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"Hi! Yes, that's correct. When `streaming` is `True`, only split names can be specified as `split`, and for slicing, you have to use `.skip`/`.take` instead.\r\n\r\nE.g. \r\n`load_dataset(\"lhoestq/demo1\",revision=None, streaming=True, split=\"train[:3]\")`\r\n\r\nrewritten with `.skip`/`.take`:\r\n`load_dataset(\"lhoestq/demo1\",revision=None, streaming=True, split=\"train\").take(3)`\r\n\r\n\r\n",
"Thank you for your quick response!"
] | 2023-01-24T14:08:17 | 2023-01-24T15:11:47 | 2023-01-24T15:11:47 | NONE | null | null | null | ### Describe the bug
When using the `load_dataset` function with streaming set to True, slicing splits is apparently not supported.
Did I miss this in the documentation?
### Steps to reproduce the bug
`load_dataset("lhoestq/demo1",revision=None, streaming=True, split="train[:3]")`
causes ValueError: Bad split: train[:3]. Available splits: ['train', 'test'] in builder.py, line 1213, in as_streaming_dataset
### Expected behavior
The first 3 entries of the dataset as a stream
### Environment info
- `datasets` version: 2.8.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.10.9
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
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"Hi! \r\n\r\nThis issue is due to our audio/image datasets not being self-contained. This allows us to save disk space (files are written only once) but also leads to the issues like this one. We plan to make all our datasets self-contained in Datasets 3.0.\r\n\r\nIn the meantime, you can run the following map to ensure your dataset is self-contained:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\n# load_dataset ...\r\ndset = dset.with_format(\"arrow\")\r\ndset.map(embed_table_storage, batched=True)\r\ndset = dset.with_format(\"python\")\r\n```\r\n",
"Understood. Thank you, Mario.\r\n\r\nPerhaps the solution could be very simple - move the extracted files into the directory of the cached dataset? Which would make it self-contained already and won't require waiting for a new major release. Unless I'm missing some back-compat nuance.\r\n\r\nBut regardless if X relies on Y - it could check if Y is still there when loading X. so not checking full consistency but just the top-level directory it relies on."
] | 2023-01-24T02:09:32 | 2023-01-24T18:14:10 | null | MEMBER | null | null | null | ### Describe the bug
I pre-built the dataset:
```
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
```
and it can be used just fine.
now I wipe out `downloads/extracted` and it no longer works.
```
rm -r ~/.cache/huggingface/datasets/downloads
```
That is I can still load it:
```
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
No config specified, defaulting to: general-pmd-synthetic-testing/100.unique
Found cached dataset general-pmd-synthetic-testing (/home/stas/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing/100.unique/1.1.1/86bc445e3e48cb5ef79de109eb4e54ff85b318cd55c3835c4ee8f86eae33d9d2)
```
but if I try to use it:
```
E stderr: Traceback (most recent call last):
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/main.py", line 116, in <module>
E stderr: train_loader, val_loader = get_dataloaders(
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 170, in get_dataloaders
E stderr: train_loader = get_dataloader_from_config(
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 443, in get_dataloader_from_config
E stderr: dataloader = get_dataloader(
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 264, in get_dataloader
E stderr: is_pmd = "meta" in hf_dataset[0] and "source" in hf_dataset[0]
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/arrow_dataset.py", line 2601, in __getitem__
E stderr: return self._getitem(
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/arrow_dataset.py", line 2586, in _getitem
E stderr: formatted_output = format_table(
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 634, in format_table
E stderr: return formatter(pa_table, query_type=query_type)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 406, in __call__
E stderr: return self.format_row(pa_table)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 442, in format_row
E stderr: row = self.python_features_decoder.decode_row(row)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 225, in decode_row
E stderr: return self.features.decode_example(row) if self.features else row
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1846, in decode_example
E stderr: return {
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1847, in <dictcomp>
E stderr: column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1304, in decode_nested_example
E stderr: return decode_nested_example([schema.feature], obj)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1296, in decode_nested_example
E stderr: if decode_nested_example(sub_schema, first_elmt) != first_elmt:
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1309, in decode_nested_example
E stderr: return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/image.py", line 144, in decode_example
E stderr: image = PIL.Image.open(path)
E stderr: File "/home/stas/anaconda3/envs/py38-pt113/lib/python3.8/site-packages/PIL/Image.py", line 3092, in open
E stderr: fp = builtins.open(filename, "rb")
E stderr: FileNotFoundError: [Errno 2] No such file or directory: '/mnt/nvme0/code/data/cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data/101/images_01.jpg'
```
Only if I wipe out the cached dir and rebuild then it starts working as `download/extracted` is back again with extracted files.
```
rm -r ~/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
```
I think there are 2 issues here:
1. why does it still rely on extracted files after `arrow` files were printed - did I do something incorrectly when creating this dataset?
2. why doesn't the dataset know that it has been gutted and loads just fine? If it has a dependency on `download/extracted` then `load_dataset` should check if it's there and fail or force rebuilding. I am sure this could be a very expensive operation, so probably really solving #1 will not require this check. and this second item is probably an overkill. Other than perhaps if it had an optional `check_consistency` flag to do that.
### Environment info
datasets@main | {
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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) | 0.012395 / 0.011353 (0.001042) | 0.006466 / 0.011008 (-0.004542) | 0.127605 / 0.038508 (0.089097) | 0.044929 / 0.023109 (0.021820) | 0.399856 / 0.275898 (0.123958) | 0.491341 / 0.323480 (0.167861) | 0.009193 / 0.007986 (0.001207) | 0.005419 / 0.004328 (0.001090) | 0.100577 / 0.004250 (0.096327) | 0.045338 / 0.037052 (0.008286) | 0.409970 / 0.258489 (0.151481) | 0.452941 / 0.293841 (0.159100) | 0.054350 / 0.128546 (-0.074197) | 0.019069 / 0.075646 (-0.056578) | 0.427036 / 0.419271 (0.007765) | 0.073616 / 0.043533 (0.030083) | 0.395384 / 0.255139 (0.140245) | 0.442381 / 0.283200 (0.159181) | 0.123185 / 0.141683 (-0.018498) | 1.797640 / 1.452155 (0.345485) | 1.888860 / 1.492716 (0.396143) |\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) | 0.211041 / 0.018006 (0.193035) | 0.539350 / 0.000490 (0.538860) | 0.001683 / 0.000200 (0.001483) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031699 / 0.037411 (-0.005712) | 0.132696 / 0.014526 (0.118170) | 0.133710 / 0.176557 (-0.042846) | 0.190074 / 0.737135 (-0.547061) | 0.142919 / 0.296338 (-0.153420) |\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) | 0.643521 / 0.215209 (0.428312) | 6.137350 / 2.077655 (4.059695) | 2.463894 / 1.504120 (0.959774) | 2.120043 / 1.541195 (0.578848) | 2.121898 / 1.468490 (0.653408) | 1.287319 / 4.584777 (-3.297458) | 5.517864 / 3.745712 (1.772151) | 5.070820 / 5.269862 (-0.199042) | 2.948967 / 4.565676 (-1.616710) | 0.175861 / 0.424275 (-0.248415) | 0.015292 / 0.007607 (0.007685) | 0.843195 / 0.226044 (0.617150) | 7.884275 / 2.268929 (5.615347) | 3.182821 / 55.444624 (-52.261803) | 2.576093 / 6.876477 (-4.300384) | 2.537160 / 2.142072 (0.395088) | 1.510029 / 4.805227 (-3.295198) | 0.249404 / 6.500664 (-6.251260) | 0.080434 / 0.075469 (0.004965) |\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) | 1.618695 / 1.841788 (-0.223093) | 18.879207 / 8.074308 (10.804899) | 21.075272 / 10.191392 (10.883880) | 0.260781 / 0.680424 (-0.419643) | 0.046387 / 0.534201 (-0.487813) | 0.570709 / 0.579283 (-0.008574) | 0.619050 / 0.434364 (0.184686) | 0.642295 / 0.540337 (0.101958) | 0.780070 / 1.386936 (-0.606866) |\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) | 0.010418 / 0.011353 (-0.000935) | 0.006104 / 0.011008 (-0.004905) | 0.133609 / 0.038508 (0.095101) | 0.035101 / 0.023109 (0.011992) | 0.471931 / 0.275898 (0.196033) | 0.504498 / 0.323480 (0.181018) | 0.007388 / 0.007986 (-0.000598) | 0.004852 / 0.004328 (0.000523) | 0.094535 / 0.004250 (0.090284) | 0.056832 / 0.037052 (0.019779) | 0.470513 / 0.258489 (0.212024) | 0.531285 / 0.293841 (0.237444) | 0.058271 / 0.128546 (-0.070276) | 0.020523 / 0.075646 (-0.055123) | 0.437398 / 0.419271 (0.018126) | 0.065390 / 0.043533 (0.021857) | 0.503702 / 0.255139 (0.248563) | 0.515876 / 0.283200 (0.232677) | 0.118615 / 0.141683 (-0.023068) | 1.865380 / 1.452155 (0.413225) | 1.990316 / 1.492716 (0.497600) |\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) | 0.246772 / 0.018006 (0.228766) | 0.560607 / 0.000490 (0.560118) | 0.005675 / 0.000200 (0.005475) | 0.000142 / 0.000054 (0.000088) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034692 / 0.037411 (-0.002719) | 0.174016 / 0.014526 (0.159490) | 0.179838 / 0.176557 (0.003282) | 0.217118 / 0.737135 (-0.520018) | 0.184811 / 0.296338 (-0.111527) |\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) | 0.675970 / 0.215209 (0.460760) | 6.787039 / 2.077655 (4.709384) | 2.932619 / 1.504120 (1.428499) | 2.545076 / 1.541195 (1.003882) | 2.566705 / 1.468490 (1.098215) | 1.287365 / 4.584777 (-3.297412) | 5.468441 / 3.745712 (1.722729) | 5.227726 / 5.269862 (-0.042136) | 2.868970 / 4.565676 (-1.696706) | 0.153535 / 0.424275 (-0.270740) | 0.020087 / 0.007607 (0.012480) | 0.860562 / 0.226044 (0.634518) | 8.656109 / 2.268929 (6.387180) | 3.749424 / 55.444624 (-51.695200) | 3.011337 / 6.876477 (-3.865139) | 3.119045 / 2.142072 (0.976973) | 1.562174 / 4.805227 (-3.243053) | 0.279161 / 6.500664 (-6.221504) | 0.084905 / 0.075469 (0.009436) |\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) | 1.638684 / 1.841788 (-0.203104) | 18.834760 / 8.074308 (10.760452) | 21.554310 / 10.191392 (11.362918) | 0.274518 / 0.680424 (-0.405906) | 0.030343 / 0.534201 (-0.503858) | 0.539094 / 0.579283 (-0.040189) | 0.627258 / 0.434364 (0.192895) | 0.624638 / 0.540337 (0.084301) | 0.742776 / 1.386936 (-0.644160) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98c9b27be45e1f5bc8c18d8bb2414478efe68055 \"CML watermark\")\n"
] | 2023-01-23T22:05:38 | 2023-01-24T11:26:47 | 2023-01-24T11:17:12 | CONTRIBUTOR | null | false | {
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"merged_at": "2023-01-24T11:17:12"
} | As described in #5418
I noticed also that the `to_json` function supports multi-workers whereas `to_parquet`, is that not possible/not needed with Parquet or something that hasn't been implemented yet? | {
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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) | 0.008372 / 0.011353 (-0.002981) | 0.004658 / 0.011008 (-0.006350) | 0.102005 / 0.038508 (0.063497) | 0.029030 / 0.023109 (0.005920) | 0.296968 / 0.275898 (0.021070) | 0.364898 / 0.323480 (0.041418) | 0.006899 / 0.007986 (-0.001087) | 0.003410 / 0.004328 (-0.000919) | 0.079705 / 0.004250 (0.075455) | 0.034265 / 0.037052 (-0.002787) | 0.305695 / 0.258489 (0.047206) | 0.343275 / 0.293841 (0.049434) | 0.033783 / 0.128546 (-0.094763) | 0.011604 / 0.075646 (-0.064042) | 0.322577 / 0.419271 (-0.096694) | 0.040540 / 0.043533 (-0.002993) | 0.299176 / 0.255139 (0.044037) | 0.333157 / 0.283200 (0.049957) | 0.087460 / 0.141683 (-0.054223) | 1.494392 / 1.452155 (0.042237) | 1.539580 / 1.492716 (0.046863) |\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) | 0.176206 / 0.018006 (0.158200) | 0.413702 / 0.000490 (0.413212) | 0.002625 / 0.000200 (0.002425) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023886 / 0.037411 (-0.013525) | 0.099758 / 0.014526 (0.085232) | 0.104349 / 0.176557 (-0.072208) | 0.147138 / 0.737135 (-0.589998) | 0.108682 / 0.296338 (-0.187657) |\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) | 0.411957 / 0.215209 (0.196748) | 4.110004 / 2.077655 (2.032349) | 1.820951 / 1.504120 (0.316831) | 1.629726 / 1.541195 (0.088532) | 1.672573 / 1.468490 (0.204083) | 0.686627 / 4.584777 (-3.898150) | 3.382665 / 3.745712 (-0.363047) | 2.875908 / 5.269862 (-2.393954) | 1.475331 / 4.565676 (-3.090345) | 0.081353 / 0.424275 (-0.342922) | 0.012521 / 0.007607 (0.004914) | 0.516226 / 0.226044 (0.290182) | 5.157658 / 2.268929 (2.888729) | 2.302012 / 55.444624 (-53.142612) | 1.950831 / 6.876477 (-4.925646) | 1.962081 / 2.142072 (-0.179992) | 0.800007 / 4.805227 (-4.005221) | 0.148462 / 6.500664 (-6.352202) | 0.064448 / 0.075469 (-0.011021) |\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) | 1.227977 / 1.841788 (-0.613810) | 13.776087 / 8.074308 (5.701779) | 13.749825 / 10.191392 (3.558433) | 0.137034 / 0.680424 (-0.543390) | 0.028461 / 0.534201 (-0.505740) | 0.392335 / 0.579283 (-0.186948) | 0.397404 / 0.434364 (-0.036960) | 0.450831 / 0.540337 (-0.089507) | 0.533716 / 1.386936 (-0.853220) |\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) | 0.006883 / 0.011353 (-0.004470) | 0.004625 / 0.011008 (-0.006383) | 0.099039 / 0.038508 (0.060531) | 0.028068 / 0.023109 (0.004958) | 0.419988 / 0.275898 (0.144090) | 0.449543 / 0.323480 (0.126063) | 0.005232 / 0.007986 (-0.002753) | 0.003527 / 0.004328 (-0.000801) | 0.076308 / 0.004250 (0.072057) | 0.040523 / 0.037052 (0.003471) | 0.420165 / 0.258489 (0.161676) | 0.463220 / 0.293841 (0.169379) | 0.032368 / 0.128546 (-0.096178) | 0.011784 / 0.075646 (-0.063863) | 0.320675 / 0.419271 (-0.098597) | 0.041861 / 0.043533 (-0.001672) | 0.424903 / 0.255139 (0.169764) | 0.443528 / 0.283200 (0.160328) | 0.090869 / 0.141683 (-0.050814) | 1.504757 / 1.452155 (0.052602) | 1.557824 / 1.492716 (0.065108) |\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) | 0.224020 / 0.018006 (0.206014) | 0.404090 / 0.000490 (0.403601) | 0.000403 / 0.000200 (0.000203) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024556 / 0.037411 (-0.012855) | 0.101280 / 0.014526 (0.086754) | 0.108017 / 0.176557 (-0.068540) | 0.146679 / 0.737135 (-0.590456) | 0.111468 / 0.296338 (-0.184870) |\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) | 0.478955 / 0.215209 (0.263746) | 4.769628 / 2.077655 (2.691973) | 2.473238 / 1.504120 (0.969118) | 2.263588 / 1.541195 (0.722393) | 2.285425 / 1.468490 (0.816935) | 0.699051 / 4.584777 (-3.885726) | 3.390495 / 3.745712 (-0.355217) | 1.858569 / 5.269862 (-3.411293) | 1.162081 / 4.565676 (-3.403596) | 0.083294 / 0.424275 (-0.340981) | 0.012410 / 0.007607 (0.004803) | 0.580786 / 0.226044 (0.354741) | 5.866868 / 2.268929 (3.597940) | 2.944358 / 55.444624 (-52.500266) | 2.596241 / 6.876477 (-4.280235) | 2.664464 / 2.142072 (0.522392) | 0.806751 / 4.805227 (-3.998476) | 0.152389 / 6.500664 (-6.348275) | 0.066945 / 0.075469 (-0.008524) |\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) | 1.290545 / 1.841788 (-0.551243) | 14.005727 / 8.074308 (5.931419) | 14.478951 / 10.191392 (4.287559) | 0.127488 / 0.680424 (-0.552935) | 0.016929 / 0.534201 (-0.517272) | 0.378380 / 0.579283 (-0.200904) | 0.387499 / 0.434364 (-0.046865) | 0.440816 / 0.540337 (-0.099522) | 0.525794 / 1.386936 (-0.861142) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#07549c6fcb2dced59d7614b4b8264d54ef573407 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008704 / 0.011353 (-0.002649) | 0.004474 / 0.011008 (-0.006534) | 0.101720 / 0.038508 (0.063212) | 0.030426 / 0.023109 (0.007317) | 0.298944 / 0.275898 (0.023046) | 0.371491 / 0.323480 (0.048011) | 0.007042 / 0.007986 (-0.000944) | 0.003479 / 0.004328 (-0.000850) | 0.078086 / 0.004250 (0.073835) | 0.037014 / 0.037052 (-0.000038) | 0.312964 / 0.258489 (0.054475) | 0.351251 / 0.293841 (0.057410) | 0.033286 / 0.128546 (-0.095260) | 0.011468 / 0.075646 (-0.064179) | 0.321784 / 0.419271 (-0.097488) | 0.040700 / 0.043533 (-0.002832) | 0.303799 / 0.255139 (0.048660) | 0.336982 / 0.283200 (0.053782) | 0.089448 / 0.141683 (-0.052235) | 1.462430 / 1.452155 (0.010275) | 1.524448 / 1.492716 (0.031732) |\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) | 0.178390 / 0.018006 (0.160384) | 0.402474 / 0.000490 (0.401984) | 0.002697 / 0.000200 (0.002497) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022679 / 0.037411 (-0.014733) | 0.097759 / 0.014526 (0.083234) | 0.105102 / 0.176557 (-0.071454) | 0.140720 / 0.737135 (-0.596415) | 0.109119 / 0.296338 (-0.187219) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414153 / 0.215209 (0.198944) | 4.131799 / 2.077655 (2.054144) | 1.852325 / 1.504120 (0.348205) | 1.646955 / 1.541195 (0.105760) | 1.662880 / 1.468490 (0.194390) | 0.693823 / 4.584777 (-3.890954) | 3.378843 / 3.745712 (-0.366869) | 1.861324 / 5.269862 (-3.408538) | 1.156916 / 4.565676 (-3.408761) | 0.082385 / 0.424275 (-0.341890) | 0.012166 / 0.007607 (0.004559) | 0.528690 / 0.226044 (0.302646) | 5.286388 / 2.268929 (3.017459) | 2.319941 / 55.444624 (-53.124684) | 1.959462 / 6.876477 (-4.917014) | 1.995102 / 2.142072 (-0.146970) | 0.817158 / 4.805227 (-3.988069) | 0.149479 / 6.500664 (-6.351185) | 0.065668 / 0.075469 (-0.009801) |\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) | 1.240228 / 1.841788 (-0.601560) | 13.770357 / 8.074308 (5.696048) | 13.940638 / 10.191392 (3.749246) | 0.152589 / 0.680424 (-0.527835) | 0.028498 / 0.534201 (-0.505703) | 0.392579 / 0.579283 (-0.186704) | 0.402843 / 0.434364 (-0.031521) | 0.455429 / 0.540337 (-0.084909) | 0.541090 / 1.386936 (-0.845846) |\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) | 0.006692 / 0.011353 (-0.004661) | 0.004514 / 0.011008 (-0.006495) | 0.097058 / 0.038508 (0.058550) | 0.027780 / 0.023109 (0.004671) | 0.415806 / 0.275898 (0.139908) | 0.443079 / 0.323480 (0.119599) | 0.005181 / 0.007986 (-0.002805) | 0.003408 / 0.004328 (-0.000921) | 0.075263 / 0.004250 (0.071013) | 0.038169 / 0.037052 (0.001116) | 0.417292 / 0.258489 (0.158803) | 0.461875 / 0.293841 (0.168034) | 0.032280 / 0.128546 (-0.096266) | 0.011571 / 0.075646 (-0.064075) | 0.319091 / 0.419271 (-0.100181) | 0.048295 / 0.043533 (0.004762) | 0.423619 / 0.255139 (0.168480) | 0.435064 / 0.283200 (0.151864) | 0.094869 / 0.141683 (-0.046814) | 1.523000 / 1.452155 (0.070846) | 1.583097 / 1.492716 (0.090381) |\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) | 0.214326 / 0.018006 (0.196320) | 0.391623 / 0.000490 (0.391134) | 0.004602 / 0.000200 (0.004403) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024306 / 0.037411 (-0.013106) | 0.101178 / 0.014526 (0.086652) | 0.108504 / 0.176557 (-0.068053) | 0.144114 / 0.737135 (-0.593022) | 0.111088 / 0.296338 (-0.185250) |\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) | 0.472573 / 0.215209 (0.257364) | 4.748929 / 2.077655 (2.671274) | 2.441602 / 1.504120 (0.937482) | 2.238841 / 1.541195 (0.697647) | 2.303303 / 1.468490 (0.834813) | 0.696618 / 4.584777 (-3.888159) | 3.373867 / 3.745712 (-0.371845) | 2.809009 / 5.269862 (-2.460852) | 1.337240 / 4.565676 (-3.228437) | 0.082682 / 0.424275 (-0.341593) | 0.012834 / 0.007607 (0.005227) | 0.569686 / 0.226044 (0.343642) | 5.723407 / 2.268929 (3.454478) | 2.882944 / 55.444624 (-52.561680) | 2.543530 / 6.876477 (-4.332947) | 2.581856 / 2.142072 (0.439784) | 0.802353 / 4.805227 (-4.002874) | 0.149947 / 6.500664 (-6.350717) | 0.065865 / 0.075469 (-0.009604) |\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) | 1.282146 / 1.841788 (-0.559642) | 13.831344 / 8.074308 (5.757036) | 14.081550 / 10.191392 (3.890157) | 0.141735 / 0.680424 (-0.538689) | 0.016677 / 0.534201 (-0.517524) | 0.378967 / 0.579283 (-0.200316) | 0.383775 / 0.434364 (-0.050589) | 0.432892 / 0.540337 (-0.107446) | 0.518042 / 1.386936 (-0.868894) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01b4a5a18b56fa7b648b0f131f6b5568b1fd436a \"CML watermark\")\n",
"Omg I love this ! cc @TevenLeScao @thomasw21 this will save your terminals from infinite streams of progress bars",
"<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) | 0.008680 / 0.011353 (-0.002673) | 0.004597 / 0.011008 (-0.006411) | 0.101154 / 0.038508 (0.062646) | 0.029831 / 0.023109 (0.006722) | 0.300619 / 0.275898 (0.024721) | 0.358259 / 0.323480 (0.034779) | 0.007284 / 0.007986 (-0.000701) | 0.003511 / 0.004328 (-0.000817) | 0.078805 / 0.004250 (0.074555) | 0.037192 / 0.037052 (0.000140) | 0.307241 / 0.258489 (0.048752) | 0.354648 / 0.293841 (0.060807) | 0.033696 / 0.128546 (-0.094851) | 0.011660 / 0.075646 (-0.063986) | 0.324266 / 0.419271 (-0.095006) | 0.043393 / 0.043533 (-0.000140) | 0.297503 / 0.255139 (0.042364) | 0.326037 / 0.283200 (0.042838) | 0.091165 / 0.141683 (-0.050517) | 1.479970 / 1.452155 (0.027816) | 1.508507 / 1.492716 (0.015791) |\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) | 0.179995 / 0.018006 (0.161989) | 0.464282 / 0.000490 (0.463793) | 0.003953 / 0.000200 (0.003753) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022696 / 0.037411 (-0.014715) | 0.099510 / 0.014526 (0.084984) | 0.103741 / 0.176557 (-0.072816) | 0.137837 / 0.737135 (-0.599299) | 0.108776 / 0.296338 (-0.187563) |\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) | 0.417034 / 0.215209 (0.201825) | 4.183479 / 2.077655 (2.105824) | 1.855329 / 1.504120 (0.351209) | 1.660675 / 1.541195 (0.119481) | 1.723936 / 1.468490 (0.255446) | 0.687815 / 4.584777 (-3.896962) | 3.331280 / 3.745712 (-0.414432) | 2.821430 / 5.269862 (-2.448432) | 1.542394 / 4.565676 (-3.023283) | 0.081665 / 0.424275 (-0.342610) | 0.012483 / 0.007607 (0.004875) | 0.524758 / 0.226044 (0.298713) | 5.277285 / 2.268929 (3.008357) | 2.278067 / 55.444624 (-53.166557) | 1.923232 / 6.876477 (-4.953245) | 1.978645 / 2.142072 (-0.163428) | 0.806225 / 4.805227 (-3.999002) | 0.147568 / 6.500664 (-6.353096) | 0.064206 / 0.075469 (-0.011263) |\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) | 1.175079 / 1.841788 (-0.666708) | 13.677443 / 8.074308 (5.603135) | 14.064103 / 10.191392 (3.872711) | 0.167462 / 0.680424 (-0.512962) | 0.028677 / 0.534201 (-0.505524) | 0.399090 / 0.579283 (-0.180193) | 0.398930 / 0.434364 (-0.035433) | 0.461604 / 0.540337 (-0.078733) | 0.540978 / 1.386936 (-0.845958) |\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) | 0.006846 / 0.011353 (-0.004507) | 0.004452 / 0.011008 (-0.006556) | 0.076169 / 0.038508 (0.037661) | 0.028290 / 0.023109 (0.005181) | 0.341105 / 0.275898 (0.065207) | 0.381465 / 0.323480 (0.057986) | 0.005038 / 0.007986 (-0.002948) | 0.003298 / 0.004328 (-0.001031) | 0.075794 / 0.004250 (0.071544) | 0.039225 / 0.037052 (0.002173) | 0.342995 / 0.258489 (0.084506) | 0.384878 / 0.293841 (0.091037) | 0.031766 / 0.128546 (-0.096780) | 0.011597 / 0.075646 (-0.064049) | 0.084849 / 0.419271 (-0.334423) | 0.041795 / 0.043533 (-0.001737) | 0.341770 / 0.255139 (0.086631) | 0.383142 / 0.283200 (0.099942) | 0.088854 / 0.141683 (-0.052829) | 1.465116 / 1.452155 (0.012961) | 1.566888 / 1.492716 (0.074171) |\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) | 0.225129 / 0.018006 (0.207123) | 0.394290 / 0.000490 (0.393801) | 0.000397 / 0.000200 (0.000197) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025492 / 0.037411 (-0.011919) | 0.100494 / 0.014526 (0.085968) | 0.110587 / 0.176557 (-0.065969) | 0.142715 / 0.737135 (-0.594420) | 0.110962 / 0.296338 (-0.185376) |\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) | 0.437240 / 0.215209 (0.222031) | 4.379191 / 2.077655 (2.301536) | 2.055059 / 1.504120 (0.550939) | 1.844643 / 1.541195 (0.303448) | 1.914678 / 1.468490 (0.446188) | 0.695607 / 4.584777 (-3.889170) | 3.353845 / 3.745712 (-0.391867) | 1.837403 / 5.269862 (-3.432459) | 1.155518 / 4.565676 (-3.410158) | 0.082753 / 0.424275 (-0.341523) | 0.012812 / 0.007607 (0.005205) | 0.537304 / 0.226044 (0.311260) | 5.387425 / 2.268929 (3.118497) | 2.506986 / 55.444624 (-52.937638) | 2.159031 / 6.876477 (-4.717445) | 2.187844 / 2.142072 (0.045772) | 0.796880 / 4.805227 (-4.008347) | 0.151850 / 6.500664 (-6.348815) | 0.067577 / 0.075469 (-0.007892) |\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) | 1.257779 / 1.841788 (-0.584009) | 13.968842 / 8.074308 (5.894534) | 13.544220 / 10.191392 (3.352828) | 0.149962 / 0.680424 (-0.530462) | 0.016875 / 0.534201 (-0.517326) | 0.394714 / 0.579283 (-0.184570) | 0.387845 / 0.434364 (-0.046519) | 0.481674 / 0.540337 (-0.058664) | 0.569820 / 1.386936 (-0.817116) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#71e50283422a93e805ea76722ce2520d1aae39c2 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009745 / 0.011353 (-0.001607) | 0.005307 / 0.011008 (-0.005702) | 0.104230 / 0.038508 (0.065722) | 0.039745 / 0.023109 (0.016635) | 0.306102 / 0.275898 (0.030204) | 0.384390 / 0.323480 (0.060910) | 0.008265 / 0.007986 (0.000279) | 0.005516 / 0.004328 (0.001187) | 0.076023 / 0.004250 (0.071772) | 0.048266 / 0.037052 (0.011213) | 0.315380 / 0.258489 (0.056891) | 0.365735 / 0.293841 (0.071895) | 0.038222 / 0.128546 (-0.090324) | 0.012397 / 0.075646 (-0.063249) | 0.348964 / 0.419271 (-0.070307) | 0.047668 / 0.043533 (0.004135) | 0.301037 / 0.255139 (0.045898) | 0.322982 / 0.283200 (0.039783) | 0.109307 / 0.141683 (-0.032376) | 1.420777 / 1.452155 (-0.031378) | 1.468290 / 1.492716 (-0.024426) |\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) | 0.262386 / 0.018006 (0.244380) | 0.557151 / 0.000490 (0.556661) | 0.000352 / 0.000200 (0.000152) | 0.000062 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029508 / 0.037411 (-0.007903) | 0.113960 / 0.014526 (0.099434) | 0.123176 / 0.176557 (-0.053381) | 0.161928 / 0.737135 (-0.575207) | 0.129196 / 0.296338 (-0.167142) |\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) | 0.407051 / 0.215209 (0.191842) | 4.072550 / 2.077655 (1.994895) | 1.899809 / 1.504120 (0.395689) | 1.751981 / 1.541195 (0.210786) | 1.841361 / 1.468490 (0.372871) | 0.713908 / 4.584777 (-3.870869) | 3.703339 / 3.745712 (-0.042373) | 2.091283 / 5.269862 (-3.178578) | 1.323810 / 4.565676 (-3.241866) | 0.084691 / 0.424275 (-0.339584) | 0.012685 / 0.007607 (0.005078) | 0.511301 / 0.226044 (0.285257) | 5.109741 / 2.268929 (2.840813) | 2.315073 / 55.444624 (-53.129551) | 2.012746 / 6.876477 (-4.863731) | 2.160074 / 2.142072 (0.018002) | 0.853025 / 4.805227 (-3.952202) | 0.165301 / 6.500664 (-6.335363) | 0.062244 / 0.075469 (-0.013225) |\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) | 1.219727 / 1.841788 (-0.622061) | 15.319675 / 8.074308 (7.245367) | 13.100883 / 10.191392 (2.909491) | 0.173451 / 0.680424 (-0.506973) | 0.029173 / 0.534201 (-0.505028) | 0.440162 / 0.579283 (-0.139122) | 0.429771 / 0.434364 (-0.004593) | 0.518689 / 0.540337 (-0.021648) | 0.608590 / 1.386936 (-0.778346) |\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) | 0.007839 / 0.011353 (-0.003514) | 0.005409 / 0.011008 (-0.005599) | 0.076468 / 0.038508 (0.037960) | 0.036568 / 0.023109 (0.013459) | 0.337568 / 0.275898 (0.061670) | 0.379353 / 0.323480 (0.055873) | 0.006208 / 0.007986 (-0.001778) | 0.005971 / 0.004328 (0.001643) | 0.073765 / 0.004250 (0.069514) | 0.056609 / 0.037052 (0.019556) | 0.344578 / 0.258489 (0.086089) | 0.405249 / 0.293841 (0.111408) | 0.037652 / 0.128546 (-0.090894) | 0.012549 / 0.075646 (-0.063097) | 0.087086 / 0.419271 (-0.332186) | 0.056669 / 0.043533 (0.013136) | 0.334121 / 0.255139 (0.078983) | 0.354582 / 0.283200 (0.071383) | 0.113293 / 0.141683 (-0.028390) | 1.437327 / 1.452155 (-0.014828) | 1.574400 / 1.492716 (0.081684) |\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) | 0.325235 / 0.018006 (0.307229) | 0.535405 / 0.000490 (0.534915) | 0.014119 / 0.000200 (0.013919) | 0.000278 / 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) | 0.030826 / 0.037411 (-0.006585) | 0.114077 / 0.014526 (0.099552) | 0.128799 / 0.176557 (-0.047758) | 0.172164 / 0.737135 (-0.564971) | 0.133665 / 0.296338 (-0.162673) |\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) | 0.430898 / 0.215209 (0.215689) | 4.285507 / 2.077655 (2.207853) | 2.089767 / 1.504120 (0.585647) | 1.899457 / 1.541195 (0.358262) | 2.042875 / 1.468490 (0.574385) | 0.690575 / 4.584777 (-3.894202) | 3.815905 / 3.745712 (0.070192) | 3.371085 / 5.269862 (-1.898776) | 1.865748 / 4.565676 (-2.699929) | 0.086678 / 0.424275 (-0.337597) | 0.013172 / 0.007607 (0.005565) | 0.552038 / 0.226044 (0.325994) | 5.275093 / 2.268929 (3.006165) | 2.561102 / 55.444624 (-52.883522) | 2.224235 / 6.876477 (-4.652242) | 2.330315 / 2.142072 (0.188243) | 0.845163 / 4.805227 (-3.960064) | 0.170675 / 6.500664 (-6.329989) | 0.068446 / 0.075469 (-0.007023) |\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) | 1.261213 / 1.841788 (-0.580575) | 15.354959 / 8.074308 (7.280651) | 15.034302 / 10.191392 (4.842910) | 0.146704 / 0.680424 (-0.533720) | 0.017986 / 0.534201 (-0.516215) | 0.425978 / 0.579283 (-0.153305) | 0.421806 / 0.434364 (-0.012558) | 0.494844 / 0.540337 (-0.045493) | 0.587870 / 1.386936 (-0.799066) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0933901bb757e9a386095aef0fb11de9f9a04085 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012765 / 0.011353 (0.001412) | 0.006429 / 0.011008 (-0.004579) | 0.133669 / 0.038508 (0.095161) | 0.041420 / 0.023109 (0.018311) | 0.419990 / 0.275898 (0.144092) | 0.505218 / 0.323480 (0.181738) | 0.010189 / 0.007986 (0.002204) | 0.005134 / 0.004328 (0.000805) | 0.100890 / 0.004250 (0.096640) | 0.045639 / 0.037052 (0.008587) | 0.440593 / 0.258489 (0.182103) | 0.476966 / 0.293841 (0.183125) | 0.059270 / 0.128546 (-0.069276) | 0.018625 / 0.075646 (-0.057021) | 0.444957 / 0.419271 (0.025686) | 0.060669 / 0.043533 (0.017136) | 0.415373 / 0.255139 (0.160234) | 0.461810 / 0.283200 (0.178610) | 0.116119 / 0.141683 (-0.025564) | 1.873691 / 1.452155 (0.421536) | 1.939891 / 1.492716 (0.447175) |\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) | 0.259529 / 0.018006 (0.241523) | 0.587213 / 0.000490 (0.586723) | 0.003729 / 0.000200 (0.003529) | 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) | 0.032064 / 0.037411 (-0.005347) | 0.140228 / 0.014526 (0.125702) | 0.147139 / 0.176557 (-0.029417) | 0.193731 / 0.737135 (-0.543405) | 0.162126 / 0.296338 (-0.134213) |\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) | 0.639262 / 0.215209 (0.424053) | 6.496491 / 2.077655 (4.418836) | 2.602044 / 1.504120 (1.097924) | 2.245891 / 1.541195 (0.704696) | 2.301321 / 1.468490 (0.832831) | 1.234088 / 4.584777 (-3.350689) | 5.883315 / 3.745712 (2.137603) | 3.166902 / 5.269862 (-2.102959) | 2.258279 / 4.565676 (-2.307398) | 0.146203 / 0.424275 (-0.278072) | 0.015490 / 0.007607 (0.007883) | 0.800188 / 0.226044 (0.574144) | 8.150866 / 2.268929 (5.881938) | 3.419508 / 55.444624 (-52.025117) | 2.712174 / 6.876477 (-4.164302) | 2.805059 / 2.142072 (0.662987) | 1.421047 / 4.805227 (-3.384180) | 0.254274 / 6.500664 (-6.246390) | 0.083886 / 0.075469 (0.008417) |\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) | 1.651962 / 1.841788 (-0.189826) | 19.453202 / 8.074308 (11.378894) | 24.643881 / 10.191392 (14.452489) | 0.263612 / 0.680424 (-0.416812) | 0.046913 / 0.534201 (-0.487288) | 0.579861 / 0.579283 (0.000578) | 0.695137 / 0.434364 (0.260773) | 0.705479 / 0.540337 (0.165142) | 0.806073 / 1.386936 (-0.580863) |\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) | 0.010384 / 0.011353 (-0.000969) | 0.007460 / 0.011008 (-0.003548) | 0.107830 / 0.038508 (0.069322) | 0.036792 / 0.023109 (0.013682) | 0.469585 / 0.275898 (0.193687) | 0.521278 / 0.323480 (0.197798) | 0.007472 / 0.007986 (-0.000513) | 0.007774 / 0.004328 (0.003446) | 0.105405 / 0.004250 (0.101154) | 0.053732 / 0.037052 (0.016680) | 0.486299 / 0.258489 (0.227810) | 0.537067 / 0.293841 (0.243226) | 0.053378 / 0.128546 (-0.075168) | 0.022018 / 0.075646 (-0.053628) | 0.127765 / 0.419271 (-0.291507) | 0.063844 / 0.043533 (0.020311) | 0.479724 / 0.255139 (0.224585) | 0.511243 / 0.283200 (0.228043) | 0.123223 / 0.141683 (-0.018460) | 1.934167 / 1.452155 (0.482013) | 2.003168 / 1.492716 (0.510451) |\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) | 0.227670 / 0.018006 (0.209664) | 0.609125 / 0.000490 (0.608635) | 0.004408 / 0.000200 (0.004208) | 0.000147 / 0.000054 (0.000092) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035905 / 0.037411 (-0.001506) | 0.142207 / 0.014526 (0.127681) | 0.154749 / 0.176557 (-0.021808) | 0.216191 / 0.737135 (-0.520944) | 0.156577 / 0.296338 (-0.139761) |\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) | 0.665085 / 0.215209 (0.449876) | 6.510923 / 2.077655 (4.433269) | 2.902438 / 1.504120 (1.398318) | 2.561427 / 1.541195 (1.020232) | 2.669556 / 1.468490 (1.201066) | 1.190340 / 4.584777 (-3.394437) | 5.933066 / 3.745712 (2.187354) | 5.627784 / 5.269862 (0.357922) | 2.971922 / 4.565676 (-1.593755) | 0.140884 / 0.424275 (-0.283391) | 0.015382 / 0.007607 (0.007775) | 0.810441 / 0.226044 (0.584396) | 8.255538 / 2.268929 (5.986609) | 3.819014 / 55.444624 (-51.625611) | 3.222479 / 6.876477 (-3.653998) | 3.181700 / 2.142072 (1.039627) | 1.483403 / 4.805227 (-3.321824) | 0.262726 / 6.500664 (-6.237939) | 0.090252 / 0.075469 (0.014783) |\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) | 1.748566 / 1.841788 (-0.093222) | 19.566894 / 8.074308 (11.492586) | 24.382155 / 10.191392 (14.190763) | 0.260118 / 0.680424 (-0.420305) | 0.028725 / 0.534201 (-0.505476) | 0.564875 / 0.579283 (-0.014408) | 0.666708 / 0.434364 (0.232344) | 0.691165 / 0.540337 (0.150827) | 0.837061 / 1.386936 (-0.549875) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fe6bf908e9f12e0b69b4059c392da8264881525d \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010098 / 0.011353 (-0.001255) | 0.005797 / 0.011008 (-0.005211) | 0.111262 / 0.038508 (0.072754) | 0.039687 / 0.023109 (0.016578) | 0.331081 / 0.275898 (0.055183) | 0.395878 / 0.323480 (0.072398) | 0.009244 / 0.007986 (0.001259) | 0.004498 / 0.004328 (0.000170) | 0.086129 / 0.004250 (0.081879) | 0.046662 / 0.037052 (0.009610) | 0.361926 / 0.258489 (0.103437) | 0.386155 / 0.293841 (0.092314) | 0.043657 / 0.128546 (-0.084889) | 0.013545 / 0.075646 (-0.062101) | 0.383735 / 0.419271 (-0.035537) | 0.055727 / 0.043533 (0.012194) | 0.355356 / 0.255139 (0.100217) | 0.358749 / 0.283200 (0.075550) | 0.123219 / 0.141683 (-0.018463) | 1.707982 / 1.452155 (0.255828) | 1.773342 / 1.492716 (0.280626) |\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) | 0.238902 / 0.018006 (0.220896) | 0.495525 / 0.000490 (0.495036) | 0.001742 / 0.000200 (0.001542) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031276 / 0.037411 (-0.006135) | 0.124286 / 0.014526 (0.109760) | 0.136236 / 0.176557 (-0.040321) | 0.180257 / 0.737135 (-0.556879) | 0.141047 / 0.296338 (-0.155292) |\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) | 0.465075 / 0.215209 (0.249865) | 4.543997 / 2.077655 (2.466342) | 2.036632 / 1.504120 (0.532512) | 1.820356 / 1.541195 (0.279161) | 1.860692 / 1.468490 (0.392202) | 0.807549 / 4.584777 (-3.777227) | 4.400369 / 3.745712 (0.654657) | 2.423372 / 5.269862 (-2.846490) | 1.741338 / 4.565676 (-2.824339) | 0.099457 / 0.424275 (-0.324818) | 0.014464 / 0.007607 (0.006857) | 0.599442 / 0.226044 (0.373398) | 5.867798 / 2.268929 (3.598870) | 2.641859 / 55.444624 (-52.802766) | 2.294246 / 6.876477 (-4.582231) | 2.329639 / 2.142072 (0.187567) | 0.981897 / 4.805227 (-3.823331) | 0.189278 / 6.500664 (-6.311386) | 0.071868 / 0.075469 (-0.003601) |\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) | 1.471800 / 1.841788 (-0.369988) | 17.149150 / 8.074308 (9.074841) | 15.818942 / 10.191392 (5.627550) | 0.174760 / 0.680424 (-0.505664) | 0.033507 / 0.534201 (-0.500694) | 0.511055 / 0.579283 (-0.068228) | 0.517107 / 0.434364 (0.082743) | 0.650813 / 0.540337 (0.110476) | 0.752515 / 1.386936 (-0.634421) |\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) | 0.008651 / 0.011353 (-0.002702) | 0.005935 / 0.011008 (-0.005073) | 0.088589 / 0.038508 (0.050081) | 0.038796 / 0.023109 (0.015687) | 0.415430 / 0.275898 (0.139532) | 0.443693 / 0.323480 (0.120213) | 0.006631 / 0.007986 (-0.001354) | 0.004638 / 0.004328 (0.000309) | 0.085779 / 0.004250 (0.081529) | 0.053994 / 0.037052 (0.016942) | 0.408349 / 0.258489 (0.149860) | 0.475441 / 0.293841 (0.181600) | 0.042792 / 0.128546 (-0.085754) | 0.013938 / 0.075646 (-0.061709) | 0.102173 / 0.419271 (-0.317098) | 0.057940 / 0.043533 (0.014407) | 0.408967 / 0.255139 (0.153828) | 0.422741 / 0.283200 (0.139541) | 0.121844 / 0.141683 (-0.019839) | 1.772779 / 1.452155 (0.320625) | 1.837706 / 1.492716 (0.344989) |\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) | 0.228896 / 0.018006 (0.210890) | 0.497964 / 0.000490 (0.497475) | 0.004402 / 0.000200 (0.004202) | 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) | 0.035626 / 0.037411 (-0.001786) | 0.132021 / 0.014526 (0.117495) | 0.145599 / 0.176557 (-0.030957) | 0.192317 / 0.737135 (-0.544818) | 0.150165 / 0.296338 (-0.146174) |\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) | 0.500216 / 0.215209 (0.285007) | 5.002916 / 2.077655 (2.925262) | 2.502439 / 1.504120 (0.998319) | 2.353019 / 1.541195 (0.811825) | 2.485082 / 1.468490 (1.016592) | 0.827694 / 4.584777 (-3.757083) | 4.569319 / 3.745712 (0.823607) | 3.739820 / 5.269862 (-1.530042) | 2.097857 / 4.565676 (-2.467819) | 0.098636 / 0.424275 (-0.325639) | 0.014608 / 0.007607 (0.007001) | 0.604411 / 0.226044 (0.378366) | 6.131702 / 2.268929 (3.862774) | 3.043988 / 55.444624 (-52.400637) | 2.642427 / 6.876477 (-4.234050) | 2.687223 / 2.142072 (0.545151) | 0.968808 / 4.805227 (-3.836419) | 0.193876 / 6.500664 (-6.306788) | 0.076931 / 0.075469 (0.001462) |\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) | 1.511820 / 1.841788 (-0.329968) | 17.971574 / 8.074308 (9.897265) | 16.512738 / 10.191392 (6.321346) | 0.223702 / 0.680424 (-0.456722) | 0.020191 / 0.534201 (-0.514010) | 0.511045 / 0.579283 (-0.068238) | 0.499813 / 0.434364 (0.065449) | 0.642147 / 0.540337 (0.101810) | 0.756029 / 1.386936 (-0.630907) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f6c7b9eb4bca89ec90c465623f7a2e6f5251062 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008909 / 0.011353 (-0.002444) | 0.005096 / 0.011008 (-0.005912) | 0.098568 / 0.038508 (0.060060) | 0.034548 / 0.023109 (0.011438) | 0.294762 / 0.275898 (0.018864) | 0.366093 / 0.323480 (0.042613) | 0.007476 / 0.007986 (-0.000510) | 0.003982 / 0.004328 (-0.000347) | 0.075975 / 0.004250 (0.071725) | 0.040499 / 0.037052 (0.003446) | 0.315050 / 0.258489 (0.056561) | 0.351273 / 0.293841 (0.057433) | 0.038327 / 0.128546 (-0.090219) | 0.011943 / 0.075646 (-0.063703) | 0.332148 / 0.419271 (-0.087124) | 0.047648 / 0.043533 (0.004115) | 0.295817 / 0.255139 (0.040678) | 0.322704 / 0.283200 (0.039504) | 0.100830 / 0.141683 (-0.040853) | 1.422162 / 1.452155 (-0.029993) | 1.468972 / 1.492716 (-0.023744) |\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) | 0.201164 / 0.018006 (0.183158) | 0.435425 / 0.000490 (0.434935) | 0.001576 / 0.000200 (0.001376) | 0.000218 / 0.000054 (0.000163) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026667 / 0.037411 (-0.010744) | 0.106161 / 0.014526 (0.091636) | 0.115836 / 0.176557 (-0.060720) | 0.151511 / 0.737135 (-0.585624) | 0.122248 / 0.296338 (-0.174091) |\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) | 0.395974 / 0.215209 (0.180765) | 3.952958 / 2.077655 (1.875303) | 1.772111 / 1.504120 (0.267991) | 1.581370 / 1.541195 (0.040175) | 1.602811 / 1.468490 (0.134321) | 0.694072 / 4.584777 (-3.890705) | 3.640238 / 3.745712 (-0.105474) | 2.028865 / 5.269862 (-3.240997) | 1.419182 / 4.565676 (-3.146495) | 0.084078 / 0.424275 (-0.340197) | 0.012248 / 0.007607 (0.004641) | 0.499768 / 0.226044 (0.273723) | 4.997449 / 2.268929 (2.728521) | 2.280711 / 55.444624 (-53.163913) | 1.971701 / 6.876477 (-4.904776) | 1.983248 / 2.142072 (-0.158824) | 0.831030 / 4.805227 (-3.974198) | 0.163008 / 6.500664 (-6.337656) | 0.061887 / 0.075469 (-0.013582) |\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) | 1.191744 / 1.841788 (-0.650043) | 14.424546 / 8.074308 (6.350238) | 14.530127 / 10.191392 (4.338735) | 0.165793 / 0.680424 (-0.514631) | 0.029099 / 0.534201 (-0.505102) | 0.447830 / 0.579283 (-0.131453) | 0.441036 / 0.434364 (0.006672) | 0.554697 / 0.540337 (0.014360) | 0.668854 / 1.386936 (-0.718082) |\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) | 0.006825 / 0.011353 (-0.004528) | 0.004998 / 0.011008 (-0.006010) | 0.074197 / 0.038508 (0.035689) | 0.032381 / 0.023109 (0.009272) | 0.335745 / 0.275898 (0.059847) | 0.360474 / 0.323480 (0.036994) | 0.005420 / 0.007986 (-0.002566) | 0.005121 / 0.004328 (0.000792) | 0.074980 / 0.004250 (0.070730) | 0.046392 / 0.037052 (0.009340) | 0.338693 / 0.258489 (0.080204) | 0.383679 / 0.293841 (0.089838) | 0.035380 / 0.128546 (-0.093166) | 0.012197 / 0.075646 (-0.063449) | 0.085738 / 0.419271 (-0.333533) | 0.049990 / 0.043533 (0.006458) | 0.342640 / 0.255139 (0.087501) | 0.355139 / 0.283200 (0.071939) | 0.102992 / 0.141683 (-0.038690) | 1.451900 / 1.452155 (-0.000254) | 1.550919 / 1.492716 (0.058202) |\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) | 0.223241 / 0.018006 (0.205235) | 0.436954 / 0.000490 (0.436464) | 0.003319 / 0.000200 (0.003120) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028042 / 0.037411 (-0.009370) | 0.106079 / 0.014526 (0.091554) | 0.122713 / 0.176557 (-0.053843) | 0.156543 / 0.737135 (-0.580593) | 0.122424 / 0.296338 (-0.173914) |\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) | 0.439482 / 0.215209 (0.224273) | 4.283112 / 2.077655 (2.205457) | 2.139705 / 1.504120 (0.635585) | 1.940898 / 1.541195 (0.399703) | 2.003906 / 1.468490 (0.535416) | 0.703269 / 4.584777 (-3.881508) | 3.780391 / 3.745712 (0.034679) | 2.079963 / 5.269862 (-3.189898) | 1.330669 / 4.565676 (-3.235007) | 0.086582 / 0.424275 (-0.337693) | 0.012497 / 0.007607 (0.004890) | 0.519329 / 0.226044 (0.293284) | 5.218117 / 2.268929 (2.949189) | 2.635982 / 55.444624 (-52.808643) | 2.301111 / 6.876477 (-4.575366) | 2.341312 / 2.142072 (0.199239) | 0.840157 / 4.805227 (-3.965070) | 0.166174 / 6.500664 (-6.334490) | 0.062890 / 0.075469 (-0.012579) |\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) | 1.257672 / 1.841788 (-0.584116) | 14.983374 / 8.074308 (6.909066) | 14.284441 / 10.191392 (4.093049) | 0.176077 / 0.680424 (-0.504347) | 0.017544 / 0.534201 (-0.516657) | 0.429619 / 0.579283 (-0.149664) | 0.426371 / 0.434364 (-0.007993) | 0.534832 / 0.540337 (-0.005506) | 0.643322 / 1.386936 (-0.743614) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0409b1435876fa97b3674b0275285e84b49d83f8 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010622 / 0.011353 (-0.000731) | 0.005856 / 0.011008 (-0.005152) | 0.108608 / 0.038508 (0.070100) | 0.039868 / 0.023109 (0.016759) | 0.327853 / 0.275898 (0.051955) | 0.396721 / 0.323480 (0.073241) | 0.008916 / 0.007986 (0.000930) | 0.004590 / 0.004328 (0.000261) | 0.085020 / 0.004250 (0.080770) | 0.046608 / 0.037052 (0.009555) | 0.356369 / 0.258489 (0.097880) | 0.391142 / 0.293841 (0.097301) | 0.040579 / 0.128546 (-0.087967) | 0.012249 / 0.075646 (-0.063397) | 0.387740 / 0.419271 (-0.031532) | 0.057794 / 0.043533 (0.014262) | 0.335763 / 0.255139 (0.080624) | 0.369847 / 0.283200 (0.086647) | 0.121276 / 0.141683 (-0.020407) | 1.605406 / 1.452155 (0.153251) | 1.709524 / 1.492716 (0.216808) |\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) | 0.226688 / 0.018006 (0.208681) | 0.493320 / 0.000490 (0.492831) | 0.002825 / 0.000200 (0.002626) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031874 / 0.037411 (-0.005538) | 0.117365 / 0.014526 (0.102840) | 0.127697 / 0.176557 (-0.048859) | 0.175589 / 0.737135 (-0.561546) | 0.137731 / 0.296338 (-0.158608) |\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) | 0.472563 / 0.215209 (0.257354) | 4.744383 / 2.077655 (2.666728) | 2.152015 / 1.504120 (0.647895) | 1.925398 / 1.541195 (0.384203) | 2.054613 / 1.468490 (0.586123) | 0.821703 / 4.584777 (-3.763074) | 4.468177 / 3.745712 (0.722465) | 4.687682 / 5.269862 (-0.582179) | 2.379674 / 4.565676 (-2.186003) | 0.101325 / 0.424275 (-0.322950) | 0.014891 / 0.007607 (0.007284) | 0.593161 / 0.226044 (0.367117) | 5.641670 / 2.268929 (3.372741) | 2.460206 / 55.444624 (-52.984419) | 2.131148 / 6.876477 (-4.745329) | 2.351067 / 2.142072 (0.208994) | 0.997634 / 4.805227 (-3.807593) | 0.195338 / 6.500664 (-6.305326) | 0.075540 / 0.075469 (0.000071) |\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) | 1.411585 / 1.841788 (-0.430203) | 17.055689 / 8.074308 (8.981381) | 16.544028 / 10.191392 (6.352636) | 0.180840 / 0.680424 (-0.499584) | 0.034549 / 0.534201 (-0.499652) | 0.510256 / 0.579283 (-0.069027) | 0.525632 / 0.434364 (0.091268) | 0.601206 / 0.540337 (0.060868) | 0.668468 / 1.386936 (-0.718469) |\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) | 0.008989 / 0.011353 (-0.002364) | 0.006065 / 0.011008 (-0.004943) | 0.088294 / 0.038508 (0.049786) | 0.040404 / 0.023109 (0.017295) | 0.405622 / 0.275898 (0.129724) | 0.454519 / 0.323480 (0.131039) | 0.006919 / 0.007986 (-0.001067) | 0.004545 / 0.004328 (0.000217) | 0.087023 / 0.004250 (0.082772) | 0.055962 / 0.037052 (0.018910) | 0.400942 / 0.258489 (0.142453) | 0.490670 / 0.293841 (0.196829) | 0.044086 / 0.128546 (-0.084461) | 0.014485 / 0.075646 (-0.061162) | 0.103333 / 0.419271 (-0.315938) | 0.059663 / 0.043533 (0.016130) | 0.404944 / 0.255139 (0.149805) | 0.425763 / 0.283200 (0.142563) | 0.123989 / 0.141683 (-0.017694) | 1.777244 / 1.452155 (0.325089) | 1.879884 / 1.492716 (0.387167) |\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) | 0.226440 / 0.018006 (0.208434) | 0.492688 / 0.000490 (0.492198) | 0.004691 / 0.000200 (0.004491) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035123 / 0.037411 (-0.002288) | 0.134288 / 0.014526 (0.119762) | 0.145542 / 0.176557 (-0.031015) | 0.195372 / 0.737135 (-0.541764) | 0.152551 / 0.296338 (-0.143787) |\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) | 0.468615 / 0.215209 (0.253406) | 4.813363 / 2.077655 (2.735708) | 2.333606 / 1.504120 (0.829486) | 2.107344 / 1.541195 (0.566149) | 2.109109 / 1.468490 (0.640619) | 0.783779 / 4.584777 (-3.800998) | 4.521448 / 3.745712 (0.775736) | 2.290532 / 5.269862 (-2.979329) | 1.553488 / 4.565676 (-3.012189) | 0.088786 / 0.424275 (-0.335489) | 0.013091 / 0.007607 (0.005484) | 0.567165 / 0.226044 (0.341120) | 5.974315 / 2.268929 (3.705386) | 2.815018 / 55.444624 (-52.629606) | 2.488954 / 6.876477 (-4.387522) | 2.461849 / 2.142072 (0.319776) | 0.934487 / 4.805227 (-3.870740) | 0.190209 / 6.500664 (-6.310455) | 0.074811 / 0.075469 (-0.000658) |\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) | 1.513476 / 1.841788 (-0.328311) | 17.902599 / 8.074308 (9.828291) | 14.308027 / 10.191392 (4.116635) | 0.201992 / 0.680424 (-0.478432) | 0.018678 / 0.534201 (-0.515523) | 0.454707 / 0.579283 (-0.124576) | 0.470643 / 0.434364 (0.036279) | 0.612534 / 0.540337 (0.072197) | 0.685773 / 1.386936 (-0.701163) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a66da3633a811eb8ea01d23469c41dfec0ffb8 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009385 / 0.011353 (-0.001968) | 0.005220 / 0.011008 (-0.005788) | 0.098722 / 0.038508 (0.060214) | 0.035382 / 0.023109 (0.012273) | 0.297114 / 0.275898 (0.021216) | 0.371443 / 0.323480 (0.047963) | 0.008070 / 0.007986 (0.000084) | 0.004204 / 0.004328 (-0.000125) | 0.075621 / 0.004250 (0.071370) | 0.046015 / 0.037052 (0.008963) | 0.304569 / 0.258489 (0.046080) | 0.345598 / 0.293841 (0.051757) | 0.037946 / 0.128546 (-0.090600) | 0.011972 / 0.075646 (-0.063674) | 0.331993 / 0.419271 (-0.087279) | 0.047250 / 0.043533 (0.003717) | 0.296588 / 0.255139 (0.041449) | 0.316070 / 0.283200 (0.032870) | 0.108211 / 0.141683 (-0.033472) | 1.447619 / 1.452155 (-0.004535) | 1.481243 / 1.492716 (-0.011473) |\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) | 0.274860 / 0.018006 (0.256854) | 0.503139 / 0.000490 (0.502649) | 0.003598 / 0.000200 (0.003398) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026752 / 0.037411 (-0.010660) | 0.109008 / 0.014526 (0.094482) | 0.119109 / 0.176557 (-0.057448) | 0.158462 / 0.737135 (-0.578673) | 0.126171 / 0.296338 (-0.170168) |\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) | 0.396396 / 0.215209 (0.181187) | 3.963055 / 2.077655 (1.885400) | 1.796308 / 1.504120 (0.292188) | 1.600565 / 1.541195 (0.059370) | 1.742409 / 1.468490 (0.273919) | 0.690942 / 4.584777 (-3.893835) | 3.713343 / 3.745712 (-0.032369) | 2.066804 / 5.269862 (-3.203058) | 1.292946 / 4.565676 (-3.272730) | 0.084344 / 0.424275 (-0.339931) | 0.012473 / 0.007607 (0.004865) | 0.513109 / 0.226044 (0.287065) | 5.175141 / 2.268929 (2.906213) | 2.266559 / 55.444624 (-53.178066) | 1.935737 / 6.876477 (-4.940740) | 2.028911 / 2.142072 (-0.113161) | 0.831191 / 4.805227 (-3.974036) | 0.163155 / 6.500664 (-6.337509) | 0.063414 / 0.075469 (-0.012055) |\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) | 1.195429 / 1.841788 (-0.646358) | 15.257933 / 8.074308 (7.183625) | 14.358815 / 10.191392 (4.167423) | 0.152677 / 0.680424 (-0.527747) | 0.028890 / 0.534201 (-0.505311) | 0.455342 / 0.579283 (-0.123941) | 0.442602 / 0.434364 (0.008238) | 0.526833 / 0.540337 (-0.013505) | 0.618296 / 1.386936 (-0.768640) |\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) | 0.007613 / 0.011353 (-0.003740) | 0.005515 / 0.011008 (-0.005493) | 0.073759 / 0.038508 (0.035251) | 0.033944 / 0.023109 (0.010835) | 0.347764 / 0.275898 (0.071866) | 0.371143 / 0.323480 (0.047664) | 0.005997 / 0.007986 (-0.001988) | 0.004322 / 0.004328 (-0.000006) | 0.073002 / 0.004250 (0.068751) | 0.053051 / 0.037052 (0.015999) | 0.340345 / 0.258489 (0.081856) | 0.383761 / 0.293841 (0.089920) | 0.037734 / 0.128546 (-0.090813) | 0.012815 / 0.075646 (-0.062831) | 0.086998 / 0.419271 (-0.332273) | 0.050165 / 0.043533 (0.006632) | 0.343864 / 0.255139 (0.088725) | 0.356734 / 0.283200 (0.073534) | 0.108955 / 0.141683 (-0.032728) | 1.464558 / 1.452155 (0.012403) | 1.560084 / 1.492716 (0.067368) |\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) | 0.327885 / 0.018006 (0.309878) | 0.515515 / 0.000490 (0.515025) | 0.000439 / 0.000200 (0.000239) | 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) | 0.030741 / 0.037411 (-0.006670) | 0.107634 / 0.014526 (0.093108) | 0.127121 / 0.176557 (-0.049436) | 0.164044 / 0.737135 (-0.573092) | 0.129097 / 0.296338 (-0.167242) |\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) | 0.435690 / 0.215209 (0.220481) | 4.350705 / 2.077655 (2.273050) | 2.199597 / 1.504120 (0.695477) | 2.022715 / 1.541195 (0.481521) | 2.265907 / 1.468490 (0.797417) | 0.695817 / 4.584777 (-3.888960) | 3.795207 / 3.745712 (0.049494) | 3.061587 / 5.269862 (-2.208274) | 1.872213 / 4.565676 (-2.693463) | 0.085265 / 0.424275 (-0.339010) | 0.012243 / 0.007607 (0.004636) | 0.547209 / 0.226044 (0.321164) | 5.383626 / 2.268929 (3.114698) | 2.707439 / 55.444624 (-52.737185) | 2.393773 / 6.876477 (-4.482703) | 2.481385 / 2.142072 (0.339312) | 0.826169 / 4.805227 (-3.979059) | 0.166643 / 6.500664 (-6.334021) | 0.065817 / 0.075469 (-0.009652) |\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) | 1.274469 / 1.841788 (-0.567318) | 15.565025 / 8.074308 (7.490717) | 14.254192 / 10.191392 (4.062800) | 0.166785 / 0.680424 (-0.513639) | 0.017830 / 0.534201 (-0.516371) | 0.430406 / 0.579283 (-0.148877) | 0.435655 / 0.434364 (0.001292) | 0.530605 / 0.540337 (-0.009732) | 0.636355 / 1.386936 (-0.750581) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#146983fdc70b9fe2cc38109368e185b6ffa7a05e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008466 / 0.011353 (-0.002887) | 0.004679 / 0.011008 (-0.006329) | 0.100534 / 0.038508 (0.062025) | 0.029513 / 0.023109 (0.006403) | 0.302866 / 0.275898 (0.026968) | 0.352816 / 0.323480 (0.029336) | 0.006912 / 0.007986 (-0.001074) | 0.003513 / 0.004328 (-0.000815) | 0.078625 / 0.004250 (0.074375) | 0.036725 / 0.037052 (-0.000327) | 0.312135 / 0.258489 (0.053646) | 0.344579 / 0.293841 (0.050738) | 0.033870 / 0.128546 (-0.094677) | 0.011563 / 0.075646 (-0.064083) | 0.318982 / 0.419271 (-0.100290) | 0.043002 / 0.043533 (-0.000531) | 0.301956 / 0.255139 (0.046817) | 0.330798 / 0.283200 (0.047599) | 0.091755 / 0.141683 (-0.049927) | 1.458577 / 1.452155 (0.006422) | 1.532642 / 1.492716 (0.039926) |\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) | 0.194853 / 0.018006 (0.176847) | 0.396844 / 0.000490 (0.396354) | 0.004401 / 0.000200 (0.004201) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022971 / 0.037411 (-0.014441) | 0.096595 / 0.014526 (0.082069) | 0.106104 / 0.176557 (-0.070452) | 0.144815 / 0.737135 (-0.592320) | 0.110036 / 0.296338 (-0.186303) |\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) | 0.415025 / 0.215209 (0.199816) | 4.138136 / 2.077655 (2.060481) | 1.861253 / 1.504120 (0.357133) | 1.653420 / 1.541195 (0.112226) | 1.703784 / 1.468490 (0.235294) | 0.698261 / 4.584777 (-3.886516) | 3.357240 / 3.745712 (-0.388472) | 3.025790 / 5.269862 (-2.244072) | 1.637191 / 4.565676 (-2.928485) | 0.085620 / 0.424275 (-0.338655) | 0.012454 / 0.007607 (0.004846) | 0.524708 / 0.226044 (0.298663) | 5.269234 / 2.268929 (3.000306) | 2.290612 / 55.444624 (-53.154012) | 1.936107 / 6.876477 (-4.940370) | 1.968216 / 2.142072 (-0.173856) | 0.810438 / 4.805227 (-3.994789) | 0.154133 / 6.500664 (-6.346531) | 0.064978 / 0.075469 (-0.010491) |\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) | 1.231782 / 1.841788 (-0.610006) | 13.545573 / 8.074308 (5.471264) | 14.558765 / 10.191392 (4.367373) | 0.140763 / 0.680424 (-0.539661) | 0.029259 / 0.534201 (-0.504942) | 0.407776 / 0.579283 (-0.171507) | 0.410244 / 0.434364 (-0.024120) | 0.477313 / 0.540337 (-0.063024) | 0.551465 / 1.386936 (-0.835471) |\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) | 0.006272 / 0.011353 (-0.005081) | 0.004397 / 0.011008 (-0.006611) | 0.077496 / 0.038508 (0.038988) | 0.026946 / 0.023109 (0.003837) | 0.342992 / 0.275898 (0.067094) | 0.374407 / 0.323480 (0.050927) | 0.004849 / 0.007986 (-0.003136) | 0.004549 / 0.004328 (0.000220) | 0.076439 / 0.004250 (0.072189) | 0.035829 / 0.037052 (-0.001224) | 0.343483 / 0.258489 (0.084994) | 0.385581 / 0.293841 (0.091740) | 0.031745 / 0.128546 (-0.096801) | 0.011617 / 0.075646 (-0.064030) | 0.087207 / 0.419271 (-0.332064) | 0.042252 / 0.043533 (-0.001281) | 0.343223 / 0.255139 (0.088084) | 0.368707 / 0.283200 (0.085508) | 0.093259 / 0.141683 (-0.048424) | 1.506904 / 1.452155 (0.054750) | 1.567583 / 1.492716 (0.074867) |\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) | 0.158962 / 0.018006 (0.140955) | 0.395982 / 0.000490 (0.395492) | 0.003604 / 0.000200 (0.003404) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025003 / 0.037411 (-0.012408) | 0.101176 / 0.014526 (0.086650) | 0.104494 / 0.176557 (-0.072062) | 0.140414 / 0.737135 (-0.596722) | 0.108398 / 0.296338 (-0.187941) |\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) | 0.436849 / 0.215209 (0.221640) | 4.369428 / 2.077655 (2.291774) | 2.070613 / 1.504120 (0.566493) | 1.867511 / 1.541195 (0.326317) | 1.866589 / 1.468490 (0.398099) | 0.700036 / 4.584777 (-3.884741) | 3.407513 / 3.745712 (-0.338199) | 3.022409 / 5.269862 (-2.247453) | 1.581423 / 4.565676 (-2.984253) | 0.083425 / 0.424275 (-0.340850) | 0.012380 / 0.007607 (0.004773) | 0.535087 / 0.226044 (0.309043) | 5.374814 / 2.268929 (3.105886) | 2.504841 / 55.444624 (-52.939784) | 2.166484 / 6.876477 (-4.709993) | 2.166363 / 2.142072 (0.024291) | 0.803692 / 4.805227 (-4.001535) | 0.150873 / 6.500664 (-6.349791) | 0.066253 / 0.075469 (-0.009216) |\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) | 1.291256 / 1.841788 (-0.550532) | 13.827843 / 8.074308 (5.753535) | 13.839334 / 10.191392 (3.647942) | 0.153530 / 0.680424 (-0.526894) | 0.016896 / 0.534201 (-0.517305) | 0.379937 / 0.579283 (-0.199346) | 0.396241 / 0.434364 (-0.038123) | 0.461808 / 0.540337 (-0.078530) | 0.553023 / 1.386936 (-0.833913) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#779ddc5c7ebbd406b2a6c9092c3f455a2cc7f5e7 \"CML watermark\")\n"
] | 2023-01-23T12:49:40 | 2023-02-13T20:23:34 | 2023-02-13T20:16:38 | CONTRIBUTOR | null | false | {
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} | Use the "shard generator approach with periodic progress updates" (used in `save_to_disk` and multi-proc `load_dataset`) in `Dataset.map` to enable having a single TQDM progress bar in the multi-proc mode.
Closes https://github.com/huggingface/datasets/issues/771, closes https://github.com/huggingface/datasets/issues/3177
TODO:
- [x] cleaner refactor of the `_map_single` decorators now that they also have to wrap generator functions (decorate `map` instead of `map_single` with the `transmit_` decorators and predict the shards' fingerprint in `map`) | {
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"Something that'd be nice to have is \"manual update of state\". One of the learning from training LLMs is the ability to skip some batches whenever we notice huge spike might be handy.",
"Your outline spec is very sound and clear, @lhoestq - thank you!\r\n\r\n@thomasw21, indeed that would be a wonderful extra feature. In Megatron-Deepspeed we manually drained the dataloader for the range we wanted. I wasn't very satisfied with the way we did it, since its behavior would change if you were to do multiple range skips. I think it should remember all the ranges it skipped and not just skip the last range - since otherwise the data is inconsistent (but we probably should discuss this in a separate issue not to derail this much bigger one)."
] | 2023-01-23T10:58:54 | 2023-01-24T01:45:48 | null | MEMBER | null | null | null | It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed)
What I have in mind (but lmk if you have other ideas or comments):
For map-style datasets, this requires to have a PyTorch Sampler state that can be saved and reloaded per node and worker.
