to the tokenizer call. \r\n\r\nI think we should mention this in the docs (cc @stevhliu)",
"I tested this tokenize function and indeed noticed a casting. However it seems to only concerns the `offset_mapping` field, which contains a list of tuples, that is converted to a list of lists. Since `pyarrow` also supports tuples, we actually don't need to convert the tuples to lists. \r\n\r\nI think this can be changed here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/src/datasets/features/features.py#L382-L383\r\n\r\n```diff\r\n- if isinstance(obj, list): \r\n+ if isinstance(obj, (list, tuple)): \r\n```\r\n\r\nand here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/src/datasets/features/features.py#L386-L387\r\n\r\n```diff\r\n- return obj if isinstance(obj, list) else [], isinstance(obj, tuple)\r\n+ return obj, False\r\n```\r\n\r\n@srobertjames can you try applying these changes and let us know if it helps ? If so, feel free to open a Pull Request to contribute this improvement if you want :)",
"Wow, adding `return_tensors=\"np\"` sped up my example by a **factor 17x** of and completely eliminated the casting! I'd recommend not only to document it, but to make that the default.\r\n\r\nThe code at https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb does not specify `return_tensors=\"np\"` but yet avoids the casting penalty. How does it do that? (The ntbk seems to do `return_overflowing_tokens=True, return_offsets_mapping=True,`).\r\n\r\nAlso, surprisingly enough, using `return_tensors=\"pt\"` (which is my eventual application) yields this error:\r\n```\r\nTypeError: Provided `function` which is applied to all elements of table returns a `dict` of types \r\n[<class 'torch.Tensor'>, <class 'torch.Tensor'>, <class 'torch.Tensor'>, <class 'torch.Tensor'>]. \r\nWhen using `batched=True`, make sure provided `function` returns a `dict` of types like \r\n`(<class 'list'>, <class 'numpy.ndarray'>)`.\r\n```",
"Setting the output to `\"np\"` makes the whole pipeline fast because it moves the data buffers from rust to python to arrow using zero-copy, and also because it does eliminate the casting completely ;)\r\n\r\nHave you had a chance to try eliminating the tuple casting using the trick above ?",
"@lhoestq I just benchmarked the two edits to `features.py` above, and they appear to solve the problem, bringing my original example to within 20% the speed of the output `\"np\"` example. Nice!\r\n\r\nFor a pull request, do you suggest simply following https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md ?",
"Cool ! Sure feel free to follow these instructions to open a PR :) thanks !",
"#take",
"Resolved via https://github.com/huggingface/datasets/pull/4993."
] | 2022-07-12T15:09:58Z
| 2022-10-03T13:01:04Z
| 2022-10-03T13:01:03Z
|
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## Describe the bug
`Dataset.map`, when fed a Huggingface Tokenizer as its map func, can sometimes spend huge amounts of time doing casts. A minimal example follows.
Not all usages suffer from this. For example, I profiled the preprocessor at https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb , and it did _not_ have this problem. However, I'm at a loss to figure out how it avoids it, as the example below is simple and minimal and still has this problem.
This casting, where it occurs, causes the `Dataset.map` to run approximately 7x slower than it runs for code which does not cause this casting.
This may be related to https://github.com/huggingface/datasets/issues/1046 . However, the tokenizer is _not_ set to return Tensors.
## Steps to reproduce the bug
A minimal, self-contained example to reproduce is below:
```python
import transformers
from transformers import AutoTokenizer
from datasets import load_dataset
import torch
import cProfile
pretrained = 'distilbert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(pretrained)
squad = load_dataset('squad')
squad_train = squad['train']
squad_tiny = squad_train.select(range(5000))
assert isinstance(tokenizer, transformers.PreTrainedTokenizerFast)
def tokenize(ds):
tokens = tokenizer(text=ds['question'],
text_pair=ds['context'],
add_special_tokens=True,
padding='max_length',
truncation='only_second',
max_length=160,
stride=32,
return_overflowing_tokens=True,
return_offsets_mapping=True,
)
return tokens
cmd = 'squad_tiny.map(tokenize, batched=True, remove_columns=squad_tiny.column_names)'
cProfile.run(cmd, sort='tottime')
```
## Actual results
The code works, but takes 10-25 sec per batch (about 7x slower than non-casting code), with the following profile. Note that `_cast_to_python_objects` is the culprit.
```
63524075 function calls (58206482 primitive calls) in 121.836 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
5274034/40 68.751 0.000 111.060 2.776 features.py:262(_cast_to_python_objects)
42223832 24.077 0.000 33.310 0.000 {built-in method builtins.isinstance}
16338/20 5.121 0.000 111.053 5.553 features.py:361(<listcomp>)
5274135 4.747 0.000 4.749 0.000 {built-in method _abc._abc_instancecheck}
80/40 4.731 0.059 116.292 2.907 {pyarrow.lib.array}
5274135 4.485 0.000 9.234 0.000 abc.py:96(__instancecheck__)
2661564/2645196 2.959 0.000 4.298 0.000 features.py:1081(_check_non_null_non_empty_recursive)
5 2.786 0.557 2.786 0.557 {method 'encode_batch' of 'tokenizers.Tokenizer' objects}
2668052 0.930 0.000 0.930 0.000 {built-in method builtins.len}
5000 0.930 0.000 0.938 0.000 tokenization_utils_fast.py:187(_convert_encoding)
5 0.750 0.150 0.808 0.162 {method 'to_pydict' of 'pyarrow.lib.Table' objects}
1 0.444 0.444 121.749 121.749 arrow_dataset.py:2501(_map_single)
40 0.375 0.009 116.291 2.907 arrow_writer.py:151(__arrow_array__)
10 0.066 0.007 0.066 0.007 {method 'write_batch' of 'pyarrow.lib._CRecordBatchWriter' objects}
1 0.060 0.060 121.835 121.835 fingerprint.py:409(wrapper)
11387/5715 0.049 0.000 0.175 0.000 {built-in method builtins.getattr}
36 0.049 0.001 0.049 0.001 {pyarrow._compute.call_function}
15000 0.040 0.000 0.040 0.000 _collections_abc.py:719(__iter__)
3 0.023 0.008 0.023 0.008 {built-in method _imp.create_dynamic}
77 0.020 0.000 0.020 0.000 {built-in method builtins.dir}
37 0.019 0.001 0.019 0.001 socket.py:543(send)
15 0.017 0.001 0.017 0.001 tokenization_utils_fast.py:460(<listcomp>)
432/421 0.015 0.000 0.024 0.000 traitlets.py:1388(_notify_observers)
5000 0.015 0.000 0.018 0.000 _collections_abc.py:672(keys)
51 0.014 0.000 0.042 0.001 traitlets.py:276(getmembers)
5 0.014 0.003 3.775 0.755 tokenization_utils_fast.py:392(_batch_encode_plus)
3/1 0.014 0.005 0.035 0.035 {built-in method _imp.exec_dynamic}
5 0.012 0.002 0.950 0.190 tokenization_utils_fast.py:438(<listcomp>)
31626 0.012 0.000 0.012 0.000 {method 'append' of 'list' objects}
1532/1001 0.011 0.000 0.189 0.000 traitlets.py:643(get)
5 0.009 0.002 3.796 0.759 arrow_dataset.py:2631(apply_function_on_filtered_inputs)
51 0.009 0.000 0.062 0.001 traitlets.py:1766(traits)
5 0.008 0.002 3.784 0.757 tokenization_utils_base.py:2632(batch_encode_plus)
368 0.007 0.000 0.044 0.000 traitlets.py:1715(_get_trait_default_generator)
26 0.007 0.000 0.022 0.001 traitlets.py:1186(setup_instance)
51 0.006 0.000 0.010 0.000 traitlets.py:1781(<listcomp>)
80/32 0.006 0.000 0.052 0.002 table.py:1758(cast_array_to_feature)
684 0.006 0.000 0.007 0.000 {method 'items' of 'dict' objects}
4344/1794 0.006 0.000 0.192 0.000 traitlets.py:675(__get__)
...
```
## Environment info
I observed this on both Google colab and my local workstation:
### Google colab
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
### Local
- `datasets` version: 2.3.2
- Platform: Windows-7-6.1.7601-SP1
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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PR_kwDODunzps5Zup2f
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fix typo in Audio dataset documentation
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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) | 0.006655 / 0.011353 (-0.004698) | 0.004115 / 0.011008 (-0.006893) | 0.083895 / 0.038508 (0.045387) | 0.072770 / 0.023109 (0.049661) | 0.311401 / 0.275898 (0.035503) | 0.341079 / 0.323480 (0.017599) | 0.005488 / 0.007986 (-0.002497) | 0.003530 / 0.004328 (-0.000799) | 0.064691 / 0.004250 (0.060441) | 0.053096 / 0.037052 (0.016044) | 0.314969 / 0.258489 (0.056480) | 0.358245 / 0.293841 (0.064404) | 0.030789 / 0.128546 (-0.097757) | 0.008868 / 0.075646 (-0.066779) | 0.288022 / 0.419271 (-0.131249) | 0.052092 / 0.043533 (0.008559) | 0.310061 / 0.255139 (0.054922) | 0.345369 / 0.283200 (0.062170) | 0.024100 / 0.141683 (-0.117582) | 1.520573 / 1.452155 (0.068418) | 1.593750 / 1.492716 (0.101033) |\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) | 0.242520 / 0.018006 (0.224514) | 0.567963 / 0.000490 (0.567473) | 0.003183 / 0.000200 (0.002983) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029473 / 0.037411 (-0.007939) | 0.083012 / 0.014526 (0.068486) | 0.262386 / 0.176557 (0.085830) | 0.155131 / 0.737135 (-0.582004) | 0.099880 / 0.296338 (-0.196458) |\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) | 0.382388 / 0.215209 (0.167179) | 3.816538 / 2.077655 (1.738884) | 1.863422 / 1.504120 (0.359302) | 1.694652 / 1.541195 (0.153457) | 1.738738 / 1.468490 (0.270248) | 0.477073 / 4.584777 (-4.107704) | 3.539244 / 3.745712 (-0.206468) | 3.238469 / 5.269862 (-2.031392) | 2.026154 / 4.565676 (-2.539523) | 0.056111 / 0.424275 (-0.368164) | 0.007615 / 0.007607 (0.000008) | 0.460620 / 0.226044 (0.234576) | 4.596383 / 2.268929 (2.327455) | 2.348645 / 55.444624 (-53.095979) | 1.977465 / 6.876477 (-4.899011) | 2.222828 / 2.142072 (0.080755) | 0.588065 / 4.805227 (-4.217162) | 0.132175 / 6.500664 (-6.368489) | 0.061322 / 0.075469 (-0.014147) |\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) | 1.260623 / 1.841788 (-0.581164) | 19.976475 / 8.074308 (11.902167) | 14.346488 / 10.191392 (4.155096) | 0.145614 / 0.680424 (-0.534810) | 0.018309 / 0.534201 (-0.515892) | 0.393644 / 0.579283 (-0.185639) | 0.405355 / 0.434364 (-0.029009) | 0.458355 / 0.540337 (-0.081982) | 0.630147 / 1.386936 (-0.756789) |\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) | 0.006769 / 0.011353 (-0.004584) | 0.004172 / 0.011008 (-0.006836) | 0.064863 / 0.038508 (0.026355) | 0.076831 / 0.023109 (0.053722) | 0.419391 / 0.275898 (0.143493) | 0.439912 / 0.323480 (0.116432) | 0.006249 / 0.007986 (-0.001737) | 0.003571 / 0.004328 (-0.000757) | 0.064877 / 0.004250 (0.060626) | 0.056023 / 0.037052 (0.018971) | 0.419899 / 0.258489 (0.161410) | 0.459334 / 0.293841 (0.165493) | 0.032217 / 0.128546 (-0.096329) | 0.008628 / 0.075646 (-0.067019) | 0.071089 / 0.419271 (-0.348183) | 0.047463 / 0.043533 (0.003930) | 0.414961 / 0.255139 (0.159822) | 0.431408 / 0.283200 (0.148209) | 0.022406 / 0.141683 (-0.119277) | 1.511890 / 1.452155 (0.059735) | 1.580268 / 1.492716 (0.087551) |\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) | 0.280805 / 0.018006 (0.262799) | 0.553766 / 0.000490 (0.553276) | 0.006155 / 0.000200 (0.005955) | 0.000102 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032980 / 0.037411 (-0.004431) | 0.092981 / 0.014526 (0.078456) | 0.108820 / 0.176557 (-0.067737) | 0.161709 / 0.737135 (-0.575426) | 0.109772 / 0.296338 (-0.186566) |\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) | 0.433659 / 0.215209 (0.218450) | 4.328577 / 2.077655 (2.250923) | 2.316899 / 1.504120 (0.812779) | 2.142645 / 1.541195 (0.601451) | 2.245518 / 1.468490 (0.777028) | 0.489448 / 4.584777 (-4.095329) | 3.630074 / 3.745712 (-0.115638) | 3.322749 / 5.269862 (-1.947112) | 2.062307 / 4.565676 (-2.503370) | 0.058153 / 0.424275 (-0.366122) | 0.007453 / 0.007607 (-0.000154) | 0.507234 / 0.226044 (0.281190) | 5.071830 / 2.268929 (2.802902) | 2.839374 / 55.444624 (-52.605250) | 2.429583 / 6.876477 (-4.446893) | 2.671940 / 2.142072 (0.529868) | 0.588256 / 4.805227 (-4.216972) | 0.135135 / 6.500664 (-6.365530) | 0.060963 / 0.075469 (-0.014506) |\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) | 1.337462 / 1.841788 (-0.504326) | 20.292912 / 8.074308 (12.218604) | 14.871809 / 10.191392 (4.680417) | 0.169214 / 0.680424 (-0.511209) | 0.020450 / 0.534201 (-0.513751) | 0.397094 / 0.579283 (-0.182189) | 0.411623 / 0.434364 (-0.022741) | 0.471560 / 0.540337 (-0.068777) | 0.647293 / 1.386936 (-0.739643) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0a068dbf3b446417ffd89d32857608394ec699e6 \"CML watermark\")\n"
] | 2023-09-06T23:17:24Z
| 2023-10-03T14:18:41Z
| 2023-09-07T15:39:09Z
|
CONTRIBUTOR
| null | null | null |
There is a typo in the section of the documentation dedicated to creating an audio dataset. The Dataset is incorrectly suffixed with a `Config`
https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia/blob/main/librivox-indonesia.py#L59
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fix docstring code example for distributed shuffle
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7166). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-24T14:39:54Z
| 2024-09-24T14:42:41Z
| 2024-09-24T14:40:14Z
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MEMBER
| null | null | null |
close https://github.com/huggingface/datasets/issues/7163
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load_dataset possibly broken for gated datasets?
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"@BradleyHsu",
"Btw, thanks very much for finding the hub rollback temporary fix and bringing the issue to our attention @KhoomeiK!",
"I see the same issue when calling `load_dataset('poloclub/diffusiondb', 'large_random_1k')` with `datasets==2.7.1` and `huggingface-hub=0.11.0`. No issue with `datasets=2.6.1` and `huggingface_hub==0.10.1`.\r\n\r\nhttps://github.com/poloclub/diffusiondb/issues/7",
"I fixed my issue by specifying `repo_type` in `hf_hub_url()`. https://github.com/poloclub/diffusiondb/commit/9eb91c79aaca98b0515a0ce45778b8af65b84652\r\n\r\nI opened a PR on the Winoground's repo: https://huggingface.co/datasets/facebook/winoground/discussions/2",
"This is a bug in the script, indeed. The most robust fix is to use a relative path instead of `hf_hub_url`, which does not depend on `huggingface_hub`'s version 🙂. I've opened a PR here: https://huggingface.co/datasets/facebook/winoground/discussions/3.",
"Awesome, big thanks to both @xiaohk and @mariosasko!",
"so, if i reproduce the bug, what should i do ? with huggingface_hub0.13.3 dataset2.6.1",
"huggingface_hub.utils._validators.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name':\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(ARGS.model_path, trust_remote_code=True)\r\n\r\nPlease handle automatically for local path and repo name inside, otherwise users always get confused about this",
"I think I'm also hitting this error, trying to load a model from a local path."
] | 2022-11-21T21:59:53Z
| 2023-05-27T00:06:14Z
| 2022-11-28T02:50:42Z
|
CONTRIBUTOR
| null | null |
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### Describe the bug
When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:
```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
165 if repo_id.count("/") > 1:
166 raise HFValidationError(
--> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
168 f" '{repo_id}'. Use `repo_type` argument if needed."
169 )
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```
### Steps to reproduce the bug
Install requirements:
```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```
Then:
```
from datasets import load_dataset
auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```
### Expected behavior
Downloading of the datset
### Environment info
Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing
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set dev version
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7462). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-03-17T16:00:53Z
| 2025-03-17T16:03:31Z
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CI is broken: No solution found when resolving dependencies
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See: https://github.com/huggingface/datasets/actions/runs/11046967444/job/30687294297
```
Run uv pip install --system -r additional-tests-requirements.txt --no-deps
× No solution found when resolving dependencies:
╰─▶ Because the current Python version (3.8.18) does not satisfy Python>=3.9
and torchdata==0.10.0a0+1a98f21 depends on Python>=3.9, we can conclude
that torchdata==0.10.0a0+1a98f21 cannot be used.
And because only torchdata==0.10.0a0+1a98f21 is available and
you require torchdata, we can conclude that your requirements are
unsatisfiable.
Error: Process completed with exit code 1.