For iterable datasets, this requires to save the state of the dataset iterator, which includes:
- the current shard idx and row position in the current shard
- the epoch number
- the rng state
- the shuffle buffer
Right now you can already resume the data loading of an iterable dataset by using `IterableDataset.skip` but it takes a lot of time because it re-iterates on all the past data until it reaches the resuming point.
cc @stas00 @sgugger | {
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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) | 0.008215 / 0.011353 (-0.003138) | 0.004510 / 0.011008 (-0.006498) | 0.099270 / 0.038508 (0.060761) | 0.028682 / 0.023109 (0.005573) | 0.332726 / 0.275898 (0.056827) | 0.371025 / 0.323480 (0.047545) | 0.006665 / 0.007986 (-0.001320) | 0.003329 / 0.004328 (-0.001000) | 0.078509 / 0.004250 (0.074259) | 0.032388 / 0.037052 (-0.004664) | 0.348540 / 0.258489 (0.090051) | 0.382212 / 0.293841 (0.088371) | 0.033307 / 0.128546 (-0.095239) | 0.011642 / 0.075646 (-0.064004) | 0.322573 / 0.419271 (-0.096699) | 0.041297 / 0.043533 (-0.002236) | 0.322710 / 0.255139 (0.067571) | 0.361593 / 0.283200 (0.078394) | 0.082276 / 0.141683 (-0.059407) | 1.481932 / 1.452155 (0.029777) | 1.531677 / 1.492716 (0.038961) |\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) | 0.194964 / 0.018006 (0.176958) | 0.406002 / 0.000490 (0.405512) | 0.001015 / 0.000200 (0.000815) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023317 / 0.037411 (-0.014095) | 0.097231 / 0.014526 (0.082705) | 0.103898 / 0.176557 (-0.072659) | 0.139864 / 0.737135 (-0.597271) | 0.106785 / 0.296338 (-0.189554) |\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) | 0.419036 / 0.215209 (0.203827) | 4.193985 / 2.077655 (2.116330) | 1.879069 / 1.504120 (0.374949) | 1.675384 / 1.541195 (0.134190) | 1.696225 / 1.468490 (0.227735) | 0.695257 / 4.584777 (-3.889520) | 3.437971 / 3.745712 (-0.307741) | 2.656037 / 5.269862 (-2.613824) | 1.463320 / 4.565676 (-3.102356) | 0.082575 / 0.424275 (-0.341700) | 0.012593 / 0.007607 (0.004986) | 0.526643 / 0.226044 (0.300599) | 5.278366 / 2.268929 (3.009437) | 2.288106 / 55.444624 (-53.156518) | 1.954875 / 6.876477 (-4.921602) | 1.950641 / 2.142072 (-0.191431) | 0.808289 / 4.805227 (-3.996938) | 0.148790 / 6.500664 (-6.351875) | 0.064775 / 0.075469 (-0.010694) |\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) | 1.215219 / 1.841788 (-0.626569) | 13.551467 / 8.074308 (5.477159) | 13.841547 / 10.191392 (3.650155) | 0.153610 / 0.680424 (-0.526814) | 0.028308 / 0.534201 (-0.505893) | 0.397087 / 0.579283 (-0.182196) | 0.401724 / 0.434364 (-0.032640) | 0.458042 / 0.540337 (-0.082296) | 0.544955 / 1.386936 (-0.841981) |\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) | 0.006321 / 0.011353 (-0.005032) | 0.004336 / 0.011008 (-0.006673) | 0.097196 / 0.038508 (0.058688) | 0.026933 / 0.023109 (0.003824) | 0.416520 / 0.275898 (0.140622) | 0.450703 / 0.323480 (0.127223) | 0.004831 / 0.007986 (-0.003155) | 0.003252 / 0.004328 (-0.001076) | 0.074981 / 0.004250 (0.070730) | 0.036136 / 0.037052 (-0.000917) | 0.423166 / 0.258489 (0.164677) | 0.460936 / 0.293841 (0.167095) | 0.031859 / 0.128546 (-0.096687) | 0.011500 / 0.075646 (-0.064146) | 0.318197 / 0.419271 (-0.101074) | 0.041472 / 0.043533 (-0.002061) | 0.419227 / 0.255139 (0.164088) | 0.444712 / 0.283200 (0.161512) | 0.088841 / 0.141683 (-0.052841) | 1.497237 / 1.452155 (0.045083) | 1.572111 / 1.492716 (0.079395) |\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) | 0.239261 / 0.018006 (0.221255) | 0.400358 / 0.000490 (0.399868) | 0.003460 / 0.000200 (0.003261) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024016 / 0.037411 (-0.013395) | 0.098414 / 0.014526 (0.083888) | 0.107220 / 0.176557 (-0.069337) | 0.143538 / 0.737135 (-0.593598) | 0.108607 / 0.296338 (-0.187731) |\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) | 0.473896 / 0.215209 (0.258687) | 4.740386 / 2.077655 (2.662731) | 2.458046 / 1.504120 (0.953926) | 2.260895 / 1.541195 (0.719700) | 2.280218 / 1.468490 (0.811728) | 0.694843 / 4.584777 (-3.889934) | 3.349795 / 3.745712 (-0.395917) | 1.846970 / 5.269862 (-3.422892) | 1.151481 / 4.565676 (-3.414195) | 0.082054 / 0.424275 (-0.342221) | 0.012664 / 0.007607 (0.005057) | 0.573400 / 0.226044 (0.347355) | 5.750648 / 2.268929 (3.481720) | 2.904257 / 55.444624 (-52.540367) | 2.555181 / 6.876477 (-4.321295) | 2.595830 / 2.142072 (0.453758) | 0.799580 / 4.805227 (-4.005647) | 0.151088 / 6.500664 (-6.349576) | 0.066639 / 0.075469 (-0.008831) |\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) | 1.251413 / 1.841788 (-0.590375) | 13.743368 / 8.074308 (5.669060) | 13.808729 / 10.191392 (3.617337) | 0.144765 / 0.680424 (-0.535659) | 0.016606 / 0.534201 (-0.517594) | 0.376503 / 0.579283 (-0.202780) | 0.381510 / 0.434364 (-0.052854) | 0.440295 / 0.540337 (-0.100043) | 0.524248 / 1.386936 (-0.862688) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eea1226779993687845da5ecd264cf047e46a128 \"CML watermark\")\n",
"Thanks a lot, @albertvillanova - I validated that your fix solves the original problem!"
] | 2023-01-23T08:57:40 | 2023-01-24T01:34:20 | 2023-01-23T10:10:42 | MEMBER | null | false | {
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"merged_at": "2023-01-23T10:10:42"
} | This PR fixes the extraction of insecure TAR files by changing the base path against which TAR members are compared:
- from: "."
- to: `output_path`
This PR also adds tests for extracting insecure TAR files.
Related to:
- #5441
- #5452
@stas00 please note this PR addresses just one of the issues you pointed out: the use of the cwd by the extractor. The other issues (actionable error messages, raise instead of log error) should be addressed in other PRs. | {
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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"
] | 2023-01-23T07:53:38 | 2023-01-23T09:40:55 | 2023-01-23T08:31:17 | MEMBER | null | false | {
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} | The log messages do not match their if-condition. This PR swaps them.
Found while investigating:
- #5441
CC: @lhoestq | {
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"Hi ! Could you share the full stack trace ? Which dataset did you try to load ?\r\n\r\nit may be related to https://github.com/huggingface/datasets/pull/5640",
"The `BadZipFile` error means the ZIP file is corrupted, so I'm closing this issue as it's not directly related to `datasets`.",
"For others that find this issue following a `BadZipFile` error, I had the same problem because I had a file in a folder dataset `my-image.target` and the datasets library was incorrectly determining that the (PNG) file was a zip archive. When it tried to extract the file, this error occurred. \r\n\r\nUpdating to `datasets==2.12.0` fixed the problem for me."
] | 2023-01-22T23:50:12 | 2023-05-23T10:35:48 | 2023-02-10T16:31:36 | NONE | null | null | null | ### Describe the bug
I'm getting the following exception:
```
lib/python3.10/zipfile.py:1353 in _RealGetContents │
│ │
│ 1350 │ │ # self.start_dir: Position of start of central directory │
│ 1351 │ │ self.start_dir = offset_cd + concat │
│ 1352 │ │ if self.start_dir < 0: │
│ ❱ 1353 │ │ │ raise BadZipFile("Bad offset for central directory") │
│ 1354 │ │ fp.seek(self.start_dir, 0) │
│ 1355 │ │ data = fp.read(size_cd) │
│ 1356 │ │ fp = io.BytesIO(data) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
BadZipFile: Bad offset for central directory
Extracting data files: 35%|█████████████████▊ | 38572/110812 [00:10<00:20, 3576.26it/s]
```
### Steps to reproduce the bug
```
load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
),
```
### Expected behavior
loads the dataset
### Environment info
datasets==2.8.0
Python 3.10.8
Linux 129-146-3-202 5.15.0-52-generic #58~20.04.1-Ubuntu SMP Thu Oct 13 13:09:46 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux | {
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"wtf",
"Couldn't find what's causing this, this will need more investigation",
"A possible hint: The function it seems to be spending a lot of time in (when iterating over the original dataset) is `_get_mp` in the PIL JPEG decoder: \r\n\r\n",
"If \"mp\" is multiprocessing, this might suggest some kind of negative interaction between the JPEG decoder and TF's handling of processes/threads. Note that we haven't merged the parallel `to_tf_dataset` PR yet, so it's not caused by that PR!",
"Update: MP isn't multiprocessing at all, it's an internal PIL method for loading metadata from JPEG files. No idea why that would be a bottleneck, but I'll see if a Python profiler can't figure out where the time is actually being spent.",
"After further profiling, the slowdown is in the C methods for JPEG decoding that are included as part of PIL. Because Python profilers can't inspect inside that, I don't have any further information on which lines exactly are responsible for the slowdown or why.\r\n\r\nIn the meantime, I'm going to suggest switching from `return_tensors=\"tf\"` to `return_tensors=\"np\"` in most of our `transformers` code - this generally works better for pre-processing. Two relevant PRs are [here](https://github.com/huggingface/transformers/pull/21266) and [here](https://github.com/huggingface/notebooks/pull/308).",
"Closing this issue as we've done what we can with this one! "
] | 2023-01-20T16:08:37 | 2023-02-13T14:13:34 | 2023-02-13T14:13:34 | MEMBER | null | null | null | ### Describe the bug
This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing)
Briefly, there are several datasets that, when you iterate over them with `to_tf_dataset` **and** a data collator that returns `tf` tensors, become very slow. We haven't been able to figure this one out - it can be intermittent, and we have no idea what could possibly cause it. The weirdest thing is that **the slowdown affects other attempts to access the underlying dataset**. If you try to iterate over the `tf.data.Dataset`, then interrupt execution, and then try to iterate over the original dataset, the original dataset is now also very slow! This is true even if the dataset format is not set to `tf` - the iteration is slow even though it's not calling TF at all!
There is a simple workaround for this - we can simply get our data collators to return `np` tensors. When we do this, the bug is never triggered and everything is fine. In general, `np` is preferred for this kind of preprocessing work anyway, when the preprocessing is not going to be compiled into a pure `tf.data` pipeline! However, the issue is fascinating, and the TF team were wondering if anyone in datasets (cc @lhoestq @mariosasko) might have an idea of what could cause this.
### Steps to reproduce the bug
Run the attached Colab.
### Expected behavior
The slowdown should go away, or at least not persist after we stop iterating over the `tf.data.Dataset`
### Environment info
The issue occurs on multiple versions of Python and TF, both on local machines and on Colab.
All testing was done using the latest versions of `transformers` and `datasets` from `main` | {
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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) | 0.008227 / 0.011353 (-0.003126) | 0.004496 / 0.011008 (-0.006512) | 0.099319 / 0.038508 (0.060811) | 0.029929 / 0.023109 (0.006820) | 0.296686 / 0.275898 (0.020788) | 0.355372 / 0.323480 (0.031892) | 0.006864 / 0.007986 (-0.001122) | 0.003458 / 0.004328 (-0.000871) | 0.077234 / 0.004250 (0.072983) | 0.037072 / 0.037052 (0.000020) | 0.311675 / 0.258489 (0.053186) | 0.338965 / 0.293841 (0.045124) | 0.033562 / 0.128546 (-0.094985) | 0.011399 / 0.075646 (-0.064248) | 0.322406 / 0.419271 (-0.096865) | 0.043034 / 0.043533 (-0.000499) | 0.298083 / 0.255139 (0.042944) | 0.323661 / 0.283200 (0.040462) | 0.089380 / 0.141683 (-0.052303) | 1.479363 / 1.452155 (0.027208) | 1.518337 / 1.492716 (0.025620) |\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) | 0.177822 / 0.018006 (0.159816) | 0.400806 / 0.000490 (0.400317) | 0.002121 / 0.000200 (0.001921) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021986 / 0.037411 (-0.015426) | 0.096749 / 0.014526 (0.082223) | 0.101443 / 0.176557 (-0.075113) | 0.137519 / 0.737135 (-0.599616) | 0.105558 / 0.296338 (-0.190780) |\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) | 0.418983 / 0.215209 (0.203774) | 4.189579 / 2.077655 (2.111924) | 1.877831 / 1.504120 (0.373711) | 1.666213 / 1.541195 (0.125019) | 1.680735 / 1.468490 (0.212245) | 0.693033 / 4.584777 (-3.891744) | 3.420553 / 3.745712 (-0.325160) | 1.819647 / 5.269862 (-3.450214) | 1.144934 / 4.565676 (-3.420743) | 0.082209 / 0.424275 (-0.342066) | 0.012433 / 0.007607 (0.004826) | 0.526781 / 0.226044 (0.300737) | 5.273689 / 2.268929 (3.004760) | 2.323468 / 55.444624 (-53.121156) | 1.960508 / 6.876477 (-4.915969) | 2.035338 / 2.142072 (-0.106735) | 0.812789 / 4.805227 (-3.992438) | 0.148429 / 6.500664 (-6.352235) | 0.064727 / 0.075469 (-0.010742) |\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) | 1.253218 / 1.841788 (-0.588569) | 13.303426 / 8.074308 (5.229118) | 13.651074 / 10.191392 (3.459682) | 0.135178 / 0.680424 (-0.545246) | 0.028483 / 0.534201 (-0.505717) | 0.393284 / 0.579283 (-0.185999) | 0.401957 / 0.434364 (-0.032407) | 0.457136 / 0.540337 (-0.083201) | 0.535835 / 1.386936 (-0.851101) |\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) | 0.006335 / 0.011353 (-0.005017) | 0.004454 / 0.011008 (-0.006554) | 0.097565 / 0.038508 (0.059057) | 0.026917 / 0.023109 (0.003808) | 0.350779 / 0.275898 (0.074881) | 0.391979 / 0.323480 (0.068499) | 0.004648 / 0.007986 (-0.003337) | 0.003204 / 0.004328 (-0.001124) | 0.076987 / 0.004250 (0.072737) | 0.035257 / 0.037052 (-0.001796) | 0.347193 / 0.258489 (0.088704) | 0.391462 / 0.293841 (0.097621) | 0.031244 / 0.128546 (-0.097302) | 0.011460 / 0.075646 (-0.064186) | 0.321606 / 0.419271 (-0.097665) | 0.041218 / 0.043533 (-0.002315) | 0.341884 / 0.255139 (0.086745) | 0.374920 / 0.283200 (0.091720) | 0.086383 / 0.141683 (-0.055300) | 1.501750 / 1.452155 (0.049595) | 1.565060 / 1.492716 (0.072344) |\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) | 0.165447 / 0.018006 (0.147441) | 0.401885 / 0.000490 (0.401395) | 0.000975 / 0.000200 (0.000775) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024494 / 0.037411 (-0.012917) | 0.097334 / 0.014526 (0.082808) | 0.105324 / 0.176557 (-0.071232) | 0.142430 / 0.737135 (-0.594705) | 0.107249 / 0.296338 (-0.189089) |\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) | 0.441632 / 0.215209 (0.226423) | 4.407729 / 2.077655 (2.330074) | 2.078167 / 1.504120 (0.574047) | 1.864210 / 1.541195 (0.323015) | 1.885948 / 1.468490 (0.417458) | 0.693974 / 4.584777 (-3.890803) | 3.386837 / 3.745712 (-0.358875) | 1.840291 / 5.269862 (-3.429571) | 1.150524 / 4.565676 (-3.415153) | 0.082240 / 0.424275 (-0.342035) | 0.012488 / 0.007607 (0.004881) | 0.537589 / 0.226044 (0.311545) | 5.404007 / 2.268929 (3.135078) | 2.537467 / 55.444624 (-52.907157) | 2.190775 / 6.876477 (-4.685702) | 2.224746 / 2.142072 (0.082674) | 0.799524 / 4.805227 (-4.005703) | 0.150639 / 6.500664 (-6.350025) | 0.066473 / 0.075469 (-0.008997) |\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) | 1.258559 / 1.841788 (-0.583228) | 13.773583 / 8.074308 (5.699275) | 13.964322 / 10.191392 (3.772930) | 0.156295 / 0.680424 (-0.524129) | 0.016824 / 0.534201 (-0.517377) | 0.377476 / 0.579283 (-0.201807) | 0.390163 / 0.434364 (-0.044201) | 0.442541 / 0.540337 (-0.097796) | 0.529404 / 1.386936 (-0.857532) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f500a5c554b213aafe87293bd593920567742c3 \"CML watermark\")\n"
] | 2023-01-20T12:53:17 | 2023-01-20T13:32:50 | 2023-01-20T13:26:03 | MEMBER | null | false | {
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} | Support fsspec 2023.1.0 in CI.
In the 2023.1.0 fsspec release, they replaced the type of `fsspec.registry`:
- from `ReadOnlyRegistry`, with an attribute called `target`
- to `MappingProxyType`, without that attribute
Consequently, we need to change our `mock_fsspec` fixtures, that were using the `target` attribute.
Fix #5448. | {
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] | null | [] | 2023-01-20T10:26:31 | 2023-01-20T13:26:05 | 2023-01-20T13:26:05 | MEMBER | null | null | null | Once we find out the root cause of:
- #5445
we should revert the temporary pin on fsspec introduced by:
- #5447 | {
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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) | 0.011875 / 0.011353 (0.000522) | 0.008188 / 0.011008 (-0.002821) | 0.131137 / 0.038508 (0.092629) | 0.038127 / 0.023109 (0.015018) | 0.383864 / 0.275898 (0.107966) | 0.458617 / 0.323480 (0.135137) | 0.010989 / 0.007986 (0.003003) | 0.004892 / 0.004328 (0.000563) | 0.101955 / 0.004250 (0.097704) | 0.045081 / 0.037052 (0.008029) | 0.409768 / 0.258489 (0.151279) | 0.446597 / 0.293841 (0.152756) | 0.058588 / 0.128546 (-0.069958) | 0.020872 / 0.075646 (-0.054774) | 0.432982 / 0.419271 (0.013711) | 0.075875 / 0.043533 (0.032342) | 0.380923 / 0.255139 (0.125784) | 0.432994 / 0.283200 (0.149795) | 0.122678 / 0.141683 (-0.019005) | 1.857865 / 1.452155 (0.405710) | 1.927801 / 1.492716 (0.435085) |\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) | 0.212941 / 0.018006 (0.194935) | 0.527977 / 0.000490 (0.527488) | 0.002996 / 0.000200 (0.002797) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030046 / 0.037411 (-0.007366) | 0.126384 / 0.014526 (0.111858) | 0.138307 / 0.176557 (-0.038250) | 0.185338 / 0.737135 (-0.551797) | 0.144733 / 0.296338 (-0.151606) |\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) | 0.627096 / 0.215209 (0.411887) | 6.418014 / 2.077655 (4.340360) | 2.547675 / 1.504120 (1.043555) | 2.195552 / 1.541195 (0.654357) | 2.200377 / 1.468490 (0.731887) | 1.289935 / 4.584777 (-3.294842) | 5.670839 / 3.745712 (1.925127) | 5.252597 / 5.269862 (-0.017265) | 2.878470 / 4.565676 (-1.687207) | 0.143754 / 0.424275 (-0.280521) | 0.014814 / 0.007607 (0.007207) | 0.810073 / 0.226044 (0.584028) | 8.183757 / 2.268929 (5.914829) | 3.375525 / 55.444624 (-52.069099) | 2.594048 / 6.876477 (-4.282428) | 2.598095 / 2.142072 (0.456023) | 1.554493 / 4.805227 (-3.250734) | 0.263159 / 6.500664 (-6.237505) | 0.089822 / 0.075469 (0.014353) |\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) | 1.660847 / 1.841788 (-0.180941) | 18.434283 / 8.074308 (10.359975) | 21.764887 / 10.191392 (11.573495) | 0.264524 / 0.680424 (-0.415900) | 0.048519 / 0.534201 (-0.485682) | 0.587468 / 0.579283 (0.008185) | 0.634142 / 0.434364 (0.199778) | 0.675374 / 0.540337 (0.135037) | 0.777510 / 1.386936 (-0.609426) |\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) | 0.010021 / 0.011353 (-0.001332) | 0.006207 / 0.011008 (-0.004801) | 0.130490 / 0.038508 (0.091982) | 0.037957 / 0.023109 (0.014848) | 0.489381 / 0.275898 (0.213483) | 0.536522 / 0.323480 (0.213042) | 0.008611 / 0.007986 (0.000626) | 0.004894 / 0.004328 (0.000565) | 0.101617 / 0.004250 (0.097367) | 0.052629 / 0.037052 (0.015577) | 0.509211 / 0.258489 (0.250721) | 0.545023 / 0.293841 (0.251182) | 0.057468 / 0.128546 (-0.071078) | 0.023393 / 0.075646 (-0.052253) | 0.431408 / 0.419271 (0.012137) | 0.064967 / 0.043533 (0.021434) | 0.495261 / 0.255139 (0.240122) | 0.527098 / 0.283200 (0.243898) | 0.113172 / 0.141683 (-0.028511) | 1.937072 / 1.452155 (0.484918) | 2.048413 / 1.492716 (0.555697) |\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) | 0.245406 / 0.018006 (0.227399) | 0.526772 / 0.000490 (0.526283) | 0.004379 / 0.000200 (0.004179) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031785 / 0.037411 (-0.005626) | 0.130949 / 0.014526 (0.116424) | 0.145660 / 0.176557 (-0.030896) | 0.186991 / 0.737135 (-0.550144) | 0.151000 / 0.296338 (-0.145338) |\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) | 0.708643 / 0.215209 (0.493434) | 7.179252 / 2.077655 (5.101597) | 3.143375 / 1.504120 (1.639255) | 2.714298 / 1.541195 (1.173103) | 2.773441 / 1.468490 (1.304951) | 1.312821 / 4.584777 (-3.271956) | 5.798396 / 3.745712 (2.052684) | 3.253215 / 5.269862 (-2.016646) | 2.147260 / 4.565676 (-2.418416) | 0.154673 / 0.424275 (-0.269602) | 0.014918 / 0.007607 (0.007311) | 0.860618 / 0.226044 (0.634573) | 8.774455 / 2.268929 (6.505527) | 3.925020 / 55.444624 (-51.519604) | 3.139361 / 6.876477 (-3.737115) | 3.208883 / 2.142072 (1.066810) | 1.547305 / 4.805227 (-3.257922) | 0.268814 / 6.500664 (-6.231850) | 0.084578 / 0.075469 (0.009109) |\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) | 1.694990 / 1.841788 (-0.146798) | 18.619183 / 8.074308 (10.544875) | 21.929886 / 10.191392 (11.738494) | 0.265763 / 0.680424 (-0.414661) | 0.028325 / 0.534201 (-0.505876) | 0.552910 / 0.579283 (-0.026373) | 0.616864 / 0.434364 (0.182500) | 0.637858 / 0.540337 (0.097521) | 0.744508 / 1.386936 (-0.642428) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f819ba3d0306748aaf9fd8ea040b981dd08e5e5 \"CML watermark\")\n"
] | 2023-01-20T10:11:02 | 2023-01-20T10:38:13 | 2023-01-20T10:28:43 | MEMBER | null | false | {
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} | Temporarily pin fsspec < 2023.1.0
Fix #5445. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@Wauplin I was testing it in a dedicated branch without opening a PR: https://github.com/huggingface/datasets/commits/test-hfh-0.12.0rc0",
"Oops, sorry @albertvillanova. I thought for next time I'll start the CIs before pinging everyone.\r\nI'm closing this one.",
"@Wauplin in your Slack message, you asked people from every major dependent library to check that our CI work. That is why I am checking it... :)\r\n\r\nAlso, I think for this purpose it is better to test it in a dedicated branch, rather than opening and closing a PR.",
"Yes, yes I know. Completely my fault on this one"
] | 2023-01-20T10:05:19 | 2023-01-20T10:43:22 | 2023-01-20T10:13:48 | CONTRIBUTOR | null | false | {
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} | DO NOT MERGE.
Only to test the CI.
cc @lhoestq @albertvillanova | {
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] | null | [] | 2023-01-20T10:03:10 | 2023-01-20T10:28:44 | 2023-01-20T10:28:44 | MEMBER | null | null | null | CI tests are broken, raising `AttributeError: 'mappingproxy' object has no attribute 'target'`. See: https://github.com/huggingface/datasets/actions/runs/3966497597/jobs/6797384185
```
...
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]/*-expected_paths4] - AttributeError: 'mappingproxy' object has no attribute 'target'
===== 2076 passed, 19 skipped, 15 warnings, 47 errors in 115.54s (0:01:55) =====
``` | {
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"Looks like a duplicate of https://github.com/huggingface/datasets/issues/1948. \r\n\r\nI also think these should be logged as INFO messages, but let's see what @lhoestq thinks.",
"It can be considered unexpected to see a `map` function return instantaneously. The warning is here to explain this case by mentioning that the cache was used. I don't expect first time users (only seeing warnings) to guess that the cache works this way",
"Oh, so it's intentional? Do all Hugging Face packages use `warning` when using cache?\r\nI guess feel free to close this issue then.",
"Yes it's intentional for `map`. For `load_dataset` it's also intentional but for a different reason: it shows where in the cache the dataset is located, in case the user wants to clear the cache.",
"OK I see. It's surprising to me that these are considered \"something unexpected happened\", the concept of cache is pretty common.\r\n\r\nHas a user every actually complained that they ran their code once, and it took a minute while the data downloaded, then ran their code again and it ran really fast (and completed successfully) but they were so baffled by the fact that it ran quickly, _and_ didn't set the log level to INFO, _and_ hadn't read the docs (or thought about it) to know that datasets are cached, that they logged an issue asking that this information be output as a warning every time they run their code?\r\n\r\nThat seems like a very niche scenario to cater for, given that the side effect is to flood the console with irrelevant warnings for every other user every other time they run a bit of `datasets` code. And the real world impact is that people TURN OFF warnings, which is a pretty bad habit to get into.\r\n\r\nAnyhoo, if there's no chance I'm going to change your mind, please close the issue :)",
"I see your point and I'm not closed to switching to INFO, but I think those logs are important to make the library less opaque. I also just checked `transformers` scripts and they default to INFO which is nice. However for colab users the default is still WARNING iirc, and it counts as one of the main env where `datasets` is used.\r\n\r\nWe also use progress bars a lot in `datasets`, that are shown if the logger is at the WARNING level. But we offer a function to disable the progress bars if necessary.",
"These kinds of messages are logged as INFO in Transformers, so we should probably be consistent with them"
] | 2023-01-20T01:19:18 | 2023-07-12T17:19:31 | 2023-07-12T17:19:31 | NONE | null | null | null | ### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from the Python docs for reference:
* INFO: Confirmation that things are working as expected.
* WARNING: An indication that something unexpected happened, or indicative of some problem in the near future (e.g. ‘disk space low’). The software is still working as expected.
In theory, a user should be able to resolve things such that there are no warnings.
### Steps to reproduce the bug
Load any dataset that's already cached.
### Expected behavior
No output when log level is at the default WARNING level.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 9.0.0
- Pandas version: 1.5.2 | {
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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) | 0.009885 / 0.011353 (-0.001468) | 0.005338 / 0.011008 (-0.005670) | 0.099967 / 0.038508 (0.061459) | 0.036860 / 0.023109 (0.013751) | 0.295283 / 0.275898 (0.019385) | 0.369504 / 0.323480 (0.046024) | 0.008267 / 0.007986 (0.000281) | 0.004375 / 0.004328 (0.000046) | 0.076294 / 0.004250 (0.072043) | 0.047058 / 0.037052 (0.010006) | 0.314463 / 0.258489 (0.055974) | 0.348125 / 0.293841 (0.054284) | 0.038334 / 0.128546 (-0.090213) | 0.012102 / 0.075646 (-0.063544) | 0.333049 / 0.419271 (-0.086223) | 0.050727 / 0.043533 (0.007195) | 0.299244 / 0.255139 (0.044105) | 0.318210 / 0.283200 (0.035010) | 0.112609 / 0.141683 (-0.029074) | 1.450377 / 1.452155 (-0.001778) | 1.485177 / 1.492716 (-0.007539) |\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) | 0.287083 / 0.018006 (0.269077) | 0.564268 / 0.000490 (0.563778) | 0.003578 / 0.000200 (0.003378) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026755 / 0.037411 (-0.010657) | 0.105857 / 0.014526 (0.091331) | 0.118291 / 0.176557 (-0.058266) | 0.155735 / 0.737135 (-0.581401) | 0.122527 / 0.296338 (-0.173812) |\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) | 0.396992 / 0.215209 (0.181783) | 3.958562 / 2.077655 (1.880908) | 1.781570 / 1.504120 (0.277451) | 1.617743 / 1.541195 (0.076549) | 1.753504 / 1.468490 (0.285013) | 0.681509 / 4.584777 (-3.903268) | 3.816910 / 3.745712 (0.071198) | 2.087359 / 5.269862 (-3.182503) | 1.328380 / 4.565676 (-3.237297) | 0.083542 / 0.424275 (-0.340733) | 0.012081 / 0.007607 (0.004473) | 0.505127 / 0.226044 (0.279082) | 5.075136 / 2.268929 (2.806208) | 2.259871 / 55.444624 (-53.184753) | 1.944302 / 6.876477 (-4.932175) | 2.102624 / 2.142072 (-0.039449) | 0.819779 / 4.805227 (-3.985448) | 0.165584 / 6.500664 (-6.335080) | 0.061774 / 0.075469 (-0.013695) |\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) | 1.208258 / 1.841788 (-0.633530) | 14.841635 / 8.074308 (6.767327) | 14.484515 / 10.191392 (4.293123) | 0.156464 / 0.680424 (-0.523959) | 0.028839 / 0.534201 (-0.505362) | 0.440860 / 0.579283 (-0.138423) | 0.433892 / 0.434364 (-0.000472) | 0.515339 / 0.540337 (-0.024998) | 0.608838 / 1.386936 (-0.778098) |\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) | 0.007548 / 0.011353 (-0.003804) | 0.005464 / 0.011008 (-0.005544) | 0.096987 / 0.038508 (0.058479) | 0.034472 / 0.023109 (0.011363) | 0.391249 / 0.275898 (0.115351) | 0.432779 / 0.323480 (0.109299) | 0.006170 / 0.007986 (-0.001816) | 0.004316 / 0.004328 (-0.000013) | 0.074184 / 0.004250 (0.069934) | 0.054254 / 0.037052 (0.017202) | 0.397947 / 0.258489 (0.139458) | 0.451253 / 0.293841 (0.157412) | 0.037098 / 0.128546 (-0.091449) | 0.012649 / 0.075646 (-0.062997) | 0.333533 / 0.419271 (-0.085739) | 0.050247 / 0.043533 (0.006714) | 0.390446 / 0.255139 (0.135307) | 0.410547 / 0.283200 (0.127347) | 0.110888 / 0.141683 (-0.030795) | 1.452160 / 1.452155 (0.000006) | 1.596331 / 1.492716 (0.103615) |\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) | 0.256061 / 0.018006 (0.238055) | 0.552674 / 0.000490 (0.552184) | 0.003362 / 0.000200 (0.003162) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030199 / 0.037411 (-0.007213) | 0.110288 / 0.014526 (0.095762) | 0.127412 / 0.176557 (-0.049145) | 0.165428 / 0.737135 (-0.571707) | 0.131658 / 0.296338 (-0.164680) |\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) | 0.441946 / 0.215209 (0.226737) | 4.414209 / 2.077655 (2.336555) | 2.284530 / 1.504120 (0.780410) | 2.110752 / 1.541195 (0.569557) | 2.210751 / 1.468490 (0.742260) | 0.698829 / 4.584777 (-3.885948) | 3.819044 / 3.745712 (0.073332) | 3.274021 / 5.269862 (-1.995840) | 1.781284 / 4.565676 (-2.784393) | 0.085264 / 0.424275 (-0.339011) | 0.012360 / 0.007607 (0.004753) | 0.553519 / 0.226044 (0.327475) | 5.466395 / 2.268929 (3.197467) | 2.825839 / 55.444624 (-52.618786) | 2.439451 / 6.876477 (-4.437026) | 2.582534 / 2.142072 (0.440462) | 0.841644 / 4.805227 (-3.963583) | 0.172288 / 6.500664 (-6.328376) | 0.067215 / 0.075469 (-0.008254) |\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) | 1.283623 / 1.841788 (-0.558165) | 15.753163 / 8.074308 (7.678855) | 14.983263 / 10.191392 (4.791871) | 0.187584 / 0.680424 (-0.492840) | 0.017999 / 0.534201 (-0.516202) | 0.427157 / 0.579283 (-0.152126) | 0.435456 / 0.434364 (0.001092) | 0.496800 / 0.540337 (-0.043537) | 0.592557 / 1.386936 (-0.794379) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8a72676689a4a3fb466cc5077884446c7302e605 \"CML watermark\")\n"
] | 2023-01-20T01:09:14 | 2023-01-20T15:44:45 | 2023-01-20T15:37:30 | MEMBER | null | false | {
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} | Based on feedback from discussion #5423, this PR updates the sharing tutorial with a mention of writing your own dataset loading script to support more advanced dataset creation options like multiple configs.
I'll open a separate PR to update the *Create a Dataset card* with the new Hub metadata UI update 😄 | {
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"Hi! \r\n\r\nWe use [`fsspec`](https://github.com/fsspec/filesystem_spec) to integrate with storage providers. You can find more info (and the usage examples) in [our docs](https://huggingface.co./docs/datasets/v2.8.0/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage).\r\n\r\n[`gdrivefs`](https://github.com/fsspec/gdrivefs) makes it possible to use Google Drive as a storage service in Datasets, but this is not the case for OneDrive, since its[ Python SDK](https://github.com/OneDrive/onedrive-sdk-python) is not integrated with `fsspec`. Can you please request the integration with `fsspec` in their repo to address this limitation?",
"I'm closing this issue as implementing a fsspec-compliant OneDrive filesystem is not our responsibility."
] | 2023-01-19T23:12:08 | 2023-02-24T16:17:51 | 2023-02-24T16:17:51 | NONE | null | null | null | ### Feature request
First of all , I would like to thank all community who are developed DataSet storage and make it free available
How to integrate our Onedrive account or any other possible storage clouds (like google drive,...) with the **HF** datasets section.