```
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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==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008798 / 0.011353 (-0.002554) | 0.005313 / 0.011008 (-0.005695) | 0.099234 / 0.038508 (0.060726) | 0.033935 / 0.023109 (0.010826) | 0.306610 / 0.275898 (0.030712) | 0.373151 / 0.323480 (0.049671) | 0.008305 / 0.007986 (0.000320) | 0.004647 / 0.004328 (0.000319) | 0.079984 / 0.004250 (0.075733) | 0.042546 / 0.037052 (0.005493) | 0.355105 / 0.258489 (0.096616) | 0.332769 / 0.293841 (0.038928) | 0.037708 / 0.128546 (-0.090839) | 0.012141 / 0.075646 (-0.063505) | 0.365338 / 0.419271 (-0.053933) | 0.048875 / 0.043533 (0.005343) | 0.301771 / 0.255139 (0.046632) | 0.323301 / 0.283200 (0.040101) | 0.099116 / 0.141683 (-0.042566) | 1.463948 / 1.452155 (0.011793) | 1.563006 / 1.492716 (0.070290) |\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) | 0.219799 / 0.018006 (0.201793) | 0.524126 / 0.000490 (0.523636) | 0.003899 / 0.000200 (0.003699) | 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) | 0.028361 / 0.037411 (-0.009050) | 0.111386 / 0.014526 (0.096860) | 0.125749 / 0.176557 (-0.050807) | 0.167026 / 0.737135 (-0.570109) | 0.132082 / 0.296338 (-0.164257) |\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) | 0.385046 / 0.215209 (0.169837) | 3.933129 / 2.077655 (1.855475) | 1.823395 / 1.504120 (0.319276) | 1.646468 / 1.541195 (0.105273) | 1.658835 / 1.468490 (0.190344) | 0.708300 / 4.584777 (-3.876477) | 4.001478 / 3.745712 (0.255766) | 2.221773 / 5.269862 (-3.048089) | 1.597925 / 4.565676 (-2.967751) | 0.088699 / 0.424275 (-0.335577) | 0.013575 / 0.007607 (0.005968) | 0.520577 / 0.226044 (0.294533) | 5.044313 / 2.268929 (2.775385) | 2.239862 / 55.444624 (-53.204763) | 2.060394 / 6.876477 (-4.816083) | 2.060684 / 2.142072 (-0.081389) | 0.844862 / 4.805227 (-3.960365) | 0.190321 / 6.500664 (-6.310343) | 0.071595 / 0.075469 (-0.003875) |\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) | 1.400048 / 1.841788 (-0.441740) | 15.684159 / 8.074308 (7.609851) | 14.369298 / 10.191392 (4.177906) | 0.164874 / 0.680424 (-0.515550) | 0.033219 / 0.534201 (-0.500982) | 0.449176 / 0.579283 (-0.130107) | 0.456560 / 0.434364 (0.022196) | 0.517978 / 0.540337 (-0.022359) | 0.635467 / 1.386936 (-0.751469) |\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) | 0.007263 / 0.011353 (-0.004089) | 0.005451 / 0.011008 (-0.005558) | 0.078785 / 0.038508 (0.040277) | 0.032656 / 0.023109 (0.009546) | 0.346384 / 0.275898 (0.070486) | 0.390778 / 0.323480 (0.067299) | 0.005848 / 0.007986 (-0.002137) | 0.004565 / 0.004328 (0.000236) | 0.077903 / 0.004250 (0.073652) | 0.048659 / 0.037052 (0.011606) | 0.368629 / 0.258489 (0.110140) | 0.401632 / 0.293841 (0.107791) | 0.038516 / 0.128546 (-0.090030) | 0.011895 / 0.075646 (-0.063752) | 0.089185 / 0.419271 (-0.330086) | 0.049875 / 0.043533 (0.006342) | 0.344771 / 0.255139 (0.089632) | 0.378237 / 0.283200 (0.095038) | 0.099184 / 0.141683 (-0.042498) | 1.505058 / 1.452155 (0.052903) | 1.555330 / 1.492716 (0.062614) |\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) | 0.209132 / 0.018006 (0.191126) | 0.479928 / 0.000490 (0.479438) | 0.005923 / 0.000200 (0.005723) | 0.000113 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029187 / 0.037411 (-0.008224) | 0.117026 / 0.014526 (0.102500) | 0.131834 / 0.176557 (-0.044722) | 0.172797 / 0.737135 (-0.564339) | 0.129098 / 0.296338 (-0.167240) |\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) | 0.450214 / 0.215209 (0.235005) | 4.323950 / 2.077655 (2.246295) | 2.210100 / 1.504120 (0.705980) | 2.058733 / 1.541195 (0.517538) | 1.968191 / 1.468490 (0.499701) | 0.694918 / 4.584777 (-3.889859) | 4.176559 / 3.745712 (0.430846) | 2.118211 / 5.269862 (-3.151651) | 1.410652 / 4.565676 (-3.155024) | 0.093606 / 0.424275 (-0.330669) | 0.013729 / 0.007607 (0.006122) | 0.528463 / 0.226044 (0.302418) | 5.311766 / 2.268929 (3.042837) | 2.522981 / 55.444624 (-52.921644) | 2.177191 / 6.876477 (-4.699285) | 2.211448 / 2.142072 (0.069375) | 0.824334 / 4.805227 (-3.980893) | 0.166642 / 6.500664 (-6.334022) | 0.062774 / 0.075469 (-0.012695) |\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) | 1.367573 / 1.841788 (-0.474215) | 15.913637 / 8.074308 (7.839328) | 13.397411 / 10.191392 (3.206019) | 0.162599 / 0.680424 (-0.517825) | 0.020325 / 0.534201 (-0.513876) | 0.438745 / 0.579283 (-0.140538) | 0.449892 / 0.434364 (0.015528) | 0.556226 / 0.540337 (0.015888) | 0.672661 / 1.386936 (-0.714275) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f810b7011a8a4ab077a1847c024d2d9e267b065 \"CML watermark\")\n"
] | 2023-02-01T09:33:42Z
| 2023-02-02T07:48:09Z
| 2023-02-02T07:40:43Z
|
MEMBER
| null | null | null |
Pin `dill` lower version compatible with `datasets`.
Related to:
- #5487
- #288
Note that the required `dill._dill` module was introduced in dill-2.8.0, however we have heuristically tested that datasets can only be installed with dill>=3.0.0 (otherwise pip hangs indefinitely while preparing metadata for multiprocess-0.70.7)
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PR_kwDODunzps499Fhe
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Update GLUE evaluation metadata
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[
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-08-29T09:43:44Z
| 2022-08-29T14:53:29Z
| 2022-08-29T14:51:18Z
|
MEMBER
| null | null | null |
This PR updates the evaluation metadata for GLUE to:
* Include defaults for all configs except `ax` (which only has a `test` split with no known labels)
* Fix the default split from `test` to `validation` since `test` splits in GLUE have no labels (they're private)
* Fix the `task_id` for some existing defaults
cc @sashavor @douwekiela
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I_kwDODunzps58DeCd
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fix os.listdir return name is empty string
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[] | 2024-01-15T05:34:36Z
| 2024-01-24T10:08:29Z
| 2024-01-24T10:08:29Z
|
CONTRIBUTOR
| null | null |
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### Describe the bug
xlistdir return name is empty string
Overloaded os.listdir
### Steps to reproduce the bug
```python
from datasets.download.streaming_download_manager import xjoin
from datasets.download.streaming_download_manager import xlistdir
config = DownloadConfig(storage_options=options)
manger = StreamingDownloadManager("ILSVRC2012",download_config=config)
input_path = "lakefs://datalab/main/imagenet/ILSVRC2012.zip"
download_files = manger.download_and_extract(input_path)
current_dir = xjoin(download_files,"ILSVRC2012/Images/ILSVRC2012_img_train")
folder_list = xlistdir(current_dir)
```
in xlistdir function
Obj ["name"] ends with "/"
last return ""
### Expected behavior
Obj ["name"] ends with "/"
return folder name
### Environment info
no
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Improve `Create a dataset` tutorial
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"I can work on this. The link to the tutorial seems to be broken though @polinaeterna. ",
"@isunitha98selvan would be great, thank you! which link are you talking about? I think it should work: https://huggingface.co/docs/datasets/create_dataset",
"Hey I don't mind working on this issue. From my understanding, we want to let the reader know that they can build datasets from `csv`, `json/jsonl`, `parquet` and `txt` files in the **folder-based builders** section and include a link to the full guide. Then in the **from local files** section, we just want to list the methods from in-memory data section such as `.from_dict()`. ",
"Hey @polinaeterna, I have a pull request for this issue. Can you review and see if it needs any changes?"
] | 2023-04-28T13:26:22Z
| 2024-07-26T21:16:13Z
| null |
CONTRIBUTOR
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Our [tutorial on how to create a dataset](https://huggingface.co/docs/datasets/create_dataset) is a bit misleading.
1. In **Folder-based builders** section it says that we have two folder-based builders as standard builders, but we also have similar builders (that can be created from directory with data of required format) for `csv`, `json/jsonl`, `parquet` and `txt` files. We have info about these loaders in separate [guide for loading](https://huggingface.co/docs/datasets/loading#local-and-remote-files) but it's worth briefly mentioning them in the beginning tutorial because they are more common and for consistency. Would be helpful to add the link to the full guide.
2. **From local files** section lists methods for creating a dataset from in-memory data which are also described in [loading guide](https://huggingface.co/docs/datasets/loading#inmemory-data).
Maybe we should actually rethink and restructure this tutorial somehow.
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Update create image dataset docs
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-31T17:45:56Z
| 2022-11-02T17:15:22Z
| 2022-11-02T17:13:02Z
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MEMBER
| null | null | null |
Based on @osanseviero and community feedback, it wasn't super clear how to upload a dataset to the Hub after creating something like an image captioning dataset. This PR adds a brief section on how to upload the dataset with `push_to_hub`.
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Swap log messages for symbolic/hard links in tar extractor
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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==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011848 / 0.011353 (0.000495) | 0.006988 / 0.011008 (-0.004020) | 0.138078 / 0.038508 (0.099570) | 0.040310 / 0.023109 (0.017201) | 0.411857 / 0.275898 (0.135959) | 0.509496 / 0.323480 (0.186016) | 0.010695 / 0.007986 (0.002709) | 0.005275 / 0.004328 (0.000946) | 0.107157 / 0.004250 (0.102907) | 0.050987 / 0.037052 (0.013935) | 0.432387 / 0.258489 (0.173898) | 0.495136 / 0.293841 (0.201295) | 0.055273 / 0.128546 (-0.073273) | 0.019573 / 0.075646 (-0.056074) | 0.460356 / 0.419271 (0.041084) | 0.060916 / 0.043533 (0.017383) | 0.426140 / 0.255139 (0.171002) | 0.430461 / 0.283200 (0.147261) | 0.124569 / 0.141683 (-0.017114) | 1.989404 / 1.452155 (0.537250) | 1.942052 / 1.492716 (0.449335) |\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) | 0.287233 / 0.018006 (0.269227) | 0.606056 / 0.000490 (0.605566) | 0.004435 / 0.000200 (0.004235) | 0.000144 / 0.000054 (0.000090) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032353 / 0.037411 (-0.005058) | 0.124237 / 0.014526 (0.109711) | 0.143280 / 0.176557 (-0.033276) | 0.182081 / 0.737135 (-0.555055) | 0.148085 / 0.296338 (-0.148253) |\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) | 0.613550 / 0.215209 (0.398341) | 6.172421 / 2.077655 (4.094766) | 2.466018 / 1.504120 (0.961898) | 2.166433 / 1.541195 (0.625238) | 2.192511 / 1.468490 (0.724021) | 1.248777 / 4.584777 (-3.336000) | 5.746150 / 3.745712 (2.000438) | 3.097184 / 5.269862 (-2.172678) | 2.078176 / 4.565676 (-2.487501) | 0.144351 / 0.424275 (-0.279924) | 0.014830 / 0.007607 (0.007223) | 0.761699 / 0.226044 (0.535655) | 7.713201 / 2.268929 (5.444272) | 3.359647 / 55.444624 (-52.084977) | 2.652595 / 6.876477 (-4.223882) | 2.721952 / 2.142072 (0.579880) | 1.493036 / 4.805227 (-3.312192) | 0.252336 / 6.500664 (-6.248328) | 0.082906 / 0.075469 (0.007436) |\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) | 1.643887 / 1.841788 (-0.197901) | 18.762775 / 8.074308 (10.688466) | 22.003583 / 10.191392 (11.812191) | 0.256361 / 0.680424 (-0.424062) | 0.048048 / 0.534201 (-0.486153) | 0.601971 / 0.579283 (0.022688) | 0.712801 / 0.434364 (0.278438) | 0.684473 / 0.540337 (0.144136) | 0.802566 / 1.386936 (-0.584370) |\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) | 0.010410 / 0.011353 (-0.000943) | 0.006719 / 0.011008 (-0.004289) | 0.132862 / 0.038508 (0.094354) | 0.036973 / 0.023109 (0.013863) | 0.470925 / 0.275898 (0.195027) | 0.502864 / 0.323480 (0.179384) | 0.007447 / 0.007986 (-0.000539) | 0.005629 / 0.004328 (0.001301) | 0.091985 / 0.004250 (0.087734) | 0.057537 / 0.037052 (0.020485) | 0.458362 / 0.258489 (0.199873) | 0.518324 / 0.293841 (0.224483) | 0.056540 / 0.128546 (-0.072007) | 0.021266 / 0.075646 (-0.054380) | 0.448289 / 0.419271 (0.029018) | 0.064211 / 0.043533 (0.020678) | 0.492596 / 0.255139 (0.237457) | 0.495030 / 0.283200 (0.211830) | 0.121858 / 0.141683 (-0.019825) | 1.823821 / 1.452155 (0.371667) | 2.012165 / 1.492716 (0.519449) |\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) | 0.296252 / 0.018006 (0.278245) | 0.601688 / 0.000490 (0.601198) | 0.006369 / 0.000200 (0.006169) | 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) | 0.035821 / 0.037411 (-0.001590) | 0.132722 / 0.014526 (0.118196) | 0.141819 / 0.176557 (-0.034738) | 0.205115 / 0.737135 (-0.532020) | 0.148917 / 0.296338 (-0.147422) |\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) | 0.678207 / 0.215209 (0.462998) | 6.969918 / 2.077655 (4.892263) | 3.077831 / 1.504120 (1.573711) | 2.689296 / 1.541195 (1.148102) | 2.706462 / 1.468490 (1.237972) | 1.249125 / 4.584777 (-3.335652) | 5.793917 / 3.745712 (2.048205) | 3.137565 / 5.269862 (-2.132297) | 2.056880 / 4.565676 (-2.508796) | 0.151918 / 0.424275 (-0.272357) | 0.015029 / 0.007607 (0.007422) | 0.833975 / 0.226044 (0.607930) | 8.575649 / 2.268929 (6.306720) | 3.812115 / 55.444624 (-51.632509) | 3.124219 / 6.876477 (-3.752258) | 3.178645 / 2.142072 (1.036572) | 1.488260 / 4.805227 (-3.316967) | 0.268239 / 6.500664 (-6.232425) | 0.089463 / 0.075469 (0.013993) |\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) | 1.645461 / 1.841788 (-0.196327) | 19.074412 / 8.074308 (11.000104) | 21.626726 / 10.191392 (11.435334) | 0.210525 / 0.680424 (-0.469899) | 0.032166 / 0.534201 (-0.502035) | 0.555572 / 0.579283 (-0.023711) | 0.654667 / 0.434364 (0.220303) | 0.632471 / 0.540337 (0.092133) | 0.756510 / 1.386936 (-0.630426) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6681c36bbaae9b8b1daa3dbbd4a96b35aaae271b \"CML watermark\")\n"
] | 2023-01-23T07:53:38Z
| 2023-01-23T09:40:55Z
| 2023-01-23T08:31:17Z
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MEMBER
| null | null | null |
The log messages do not match their if-condition. This PR swaps them.
Found while investigating:
- #5441
CC: @lhoestq
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Checking that split name is correct happens only after the data is downloaded
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[] | 2023-02-10T19:13:03Z
| 2023-02-10T19:14:50Z
| null |
CONTRIBUTOR
| null | null |
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### Describe the bug
Verification of split names (=indexing data by split) happens after downloading the data. So when the split name is incorrect, users learn about that only after the data is fully downloaded, for large datasets it might take a lot of time.
### Steps to reproduce the bug
Load any dataset with random split name, for example:
```python
from datasets import load_dataset
load_dataset("mozilla-foundation/common_voice_11_0", "en", split="blabla")
```
and the download will start smoothly, despite there is no split named "blabla".
### Expected behavior
Raise error when split name is incorrect.
### Environment info
`datasets==2.9.1.dev0`
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Random 400 Client Error when pushing dataset
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[
"did you ever fix this? I'm experiencing the same",
"I am having the same issue. Even the simple example from the documentation gives me the 400 Error\r\n\r\n\r\n> from datasets import load_dataset\r\n> \r\n> dataset = load_dataset(\"stevhliu/demo\")\r\n> dataset.push_to_hub(\"processed_demo\")\r\n\r\n\r\n`requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/REDACTED/commit/main (Request ID: e-tPnYTiCdB5KPmSL86dQ)`\r\n\r\nI \"fixed\" it by initializing a new virtual environment with only datasets==2.5.2 installed.\r\n\r\nThe workaround consists of saving to disk then loading from disk and pushing to hub but from the new clean virtual environment."
] | 2022-07-12T15:56:44Z
| 2023-02-07T13:54:10Z
| 2023-02-07T13:54:10Z
|
NONE
| null | null |
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## Describe the bug
When pushing a dataset, the client errors randomly with `Bad Request for url:...`.
At the next call, a new parquet file is created for each shard.
The client may fail at any random shard.
## Steps to reproduce the bug
```python
dataset.push_to_hub("ORG/DATASET", private=True, branch="main")
```
## Expected results
Push all the dataset to the Hub with no duplicates.
If it fails, it should retry or fail, but continue from the last failed shard.
## Actual results
```
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
testing.ipynb Cell 29 in <cell line: 1>()
----> [1](testing.ipynb?line=0) dataset.push_to_hub("ORG/DATASET", private=True, branch="main")
File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4297, in Dataset.push_to_hub(self, repo_id, split, private, token, branch, max_shard_size, shard_size, embed_external_files)
4291 warnings.warn(
4292 "'shard_size' was renamed to 'max_shard_size' in version 2.1.1 and will be removed in 2.4.0.",
4293 FutureWarning,
4294 )
4295 max_shard_size = shard_size
-> 4297 repo_id, split, uploaded_size, dataset_nbytes, repo_files, deleted_size = self._push_parquet_shards_to_hub(
4298 repo_id=repo_id,
4299 split=split,
4300 private=private,
4301 token=token,
4302 branch=branch,
4303 max_shard_size=max_shard_size,
4304 embed_external_files=embed_external_files,
4305 )
4306 organization, dataset_name = repo_id.split("/")
4307 info_to_dump = self.info.copy()
File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4195, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, embed_external_files)
4193 shard.to_parquet(buffer)
4194 uploaded_size += buffer.tell()
-> 4195 _retry(
4196 api.upload_file,
4197 func_kwargs=dict(
4198 path_or_fileobj=buffer.getvalue(),
4199 path_in_repo=shard_path_in_repo,
4200 repo_id=repo_id,
4201 token=token,
4202 repo_type="dataset",
4203 revision=branch,
4204 identical_ok=False,
4205 ),
4206 exceptions=HTTPError,
4207 status_codes=[504],
4208 base_wait_time=2.0,
4209 max_retries=5,
4210 max_wait_time=20.0,
4211 )
4212 shards_path_in_repo.append(shard_path_in_repo)
4214 # Cleanup to remove unused files
File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:284, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
282 except exceptions as err:
283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
--> 284 raise err
285 else:
286 sleep_time = min(max_wait_time, base_wait_time * 2**retry) # Exponential backoff
File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:281, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
279 while True:
280 try:
--> 281 return func(*func_args, **func_kwargs)
282 except exceptions as err:
283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1967, in HfApi.upload_file(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, identical_ok, commit_message, commit_description, create_pr)
1957 commit_message = (
1958 commit_message
1959 if commit_message is not None
1960 else f"Upload {path_in_repo} with huggingface_hub"
1961 )
1962 operation = CommitOperationAdd(
1963 path_or_fileobj=path_or_fileobj,
1964 path_in_repo=path_in_repo,
1965 )
-> 1967 pr_url = self.create_commit(
1968 repo_id=repo_id,
1969 repo_type=repo_type,
1970 operations=[operation],
1971 commit_message=commit_message,
1972 commit_description=commit_description,
1973 token=token,
1974 revision=revision,
1975 create_pr=create_pr,
1976 )
1977 if pr_url is not None:
1978 re_match = re.match(REGEX_DISCUSSION_URL, pr_url)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1844, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads)
1836 commit_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/commit/{revision}"
1838 commit_resp = requests.post(
1839 url=commit_url,
1840 headers={"Authorization": f"Bearer {token}"},
1841 json=commit_payload,
1842 params={"create_pr": 1} if create_pr else None,
1843 )
-> 1844 _raise_for_status(commit_resp)
1845 return commit_resp.json().get("pullRequestUrl", None)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:84, in _raise_for_status(request)
76 if request.status_code == 401:
77 # The repo was not found and the user is not Authenticated
78 raise RepositoryNotFoundError(
79 f"401 Client Error: Repository Not Found for url: {request.url}. If the"
80 " repo is private, make sure you are authenticated. (Request ID:"
81 f" {request_id})"
82 )
---> 84 _raise_with_request_id(request)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:95, in _raise_with_request_id(request)
92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str):
93 e.args = (e.args[0] + f" (Request ID: {request_id})",) + e.args[1:]
---> 95 raise e
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:90, in _raise_with_request_id(request)
88 request_id = request.headers.get("X-Request-Id")
89 try:
---> 90 request.raise_for_status()
91 except Exception as e:
92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str):
File ~/.local/lib/python3.9/site-packages/requests/models.py:1021, in Response.raise_for_status(self)
1016 http_error_msg = (
1017 f"{self.status_code} Server Error: {reason} for url: {self.url}"
1018 )
1020 if http_error_msg:
-> 1021 raise HTTPError(http_error_msg, response=self)
HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/ORG/DATASET/commit/main (Request ID: a_F0IQAHJdxGKVRYyu1cF)
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.31
- Python version: 3.9.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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|
Implicit type conversion of lists in to_pandas
|
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[
"I think this behavior comes from PyArrow:\r\n```python\r\nimport pyarrow as pa\r\nt = pa.table({\"a\": [[0]]})\r\nt.to_pandas().a.values[0]\r\n# array([0])\r\n```\r\n\r\nI believe this has to do with zero-copy: you can get a pandas DataFrame without copying the buffers from arrow, and therefore end up with numpy arrays.",
"That's interesting, I guess not much to do here then."