For example, if I have **50GB** on my **Onedrive** account and I want to move between drive and Hugging face repo or vis versa
### Motivation
make the dataset section more flexible with other possible storage
like the integration between Google Collab and Google drive the storage
### Your contribution
Can be done using Hugging face CLI | {
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"<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) | 0.011815 / 0.011353 (0.000463) | 0.006407 / 0.011008 (-0.004601) | 0.132937 / 0.038508 (0.094429) | 0.040634 / 0.023109 (0.017525) | 0.398049 / 0.275898 (0.122151) | 0.498207 / 0.323480 (0.174727) | 0.010111 / 0.007986 (0.002126) | 0.007282 / 0.004328 (0.002954) | 0.103661 / 0.004250 (0.099411) | 0.046223 / 0.037052 (0.009171) | 0.411490 / 0.258489 (0.153001) | 0.480973 / 0.293841 (0.187132) | 0.058397 / 0.128546 (-0.070149) | 0.019952 / 0.075646 (-0.055695) | 0.440734 / 0.419271 (0.021463) | 0.064585 / 0.043533 (0.021052) | 0.392556 / 0.255139 (0.137417) | 0.437842 / 0.283200 (0.154643) | 0.130684 / 0.141683 (-0.010999) | 1.910552 / 1.452155 (0.458397) | 1.984644 / 1.492716 (0.491927) |\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) | 0.264417 / 0.018006 (0.246411) | 0.676519 / 0.000490 (0.676030) | 0.003369 / 0.000200 (0.003169) | 0.000125 / 0.000054 (0.000071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034558 / 0.037411 (-0.002854) | 0.126561 / 0.014526 (0.112035) | 0.134478 / 0.176557 (-0.042079) | 0.202125 / 0.737135 (-0.535010) | 0.143273 / 0.296338 (-0.153066) |\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) | 0.618592 / 0.215209 (0.403383) | 6.224435 / 2.077655 (4.146780) | 2.636689 / 1.504120 (1.132569) | 2.243507 / 1.541195 (0.702313) | 2.312449 / 1.468490 (0.843959) | 1.188499 / 4.584777 (-3.396277) | 5.738347 / 3.745712 (1.992635) | 4.891933 / 5.269862 (-0.377929) | 2.697631 / 4.565676 (-1.868046) | 0.140200 / 0.424275 (-0.284076) | 0.015484 / 0.007607 (0.007877) | 0.781947 / 0.226044 (0.555903) | 7.946600 / 2.268929 (5.677671) | 3.365574 / 55.444624 (-52.079050) | 2.783443 / 6.876477 (-4.093034) | 2.738634 / 2.142072 (0.596561) | 1.487247 / 4.805227 (-3.317980) | 0.255681 / 6.500664 (-6.244983) | 0.084607 / 0.075469 (0.009138) |\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) | 1.717846 / 1.841788 (-0.123941) | 18.405566 / 8.074308 (10.331258) | 20.508578 / 10.191392 (10.317186) | 0.262364 / 0.680424 (-0.418060) | 0.050881 / 0.534201 (-0.483319) | 0.587516 / 0.579283 (0.008232) | 0.650900 / 0.434364 (0.216536) | 0.656168 / 0.540337 (0.115830) | 0.778876 / 1.386936 (-0.608061) |\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) | 0.010817 / 0.011353 (-0.000536) | 0.007338 / 0.011008 (-0.003670) | 0.131949 / 0.038508 (0.093441) | 0.037244 / 0.023109 (0.014135) | 0.565994 / 0.275898 (0.290096) | 0.567434 / 0.323480 (0.243954) | 0.007733 / 0.007986 (-0.000252) | 0.005216 / 0.004328 (0.000887) | 0.096578 / 0.004250 (0.092328) | 0.056001 / 0.037052 (0.018949) | 0.538209 / 0.258489 (0.279720) | 0.580385 / 0.293841 (0.286544) | 0.053654 / 0.128546 (-0.074892) | 0.019471 / 0.075646 (-0.056176) | 0.448781 / 0.419271 (0.029509) | 0.064774 / 0.043533 (0.021241) | 0.540222 / 0.255139 (0.285083) | 0.563058 / 0.283200 (0.279858) | 0.122716 / 0.141683 (-0.018967) | 1.839402 / 1.452155 (0.387247) | 1.915523 / 1.492716 (0.422806) |\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) | 0.310448 / 0.018006 (0.292442) | 0.603664 / 0.000490 (0.603175) | 0.004833 / 0.000200 (0.004633) | 0.000145 / 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) | 0.032340 / 0.037411 (-0.005072) | 0.130115 / 0.014526 (0.115589) | 0.154192 / 0.176557 (-0.022364) | 0.200655 / 0.737135 (-0.536480) | 0.144961 / 0.296338 (-0.151377) |\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) | 0.671588 / 0.215209 (0.456379) | 6.691642 / 2.077655 (4.613988) | 2.915230 / 1.504120 (1.411110) | 2.573337 / 1.541195 (1.032143) | 2.578204 / 1.468490 (1.109714) | 1.249028 / 4.584777 (-3.335749) | 5.808539 / 3.745712 (2.062827) | 3.079317 / 5.269862 (-2.190545) | 2.033308 / 4.565676 (-2.532369) | 0.142411 / 0.424275 (-0.281864) | 0.015525 / 0.007607 (0.007918) | 0.800389 / 0.226044 (0.574345) | 8.228236 / 2.268929 (5.959308) | 3.660207 / 55.444624 (-51.784417) | 3.021033 / 6.876477 (-3.855444) | 3.088335 / 2.142072 (0.946263) | 1.380137 / 4.805227 (-3.425091) | 0.252065 / 6.500664 (-6.248599) | 0.084302 / 0.075469 (0.008833) |\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) | 1.709429 / 1.841788 (-0.132359) | 18.358770 / 8.074308 (10.284462) | 21.109844 / 10.191392 (10.918452) | 0.231549 / 0.680424 (-0.448875) | 0.029251 / 0.534201 (-0.504950) | 0.560719 / 0.579283 (-0.018564) | 0.610125 / 0.434364 (0.175761) | 0.630015 / 0.540337 (0.089678) | 0.751656 / 1.386936 (-0.635280) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18baf4eebf71c0db1d9980f7ee164f1272ff8f26 \"CML watermark\")\n",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5441). All of your documentation changes will be reflected on that endpoint.",
"I think I managed to reproduce it:\r\n\r\n```\r\nrm -rf ~/.cache/huggingface/datasets/HuggingFaceM4___cm4-synthetic-testing\r\nmkdir -p /tmp/xxx/hf-data\r\nsudo ln -s /tmp/xxx /test\r\nmkdir -p /tmp/yyy\r\nln -sf /test/hf-data /tmp/yyy/data\r\ncd /tmp/yyy\r\npython -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/cm4-synthetic-testing\r\n```\r\n\r\nPlease note it includes a creation of a symlink from the `/` (so `sudo`) - may be there is a simpler way but I'm just trying to replicate the real setup. Of course please be careful - it's mostly under `/tmp` not to destroy anything if you try to run this.\r\n\r\nthis fails with:\r\n\r\n```\r\nNo config specified, defaulting to: cm4-synthetic-testing/100.unique\r\nDownloading and preparing dataset cm4-synthetic-testing/100.unique (download: 20.71 KiB, generated: 49.99 MiB, post-processed: Unknown size, total: 50.01 MiB) to /home/stas/.cache/huggingface/datasets/HuggingFaceM4___cm4-synthetic-testing/100.unique/1.1.1/2e33dcc086c7209b8ccff4b19e44f1d41b5be53262e7d793142b96c2e984602b...\r\nExtraction of data is blocked (illegal path: /tmp/yyy)\r\n[...]\r\nExtraction of data/115/texts_03.txt is blocked (illegal path: /tmp/yyy)\r\nGenerating 100.unique split: 0%| | 0/100 [00:00<?, ? examples/s]Generating 100-long unique records split\r\n\r\nTraceback (most recent call last):\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1571, in _prepare_split_single\r\n for key, record in generator:\r\n File \"/home/stas/.cache/huggingface/modules/datasets_modules/datasets/HuggingFaceM4--cm4-synthetic-testing/2e33dcc086c7209b8ccff4b19e44f1d41b5be53262e7d793142b96c2e984602b/cm4-synthetic-testing.py\", line 190, in _generate_examples\r\n raise ValueError(f\"can't find any data - check {data_path}\")\r\nValueError: can't find any data - check /home/stas/.cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data\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 \"<string>\", line 1, in <module>\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/load.py\", line 1757, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 860, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1612, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 953, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1450, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1607, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\ndatasets.builder.DatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n\r\nnote that `illegal path: /tmp/yyy` is now with the mods of this PR.\r\n\r\n----------------------\r\n\r\nAlso I think the whole thing should have failed at the first `illegal path` and not continue running. But as it continued and gave:\r\n\r\n\r\n> ValueError: can't find any data - check /home/stas/.cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data\r\n\r\nwhat can a user do with that other than confirming that that dir is indeed empty, but no clue is given to why and it's far from obvious that one needs to scroll up and discover earlier issues. Most users won't do that.\r\n\r\n(my apologies for writing out so much - was trying to make the situation clear)",
"Thank you, Albert, for the explanation.\r\n\r\nTo summarize I think what's needed is:\r\n\r\n1. add a comment in the code to why this is done for someone being puzzled over the odd code\r\n2. and to use an actionable by the user error message\r\n3. perform an untrapped assert on that tar extract error and not continue, so that the user will not get a later misleading error that the folder is empty and is completely not actionable and it's is far from obvious that one needs to scroll up to find earlier errors, which were trapped.\r\n\r\nAfter reading the advisory I'm still not sure why `cwd` is used and not a designated `~/.cache/huggingface/datasets/downloads/extracted`, I can't see what difference does it make since I could `chdir` to the designated directory and it would be `cwd`. The security solution is trying to ensure that `/etc/passwd` won't get overriden. So why is the check done in `.` and not the real target base directory, since the extraction isn't done in the current working dir. By not using `.` you lower the chances that the user will have all sorts of local symlinks that could trigger the issue since `datasets` typically is the only one managing it's `~/.cache/huggingface/datasets` domain and 99.9% of the time the user won't manually create files in it.\r\n\r\nthank you!\r\n"
] | 2023-01-19T02:17:21 | 2023-01-20T16:49:22 | null | MEMBER | null | false | {
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} | ok, every so often, I have been getting a strange failure on dataset install:
```
$ python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
No config specified, defaulting to: general-pmd-synthetic-testing/100.unique
Downloading and preparing dataset general-pmd-synthetic-testing/100.unique (download: 3.21 KiB, generated: 16.01 MiB, post-processed: Unknown size, total: 16.02 MiB) to /home/stas/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing/100.unique/1.1.1/86bc445e3e48cb5ef79de109eb4e54ff85b318cd55c3835c4ee8f86eae33d9d2...
Extraction of data is blocked (illegal path)
Extraction of data/1 is blocked (illegal path)
Extraction of data/1/text.null is blocked (illegal path)
[...]
```
I had no idea what to do with that - what in the world does **illegal path** mean?
I started looking at the code in `TarExtractor` and added a debug print of `base` so that told me that there was a problem with the current directory - which was a clone of one of the hf repos.
This particular dataset extracts into a directory `data` and the current dir I was running the tests from already had `data` in it which was a symbolic link to another partition and somehow all that `badpath` code was blowing up there.
https://github.com/huggingface/datasets/blob/80eb8db74f49b7ee9c0f73a819c22177fabd61db/src/datasets/utils/extract.py#L113-L114
I tried hard to come up with a repro, but no matter what I tried it only fails in that particular clone directory that has a `data` symlink and not anywhere else.
In any case, in this PR I'm proposing to at least give a user a hint of what seems to be an issue.
I'm not at all happy with the info I got with this proposed change, but at least it gave me a hint that `TarExtractor` tries to extract into the current directory without any respect to pre-existing files. Say what?
https://github.com/huggingface/datasets/blob/80eb8db74f49b7ee9c0f73a819c22177fabd61db/src/datasets/utils/extract.py#L110
why won't it use the `datasets` designated directory for that? There would never be a problem if it were to do that.
I had to look at all those `resolved`, `badpath` calls and see what it did and why it failed, since it was far from obvious. It appeared like it resolved a symlink and compared it to the original path which of course wasn't matching.
So perhaps you have a better solution than what I proposed in this PR. I think that code line I quoted is the one that should be fixed instead.
But if you can't think of a better solution let's merge this at least so that the user will have a clue that the current dir is somehow involved.
p.s. I double checked that if I remove the pre-existing `data` symlink in the current dir I'm running the dataset install command from, the problem goes away too.
Thanks.
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https://api.github.com/repos/huggingface/datasets/issues/5440 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5440/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5440/comments | https://api.github.com/repos/huggingface/datasets/issues/5440/events | https://github.com/huggingface/datasets/pull/5440 | 1,538,361,143 | PR_kwDODunzps5HpRbF | 5,440 | Fix documentation about batch samplers | {
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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) | 0.008874 / 0.011353 (-0.002479) | 0.004685 / 0.011008 (-0.006323) | 0.101478 / 0.038508 (0.062970) | 0.031409 / 0.023109 (0.008300) | 0.305429 / 0.275898 (0.029531) | 0.371777 / 0.323480 (0.048297) | 0.007282 / 0.007986 (-0.000704) | 0.005545 / 0.004328 (0.001217) | 0.078583 / 0.004250 (0.074333) | 0.037171 / 0.037052 (0.000118) | 0.320186 / 0.258489 (0.061696) | 0.347881 / 0.293841 (0.054040) | 0.034005 / 0.128546 (-0.094541) | 0.011534 / 0.075646 (-0.064113) | 0.326079 / 0.419271 (-0.093193) | 0.040856 / 0.043533 (-0.002677) | 0.307327 / 0.255139 (0.052188) | 0.323521 / 0.283200 (0.040321) | 0.090407 / 0.141683 (-0.051276) | 1.481994 / 1.452155 (0.029840) | 1.490372 / 1.492716 (-0.002345) |\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) | 0.175161 / 0.018006 (0.157155) | 0.447009 / 0.000490 (0.446519) | 0.003570 / 0.000200 (0.003370) | 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) | 0.023868 / 0.037411 (-0.013543) | 0.100791 / 0.014526 (0.086265) | 0.108131 / 0.176557 (-0.068425) | 0.147993 / 0.737135 (-0.589142) | 0.111205 / 0.296338 (-0.185133) |\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) | 0.425369 / 0.215209 (0.210160) | 4.241694 / 2.077655 (2.164040) | 2.145403 / 1.504120 (0.641283) | 1.913517 / 1.541195 (0.372322) | 1.887307 / 1.468490 (0.418817) | 0.691615 / 4.584777 (-3.893162) | 3.402233 / 3.745712 (-0.343480) | 1.992532 / 5.269862 (-3.277330) | 1.322292 / 4.565676 (-3.243385) | 0.082862 / 0.424275 (-0.341413) | 0.012595 / 0.007607 (0.004988) | 0.528490 / 0.226044 (0.302445) | 5.313338 / 2.268929 (3.044409) | 2.645037 / 55.444624 (-52.799587) | 2.326279 / 6.876477 (-4.550198) | 2.396955 / 2.142072 (0.254883) | 0.819354 / 4.805227 (-3.985873) | 0.150889 / 6.500664 (-6.349775) | 0.066517 / 0.075469 (-0.008952) |\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) | 1.233673 / 1.841788 (-0.608114) | 14.563293 / 8.074308 (6.488985) | 14.317989 / 10.191392 (4.126597) | 0.150767 / 0.680424 (-0.529657) | 0.028972 / 0.534201 (-0.505229) | 0.400547 / 0.579283 (-0.178736) | 0.402267 / 0.434364 (-0.032097) | 0.459375 / 0.540337 (-0.080962) | 0.544419 / 1.386936 (-0.842517) |\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) | 0.006817 / 0.011353 (-0.004536) | 0.004588 / 0.011008 (-0.006421) | 0.099224 / 0.038508 (0.060716) | 0.027730 / 0.023109 (0.004621) | 0.412310 / 0.275898 (0.136412) | 0.445731 / 0.323480 (0.122252) | 0.005197 / 0.007986 (-0.002788) | 0.003601 / 0.004328 (-0.000728) | 0.076200 / 0.004250 (0.071950) | 0.041813 / 0.037052 (0.004761) | 0.415282 / 0.258489 (0.156793) | 0.457182 / 0.293841 (0.163341) | 0.031920 / 0.128546 (-0.096626) | 0.011712 / 0.075646 (-0.063934) | 0.320859 / 0.419271 (-0.098412) | 0.041466 / 0.043533 (-0.002067) | 0.418156 / 0.255139 (0.163017) | 0.435501 / 0.283200 (0.152302) | 0.090727 / 0.141683 (-0.050955) | 1.484014 / 1.452155 (0.031859) | 1.568072 / 1.492716 (0.075356) |\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) | 0.263356 / 0.018006 (0.245350) | 0.410768 / 0.000490 (0.410278) | 0.015983 / 0.000200 (0.015783) | 0.000301 / 0.000054 (0.000246) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024522 / 0.037411 (-0.012889) | 0.103986 / 0.014526 (0.089460) | 0.109253 / 0.176557 (-0.067303) | 0.142308 / 0.737135 (-0.594827) | 0.114037 / 0.296338 (-0.182302) |\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) | 0.452617 / 0.215209 (0.237407) | 4.505215 / 2.077655 (2.427560) | 2.185546 / 1.504120 (0.681426) | 1.995540 / 1.541195 (0.454345) | 1.962875 / 1.468490 (0.494385) | 0.690237 / 4.584777 (-3.894540) | 3.448311 / 3.745712 (-0.297401) | 1.901572 / 5.269862 (-3.368289) | 1.170832 / 4.565676 (-3.394844) | 0.082333 / 0.424275 (-0.341942) | 0.012569 / 0.007607 (0.004962) | 0.547822 / 0.226044 (0.321778) | 5.504180 / 2.268929 (3.235251) | 2.693981 / 55.444624 (-52.750644) | 2.320710 / 6.876477 (-4.555767) | 2.270508 / 2.142072 (0.128435) | 0.803145 / 4.805227 (-4.002083) | 0.152168 / 6.500664 (-6.348496) | 0.067408 / 0.075469 (-0.008061) |\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) | 1.260689 / 1.841788 (-0.581099) | 14.281112 / 8.074308 (6.206804) | 14.549742 / 10.191392 (4.358350) | 0.129337 / 0.680424 (-0.551087) | 0.017181 / 0.534201 (-0.517020) | 0.380473 / 0.579283 (-0.198810) | 0.387689 / 0.434364 (-0.046675) | 0.446734 / 0.540337 (-0.093603) | 0.532479 / 1.386936 (-0.854457) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7972a0b5f1ad2c36023a79686f6ef026f4ffa64f \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008953 / 0.011353 (-0.002400) | 0.004917 / 0.011008 (-0.006091) | 0.098699 / 0.038508 (0.060191) | 0.034460 / 0.023109 (0.011351) | 0.294604 / 0.275898 (0.018706) | 0.322709 / 0.323480 (-0.000770) | 0.007780 / 0.007986 (-0.000206) | 0.004061 / 0.004328 (-0.000267) | 0.076134 / 0.004250 (0.071883) | 0.043786 / 0.037052 (0.006734) | 0.302155 / 0.258489 (0.043666) | 0.339779 / 0.293841 (0.045938) | 0.038305 / 0.128546 (-0.090241) | 0.012131 / 0.075646 (-0.063515) | 0.332656 / 0.419271 (-0.086615) | 0.048029 / 0.043533 (0.004496) | 0.303859 / 0.255139 (0.048720) | 0.315861 / 0.283200 (0.032662) | 0.100758 / 0.141683 (-0.040925) | 1.468072 / 1.452155 (0.015918) | 1.521325 / 1.492716 (0.028609) |\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) | 0.244975 / 0.018006 (0.226969) | 0.524392 / 0.000490 (0.523902) | 0.003720 / 0.000200 (0.003520) | 0.000087 / 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) | 0.027704 / 0.037411 (-0.009707) | 0.109048 / 0.014526 (0.094522) | 0.118298 / 0.176557 (-0.058259) | 0.158748 / 0.737135 (-0.578388) | 0.125654 / 0.296338 (-0.170684) |\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) | 0.406973 / 0.215209 (0.191764) | 4.057502 / 2.077655 (1.979847) | 1.939847 / 1.504120 (0.435727) | 1.746457 / 1.541195 (0.205262) | 1.698866 / 1.468490 (0.230376) | 0.692884 / 4.584777 (-3.891893) | 3.736988 / 3.745712 (-0.008724) | 2.050122 / 5.269862 (-3.219740) | 1.299808 / 4.565676 (-3.265868) | 0.085285 / 0.424275 (-0.338990) | 0.012768 / 0.007607 (0.005161) | 0.510814 / 0.226044 (0.284770) | 5.105319 / 2.268929 (2.836391) | 2.304003 / 55.444624 (-53.140621) | 1.951123 / 6.876477 (-4.925354) | 1.998504 / 2.142072 (-0.143568) | 0.840235 / 4.805227 (-3.964993) | 0.164521 / 6.500664 (-6.336143) | 0.064215 / 0.075469 (-0.011254) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272520 / 1.841788 (-0.569268) | 14.648110 / 8.074308 (6.573802) | 14.573754 / 10.191392 (4.382362) | 0.170053 / 0.680424 (-0.510371) | 0.029389 / 0.534201 (-0.504811) | 0.438924 / 0.579283 (-0.140359) | 0.433572 / 0.434364 (-0.000792) | 0.517702 / 0.540337 (-0.022635) | 0.600389 / 1.386936 (-0.786547) |\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) | 0.007362 / 0.011353 (-0.003991) | 0.005451 / 0.011008 (-0.005557) | 0.099336 / 0.038508 (0.060828) | 0.033284 / 0.023109 (0.010174) | 0.377143 / 0.275898 (0.101245) | 0.423724 / 0.323480 (0.100244) | 0.006194 / 0.007986 (-0.001792) | 0.004208 / 0.004328 (-0.000121) | 0.074473 / 0.004250 (0.070223) | 0.049874 / 0.037052 (0.012821) | 0.376012 / 0.258489 (0.117523) | 0.439942 / 0.293841 (0.146101) | 0.037860 / 0.128546 (-0.090686) | 0.012546 / 0.075646 (-0.063100) | 0.349123 / 0.419271 (-0.070148) | 0.048980 / 0.043533 (0.005447) | 0.391205 / 0.255139 (0.136066) | 0.396474 / 0.283200 (0.113274) | 0.105846 / 0.141683 (-0.035836) | 1.502475 / 1.452155 (0.050321) | 1.612303 / 1.492716 (0.119587) |\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) | 0.300815 / 0.018006 (0.282809) | 0.542171 / 0.000490 (0.541681) | 0.005465 / 0.000200 (0.005265) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028904 / 0.037411 (-0.008508) | 0.110352 / 0.014526 (0.095827) | 0.123275 / 0.176557 (-0.053282) | 0.161958 / 0.737135 (-0.575178) | 0.133595 / 0.296338 (-0.162743) |\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) | 0.438724 / 0.215209 (0.223515) | 4.373633 / 2.077655 (2.295979) | 2.178981 / 1.504120 (0.674861) | 1.992442 / 1.541195 (0.451247) | 2.063149 / 1.468490 (0.594659) | 0.696688 / 4.584777 (-3.888089) | 3.849370 / 3.745712 (0.103658) | 3.509495 / 5.269862 (-1.760367) | 1.923320 / 4.565676 (-2.642356) | 0.085554 / 0.424275 (-0.338721) | 0.012510 / 0.007607 (0.004903) | 0.535953 / 0.226044 (0.309909) | 5.365684 / 2.268929 (3.096755) | 2.686902 / 55.444624 (-52.757723) | 2.330922 / 6.876477 (-4.545554) | 2.353445 / 2.142072 (0.211373) | 0.878336 / 4.805227 (-3.926891) | 0.167296 / 6.500664 (-6.333368) | 0.064564 / 0.075469 (-0.010905) |\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) | 1.244696 / 1.841788 (-0.597091) | 15.027981 / 8.074308 (6.953673) | 14.545797 / 10.191392 (4.354405) | 0.147229 / 0.680424 (-0.533194) | 0.018007 / 0.534201 (-0.516194) | 0.446196 / 0.579283 (-0.133087) | 0.437418 / 0.434364 (0.003054) | 0.510732 / 0.540337 (-0.029606) | 0.594814 / 1.386936 (-0.792122) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#80eb8db74f49b7ee9c0f73a819c22177fabd61db \"CML watermark\")\n"
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"@polinaeterna any tentative date on when the Common Voice 12.0 dataset will be added ?",
"This dataset is now hosted on the Hub here: https://huggingface.co./datasets/mozilla-foundation/common_voice_12_0"
] | 2023-01-18T13:07:05 | 2023-07-21T14:26:10 | 2023-07-21T14:26:09 | NONE | null | null | null | ### Feature request
Please add the common voice 12_0 datasets. Apart from English, a significant amount of audio-data has been added to the other minor-language datasets.
### Motivation
The dataset link:
https://commonvoice.mozilla.org/en/datasets
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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) | 0.008470 / 0.011353 (-0.002883) | 0.004721 / 0.011008 (-0.006287) | 0.099024 / 0.038508 (0.060516) | 0.029831 / 0.023109 (0.006722) | 0.325887 / 0.275898 (0.049989) | 0.380753 / 0.323480 (0.057273) | 0.007101 / 0.007986 (-0.000885) | 0.004734 / 0.004328 (0.000406) | 0.077576 / 0.004250 (0.073326) | 0.037207 / 0.037052 (0.000154) | 0.320463 / 0.258489 (0.061974) | 0.369284 / 0.293841 (0.075443) | 0.033411 / 0.128546 (-0.095135) | 0.011610 / 0.075646 (-0.064037) | 0.321460 / 0.419271 (-0.097811) | 0.041315 / 0.043533 (-0.002217) | 0.349186 / 0.255139 (0.094047) | 0.384546 / 0.283200 (0.101347) | 0.088045 / 0.141683 (-0.053637) | 1.536341 / 1.452155 (0.084186) | 1.527806 / 1.492716 (0.035089) |\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) | 0.193435 / 0.018006 (0.175429) | 0.451732 / 0.000490 (0.451243) | 0.003165 / 0.000200 (0.002965) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023203 / 0.037411 (-0.014208) | 0.096211 / 0.014526 (0.081685) | 0.105665 / 0.176557 (-0.070891) | 0.141074 / 0.737135 (-0.596061) | 0.108584 / 0.296338 (-0.187755) |\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) | 0.419041 / 0.215209 (0.203832) | 4.187915 / 2.077655 (2.110261) | 1.855336 / 1.504120 (0.351216) | 1.660046 / 1.541195 (0.118851) | 1.674646 / 1.468490 (0.206156) | 0.692257 / 4.584777 (-3.892520) | 3.466853 / 3.745712 (-0.278860) | 1.900925 / 5.269862 (-3.368936) | 1.294696 / 4.565676 (-3.270980) | 0.082792 / 0.424275 (-0.341483) | 0.012808 / 0.007607 (0.005201) | 0.529622 / 0.226044 (0.303578) | 5.337025 / 2.268929 (3.068096) | 2.326558 / 55.444624 (-53.118066) | 1.956256 / 6.876477 (-4.920221) | 2.035911 / 2.142072 (-0.106161) | 0.815824 / 4.805227 (-3.989403) | 0.148720 / 6.500664 (-6.351944) | 0.064226 / 0.075469 (-0.011243) |\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) | 1.231347 / 1.841788 (-0.610440) | 13.724596 / 8.074308 (5.650288) | 13.933878 / 10.191392 (3.742486) | 0.150913 / 0.680424 (-0.529511) | 0.028460 / 0.534201 (-0.505741) | 0.393564 / 0.579283 (-0.185719) | 0.407185 / 0.434364 (-0.027179) | 0.458250 / 0.540337 (-0.082087) | 0.547993 / 1.386936 (-0.838943) |\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) | 0.006653 / 0.011353 (-0.004699) | 0.004615 / 0.011008 (-0.006393) | 0.098062 / 0.038508 (0.059554) | 0.027849 / 0.023109 (0.004740) | 0.409116 / 0.275898 (0.133218) | 0.448770 / 0.323480 (0.125290) | 0.004856 / 0.007986 (-0.003130) | 0.003427 / 0.004328 (-0.000901) | 0.075748 / 0.004250 (0.071498) | 0.037942 / 0.037052 (0.000889) | 0.410232 / 0.258489 (0.151743) | 0.457394 / 0.293841 (0.163553) | 0.031927 / 0.128546 (-0.096620) | 0.011618 / 0.075646 (-0.064028) | 0.321231 / 0.419271 (-0.098040) | 0.041416 / 0.043533 (-0.002117) | 0.413535 / 0.255139 (0.158396) | 0.438196 / 0.283200 (0.154997) | 0.089551 / 0.141683 (-0.052132) | 1.459298 / 1.452155 (0.007143) | 1.552594 / 1.492716 (0.059878) |\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) | 0.228186 / 0.018006 (0.210180) | 0.404393 / 0.000490 (0.403904) | 0.006944 / 0.000200 (0.006744) | 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) | 0.025167 / 0.037411 (-0.012244) | 0.101282 / 0.014526 (0.086756) | 0.107282 / 0.176557 (-0.069275) | 0.139797 / 0.737135 (-0.597339) | 0.110477 / 0.296338 (-0.185861) |\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) | 0.479121 / 0.215209 (0.263912) | 4.778210 / 2.077655 (2.700555) | 2.464687 / 1.504120 (0.960567) | 2.255312 / 1.541195 (0.714118) | 2.287348 / 1.468490 (0.818858) | 0.694769 / 4.584777 (-3.890008) | 3.460860 / 3.745712 (-0.284852) | 3.078881 / 5.269862 (-2.190980) | 1.297726 / 4.565676 (-3.267950) | 0.082699 / 0.424275 (-0.341576) | 0.012652 / 0.007607 (0.005045) | 0.583308 / 0.226044 (0.357263) | 5.839199 / 2.268929 (3.570271) | 2.893724 / 55.444624 (-52.550900) | 2.546503 / 6.876477 (-4.329974) | 2.559570 / 2.142072 (0.417498) | 0.802357 / 4.805227 (-4.002870) | 0.151890 / 6.500664 (-6.348774) | 0.068593 / 0.075469 (-0.006876) |\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) | 1.262421 / 1.841788 (-0.579367) | 13.771848 / 8.074308 (5.697540) | 14.046017 / 10.191392 (3.854625) | 0.140950 / 0.680424 (-0.539474) | 0.016839 / 0.534201 (-0.517362) | 0.378870 / 0.579283 (-0.200413) | 0.385908 / 0.434364 (-0.048456) | 0.438539 / 0.540337 (-0.101799) | 0.522761 / 1.386936 (-0.864175) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8145ebfd4fc3508d0be0de9a0f9c58877f2b32f8 \"CML watermark\")\n"
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} | This PR updates the "checkout" GitHub Action to its latest version, as previous ones are deprecated: https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/ | {
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"Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\r\n\r\n",
"> Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\n> \n> \n\nI have only 1 folder that I use in the load_dataset function with the name \"IMGDATA\" and all my 9000 images are located in this folder.\n`\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"IMGDATA\")\n`\nAt the same time, using another data set with images consisting of 3 RGB channels, everything works",
"Okay, I figured out what was wrong. When uploading my dataset via Google Drive, the images broke and Pillow couldn't open them. As a result, I solved the problem by downloading the ZIP archive"
] | 2023-01-17T18:22:27 | 2023-01-18T20:20:15 | 2023-01-18T20:20:15 | NONE | null | null | null | I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand. | {
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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) | 0.013736 / 0.011353 (0.002383) | 0.006253 / 0.011008 (-0.004755) | 0.127076 / 0.038508 (0.088568) | 0.040997 / 0.023109 (0.017888) | 0.394744 / 0.275898 (0.118846) | 0.454285 / 0.323480 (0.130805) | 0.009864 / 0.007986 (0.001878) | 0.005093 / 0.004328 (0.000765) | 0.098714 / 0.004250 (0.094464) | 0.044308 / 0.037052 (0.007255) | 0.421951 / 0.258489 (0.163462) | 0.462280 / 0.293841 (0.168439) | 0.059979 / 0.128546 (-0.068567) | 0.020607 / 0.075646 (-0.055039) | 0.443593 / 0.419271 (0.024321) | 0.062332 / 0.043533 (0.018799) | 0.411335 / 0.255139 (0.156196) | 0.426524 / 0.283200 (0.143324) | 0.118233 / 0.141683 (-0.023450) | 1.877681 / 1.452155 (0.425527) | 1.865271 / 1.492716 (0.372555) |\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) | 0.234791 / 0.018006 (0.216784) | 0.557322 / 0.000490 (0.556833) | 0.000528 / 0.000200 (0.000328) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030260 / 0.037411 (-0.007151) | 0.122594 / 0.014526 (0.108068) | 0.142142 / 0.176557 (-0.034414) | 0.197098 / 0.737135 (-0.540037) | 0.150978 / 0.296338 (-0.145360) |\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) | 0.622644 / 0.215209 (0.407435) | 6.320078 / 2.077655 (4.242423) | 2.552755 / 1.504120 (1.048635) | 2.188647 / 1.541195 (0.647453) | 2.226602 / 1.468490 (0.758112) | 1.288083 / 4.584777 (-3.296694) | 5.624143 / 3.745712 (1.878431) | 3.208382 / 5.269862 (-2.061480) | 2.115222 / 4.565676 (-2.450455) | 0.146420 / 0.424275 (-0.277856) | 0.014464 / 0.007607 (0.006857) | 0.816470 / 0.226044 (0.590425) | 7.984049 / 2.268929 (5.715120) | 3.364942 / 55.444624 (-52.079682) | 2.552306 / 6.876477 (-4.324171) | 2.664575 / 2.142072 (0.522503) | 1.556177 / 4.805227 (-3.249050) | 0.263389 / 6.500664 (-6.237275) | 0.076861 / 0.075469 (0.001391) |\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) | 1.553734 / 1.841788 (-0.288054) | 18.365029 / 8.074308 (10.290721) | 20.993993 / 10.191392 (10.802601) | 0.235642 / 0.680424 (-0.444782) | 0.047084 / 0.534201 (-0.487117) | 0.555610 / 0.579283 (-0.023673) | 0.659413 / 0.434364 (0.225049) | 0.639284 / 0.540337 (0.098947) | 0.756317 / 1.386936 (-0.630620) |\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) | 0.014709 / 0.011353 (0.003356) | 0.006673 / 0.011008 (-0.004335) | 0.133718 / 0.038508 (0.095210) | 0.035699 / 0.023109 (0.012590) | 0.459089 / 0.275898 (0.183191) | 0.538071 / 0.323480 (0.214591) | 0.007376 / 0.007986 (-0.000610) | 0.004688 / 0.004328 (0.000360) | 0.104909 / 0.004250 (0.100659) | 0.064942 / 0.037052 (0.027890) | 0.466158 / 0.258489 (0.207669) | 0.566100 / 0.293841 (0.272259) | 0.057368 / 0.128546 (-0.071178) | 0.021572 / 0.075646 (-0.054075) | 0.413826 / 0.419271 (-0.005446) | 0.079543 / 0.043533 (0.036010) | 0.493313 / 0.255139 (0.238174) | 0.517787 / 0.283200 (0.234587) | 0.119836 / 0.141683 (-0.021847) | 1.833956 / 1.452155 (0.381801) | 2.003288 / 1.492716 (0.510572) |\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) | 0.276013 / 0.018006 (0.258007) | 0.549194 / 0.000490 (0.548704) | 0.010939 / 0.000200 (0.010739) | 0.000129 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034983 / 0.037411 (-0.002428) | 0.131576 / 0.014526 (0.117050) | 0.140651 / 0.176557 (-0.035906) | 0.186455 / 0.737135 (-0.550681) | 0.146309 / 0.296338 (-0.150029) |\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) | 0.675973 / 0.215209 (0.460763) | 6.821862 / 2.077655 (4.744208) | 3.090307 / 1.504120 (1.586187) | 2.710679 / 1.541195 (1.169484) | 2.891577 / 1.468490 (1.423087) | 1.306160 / 4.584777 (-3.278617) | 5.629763 / 3.745712 (1.884051) | 4.662578 / 5.269862 (-0.607283) | 2.670195 / 4.565676 (-1.895482) | 0.153867 / 0.424275 (-0.270408) | 0.016028 / 0.007607 (0.008421) | 0.878702 / 0.226044 (0.652658) | 8.801612 / 2.268929 (6.532683) | 4.005520 / 55.444624 (-51.439104) | 3.124755 / 6.876477 (-3.751721) | 3.382132 / 2.142072 (1.240060) | 1.525951 / 4.805227 (-3.279277) | 0.263350 / 6.500664 (-6.237315) | 0.079285 / 0.075469 (0.003815) |\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) | 1.647591 / 1.841788 (-0.194197) | 18.281646 / 8.074308 (10.207338) | 21.072142 / 10.191392 (10.880750) | 0.232236 / 0.680424 (-0.448188) | 0.026126 / 0.534201 (-0.508075) | 0.546926 / 0.579283 (-0.032357) | 0.634496 / 0.434364 (0.200132) | 0.604345 / 0.540337 (0.064007) | 0.730159 / 1.386936 (-0.656777) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cfe8a6aa4cd2d3d0d7067f390152d1a4aeb4c710 \"CML watermark\")\n"
] | 2023-01-17T15:59:50 | 2023-01-18T09:05:49 | 2023-01-18T06:29:06 | CONTRIBUTOR | null | false | {
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} | Closes #5433, reverts #5432, and also:
* Uses [ghcr.io container images](https://cml.dev/doc/self-hosted-runners/#docker-images) for extra speed
* Updates `actions/checkout` to `v3` (note that `v2` is [deprecated](https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/))
* Follows the new naming convention for environment variables introduced with [iterative/cml#1272](https://github.com/iterative/cml/pull/1272) | {
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https://api.github.com/repos/huggingface/datasets/issues/5435 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5435/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5435/comments | https://api.github.com/repos/huggingface/datasets/issues/5435/events | https://github.com/huggingface/datasets/issues/5435 | 1,536,099,300 | I_kwDODunzps5bjwPk | 5,435 | Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage | {
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"Just for your information, Tensorflow confirmed this issue [here.](https://github.com/tensorflow/tensorflow/issues/59279)",
"Thanks for reporting, @HaoyuYang59.\r\n\r\nPlease note that these are different \"dataset\" objects: our docs refer to Hugging Face `datasets.Dataset` and not to TensorFlow `tf.data.Dataset`.\r\n\r\nOur `datasets.Dataset.shuffle` method does not have a `reshuffle_each_iteration` argument. Therefore, I would say the statement in our docs is True because they refer to `datasets.Dataset.shuffle`, `datasets.Dataset.skip` and `datasets.Dataset.take`.\r\n\r\nI think this issue is restricted to TensorFlow dataset, and this would be addressed by them in the issue you opened in their repo: https://github.com/tensorflow/tensorflow/issues/59279",
"Also note that you are referring to an outdated documentation page: datasets 1.10.2 version\r\n\r\nCurrent datasets version is 2.8.0 and the corresponding documentation page is: https://huggingface.co./docs/datasets/stream#split-dataset",
"Hi @albertvillanova thanks for your reply and your explaination here. \r\n\r\nSorry for the confusion as I'm not actually a user of your repo and I just happen to find the thread by Google (and didn't read carefully).\r\n\r\nGreat to know that and you made everything very clear now.\r\n\r\nThanks for your time and sorry for the consusion.\r\n\r\nWishing you a wonderful time. \r\n\r\nRegards"
] | 2023-01-17T10:04:16 | 2023-01-19T09:56:03 | 2023-01-19T09:56:03 | NONE | null | null | null | ### Describe the bug
In the [Split your dataset with take and skip](https://huggingface.co./docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:
> Using take (or skip) prevents future calls to shuffle from shuffling the dataset shards order, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer. Therefore it is advised to shuffle the dataset before splitting using take or skip. See more details in the [Shuffling the dataset: shuffle](https://huggingface.co./docs/datasets/v1.10.2/dataset_streaming.html#iterable-dataset-shuffling) section.`
>> \# You can also create splits from a shuffled dataset
>> train_dataset = shuffled_dataset.skip(1000)
>> eval_dataset = shuffled_dataset.take(1000)
Where the shuffled dataset comes from:
`shuffled_dataset = dataset.shuffle(buffer_size=10_000, seed=42)`
At least in Tensorflow 2.9/2.10/2.11, [docs](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) states the `reshuffle_each_iteration` argument is `True` by default. This means the dataset would be shuffled after each epoch, and as a result **the validation data would leak into training test**.