] | 2022-11-09T08:40:18Z
| 2022-11-10T16:12:26Z
| 2022-11-10T16:12:26Z
|
CONTRIBUTOR
| null | null |
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### Describe the bug
```
ds = Dataset.from_list([{'a':[1,2,3]}])
ds.to_pandas().a.values[0]
```
Results in `array([1, 2, 3])` -- a rather unexpected conversion of types which made downstream tools expecting lists not happy.
### Steps to reproduce the bug
See snippet
### Expected behavior
Keep the original type
### Environment info
datasets 2.6.1
python 3.8.10
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[
"Hi ! From the `adlfs` docs, there are two filesystems you can use:\r\n> To use the Gen1 filesystem:\r\n> - known_implementations[‘adl’] = {‘class’: ‘adlfs.AzureDatalakeFileSystem’}\r\n> \r\n> To use the Gen2 filesystem:\r\n> - known_implementations[‘abfs’] = {‘class’: ‘adlfs.AzureBlobFileSystem’}\r\n\r\nIf I'm not mistaken you're using the second one - so you should use `abfs://` instead of `adl://`, and also run this at the beginning of your script:\r\n```python\r\nfrom fsspec.registry import known_implementations\r\nknown_implementations['abfs'] = {'class': 'adlfs.AzureDatalakeFileSystem'}\r\n```\r\n\r\n",
"Thank you @lhoestq . Great call.\r\nUsing the default class from `known_implementations` dict solved my problem\r\n```\r\nknown_implementations[‘abfs’] = {‘class’: ‘adlfs.AzureBlobFileSystem’}\r\n```\r\nI'm closing this issue.",
"> Thank you @lhoestq . Great call. Using the default class from `known_implementations` dict solved my problem\r\n> \r\n> ```\r\n> known_implementations[‘abfs’] = {‘class’: ‘adlfs.AzureBlobFileSystem’}\r\n> ```\r\n> \r\n> I'm closing this issue.\r\n\r\nHi so here `Saving serialized datasets\r\n\r\nAfter you have processed your dataset, you can save it to your cloud storage with [Dataset.save_to_disk()](https://huggingface.co/docs/datasets/v2.17.0/en/package_reference/main_classes#datasets.Dataset.save_to_disk):` what is the encoded dataset I have failed to save it ",
"Uploading failed ? Did you get an error message ?"
] | 2022-10-25T00:43:18Z
| 2024-02-15T09:48:36Z
| 2022-11-17T23:37:08Z
|
NONE
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### Describe the bug
When using the DatasetBuilder method with the credentials for the cloud storage Azure Data Lake (adl) Gen2, the following error is showed:
```
Traceback (most recent call last):
File "download_hf_dataset.py", line 143, in <module>
main()
File "download_hf_dataset.py", line 102, in main
builder.download_and_prepare(save_dir, storage_options=storage_options, max_shard_size="250MB", file_format="parquet")
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/datasets/builder.py", line 671, in download_and_prepare
fs_token_paths = fsspec.get_fs_token_paths(output_dir, storage_options=storage_options)
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/fsspec/core.py", line 639, in get_fs_token_paths
fs = cls(**options)
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/fsspec/spec.py", line 76, in __call__
obj = super().__call__(*args, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'account_name'
```
If I don't pass the storage_options argument (leave it as None), it requires the credentials used in ADL Gen 1:
`TypeError: __init__() missing 3 required positional arguments: 'tenant_id', 'client_id', and 'client_secret'`
Thus, it is not possible to download a dataset from the cloud using Azure Data Lake (adl) Gen2.
### Steps to reproduce the bug
Assuming that you have an account on Azure and at Storage Account that can be used for reproduce:
1. Create a dict with the format to connect to Azure Data Lake Gen 2
```
storage_options = {"account_name": ACCOUNT_NAME, "account_key": ACCOUNT_KEY) # gen 2 filesystem
```
2. Create a dataset builder for any HF hosted dataset
```
builder = load_dataset_builder(dataset_name)
```
3. Try to download the dataset passing the storage_options as an argument
```
save_dir = 'adl://my_save_dir'
builder.download_and_prepare(save_dir, storage_options=storage_options, max_shard_size="250MB", file_format="parquet")
```
### Expected behavior
Not seeing the error mentioned above and being able to download the dataset to the provided path on ADL
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1
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[] | 2024-02-19T09:47:37Z
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`Node.js` 16 GitHub Actions are deprecated. See: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/
We should update them to Node 20.
See warnings in our CI, e.g.: https://github.com/huggingface/datasets/actions/runs/7957295009?pr=6678
> Node.js 16 actions are deprecated. Please update the following actions to use Node.js 20: actions/checkout@v3, actions/setup-python@v4. For more information see: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/.
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support LargeListArray in pyarrow
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4800). All of your documentation changes will be reflected on that endpoint.",
"Hi, thanks for working on this! Can you run `make style` at the repo root to fix the code quality error in CI and add a test?",
"Hi, I have fixed the code quality error and added a test",
"It seems that CI fails due to the lack of memory for allocating a large array, while I pass the test locally.",
"Also, the current implementation of the NumPy-to-PyArrow conversion creates a lot of copies, which is not ideal for large arrays.\r\n\r\nWe can improve performance significantly if we rewrite this part:\r\nhttps://github.com/huggingface/datasets/blob/83f695c14507a3a38e9f4d84612cf49e5f50c153/src/datasets/features/features.py#L1322-L1323\r\n\r\nas\r\n```python\r\n values = pa.array(arr.ravel(), type=type) \r\n```",
"@xwwwwww Feel free to ignore https://github.com/huggingface/datasets/pull/4800#issuecomment-1212280549 and revert the changes you've made to address it. \r\n\r\nWithout copying the array, this would be possible:\r\n```python\r\narr = np.array([\r\n [1, 2, 3],\r\n [4, 5, 6]\r\n])\r\n\r\ndset = Dataset.from_dict({\"data\": [arr]})\r\n\r\narr[0][0] = 100 # this change would be reflected in dset's PyArrow table -> a breaking change and also probably unexpected by the user \r\n```",
"> @xwwwwww Feel free to ignore [#4800 (comment)](https://github.com/huggingface/datasets/pull/4800#issuecomment-1212280549) and revert the changes you've made to address it.\r\n> \r\n> Without copying the array, this would be possible:\r\n> \r\n> ```python\r\n> arr = np.array([\r\n> [1, 2, 3],\r\n> [4, 5, 6]\r\n> ])\r\n> \r\n> dset = Dataset.from_dict({\"data\": [arr]})\r\n> \r\n> arr[0][0] = 100 # this change would be reflected in dset's PyArrow table -> a breaking change and also probably unexpected by the user \r\n> ```\r\n\r\nOh, that makes sense.",
"passed tests in ubuntu while failed in windows",
"@mariosasko Hi, do you have any clue about this failure in windows?",
"Perhaps we can skip the added test on Windows then.\r\n\r\nNot sure if this can help, but the ERR tool available on Windows outputs the following for the returned error code `-1073741819`:\r\n```\r\n# for decimal -1073741819 / hex 0xc0000005\r\n ISCSI_ERR_SETUP_NETWORK_NODE iscsilog.h\r\n# Failed to setup initiator portal. Error status is given in\r\n# the dump data.\r\n STATUS_ACCESS_VIOLATION ntstatus.h\r\n# The instruction at 0x%p referenced memory at 0x%p. The\r\n# memory could not be %s.\r\n USBD_STATUS_DEV_NOT_RESPONDING usb.h\r\n# as an HRESULT: Severity: FAILURE (1), FACILITY_NONE (0x0), Code 0x5\r\n# for decimal 5 / hex 0x5\r\n WINBIO_FP_TOO_FAST winbio_err.h\r\n# Move your finger more slowly on the fingerprint reader.\r\n# as an HRESULT: Severity: FAILURE (1), FACILITY_NULL (0x0), Code 0x5\r\n ERROR_ACCESS_DENIED winerror.h\r\n# Access is denied.\r\n# 5 matches found for \"-1073741819\"\r\n```",
"What's the proper way to skip the added test in windows?\r\nI tried `if platform.system() == 'Linux'`, but the CI test seems stuck",
"@mariosasko Hi, any idea about this :)",
"Hi again! We want to skip the test on Windows but not on Linux. You can use this decorator to do so: \r\n```python\r\n@pytest.mark.skipif(os.name == \"nt\" and (os.getenv(\"CIRCLECI\") == \"true\" or os.getenv(\"GITHUB_ACTIONS\") == \"true\"), reason=\"The Windows CI runner does not have enough RAM to run this test\")\r\n@pytest.mark.parametrize(...)\r\ndef test_large_array_xd_with_np(...):\r\n ...\r\n```",
"> Hi again! We want to skip the test on Windows but not on Linux. You can use this decorator to do so:\r\n> \r\n> ```python\r\n> @pytest.mark.skipif(os.name == \"nt\" and (os.getenv(\"CIRCLECI\") == \"true\" or os.getenv(\"GITHUB_ACTIONS\") == \"true\"), reason=\"The Windows CI runner does not have enough RAM to run this test\")\r\n> @pytest.mark.parametrize(...)\r\n> def test_large_array_xd_with_np(...):\r\n> ...\r\n> ```\r\n\r\nCI on windows still stucks :(",
"@mariosasko Hi, could you please take a look at this issue",
"@mariosasko Hi, all checks have passed, and we are finally ready to merge this PR :)",
"@lhoestq @albertvillanova Perhaps other maintainers can take a look and merge this PR :)",
"same issus come from pyarrow.Is there a solution for this?\r\nfile parquet:50GB\r\ndatasets version: 2.14.4\r\npyarrow :12.0.1\r\n\r\nGenerating train split: 0 examples [01:22, ? examples/s]\r\nTraceback (most recent call last):\r\n File \"/opt/conda/lib/python3.10/site-packages/datasets/builder.py\", line 1925, in _prepare_split_single\r\n for _, table in generator:\r\n File \"/opt/conda/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py\", line 79, in _generate_tables\r\n for batch_idx, record_batch in enumerate(\r\n File \"pyarrow/_parquet.pyx\", line 1315, in iter_batches\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\nOSError: List index overflow.",
"when this feature adds to the newest version?",
"LargeListArray support is not ready yet, there is one remaining change:\r\n\r\n> I think the key is to add the large parameter to Sequence and update the functions you modified in this PR to use pa.list_() if large is False, and pa.large_list otherwise",
"Gents, any move on this. Convert largse list of dicts to Datasets is a nightmare and took all RAM possible. Is there any other alternative?\r\n\r\nThanks,\r\nSteve\r\n",
"Arrow large_list is supported since datasets 2.21.0. See: https://github.com/huggingface/datasets/releases/tag/2.21.0\r\n- #7019"
] | 2022-08-08T03:58:46Z
| 2024-09-27T09:54:17Z
| 2024-08-12T14:43:46Z
|
CONTRIBUTOR
| null | null | null |
```python
import numpy as np
import datasets
a = np.zeros((5000000, 768))
res = datasets.Dataset.from_dict({'embedding': a})
'''
File '/home/wenjiaxin/anaconda3/envs/data/lib/python3.8/site-packages/datasets/arrow_writer.py', line 178, in __arrow_array__
out = numpy_to_pyarrow_listarray(data)
File "/home/wenjiaxin/anaconda3/envs/data/lib/python3.8/site-packages/datasets/features/features.py", line 1173, in numpy_to_pyarrow_listarray
offsets = pa.array(np.arange(n_offsets + 1) * step_offsets, type=pa.int32())
File "pyarrow/array.pxi", line 312, in pyarrow.lib.array
File "pyarrow/array.pxi", line 83, in pyarrow.lib._ndarray_to_array
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Integer value 2147483904 not in range: -2147483648 to 2147483647
'''
```
Loading a large numpy array currently raises the error above as the type of offsets is `int32`.
And pyarrow has supported [LargeListArray](https://arrow.apache.org/docs/python/generated/pyarrow.LargeListArray.html) for this case.
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I_kwDODunzps5tb4w6
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Lookahead streaming support?
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"In which format is your dataset? We could expose the `pre_buffer` flag for Parquet to use PyArrow's background thread pool to speed up loading. "
] | 2023-08-04T04:01:52Z
| 2023-08-17T17:48:42Z
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### Feature request
From what I understand, streaming dataset currently pulls the data, and process the data as it is requested.
This can introduce significant latency delays when data is loaded into the training process, needing to wait for each segment.
While the delays might be dataset specific (or even mapping instruction/tokenizer specific)
Is it possible to introduce a `streaming_lookahead` parameter, which is used for predictable workloads (even shuffled dataset with fixed seed). As we can predict in advance what the next few datasamples will be. And fetch them while the current set is being trained.
With enough CPU & bandwidth to keep up with the training process, and a sufficiently large lookahead, this will reduce the various latency involved while waiting for the dataset to be ready between batches.
### Motivation
Faster streaming performance, while training over extra large TB sized datasets
### Your contribution
I currently use HF dataset, with pytorch lightning trainer for RWKV project, and would be able to help test this feature if supported.
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I_kwDODunzps6itILT
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Allow manual configuration of Dataset Viewer for datasets not created with the `datasets` library
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"Hi @diarray-hub , thanks for opening the issue :) Let me ping @lhoestq and @severo from the dataset viewer team :hugs: ",
"amazing :)",
"Hi ! why not modify the manifest.json file directly ? this way users see in the viewer the dataset as is instead which makes it easier to use using e.g. the `datasets` library",
"Can I create and push the dataset with the dataset library while also pushing the dataset directory, mainting its structure and all the files as with git? ",
"(I transferred to the issue to the `datasets` repo as it's not related to `huggingface_hub`)",
"> Can I create and push the dataset with the dataset library while also pushing the dataset directory, mainting its structure and all the files as with git?\r\n\r\nyes push_to_hub simply uploads Parquet files in a directory named \"data\" in the git repository\r\n",
"That's the problem actually, I need that the data stays in the same format and the directory they are in keep the same structure in order to go quick with Nemo training so users of Nvidia's Nemo framework don't need to write any preprocessing code before starting training. That's why I used git instead of push_to_hub so me and other users working with Nemo can just:\r\n1. git clone\r\n2. asr_model.setup_training_data(train_data_config={'manifest_filepath': training_manifest_filepath})\r\n\r\nAnd start training already. It may be not very kind of me to prioritize users of a specific framework but I noticed that it take much more code to convert an huggingFace dataset with the parquet file to Nemo manifest format than the inverse :haha: ",
"Happy to help if you think the Nemo dataset format should be supported in `datasets` (and therefore in the HF Viewer that is based on `datasets`). Maybe the Nemo team could help as well\r\n\r\nThough I'm not sure if there is only one but actually many formats/structure in Nemo depending on the task ?",
"Yeah, you're right Quentin, it depends of the task. This one is for ASR. And, yes maybe they can help. I noticed that they already share their models through HF. Maybe someone in your teams already have a contact point there. Anyway it's not really a big issues since people can easily understand the dataset and its format with the dataset card but it's a little annoying for those who wanna visually explore each features with the viewer as for regular HF datasets",
"In that case I'd recommend you to upload the dataset in Nemo format and \r\n1) add the \"nemo\" tag\r\n2) add how to use the dataset in Nemo in the dataset README.md\r\n\r\nThe viewer is likely to show the audio content by default but without the transcriptions. You can also configure the viewer to show the transcriptions instead (without the audio).",
"I already did, it's just a little bit \"dommage\" (Hope you'll understand, you speak french right? Cause I don't know any english word for this) that I have to choose which one the viewer displays. But it's no problem for the usability of the dataset. Thanks Quentin :+1: ",
"It's \"dommage\" for now, but feel free to ping the Nemo people if you think there is room for making this better together :)\r\n\r\nKinda related, but the `datasets` AudioFolder structure looks similar and simply asks for a `metadata.jsonl` with a field named \"file_name\" to link the transcriptions to the audio files - you could also add this file to your repository to make the viewer show audio + transcripts.\r\n\r\nAlternatively maybe we can expand the AudioFolder configuration to allow you to set the metadata file to be the \"manifest.json\" and the linking field to be \"audio_file_name\" (we just need to agree on something general - not just for Nemo)",
"Right, actually that was my idea when I opened this issues. That's what I suggested, taking my case as an exemple but you should think of a more general approach like adding a field to configure the viewer as you wish in the metadata (in the dataset card) or a config.yaml or json file. With a level of abstraction like the solution I proposed ot even higher abstraction, it would allow for more customizability :)"
] | 2024-12-07T16:37:12Z
| 2024-12-11T11:05:22Z
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#### **Problem Description**
Currently, the Hugging Face Dataset Viewer automatically interprets dataset fields for datasets created with the `datasets` library. However, for datasets pushed directly via `git`, the Viewer:
- Defaults to generic columns like `label` with `null` values if no explicit mapping is provided.