### Steps to reproduce the bug
N/A
### Expected behavior
The `reshuffle_each_iteration` argument should be set to `False`.
### Environment info
Tensorflow 2.9/2.10/2.11 | {
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https://api.github.com/repos/huggingface/datasets/issues/5434 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5434/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5434/comments | https://api.github.com/repos/huggingface/datasets/issues/5434/events | https://github.com/huggingface/datasets/issues/5434 | 1,536,090,042 | I_kwDODunzps5bjt-6 | 5,434 | sample_dataset module not found | {
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"Hi! Can you describe what the actual error is?",
"working on the setfit example script\r\n\r\n from setfit import SetFitModel, SetFitTrainer, sample_dataset\r\n\r\nImportError: cannot import name 'sample_dataset' from 'setfit' (C:\\Python\\Python38\\lib\\site-packages\\setfit\\__init__.py)\r\n\r\n apart from that, I also had to hack these loads to import thses modules:\r\n from datasets.load import load_dataset \r\n from datasets.arrow_dataset import Dataset\r\n from datasets.dataset_dict import DatasetDict",
"Hi! This issue is related to the [SetFit](https://github.com/huggingface/setfit) project, so can you please open it there?"
] | 2023-01-17T09:57:54 | 2023-01-19T13:52:12 | 2023-01-19T07:55:11 | NONE | null | null | null | null | {
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"Sorry, it was us:[^1] https://github.com/iterative/cml/pull/1317 & https://github.com/iterative/cml/issues/1319#issuecomment-1385599559; should be fixed with [v0.18.17](https://github.com/iterative/cml/releases/tag/v0.18.17).\r\n\r\n[^1]: More or less, see https://github.com/yargs/yargs/issues/873.",
"Opened https://github.com/huggingface/datasets/pull/5436 unpinning again the container image.",
"Hi @0x2b3bfa0, thanks a lot for the investigation, the context about the the root cause and for fixing it!!\r\n\r\nWe are reviewing your PR to unpin the container image."
] | 2023-01-17T09:06:08 | 2023-01-18T06:29:08 | 2023-01-18T06:29:08 | MEMBER | null | null | null | Once we find out the root cause of:
- #5431
we should revert the temporary pin on the Docker image version introduced by:
- #5432 | {
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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) | 0.008519 / 0.011353 (-0.002834) | 0.004451 / 0.011008 (-0.006558) | 0.102401 / 0.038508 (0.063893) | 0.029779 / 0.023109 (0.006669) | 0.302654 / 0.275898 (0.026756) | 0.366002 / 0.323480 (0.042522) | 0.007044 / 0.007986 (-0.000942) | 0.003350 / 0.004328 (-0.000978) | 0.078213 / 0.004250 (0.073963) | 0.035208 / 0.037052 (-0.001844) | 0.312980 / 0.258489 (0.054491) | 0.344217 / 0.293841 (0.050376) | 0.033089 / 0.128546 (-0.095457) | 0.011443 / 0.075646 (-0.064203) | 0.353143 / 0.419271 (-0.066128) | 0.040851 / 0.043533 (-0.002682) | 0.304501 / 0.255139 (0.049362) | 0.329118 / 0.283200 (0.045918) | 0.087399 / 0.141683 (-0.054284) | 1.500200 / 1.452155 (0.048046) | 1.536176 / 1.492716 (0.043459) |\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) | 0.209626 / 0.018006 (0.191619) | 0.425551 / 0.000490 (0.425061) | 0.001168 / 0.000200 (0.000968) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023664 / 0.037411 (-0.013748) | 0.096792 / 0.014526 (0.082266) | 0.105652 / 0.176557 (-0.070905) | 0.140796 / 0.737135 (-0.596340) | 0.109319 / 0.296338 (-0.187019) |\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) | 0.414802 / 0.215209 (0.199593) | 4.152619 / 2.077655 (2.074964) | 1.814403 / 1.504120 (0.310283) | 1.611392 / 1.541195 (0.070198) | 1.667350 / 1.468490 (0.198860) | 0.691855 / 4.584777 (-3.892922) | 3.406584 / 3.745712 (-0.339128) | 1.940332 / 5.269862 (-3.329530) | 1.279061 / 4.565676 (-3.286615) | 0.082938 / 0.424275 (-0.341337) | 0.012388 / 0.007607 (0.004781) | 0.521738 / 0.226044 (0.295693) | 5.233764 / 2.268929 (2.964835) | 2.306573 / 55.444624 (-53.138051) | 1.954631 / 6.876477 (-4.921845) | 2.048315 / 2.142072 (-0.093757) | 0.816921 / 4.805227 (-3.988306) | 0.150983 / 6.500664 (-6.349681) | 0.066628 / 0.075469 (-0.008842) |\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) | 1.235939 / 1.841788 (-0.605849) | 14.047114 / 8.074308 (5.972806) | 14.149842 / 10.191392 (3.958450) | 0.152836 / 0.680424 (-0.527588) | 0.028837 / 0.534201 (-0.505364) | 0.396232 / 0.579283 (-0.183051) | 0.409950 / 0.434364 (-0.024414) | 0.460296 / 0.540337 (-0.080041) | 0.556787 / 1.386936 (-0.830149) |\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) | 0.006582 / 0.011353 (-0.004771) | 0.004491 / 0.011008 (-0.006518) | 0.100093 / 0.038508 (0.061585) | 0.026826 / 0.023109 (0.003717) | 0.413971 / 0.275898 (0.138073) | 0.445625 / 0.323480 (0.122145) | 0.004892 / 0.007986 (-0.003094) | 0.003295 / 0.004328 (-0.001034) | 0.077879 / 0.004250 (0.073628) | 0.039177 / 0.037052 (0.002125) | 0.353299 / 0.258489 (0.094810) | 0.406566 / 0.293841 (0.112725) | 0.031633 / 0.128546 (-0.096913) | 0.011517 / 0.075646 (-0.064130) | 0.320939 / 0.419271 (-0.098332) | 0.041487 / 0.043533 (-0.002046) | 0.353735 / 0.255139 (0.098596) | 0.434786 / 0.283200 (0.151586) | 0.087722 / 0.141683 (-0.053961) | 1.515134 / 1.452155 (0.062979) | 1.588908 / 1.492716 (0.096191) |\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) | 0.225312 / 0.018006 (0.207305) | 0.398324 / 0.000490 (0.397834) | 0.000453 / 0.000200 (0.000253) | 0.000064 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024645 / 0.037411 (-0.012766) | 0.099399 / 0.014526 (0.084873) | 0.107006 / 0.176557 (-0.069550) | 0.145090 / 0.737135 (-0.592045) | 0.110046 / 0.296338 (-0.186292) |\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) | 0.450573 / 0.215209 (0.235364) | 4.498030 / 2.077655 (2.420375) | 2.193164 / 1.504120 (0.689044) | 1.940103 / 1.541195 (0.398908) | 1.957137 / 1.468490 (0.488647) | 0.697599 / 4.584777 (-3.887178) | 3.465146 / 3.745712 (-0.280566) | 1.918209 / 5.269862 (-3.351653) | 1.183921 / 4.565676 (-3.381756) | 0.082540 / 0.424275 (-0.341735) | 0.012495 / 0.007607 (0.004888) | 0.549702 / 0.226044 (0.323658) | 5.526841 / 2.268929 (3.257912) | 2.658611 / 55.444624 (-52.786014) | 2.259542 / 6.876477 (-4.616935) | 2.310139 / 2.142072 (0.168066) | 0.810550 / 4.805227 (-3.994677) | 0.152369 / 6.500664 (-6.348295) | 0.066295 / 0.075469 (-0.009174) |\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) | 1.289240 / 1.841788 (-0.552547) | 14.032143 / 8.074308 (5.957834) | 13.973492 / 10.191392 (3.782100) | 0.140082 / 0.680424 (-0.540342) | 0.017113 / 0.534201 (-0.517088) | 0.386534 / 0.579283 (-0.192749) | 0.393723 / 0.434364 (-0.040641) | 0.448891 / 0.540337 (-0.091446) | 0.533085 / 1.386936 (-0.853851) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2023-01-17T07:15:31 | 2023-01-17T08:58:22 | 2023-01-17T08:51:17 | MEMBER | null | false | {
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} | This PR fixes CI benchmarks, by temporarily pinning Docker image version, instead of "latest" tag.
It also updates deprecated `cml-send-comment` command and using `cml comment create` instead.
Fix #5431. | {
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] | null | [] | 2023-01-17T06:49:57 | 2023-01-18T06:33:24 | 2023-01-17T08:51:18 | MEMBER | null | null | null | Our CI benchmarks are broken, raising `Unknown arguments` error: https://github.com/huggingface/datasets/actions/runs/3932397079/jobs/6724905161
```
Unknown arguments: runnerPath, path
```
Stack trace:
```
100%|██████████| 500/500 [00:01<00:00, 338.98ba/s]
Updating lock file 'dvc.lock'
To track the changes with git, run:
git add dvc.lock
To enable auto staging, run:
dvc config core.autostage true
Use `dvc push` to send your updates to remote storage.
cml send-comment <markdown file>
Global Options:
--log Logging verbosity
[string] [choices: "error", "warn", "info", "debug"] [default: "info"]
--driver Git provider where the repository is hosted
[string] [choices: "github", "gitlab", "bitbucket"] [default: infer from the
environment]
--repo Repository URL or slug
[string] [default: infer from the environment]
--driver-token, --token CI driver personal/project access token (PAT)
[string] [default: infer from the environment]
--help Show help [boolean]
Options:
--target Comment type (`commit`, `pr`, `commit/f00bar`,
`pr/42`, `issue/1337`),default is automatic (`pr`
but fallback to `commit`). [string]
--watch Watch for changes and automatically update the
comment [boolean]
--publish Upload any local images found in the Markdown
report [boolean] [default: true]
--publish-url Self-hosted image server URL
[string] [default: "https://asset.cml.dev/"]
--publish-native, --native Uses driver's native capabilities to upload assets
instead of CML's storage; not available on GitHub
[boolean]
--watermark-title Hidden comment marker (used for targeting in
subsequent `cml comment update`); "{workflow}" &
"{run}" are auto-replaced [string] [default: ""]
Unknown arguments: runnerPath, path
Error: Process completed with exit code 1.
```
Issue reported to iterative/cml:
- iterative/cml#1319 | {
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"Some of the shard files now have 0 number of rows.\r\n\r\nWe have opened an issue in the Apache Beam repo:\r\n- https://github.com/apache/beam/issues/25041"
] | 2023-01-17T06:42:12 | 2023-01-17T16:12:18 | null | MEMBER | null | null | null | Once we find out the root cause of:
- #5426
we should revert the temporary pin on apache-beam introduced by:
- #5429 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2023-01-16T16:20:09 | 2023-01-16T16:51:42 | 2023-01-16T16:49:03 | MEMBER | null | false | {
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} | Temporarily pin apache-beam < 2.44.0
Fix #5426. | {
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https://api.github.com/repos/huggingface/datasets/issues/5428 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5428/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5428/comments | https://api.github.com/repos/huggingface/datasets/issues/5428/events | https://github.com/huggingface/datasets/issues/5428 | 1,535,166,139 | I_kwDODunzps5bgMa7 | 5,428 | Load/Save FAISS index using fsspec | {
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"Hi! Sure, feel free to submit a PR. Maybe if we want to be consistent with the existing API, it would be cleaner to directly add support for `fsspec` paths in `Dataset.load_faiss_index`/`Dataset.save_faiss_index` in the same manner as it was done in `Dataset.load_from_disk`/`Dataset.save_to_disk`.",
"That's a great idea! I'll do that instead. "
] | 2023-01-16T16:08:12 | 2023-03-27T15:18:22 | 2023-03-27T15:18:22 | CONTRIBUTOR | null | null | null | ### Feature request
From what I understand `faiss` already support this [link](https://github.com/facebookresearch/faiss/wiki/Index-IO,-cloning-and-hyper-parameter-tuning#generic-io-support)
I would like to use a stream as input to `Dataset.load_faiss_index` and `Dataset.save_faiss_index`.
### Motivation
In my case, I'm saving faiss index in cloud storage and use `fsspec` to load them. It would be ideal if I could send the stream directly instead of copying the file locally (or mounting the bucket) and then load the index.
### Your contribution
I can submit the PR | {
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https://api.github.com/repos/huggingface/datasets/issues/5427 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5427/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5427/comments | https://api.github.com/repos/huggingface/datasets/issues/5427/events | https://github.com/huggingface/datasets/issues/5427 | 1,535,162,889 | I_kwDODunzps5bgLoJ | 5,427 | Unable to download dataset id_clickbait | {
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"Thanks for reporting, @ilos-vigil.\r\n\r\nWe have transferred this issue to the corresponding dataset on the Hugging Face Hub: https://huggingface.co./datasets/id_clickbait/discussions/1 "
] | 2023-01-16T16:05:36 | 2023-01-18T09:51:28 | 2023-01-18T09:25:19 | NONE | null | null | null | ### Describe the bug
I tried to download dataset `id_clickbait`, but receive this error message.
```
FileNotFoundError: Couldn't find file at https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/k42j7x2kpn-1.zip
```
When i open the link using browser, i got this XML data.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<Error><Code>NoSuchBucket</Code><Message>The specified bucket does not exist</Message><BucketName>md-datasets-cache-zipfiles-prod</BucketName><RequestId>NVRM6VEEQD69SD00</RequestId><HostId>W/SPDxLGvlCGi0OD6d7mSDvfOAUqLAfvs9nTX50BkJrjMny+X9Jnqp/Li2lG9eTUuT4MUkAA2jjTfCrCiUmu7A==</HostId></Error>
```
### Steps to reproduce the bug
Code snippet:
```
from datasets import load_dataset
load_dataset('id_clickbait', 'annotated')
load_dataset('id_clickbait', 'raw')
```
Link to Kaggle notebook: https://www.kaggle.com/code/ilosvigil/bug-check-on-id-clickbait-dataset
### Expected behavior
Successfully download and load `id_newspaper` dataset.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5 | {
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] | null | [] | 2023-01-16T16:02:07 | 2023-06-02T06:40:32 | 2023-01-16T16:49:04 | MEMBER | null | null | null | CI test (unit, ubuntu-latest, deps-minimum) is broken, raising a `SchemaInferenceError`: see https://github.com/huggingface/datasets/actions/runs/3930901593/jobs/6721492004
```
FAILED tests/test_beam.py::BeamBuilderTest::test_download_and_prepare_sharded - datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
```
Stack trace:
```
______________ BeamBuilderTest.test_download_and_prepare_sharded _______________
[gw1] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
self = <tests.test_beam.BeamBuilderTest testMethod=test_download_and_prepare_sharded>
@require_beam
def test_download_and_prepare_sharded(self):
import apache_beam as beam
original_write_parquet = beam.io.parquetio.WriteToParquet
expected_num_examples = len(get_test_dummy_examples())
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = DummyBeamDataset(cache_dir=tmp_cache_dir, beam_runner="DirectRunner")
with patch("apache_beam.io.parquetio.WriteToParquet") as write_parquet_mock:
write_parquet_mock.side_effect = partial(original_write_parquet, num_shards=2)
> builder.download_and_prepare()
tests/test_beam.py:97:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:864: in download_and_prepare
**download_and_prepare_kwargs,
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:1976: in _download_and_prepare
num_examples, num_bytes = beam_writer.finalize(metrics.query(m_filter))
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:694: in finalize
shard_num_bytes, _ = parquet_to_arrow(source, destination)
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:740: in parquet_to_arrow
num_bytes, num_examples = writer.finalize()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <datasets.arrow_writer.ArrowWriter object at 0x7f6dcbb3e810>
close_stream = True
def finalize(self, close_stream=True):
self.write_rows_on_file()
# In case current_examples < writer_batch_size, but user uses finalize()
if self._check_duplicates:
self.check_duplicate_keys()
# Re-intializing to empty list for next batch
self.hkey_record = []
self.write_examples_on_file()
# If schema is known, infer features even if no examples were written
if self.pa_writer is None and self.schema:
self._build_writer(self.schema)
if self.pa_writer is not None:
self.pa_writer.close()
self.pa_writer = None
if close_stream:
self.stream.close()
else:
if close_stream:
self.stream.close()
> raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
E datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:593: SchemaInferenceError
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5425 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5425/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5425/comments | https://api.github.com/repos/huggingface/datasets/issues/5425/events | https://github.com/huggingface/datasets/issues/5425 | 1,534,581,850 | I_kwDODunzps5bd9xa | 5,425 | Sort on multiple keys with datasets.Dataset.sort() | {
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] | closed | false | null | [] | null | [
"Hi! \r\n\r\n`Dataset.sort` calls `df.sort_values` internally, and `df.sort_values` brings all the \"sort\" columns in memory, so sorting on multiple keys could be very expensive. This makes me think that maybe we can replace `df.sort_values` with `pyarrow.compute.sort_indices` - the latter can also sort on multiple keys and currently loads the data into memory; however, there is a plan to eventually implement \"memory-map\" friendly kernels for the Arrow compute ops (using the Acero execution engine). \r\n\r\nSo to address this issue, you should replace `df.sort_values` with `pyarrow.compute.sort_indices` in `Dataset.sort` and adjust the signature of this function (deprecate the `kind` parameter, etc.).\r\n\r\nPS: Feel free to ping us if you need some additional help/pointers",
"@mariosasko If I understand the code right, using `pyarrow.compute.sort_indices` would also require changes to the `select` method if it is meant to sort multiple keys. That's because `select` only accepts 1D input for `indices`, not an iterable or similar which would be required for multiple keys unless you want some looping over selects. Doesn't seem that straight-forward but I might be missing something here... ",
"@MichlF No, it doesn't require modifying select because sorting on multiple keys also returns a 1D array.\r\n\r\nIt's easier to understand with an example:\r\n```python\r\n>>> import pyarrow as pa\r\n>>> import pyarrow.compute as pc\r\n>>> table = pa.table({\r\n... \"name\": [\"John\", \"Eve\", \"Peter\", \"John\"],\r\n... \"surname\": [\"Johnson\", \"Smith\", \"Smith\", \"Doe\"],\r\n... \"age\": [20, 40, 30, 50],\r\n... })\r\n>>> indices = pc.sort_indices(table, sort_keys=[(\"name\", \"ascending\"), (\"surname\", \"ascending\")])\r\n>>> print(indices)\r\n[\r\n 1,\r\n 3,\r\n 0,\r\n 2\r\n]\r\n```\r\n\r\n",
"Thanks for clarifying.\r\nI can prepare a PR to address this issue. This would be my first PR here so I have a few maybe silly questions but:\r\n- What is the preferred input type of `sort_keys` for the sort method? A sequence with name, order tuples like pyarrow's `sort_indices` requires?\r\n- What about backwards compatability: is it supposed to also accept the old way of calling sort() or should both `column` and `kind` be deprecated?\r\n- If `sort_keys` is provided in the same format as for pyarrow's `sort_indices` - i.e. along with order for each column -, `reverse` doesn't make much sense either and should be deprecated as well I assume.",
"I think we can have the following signature:\r\n```python\r\ndef sort(\r\n self,\r\n column_names: Union[str, Sequence[str]],\r\n reverse: Union[bool, Sequence[bool]] = False,\r\n kind=\"deprecated\",\r\n null_placement: str = \"last\",\r\n keep_in_memory: bool = False,\r\n load_from_cache_file: bool = True,\r\n indices_cache_file_name: Optional[str] = None,\r\n writer_batch_size: Optional[int] = 1000,\r\n new_fingerprint: Optional[str] = None,\r\n ) -> \"Dataset\":\r\n``` \r\n\r\nSo we should:\r\n* rename`column` to `column_names`. `column` is a positional argument, so it's OK to rename it (not marked as positional-only with \"/\", but still should be fine)\r\n* deprecate `kind`\r\n* keep `reverse` instead of introducing `sort_keys`, but we should allow passing a list of booleans that defines the sort order of each column from `column_names` to it (`reverse = False` would be equal to `[False] * len(column_names)` and `reverse = True` to `[True] * len(column_names)`)",
"I am pretty much done with the PR. Just one clarification: `Sequence` in `arrow_dataset.py` is a custom dataclass from `features.py` instead of the `type.hinting` class `Sequence` from Python. Do you suggest using that custom `Sequence` class somehow ? Otherwise signature currently reads instead:\r\n```Python\r\n def sort(\r\n self,\r\n column_names: Union[str, List[str]],\r\n reverse: Union[bool, List[bool]] = False,\r\n kind = \"deprecated\",\r\n null_placement: str = \"last\",\r\n keep_in_memory: bool = False,\r\n load_from_cache_file: bool = True,\r\n indices_cache_file_name: Optional[str] = None,\r\n writer_batch_size: Optional[int] = 1000,\r\n new_fingerprint: Optional[str] = None,\r\n )\r\n```\r\n\r\nAlso, to maintain backwards compatibility, I added conditionals for `null_placement`, because pyarrow's `null_placement` only accepts `at_start` and `at_end`, and not `last` and `first`.\r\nIf that is all good, I think I can open the PR.",
"I meant `typing.Sequence` (`datasets.Sequence` is a feature type). \r\n\r\nRegarding `null_placement`, I think we can support both `at_start` and `at_end`, and `last` and `first` (for backward compatibility; convert internally to `at_end` and `at_start` respectively).",
"> I meant typing.Sequence (datasets.Sequence is a feature type).\r\n\r\nSorry, I actually meant `typing.Sequence` and not `type.hinting`. However, the issue is still that `dataset.Sequence` is imported in `arrow_dataset.py` so I cannot import and use `typing.Sequence` for the `sort`'s signature without overwriting the `dataset.Sequence` import. The latter is used in the `align_labels_with_mapping` method so it's a necessary import for `arrow_dataset.py`. \r\nTo import `typing.Sequence` as something else than `Sequence` to avoid overwriting may only be confusing and doesn't seem good practice!? The other solution is to keep `List` type hinting as in the signature I posted in my previous post but this excludes other Sequence types and may cause problems further down the line.\r\nPlease advise,\r\nThanks for all the clarifications!",
"You can avoid the name collision by renaming `typing.Sequence` to `Sequence_` when importing:\r\n```python\r\nfrom typing import Sequence as Sequence_\r\n```",
"Resolved via #5502 "
] | 2023-01-16T09:22:26 | 2023-02-24T16:15:11 | 2023-02-24T16:15:11 | NONE | null | null | null | ### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to pandas and be able to specify multiple columns for sorting. We’re already using pandas under the hood to do the sorting in datasets.
The suggested workaround:
> convert your dataset to pandas and use `df.sort_values()`
### Motivation
Preserved ordering when sorting is very handy when one needs to sort on multiple columns, A and B, so that e.g. whenever A is equal for two or more rows, B is kept sorted.
Having a parameter to do this in 🤗datasets would be cleaner than going through pandas and back, and it wouldn't add much complexity to the library.
Alternatives:
- the possibility to specify multiple keys to sort by with decreasing priority (suggested solution),
- the ability to provide a key function for sorting, so that one can manually specify the sorting criteria.
### Your contribution
I'll be happy to contribute by submitting a PR. Will get documented on `CONTRIBUTING.MD`.
Would love to get thoughts on this, if anyone has anything to add. | {
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https://api.github.com/repos/huggingface/datasets/issues/5424 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5424/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5424/comments | https://api.github.com/repos/huggingface/datasets/issues/5424/events | https://github.com/huggingface/datasets/issues/5424 | 1,534,394,756 | I_kwDODunzps5bdQGE | 5,424 | When applying `ReadInstruction` to custom load it's not DatasetDict but list of Dataset? | {
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"Hi! You can get a `DatasetDict` if you pass a dictionary with read instructions as follows:\r\n```python\r\ninstructions = [\r\n ReadInstruction(split_name=\"train\", from_=0, to=10, unit='%', rounding='closest'),\r\n ReadInstruction(split_name=\"dev\", from_=0, to=10, unit='%', rounding='closest'),\r\n ReadInstruction(split_name=\"test\", from_=0, to=5, unit='%', rounding='closest')\r\n]\r\n\r\ndataset = load_dataset('csv', data_dir=\"data/\", data_files={\"train\":\"train.tsv\", \"dev\":\"dev.tsv\", \"test\":\"test.tsv\"}, delimiter=\"\\t\", split={inst.split_name: inst for inst in instructions})\r\n```\r\n"
] | 2023-01-16T06:54:28 | 2023-02-24T16:19:00 | 2023-02-24T16:19:00 | NONE | null | null | null | ### Describe the bug
I am loading datasets from custom `tsv` files stored locally and applying split instructions for each split. Although the ReadInstruction is being applied correctly and I was expecting it to be `DatasetDict` but instead it is a list of `Dataset`.
### Steps to reproduce the bug
Steps to reproduce the behaviour:
1. Import
`from datasets import load_dataset, ReadInstruction`
2. Instruction to load the dataset
```
instructions = [
ReadInstruction(split_name="train", from_=0, to=10, unit='%', rounding='closest'),
ReadInstruction(split_name="dev", from_=0, to=10, unit='%', rounding='closest'),
ReadInstruction(split_name="test", from_=0, to=5, unit='%', rounding='closest')
]
```
3. Load
`dataset = load_dataset('csv', data_dir="data/", data_files={"train":"train.tsv", "dev":"dev.tsv", "test":"test.tsv"}, delimiter="\t", split=instructions)`
### Expected behavior
**Current behaviour**
:
**Expected behaviour**
### Environment info
``datasets==2.8.0
``
`Python==3.8.5
`
`Platform - Ubuntu 20.04.4 LTS` | {
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https://api.github.com/repos/huggingface/datasets/issues/5422 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5422/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5422/comments | https://api.github.com/repos/huggingface/datasets/issues/5422/events | https://github.com/huggingface/datasets/issues/5422 | 1,533,385,239 | I_kwDODunzps5bZZoX | 5,422 | Datasets load error for saved github issues | {
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"I can confirm that the error exists!\r\nI'm trying to read 3 parquet files locally:\r\n```python\r\nfrom datasets import load_dataset, Features, Value, ClassLabel\r\n\r\nreview_dataset = load_dataset(\r\n \"parquet\",\r\n data_files={\r\n \"train\": os.path.join(sentiment_analysis_data_path, \"train.parquet\"),\r\n \"validation\": os.path.join(sentiment_analysis_data_path, \"validation.parquet\"),\r\n \"test\": os.path.join(sentiment_analysis_data_path, \"test.parquet\"),\r\n },\r\n)\r\n```\r\n\r\nBut you can fix it, by specifying `features` for `load_dataset()` function like this:\r\n```python\r\nfrom datasets import load_dataset, Features, Value, ClassLabel\r\n\r\nfeatures = Features(\r\n {\r\n \"label\": ClassLabel(\r\n num_classes=3,\r\n names=[\"negative\", \"neutral\", \"positive\"],\r\n ),\r\n \"text\": Value(dtype=\"string\"),\r\n }\r\n)\r\n\r\nreview_dataset = load_dataset(\r\n \"parquet\",\r\n data_files={\r\n \"train\": os.path.join(sentiment_analysis_data_path, \"train.parquet\"),\r\n \"validation\": os.path.join(sentiment_analysis_data_path, \"validation.parquet\"),\r\n \"test\": os.path.join(sentiment_analysis_data_path, \"test.parquet\"),\r\n },\r\n features=features,\r\n)\r\n\r\nprint(review_dataset)\r\n```",
"@Extremesarova I think this is a different issue, but understand using features could be a work-around.\r\nIt seems the field `closed_at` is `null` in many cases.\r\n\r\nI've not found a way to specify only a single feature without (succesfully) specifiying the full and quite detailed set of expected features. Using this features set gives an error the column names don't match.\r\n`features = Features({'closed_at': Value(dtype='timestamp[s]', id=None)})`\r\n\r\n",
"Found this when searching for the same error, looks like based on #3965 it's just an issue with the data. I found that changing `df = pd.DataFrame.from_records(all_issues)` to `df = pd.DataFrame.from_records(all_issues).dropna(axis=1, how='all').drop(['milestone'], axis=1)` from the fetch_issues function fixed the issue. \r\nThe \"milestone\" column seemed to be problematic (only ~50 non null rows) and dropped any columns that were all null as well just in case.",
"I have this same issue. I saved a dataset to disk and now I can't load it.",
"Ok the solution was to use load_from_disk instead of load_dataset."
] | 2023-01-14T17:29:38 | 2023-05-05T19:25:08 | null | NONE | null | null | null | ### Describe the bug
Loading a previously downloaded & saved dataset as described in the HuggingFace course:
issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")
Gives this error:
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
A work-around I found was to use streaming.
### Steps to reproduce the bug
Reproduce by executing the code provided:
https://huggingface.co./course/chapter5/5?fw=pt
From the heading:
'let’s create a function that can download all the issues from a GitHub repository'
### Expected behavior
No error
### Environment info
Datasets version 2.8.0. Note that version 2.6.1 gives the same error (related to null timestamp).
**[EDIT]**
This is the complete error trace confirming the issue is related to the timestamp (`Couldn't cast array of type timestamp[s] to null`)
```
Using custom data configuration default-950028611d2860c8
Downloading and preparing dataset json/default to [...]/.cache/huggingface/datasets/json/default-950028611d2860c8/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...
Downloading data files: 100%|██████████| 1/1 [00:00<?, ?it/s]
Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 500.63it/s]
Generating train split: 2619 examples [00:00, 7155.72 examples/s]Traceback (most recent call last):
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\arrow_writer.py", line 567, in write_table
pa_table = table_cast(pa_table, self._schema)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2282, in table_cast
return cast_table_to_schema(table, schema)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2241, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2241, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1807, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1807, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2035, in cast_array_to_feature
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2035, in <listcomp>
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1809, in wrapper
return func(array, *args, **kwargs)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2101, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1809, in wrapper
return func(array, *args, **kwargs)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1990, in array_cast
raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
TypeError: Couldn't cast array of type timestamp[s] to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\pydevconsole.py", line 364, in runcode
coro = func()
File "<input>", line 1, in <module>
File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "[...]\PycharmProjects\TransformersTesting\dataset_issues.py", line 20, in <module>
issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\load.py", line 1757, in load_dataset
builder_instance.download_and_prepare(
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 860, in download_and_prepare
self._download_and_prepare(
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 953, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1706, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1849, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
Generating train split: 2619 examples [00:19, 7155.72 examples/s]
``` | {
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"Closing as case-insensitivity should be only for URL redirection on the Hub. In the APIs, we will only support the canonical name (https://github.com/huggingface/moon-landing/pull/2399#issuecomment-1382085611)"
] | 2023-01-13T13:07:07 | 2023-01-13T20:12:32 | 2023-01-13T20:12:32 | CONTRIBUTOR | null | null | null | ### Feature request
The dataset name on the Hub is case-insensitive (see https://github.com/huggingface/moon-landing/pull/2399, internal issue), i.e., https://huggingface.co./datasets/GLUE redirects to https://huggingface.co./datasets/glue.
Ideally, we could load the glue dataset using the following:
```
from datasets import load_dataset
load_dataset('GLUE', 'cola')
```
It breaks because the loading script `GLUE.py` does not exist (`glue.py` should be selected instead).
Minor additional comment: in other cases without a loading script, we can load the dataset, but the automatically generated config name depends on the casing:
- `load_dataset('severo/danish-wit')` generates the config name `severo--danish-wit-e6fda5b070deb133`, while
- `load_dataset('severo/danish-WIT')` generates the config name `severo--danish-WIT-e6fda5b070deb133`
### Motivation
To follow the same UX on the Hub and in the datasets library.