- Does not allow dataset creators to configure field mappings or suppress default fields unless the dataset is recreated and pushed using the `datasets` library.
This creates a limitation for creators who:
- Use custom workflows to prepare datasets (e.g., manifest files with audio-transcription mappings).
- Push large datasets directly via `git` and cannot easily restructure them to conform to the `datasets` library format.
#### **Proposed Solution**
Introduce a feature that allows dataset creators to manually configure the Dataset Viewer behavior for datasets not created with the `datasets` library. This could be achieved by:
1. **Using the YAML Metadata in `README.md`:**
- Add support for defining the dataset's field mappings directly in the `README.md` YAML section.
- Example:
```yaml
viewer:
fields:
- name: "audio"
type: "audio_path" / "text"
source: "manifest['audio']"
- name: "bambara_transcription"
type: "text"
source: "manifest['bambara']"
- name: "french_translation"
type: "text"
source: "manifest['french']"
```
With manifest being a csv or json like format file in the repository so that the viewer understands that it should look for the values of each field in that file.
#### **Benefits**
- Improves flexibility for dataset creators who push datasets via `git`.
- Enhances dataset discoverability and usability on the Hugging Face Hub by allowing creators to present meaningful field mappings without restructuring their data.
- Reduces overhead for creators of large or complex datasets.
#### **Examples of Use Case**
- An audio dataset with transcriptions in multiple languages stored in a `manifest.json` file, where the user wants the Viewer to:
- Display the `audio` column and Explicitly map features that he defined such as `bambara_transcription` and `french_translation` from the manifest.
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don't import soundfile in tests
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7340). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-12-17T16:49:55Z
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Support pyarrow 14.0.0
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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) | 0.007561 / 0.011353 (-0.003792) | 0.004824 / 0.011008 (-0.006184) | 0.110372 / 0.038508 (0.071864) | 0.076767 / 0.023109 (0.053657) | 0.357094 / 0.275898 (0.081196) | 0.420566 / 0.323480 (0.097086) | 0.004753 / 0.007986 (-0.003232) | 0.004734 / 0.004328 (0.000405) | 0.072926 / 0.004250 (0.068675) | 0.058045 / 0.037052 (0.020992) | 0.401109 / 0.258489 (0.142620) | 0.444585 / 0.293841 (0.150744) | 0.046492 / 0.128546 (-0.082055) | 0.013948 / 0.075646 (-0.061698) | 0.305188 / 0.419271 (-0.114083) | 0.063112 / 0.043533 (0.019579) | 0.384711 / 0.255139 (0.129572) | 0.411375 / 0.283200 (0.128175) | 0.048147 / 0.141683 (-0.093536) | 1.632357 / 1.452155 (0.180202) | 1.661021 / 1.492716 (0.168304) |\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) | 0.281104 / 0.018006 (0.263098) | 0.567152 / 0.000490 (0.566662) | 0.007178 / 0.000200 (0.006978) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029337 / 0.037411 (-0.008075) | 0.081644 / 0.014526 (0.067118) | 0.103326 / 0.176557 (-0.073230) | 0.155299 / 0.737135 (-0.581836) | 0.093518 / 0.296338 (-0.202821) |\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) | 0.517979 / 0.215209 (0.302769) | 5.250052 / 2.077655 (3.172397) | 2.220543 / 1.504120 (0.716424) | 1.901087 / 1.541195 (0.359892) | 1.920564 / 1.468490 (0.452073) | 0.766289 / 4.584777 (-3.818488) | 5.130968 / 3.745712 (1.385256) | 4.561874 / 5.269862 (-0.707988) | 2.702808 / 4.565676 (-1.862868) | 0.078929 / 0.424275 (-0.345346) | 0.007834 / 0.007607 (0.000226) | 0.636628 / 0.226044 (0.410583) | 6.309391 / 2.268929 (4.040463) | 2.942180 / 55.444624 (-52.502445) | 2.369557 / 6.876477 (-4.506920) | 2.347528 / 2.142072 (0.205456) | 0.911110 / 4.805227 (-3.894117) | 0.189102 / 6.500664 (-6.311562) | 0.068012 / 0.075469 (-0.007457) |\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) | 1.494431 / 1.841788 (-0.347356) | 22.161476 / 8.074308 (14.087168) | 19.426403 / 10.191392 (9.235011) | 0.211154 / 0.680424 (-0.469270) | 0.030655 / 0.534201 (-0.503546) | 0.440449 / 0.579283 (-0.138834) | 0.526522 / 0.434364 (0.092158) | 0.517494 / 0.540337 (-0.022844) | 0.727387 / 1.386936 (-0.659549) |\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) | 0.008354 / 0.011353 (-0.002999) | 0.006108 / 0.011008 (-0.004900) | 0.069079 / 0.038508 (0.030571) | 0.080402 / 0.023109 (0.057292) | 0.452166 / 0.275898 (0.176268) | 0.440264 / 0.323480 (0.116784) | 0.005942 / 0.007986 (-0.002043) | 0.003397 / 0.004328 (-0.000932) | 0.079856 / 0.004250 (0.075606) | 0.056329 / 0.037052 (0.019276) | 0.424261 / 0.258489 (0.165772) | 0.464362 / 0.293841 (0.170521) | 0.051968 / 0.128546 (-0.076578) | 0.015204 / 0.075646 (-0.060442) | 0.085940 / 0.419271 (-0.333332) | 0.066673 / 0.043533 (0.023140) | 0.436481 / 0.255139 (0.181342) | 0.445285 / 0.283200 (0.162085) | 0.035188 / 0.141683 (-0.106495) | 1.579442 / 1.452155 (0.127288) | 1.686120 / 1.492716 (0.193404) |\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) | 0.319039 / 0.018006 (0.301032) | 0.655080 / 0.000490 (0.654591) | 0.005445 / 0.000200 (0.005245) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028566 / 0.037411 (-0.008845) | 0.092131 / 0.014526 (0.077605) | 0.103654 / 0.176557 (-0.072902) | 0.158082 / 0.737135 (-0.579054) | 0.107520 / 0.296338 (-0.188819) |\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) | 0.573479 / 0.215209 (0.358270) | 5.629751 / 2.077655 (3.552096) | 2.501722 / 1.504120 (0.997602) | 2.156255 / 1.541195 (0.615061) | 2.251296 / 1.468490 (0.782805) | 0.767686 / 4.584777 (-3.817091) | 5.080866 / 3.745712 (1.335154) | 4.353351 / 5.269862 (-0.916510) | 2.818707 / 4.565676 (-1.746970) | 0.082617 / 0.424275 (-0.341658) | 0.008045 / 0.007607 (0.000438) | 0.665462 / 0.226044 (0.439417) | 6.961380 / 2.268929 (4.692452) | 3.308717 / 55.444624 (-52.135907) | 2.664239 / 6.876477 (-4.212238) | 2.782790 / 2.142072 (0.640718) | 0.919567 / 4.805227 (-3.885660) | 0.186731 / 6.500664 (-6.313933) | 0.063437 / 0.075469 (-0.012032) |\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) | 1.668076 / 1.841788 (-0.173712) | 22.720187 / 8.074308 (14.645879) | 19.803359 / 10.191392 (9.611967) | 0.237201 / 0.680424 (-0.443223) | 0.041156 / 0.534201 (-0.493045) | 0.458974 / 0.579283 (-0.120309) | 0.620276 / 0.434364 (0.185912) | 0.544079 / 0.540337 (0.003741) | 0.722715 / 1.386936 (-0.664221) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ed9306b6c512befb721b681fba3222221c8468e \"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) | 0.006882 / 0.011353 (-0.004471) | 0.004238 / 0.011008 (-0.006770) | 0.084042 / 0.038508 (0.045534) | 0.074175 / 0.023109 (0.051065) | 0.308771 / 0.275898 (0.032873) | 0.346300 / 0.323480 (0.022820) | 0.005455 / 0.007986 (-0.002530) | 0.003638 / 0.004328 (-0.000690) | 0.065326 / 0.004250 (0.061076) | 0.056080 / 0.037052 (0.019028) | 0.326324 / 0.258489 (0.067834) | 0.360133 / 0.293841 (0.066292) | 0.031577 / 0.128546 (-0.096969) | 0.008675 / 0.075646 (-0.066971) | 0.288051 / 0.419271 (-0.131221) | 0.052769 / 0.043533 (0.009236) | 0.308689 / 0.255139 (0.053550) | 0.328270 / 0.283200 (0.045070) | 0.025028 / 0.141683 (-0.116655) | 1.520670 / 1.452155 (0.068515) | 1.585229 / 1.492716 (0.092513) |\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) | 0.284078 / 0.018006 (0.266072) | 0.558134 / 0.000490 (0.557644) | 0.015042 / 0.000200 (0.014842) | 0.000429 / 0.000054 (0.000375) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028747 / 0.037411 (-0.008664) | 0.083816 / 0.014526 (0.069290) | 0.207467 / 0.176557 (0.030911) | 0.163527 / 0.737135 (-0.573608) | 0.100148 / 0.296338 (-0.196190) |\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) | 0.376109 / 0.215209 (0.160900) | 3.749639 / 2.077655 (1.671984) | 1.827081 / 1.504120 (0.322961) | 1.662021 / 1.541195 (0.120827) | 1.734655 / 1.468490 (0.266165) | 0.483701 / 4.584777 (-4.101075) | 3.454772 / 3.745712 (-0.290941) | 3.465079 / 5.269862 (-1.804783) | 2.070874 / 4.565676 (-2.494802) | 0.056714 / 0.424275 (-0.367561) | 0.007786 / 0.007607 (0.000179) | 0.455980 / 0.226044 (0.229936) | 4.530612 / 2.268929 (2.261683) | 2.345757 / 55.444624 (-53.098867) | 2.030289 / 6.876477 (-4.846188) | 2.068440 / 2.142072 (-0.073632) | 0.576502 / 4.805227 (-4.228725) | 0.131787 / 6.500664 (-6.368878) | 0.060038 / 0.075469 (-0.015431) |\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) | 1.272225 / 1.841788 (-0.569563) | 19.373635 / 8.074308 (11.299327) | 14.167831 / 10.191392 (3.976439) | 0.166336 / 0.680424 (-0.514088) | 0.018420 / 0.534201 (-0.515781) | 0.387878 / 0.579283 (-0.191405) | 0.413105 / 0.434364 (-0.021259) | 0.458618 / 0.540337 (-0.081720) | 0.639031 / 1.386936 (-0.747905) |\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) | 0.007122 / 0.011353 (-0.004230) | 0.004193 / 0.011008 (-0.006815) | 0.066194 / 0.038508 (0.027686) | 0.077775 / 0.023109 (0.054666) | 0.349780 / 0.275898 (0.073882) | 0.383417 / 0.323480 (0.059937) | 0.006416 / 0.007986 (-0.001570) | 0.003651 / 0.004328 (-0.000677) | 0.064837 / 0.004250 (0.060587) | 0.058012 / 0.037052 (0.020959) | 0.351085 / 0.258489 (0.092596) | 0.387302 / 0.293841 (0.093462) | 0.032447 / 0.128546 (-0.096099) | 0.008636 / 0.075646 (-0.067011) | 0.071962 / 0.419271 (-0.347309) | 0.047839 / 0.043533 (0.004306) | 0.349508 / 0.255139 (0.094369) | 0.361892 / 0.283200 (0.078693) | 0.024129 / 0.141683 (-0.117554) | 1.523828 / 1.452155 (0.071673) | 1.607371 / 1.492716 (0.114655) |\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) | 0.245928 / 0.018006 (0.227922) | 0.567708 / 0.000490 (0.567218) | 0.003789 / 0.000200 (0.003589) | 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) | 0.034107 / 0.037411 (-0.003304) | 0.092539 / 0.014526 (0.078014) | 0.110735 / 0.176557 (-0.065821) | 0.163251 / 0.737135 (-0.573884) | 0.110353 / 0.296338 (-0.185985) |\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) | 0.399992 / 0.215209 (0.184783) | 3.976526 / 2.077655 (1.898872) | 2.056182 / 1.504120 (0.552062) | 1.856624 / 1.541195 (0.315429) | 1.941540 / 1.468490 (0.473050) | 0.484662 / 4.584777 (-4.100115) | 3.548228 / 3.745712 (-0.197484) | 3.352900 / 5.269862 (-1.916962) | 2.056310 / 4.565676 (-2.509366) | 0.056952 / 0.424275 (-0.367323) | 0.007284 / 0.007607 (-0.000323) | 0.473749 / 0.226044 (0.247704) | 4.736510 / 2.268929 (2.467581) | 2.570711 / 55.444624 (-52.873913) | 2.204237 / 6.876477 (-4.672239) | 2.438512 / 2.142072 (0.296439) | 0.575542 / 4.805227 (-4.229685) | 0.129260 / 6.500664 (-6.371404) | 0.057704 / 0.075469 (-0.017765) |\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) | 1.316659 / 1.841788 (-0.525128) | 20.103340 / 8.074308 (12.029032) | 14.488385 / 10.191392 (4.296993) | 0.171841 / 0.680424 (-0.508583) | 0.020148 / 0.534201 (-0.514053) | 0.398456 / 0.579283 (-0.180828) | 0.443516 / 0.434364 (0.009152) | 0.479597 / 0.540337 (-0.060741) | 0.643665 / 1.386936 (-0.743271) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#370be814b0c18769ea8e699e3647fadcf431e6df \"CML watermark\")\n"
] | 2023-11-02T10:25:10Z
| 2023-11-02T15:24:28Z
| 2023-11-02T15:15:44Z
|
MEMBER
| null | null | null |
Support `pyarrow` 14.0.0.
Fix #6377 and fix #6374 (root cause).
This fix is analog to a previous one:
- #6175
|
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I_kwDODunzps52XyXs
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TorchFormatter images (H, W, C) instead of (C, H, W) format
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"Here's a PR for that. https://github.com/huggingface/datasets/pull/6402\r\n\r\nIt's not backward compatible, unfortunately. ",
"Just ran into this working on data lib that's attempting to achieve common interfaces across hf datasets, webdataset, native torch style datasets. The defacto standards for image tensors are numpy == HWC, torch.Tensor == CHW. \r\n\r\nI had to drop use of 'torch' formatting because as is (H, W, C) makes it incompatible with pretty much all standard torch vision processing (torchvision, etc) including model inputs themselves... not sure what the breakage scope would be, but might be worth considering a breaking change since I'm not aware of many use cases where a torch.Tensor image is expected to be in HWC form. And if I set the format to 'torch', I'd expect to be able to apply torchvision transforms, etc directly to the output...\r\n\r\nEDIT: For 'torch' output to be compatible with torch conventions (namely torchvision for images), should follow this https://pytorch.org/vision/0.17/transforms.html#supported-input-types-and-conventions\r\n\r\nattn @lhoestq \r\n\r\n",
"We can define something like `.with_format(\"torch\", image_data_format=\"channels_first\")` and recommend using this in the docs maybe ? also cc @NielsRogge ",
"Sounds good to me. I guess it's not allowed to use the channels first format by default for backwards compatibility purposes?",
"This works, but am wondering how widespread the use of the function is for image datasets? My hunch would be that it's not used widely enough with image datasets to favour backwards compat (keeping default channels_last) over clumsiness of needing this to be 'correct' for typical use.. but don't have the data to back that up.",
"I see. I just checked in the HF libraries and it shouldn't break anything. And to be consistent with them we should actually use C H W. For example `transformers` image processors use C H W by default too.\r\n\r\nSo I'm ok with doing a breaking change to make it consistent with `transformers`, `torchvision`, etc.",
"Since it is quite connected, the proposed PR #6402 will not work for monochrome `PIL` images since they only have 2 dimensions as `numpy `arrays. [Torchvision ](https://pytorch.org/vision/stable/_modules/torchvision/transforms/functional.html#pil_to_tensor) adds a channel before permuting. Would that make sense here as well?",
"@Modexus yes, indeed that would make sense as torch expects 1, H, W for monochrome, not H,W as you'd often see in numpy (via PIL), OpenCV, etc.\r\n\r\nThe reference should be the torchvision fn https://pytorch.org/vision/main/_modules/torchvision/transforms/functional.html#pil_to_tensor",
"My PR now should handle monochrome PIL image. Thanks for the heads up :)"
] | 2023-11-09T16:02:15Z
| 2024-04-11T12:40:16Z
| 2024-04-11T12:40:16Z
|
CONTRIBUTOR
| null | null |
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### Describe the bug
Using .set_format("torch") leads to images having shape (H, W, C), the same as in numpy.
However, pytorch normally uses (C, H, W) format.
Maybe I'm missing something but this makes the format a lot less useful as I then have to permute it anyways.
If not using the format it is possible to directly use torchvision transforms but any non-transformed value will not be a tensor.
Is there a reason for this choice?