### Your contribution
... | {
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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) | 0.008450 / 0.011353 (-0.002902) | 0.004478 / 0.011008 (-0.006530) | 0.100440 / 0.038508 (0.061931) | 0.029568 / 0.023109 (0.006459) | 0.296705 / 0.275898 (0.020807) | 0.354565 / 0.323480 (0.031085) | 0.006887 / 0.007986 (-0.001098) | 0.003415 / 0.004328 (-0.000914) | 0.078876 / 0.004250 (0.074626) | 0.034927 / 0.037052 (-0.002125) | 0.307695 / 0.258489 (0.049206) | 0.340917 / 0.293841 (0.047076) | 0.033630 / 0.128546 (-0.094916) | 0.011626 / 0.075646 (-0.064020) | 0.322644 / 0.419271 (-0.096627) | 0.040254 / 0.043533 (-0.003279) | 0.297419 / 0.255139 (0.042280) | 0.321584 / 0.283200 (0.038384) | 0.086202 / 0.141683 (-0.055481) | 1.465579 / 1.452155 (0.013425) | 1.521456 / 1.492716 (0.028740) |\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) | 0.200890 / 0.018006 (0.182884) | 0.410300 / 0.000490 (0.409811) | 0.001647 / 0.000200 (0.001447) | 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) | 0.022569 / 0.037411 (-0.014843) | 0.096062 / 0.014526 (0.081536) | 0.102474 / 0.176557 (-0.074082) | 0.138596 / 0.737135 (-0.598539) | 0.106262 / 0.296338 (-0.190077) |\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) | 0.415976 / 0.215209 (0.200766) | 4.144322 / 2.077655 (2.066667) | 1.871783 / 1.504120 (0.367663) | 1.669478 / 1.541195 (0.128283) | 1.718214 / 1.468490 (0.249724) | 0.687870 / 4.584777 (-3.896907) | 3.362084 / 3.745712 (-0.383628) | 1.844127 / 5.269862 (-3.425735) | 1.149611 / 4.565676 (-3.416066) | 0.081410 / 0.424275 (-0.342865) | 0.012278 / 0.007607 (0.004671) | 0.518245 / 0.226044 (0.292200) | 5.185164 / 2.268929 (2.916236) | 2.299029 / 55.444624 (-53.145595) | 1.960021 / 6.876477 (-4.916456) | 2.009751 / 2.142072 (-0.132322) | 0.803759 / 4.805227 (-4.001468) | 0.147340 / 6.500664 (-6.353324) | 0.063896 / 0.075469 (-0.011573) |\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) | 1.254142 / 1.841788 (-0.587646) | 13.799683 / 8.074308 (5.725375) | 13.940387 / 10.191392 (3.748995) | 0.151246 / 0.680424 (-0.529178) | 0.028709 / 0.534201 (-0.505491) | 0.391600 / 0.579283 (-0.187683) | 0.405750 / 0.434364 (-0.028614) | 0.455479 / 0.540337 (-0.084858) | 0.541022 / 1.386936 (-0.845914) |\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) | 0.006462 / 0.011353 (-0.004891) | 0.004462 / 0.011008 (-0.006547) | 0.096588 / 0.038508 (0.058080) | 0.026931 / 0.023109 (0.003822) | 0.344595 / 0.275898 (0.068697) | 0.378743 / 0.323480 (0.055264) | 0.005672 / 0.007986 (-0.002314) | 0.003345 / 0.004328 (-0.000984) | 0.074363 / 0.004250 (0.070112) | 0.037300 / 0.037052 (0.000248) | 0.346895 / 0.258489 (0.088406) | 0.388585 / 0.293841 (0.094744) | 0.031459 / 0.128546 (-0.097088) | 0.011522 / 0.075646 (-0.064124) | 0.318507 / 0.419271 (-0.100764) | 0.041145 / 0.043533 (-0.002388) | 0.343866 / 0.255139 (0.088727) | 0.366490 / 0.283200 (0.083291) | 0.086793 / 0.141683 (-0.054890) | 1.483859 / 1.452155 (0.031704) | 1.574006 / 1.492716 (0.081290) |\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) | 0.220436 / 0.018006 (0.202430) | 0.402988 / 0.000490 (0.402498) | 0.000435 / 0.000200 (0.000235) | 0.000063 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024573 / 0.037411 (-0.012838) | 0.099190 / 0.014526 (0.084664) | 0.106796 / 0.176557 (-0.069761) | 0.142387 / 0.737135 (-0.594748) | 0.109991 / 0.296338 (-0.186347) |\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) | 0.473452 / 0.215209 (0.258243) | 4.749554 / 2.077655 (2.671899) | 2.433482 / 1.504120 (0.929362) | 2.224276 / 1.541195 (0.683082) | 2.261579 / 1.468490 (0.793088) | 0.699876 / 4.584777 (-3.884901) | 3.378366 / 3.745712 (-0.367346) | 1.835062 / 5.269862 (-3.434799) | 1.161249 / 4.565676 (-3.404427) | 0.082967 / 0.424275 (-0.341308) | 0.012745 / 0.007607 (0.005138) | 0.580006 / 0.226044 (0.353962) | 5.789868 / 2.268929 (3.520939) | 2.909496 / 55.444624 (-52.535128) | 2.539196 / 6.876477 (-4.337280) | 2.617737 / 2.142072 (0.475665) | 0.810320 / 4.805227 (-3.994907) | 0.152501 / 6.500664 (-6.348163) | 0.067201 / 0.075469 (-0.008268) |\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) | 1.257844 / 1.841788 (-0.583943) | 13.865295 / 8.074308 (5.790987) | 14.169073 / 10.191392 (3.977680) | 0.135655 / 0.680424 (-0.544769) | 0.016597 / 0.534201 (-0.517604) | 0.374915 / 0.579283 (-0.204368) | 0.382771 / 0.434364 (-0.051593) | 0.431934 / 0.540337 (-0.108403) | 0.524617 / 1.386936 (-0.862319) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008748 / 0.011353 (-0.002605) | 0.004489 / 0.011008 (-0.006519) | 0.100923 / 0.038508 (0.062415) | 0.031436 / 0.023109 (0.008326) | 0.306508 / 0.275898 (0.030610) | 0.365110 / 0.323480 (0.041630) | 0.007161 / 0.007986 (-0.000824) | 0.005489 / 0.004328 (0.001160) | 0.078909 / 0.004250 (0.074658) | 0.036097 / 0.037052 (-0.000955) | 0.307907 / 0.258489 (0.049418) | 0.370277 / 0.293841 (0.076436) | 0.034184 / 0.128546 (-0.094362) | 0.011613 / 0.075646 (-0.064033) | 0.322896 / 0.419271 (-0.096375) | 0.041829 / 0.043533 (-0.001704) | 0.299669 / 0.255139 (0.044530) | 0.322217 / 0.283200 (0.039017) | 0.087751 / 0.141683 (-0.053932) | 1.476277 / 1.452155 (0.024122) | 1.548196 / 1.492716 (0.055480) |\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) | 0.183002 / 0.018006 (0.164995) | 0.415627 / 0.000490 (0.415138) | 0.003272 / 0.000200 (0.003072) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024881 / 0.037411 (-0.012531) | 0.103424 / 0.014526 (0.088898) | 0.106446 / 0.176557 (-0.070110) | 0.142806 / 0.737135 (-0.594330) | 0.110938 / 0.296338 (-0.185401) |\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) | 0.421669 / 0.215209 (0.206460) | 4.207457 / 2.077655 (2.129802) | 1.882176 / 1.504120 (0.378056) | 1.677609 / 1.541195 (0.136415) | 1.734065 / 1.468490 (0.265575) | 0.695915 / 4.584777 (-3.888862) | 3.416731 / 3.745712 (-0.328981) | 1.872575 / 5.269862 (-3.397286) | 1.163612 / 4.565676 (-3.402064) | 0.082710 / 0.424275 (-0.341565) | 0.012659 / 0.007607 (0.005052) | 0.528785 / 0.226044 (0.302741) | 5.305328 / 2.268929 (3.036399) | 2.299850 / 55.444624 (-53.144774) | 1.968137 / 6.876477 (-4.908339) | 2.028326 / 2.142072 (-0.113746) | 0.813157 / 4.805227 (-3.992070) | 0.149997 / 6.500664 (-6.350668) | 0.066739 / 0.075469 (-0.008730) |\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) | 1.206332 / 1.841788 (-0.635456) | 13.795510 / 8.074308 (5.721202) | 14.367695 / 10.191392 (4.176303) | 0.138106 / 0.680424 (-0.542318) | 0.028760 / 0.534201 (-0.505441) | 0.394822 / 0.579283 (-0.184461) | 0.403291 / 0.434364 (-0.031073) | 0.463273 / 0.540337 (-0.077065) | 0.540881 / 1.386936 (-0.846055) |\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) | 0.006830 / 0.011353 (-0.004523) | 0.004606 / 0.011008 (-0.006402) | 0.097763 / 0.038508 (0.059255) | 0.027832 / 0.023109 (0.004723) | 0.422970 / 0.275898 (0.147072) | 0.460313 / 0.323480 (0.136833) | 0.005110 / 0.007986 (-0.002876) | 0.003428 / 0.004328 (-0.000901) | 0.075047 / 0.004250 (0.070797) | 0.038374 / 0.037052 (0.001322) | 0.422762 / 0.258489 (0.164273) | 0.469886 / 0.293841 (0.176045) | 0.032391 / 0.128546 (-0.096155) | 0.011804 / 0.075646 (-0.063843) | 0.320439 / 0.419271 (-0.098832) | 0.041939 / 0.043533 (-0.001594) | 0.422521 / 0.255139 (0.167382) | 0.446420 / 0.283200 (0.163220) | 0.090715 / 0.141683 (-0.050968) | 1.484578 / 1.452155 (0.032423) | 1.556154 / 1.492716 (0.063438) |\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) | 0.260735 / 0.018006 (0.242728) | 0.415586 / 0.000490 (0.415096) | 0.026960 / 0.000200 (0.026760) | 0.000296 / 0.000054 (0.000241) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024926 / 0.037411 (-0.012486) | 0.099651 / 0.014526 (0.085125) | 0.107810 / 0.176557 (-0.068747) | 0.148685 / 0.737135 (-0.588451) | 0.112725 / 0.296338 (-0.183614) |\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) | 0.472669 / 0.215209 (0.257460) | 4.718827 / 2.077655 (2.641172) | 2.475583 / 1.504120 (0.971463) | 2.260862 / 1.541195 (0.719667) | 2.307820 / 1.468490 (0.839330) | 0.699464 / 4.584777 (-3.885313) | 3.376282 / 3.745712 (-0.369431) | 1.872650 / 5.269862 (-3.397211) | 1.176399 / 4.565676 (-3.389277) | 0.082854 / 0.424275 (-0.341421) | 0.012845 / 0.007607 (0.005237) | 0.582088 / 0.226044 (0.356044) | 5.861609 / 2.268929 (3.592681) | 2.930728 / 55.444624 (-52.513896) | 2.624310 / 6.876477 (-4.252167) | 2.762130 / 2.142072 (0.620058) | 0.811902 / 4.805227 (-3.993325) | 0.152516 / 6.500664 (-6.348149) | 0.067670 / 0.075469 (-0.007799) |\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) | 1.289790 / 1.841788 (-0.551997) | 14.267607 / 8.074308 (6.193299) | 14.120655 / 10.191392 (3.929263) | 0.128442 / 0.680424 (-0.551982) | 0.017079 / 0.534201 (-0.517121) | 0.381807 / 0.579283 (-0.197476) | 0.400546 / 0.434364 (-0.033818) | 0.447629 / 0.540337 (-0.092709) | 0.532006 / 1.386936 (-0.854930) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2023-01-13T12:58:43 | 2023-01-13T13:36:00 | 2023-01-13T13:29:01 | CONTRIBUTOR | null | false | {
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} | add-dataset is not needed anymore since the "canonical" datasets are on the Hub. And dataset-viewer is managed within the datasets-server project.
See https://github.com/huggingface/datasets/issues/new/choose
<img width="1245" alt="Capture d’écran 2023-01-13 à 13 59 58" src="https://user-images.githubusercontent.com/1676121/212325813-2d4c30e2-343e-4aa2-8cce-b2b77f45628e.png">
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https://api.github.com/repos/huggingface/datasets/issues/5419 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5419/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5419/comments | https://api.github.com/repos/huggingface/datasets/issues/5419/events | https://github.com/huggingface/datasets/issues/5419 | 1,531,999,850 | I_kwDODunzps5bUHZq | 5,419 | label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator | {
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"Hi! Thanks for pointing out this inconsistency. Changing the default value at this point is probably not worth it, considering we've started discussing the state of the task API internally - we will most likely deprecate the current one and replace it with a more robust solution that relies on the `train_eval_index` field stored in the YAML section of the dataset cards.",
"The task templates API has been deprecated (will be removed in version 3.0), so I'm closing this issue."
] | 2023-01-13T09:40:07 | 2023-07-21T14:27:08 | 2023-07-21T14:27:08 | NONE | null | null | null | ### Describe the bug
When preparing a dataset for a task using `datasets.TextClassification`, the output feature is named `labels`. When preparing the trainer using the `transformers.DataCollator` the default column name is `label` if binary or `label_ids` if multi-class problem.
It is required to rename the column accordingly to the expected name : `label` or `label_ids`
### Steps to reproduce the bug
```python
from datasets import TextClassification, AutoTokenized, DataCollatorWithPadding
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0'))
print(ds_prepared)
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
ds_tokenized = ds_prepared.map(lambda x: tokenizer(x['text'], truncation=True), batched=True)
print(ds_tokenized)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
tf_data = model.prepare_tf_dataset(ds_tokenized, shuffle=True, batch_size=16, collate_fn=data_collator)
print(tf_data)
```
### Expected behavior
Without renaming the the column, the target column is not in the final tf_data since it is not in the column name expected by the data_collator.
To correct this, we have to rename the column:
```python
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0')).rename_column('labels', 'label')
```
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
- `transformers` version: 4.26.0.dev0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.11.1
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): 2.11.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in> | {
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"Thanks for your proposal, @zanussbaum. Yes, I agree that would definitely be a nice feature to have!",
"@albertvillanova I’m happy to make a quick PR for the feature! let me know ",
"That would be awesome ! You can comment `#self-assign` to assign you to this issue and open a PR :) Will be happy to review",
"Closing as this has been merged @lhoestq "
] | 2023-01-12T05:06:20 | 2023-01-24T18:18:24 | 2023-01-24T18:18:24 | CONTRIBUTOR | null | null | null | ### Feature request
Add a progress bar for `Dataset.to_parquet`, similar to how `to_json` works.
### Motivation
It's a bit frustrating to not know how long a dataset will take to write to file and if it's stuck or not without a progress bar
### Your contribution
Sure I can help if needed | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"By the way, do we know how many datasets are impacted by this issue?\r\n\r\nMaybe we should do a patch release with this fix.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009256 / 0.011353 (-0.002097) | 0.005033 / 0.011008 (-0.005975) | 0.099346 / 0.038508 (0.060838) | 0.035204 / 0.023109 (0.012095) | 0.303017 / 0.275898 (0.027119) | 0.335632 / 0.323480 (0.012152) | 0.007953 / 0.007986 (-0.000033) | 0.005806 / 0.004328 (0.001477) | 0.076121 / 0.004250 (0.071871) | 0.041164 / 0.037052 (0.004112) | 0.305536 / 0.258489 (0.047047) | 0.348452 / 0.293841 (0.054611) | 0.037704 / 0.128546 (-0.090842) | 0.011982 / 0.075646 (-0.063664) | 0.333264 / 0.419271 (-0.086008) | 0.047738 / 0.043533 (0.004205) | 0.310126 / 0.255139 (0.054987) | 0.318719 / 0.283200 (0.035519) | 0.098933 / 0.141683 (-0.042750) | 1.421058 / 1.452155 (-0.031096) | 1.554771 / 1.492716 (0.062054) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.258627 / 0.018006 (0.240620) | 0.450814 / 0.000490 (0.450324) | 0.011288 / 0.000200 (0.011088) | 0.000136 / 0.000054 (0.000081) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027004 / 0.037411 (-0.010407) | 0.109067 / 0.014526 (0.094541) | 0.120401 / 0.176557 (-0.056155) | 0.158336 / 0.737135 (-0.578799) | 0.126244 / 0.296338 (-0.170094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401847 / 0.215209 (0.186638) | 4.006003 / 2.077655 (1.928348) | 1.806342 / 1.504120 (0.302223) | 1.619751 / 1.541195 (0.078556) | 1.709660 / 1.468490 (0.241170) | 0.692444 / 4.584777 (-3.892333) | 3.853691 / 3.745712 (0.107979) | 2.143910 / 5.269862 (-3.125951) | 1.471600 / 4.565676 (-3.094076) | 0.084589 / 0.424275 (-0.339686) | 0.012276 / 0.007607 (0.004669) | 0.506529 / 0.226044 (0.280485) | 5.028361 / 2.268929 (2.759432) | 2.277660 / 55.444624 (-53.166964) | 1.930365 / 6.876477 (-4.946112) | 1.965494 / 2.142072 (-0.176579) | 0.826752 / 4.805227 (-3.978475) | 0.165050 / 6.500664 (-6.335614) | 0.062702 / 0.075469 (-0.012767) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.234539 / 1.841788 (-0.607249) | 15.067401 / 8.074308 (6.993093) | 14.041920 / 10.191392 (3.850528) | 0.162590 / 0.680424 (-0.517834) | 0.028941 / 0.534201 (-0.505260) | 0.438518 / 0.579283 (-0.140765) | 0.443787 / 0.434364 (0.009423) | 0.516671 / 0.540337 (-0.023666) | 0.609036 / 1.386936 (-0.777900) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007535 / 0.011353 (-0.003818) | 0.005283 / 0.011008 (-0.005725) | 0.097116 / 0.038508 (0.058608) | 0.033357 / 0.023109 (0.010247) | 0.383398 / 0.275898 (0.107500) | 0.425516 / 0.323480 (0.102037) | 0.006039 / 0.007986 (-0.001947) | 0.004074 / 0.004328 (-0.000255) | 0.073207 / 0.004250 (0.068956) | 0.052153 / 0.037052 (0.015101) | 0.386385 / 0.258489 (0.127896) | 0.429900 / 0.293841 (0.136059) | 0.038341 / 0.128546 (-0.090205) | 0.012417 / 0.075646 (-0.063230) | 0.333859 / 0.419271 (-0.085413) | 0.051157 / 0.043533 (0.007625) | 0.395022 / 0.255139 (0.139883) | 0.402462 / 0.283200 (0.119262) | 0.105207 / 0.141683 (-0.036475) | 1.510679 / 1.452155 (0.058524) | 1.584205 / 1.492716 (0.091489) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225805 / 0.018006 (0.207799) | 0.452109 / 0.000490 (0.451619) | 0.000429 / 0.000200 (0.000229) | 0.000057 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029653 / 0.037411 (-0.007759) | 0.112609 / 0.014526 (0.098083) | 0.121828 / 0.176557 (-0.054728) | 0.159003 / 0.737135 (-0.578133) | 0.129306 / 0.296338 (-0.167033) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453001 / 0.215209 (0.237792) | 4.514882 / 2.077655 (2.437228) | 2.277494 / 1.504120 (0.773374) | 2.073870 / 1.541195 (0.532675) | 2.153346 / 1.468490 (0.684856) | 0.698363 / 4.584777 (-3.886414) | 3.921763 / 3.745712 (0.176051) | 2.123133 / 5.269862 (-3.146729) | 1.347618 / 4.565676 (-3.218058) | 0.085654 / 0.424275 (-0.338621) | 0.012059 / 0.007607 (0.004452) | 0.568183 / 0.226044 (0.342139) | 5.720047 / 2.268929 (3.451119) | 2.777973 / 55.444624 (-52.666651) | 2.453426 / 6.876477 (-4.423051) | 2.523977 / 2.142072 (0.381905) | 0.841979 / 4.805227 (-3.963248) | 0.167958 / 6.500664 (-6.332706) | 0.064929 / 0.075469 (-0.010540) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235297 / 1.841788 (-0.606491) | 15.883598 / 8.074308 (7.809290) | 14.395328 / 10.191392 (4.203936) | 0.162401 / 0.680424 (-0.518022) | 0.017806 / 0.534201 (-0.516394) | 0.423853 / 0.579283 (-0.155430) | 0.423266 / 0.434364 (-0.011098) | 0.490351 / 0.540337 (-0.049986) | 0.588116 / 1.386936 (-0.798820) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb3fbfa162bb4700e23d084826b4b7f6d97284be \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010759 / 0.011353 (-0.000594) | 0.005748 / 0.011008 (-0.005260) | 0.119195 / 0.038508 (0.080687) | 0.033476 / 0.023109 (0.010367) | 0.364081 / 0.275898 (0.088183) | 0.422456 / 0.323480 (0.098976) | 0.009780 / 0.007986 (0.001795) | 0.006170 / 0.004328 (0.001841) | 0.093242 / 0.004250 (0.088991) | 0.041049 / 0.037052 (0.003997) | 0.372132 / 0.258489 (0.113643) | 0.442501 / 0.293841 (0.148660) | 0.054889 / 0.128546 (-0.073657) | 0.018302 / 0.075646 (-0.057345) | 0.378899 / 0.419271 (-0.040373) | 0.058455 / 0.043533 (0.014922) | 0.356141 / 0.255139 (0.101002) | 0.400866 / 0.283200 (0.117666) | 0.103384 / 0.141683 (-0.038299) | 1.629867 / 1.452155 (0.177713) | 1.693939 / 1.492716 (0.201222) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240484 / 0.018006 (0.222478) | 0.509137 / 0.000490 (0.508648) | 0.000450 / 0.000200 (0.000250) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025856 / 0.037411 (-0.011555) | 0.113214 / 0.014526 (0.098689) | 0.119420 / 0.176557 (-0.057136) | 0.158663 / 0.737135 (-0.578473) | 0.123542 / 0.296338 (-0.172797) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.555900 / 0.215209 (0.340691) | 5.580295 / 2.077655 (3.502640) | 2.216640 / 1.504120 (0.712520) | 1.904944 / 1.541195 (0.363749) | 1.865839 / 1.468490 (0.397349) | 1.158325 / 4.584777 (-3.426452) | 5.097420 / 3.745712 (1.351708) | 2.881775 / 5.269862 (-2.388087) | 2.068896 / 4.565676 (-2.496780) | 0.129028 / 0.424275 (-0.295247) | 0.014075 / 0.007607 (0.006468) | 0.698874 / 0.226044 (0.472830) | 7.131225 / 2.268929 (4.862296) | 2.901686 / 55.444624 (-52.542939) | 2.186146 / 6.876477 (-4.690330) | 2.251172 / 2.142072 (0.109100) | 1.342264 / 4.805227 (-3.462963) | 0.232045 / 6.500664 (-6.268619) | 0.073520 / 0.075469 (-0.001949) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.431314 / 1.841788 (-0.410474) | 16.313055 / 8.074308 (8.238747) | 18.451552 / 10.191392 (8.260160) | 0.232875 / 0.680424 (-0.447549) | 0.042170 / 0.534201 (-0.492031) | 0.495261 / 0.579283 (-0.084022) | 0.582901 / 0.434364 (0.148537) | 0.582049 / 0.540337 (0.041712) | 0.681122 / 1.386936 (-0.705814) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008131 / 0.011353 (-0.003222) | 0.006162 / 0.011008 (-0.004847) | 0.113721 / 0.038508 (0.075213) | 0.030797 / 0.023109 (0.007688) | 0.413108 / 0.275898 (0.137210) | 0.449968 / 0.323480 (0.126488) | 0.006126 / 0.007986 (-0.001860) | 0.004848 / 0.004328 (0.000519) | 0.085465 / 0.004250 (0.081214) | 0.045817 / 0.037052 (0.008764) | 0.419360 / 0.258489 (0.160871) | 0.489077 / 0.293841 (0.195236) | 0.050841 / 0.128546 (-0.077705) | 0.020646 / 0.075646 (-0.055000) | 0.379838 / 0.419271 (-0.039434) | 0.068897 / 0.043533 (0.025365) | 0.422182 / 0.255139 (0.167043) | 0.435529 / 0.283200 (0.152330) | 0.115299 / 0.141683 (-0.026384) | 1.655134 / 1.452155 (0.202979) | 1.835198 / 1.492716 (0.342481) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207041 / 0.018006 (0.189034) | 0.491263 / 0.000490 (0.490773) | 0.003554 / 0.000200 (0.003354) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030830 / 0.037411 (-0.006582) | 0.127003 / 0.014526 (0.112477) | 0.142901 / 0.176557 (-0.033656) | 0.177570 / 0.737135 (-0.559565) | 0.137758 / 0.296338 (-0.158580) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.632820 / 0.215209 (0.417611) | 6.215535 / 2.077655 (4.137880) | 2.615310 / 1.504120 (1.111190) | 2.261431 / 1.541195 (0.720236) | 2.220570 / 1.468490 (0.752080) | 1.215820 / 4.584777 (-3.368957) | 5.247680 / 3.745712 (1.501968) | 3.120054 / 5.269862 (-2.149807) | 1.950947 / 4.565676 (-2.614730) | 0.149980 / 0.424275 (-0.274295) | 0.015241 / 0.007607 (0.007634) | 0.879714 / 0.226044 (0.653670) | 7.941913 / 2.268929 (5.672984) | 3.512456 / 55.444624 (-51.932168) | 2.693833 / 6.876477 (-4.182644) | 2.772780 / 2.142072 (0.630708) | 1.459581 / 4.805227 (-3.345646) | 0.264820 / 6.500664 (-6.235844) | 0.076698 / 0.075469 (0.001228) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.437719 / 1.841788 (-0.404068) | 16.750309 / 8.074308 (8.676001) | 18.646776 / 10.191392 (8.455384) | 0.227858 / 0.680424 (-0.452566) | 0.024239 / 0.534201 (-0.509962) | 0.486172 / 0.579283 (-0.093111) | 0.574731 / 0.434364 (0.140367) | 0.557776 / 0.540337 (0.017439) | 0.672921 / 1.386936 (-0.714015) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb3fbfa162bb4700e23d084826b4b7f6d97284be \"CML watermark\")\n"
] | 2023-01-10T08:43:19 | 2023-01-18T17:12:17 | 2023-01-18T14:09:02 | MEMBER | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5416",
"html_url": "https://github.com/huggingface/datasets/pull/5416",
"diff_url": "https://github.com/huggingface/datasets/pull/5416.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5416.patch",
"merged_at": "2023-01-18T14:09:02"
} | This PR fixes the RuntimeError: Sharding is ambiguous for this dataset.
The error for ambiguous sharding will be raised only if num_proc > 1.
Fix #5415, fix #5414.
Fix https://huggingface.co./datasets/ami/discussions/3. | {
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https://api.github.com/repos/huggingface/datasets/issues/5415 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5415/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5415/comments | https://api.github.com/repos/huggingface/datasets/issues/5415/events | https://github.com/huggingface/datasets/issues/5415 | 1,526,904,861 | I_kwDODunzps5bArgd | 5,415 | RuntimeError: Sharding is ambiguous for this dataset | {
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] | null | [] | 2023-01-10T07:36:11 | 2023-01-18T14:09:04 | 2023-01-18T14:09:03 | MEMBER | null | null | null | ### Describe the bug
When loading some datasets, a RuntimeError is raised.
For example, for "ami" dataset: https://huggingface.co./datasets/ami/discussions/3
```
.../huggingface/datasets/src/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1415 fpath = path_join(self._output_dir, fname)
1416
-> 1417 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
1418 if num_input_shards <= 1 and num_proc is not None:
1419 logger.warning(
.../huggingface/datasets/src/datasets/utils/sharding.py in _number_of_shards_in_gen_kwargs(gen_kwargs)
10 lists_lengths = {key: len(value) for key, value in gen_kwargs.items() if isinstance(value, list)}
11 if len(set(lists_lengths.values())) > 1:
---> 12 raise RuntimeError(
13 (
14 "Sharding is ambiguous for this dataset: "
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key samples_paths has length 6
- key ids has length 7
- key verification_ids has length 6
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.
```
This behavior was introduced when implementing multiprocessing by PR:
- #5107
### Steps to reproduce the bug
```python
ds = load_dataset("ami", "microphone-single", split="train", revision="2d7620bb7c3f1aab9f329615c3bdb598069d907a")
```
### Expected behavior
No error raised.
### Environment info
Since datasets 2.7.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5414 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5414/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5414/comments | https://api.github.com/repos/huggingface/datasets/issues/5414/events | https://github.com/huggingface/datasets/issues/5414 | 1,525,733,818 | I_kwDODunzps5a8Nm6 | 5,414 | Sharding error with Multilingual LibriSpeech | {
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"Thanks for reporting, @Nithin-Holla.\r\n\r\nThis is a known issue for multiple datasets and we are investigating it:\r\n- See e.g.: https://huggingface.co./datasets/ami/discussions/3",
"Main issue:\r\n- #5415",
"@albertvillanova Thanks! As a workaround for now, can I use the dataset in streaming mode?",
"Yes, @Nithin-Holla, in the meantime you can use this dataset in streaming mode."
] | 2023-01-09T14:45:31 | 2023-01-18T14:09:04 | 2023-01-18T14:09:04 | NONE | null | null | null | ### Describe the bug
Loading the German Multilingual LibriSpeech dataset results in a RuntimeError regarding sharding with the following stacktrace:
```
Downloading and preparing dataset multilingual_librispeech/german to /home/nithin/datadrive/cache/huggingface/datasets/facebook___multilingual_librispeech/german/2.1.0/1904af50f57a5c370c9364cc337699cfe496d4e9edcae6648a96be23086362d0...
Downloading data files: 100%
3/3 [00:00<00:00, 107.23it/s]
Downloading data files: 100%
1/1 [00:00<00:00, 35.08it/s]
Downloading data files: 100%
6/6 [00:00<00:00, 303.36it/s]
Downloading data files: 100%
3/3 [00:00<00:00, 130.37it/s]
Downloading data files: 100%
1049/1049 [00:00<00:00, 4491.40it/s]
Downloading data files: 100%
37/37 [00:00<00:00, 1096.78it/s]
Downloading data files: 100%
40/40 [00:00<00:00, 1003.93it/s]
Extracting data files: 100%
3/3 [00:11<00:00, 2.62s/it]
Generating train split:
469942/0 [34:13<00:00, 273.21 examples/s]
Output exceeds the size limit. Open the full output data in a text editor
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-14-74fa6d092bdc> in <module>
----> 1 mls = load_dataset(MLS_DATASET,
2 LANGUAGE,
3 cache_dir="~/datadrive/cache/huggingface/datasets",
4 ignore_verifications=True)
/anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1755
1756 # Download and prepare data
-> 1757 builder_instance.download_and_prepare(
1758 download_config=download_config,
1759 download_mode=download_mode,
/anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
858 if num_proc is not None:
859 prepare_split_kwargs["num_proc"] = num_proc
--> 860 self._download_and_prepare(
861 dl_manager=dl_manager,
862 verify_infos=verify_infos,
/anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
1609
1610 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs):
...
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_archives has length 1049
- key local_extracted_archive has length 1049
- key limited_ids_paths has length 1
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.
```
### Steps to reproduce the bug
Here is the code to reproduce it:
```python
from datasets import load_dataset
MLS_DATASET = "facebook/multilingual_librispeech"
LANGUAGE = "german"
mls = load_dataset(MLS_DATASET,
LANGUAGE,
cache_dir="~/datadrive/cache/huggingface/datasets",
ignore_verifications=True)
```
### Expected behavior
The expected behaviour is that the dataset is successfully loaded.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.4.0-1094-azure-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 10.0.1
- Pandas version: 1.2.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/5413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5413/comments | https://api.github.com/repos/huggingface/datasets/issues/5413/events | https://github.com/huggingface/datasets/issues/5413 | 1,524,591,837 | I_kwDODunzps5a32zd | 5,413 | concatenate_datasets fails when two dataset with shards > 1 and unequal shard numbers | {
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"Hi ! Thanks for reporting :)\r\n\r\nI managed to reproduce the hub using\r\n```python\r\n\r\nfrom datasets import concatenate_datasets, Dataset, load_from_disk\r\n\r\nDataset.from_dict({\"a\": range(9)}).save_to_disk(\"tmp/ds1\")\r\nds1 = load_from_disk(\"tmp/ds1\")\r\nds1 = concatenate_datasets([ds1, ds1])\r\n\r\nDataset.from_dict({\"b\": range(6)}).save_to_disk(\"tmp/ds2\")\r\nds2 = load_from_disk(\"tmp/ds2\")\r\nds2 = concatenate_datasets([ds2, ds2, ds2])\r\n\r\nconcatenate_datasets([ds1, ds2], axis=1)\r\n```\r\nand I get\r\n```python\r\nTraceback (most recent call last): \r\n File \"test.py\", line 98, in <module>\r\n dds = concatenate_datasets([ds1, ds2], axis=1)\r\n File \"/Users/.../datasets/combine.py\", line 182, in concatenate_datasets\r\n return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)\r\n File \"/Users/.../datasets/arrow_dataset.py\", line 5499, in _concatenate_map_style_datasets\r\n table = concat_tables([dset._data for dset in dsets], axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1778, in concat_tables\r\n return ConcatenationTable.from_tables(tables, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1483, in from_tables\r\n blocks = _extend_blocks(blocks, table_blocks, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1477, in _extend_blocks\r\n result[i].extend(row_blocks)\r\nIndexError: list index out of range\r\n```\r\n\r\nIt appears to happen when the two datasets have a number of shards that is not the same"
] | 2023-01-08T17:01:52 | 2023-01-26T09:27:21 | 2023-01-26T09:27:21 | NONE | null | null | null | ### Describe the bug
When using `concatenate_datasets([dataset1, dataset2], axis = 1)` to concatenate two datasets with shards > 1, it fails:
```
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/combine.py", line 182, in concatenate_datasets
return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 5499, in _concatenate_map_style_datasets
table = concat_tables([dset._data for dset in dsets], axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1778, in concat_tables
return ConcatenationTable.from_tables(tables, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1483, in from_tables
blocks = _extend_blocks(blocks, table_blocks, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1477, in _extend_blocks
result[i].extend(row_blocks)
IndexError: list index out of range
```
### Steps to reproduce the bug
dataset = concatenate_datasets([dataset1, dataset2], axis = 1)
### Expected behavior
The datasets are correctly concatenated.
### Environment info
datasets==2.8.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5412 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5412/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5412/comments | https://api.github.com/repos/huggingface/datasets/issues/5412/events | https://github.com/huggingface/datasets/issues/5412 | 1,524,250,269 | I_kwDODunzps5a2jad | 5,412 | load_dataset() cannot find dataset_info.json with multiple training runs in parallel | {
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"Hi ! It fails because the dataset is already being prepared by your first run. I'd encourage you to prepare your dataset before using it for multiple trainings.\r\n\r\nYou can also specify another cache directory by passing `cache_dir=` to `load_dataset()`.",
"Thank you! What do you mean by prepare it beforehand? I am unclear how to conduct dataset preparation outside of using the `load_dataset` function.",
"You can have a separate script that does load_dataset + map + save_to_disk to save your prepared dataset somewhere. Then in your training script you can reload the dataset with load_from_disk",
"Thank you! I believe I was running additional map steps after loading, resulting in the cache conflict. "
] | 2023-01-08T00:44:32 | 2023-01-19T20:28:43 | 2023-01-19T20:28:43 | NONE | null | null | null | ### Describe the bug
I have a custom local dataset in JSON form. I am trying to do multiple training runs in parallel. The first training run runs with no issue. However, when I start another run on another GPU, the following code throws this error.
If there is a workaround to ignore the cache I think that would solve my problem too.
I am using datasets version 2.8.0.