### Steps to reproduce the bug
```python
from datasets import Dataset, Features, Audio, Image
images = ["path/to/image.png"] * 10
features = Features({"image": Image()})
ds = Dataset.from_dict({"image": images}, features=features)
ds = ds.with_format("torch")
ds[0]["image"].shape
```
```python
torch.Size([512, 512, 4])
```
### Expected behavior
```python
from datasets import Dataset, Features, Audio, Image
images = ["path/to/image.png"] * 10
features = Features({"image": Image()})
ds = Dataset.from_dict({"image": images}, features=features)
ds = ds.with_format("torch")
ds[0]["image"].shape
```
```python
torch.Size([4, 512, 512])
```
### Environment info
- `datasets` version: 2.14.6
- Platform: Linux-6.5.9-100.fc37.x86_64-x86_64-with-glibc2.31
- Python version: 3.11.6
- Huggingface_hub version: 0.18.0
- PyArrow version: 14.0.1
- Pandas version: 2.1.2
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load_dataset("squad") doesn't work in 2.7.1 and 2.10.1
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[
"Thanks for reporting, @yaseen157.\r\n\r\nCould you please give the complete error stack trace?",
"I am not able to reproduce your issue: the dataset loads perfectly on my local machine and on a Colab notebook: https://colab.research.google.com/drive/1Fbdoa1JdNz8DOdX6gmIsOK1nCT8Abj4O?usp=sharing\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"squad\")\r\nDownloading builder script: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.27k/5.27k [00:00<00:00, 3.22MB/s]\r\nDownloading metadata: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.36k/2.36k [00:00<00:00, 1.60MB/s]\r\nDownloading readme: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.67k/7.67k [00:00<00:00, 4.58MB/s]\r\nDownloading and preparing dataset squad/plain_text to ...t/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453...\r\nDownloading data: 30.3MB [00:00, 91.8MB/s] | 0/2 [00:00<?, ?it/s]\r\nDownloading data: 4.85MB [00:00, 75.3MB/s] \r\nDownloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.31it/s]\r\nExtracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2157.01it/s]\r\nDataset squad downloaded and prepared to .../.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 463.95it/s]\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 87599\r\n })\r\n validation: Dataset({\r\n features: ['id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 10570\r\n })\r\n})\r\n```",
"I am at a complete loss for what's happening here. A quick summary, I have 3 machines to try this with:\r\n1) My windows 10 laptop\r\n2) Linux machine1, super computer login node\r\n3) Linux machine2, super computer compute node\r\n\r\nLet's define the following as a test script for the machines:\r\n\r\n```\r\nimport traceback\r\nimport datasets\r\nprint(f\"{datasets.__version__=}\")\r\ntry:\r\n ds = datasets.load_dataset(\"squad\")\r\nexcept:\r\n traceback.print_exc()\r\nelse:\r\n print(\"Success!\")\r\n```\r\n\r\nThe Windows laptop enters some sort of traceback recursion loop:\r\n\r\n> datasets.__version__='2.7.1'\r\n> Downloading and preparing dataset squad/plain_text to C:/Users/yr3g17/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453...\r\n> Downloading data files: 100%|██████████| 2/2 [00:00<?, ?it/s]\r\n> Traceback (most recent call last):\r\n> File \"<string>\", line 1, in <module>\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 116, in spawn_main\r\n> exitcode = _main(fd, parent_sentinel)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 125, in _main\r\n> prepare(preparation_data)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 236, in prepare\r\n> _fixup_main_from_path(data['init_main_from_path'])\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 287, in _fixup_main_from_path\r\n> main_content = runpy.run_path(main_path,\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 267, in run_path\r\n> code, fname = _get_code_from_file(run_name, path_name)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 237, in _get_code_from_file\r\n> with io.open_code(decoded_path) as f:\r\n> OSError: [Errno 22] Invalid argument: 'C:\\\\Users\\\\yr3g17\\\\OneDrive - University of Southampton\\\\Documents\\\\PhD-repository\\\\<input>'\r\n> Traceback (most recent call last):\r\n> File \"<string>\", line 1, in <module>\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 116, in spawn_main\r\n> exitcode = _main(fd, parent_sentinel)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 125, in _main\r\n> prepare(preparation_data)\r\n**this error traceback is endlessly recursive**\r\n\r\nThis is a brand new issue that started today and I didn't even realise was a thing, as I had been using my windows machine to follow tracebacks for the other machines...\r\n\r\nI suspect this issue had something to do with my filepath naming, but I couldn't confirm this when I spent time trying to debug this myself weeks ago, something to do with files being locked and never released. I'm not too concerned about my laptop not working here because I've had so many issues with Microsoft OneDrive and my filesystem.\r\n\r\nLinux machines 1 and 2 were working fine for months, but have all of a sudden stopped working. Trying to run linux machine 1 (login node), I get:\r\n\r\n> datasets.__version__='2.10.1'\r\n> Downloading and preparing dataset json/squad to /home/yr3g17/.cache/hugg\r\ningface/datasets/json/squad-d733af945be1d2c2/0.0.0/0f7e3662623656454fcd2\r\nb650f34e886a7db4b9104504885bd462096cc7a9f51...\r\n> Downloading data files: 100%|███████████████████████████████████████████\r\n█████████████████████████████████████████████| 2/2 [00:00<00:00, 4042.70\r\nit/s]\r\n>Extracting data files: 100%|███████████████████████████████████████\r\n███████████████████████████████████████████████████| 2/2 [00:00<00:00, 1\r\n11.15it/s]\r\n> Generating train split: 0 examples [00:00, ? examples/s]\r\n\r\n and hangs here. This has not happened to me before on the Linux machine. If I forcefully keyboard interrupt, I get:\r\n \r\n> Traceback (most recent call last):\r\n> File \"<stdin>\", line 2, in <module>\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/load.py\", line 1782, in load_dataset\r\n> builder_instance.download_and_prepare(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/builder.py\", line 793, in download_and_prepare\r\n> with FileLock(lock_path) if is_local else contextlib.nullcontext():\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/utils/filelock.py\", line 320, in __enter__\r\n> self.acquire()\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/utils/filelock.py\", line 282, in acquire\r\n> time.sleep(poll_intervall)\r\n\r\nWhich also appears to be file lock related! I resolved this by navigating to my ~/.cache/huggingface/datasets directory and wiping out anything to do with the squad dataset in *.lock files. Now I get:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset_load(\"squad\")\r\n\r\n```\r\n> Downloading and preparing dataset squad/plain_text to /home/yr3g17/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb\r\n> 2511d223b9150cce08a837ef62ffea453...\r\n> Downloading data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 44.75it/s]\r\n> Extracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 8.54it/s]\r\n> Dataset squad downloaded and prepared to /home/yr3g17/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150\r\n> cce08a837ef62ffea453. Subsequent calls will reuse this data.\r\n> 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 19.77it/s]\r\n> DatasetDict({\r\n> train: Dataset({\r\n> features: ['id', 'title', 'context', 'question', 'answers'],\r\n> num_rows: 87599\r\n> })\r\n> validation: Dataset({\r\n> features: ['id', 'title', 'context', 'question', 'answers'],\r\n> num_rows: 10570\r\n> })\r\n> })\r\n> \r\n\r\nWhich all seems fine right, it's doing what it should be. But now, without ever leaving the IDE, I \"make a subsequent call\" to reuse the data by repeating the command. I encounter the following traceback\r\n\r\n`load_dataset(\"squad\")`\r\n\r\n> Traceback (most recent call last):\r\n> File \"<stdin>\", line 1, in <module>\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1759, in load_dataset\r\n> builder_instance = load_dataset_builder(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1496, in load_dataset_builder\r\n> dataset_module = dataset_module_factory(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1151, in dataset_module_factory\r\n> ).get_module()\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 631, in get_module\r\n> data_files = DataFilesDict.from_local_or_remote(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/data_files.py\", line 796, in from_local_or_remote\r\n> DataFilesList.from_local_or_remote(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/data_files.py\", line 764, in from_local_or_remote\r\n> data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/data_files.py\", line 369, in resolve_patterns_locally_or_by_urls\r\n> raise FileNotFoundError(error_msg)\r\n> FileNotFoundError: Unable to resolve any data file that matches '['train[-._ 0-9/]**', '**[-._ 0-9/]train[-._ 0-9/]**', 'training[-._ 0-9/]**', '**[-\r\n> ._ 0-9/]training[-._ 0-9/]**']' at /mainfs/home/yr3g17/.cache/huggingface/datasets/squad with any supported extension ['csv', 'tsv', 'json', 'jsonl',\r\n> 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'gr\r\n> ib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', '\r\n> mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', '\r\n> emf', 'xbm', 'xpm', 'BLP', 'BMP', 'DIB', 'BUFR', 'CUR', 'PCX', 'DCX', 'DDS', 'PS', 'EPS', 'FIT', 'FITS', 'FLI', 'FLC', 'FTC', 'FTU', 'GBR', 'GIF', 'G\r\n> RIB', 'H5', 'HDF', 'PNG', 'APNG', 'JP2', 'J2K', 'JPC', 'JPF', 'JPX', 'J2C', 'ICNS', 'ICO', 'IM', 'IIM', 'TIF', 'TIFF', 'JFIF', 'JPE', 'JPG', 'JPEG',\r\n> 'MPG', 'MPEG', 'MSP', 'PCD', 'PXR', 'PBM', 'PGM', 'PPM', 'PNM', 'PSD', 'BW', 'RGB', 'RGBA', 'SGI', 'RAS', 'TGA', 'ICB', 'VDA', 'VST', 'WEBP', 'WMF',\r\n> 'EMF', 'XBM', 'XPM', 'aiff', 'au', 'avr', 'caf', 'flac', 'htk', 'svx', 'mat4', 'mat5', 'mpc2k', 'ogg', 'paf', 'pvf', 'raw', 'rf64', 'sd2', 'sds', 'ir\r\n> cam', 'voc', 'w64', 'wav', 'nist', 'wavex', 'wve', 'xi', 'mp3', 'opus', 'AIFF', 'AU', 'AVR', 'CAF', 'FLAC', 'HTK', 'SVX', 'MAT4', 'MAT5', 'MPC2K', 'O\r\n> GG', 'PAF', 'PVF', 'RAW', 'RF64', 'SD2', 'SDS', 'IRCAM', 'VOC', 'W64', 'WAV', 'NIST', 'WAVEX', 'WVE', 'XI', 'MP3', 'OPUS', 'zip']\r\n\r\nIt doesn't even appear like I can reliably repeat this process. I'll nuke squad files in my dataset cache and run the Python code again (which downloads a new copy of the dataset to cache). It will either fail (as it just did in the quote above), or it will successfully recall the dataset.\r\n\r\nI repeated this nuking process a few times until calling load_dataset was reliably giving me the correct result (no filelocking issues or tracebacks). I then sent the test script as a job to the supercomputer compute nodes (which do not have internet access and therefore depend on cached data from Linux machine 1 login nodes)\r\n\r\n> Using the latest cached version of the module from /home/yr3g17/.cache/huggingface/modules/datasets_modules/datasets/squad/8730650fed465361f38ac4d810\r\n> ccdd16e8fc87b56498e52fb7e2cadaefc1f177 (last modified on Tue Feb 14 10:12:56 2023) since it couldn't be found locally at squad., or remotely on the Hugging Face Hub.\r\n> Traceback (most recent call last):\r\n> File \"/mainfs/scratch/yr3g17/squad_qanswering/3054408/0/../../main.py\", line 5, in <module>\r\n> dataset = load_dataset(\"squad\")\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1759, in load_dataset\r\n> builder_instance = load_dataset_builder(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1522, in load_dataset_builder\r\n> builder_instance: DatasetBuilder = builder_cls(\r\n> TypeError: 'NoneType' object is not callable\r\n\r\nand I have absolutely no idea why the second and third machines are producing different tracebacks. I have previously run these exact scripts successfully on the login and compute nodes of the supercomputer, this issue I'm raising has appeared fairly recently for me. This, is where I encounter the TypeError that I opened this issue with, which I was able to traceback (using my laptop before it too started not working) to whatever was dynamically importing \"builder_cls\". That bit of code wasn't doing importing builder_cls correctly and would effectively make the assignment \"builder_cls=None\" resulting in the TypeError. Does any of this help?",
"I'm back on linux machine 1 (login node) now. After submitting that as a job to machine 2 and it failing with TypeError, linux machine 1 now produces identical traceback to machine 2:\r\n\r\n> (arkroyal) [yr3g17@cyan52 squad_qanswering]$ python\r\n> Python 3.10.8 (main, Nov 24 2022, 14:13:03) [GCC 11.2.0] on linux\r\n> Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>\r\n> from datasets import load_dataset\r\n> load_dataset(\"squad\")\r\n>\r\n> Traceback (most recent call last):\r\n> File \"<stdin>\", line 1, in <module>\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1759, in load_dataset\r\n> builder_instance = load_dataset_builder(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1522, in load_dataset_builder\r\n> builder_instance: DatasetBuilder = builder_cls(\r\n> TypeError: 'NoneType' object is not callable\r\n\r\nI thought it might be useful to provide you with my cache file structure:\r\n\r\n>_home_yr3g17_.cache_huggingface_datasets_casino_default_1.1.0_302c3b1ac78c48091deabe83a11f4003c7b472a4e11a8eb92799653785bd5da1.lock\r\n>_home_yr3g17_.cache_huggingface_datasets_imdb_plain_text_1.0.0_2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1.lock\r\n>_home_yr3g17_.cache_huggingface_datasets_squad_plain_text_1.0.0_d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453.lock\r\n>_home_yr3g17_.cache_huggingface_datasets_yelp_review_full_yelp_review_full_1.0.0_e8e18e19d7be9e75642fc66b198abadb116f73599ec89a69ba5dd8d1e57ba0bf.lock\r\n> casino\r\n> downloads\r\n> imdb\r\n> json\r\n> squad\r\n> squad_v2\r\n> yelp_review_full\r\n\r\nThe inside of squad/plain_text/1.0.0/ looks like\r\n\r\n> d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453\r\n> d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453.incomplete_info.lock\r\n> d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453_builder.lock\r\n",
"I see this is quite a complex use case...\r\n\r\nLet's try multiple things:\r\n- First, update `datasets` and make sure you use the same version in all machines, so that we can easily compare different behaviors.\r\n ```\r\n pip install -U datasets\r\n ```\r\n- Second, wherever you run the `load_dataset(\"squad\")` command, make sure there is not a local directory named \"squad\". The datasets library gives priority to any local file/directory over the datasets on the Hugging Face Hub\r\n - I tell you this, because in one of your trace backs, it seems it refers to a local directory:\r\n ```\r\n Downloading and preparing dataset json/squad to /home/yr3g17/.cache/huggingface/datasets/json/squad-d733af945be1d2c2/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\r\n ```\r\n- Third, to use the \"squad\" dataset from the Hub, you need to have internet connection, so that you can download the \"squad\" Python loading script from the Hub. Do all your machines have internet connection?\r\n - I ask this because of this error message:\r\n ```\r\n Using the latest cached version of the module from /home/yr3g17/.cache/huggingface/modules/datasets_modules/datasets/squad/8730650fed465361f38ac4d810ccdd16e8fc87b56498e52fb7e2cadaefc1f177 (last modified on Tue Feb 14 10:12:56 2023) since it couldn't be found locally at squad., or remotely on the Hugging Face Hub.\r\n ```\r\n- Fourth, to be sure that we avoid any issues with the cache, it is a good idea to remove it and regenerate it. Remove `.cache/huggingface/datasets` and also `.cache/huggingface/modules`\r\n- Fifth, as an additional debugging tool, let's be sure we use the latest \"squad\" Python loading script by passing the revision parameter:\r\n ```\r\n ds = load_dataset(\"squad\", revision=\"5fe18c4c680f9922d794e3f4dd673a751c74ee37\")\r\n ```",
"Additionally, we just had an infrastructure issue on the Hugging Face Hub at around 11:30 today. That might have contributed to the connectivity issue... It is fixed now.\r\n\r\nhttps://status.huggingface.co/",
"Hi again, thanks for your help and insight Albert Villanova.\r\n\r\nIt's all working now, so thank you for that. For the benefit of anyone else who ends up in this thread, I solved the problem by addressing Albert's advice:\r\n\r\n(1) Both Windows and Linux machine 1 (have internet access) and can now access the SQuAD dataset. The supercomputer login node can only access version 2.7.1, but my Windows laptop is running on datasets 2.11.0 just fine. I suspect it was just a perfect storm alongside the aforementioned \"infrastructure issue\".\r\n\r\n(2) I did have a local directory called squad, because I was using a local copy of evaluate's \"SQuAD\" metric. The supercomputer compute nodes do not have internet access and treat `metric = evaluate.load('<x>')` as a way of loading a metric at the local path `./<x>/<x>.py`, which could've been a related issue as I was storing the metric under `squad/squad.py`. Don't be lazy like me and store the evaluation code under a path with a name that can be misinterpreted.\r\n\r\n(3) I can't give internet access to the supercomputer compute nodes, so local files do just fine here.\r\n\r\n(4) The windows and Linux machine 1 can both access the internet and were getting fresh copies of the dataset from the huggingface hub. Linux machine 2 was working after I cleared the contents of ~/.cache/huggingface/....\r\n\r\nI feel silly now, knowing it was all so simple! Sorry about that Albert, and thanks again for the help. I've not raised a Github issue like this before, so I'm not sure if I should be close my own issues or if this is something you guys do?",
"Thanks for your detailed feedback which for sure will be useful to other community members."
] | 2023-04-18T07:10:56Z
| 2023-04-20T10:27:23Z
| 2023-04-20T10:27:22Z
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### Describe the bug
There is an issue that seems to be unique to the "squad" dataset, in which it cannot be loaded using standard methods. This issue is most quickly reproduced from the command line, using the HF examples to verify a dataset is loaded properly.
This is not a problem with "squad_v2" dataset for example.
### Steps to reproduce the bug
cmd line
> $ python -c "from datasets import load_dataset; print(load_dataset('squad', split='train')[0])"
OR
Python IDE
> from datasets import load_dataset
> load_dataset("squad")
### Expected behavior
I expected to either see the output described here from running the very same command in command line ([https://huggingface.co/docs/datasets/installation]), or any output that does not raise Python's TypeError.
There is some funky behaviour in the dataset builder portion of the codebase that means it is trying to import the squad dataset with an incorrect path, or the squad dataset couldn't be downloaded. I'm not really sure what the problem is beyond that. Messing around with caching I did manage to get it to load the dataset once, and then couldn't repeat this.
### Environment info
datasets=2.7.1 **or** 2.10.1, python=3.10.8, Linux 3.10.0-1160.36.2.el7.x86_64 **or** Windows 10-64
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pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int'
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[] | 2025-03-12T07:48:37Z
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### Describe the bug
A dict with its keys are all str but get following error
```python
test_data=[{'input_ids':[1,2,3],'labels':[[Counter({2:1})]]}]
dataset = datasets.Dataset.from_list(test_data)
```
```bash
pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int'
```
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
datasets 3.3.2
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When calling load_dataset, raise error: pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
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[
"I solved the problem by modifying the \"self DEFAULT_WRITER_BATCH_SIZE\" in \"class MyDataset (datasets. GeneratorBasedBuilder) : __init__\"",
"same problem, and this solution worked me also - you can set this var by setting the keyword argument `writer_batch_size=...` in `load_dataset(...,writer_batch_size=...)`"
] | 2023-09-04T04:14:00Z
| 2024-04-17T15:53:29Z
| 2023-09-04T06:05:49Z
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### Describe the bug
When calling load_dataset, raise error
```
Traceback (most recent call last):
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1694, in _pre
pare_split_single
writer.write(example, key)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 490, in
write
self.write_examples_on_file()
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 448, in
write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 559, in
write_batch
self.write_table(pa_table, writer_batch_size)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 571, in
write_table
pa_table = pa_table.combine_chunks()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 3439, in pyarrow.lib.Table.combine_chunks
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
dataset = load_dataset(
^^^^^^^^^^^^^
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py", line 2133, in load_da
taset
builder_instance.download_and_prepare(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 954, in downl
oad_and_prepare
self._download_and_prepare(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _dow
nload_and_prepare
super()._download_and_prepare(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _dow
nload_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _pre
pare_split
for job_id, done, content in self._prepare_split_single(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1712, in _pre
pare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
Setting num_proc from 8 back to 1 for the train split to disable multiprocessing as it only contains one shard.
09/04/2023 12:02:04 - WARNING - datasets.builder - Setting num_proc from 8 back to 1 for the train split to dis
able multiprocessing as it only contains one shard.
```
### Steps to reproduce the bug
Call load_dataset with the large image as feature
### Expected behavior
no error
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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Can't add iterable datasets to a Dataset Dict.
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[
"Transferring as this is relating to the 🤗 Datasets library",
"You need to use `IterableDatasetDict` instead of `DatasetDict` for iterable datasets."
] | 2023-05-11T02:09:29Z
| 2023-05-25T04:51:59Z
| 2023-05-25T04:51:59Z
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### System Info
standard env
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Get the following error:
TypeError: Values in `DatasetDict` should be of type `Dataset` but got type '<class 'datasets.iterable_dataset.IterableDataset'>'
### Expected behavior
should be able to add iterable datasets to a dataset dict
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I_kwDODunzps5yy9iW
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I'm trying to fine tune the openai/whisper model from huggingface using jupyter notebook and i keep getting this error
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[
"Since you are using Windows, maybe moving the `map` call inside `if __name__ == \"__main__\"` can fix the issue:\r\n```python\r\nif __name__ == \"__main__\":\r\n common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=4)\r\n```\r\n\r\nOtherwise, the only solution is to set `num_proc=1`.",
"> Since you are using Windows, maybe moving the `map` call inside `if __name__ == \"__main__\"` can fix the issue:\r\n> \r\n> ```python\r\n> if __name__ == \"__main__\":\r\n> common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=4)\r\n> ```\r\n> \r\n> Otherwise, the only solution is to set `num_proc=1`.\r\n\r\nThank you very much for the response, i eventually tried setting `num_proc=1` and now the jupyter notebook kernel keers dying after running the command, what do you think the issue could be, could it be that my system is not capable of running the command \"i'm using a Lenovo Thinkpad T440 with no GPU\"",
"Firstly, you didn't define feature_extractor variable. Secondly, it is large nlp model. Hence you should use proper gpu, otherwise your machine's cpu will be overclock and you can do nothing."
] | 2023-10-04T11:03:41Z
| 2023-11-27T10:39:16Z
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### Describe the bug
I'm trying to fine tune the openai/whisper model from huggingface using jupyter notebook and i keep getting this error, i'm following the steps in this blog post
https://huggingface.co/blog/fine-tune-whisper
I tried google collab and it works but because I'm on the free version the training doesn't complete
the error comes in jupyter notebook when i run this line
`common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)`
here is the error message
```
Map (num_proc=4): 0% 0/2506 [00:52<?, ? examples/s]
The above exception was the direct cause of the following exception:
NameError Traceback (most recent call last) Cell In[19], line 1 ----> 1 common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)
File ~\anaconda\Lib\site-packages\datasets\dataset_dict.py:853, in DatasetDict.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc) 850 if cache_file_names is None: 851 cache_file_names = {k: None for k in self} 852 return DatasetDict( --> 853 { 854 k: dataset.map( 855 function=function, 856 with_indices=with_indices, 857 with_rank=with_rank, 858 input_columns=input_columns, 859 batched=batched, 860 batch_size=batch_size, 861 drop_last_batch=drop_last_batch, 862 remove_columns=remove_columns, 863 keep_in_memory=keep_in_memory, 864 load_from_cache_file=load_from_cache_file, 865 cache_file_name=cache_file_names[k], 866 writer_batch_size=writer_batch_size, 867 features=features, 868 disable_nullable=disable_nullable, 869 fn_kwargs=fn_kwargs, 870 num_proc=num_proc, 871 desc=desc, 872 ) 873 for k, dataset in self.items() 874 } 875 )
File ~\anaconda\Lib\site-packages\datasets\dataset_dict.py:854, in <dictcomp>(.0) 850 if cache_file_names is None: 851 cache_file_names = {k: None for k in self} 852 return DatasetDict( 853 { --> 854 k: dataset.map( 855 function=function, 856 with_indices=with_indices, 857 with_rank=with_rank, 858 input_columns=input_columns, 859 batched=batched, 860 batch_size=batch_size, 861 drop_last_batch=drop_last_batch, 862 remove_columns=remove_columns, 863 keep_in_memory=keep_in_memory, 864 load_from_cache_file=load_from_cache_file, 865 cache_file_name=cache_file_names[k], 866 writer_batch_size=writer_batch_size, 867 features=features, 868 disable_nullable=disable_nullable, 869 fn_kwargs=fn_kwargs, 870 num_proc=num_proc, 871 desc=desc, 872 ) 873 for k, dataset in self.items() 874 } 875 )
File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:592, in transmit_tasks.<locals>.wrapper(*args, **kwargs) 590 self: "Dataset" = kwargs.pop("self") 591 # apply actual function --> 592 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 593 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 594 for dataset in datasets: 595 # Remove task templates if a column mapping of the template is no longer valid
File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:557, in transmit_format.<locals>.wrapper(*args, **kwargs) 550 self_format = { 551 "type": self._format_type, 552 "format_kwargs": self._format_kwargs, 553 "columns": self._format_columns, 554 "output_all_columns": self._output_all_columns, 555 } 556 # apply actual function --> 557 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 558 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 559 # re-apply format to the output
File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:3189, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 3182 logger.info(f"Spawning {num_proc} processes") 3183 with logging.tqdm( 3184 disable=not logging.is_progress_bar_enabled(), 3185 unit=" examples", 3186 total=pbar_total, 3187 desc=(desc or "Map") + f" (num_proc={num_proc})", 3188 ) as pbar: -> 3189 for rank, done, content in iflatmap_unordered( 3190 pool, Dataset._map_single, kwargs_iterable=kwargs_per_job 3191 ): 3192 if done: 3193 shards_done += 1
File ~\anaconda\Lib\site-packages\datasets\utils\py_utils.py:1394, in iflatmap_unordered(pool, func, kwargs_iterable) 1391 finally: 1392 if not pool_changed: 1393 # we get the result in case there's an error to raise -> 1394 [async_result.get(timeout=0.05) for async_result in async_results]
File ~\anaconda\Lib\site-packages\datasets\utils\py_utils.py:1394, in <listcomp>(.0) 1391 finally: 1392 if not pool_changed: 1393 # we get the result in case there's an error to raise -> 1394 [async_result.get(timeout=0.05) for async_result in async_results]
File ~\anaconda\Lib\site-packages\multiprocess\pool.py:774, in ApplyResult.get(self, timeout) 772 return self._value 773 else: --> 774 raise self._value
NameError: name 'feature_extractor' is not defined
```
### Steps to reproduce the bug
1. follow the steps in this blog post
https://huggingface.co/blog/fine-tune-whisper
2. run this line of code
`common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)`
3. I'm using jupyter notebook from anaconda
### Expected behavior
No error message
### Environment info
datasets version: 2.8.0
Python version: 3.11
Windows 10
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Fix missing use_auth_token in streaming docstrings
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-21T09:27:03Z
| 2022-09-21T16:24:01Z
| 2022-09-21T16:20:59Z
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MEMBER
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This PRs fixes docstrings:
- adds the missing `use_auth_token` param
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- fixes return/yield types
- fixes syntax
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/flickr30k-captions)."
] | 2022-07-07T01:59:08Z
| 2022-07-14T02:09:45Z
| 2022-07-14T02:09:45Z
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## Adding a Dataset
- **Name:** *Flickr 30k*
- **Description:** *To produce the denotation graph, we have created an image caption corpus consisting of 158,915 crowd-sourced captions describing 31,783 images. This is an extension of our previous Flickr 8k Dataset. The new images and captions focus on people involved in everyday activities and events.*
- **Paper:** *https://transacl.org/ojs/index.php/tacl/article/view/229/33*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/flickr30k_captions.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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https://github.com/huggingface/datasets/pull/6316
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PR_kwDODunzps5dQGpg
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Fix loading Hub datasets with CSV metadata file
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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) | 0.008896 / 0.011353 (-0.002456) | 0.005811 / 0.011008 (-0.005197) | 0.108582 / 0.038508 (0.070074) | 0.096509 / 0.023109 (0.073399) | 0.481725 / 0.275898 (0.205827) | 0.534743 / 0.323480 (0.211263) | 0.005517 / 0.007986 (-0.002468) | 0.006479 / 0.004328 (0.002151) | 0.081313 / 0.004250 (0.077062) | 0.063578 / 0.037052 (0.026525) | 0.493977 / 0.258489 (0.235488) | 0.551897 / 0.293841 (0.258056) | 0.051835 / 0.128546 (-0.076711) | 0.014105 / 0.075646 (-0.061541) | 0.385866 / 0.419271 (-0.033405) | 0.069131 / 0.043533 (0.025598) | 0.484780 / 0.255139 (0.229641) | 0.493221 / 0.283200 (0.210021) | 0.039560 / 0.141683 (-0.102123) | 1.782331 / 1.452155 (0.330176) | 1.899193 / 1.492716 (0.406477) |\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) | 0.329978 / 0.018006 (0.311972) | 0.600839 / 0.000490 (0.600349) | 0.013187 / 0.000200 (0.012987) | 0.000499 / 0.000054 (0.000444) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031835 / 0.037411 (-0.005576) | 0.103740 / 0.014526 (0.089214) | 0.115875 / 0.176557 (-0.060681) | 0.189880 / 0.737135 (-0.547255) | 0.132614 / 0.296338 (-0.163725) |\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) | 0.596255 / 0.215209 (0.381046) | 5.967993 / 2.077655 (3.890339) | 2.612675 / 1.504120 (1.108555) | 2.251461 / 1.541195 (0.710266) | 2.308585 / 1.468490 (0.840095) | 0.816516 / 4.584777 (-3.768261) | 5.241791 / 3.745712 (1.496079) | 4.680745 / 5.269862 (-0.589117) | 2.997370 / 4.565676 (-1.568307) | 0.098632 / 0.424275 (-0.325643) | 0.010912 / 0.007607 (0.003305) | 0.659092 / 0.226044 (0.433047) | 6.825562 / 2.268929 (4.556634) | 3.323844 / 55.444624 (-52.120780) | 2.796203 / 6.876477 (-4.080274) | 2.946994 / 2.142072 (0.804922) | 1.002814 / 4.805227 (-3.802413) | 0.202613 / 6.500664 (-6.298051) | 0.072011 / 0.075469 (-0.003459) |\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) | 1.613873 / 1.841788 (-0.227914) | 24.500990 / 8.074308 (16.426682) | 21.941599 / 10.191392 (11.750207) | 0.214450 / 0.680424 (-0.465974) | 0.031227 / 0.534201 (-0.502974) | 0.498297 / 0.579283 (-0.080986) | 0.597460 / 0.434364 (0.163096) | 0.558152 / 0.540337 (0.017815) | 0.789693 / 1.386936 (-0.597243) |\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) | 0.011299 / 0.011353 (-0.000053) | 0.005103 / 0.011008 (-0.005905) | 0.083161 / 0.038508 (0.044653) | 0.094201 / 0.023109 (0.071092) | 0.560457 / 0.275898 (0.284559) | 0.590459 / 0.323480 (0.266980) | 0.007059 / 0.007986 (-0.000926) | 0.004418 / 0.004328 (0.000090) | 0.081343 / 0.004250 (0.077093) | 0.067069 / 0.037052 (0.030016) | 0.538137 / 0.258489 (0.279648) | 0.600416 / 0.293841 (0.306575) | 0.049046 / 0.128546 (-0.079500) | 0.014299 / 0.075646 (-0.061347) | 0.093631 / 0.419271 (-0.325641) | 0.062536 / 0.043533 (0.019003) | 0.557238 / 0.255139 (0.302099) | 0.571050 / 0.283200 (0.287850) | 0.035881 / 0.141683 (-0.105802) | 1.918487 / 1.452155 (0.466332) | 2.013979 / 1.492716 (0.521263) |\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) | 0.400995 / 0.018006 (0.382989) | 0.634898 / 0.000490 (0.634408) | 0.041809 / 0.000200 (0.041609) | 0.000279 / 0.000054 (0.000224) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034160 / 0.037411 (-0.003251) | 0.109996 / 0.014526 (0.095470) | 0.124335 / 0.176557 (-0.052222) | 0.188100 / 0.737135 (-0.549035) | 0.135897 / 0.296338 (-0.160442) |\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) | 0.639751 / 0.215209 (0.424542) | 6.403312 / 2.077655 (4.325657) | 3.146453 / 1.504120 (1.642333) | 2.840358 / 1.541195 (1.299164) | 2.908667 / 1.468490 (1.440177) | 0.818767 / 4.584777 (-3.766010) | 5.416939 / 3.745712 (1.671227) | 4.853498 / 5.269862 (-0.416364) | 3.023526 / 4.565676 (-1.542150) | 0.110850 / 0.424275 (-0.313425) | 0.013103 / 0.007607 (0.005496) | 0.799720 / 0.226044 (0.573676) | 7.837704 / 2.268929 (5.568775) | 4.016526 / 55.444624 (-51.428099) | 3.338965 / 6.876477 (-3.537512) | 3.715721 / 2.142072 (1.573648) | 1.088340 / 4.805227 (-3.716887) | 0.213610 / 6.500664 (-6.287054) | 0.079244 / 0.075469 (0.003775) |\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) | 1.833175 / 1.841788 (-0.008612) | 25.307218 / 8.074308 (17.232910) | 23.716075 / 10.191392 (13.524683) | 0.259114 / 0.680424 (-0.421310) | 0.035171 / 0.534201 (-0.499029) | 0.530128 / 0.579283 (-0.049155) | 0.651484 / 0.434364 (0.217120) | 0.589414 / 0.540337 (0.049077) | 0.862691 / 1.386936 (-0.524245) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bdfba93b8a739b9d885b8fb1909d47ff689bbc2 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"Me too, I thought the same... quite surprised... :open_mouth: ",
"<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) | 0.006929 / 0.011353 (-0.004423) | 0.004345 / 0.011008 (-0.006663) | 0.085522 / 0.038508 (0.047014) | 0.083380 / 0.023109 (0.060271) | 0.310332 / 0.275898 (0.034434) | 0.350525 / 0.323480 (0.027045) | 0.004367 / 0.007986 (-0.003618) | 0.005503 / 0.004328 (0.001175) | 0.066311 / 0.004250 (0.062061) | 0.059545 / 0.037052 (0.022492) | 0.314090 / 0.258489 (0.055601) | 0.366661 / 0.293841 (0.072821) | 0.031581 / 0.128546 (-0.096965) | 0.008852 / 0.075646 (-0.066794) | 0.289312 / 0.419271 (-0.129960) | 0.052960 / 0.043533 (0.009427) | 0.308134 / 0.255139 (0.052995) | 0.330342 / 0.283200 (0.047142) | 0.026157 / 0.141683 (-0.115526) | 1.488463 / 1.452155 (0.036308) | 1.561441 / 1.492716 (0.068725) |\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) | 0.327735 / 0.018006 (0.309729) | 0.568162 / 0.000490 (0.567672) | 0.012097 / 0.000200 (0.011897) | 0.000438 / 0.000054 (0.000383) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029503 / 0.037411 (-0.007909) | 0.084327 / 0.014526 (0.069801) | 0.102065 / 0.176557 (-0.074492) | 0.157392 / 0.737135 (-0.579744) | 0.101428 / 0.296338 (-0.194910) |\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) | 0.386767 / 0.215209 (0.171558) | 3.870757 / 2.077655 (1.793102) | 1.870048 / 1.504120 (0.365928) | 1.678221 / 1.541195 (0.137026) | 1.799423 / 1.468490 (0.330933) | 0.477718 / 4.584777 (-4.107059) | 3.618351 / 3.745712 (-0.127361) | 3.577921 / 5.269862 (-1.691941) | 2.146217 / 4.565676 (-2.419459) | 0.056290 / 0.424275 (-0.367985) | 0.007378 / 0.007607 (-0.000229) | 0.460678 / 0.226044 (0.234633) | 4.606243 / 2.268929 (2.337314) | 2.303460 / 55.444624 (-53.141164) | 1.982662 / 6.876477 (-4.893814) | 2.103891 / 2.142072 (-0.038182) | 0.570700 / 4.805227 (-4.234527) | 0.131747 / 6.500664 (-6.368918) | 0.060915 / 0.075469 (-0.014554) |\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) | 1.286364 / 1.841788 (-0.555424) | 20.106330 / 8.074308 (12.032022) | 14.780833 / 10.191392 (4.589441) | 0.164301 / 0.680424 (-0.516123) | 0.018730 / 0.534201 (-0.515471) | 0.398530 / 0.579283 (-0.180754) | 0.418084 / 0.434364 (-0.016280) | 0.468735 / 0.540337 (-0.071602) | 0.690122 / 1.386936 (-0.696814) |\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) | 0.007262 / 0.011353 (-0.004091) | 0.004228 / 0.011008 (-0.006780) | 0.065866 / 0.038508 (0.027358) | 0.096151 / 0.023109 (0.073042) | 0.409352 / 0.275898 (0.133454) | 0.441234 / 0.323480 (0.117754) | 0.005946 / 0.007986 (-0.002039) | 0.003630 / 0.004328 (-0.000698) | 0.066271 / 0.004250 (0.062020) | 0.061567 / 0.037052 (0.024515) | 0.409097 / 0.258489 (0.150608) | 0.447675 / 0.293841 (0.153834) | 0.032804 / 0.128546 (-0.095743) | 0.008793 / 0.075646 (-0.066853) | 0.070790 / 0.419271 (-0.348482) | 0.048650 / 0.043533 (0.005117) | 0.411021 / 0.255139 (0.155882) | 0.421398 / 0.283200 (0.138198) | 0.025305 / 0.141683 (-0.116378) | 1.494826 / 1.452155 (0.042671) | 1.580441 / 1.492716 (0.087724) |\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) | 0.321871 / 0.018006 (0.303865) | 0.526471 / 0.000490 (0.525982) | 0.006913 / 0.000200 (0.006713) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034889 / 0.037411 (-0.002522) | 0.096096 / 0.014526 (0.081570) | 0.111920 / 0.176557 (-0.064636) | 0.166103 / 0.737135 (-0.571032) | 0.111162 / 0.296338 (-0.185176) |\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) | 0.428037 / 0.215209 (0.212828) | 4.294150 / 2.077655 (2.216495) | 2.270331 / 1.504120 (0.766211) | 2.108235 / 1.541195 (0.567041) | 2.242560 / 1.468490 (0.774070) | 0.489941 / 4.584777 (-4.094836) | 3.688111 / 3.745712 (-0.057601) | 3.450180 / 5.269862 (-1.819681) | 2.175106 / 4.565676 (-2.390570) | 0.057657 / 0.424275 (-0.366619) | 0.007478 / 0.007607 (-0.000130) | 0.505242 / 0.226044 (0.279198) | 5.047817 / 2.268929 (2.778888) | 2.724125 / 55.444624 (-52.720500) | 2.419765 / 6.876477 (-4.456711) | 2.723231 / 2.142072 (0.581159) | 0.602382 / 4.805227 (-4.202846) | 0.132362 / 6.500664 (-6.368302) | 0.060600 / 0.075469 (-0.014869) |\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) | 1.363356 / 1.841788 (-0.478431) | 21.446474 / 8.074308 (13.372165) | 15.074732 / 10.191392 (4.883340) | 0.191837 / 0.680424 (-0.488587) | 0.020565 / 0.534201 (-0.513636) | 0.396692 / 0.579283 (-0.182591) | 0.432390 / 0.434364 (-0.001974) | 0.491747 / 0.540337 (-0.048591) | 0.699203 / 1.386936 (-0.687733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3a8a87c841426495d3a7ed1863c26660a6a551f \"CML watermark\")\n"
] | 2023-10-19T10:21:34Z
| 2023-10-20T06:23:21Z
| 2023-10-20T06:14:09Z
|
MEMBER
| null | null | null |
Currently, the reading of the metadata file infers the file extension (.jsonl or .csv) from the passed filename. However, downloaded files from the Hub don't have file extension. For example:
- the original file: `hf://datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5916a4-16977085077831/metadata.jsonl`
- corresponds to the downloaded path: `/tmp/pytest-of-username/pytest-46/cache/datasets/downloads/9f5374dbb470f711f6b89d66a5eec1f19cc96324b26bcbebe29138bda6cb20e6`, which does not have extension
In the case where the metadata file does not have an extension, the reader assumes it is a JSONL file, thus the reported error when trying to read a CSV file as a JSONL one: `ArrowInvalid: JSON parse error: Invalid value. in row 0`
This behavior was introduced by:
- #4837
This PR extracts the metadata file extension from the original filename (instead of the downloaded one) and passes it as a parameter to the read_metadata function.
Fix #6315.
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I_kwDODunzps6v4ABr
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deal with python `10_000` legal number in slice syntax
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"should be an easy fix, I opened a PR"
] | 2025-03-26T20:10:54Z
| 2025-03-28T16:20:44Z
| 2025-03-28T16:20:44Z
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NONE
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### Feature request
```
In [6]: ds = datasets.load_dataset("HuggingFaceH4/ultrachat_200k", split="train_sft[:1000]")
In [7]: ds = datasets.load_dataset("HuggingFaceH4/ultrachat_200k", split="train_sft[:1_000]")
[dozens of frames skipped]
File /usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py:444, in _str_to_read_instruction(spec)
442 res = _SUB_SPEC_RE.match(spec)
443 if not res:
--> 444 raise ValueError(f"Unrecognized instruction format: {spec}")
ValueError: Unrecognized instruction format: train_sft[:1_000]
```
It took me a while to understand what the problem was. But apparently `pyarrow` doesn't allow python numbers that may include `_` as in `1_000`. The `_` aids readability since `10_000_000` vs `10000000` is obviously easier to grasp of what the actual number is.
Feature request:
ideally `datasets` being a python module will do the right thing and convert python numbers into whatever pyarrow supports - in this case stripping `_`s.
Second best it'd err and tell the user that using numbers with `_` in split slices is not acceptible, so that the user won't have to deal with a huge pyarrow assert they know nothing about.
Thank you!
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Push to hub in a pull request
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5528). All of your documentation changes will be reflected on that endpoint.",
"It seems that the parameter `create_pr` is available for [`0.8.0`](https://huggingface.co/docs/huggingface_hub/v0.8.1/en/package_reference/hf_api#huggingface_hub.HfApi.upload_file) (its not here: [`0.7.0`](https://huggingface.co/docs/huggingface_hub/v0.7.0.rc0/en/package_reference/hf_api#huggingface_hub.HfApi.upload_file)) and onwards. I included a warning, informing the user that no PR was created.",
"@nateraw you are completely right! Actually, the dataset shards is never added to the created pr, only the metadata, as the code is now. Ill look into you suggestion asap. Thank!",
"@nateraw Nothing more to add, that's a perfect usage of `huggingface_hub` as far as I can tell ! :fire: \r\n\r\nA very nit improvement would be to use the [for .. else ... python statement](https://book.pythontips.com/en/latest/for_-_else.html).\r\ni.e:\r\n\r\n```py\r\nif create_pr is True and revision is not None:\r\n for discussion in get_repo_discussions(repo_id, repo_type='dataset'):\r\n if discussion.is_pull_request and discussion.git_reference == revision:\r\n create_pr = False\r\n break\r\n else:\r\n raise ValueError(\"Provided revision not found\")\r\n```\r\nNo need for the `revision_found` temporary flag when do so. Yeah ok, it's niche :wink: ",
"I added the suggestions from @nateraw and @Wauplin .",
"> Thanks. Some comments/suggestions below...\r\n> \r\n> Why have you removed the test for create_pr? You could add it again and just add a pytest skipif when version of huggingface_hub is lower than 0.8.1.\r\n\r\nI have added the test again. I removed it because i kept getting errors when calling `create_pull_request` with `repo_id=ds_name` where `temporary_repo = ds_name`, and thought i might look more thoroughly at it later. I have added a test called `test_test` showing this, it gives:\r\n```\r\ntests/test_upstream_hub.py:360: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\n.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n return fn(*args, **kwargs)\r\n.venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3451: in create_pull_request\r\n return self.create_discussion(\r\n.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n return fn(*args, **kwargs)\r\n.venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3393: in create_discussion\r\n hf_raise_for_status(resp)\r\n(...)\r\nE huggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: Root=1-63ecd2cb-2cf2557a332c86ad27f687b3)\r\nE \r\nE Repository Not Found for url: https://huggingface.co/api/models/__DUMMY_TRANSFORMERS_USER__/test-16764648321590/discussions.\r\nE Please make sure you specified the correct `repo_id` and `repo_type`.\r\nE If you are trying to access a private or gated repo, make sure you are authenticated.\r\nE Invalid username or password.\r\n```",
"> > Thanks. Some comments/suggestions below...\r\n> > Why have you removed the test for create_pr? You could add it again and just add a pytest skipif when version of huggingface_hub is lower than 0.8.1.\r\n> \r\n> I have added the test again. I removed it because i kept getting errors when calling `create_pull_request` with `repo_id=ds_name` where `temporary_repo = ds_name`, and thought i might look more thoroughly at it later. I have added a test called `test_test` showing this, it gives:\r\n> \r\n> ```\r\n> tests/test_upstream_hub.py:360: \r\n> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n> return fn(*args, **kwargs)\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3451: in create_pull_request\r\n> return self.create_discussion(\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n> return fn(*args, **kwargs)\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3393: in create_discussion\r\n> hf_raise_for_status(resp)\r\n> (...)\r\n> E huggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: Root=1-63ecd2cb-2cf2557a332c86ad27f687b3)\r\n> E \r\n> E Repository Not Found for url: https://huggingface.co/api/models/__DUMMY_TRANSFORMERS_USER__/test-16764648321590/discussions.\r\n> E Please make sure you specified the correct `repo_id` and `repo_type`.\r\n> E If you are trying to access a private or gated repo, make sure you are authenticated.\r\n> E Invalid username or password.\r\n> ```\r\n\r\n@albertvillanova, @lhoestq , FYI I have looked at this again, and i haven't figured it out, so the test`test_push_dataset_to_hub_with_pull_request` and the minimal example `test_test` are still failing locally, while the other tests succeed. Do you have any advice?",
"I tried to move all of the \"create pr safely\"-logic to a seperate function in `_hf_hub_fixes`. I looked at how the exceptions were raised before `huggingface_hub.utils.RepositoryNotFoundError`existed, and make changes accordingly. ",
"`create_pr` was set during `push_to_hub`, even though it was `None` from the outset, hence causing tests to fail for older versions of `huggingface_hub`. This is now fixed.\r\n\r\nWith the implementation of `_hf_hub_fixes.upload_file` the function call expected `commit_message`, `commit_description`. If these are not set we call the function without them, even though we are on a version of `huggingface_hub` where they are not available in `upload_file`.\r\n\r\nWhen `huggingface_hub < 0.5.0` we assume `repo_id` of them form `organisation/name`, so now that we are calling `create_repo` in the tests with `repo_id` not of this form, we need to handle this case, this is now done.\r\n\r\nMany tests failed for `dataset_dict` for the above reasons, so the fixes from `arrow_dataset.py` were also added to `dataset_dict.py`. \r\n\r\n**All tests are now passing locally for `huggingface_hub==0.2.0` and `huggingface_hub==0.12.1`…** Im sorry I should have downgraded and went through this a long time ago, but I didn’t realise the extend of these version fixes until recently…",
"Hi ! FYI bumped the `huggingface-hub` dependency to 0.11 and removed the `_hf_hub_fixes.py` - which should make this PR much easier",
"Just now finding this - seems like a cool issue to contribute to. If any more help is needed please ping me! @AJDERS "
] | 2023-02-13T11:43:47Z
| 2023-10-06T21:58:02Z
| null |
NONE
| null | null | null |
Fixes #5492.
Introduce new kwarg `create_pr` in `push_to_hub`, which is passed to `HFapi.upload_file`.
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I_kwDODunzps5NSlFX
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Add Quora Question Triplets Dataset
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/QQP_triplets)."
] | 2022-07-07T02:43:42Z
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## Adding a Dataset
- **Name:** *Quora Question Triplets*
- **Description:** *This dataset consists of over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line truly contains a duplicate pair.*
- **Paper:**
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/quora_duplicates_triplets.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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PR_kwDODunzps5ShUxQ
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Modify levels of some logging messages
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[
"I've addressed this as part of #6019, so feel free to close this PR. ",
"Thanks !"
] | 2023-06-08T13:31:44Z
| 2023-07-12T18:21:03Z
| 2023-07-12T18:21:02Z
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CONTRIBUTOR
| null | null | null |
Some warning messages didn't quite sound like warnings so I modified their logging levels to info.
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Concatenate_datasets loads everything into RAM
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## Describe the bug
When loading the datasets seperately and saving them on disk, I want to concatenate them. But `concatenate_datasets` is filling up my RAM and the process gets killed. Is there a way to prevent this from happening or is this intended behaviour? Thanks in advance
## Steps to reproduce the bug
```python
gcs = gcsfs.GCSFileSystem(project='project')
datasets = [load_from_disk(f'path/to/slice/of/data/{i}', fs=gcs, keep_in_memory=False) for i in range(10)]
dataset = concatenate_datasets(datasets)
```
## Expected results
A concatenated dataset which is stored on my disk.
## Actual results
Concatenated dataset gets loaded into RAM and overflows it which gets the process killed.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 8.0.1
- Pandas version: 1.4.3
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Create dataset from list of dicts
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[
"Hi @sanderland, thanks for your enhancement proposal.\r\n\r\nI agree with you that this would be useful.\r\n\r\nPlease note that under the hood, we use PyArrow tables as backend:\r\n- The implementation of `Dataset.from_dict` uses the PyArrow `Table.from_pydict`\r\n\r\nTherefore, I would suggest:\r\n- Implementing `Dataset.from_list` using the PyArrow `Table.from_pylist`\r\n\r\nWhat do you think?\r\nLet's see if other people have other suggestions...",
"Thanks for the quick and positive reply @albertvillanova! \r\n`from_list` seems sensible. Have opened a PR so we can discuss details there.",
"Resolved via #4890."
] | 2022-08-24T10:01:24Z
| 2022-09-08T16:02:52Z
| 2022-09-08T16:02:52Z
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CONTRIBUTOR
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I often find myself with data from a variety of sources, and a list of dicts is very common among these.
However, converting this to a Dataset is a little awkward, requiring either
```Dataset.from_pandas(pd.DataFrame(formatted_training_data))```
Which can error out on some more exotic values as 2-d arrays for reasons that are not entirely clear
> ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column labels with type object')
Alternatively:
```Dataset.from_dict({k: [s[k] for s in formatted_training_data] for k in formatted_training_data[0].keys()})```
Which works, but is a little ugly.
**Describe the solution you'd like**
Either `.from_dict` accepting a list of dicts, or a `.from_records` function accepting such.
I am happy to PR this, just wanted to check you are happy to accept this I haven't missed something obvious, and which of the solutions would be prefered.
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Support all split in streaming mode
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4640). All of your documentation changes will be reflected on that endpoint."
] | 2022-07-06T08:56:38Z
| 2022-07-06T15:19:55Z
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MEMBER
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Fix #4637.
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I_kwDODunzps5R_wXG
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How to convert torch.utils.data.Dataset to huggingface dataset?
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"Hi! I think you can use the newly-added `from_generator` method for that:\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndef gen():\r\n for idx in len(torch_dataset):\r\n yield torch_dataset[idx] # this has to be a dictionary\r\n ## or if it's an IterableDataset\r\n # for ex in torch_dataset:\r\n # yield ex\r\n\r\ndset = Dataset.from_generator(gen)\r\n```",
"Maybe `Dataset.from_list` can work as well no ?\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndset = Dataset.from_list(torch_dataset)\r\n```",
"> ```python\r\n> from datasets import Dataset\r\n> \r\n> def gen():\r\n> for idx in len(torch_dataset):\r\n> yield torch_dataset[idx] # this has to be a dictionary\r\n> ## or if it's an IterableDataset\r\n> # for ex in torch_dataset:\r\n> # yield ex\r\n> \r\n> dset = Dataset.from_generator(gen)\r\n> ```\r\n\r\nI try to use `Dataset.from_generator()` method, and it returns an error:\r\n```bash\r\nAttributeError: type object 'Dataset' has no attribute 'from_generator'\r\n```\r\nAnd I think it maybe the version of my datasets package is out-of-date, so I update it\r\n```bash\r\npip install --upgrade datasets\r\n```\r\nBut after that, the code still return the above Error. ",
"> ```python\r\n> dset = Dataset.from_list(torch_dataset)\r\n> ```\r\n\r\nIt seems that Dataset also has no `from_list` method 😂\r\n```bash\r\nAttributeError: type object 'Dataset' has no attribute 'from_list'\r\n```",
"> I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below:\r\n> \r\n> ```python\r\n> from datasets import Dataset\r\n> data = [[1, 2],[3, 4]]\r\n> ds = Dataset.from_dict({\"data\": data})\r\n> ds = ds.with_format(\"torch\")\r\n> ds[0]\r\n> ds[:2]\r\n> ```\r\n> \r\n> So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert? Thanks.\r\n\r\nMy dummy code is like:\r\n```python\r\nimport os\r\nimport json\r\nfrom torch.utils import data\r\nimport datasets\r\n\r\ndef gen(torch_dataset):\r\n for idx in len(torch_dataset):\r\n yield torch_dataset[idx] # this has to be a dictionary\r\n\r\nclass MyDataset(data.Dataset):\r\n def __init__(self, path):\r\n self.dict = []\r\n for line in open(path, 'r', encoding='utf-8'):\r\n j_dict = json.loads(line)\r\n self.dict.append(j_dict['context'])\r\n \r\n def __getitem__(self, idx):\r\n return self.dict[idx]\r\n\r\n def __len__(self):\r\n return len(self.dict)\r\n\r\nroot_path = os.path.dirname(os.path.abspath(__file__))\r\npath = os.path.join(root_path, 'dataset', 'train.json')\r\ntorch_dataset = MyDataset(path)\r\n\r\ndit = []\r\nfor line in open(path, 'r', encoding='utf-8'):\r\n j_dict = json.loads(line)\r\n dit.append(j_dict['context'])\r\ndset1 = datasets.Dataset.from_list(dit)\r\nprint(dset1)\r\ndset2 = datasets.Dataset.from_generator(gen)\r\nprint(dset2)\r\n```",
"We're releasing `from_generator` and `from_list` today :)\r\nIn the meantime you can play with them by installing `datasets` from source",
"> We're releasing `from_generator` and `from_list` today :) In the meantime you can play with them by installing `datasets` from source\r\n\r\nThanks a lot for your work!",
"> > I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below:\r\n> > ```python\r\n> > from datasets import Dataset\r\n> > data = [[1, 2],[3, 4]]\r\n> > ds = Dataset.from_dict({\"data\": data})\r\n> > ds = ds.with_format(\"torch\")\r\n> > ds[0]\r\n> > ds[:2]\r\n> > ```\r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert? Thanks.\r\n> \r\n> My dummy code is like:\r\n> \r\n> ```python\r\n> import os\r\n> import json\r\n> from torch.utils import data\r\n> import datasets\r\n> \r\n> def gen(torch_dataset):\r\n> for idx in len(torch_dataset):\r\n> yield torch_dataset[idx] # this has to be a dictionary\r\n> \r\n> class MyDataset(data.Dataset):\r\n> def __init__(self, path):\r\n> self.dict = []\r\n> for line in open(path, 'r', encoding='utf-8'):\r\n> j_dict = json.loads(line)\r\n> self.dict.append(j_dict['context'])\r\n> \r\n> def __getitem__(self, idx):\r\n> return self.dict[idx]\r\n> \r\n> def __len__(self):\r\n> return len(self.dict)\r\n> \r\n> root_path = os.path.dirname(os.path.abspath(__file__))\r\n> path = os.path.join(root_path, 'dataset', 'train.json')\r\n> torch_dataset = MyDataset(path)\r\n> \r\n> dit = []\r\n> for line in open(path, 'r', encoding='utf-8'):\r\n> j_dict = json.loads(line)\r\n> dit.append(j_dict['context'])\r\n> dset1 = datasets.Dataset.from_list(dit)\r\n> print(dset1)\r\n> dset2 = datasets.Dataset.from_generator(gen)\r\n> print(dset2)\r\n> ```\r\nHi, when I am using this code to build my own dataset, ` datasets.Dataset.from_generator(gen)` report `TypeError: cannot pickle generator object` whre MyDataset returns a dict like {'image': bytes, 'text': string}. How can I resolve this? Thanks a lot!",
"Hi ! Right now generator functions are expected to be picklable, so that `datasets` can hash it and use the hash to cache the resulting Dataset on disk. Maybe this can be improved.\r\n\r\nIn the meantime, can you check that you're not using unpickable objects. In your case it looks like you're using a generator object that is unpickable. It might come from an opened file, e.g. this doesn't work:\r\n```python\r\nwith open(...) as f:\r\n\r\n def gen():\r\n for x in f:\r\n yield json.loads(x)\r\n\r\n ds = Dataset.from_generator(gen)\r\n```\r\nbut this does work:\r\n```python\r\ndef gen():\r\n with open(...) as f:\r\n for x in f:\r\n yield json.loads(x)\r\n\r\nds = Dataset.from_generator(gen)\r\n```",
"> Hi ! Right now generator functions are expected to be picklable, so that `datasets` can hash it and use the hash to cache the resulting Dataset on disk. Maybe this can be improved.\r\n> \r\n> In the meantime, can you check that you're not using unpickable objects. In your case it looks like you're using a generator object that is unpickable. It might come from an opened file, e.g. this doesn't work:\r\n> \r\n> ```python\r\n> with open(...) as f:\r\n> \r\n> def gen():\r\n> for x in f:\r\n> yield json.loads(x)\r\n> \r\n> ds = Dataset.from_generator(gen)\r\n> ```\r\n> \r\n> but this does work:\r\n> \r\n> ```python\r\n> def gen():\r\n> with open(...) as f:\r\n> for x in f:\r\n> yield json.loads(x)\r\n> \r\n> ds = Dataset.from_generator(gen)\r\n> ```\r\n\r\nThanks a lot! That's the reason why I have encountered this issue. Sorry for bothering you again with another problem, since my dataset is large and I use IterableDataset.from_generator which has no attribute with_transform, how can I equip it with some customed preprocessings like Dataset.from_generator? Should I move the preprocessing to the my torch Dataset?",
"Iterable datasets are lazy: exactly like `with_transform` they apply processing on the fly when accessing the examples.\r\n\r\nTherefore you can use `my_iterable_dataset.map()` instead :)",
"@lhoestq thanks a lot and I have successfully made it work~",
"@lhoestq I am having a similar issue. Can you help me understand which kinds of generators are picklable? I previously thought that no generators are picklable so I'm intrigued to hear this.",
"Generator functions are generally picklable. E.g.\r\n```python\r\nimport dill as pickle\r\n\r\ndef generator_fn():\r\n for i in range(10):\r\n yield i\r\n\r\npickle.dumps(generator_fn)\r\n```\r\n\r\nhowever generators are not picklable\r\n```python\r\ngenerator = generator_fn()\r\npickle.dumps(generator)\r\n# TypeError: cannot pickle 'generator' object\r\n```\r\n\r\nThough it can happen that some generator functions are not recursively picklable if they use global objects that are not picklable:\r\n```python\r\ndef generator_fn_not_picklable():\r\n for i in generator:\r\n yield i\r\n\r\npickle.dumps(generator_fn_not_picklable, recurse=True)\r\n# TypeError: cannot pickle 'generator' object\r\n````",
"I'm trying to create an IterableDataset from a generator but I get this error:\r\n`PicklingError: Can't pickle <built-in function input>: it's not the same object as builtins.input`\r\n\r\nWhat can I do?"
] | 2022-09-16T09:15:10Z
| 2023-12-14T20:54:15Z
| 2022-09-20T11:23:43Z
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I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below:
```python
from datasets import Dataset
data = [[1, 2],[3, 4]]
ds = Dataset.from_dict({"data": data})
ds = ds.with_format("torch")
ds[0]
ds[:2]
```
So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert?
Thanks.
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Dataset Viewer issue for codeparrot/xlcost-text-to-code
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"There seems to be an issue with the `C++-snippet-level` config:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"codeparrot/xlcost-text-to-code\", \"C++-snippet-level\")\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 352, in get_dataset_config_info\r\n info.splits = {\r\nTypeError: 'NoneType' object is not iterable\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nI remove the dataset-viewer tag since it's not directly related.\r\n\r\nPinging @huggingface/datasets ",
"Thanks I found that this subset wasn't properly defined the the config, I fixed it. Now I can see the subsets but I get this error for the viewer\r\n````\r\nStatus code: 400\r\nException: Status400Error\r\nMessage: The split cache is empty.\r\n```",
"Yes, the cache is being refreshed, hopefully, it will work in some minutes for all the splits. Some are already here:\r\n\r\nhttps://huggingface.co/datasets/codeparrot/xlcost-text-to-code/viewer/Python-snippet-level/train\r\n\r\n<img width=\"1533\" alt=\"Capture d’écran 2022-07-18 à 12 04 06\" src=\"https://user-images.githubusercontent.com/1676121/179553933-64d874fa-ada9-4b82-900e-082619523c20.png\">\r\n",
"I think all the splits are working as expected now",
"Perfect, thank you!"
] | 2022-07-14T09:45:50Z
| 2022-07-18T16:37:00Z
| 2022-07-18T16:04:36Z
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### Link
https://huggingface.co/datasets/codeparrot/xlcost-text-to-code
### Description
Error
```
Server Error
Status code: 400
Exception: TypeError
Message: 'NoneType' object is not iterable
```
Before I did a minor change in the dataset script (removing some comments), the viewer was working but not properely, it wasn't showing the dataset subsets. But the data can be loaded successfully.
Thanks!
### Owner
Yes
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How to get the original dataset name with username?
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"Hi ! why not pass the dataset id to Ray and let it check the parquet files ? Or pass the parquet files lists directly ?",
"I'm not sure why ray design an API like this to accept a `Dataset` object, so they need to verify the `Dataset` is the original one and use the `DatasetInfo` to query the huggingface hub. I'll advise the ray data team to use dataset id instead of dataset for the `HuggingFaceDatasource` API."
] | 2024-12-08T07:18:14Z
| 2025-01-09T10:48:02Z
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CONTRIBUTOR
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### Feature request
The issue is related to ray data https://github.com/ray-project/ray/issues/49008 which it requires to check if the dataset is the original one just after `load_dataset` and parquet files are already available on hf hub.
The solution used now is to get the dataset name, config and split, then `load_dataset` again and check the fingerprint. But it's unable to get the correct dataset name if it contains username. So how to get the dataset name with username prefix, or is there another way to query if a dataset is the original one with parquet available?
@lhoestq
### Motivation
https://github.com/ray-project/ray/issues/49008
### Your contribution
Would like to fix that.
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🧪 Do not mock create_commit
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7076). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-07-26T13:44:42Z
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6047). 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) | 0.006384 / 0.011353 (-0.004969) | 0.003872 / 0.011008 (-0.007136) | 0.083454 / 0.038508 (0.044946) | 0.069120 / 0.023109 (0.046011) | 0.312573 / 0.275898 (0.036675) | 0.345814 / 0.323480 (0.022334) | 0.005729 / 0.007986 (-0.002257) | 0.003225 / 0.004328 (-0.001103) | 0.063950 / 0.004250 (0.059700) | 0.053998 / 0.037052 (0.016946) | 0.316492 / 0.258489 (0.058003) | 0.350738 / 0.293841 (0.056897) | 0.030770 / 0.128546 (-0.097776) | 0.008474 / 0.075646 (-0.067173) | 0.286989 / 0.419271 (-0.132282) | 0.052473 / 0.043533 (0.008940) | 0.314361 / 0.255139 (0.059222) | 0.335170 / 0.283200 (0.051970) | 0.022885 / 0.141683 (-0.118798) | 1.465430 / 1.452155 (0.013275) | 1.527799 / 1.492716 (0.035083) |\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) | 0.209377 / 0.018006 (0.191371) | 0.455583 / 0.000490 (0.455094) | 0.003352 / 0.000200 (0.003152) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026284 / 0.037411 (-0.011127) | 0.080710 / 0.014526 (0.066185) | 0.091741 / 0.176557 (-0.084816) | 0.147602 / 0.737135 (-0.589534) | 0.091173 / 0.296338 (-0.205166) |\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) | 0.386592 / 0.215209 (0.171383) | 3.856665 / 2.077655 (1.779011) | 1.835745 / 1.504120 (0.331625) | 1.671814 / 1.541195 (0.130619) | 1.711224 / 1.468490 (0.242734) | 0.484704 / 4.584777 (-4.100073) | 3.649239 / 3.745712 (-0.096473) | 3.784051 / 5.269862 (-1.485810) | 2.241195 / 4.565676 (-2.324482) | 0.056613 / 0.424275 (-0.367662) | 0.007140 / 0.007607 (-0.000467) | 0.464585 / 0.226044 (0.238540) | 4.616537 / 2.268929 (2.347609) | 2.371969 / 55.444624 (-53.072656) | 1.977754 / 6.876477 (-4.898723) | 2.083385 / 2.142072 (-0.058687) | 0.582330 / 4.805227 (-4.222897) | 0.132744 / 6.500664 (-6.367920) | 0.059822 / 0.075469 (-0.015647) |\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) | 1.259566 / 1.841788 (-0.582221) | 18.990166 / 8.074308 (10.915858) | 13.992069 / 10.191392 (3.800677) | 0.160001 / 0.680424 (-0.520423) | 0.018622 / 0.534201 (-0.515579) | 0.392921 / 0.579283 (-0.186362) | 0.418225 / 0.434364 (-0.016139) | 0.471252 / 0.540337 (-0.069086) | 0.653227 / 1.386936 (-0.733709) |\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) | 0.006641 / 0.011353 (-0.004712) | 0.003738 / 0.011008 (-0.007271) | 0.064053 / 0.038508 (0.025545) | 0.069467 / 0.023109 (0.046357) | 0.360625 / 0.275898 (0.084727) | 0.394291 / 0.323480 (0.070811) | 0.005236 / 0.007986 (-0.002750) | 0.003304 / 0.004328 (-0.001024) | 0.064078 / 0.004250 (0.059827) | 0.054605 / 0.037052 (0.017552) | 0.374567 / 0.258489 (0.116078) | 0.411227 / 0.293841 (0.117386) | 0.031614 / 0.128546 (-0.096933) | 0.008323 / 0.075646 (-0.067324) | 0.070616 / 0.419271 (-0.348656) | 0.050077 / 0.043533 (0.006544) | 0.362229 / 0.255139 (0.107090) | 0.388310 / 0.283200 (0.105110) | 0.024053 / 0.141683 (-0.117630) | 1.508913 / 1.452155 (0.056759) | 1.562140 / 1.492716 (0.069423) |\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) | 0.230172 / 0.018006 (0.212165) | 0.449363 / 0.000490 (0.448873) | 0.002374 / 0.000200 (0.002174) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029813 / 0.037411 (-0.007598) | 0.087298 / 0.014526 (0.072772) | 0.096712 / 0.176557 (-0.079845) | 0.152864 / 0.737135 (-0.584271) | 0.098204 / 0.296338 (-0.198135) |\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) | 0.408664 / 0.215209 (0.193455) | 4.075068 / 2.077655 (1.997414) | 2.096365 / 1.504120 (0.592245) | 1.936096 / 1.541195 (0.394901) | 1.961872 / 1.468490 (0.493382) | 0.483383 / 4.584777 (-4.101394) | 3.686926 / 3.745712 (-0.058787) | 4.798824 / 5.269862 (-0.471037) | 2.652279 / 4.565676 (-1.913398) | 0.056695 / 0.424275 (-0.367580) | 0.007592 / 0.007607 (-0.000016) | 0.484710 / 0.226044 (0.258665) | 4.842153 / 2.268929 (2.573225) | 2.636828 / 55.444624 (-52.807796) | 2.243666 / 6.876477 (-4.632811) | 2.375972 / 2.142072 (0.233899) | 0.578544 / 4.805227 (-4.226683) | 0.132579 / 6.500664 (-6.368085) | 0.061287 / 0.075469 (-0.014182) |\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) | 1.360287 / 1.841788 (-0.481501) | 19.464110 / 8.074308 (11.389802) | 14.530875 / 10.191392 (4.339483) | 0.149479 / 0.680424 (-0.530944) | 0.018471 / 0.534201 (-0.515730) | 0.395399 / 0.579283 (-0.183884) | 0.412897 / 0.434364 (-0.021467) | 0.465194 / 0.540337 (-0.075144) | 0.611752 / 1.386936 (-0.775184) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79a535de98b590da7bc223a6498c59790882f14a \"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) | 0.008986 / 0.011353 (-0.002367) | 0.005104 / 0.011008 (-0.005905) | 0.108371 / 0.038508 (0.069863) | 0.091655 / 0.023109 (0.068546) | 0.430183 / 0.275898 (0.154285) | 0.481387 / 0.323480 (0.157907) | 0.006662 / 0.007986 (-0.001324) | 0.004681 / 0.004328 (0.000353) | 0.089325 / 0.004250 (0.085075) | 0.065096 / 0.037052 (0.028044) | 0.435021 / 0.258489 (0.176532) | 0.478635 / 0.293841 (0.184794) | 0.047628 / 0.128546 (-0.080918) | 0.013496 / 0.075646 (-0.062150) | 0.389661 / 0.419271 (-0.029611) | 0.082260 / 0.043533 (0.038727) | 0.474165 / 0.255139 (0.219026) | 0.464877 / 0.283200 (0.181677) | 0.039784 / 0.141683 (-0.101899) | 1.874694 / 1.452155 (0.422539) | 1.980183 / 1.492716 (0.487467) |\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) | 0.254044 / 0.018006 (0.236038) | 0.631495 / 0.000490 (0.631005) | 0.000628 / 0.000200 (0.000428) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038773 / 0.037411 (0.001362) | 0.103681 / 0.014526 (0.089156) | 0.125081 / 0.176557 (-0.051476) | 0.198345 / 0.737135 (-0.538790) | 0.122217 / 0.296338 (-0.174121) |\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) | 0.611677 / 0.215209 (0.396468) | 6.220790 / 2.077655 (4.143135) | 2.729858 / 1.504120 (1.225739) | 2.351944 / 1.541195 (0.810749) | 2.449137 / 1.468490 (0.980647) | 0.896842 / 4.584777 (-3.687935) | 5.537491 / 3.745712 (1.791778) | 8.480182 / 5.269862 (3.210320) | 5.251404 / 4.565676 (0.685728) | 0.100449 / 0.424275 (-0.323826) | 0.009008 / 0.007607 (0.001401) | 0.750060 / 0.226044 (0.524016) | 7.390940 / 2.268929 (5.122011) | 3.478256 / 55.444624 (-51.966369) | 2.883597 / 6.876477 (-3.992880) | 3.082256 / 2.142072 (0.940183) | 1.114339 / 4.805227 (-3.690889) | 0.225389 / 6.500664 (-6.275275) | 0.083972 / 0.075469 (0.008503) |\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) | 1.741522 / 1.841788 (-0.100266) | 25.674700 / 8.074308 (17.600392) | 24.324412 / 10.191392 (14.133020) | 0.257878 / 0.680424 (-0.422546) | 0.038384 / 0.534201 (-0.495817) | 0.508302 / 0.579283 (-0.070981) | 0.612979 / 0.434364 (0.178615) | 0.584366 / 0.540337 (0.044029) | 0.881115 / 1.386936 (-0.505821) |\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) | 0.009114 / 0.011353 (-0.002239) | 0.005333 / 0.011008 (-0.005675) | 0.094944 / 0.038508 (0.056436) | 0.099178 / 0.023109 (0.076068) | 0.529813 / 0.275898 (0.253915) | 0.551282 / 0.323480 (0.227802) | 0.006442 / 0.007986 (-0.001543) | 0.004283 / 0.004328 (-0.000045) | 0.084257 / 0.004250 (0.080007) | 0.067557 / 0.037052 (0.030504) | 0.514733 / 0.258489 (0.256244) | 0.568200 / 0.293841 (0.274359) | 0.050969 / 0.128546 (-0.077577) | 0.014495 / 0.075646 (-0.061151) | 0.097089 / 0.419271 (-0.322182) | 0.063142 / 0.043533 (0.019609) | 0.513327 / 0.255139 (0.258188) | 0.520593 / 0.283200 (0.237394) | 0.036824 / 0.141683 (-0.104859) | 1.954875 / 1.452155 (0.502720) | 1.976307 / 1.492716 (0.483591) |\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) | 0.304070 / 0.018006 (0.286063) | 0.611073 / 0.000490 (0.610583) | 0.005027 / 0.000200 (0.004827) | 0.000113 / 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) | 0.037993 / 0.037411 (0.000582) | 0.115876 / 0.014526 (0.101350) | 0.118087 / 0.176557 (-0.058469) | 0.186437 / 0.737135 (-0.550699) | 0.129883 / 0.296338 (-0.166456) |\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) | 0.658292 / 0.215209 (0.443083) | 6.618257 / 2.077655 (4.540602) | 3.203786 / 1.504120 (1.699667) | 2.858714 / 1.541195 (1.317519) | 2.940974 / 1.468490 (1.472484) | 0.856238 / 4.584777 (-3.728538) | 5.427708 / 3.745712 (1.681996) | 4.810048 / 5.269862 (-0.459813) | 3.120006 / 4.565676 (-1.445671) | 0.098098 / 0.424275 (-0.326177) | 0.010077 / 0.007607 (0.002470) | 0.790890 / 0.226044 (0.564845) | 7.956679 / 2.268929 (5.687750) | 3.955710 / 55.444624 (-51.488914) | 3.446419 / 6.876477 (-3.430057) | 3.541228 / 2.142072 (1.399156) | 1.013420 / 4.805227 (-3.791808) | 0.213741 / 6.500664 (-6.286923) | 0.080857 / 0.075469 (0.005388) |\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) | 1.813265 / 1.841788 (-0.028522) | 25.965199 / 8.074308 (17.890891) | 21.892761 / 10.191392 (11.701369) | 0.257843 / 0.680424 (-0.422580) | 0.029388 / 0.534201 (-0.504813) | 0.510609 / 0.579283 (-0.068674) | 0.626579 / 0.434364 (0.192215) | 0.576865 / 0.540337 (0.036528) | 0.826610 / 1.386936 (-0.560326) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1a9c00249b330f97f66ceb86c2939261091f4fe \"CML watermark\")\n"
] | 2023-07-18T10:15:39Z
| 2023-07-18T10:28:01Z
| 2023-07-18T10:15:52Z
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MEMBER
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workaround to fix an issue with transformers CI
https://github.com/huggingface/transformers/pull/24867#discussion_r1266519626
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PR_kwDODunzps5teFoJ
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Support downloading specific splits in `load_dataset`
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6832). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Friendly ping on this! This feature would be really helpful and useful to me (and likely others with limited download speed and storage space!). Thanks so much!",
"No one is working on this atm afaik :/",
"No worries! I've patched the ImageNet dataset in: <https://huggingface.co/datasets/ILSVRC/imagenet-1k/blob/refs%2Fpr%2F20/imagenet-1k.py> \r\n\r\nTogether with:\r\n\r\n```python\r\ndataset = load_dataset(\r\n \"ILSVRC/imagenet-1k\",\r\n split=\"validation\",\r\n data_files={\"val\": \"data/val_images.tar.gz\"},\r\n revision=\"refs/pr/20\",\r\n trust_remote_code=True,\r\n download_config=DownloadConfig(resume_download=True),\r\n verification_mode=VerificationMode.NO_CHECKS,\r\n )\r\n```\r\n\r\nIt only downloads the validation set this way (NO_CHECKS is a bit annoying because I'd rather have md5 checks, but I guess I can't have everything) ^^' The patch is not perfect, but it does the job.\r\n\r\n"
] | 2024-04-23T12:32:27Z
| 2024-08-19T15:19:38Z
| null |
COLLABORATOR
| null | null | null |
This PR builds on https://github.com/huggingface/datasets/pull/6639 to support downloading only the specified splits in `load_dataset`. For this to work, a builder's `_split_generators` need to be able to accept the requested splits (as a list) via a `splits` argument to avoid processing the non-requested ones. Also, the builder has to define a `_available_splits` method that lists all the possible `splits` values.
Close https://github.com/huggingface/datasets/issues/4101, close https://github.com/huggingface/datasets/issues/2538 (I'm probably missing some)
Should also make it possible to address https://github.com/huggingface/datasets/issues/6793
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I_kwDODunzps6QaWt0
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None values are skipped when reading jsonl in subobjects
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[] | 2024-07-22T13:02:42Z
| 2024-07-22T13:02:53Z
| null |
NONE
| null | null |
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### Describe the bug
I have been fighting against my machine since this morning only to find out this is some kind of a bug.
When loading a dataset composed of `metadata.jsonl`, if you have nullable values (Optional[str]), they can be ignored by the parser, shifting things around.
E.g., let's take this example
Here are two version of a same dataset:
[not-buggy.tar.gz](https://github.com/user-attachments/files/16333532/not-buggy.tar.gz)
[buggy.tar.gz](https://github.com/user-attachments/files/16333553/buggy.tar.gz)
### Steps to reproduce the bug
1. Load the `buggy.tar.gz` dataset
2. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
3. Load the `not-buggy.tar.gz` dataset
4. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
### Expected behavior
Both should have 4 baseline entries:
1. Buggy should have None followed by three lists
2. Non-Buggy should have four lists, and the first one should be an empty list.
One does not work, 2 works. Despite accepting None in another position than the first one.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
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