### Steps to reproduce the bug
1. Start training run of GPU 0 loading dataset from
```
load_dataset(
"json",
data_files=tr_dataset_path,
split=f"train",
download_mode="force_redownload",
)
```
2. While GPU 0 is training, start an identical run on GPU 1. GPU 1 will produce the following error:
```
Traceback (most recent call last):
File "/local-scratch1/data/mt/code/qq/train.py", line 198, in <module>
main()
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1130, in __call__
return self.main(*args, **kwargs)
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1055, in main
rv = self.invoke(ctx)
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 760, in invoke
return __callback(*args, **kwargs)
File "/local-scratch1/data/mt/code/qq/train.py", line 113, in main
load_dataset(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/load.py", line 1734, in load_dataset
builder_instance = load_dataset_builder(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/load.py", line 1518, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/builder.py", line 366, in __init__
self.info = DatasetInfo.from_directory(self._cache_dir)
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/info.py", line 313, in from_directory
with fs.open(path_join(dataset_info_dir, config.DATASET_INFO_FILENAME), "r", encoding="utf-8") as f:
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/spec.py", line 1094, in open
self.open(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/spec.py", line 1106, in open
f = self._open(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 175, in _open
return LocalFileOpener(path, mode, fs=self, **kwargs)
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 273, in __init__
self._open()
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 278, in _open
self.f = open(self.path, mode=self.mode)
FileNotFoundError: [Errno 2] No such file or directory: '/home/username/.cache/huggingface/datasets/json/default-43d06a4aedb25e6d/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/dataset_info.json'
```
### Expected behavior
Expected behavior: 2nd GPU training run should run the same as 1st GPU training run.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.8.0
- Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.10
- Python version: 3.8.15
- PyArrow version: 9.0.0
- Pandas version: 1.5.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5411 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5411/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5411/comments | https://api.github.com/repos/huggingface/datasets/issues/5411/events | https://github.com/huggingface/datasets/pull/5411 | 1,523,297,786 | PR_kwDODunzps5G23-T | 5,411 | Update docs of S3 filesystem with async aiobotocore | {
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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) | 0.008587 / 0.011353 (-0.002766) | 0.004613 / 0.011008 (-0.006395) | 0.100446 / 0.038508 (0.061938) | 0.029606 / 0.023109 (0.006497) | 0.302102 / 0.275898 (0.026204) | 0.357364 / 0.323480 (0.033884) | 0.007031 / 0.007986 (-0.000954) | 0.003593 / 0.004328 (-0.000735) | 0.078110 / 0.004250 (0.073860) | 0.035495 / 0.037052 (-0.001557) | 0.312522 / 0.258489 (0.054033) | 0.349336 / 0.293841 (0.055495) | 0.033719 / 0.128546 (-0.094827) | 0.011449 / 0.075646 (-0.064197) | 0.321760 / 0.419271 (-0.097512) | 0.043697 / 0.043533 (0.000165) | 0.304476 / 0.255139 (0.049337) | 0.333126 / 0.283200 (0.049926) | 0.092756 / 0.141683 (-0.048927) | 1.506734 / 1.452155 (0.054579) | 1.547381 / 1.492716 (0.054664) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.178177 / 0.018006 (0.160171) | 0.427814 / 0.000490 (0.427324) | 0.002505 / 0.000200 (0.002305) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023039 / 0.037411 (-0.014372) | 0.097113 / 0.014526 (0.082587) | 0.105014 / 0.176557 (-0.071543) | 0.141185 / 0.737135 (-0.595950) | 0.108843 / 0.296338 (-0.187495) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424148 / 0.215209 (0.208939) | 4.247599 / 2.077655 (2.169944) | 2.130720 / 1.504120 (0.626600) | 1.916349 / 1.541195 (0.375154) | 1.831515 / 1.468490 (0.363025) | 0.688301 / 4.584777 (-3.896476) | 3.381749 / 3.745712 (-0.363963) | 2.900045 / 5.269862 (-2.369817) | 1.576248 / 4.565676 (-2.989428) | 0.082354 / 0.424275 (-0.341921) | 0.012200 / 0.007607 (0.004593) | 0.525753 / 0.226044 (0.299709) | 5.277672 / 2.268929 (3.008743) | 2.603870 / 55.444624 (-52.840754) | 2.296203 / 6.876477 (-4.580273) | 2.308014 / 2.142072 (0.165942) | 0.809056 / 4.805227 (-3.996171) | 0.148122 / 6.500664 (-6.352542) | 0.066097 / 0.075469 (-0.009372) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.214059 / 1.841788 (-0.627728) | 13.671332 / 8.074308 (5.597024) | 13.694554 / 10.191392 (3.503162) | 0.151454 / 0.680424 (-0.528970) | 0.028514 / 0.534201 (-0.505687) | 0.391480 / 0.579283 (-0.187804) | 0.404499 / 0.434364 (-0.029865) | 0.458111 / 0.540337 (-0.082226) | 0.539454 / 1.386936 (-0.847482) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006795 / 0.011353 (-0.004558) | 0.004463 / 0.011008 (-0.006545) | 0.099542 / 0.038508 (0.061034) | 0.027588 / 0.023109 (0.004479) | 0.423023 / 0.275898 (0.147125) | 0.458459 / 0.323480 (0.134979) | 0.004981 / 0.007986 (-0.003005) | 0.003321 / 0.004328 (-0.001008) | 0.075727 / 0.004250 (0.071477) | 0.040541 / 0.037052 (0.003489) | 0.423724 / 0.258489 (0.165235) | 0.468334 / 0.293841 (0.174493) | 0.031732 / 0.128546 (-0.096814) | 0.011478 / 0.075646 (-0.064168) | 0.319807 / 0.419271 (-0.099465) | 0.041215 / 0.043533 (-0.002318) | 0.423060 / 0.255139 (0.167921) | 0.446157 / 0.283200 (0.162957) | 0.088884 / 0.141683 (-0.052799) | 1.553404 / 1.452155 (0.101250) | 1.607797 / 1.492716 (0.115080) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208314 / 0.018006 (0.190307) | 0.411627 / 0.000490 (0.411137) | 0.002416 / 0.000200 (0.002216) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024641 / 0.037411 (-0.012770) | 0.101047 / 0.014526 (0.086521) | 0.108410 / 0.176557 (-0.068147) | 0.142860 / 0.737135 (-0.594276) | 0.112486 / 0.296338 (-0.183852) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.485520 / 0.215209 (0.270311) | 4.864009 / 2.077655 (2.786355) | 2.541865 / 1.504120 (1.037745) | 2.339569 / 1.541195 (0.798374) | 2.378258 / 1.468490 (0.909768) | 0.698000 / 4.584777 (-3.886777) | 3.343137 / 3.745712 (-0.402575) | 1.842264 / 5.269862 (-3.427597) | 1.154707 / 4.565676 (-3.410969) | 0.082826 / 0.424275 (-0.341449) | 0.012379 / 0.007607 (0.004772) | 0.583335 / 0.226044 (0.357291) | 5.885934 / 2.268929 (3.617006) | 2.997769 / 55.444624 (-52.446856) | 2.653681 / 6.876477 (-4.222796) | 2.761656 / 2.142072 (0.619583) | 0.799883 / 4.805227 (-4.005344) | 0.151398 / 6.500664 (-6.349266) | 0.067445 / 0.075469 (-0.008024) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.292009 / 1.841788 (-0.549779) | 13.976180 / 8.074308 (5.901872) | 14.219469 / 10.191392 (4.028077) | 0.127810 / 0.680424 (-0.552614) | 0.016919 / 0.534201 (-0.517282) | 0.376401 / 0.579283 (-0.202882) | 0.388563 / 0.434364 (-0.045801) | 0.444904 / 0.540337 (-0.095433) | 0.532290 / 1.386936 (-0.854646) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#41d4378831cac1fe5fc624bf97a97b3cf81e0b8a \"CML watermark\")\n"
] | 2023-01-06T23:19:17 | 2023-01-18T11:18:59 | 2023-01-18T11:12:04 | CONTRIBUTOR | null | false | {
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} | [s3fs has migrated to all async calls](https://github.com/fsspec/s3fs/commit/0de2c6fb3d87c08ea694de96dca0d0834034f8bf).
Updating documentation to use `AioSession` while using s3fs for download manager as well as working with datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/5410 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5410/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5410/comments | https://api.github.com/repos/huggingface/datasets/issues/5410/events | https://github.com/huggingface/datasets/pull/5410 | 1,521,168,032 | PR_kwDODunzps5GvnJH | 5,410 | Map-style Dataset to IterableDataset | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009812 / 0.011353 (-0.001540) | 0.005290 / 0.011008 (-0.005719) | 0.099728 / 0.038508 (0.061220) | 0.036712 / 0.023109 (0.013602) | 0.305924 / 0.275898 (0.030026) | 0.349844 / 0.323480 (0.026365) | 0.008353 / 0.007986 (0.000368) | 0.004464 / 0.004328 (0.000135) | 0.075329 / 0.004250 (0.071079) | 0.046146 / 0.037052 (0.009094) | 0.304197 / 0.258489 (0.045708) | 0.354245 / 0.293841 (0.060404) | 0.039270 / 0.128546 (-0.089276) | 0.012496 / 0.075646 (-0.063151) | 0.334390 / 0.419271 (-0.084882) | 0.049428 / 0.043533 (0.005896) | 0.297318 / 0.255139 (0.042179) | 0.315646 / 0.283200 (0.032447) | 0.106746 / 0.141683 (-0.034937) | 1.443562 / 1.452155 (-0.008593) | 1.546022 / 1.492716 (0.053305) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303419 / 0.018006 (0.285413) | 0.536971 / 0.000490 (0.536481) | 0.001335 / 0.000200 (0.001135) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030484 / 0.037411 (-0.006927) | 0.110043 / 0.014526 (0.095518) | 0.125265 / 0.176557 (-0.051291) | 0.171410 / 0.737135 (-0.565725) | 0.128978 / 0.296338 (-0.167361) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398354 / 0.215209 (0.183145) | 3.984180 / 2.077655 (1.906526) | 1.781134 / 1.504120 (0.277014) | 1.589656 / 1.541195 (0.048462) | 1.704192 / 1.468490 (0.235702) | 0.682271 / 4.584777 (-3.902506) | 3.731504 / 3.745712 (-0.014208) | 2.243520 / 5.269862 (-3.026342) | 1.511334 / 4.565676 (-3.054343) | 0.084243 / 0.424275 (-0.340032) | 0.012261 / 0.007607 (0.004654) | 0.507499 / 0.226044 (0.281454) | 5.066037 / 2.268929 (2.797109) | 2.246107 / 55.444624 (-53.198517) | 1.921032 / 6.876477 (-4.955444) | 2.144111 / 2.142072 (0.002039) | 0.845233 / 4.805227 (-3.959995) | 0.165392 / 6.500664 (-6.335272) | 0.064201 / 0.075469 (-0.011268) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.217649 / 1.841788 (-0.624138) | 15.890487 / 8.074308 (7.816179) | 14.772039 / 10.191392 (4.580647) | 0.192901 / 0.680424 (-0.487523) | 0.029119 / 0.534201 (-0.505082) | 0.442904 / 0.579283 (-0.136380) | 0.451035 / 0.434364 (0.016671) | 0.520788 / 0.540337 (-0.019550) | 0.623588 / 1.386936 (-0.763348) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007452 / 0.011353 (-0.003901) | 0.005426 / 0.011008 (-0.005582) | 0.096488 / 0.038508 (0.057980) | 0.033575 / 0.023109 (0.010465) | 0.375688 / 0.275898 (0.099790) | 0.412393 / 0.323480 (0.088913) | 0.006050 / 0.007986 (-0.001936) | 0.004424 / 0.004328 (0.000095) | 0.073102 / 0.004250 (0.068852) | 0.052672 / 0.037052 (0.015620) | 0.379352 / 0.258489 (0.120862) | 0.436065 / 0.293841 (0.142224) | 0.036594 / 0.128546 (-0.091952) | 0.012380 / 0.075646 (-0.063266) | 0.332899 / 0.419271 (-0.086373) | 0.048859 / 0.043533 (0.005326) | 0.373215 / 0.255139 (0.118076) | 0.386990 / 0.283200 (0.103791) | 0.105166 / 0.141683 (-0.036517) | 1.490762 / 1.452155 (0.038607) | 1.611310 / 1.492716 (0.118593) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.333142 / 0.018006 (0.315136) | 0.537137 / 0.000490 (0.536647) | 0.000452 / 0.000200 (0.000252) | 0.000063 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030368 / 0.037411 (-0.007043) | 0.109608 / 0.014526 (0.095083) | 0.124220 / 0.176557 (-0.052336) | 0.162834 / 0.737135 (-0.574301) | 0.128037 / 0.296338 (-0.168302) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440991 / 0.215209 (0.225782) | 4.400825 / 2.077655 (2.323170) | 2.158768 / 1.504120 (0.654648) | 1.968158 / 1.541195 (0.426963) | 2.085115 / 1.468490 (0.616625) | 0.710757 / 4.584777 (-3.874020) | 3.835441 / 3.745712 (0.089729) | 2.204118 / 5.269862 (-3.065744) | 1.378909 / 4.565676 (-3.186767) | 0.089149 / 0.424275 (-0.335126) | 0.013066 / 0.007607 (0.005459) | 0.539165 / 0.226044 (0.313121) | 5.414176 / 2.268929 (3.145248) | 2.677020 / 55.444624 (-52.767604) | 2.328334 / 6.876477 (-4.548143) | 2.518933 / 2.142072 (0.376860) | 0.840902 / 4.805227 (-3.964325) | 0.170365 / 6.500664 (-6.330299) | 0.063909 / 0.075469 (-0.011561) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237205 / 1.841788 (-0.604583) | 15.678776 / 8.074308 (7.604468) | 14.118576 / 10.191392 (3.927184) | 0.167236 / 0.680424 (-0.513188) | 0.018177 / 0.534201 (-0.516024) | 0.426680 / 0.579283 (-0.152603) | 0.425126 / 0.434364 (-0.009238) | 0.501755 / 0.540337 (-0.038582) | 0.592754 / 1.386936 (-0.794182) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008708 / 0.011353 (-0.002645) | 0.004462 / 0.011008 (-0.006546) | 0.100159 / 0.038508 (0.061651) | 0.029543 / 0.023109 (0.006434) | 0.304056 / 0.275898 (0.028158) | 0.367098 / 0.323480 (0.043618) | 0.007049 / 0.007986 (-0.000937) | 0.003294 / 0.004328 (-0.001034) | 0.076954 / 0.004250 (0.072703) | 0.036850 / 0.037052 (-0.000202) | 0.307556 / 0.258489 (0.049067) | 0.348327 / 0.293841 (0.054486) | 0.033520 / 0.128546 (-0.095026) | 0.011312 / 0.075646 (-0.064334) | 0.317588 / 0.419271 (-0.101684) | 0.040196 / 0.043533 (-0.003337) | 0.298330 / 0.255139 (0.043191) | 0.333821 / 0.283200 (0.050622) | 0.086584 / 0.141683 (-0.055099) | 1.480205 / 1.452155 (0.028050) | 1.520975 / 1.492716 (0.028259) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.186641 / 0.018006 (0.168635) | 0.414420 / 0.000490 (0.413930) | 0.003021 / 0.000200 (0.002821) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022953 / 0.037411 (-0.014458) | 0.097338 / 0.014526 (0.082812) | 0.104985 / 0.176557 (-0.071572) | 0.139208 / 0.737135 (-0.597927) | 0.108031 / 0.296338 (-0.188307) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417969 / 0.215209 (0.202759) | 4.173189 / 2.077655 (2.095534) | 1.862813 / 1.504120 (0.358693) | 1.653226 / 1.541195 (0.112031) | 1.725917 / 1.468490 (0.257426) | 0.701038 / 4.584777 (-3.883739) | 3.350500 / 3.745712 (-0.395213) | 1.913156 / 5.269862 (-3.356705) | 1.267597 / 4.565676 (-3.298079) | 0.082197 / 0.424275 (-0.342078) | 0.012499 / 0.007607 (0.004892) | 0.520173 / 0.226044 (0.294128) | 5.219981 / 2.268929 (2.951053) | 2.306029 / 55.444624 (-53.138595) | 1.948169 / 6.876477 (-4.928307) | 2.013160 / 2.142072 (-0.128912) | 0.813325 / 4.805227 (-3.991902) | 0.149729 / 6.500664 (-6.350935) | 0.065492 / 0.075469 (-0.009977) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.194163 / 1.841788 (-0.647625) | 13.739562 / 8.074308 (5.665254) | 13.881988 / 10.191392 (3.690596) | 0.138180 / 0.680424 (-0.542244) | 0.029031 / 0.534201 (-0.505170) | 0.387858 / 0.579283 (-0.191425) | 0.395171 / 0.434364 (-0.039193) | 0.446349 / 0.540337 (-0.093988) | 0.527073 / 1.386936 (-0.859863) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006504 / 0.011353 (-0.004849) | 0.004564 / 0.011008 (-0.006444) | 0.099108 / 0.038508 (0.060599) | 0.027420 / 0.023109 (0.004311) | 0.340712 / 0.275898 (0.064814) | 0.391613 / 0.323480 (0.068133) | 0.004977 / 0.007986 (-0.003009) | 0.003375 / 0.004328 (-0.000953) | 0.076403 / 0.004250 (0.072152) | 0.036650 / 0.037052 (-0.000402) | 0.341948 / 0.258489 (0.083459) | 0.392065 / 0.293841 (0.098224) | 0.031802 / 0.128546 (-0.096745) | 0.011659 / 0.075646 (-0.063987) | 0.320099 / 0.419271 (-0.099173) | 0.041615 / 0.043533 (-0.001918) | 0.342125 / 0.255139 (0.086986) | 0.372833 / 0.283200 (0.089633) | 0.089032 / 0.141683 (-0.052650) | 1.486691 / 1.452155 (0.034536) | 1.567326 / 1.492716 (0.074610) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.193123 / 0.018006 (0.175117) | 0.404062 / 0.000490 (0.403573) | 0.003460 / 0.000200 (0.003260) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024565 / 0.037411 (-0.012846) | 0.098958 / 0.014526 (0.084432) | 0.108701 / 0.176557 (-0.067855) | 0.142567 / 0.737135 (-0.594569) | 0.111048 / 0.296338 (-0.185290) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474549 / 0.215209 (0.259340) | 4.753776 / 2.077655 (2.676121) | 2.435528 / 1.504120 (0.931409) | 2.234491 / 1.541195 (0.693297) | 2.269474 / 1.468490 (0.800984) | 0.695636 / 4.584777 (-3.889141) | 3.367816 / 3.745712 (-0.377896) | 1.854828 / 5.269862 (-3.415034) | 1.159729 / 4.565676 (-3.405948) | 0.082267 / 0.424275 (-0.342008) | 0.012483 / 0.007607 (0.004876) | 0.578490 / 0.226044 (0.352446) | 5.814490 / 2.268929 (3.545561) | 2.893310 / 55.444624 (-52.551314) | 2.540555 / 6.876477 (-4.335922) | 2.573705 / 2.142072 (0.431633) | 0.800545 / 4.805227 (-4.004682) | 0.151306 / 6.500664 (-6.349358) | 0.067925 / 0.075469 (-0.007544) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294645 / 1.841788 (-0.547142) | 13.641842 / 8.074308 (5.567534) | 14.015200 / 10.191392 (3.823808) | 0.128829 / 0.680424 (-0.551595) | 0.016870 / 0.534201 (-0.517331) | 0.389137 / 0.579283 (-0.190146) | 0.388384 / 0.434364 (-0.045980) | 0.447711 / 0.540337 (-0.092627) | 0.540637 / 1.386936 (-0.846299) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45ad185b9040a68285080b6099ed3af58442ccb2 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012282 / 0.011353 (0.000929) | 0.006328 / 0.011008 (-0.004680) | 0.129666 / 0.038508 (0.091158) | 0.039403 / 0.023109 (0.016294) | 0.375464 / 0.275898 (0.099566) | 0.463167 / 0.323480 (0.139687) | 0.010329 / 0.007986 (0.002344) | 0.005111 / 0.004328 (0.000782) | 0.108727 / 0.004250 (0.104476) | 0.047156 / 0.037052 (0.010103) | 0.381869 / 0.258489 (0.123380) | 0.441936 / 0.293841 (0.148095) | 0.054750 / 0.128546 (-0.073796) | 0.019809 / 0.075646 (-0.055837) | 0.436389 / 0.419271 (0.017118) | 0.066585 / 0.043533 (0.023052) | 0.402108 / 0.255139 (0.146969) | 0.424571 / 0.283200 (0.141371) | 0.118326 / 0.141683 (-0.023357) | 1.870175 / 1.452155 (0.418020) | 1.878720 / 1.492716 (0.386004) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012863 / 0.018006 (-0.005144) | 0.528670 / 0.000490 (0.528181) | 0.006057 / 0.000200 (0.005857) | 0.000124 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030091 / 0.037411 (-0.007320) | 0.136143 / 0.014526 (0.121618) | 0.148931 / 0.176557 (-0.027626) | 0.179578 / 0.737135 (-0.557558) | 0.144528 / 0.296338 (-0.151810) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.594080 / 0.215209 (0.378871) | 6.029101 / 2.077655 (3.951446) | 2.443084 / 1.504120 (0.938964) | 2.123949 / 1.541195 (0.582754) | 2.183021 / 1.468490 (0.714531) | 1.235453 / 4.584777 (-3.349324) | 5.585121 / 3.745712 (1.839408) | 3.208510 / 5.269862 (-2.061351) | 2.090334 / 4.565676 (-2.475342) | 0.150353 / 0.424275 (-0.273922) | 0.016787 / 0.007607 (0.009180) | 0.797561 / 0.226044 (0.571516) | 7.756291 / 2.268929 (5.487363) | 3.283638 / 55.444624 (-52.160986) | 2.527441 / 6.876477 (-4.349036) | 2.590765 / 2.142072 (0.448692) | 1.446818 / 4.805227 (-3.358409) | 0.250563 / 6.500664 (-6.250101) | 0.077919 / 0.075469 (0.002450) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.612022 / 1.841788 (-0.229765) | 18.363316 / 8.074308 (10.289008) | 22.578570 / 10.191392 (12.387178) | 0.232801 / 0.680424 (-0.447623) | 0.048232 / 0.534201 (-0.485969) | 0.549518 / 0.579283 (-0.029766) | 0.624663 / 0.434364 (0.190299) | 0.674745 / 0.540337 (0.134408) | 0.803489 / 1.386936 (-0.583447) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009872 / 0.011353 (-0.001481) | 0.006593 / 0.011008 (-0.004415) | 0.139248 / 0.038508 (0.100740) | 0.035708 / 0.023109 (0.012598) | 0.551335 / 0.275898 (0.275437) | 0.544995 / 0.323480 (0.221515) | 0.007085 / 0.007986 (-0.000900) | 0.004742 / 0.004328 (0.000413) | 0.095823 / 0.004250 (0.091572) | 0.051674 / 0.037052 (0.014621) | 0.463405 / 0.258489 (0.204916) | 0.640392 / 0.293841 (0.346551) | 0.055242 / 0.128546 (-0.073304) | 0.022602 / 0.075646 (-0.053044) | 0.419171 / 0.419271 (-0.000100) | 0.062986 / 0.043533 (0.019453) | 0.503683 / 0.255139 (0.248544) | 0.568719 / 0.283200 (0.285519) | 0.113906 / 0.141683 (-0.027777) | 1.825248 / 1.452155 (0.373094) | 1.985667 / 1.492716 (0.492951) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237478 / 0.018006 (0.219472) | 0.528861 / 0.000490 (0.528371) | 0.008507 / 0.000200 (0.008307) | 0.000158 / 0.000054 (0.000103) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033536 / 0.037411 (-0.003875) | 0.144202 / 0.014526 (0.129677) | 0.139472 / 0.176557 (-0.037084) | 0.184540 / 0.737135 (-0.552596) | 0.147818 / 0.296338 (-0.148520) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.671654 / 0.215209 (0.456445) | 6.616368 / 2.077655 (4.538713) | 2.805634 / 1.504120 (1.301514) | 2.482890 / 1.541195 (0.941695) | 2.547686 / 1.468490 (1.079195) | 1.289169 / 4.584777 (-3.295608) | 5.551436 / 3.745712 (1.805724) | 5.228500 / 5.269862 (-0.041362) | 2.456706 / 4.565676 (-2.108970) | 0.148556 / 0.424275 (-0.275720) | 0.015290 / 0.007607 (0.007683) | 0.837090 / 0.226044 (0.611045) | 8.373561 / 2.268929 (6.104632) | 3.663910 / 55.444624 (-51.780714) | 2.927117 / 6.876477 (-3.949360) | 2.976785 / 2.142072 (0.834712) | 1.501618 / 4.805227 (-3.303609) | 0.263321 / 6.500664 (-6.237343) | 0.082644 / 0.075469 (0.007175) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.707419 / 1.841788 (-0.134368) | 18.371117 / 8.074308 (10.296809) | 22.015154 / 10.191392 (11.823762) | 0.232066 / 0.680424 (-0.448357) | 0.027149 / 0.534201 (-0.507052) | 0.544450 / 0.579283 (-0.034833) | 0.605134 / 0.434364 (0.170770) | 0.656063 / 0.540337 (0.115725) | 0.788121 / 1.386936 (-0.598815) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1e0ec31e07e4bc0469f4acfed601d9c71e9a459 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008952 / 0.011353 (-0.002401) | 0.005592 / 0.011008 (-0.005416) | 0.101138 / 0.038508 (0.062630) | 0.035573 / 0.023109 (0.012464) | 0.295959 / 0.275898 (0.020060) | 0.365347 / 0.323480 (0.041867) | 0.008136 / 0.007986 (0.000150) | 0.004479 / 0.004328 (0.000150) | 0.078806 / 0.004250 (0.074556) | 0.045180 / 0.037052 (0.008127) | 0.321687 / 0.258489 (0.063198) | 0.345874 / 0.293841 (0.052033) | 0.038720 / 0.128546 (-0.089826) | 0.012534 / 0.075646 (-0.063112) | 0.335571 / 0.419271 (-0.083700) | 0.049048 / 0.043533 (0.005515) | 0.294756 / 0.255139 (0.039617) | 0.327496 / 0.283200 (0.044296) | 0.109181 / 0.141683 (-0.032502) | 1.417068 / 1.452155 (-0.035087) | 1.455473 / 1.492716 (-0.037244) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267774 / 0.018006 (0.249768) | 0.538546 / 0.000490 (0.538056) | 0.001755 / 0.000200 (0.001555) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026839 / 0.037411 (-0.010572) | 0.105862 / 0.014526 (0.091336) | 0.118278 / 0.176557 (-0.058279) | 0.157926 / 0.737135 (-0.579209) | 0.124700 / 0.296338 (-0.171638) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399060 / 0.215209 (0.183851) | 3.991409 / 2.077655 (1.913754) | 1.763569 / 1.504120 (0.259449) | 1.579602 / 1.541195 (0.038407) | 1.652928 / 1.468490 (0.184438) | 0.692962 / 4.584777 (-3.891815) | 3.784635 / 3.745712 (0.038922) | 3.249341 / 5.269862 (-2.020521) | 1.815711 / 4.565676 (-2.749966) | 0.084384 / 0.424275 (-0.339891) | 0.012546 / 0.007607 (0.004939) | 0.521397 / 0.226044 (0.295352) | 5.075824 / 2.268929 (2.806895) | 2.258353 / 55.444624 (-53.186272) | 1.925220 / 6.876477 (-4.951256) | 2.002821 / 2.142072 (-0.139252) | 0.830507 / 4.805227 (-3.974720) | 0.165845 / 6.500664 (-6.334819) | 0.063905 / 0.075469 (-0.011565) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198726 / 1.841788 (-0.643061) | 14.804448 / 8.074308 (6.730139) | 12.855167 / 10.191392 (2.663775) | 0.167932 / 0.680424 (-0.512492) | 0.028643 / 0.534201 (-0.505558) | 0.441224 / 0.579283 (-0.138059) | 0.434924 / 0.434364 (0.000560) | 0.516188 / 0.540337 (-0.024150) | 0.605017 / 1.386936 (-0.781919) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007031 / 0.011353 (-0.004322) | 0.005157 / 0.011008 (-0.005851) | 0.086943 / 0.038508 (0.048434) | 0.031377 / 0.023109 (0.008268) | 0.334810 / 0.275898 (0.058912) | 0.368590 / 0.323480 (0.045110) | 0.005973 / 0.007986 (-0.002013) | 0.004173 / 0.004328 (-0.000155) | 0.067033 / 0.004250 (0.062783) | 0.054070 / 0.037052 (0.017018) | 0.332232 / 0.258489 (0.073743) | 0.384982 / 0.293841 (0.091141) | 0.034023 / 0.128546 (-0.094524) | 0.011301 / 0.075646 (-0.064345) | 0.295644 / 0.419271 (-0.123628) | 0.045589 / 0.043533 (0.002056) | 0.330739 / 0.255139 (0.075600) | 0.352841 / 0.283200 (0.069642) | 0.104829 / 0.141683 (-0.036854) | 1.329360 / 1.452155 (-0.122794) | 1.437956 / 1.492716 (-0.054760) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.299187 / 0.018006 (0.281181) | 0.563407 / 0.000490 (0.562917) | 0.004179 / 0.000200 (0.003979) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027405 / 0.037411 (-0.010006) | 0.097498 / 0.014526 (0.082972) | 0.114265 / 0.176557 (-0.062292) | 0.146823 / 0.737135 (-0.590313) | 0.117948 / 0.296338 (-0.178391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.378756 / 0.215209 (0.163547) | 3.774804 / 2.077655 (1.697150) | 1.804149 / 1.504120 (0.300029) | 1.626312 / 1.541195 (0.085117) | 1.731111 / 1.468490 (0.262620) | 0.633493 / 4.584777 (-3.951284) | 3.488220 / 3.745712 (-0.257492) | 3.064710 / 5.269862 (-2.205151) | 1.690647 / 4.565676 (-2.875029) | 0.076093 / 0.424275 (-0.348182) | 0.010820 / 0.007607 (0.003213) | 0.465091 / 0.226044 (0.239046) | 4.676842 / 2.268929 (2.407913) | 2.297381 / 55.444624 (-53.147244) | 1.960355 / 6.876477 (-4.916122) | 1.983742 / 2.142072 (-0.158330) | 0.739525 / 4.805227 (-4.065702) | 0.152663 / 6.500664 (-6.348001) | 0.057316 / 0.075469 (-0.018153) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.104721 / 1.841788 (-0.737067) | 14.577171 / 8.074308 (6.502863) | 13.680402 / 10.191392 (3.489010) | 0.182234 / 0.680424 (-0.498190) | 0.018853 / 0.534201 (-0.515348) | 0.426194 / 0.579283 (-0.153089) | 0.429202 / 0.434364 (-0.005162) | 0.543125 / 0.540337 (0.002788) | 0.645887 / 1.386936 (-0.741049) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f830952573bdc59f8732b8f1a13f70d9187e0a65 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010055 / 0.011353 (-0.001298) | 0.005576 / 0.011008 (-0.005432) | 0.100059 / 0.038508 (0.061551) | 0.038535 / 0.023109 (0.015425) | 0.297538 / 0.275898 (0.021640) | 0.368117 / 0.323480 (0.044637) | 0.008540 / 0.007986 (0.000555) | 0.004469 / 0.004328 (0.000141) | 0.075801 / 0.004250 (0.071551) | 0.046604 / 0.037052 (0.009552) | 0.307242 / 0.258489 (0.048753) | 0.343949 / 0.293841 (0.050108) | 0.039353 / 0.128546 (-0.089194) | 0.012446 / 0.075646 (-0.063200) | 0.334628 / 0.419271 (-0.084643) | 0.051628 / 0.043533 (0.008095) | 0.298726 / 0.255139 (0.043587) | 0.316010 / 0.283200 (0.032810) | 0.120564 / 0.141683 (-0.021119) | 1.459396 / 1.452155 (0.007241) | 1.493682 / 1.492716 (0.000965) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011702 / 0.018006 (-0.006304) | 0.570261 / 0.000490 (0.569771) | 0.003760 / 0.000200 (0.003560) | 0.000091 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028806 / 0.037411 (-0.008605) | 0.112150 / 0.014526 (0.097625) | 0.123140 / 0.176557 (-0.053417) | 0.173055 / 0.737135 (-0.564080) | 0.130060 / 0.296338 (-0.166279) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398216 / 0.215209 (0.183007) | 3.978677 / 2.077655 (1.901022) | 1.754229 / 1.504120 (0.250109) | 1.561892 / 1.541195 (0.020697) | 1.679138 / 1.468490 (0.210648) | 0.690254 / 4.584777 (-3.894523) | 3.817698 / 3.745712 (0.071986) | 2.177854 / 5.269862 (-3.092008) | 1.361860 / 4.565676 (-3.203816) | 0.084108 / 0.424275 (-0.340167) | 0.012640 / 0.007607 (0.005033) | 0.504385 / 0.226044 (0.278341) | 5.034103 / 2.268929 (2.765174) | 2.254032 / 55.444624 (-53.190593) | 1.910439 / 6.876477 (-4.966038) | 2.003515 / 2.142072 (-0.138558) | 0.839747 / 4.805227 (-3.965480) | 0.165654 / 6.500664 (-6.335010) | 0.063483 / 0.075469 (-0.011986) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.187521 / 1.841788 (-0.654267) | 15.381121 / 8.074308 (7.306812) | 14.579418 / 10.191392 (4.388026) | 0.199221 / 0.680424 (-0.481202) | 0.029335 / 0.534201 (-0.504866) | 0.443159 / 0.579283 (-0.136124) | 0.447772 / 0.434364 (0.013408) | 0.545071 / 0.540337 (0.004733) | 0.650494 / 1.386936 (-0.736442) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007675 / 0.011353 (-0.003677) | 0.005364 / 0.011008 (-0.005644) | 0.097921 / 0.038508 (0.059413) | 0.033645 / 0.023109 (0.010536) | 0.404818 / 0.275898 (0.128920) | 0.429983 / 0.323480 (0.106503) | 0.006106 / 0.007986 (-0.001879) | 0.005281 / 0.004328 (0.000953) | 0.073762 / 0.004250 (0.069512) | 0.053065 / 0.037052 (0.016012) | 0.400657 / 0.258489 (0.142168) | 0.447743 / 0.293841 (0.153902) | 0.036782 / 0.128546 (-0.091765) | 0.012593 / 0.075646 (-0.063054) | 0.332825 / 0.419271 (-0.086446) | 0.049424 / 0.043533 (0.005891) | 0.400397 / 0.255139 (0.145258) | 0.414794 / 0.283200 (0.131594) | 0.106555 / 0.141683 (-0.035128) | 1.466917 / 1.452155 (0.014762) | 1.571351 / 1.492716 (0.078635) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254337 / 0.018006 (0.236331) | 0.568360 / 0.000490 (0.567870) | 0.000445 / 0.000200 (0.000245) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031044 / 0.037411 (-0.006367) | 0.112282 / 0.014526 (0.097756) | 0.127205 / 0.176557 (-0.049352) | 0.166551 / 0.737135 (-0.570584) | 0.130520 / 0.296338 (-0.165818) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442906 / 0.215209 (0.227697) | 4.430218 / 2.077655 (2.352563) | 2.287251 / 1.504120 (0.783132) | 2.112345 / 1.541195 (0.571150) | 2.240952 / 1.468490 (0.772462) | 0.713800 / 4.584777 (-3.870977) | 3.884161 / 3.745712 (0.138449) | 2.166901 / 5.269862 (-3.102960) | 1.374490 / 4.565676 (-3.191187) | 0.087548 / 0.424275 (-0.336727) | 0.012369 / 0.007607 (0.004761) | 0.540783 / 0.226044 (0.314739) | 5.396187 / 2.268929 (3.127258) | 2.779636 / 55.444624 (-52.664988) | 2.434220 / 6.876477 (-4.442257) | 2.508180 / 2.142072 (0.366107) | 0.852470 / 4.805227 (-3.952757) | 0.171266 / 6.500664 (-6.329398) | 0.065463 / 0.075469 (-0.010006) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.241720 / 1.841788 (-0.600067) | 15.332568 / 8.074308 (7.258260) | 13.688723 / 10.191392 (3.497331) | 0.145150 / 0.680424 (-0.535273) | 0.017694 / 0.534201 (-0.516507) | 0.426078 / 0.579283 (-0.153205) | 0.441189 / 0.434364 (0.006825) | 0.540284 / 0.540337 (-0.000054) | 0.657548 / 1.386936 (-0.729388) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c47ecf71362f6b6290b6471b30e77184a5e1df31 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008604 / 0.011353 (-0.002749) | 0.004566 / 0.011008 (-0.006442) | 0.099607 / 0.038508 (0.061099) | 0.029628 / 0.023109 (0.006519) | 0.300481 / 0.275898 (0.024583) | 0.342596 / 0.323480 (0.019116) | 0.007003 / 0.007986 (-0.000982) | 0.003408 / 0.004328 (-0.000920) | 0.079076 / 0.004250 (0.074826) | 0.034104 / 0.037052 (-0.002948) | 0.303856 / 0.258489 (0.045367) | 0.348729 / 0.293841 (0.054888) | 0.033752 / 0.128546 (-0.094794) | 0.011497 / 0.075646 (-0.064149) | 0.321568 / 0.419271 (-0.097704) | 0.041472 / 0.043533 (-0.002061) | 0.303396 / 0.255139 (0.048257) | 0.331121 / 0.283200 (0.047921) | 0.086203 / 0.141683 (-0.055480) | 1.476995 / 1.452155 (0.024840) | 1.539428 / 1.492716 (0.046712) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215810 / 0.018006 (0.197803) | 0.414292 / 0.000490 (0.413802) | 0.000388 / 0.000200 (0.000188) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023441 / 0.037411 (-0.013970) | 0.098463 / 0.014526 (0.083938) | 0.105435 / 0.176557 (-0.071121) | 0.139736 / 0.737135 (-0.597399) | 0.109467 / 0.296338 (-0.186872) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418244 / 0.215209 (0.203035) | 4.160693 / 2.077655 (2.083039) | 1.878895 / 1.504120 (0.374775) | 1.679338 / 1.541195 (0.138143) | 1.730384 / 1.468490 (0.261894) | 0.688603 / 4.584777 (-3.896174) | 3.393542 / 3.745712 (-0.352170) | 1.901337 / 5.269862 (-3.368525) | 1.447269 / 4.565676 (-3.118408) | 0.083003 / 0.424275 (-0.341272) | 0.012574 / 0.007607 (0.004967) | 0.526363 / 0.226044 (0.300318) | 5.275159 / 2.268929 (3.006230) | 2.323642 / 55.444624 (-53.120982) | 1.982929 / 6.876477 (-4.893548) | 2.014081 / 2.142072 (-0.127991) | 0.809466 / 4.805227 (-3.995761) | 0.149038 / 6.500664 (-6.351626) | 0.064394 / 0.075469 (-0.011075) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.207439 / 1.841788 (-0.634349) | 13.691048 / 8.074308 (5.616740) | 13.880965 / 10.191392 (3.689573) | 0.148553 / 0.680424 (-0.531871) | 0.028397 / 0.534201 (-0.505804) | 0.391818 / 0.579283 (-0.187465) | 0.407181 / 0.434364 (-0.027183) | 0.481163 / 0.540337 (-0.059175) | 0.570689 / 1.386936 (-0.816247) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006361 / 0.011353 (-0.004992) | 0.004520 / 0.011008 (-0.006488) | 0.097679 / 0.038508 (0.059171) | 0.027223 / 0.023109 (0.004113) | 0.407966 / 0.275898 (0.132068) | 0.439868 / 0.323480 (0.116388) | 0.004625 / 0.007986 (-0.003360) | 0.004039 / 0.004328 (-0.000289) | 0.074548 / 0.004250 (0.070298) | 0.034957 / 0.037052 (-0.002095) | 0.412762 / 0.258489 (0.154273) | 0.449716 / 0.293841 (0.155875) | 0.031272 / 0.128546 (-0.097274) | 0.011598 / 0.075646 (-0.064049) | 0.320922 / 0.419271 (-0.098349) | 0.041250 / 0.043533 (-0.002283) | 0.411439 / 0.255139 (0.156300) | 0.429722 / 0.283200 (0.146523) | 0.087161 / 0.141683 (-0.054522) | 1.512573 / 1.452155 (0.060418) | 1.569385 / 1.492716 (0.076668) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222612 / 0.018006 (0.204606) | 0.409086 / 0.000490 (0.408596) | 0.004246 / 0.000200 (0.004046) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024324 / 0.037411 (-0.013087) | 0.099055 / 0.014526 (0.084530) | 0.106809 / 0.176557 (-0.069748) | 0.141275 / 0.737135 (-0.595860) | 0.109426 / 0.296338 (-0.186913) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.469736 / 0.215209 (0.254527) | 4.686900 / 2.077655 (2.609246) | 2.413392 / 1.504120 (0.909272) | 2.217366 / 1.541195 (0.676171) | 2.266957 / 1.468490 (0.798467) | 0.698647 / 4.584777 (-3.886129) | 3.389317 / 3.745712 (-0.356395) | 1.862315 / 5.269862 (-3.407546) | 1.160931 / 4.565676 (-3.404746) | 0.082829 / 0.424275 (-0.341446) | 0.012627 / 0.007607 (0.005020) | 0.568027 / 0.226044 (0.341983) | 5.683220 / 2.268929 (3.414291) | 2.865701 / 55.444624 (-52.578924) | 2.522401 / 6.876477 (-4.354076) | 2.542395 / 2.142072 (0.400323) | 0.801224 / 4.805227 (-4.004003) | 0.149946 / 6.500664 (-6.350718) | 0.065447 / 0.075469 (-0.010023) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283756 / 1.841788 (-0.558032) | 13.903662 / 8.074308 (5.829354) | 13.238389 / 10.191392 (3.046997) | 0.142304 / 0.680424 (-0.538120) | 0.016922 / 0.534201 (-0.517279) | 0.377797 / 0.579283 (-0.201487) | 0.382460 / 0.434364 (-0.051904) | 0.464645 / 0.540337 (-0.075692) | 0.556270 / 1.386936 (-0.830666) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#675cf2910c5e6f083ed6664a7bffba9a58f78309 \"CML watermark\")\n",
"> I think this would be more of a Conceptual Guide doc since this is more explanatory and compares the differences between a Dataset and an IterableDataset\r\n\r\nsounds good to me !\r\n\r\n> There are definitely places in the docs where we can add a nice and link to this doc though to build up the user's understanding of this topic. For example, in the Know your dataset [tutorial](https://huggingface.co./docs/datasets/access), we only introduce the regular Dataset object and not the IterableDataset. We can add a section there for IterableDataset and then link to this doc that explains the difference between the two 🙂\r\n\r\ngood idea, thanks :)",
"I'll open a PR to add a section on `IterableDataset`'s in the tutorial, and once you're done editing this doc I can give it a final polish! 😄 ",
"I moved the doc page to conceptual guides and took your suggestions into account :)\r\n\r\nI think this is ready for final review now",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009890 / 0.011353 (-0.001463) | 0.005156 / 0.011008 (-0.005852) | 0.099493 / 0.038508 (0.060984) | 0.036671 / 0.023109 (0.013562) | 0.304686 / 0.275898 (0.028788) | 0.339070 / 0.323480 (0.015590) | 0.008466 / 0.007986 (0.000481) | 0.005863 / 0.004328 (0.001534) | 0.075082 / 0.004250 (0.070832) | 0.045926 / 0.037052 (0.008874) | 0.303157 / 0.258489 (0.044668) | 0.363710 / 0.293841 (0.069870) | 0.038497 / 0.128546 (-0.090049) | 0.012063 / 0.075646 (-0.063583) | 0.334463 / 0.419271 (-0.084808) | 0.048161 / 0.043533 (0.004628) | 0.300431 / 0.255139 (0.045292) | 0.330344 / 0.283200 (0.047145) | 0.105509 / 0.141683 (-0.036174) | 1.475242 / 1.452155 (0.023087) | 1.550624 / 1.492716 (0.057908) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245749 / 0.018006 (0.227743) | 0.575091 / 0.000490 (0.574601) | 0.001556 / 0.000200 (0.001357) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030447 / 0.037411 (-0.006964) | 0.110982 / 0.014526 (0.096456) | 0.126760 / 0.176557 (-0.049797) | 0.173375 / 0.737135 (-0.563760) | 0.128799 / 0.296338 (-0.167539) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.392861 / 0.215209 (0.177651) | 3.911231 / 2.077655 (1.833576) | 1.757413 / 1.504120 (0.253293) | 1.563287 / 1.541195 (0.022093) | 1.658678 / 1.468490 (0.190188) | 0.677244 / 4.584777 (-3.907533) | 3.754917 / 3.745712 (0.009205) | 3.779417 / 5.269862 (-1.490444) | 1.993159 / 4.565676 (-2.572517) | 0.084425 / 0.424275 (-0.339850) | 0.012500 / 0.007607 (0.004893) | 0.501788 / 0.226044 (0.275743) | 5.003173 / 2.268929 (2.734244) | 2.273547 / 55.444624 (-53.171077) | 1.909766 / 6.876477 (-4.966711) | 1.968287 / 2.142072 (-0.173785) | 0.834895 / 4.805227 (-3.970332) | 0.165312 / 6.500664 (-6.335352) | 0.062202 / 0.075469 (-0.013267) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.203080 / 1.841788 (-0.638708) | 15.158284 / 8.074308 (7.083976) | 14.174484 / 10.191392 (3.983092) | 0.171540 / 0.680424 (-0.508883) | 0.028604 / 0.534201 (-0.505597) | 0.438379 / 0.579283 (-0.140904) | 0.429447 / 0.434364 (-0.004917) | 0.540979 / 0.540337 (0.000642) | 0.630322 / 1.386936 (-0.756614) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007600 / 0.011353 (-0.003753) | 0.005400 / 0.011008 (-0.005608) | 0.097983 / 0.038508 (0.059475) | 0.033407 / 0.023109 (0.010297) | 0.384429 / 0.275898 (0.108531) | 0.415880 / 0.323480 (0.092400) | 0.006085 / 0.007986 (-0.001900) | 0.004330 / 0.004328 (0.000002) | 0.074654 / 0.004250 (0.070403) | 0.053076 / 0.037052 (0.016024) | 0.383958 / 0.258489 (0.125469) | 0.427289 / 0.293841 (0.133448) | 0.036710 / 0.128546 (-0.091836) | 0.012400 / 0.075646 (-0.063246) | 0.332712 / 0.419271 (-0.086560) | 0.058390 / 0.043533 (0.014857) | 0.377747 / 0.255139 (0.122608) | 0.398997 / 0.283200 (0.115798) | 0.117370 / 0.141683 (-0.024313) | 1.464211 / 1.452155 (0.012057) | 1.596465 / 1.492716 (0.103749) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212989 / 0.018006 (0.194983) | 0.554968 / 0.000490 (0.554479) | 0.004305 / 0.000200 (0.004105) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029167 / 0.037411 (-0.008244) | 0.109156 / 0.014526 (0.094631) | 0.122575 / 0.176557 (-0.053982) | 0.163058 / 0.737135 (-0.574077) | 0.127431 / 0.296338 (-0.168908) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445395 / 0.215209 (0.230185) | 4.447534 / 2.077655 (2.369879) | 2.259186 / 1.504120 (0.755066) | 2.082956 / 1.541195 (0.541761) | 2.259126 / 1.468490 (0.790636) | 0.692271 / 4.584777 (-3.892506) | 3.795759 / 3.745712 (0.050047) | 3.603000 / 5.269862 (-1.666862) | 1.948556 / 4.565676 (-2.617120) | 0.084589 / 0.424275 (-0.339687) | 0.012751 / 0.007607 (0.005144) | 0.544783 / 0.226044 (0.318738) | 5.452278 / 2.268929 (3.183349) | 2.809467 / 55.444624 (-52.635157) | 2.479297 / 6.876477 (-4.397180) | 2.587756 / 2.142072 (0.445683) | 0.832258 / 4.805227 (-3.972970) | 0.167424 / 6.500664 (-6.333240) | 0.066064 / 0.075469 (-0.009405) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.262719 / 1.841788 (-0.579069) | 15.917869 / 8.074308 (7.843561) | 13.879301 / 10.191392 (3.687909) | 0.187712 / 0.680424 (-0.492712) | 0.018175 / 0.534201 (-0.516026) | 0.425840 / 0.579283 (-0.153443) | 0.426164 / 0.434364 (-0.008200) | 0.527465 / 0.540337 (-0.012872) | 0.629478 / 1.386936 (-0.757458) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f7e178d6373e7d66a60662a22fd60af117f0885 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009064 / 0.011353 (-0.002289) | 0.004824 / 0.011008 (-0.006184) | 0.100869 / 0.038508 (0.062361) | 0.030803 / 0.023109 (0.007694) | 0.350880 / 0.275898 (0.074982) | 0.423816 / 0.323480 (0.100336) | 0.007581 / 0.007986 (-0.000405) | 0.003642 / 0.004328 (-0.000686) | 0.077682 / 0.004250 (0.073432) | 0.039856 / 0.037052 (0.002803) | 0.366097 / 0.258489 (0.107608) | 0.409226 / 0.293841 (0.115385) | 0.033698 / 0.128546 (-0.094848) | 0.011730 / 0.075646 (-0.063916) | 0.321683 / 0.419271 (-0.097588) | 0.041794 / 0.043533 (-0.001739) | 0.351175 / 0.255139 (0.096036) | 0.374328 / 0.283200 (0.091128) | 0.091833 / 0.141683 (-0.049850) | 1.507082 / 1.452155 (0.054927) | 1.543289 / 1.492716 (0.050572) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010670 / 0.018006 (-0.007337) | 0.429674 / 0.000490 (0.429184) | 0.003246 / 0.000200 (0.003046) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025015 / 0.037411 (-0.012397) | 0.102155 / 0.014526 (0.087629) | 0.107010 / 0.176557 (-0.069546) | 0.144265 / 0.737135 (-0.592870) | 0.110635 / 0.296338 (-0.185703) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414211 / 0.215209 (0.199002) | 4.125582 / 2.077655 (2.047928) | 1.997856 / 1.504120 (0.493736) | 1.847676 / 1.541195 (0.306481) | 1.994100 / 1.468490 (0.525610) | 0.694975 / 4.584777 (-3.889802) | 3.373629 / 3.745712 (-0.372083) | 2.863255 / 5.269862 (-2.406606) | 1.565723 / 4.565676 (-2.999953) | 0.082539 / 0.424275 (-0.341736) | 0.012650 / 0.007607 (0.005043) | 0.522989 / 0.226044 (0.296945) | 5.205720 / 2.268929 (2.936792) | 2.352292 / 55.444624 (-53.092332) | 2.080467 / 6.876477 (-4.796010) | 2.231014 / 2.142072 (0.088942) | 0.811252 / 4.805227 (-3.993975) | 0.149171 / 6.500664 (-6.351493) | 0.065207 / 0.075469 (-0.010262) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.203137 / 1.841788 (-0.638651) | 14.244903 / 8.074308 (6.170595) | 14.454368 / 10.191392 (4.262976) | 0.139090 / 0.680424 (-0.541334) | 0.028738 / 0.534201 (-0.505463) | 0.396394 / 0.579283 (-0.182889) | 0.407207 / 0.434364 (-0.027156) | 0.478036 / 0.540337 (-0.062302) | 0.568488 / 1.386936 (-0.818448) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006878 / 0.011353 (-0.004475) | 0.004636 / 0.011008 (-0.006372) | 0.099118 / 0.038508 (0.060610) | 0.028076 / 0.023109 (0.004967) | 0.416097 / 0.275898 (0.140199) | 0.451722 / 0.323480 (0.128242) | 0.005364 / 0.007986 (-0.002622) | 0.003506 / 0.004328 (-0.000822) | 0.075791 / 0.004250 (0.071541) | 0.041373 / 0.037052 (0.004321) | 0.416358 / 0.258489 (0.157869) | 0.458440 / 0.293841 (0.164599) | 0.031870 / 0.128546 (-0.096676) | 0.011751 / 0.075646 (-0.063896) | 0.321748 / 0.419271 (-0.097524) | 0.041780 / 0.043533 (-0.001752) | 0.425037 / 0.255139 (0.169898) | 0.444169 / 0.283200 (0.160969) | 0.093145 / 0.141683 (-0.048538) | 1.472151 / 1.452155 (0.019996) | 1.542942 / 1.492716 (0.050226) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224287 / 0.018006 (0.206281) | 0.415303 / 0.000490 (0.414813) | 0.003180 / 0.000200 (0.002980) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026377 / 0.037411 (-0.011035) | 0.106222 / 0.014526 (0.091696) | 0.113873 / 0.176557 (-0.062684) | 0.143255 / 0.737135 (-0.593880) | 0.112642 / 0.296338 (-0.183697) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444149 / 0.215209 (0.228940) | 4.421434 / 2.077655 (2.343779) | 2.082198 / 1.504120 (0.578078) | 1.879909 / 1.541195 (0.338715) | 1.968526 / 1.468490 (0.500036) | 0.697230 / 4.584777 (-3.887546) | 3.430800 / 3.745712 (-0.314912) | 1.893353 / 5.269862 (-3.376509) | 1.173271 / 4.565676 (-3.392406) | 0.082636 / 0.424275 (-0.341639) | 0.012357 / 0.007607 (0.004750) | 0.544008 / 0.226044 (0.317964) | 5.465472 / 2.268929 (3.196543) | 2.530017 / 55.444624 (-52.914608) | 2.178462 / 6.876477 (-4.698014) | 2.279570 / 2.142072 (0.137498) | 0.804890 / 4.805227 (-4.000337) | 0.152091 / 6.500664 (-6.348573) | 0.069442 / 0.075469 (-0.006027) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256722 / 1.841788 (-0.585065) | 14.554131 / 8.074308 (6.479823) | 13.499913 / 10.191392 (3.308521) | 0.144350 / 0.680424 (-0.536074) | 0.016977 / 0.534201 (-0.517224) | 0.378836 / 0.579283 (-0.200447) | 0.392004 / 0.434364 (-0.042360) | 0.468423 / 0.540337 (-0.071914) | 0.584711 / 1.386936 (-0.802225) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e4894fcdf2a82b3355bb6a2dc5557c8e23f8144 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008542 / 0.011353 (-0.002811) | 0.004552 / 0.011008 (-0.006456) | 0.100543 / 0.038508 (0.062035) | 0.029717 / 0.023109 (0.006608) | 0.301948 / 0.275898 (0.026050) | 0.360211 / 0.323480 (0.036731) | 0.006881 / 0.007986 (-0.001105) | 0.003433 / 0.004328 (-0.000896) | 0.077760 / 0.004250 (0.073510) | 0.037069 / 0.037052 (0.000017) | 0.314084 / 0.258489 (0.055595) | 0.347759 / 0.293841 (0.053918) | 0.033255 / 0.128546 (-0.095291) | 0.011487 / 0.075646 (-0.064160) | 0.323873 / 0.419271 (-0.095399) | 0.041203 / 0.043533 (-0.002330) | 0.298397 / 0.255139 (0.043258) | 0.327174 / 0.283200 (0.043974) | 0.088892 / 0.141683 (-0.052791) | 1.560114 / 1.452155 (0.107959) | 1.532475 / 1.492716 (0.039759) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226080 / 0.018006 (0.208074) | 0.467492 / 0.000490 (0.467003) | 0.002198 / 0.000200 (0.001998) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023627 / 0.037411 (-0.013784) | 0.096696 / 0.014526 (0.082170) | 0.106196 / 0.176557 (-0.070360) | 0.140496 / 0.737135 (-0.596639) | 0.108859 / 0.296338 (-0.187480) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422335 / 0.215209 (0.207126) | 4.214879 / 2.077655 (2.137224) | 1.865866 / 1.504120 (0.361747) | 1.660914 / 1.541195 (0.119719) | 1.691869 / 1.468490 (0.223379) | 0.688164 / 4.584777 (-3.896613) | 3.432708 / 3.745712 (-0.313004) | 1.856852 / 5.269862 (-3.413010) | 1.243685 / 4.565676 (-3.321991) | 0.081552 / 0.424275 (-0.342723) | 0.012491 / 0.007607 (0.004884) | 0.524331 / 0.226044 (0.298287) | 5.255090 / 2.268929 (2.986162) | 2.269705 / 55.444624 (-53.174919) | 1.936722 / 6.876477 (-4.939755) | 2.018958 / 2.142072 (-0.123114) | 0.800658 / 4.805227 (-4.004569) | 0.148665 / 6.500664 (-6.351999) | 0.064210 / 0.075469 (-0.011259) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235422 / 1.841788 (-0.606365) | 14.156755 / 8.074308 (6.082447) | 14.005916 / 10.191392 (3.814524) | 0.150983 / 0.680424 (-0.529441) | 0.028500 / 0.534201 (-0.505701) | 0.393013 / 0.579283 (-0.186270) | 0.408191 / 0.434364 (-0.026173) | 0.481017 / 0.540337 (-0.059320) | 0.581711 / 1.386936 (-0.805225) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006950 / 0.011353 (-0.004403) | 0.004575 / 0.011008 (-0.006434) | 0.076702 / 0.038508 (0.038194) | 0.028050 / 0.023109 (0.004941) | 0.342916 / 0.275898 (0.067018) | 0.378861 / 0.323480 (0.055381) | 0.005315 / 0.007986 (-0.002671) | 0.004822 / 0.004328 (0.000494) | 0.075560 / 0.004250 (0.071310) | 0.040441 / 0.037052 (0.003388) | 0.344284 / 0.258489 (0.085795) | 0.386519 / 0.293841 (0.092678) | 0.032122 / 0.128546 (-0.096424) | 0.011843 / 0.075646 (-0.063803) | 0.085798 / 0.419271 (-0.333473) | 0.043027 / 0.043533 (-0.000506) | 0.342910 / 0.255139 (0.087771) | 0.366618 / 0.283200 (0.083418) | 0.094766 / 0.141683 (-0.046917) | 1.492981 / 1.452155 (0.040827) | 1.566994 / 1.492716 (0.074278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.166083 / 0.018006 (0.148076) | 0.409315 / 0.000490 (0.408826) | 0.003189 / 0.000200 (0.002989) | 0.000127 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024753 / 0.037411 (-0.012658) | 0.099112 / 0.014526 (0.084586) | 0.106668 / 0.176557 (-0.069889) | 0.142562 / 0.737135 (-0.594573) | 0.110648 / 0.296338 (-0.185690) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452668 / 0.215209 (0.237459) | 4.501188 / 2.077655 (2.423534) | 2.086197 / 1.504120 (0.582077) | 1.873955 / 1.541195 (0.332761) | 1.935610 / 1.468490 (0.467120) | 0.708290 / 4.584777 (-3.876487) | 3.426986 / 3.745712 (-0.318726) | 2.805852 / 5.269862 (-2.464009) | 1.516918 / 4.565676 (-3.048759) | 0.084067 / 0.424275 (-0.340208) | 0.012776 / 0.007607 (0.005169) | 0.548853 / 0.226044 (0.322809) | 5.488198 / 2.268929 (3.219270) | 2.704464 / 55.444624 (-52.740161) | 2.377817 / 6.876477 (-4.498660) | 2.366152 / 2.142072 (0.224079) | 0.818192 / 4.805227 (-3.987035) | 0.152649 / 6.500664 (-6.348015) | 0.066914 / 0.075469 (-0.008555) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273803 / 1.841788 (-0.567985) | 14.071633 / 8.074308 (5.997325) | 13.655586 / 10.191392 (3.464194) | 0.149471 / 0.680424 (-0.530953) | 0.016745 / 0.534201 (-0.517456) | 0.386850 / 0.579283 (-0.192434) | 0.393595 / 0.434364 (-0.040769) | 0.480396 / 0.540337 (-0.059942) | 0.573708 / 1.386936 (-0.813228) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b2c7de67b326a635c0dc39ea5dd1ae982c958d6 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008173 / 0.011353 (-0.003180) | 0.004461 / 0.011008 (-0.006547) | 0.100284 / 0.038508 (0.061776) | 0.028900 / 0.023109 (0.005791) | 0.293639 / 0.275898 (0.017741) | 0.359450 / 0.323480 (0.035971) | 0.007567 / 0.007986 (-0.000418) | 0.003434 / 0.004328 (-0.000894) | 0.077913 / 0.004250 (0.073663) | 0.036313 / 0.037052 (-0.000740) | 0.308484 / 0.258489 (0.049995) | 0.347575 / 0.293841 (0.053734) | 0.033367 / 0.128546 (-0.095179) | 0.011508 / 0.075646 (-0.064138) | 0.323490 / 0.419271 (-0.095782) | 0.042285 / 0.043533 (-0.001248) | 0.295696 / 0.255139 (0.040557) | 0.332475 / 0.283200 (0.049276) | 0.089980 / 0.141683 (-0.051703) | 1.461851 / 1.452155 (0.009697) | 1.493030 / 1.492716 (0.000314) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191068 / 0.018006 (0.173062) | 0.396768 / 0.000490 (0.396278) | 0.002355 / 0.000200 (0.002155) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023117 / 0.037411 (-0.014294) | 0.096155 / 0.014526 (0.081630) | 0.102424 / 0.176557 (-0.074132) | 0.142148 / 0.737135 (-0.594987) | 0.105954 / 0.296338 (-0.190384) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421227 / 0.215209 (0.206018) | 4.200403 / 2.077655 (2.122748) | 1.899410 / 1.504120 (0.395290) | 1.684091 / 1.541195 (0.142896) | 1.698084 / 1.468490 (0.229594) | 0.696195 / 4.584777 (-3.888582) | 3.364116 / 3.745712 (-0.381596) | 1.899133 / 5.269862 (-3.370728) | 1.281405 / 4.565676 (-3.284272) | 0.082958 / 0.424275 (-0.341317) | 0.012433 / 0.007607 (0.004826) | 0.521856 / 0.226044 (0.295812) | 5.217626 / 2.268929 (2.948698) | 2.309228 / 55.444624 (-53.135396) | 1.956828 / 6.876477 (-4.919648) | 2.018964 / 2.142072 (-0.123108) | 0.816855 / 4.805227 (-3.988373) | 0.152867 / 6.500664 (-6.347798) | 0.064764 / 0.075469 (-0.010705) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219020 / 1.841788 (-0.622768) | 13.509058 / 8.074308 (5.434750) | 13.637826 / 10.191392 (3.446434) | 0.156620 / 0.680424 (-0.523804) | 0.028518 / 0.534201 (-0.505683) | 0.399138 / 0.579283 (-0.180146) | 0.399931 / 0.434364 (-0.034433) | 0.482902 / 0.540337 (-0.057435) | 0.574089 / 1.386936 (-0.812847) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006232 / 0.011353 (-0.005121) | 0.004467 / 0.011008 (-0.006542) | 0.075494 / 0.038508 (0.036986) | 0.026891 / 0.023109 (0.003782) | 0.356603 / 0.275898 (0.080705) | 0.371977 / 0.323480 (0.048497) | 0.004709 / 0.007986 (-0.003276) | 0.003230 / 0.004328 (-0.001099) | 0.074338 / 0.004250 (0.070088) | 0.035588 / 0.037052 (-0.001464) | 0.349554 / 0.258489 (0.091065) | 0.389672 / 0.293841 (0.095831) | 0.031524 / 0.128546 (-0.097022) | 0.011493 / 0.075646 (-0.064153) | 0.084584 / 0.419271 (-0.334688) | 0.041945 / 0.043533 (-0.001588) | 0.341057 / 0.255139 (0.085918) | 0.367876 / 0.283200 (0.084677) | 0.090113 / 0.141683 (-0.051569) | 1.507104 / 1.452155 (0.054949) | 1.567810 / 1.492716 (0.075094) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210939 / 0.018006 (0.192933) | 0.392600 / 0.000490 (0.392110) | 0.002188 / 0.000200 (0.001988) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024294 / 0.037411 (-0.013118) | 0.100325 / 0.014526 (0.085799) | 0.104027 / 0.176557 (-0.072530) | 0.141189 / 0.737135 (-0.595947) | 0.107438 / 0.296338 (-0.188901) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443314 / 0.215209 (0.228105) | 4.429612 / 2.077655 (2.351957) | 2.129275 / 1.504120 (0.625156) | 1.940016 / 1.541195 (0.398821) | 2.008975 / 1.468490 (0.540485) | 0.695434 / 4.584777 (-3.889343) | 3.355137 / 3.745712 (-0.390575) | 2.606262 / 5.269862 (-2.663600) | 1.451283 / 4.565676 (-3.114394) | 0.082875 / 0.424275 (-0.341400) | 0.012398 / 0.007607 (0.004791) | 0.544262 / 0.226044 (0.318218) | 5.450829 / 2.268929 (3.181900) | 2.582074 / 55.444624 (-52.862550) | 2.220037 / 6.876477 (-4.656439) | 2.232473 / 2.142072 (0.090401) | 0.802094 / 4.805227 (-4.003134) | 0.150188 / 6.500664 (-6.350476) | 0.066543 / 0.075469 (-0.008926) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269098 / 1.841788 (-0.572690) | 13.764780 / 8.074308 (5.690472) | 13.461490 / 10.191392 (3.270098) | 0.143841 / 0.680424 (-0.536583) | 0.016687 / 0.534201 (-0.517514) | 0.388548 / 0.579283 (-0.190736) | 0.385229 / 0.434364 (-0.049135) | 0.478966 / 0.540337 (-0.061371) | 0.570355 / 1.386936 (-0.816581) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0ba81f5b299f0918cb0c0c071412feadd0ea3ef5 \"CML watermark\")\n",
"I took your comments into account :)\r\n\r\n> Regarding the docs, I think it would be better to add this info as notes/tips/sections to the existing docs (Process/Stream; e.g. a tip under Dataset.shuffle that explains how to make this operation more performant by using to_iterable + shuffle, etc.) rather than introducing a new doc page.\r\n\r\nI added a paragraph in the Dataset.shuffle docstring, and a note in the Process doc page",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010906 / 0.011353 (-0.000447) | 0.005995 / 0.011008 (-0.005014) | 0.120183 / 0.038508 (0.081675) | 0.042166 / 0.023109 (0.019057) | 0.350945 / 0.275898 (0.075046) | 0.433055 / 0.323480 (0.109575) | 0.009093 / 0.007986 (0.001107) | 0.004695 / 0.004328 (0.000366) | 0.090362 / 0.004250 (0.086112) | 0.051402 / 0.037052 (0.014350) | 0.368677 / 0.258489 (0.110188) | 0.410926 / 0.293841 (0.117086) | 0.044471 / 0.128546 (-0.084075) | 0.014051 / 0.075646 (-0.061595) | 0.397765 / 0.419271 (-0.021507) | 0.057227 / 0.043533 (0.013694) | 0.357587 / 0.255139 (0.102448) | 0.377470 / 0.283200 (0.094270) | 0.119482 / 0.141683 (-0.022201) | 1.719799 / 1.452155 (0.267645) | 1.758228 / 1.492716 (0.265511) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224385 / 0.018006 (0.206379) | 0.505070 / 0.000490 (0.504580) | 0.004863 / 0.000200 (0.004663) | 0.000379 / 0.000054 (0.000324) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030366 / 0.037411 (-0.007046) | 0.130481 / 0.014526 (0.115955) | 0.136429 / 0.176557 (-0.040128) | 0.182263 / 0.737135 (-0.554872) | 0.142871 / 0.296338 (-0.153468) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.467623 / 0.215209 (0.252414) | 4.665522 / 2.077655 (2.587868) | 2.130885 / 1.504120 (0.626766) | 1.903810 / 1.541195 (0.362615) | 2.019077 / 1.468490 (0.550587) | 0.820868 / 4.584777 (-3.763909) | 4.543118 / 3.745712 (0.797406) | 2.491541 / 5.269862 (-2.778321) | 1.585377 / 4.565676 (-2.980299) | 0.101850 / 0.424275 (-0.322426) | 0.014737 / 0.007607 (0.007129) | 0.597241 / 0.226044 (0.371197) | 5.938445 / 2.268929 (3.669516) | 2.695799 / 55.444624 (-52.748825) | 2.286890 / 6.876477 (-4.589587) | 2.363064 / 2.142072 (0.220991) | 0.986670 / 4.805227 (-3.818557) | 0.194407 / 6.500664 (-6.306257) | 0.074767 / 0.075469 (-0.000702) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.420630 / 1.841788 (-0.421158) | 17.537702 / 8.074308 (9.463394) | 16.521804 / 10.191392 (6.330412) | 0.173622 / 0.680424 (-0.506802) | 0.033944 / 0.534201 (-0.500257) | 0.520461 / 0.579283 (-0.058822) | 0.541283 / 0.434364 (0.106919) | 0.651906 / 0.540337 (0.111569) | 0.771724 / 1.386936 (-0.615212) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008448 / 0.011353 (-0.002905) | 0.005893 / 0.011008 (-0.005115) | 0.087995 / 0.038508 (0.049487) | 0.038602 / 0.023109 (0.015493) | 0.400048 / 0.275898 (0.124150) | 0.436998 / 0.323480 (0.113518) | 0.006414 / 0.007986 (-0.001572) | 0.004478 / 0.004328 (0.000149) | 0.086444 / 0.004250 (0.082194) | 0.056535 / 0.037052 (0.019483) | 0.402066 / 0.258489 (0.143577) | 0.458730 / 0.293841 (0.164889) | 0.041622 / 0.128546 (-0.086924) | 0.014014 / 0.075646 (-0.061632) | 0.101382 / 0.419271 (-0.317889) | 0.056986 / 0.043533 (0.013453) | 0.404527 / 0.255139 (0.149388) | 0.428105 / 0.283200 (0.144906) | 0.118321 / 0.141683 (-0.023361) | 1.716940 / 1.452155 (0.264785) | 1.834683 / 1.492716 (0.341967) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252917 / 0.018006 (0.234910) | 0.485950 / 0.000490 (0.485461) | 0.000489 / 0.000200 (0.000289) | 0.000066 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035023 / 0.037411 (-0.002388) | 0.139055 / 0.014526 (0.124529) | 0.144165 / 0.176557 (-0.032392) | 0.189559 / 0.737135 (-0.547577) | 0.153213 / 0.296338 (-0.143126) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.505069 / 0.215209 (0.289860) | 5.024620 / 2.077655 (2.946965) | 2.429469 / 1.504120 (0.925349) | 2.186210 / 1.541195 (0.645015) | 2.275971 / 1.468490 (0.807481) | 0.829432 / 4.584777 (-3.755345) | 4.518600 / 3.745712 (0.772888) | 2.466418 / 5.269862 (-2.803443) | 1.558910 / 4.565676 (-3.006767) | 0.102017 / 0.424275 (-0.322258) | 0.015191 / 0.007607 (0.007584) | 0.619092 / 0.226044 (0.393048) | 6.241105 / 2.268929 (3.972176) | 3.044213 / 55.444624 (-52.400411) | 2.630194 / 6.876477 (-4.246282) | 2.723685 / 2.142072 (0.581613) | 0.994018 / 4.805227 (-3.811210) | 0.198722 / 6.500664 (-6.301942) | 0.075812 / 0.075469 (0.000343) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.545497 / 1.841788 (-0.296291) | 18.305250 / 8.074308 (10.230942) | 16.035275 / 10.191392 (5.843883) | 0.209339 / 0.680424 (-0.471085) | 0.020903 / 0.534201 (-0.513298) | 0.499909 / 0.579283 (-0.079374) | 0.488775 / 0.434364 (0.054411) | 0.581990 / 0.540337 (0.041653) | 0.697786 / 1.386936 (-0.689150) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78dca62e8aaddb9e0cf0212841f2c8d861fe74c8 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011706 / 0.011353 (0.000353) | 0.008406 / 0.011008 (-0.002602) | 0.130887 / 0.038508 (0.092379) | 0.037468 / 0.023109 (0.014359) | 0.385043 / 0.275898 (0.109145) | 0.458837 / 0.323480 (0.135357) | 0.013400 / 0.007986 (0.005414) | 0.004885 / 0.004328 (0.000557) | 0.107156 / 0.004250 (0.102905) | 0.046958 / 0.037052 (0.009906) | 0.419314 / 0.258489 (0.160825) | 0.456061 / 0.293841 (0.162220) | 0.058859 / 0.128546 (-0.069687) | 0.016682 / 0.075646 (-0.058965) | 0.428401 / 0.419271 (0.009129) | 0.062908 / 0.043533 (0.019376) | 0.370902 / 0.255139 (0.115763) | 0.433897 / 0.283200 (0.150697) | 0.125672 / 0.141683 (-0.016011) | 1.818279 / 1.452155 (0.366124) | 1.935767 / 1.492716 (0.443050) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011928 / 0.018006 (-0.006078) | 0.591995 / 0.000490 (0.591506) | 0.008416 / 0.000200 (0.008216) | 0.000122 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029640 / 0.037411 (-0.007772) | 0.121044 / 0.014526 (0.106518) | 0.141840 / 0.176557 (-0.034716) | 0.195856 / 0.737135 (-0.541280) | 0.146460 / 0.296338 (-0.149879) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.591838 / 0.215209 (0.376629) | 5.817309 / 2.077655 (3.739654) | 2.411864 / 1.504120 (0.907744) | 2.098517 / 1.541195 (0.557323) | 2.214609 / 1.468490 (0.746119) | 1.217542 / 4.584777 (-3.367235) | 5.658394 / 3.745712 (1.912682) | 5.155807 / 5.269862 (-0.114055) | 2.797313 / 4.565676 (-1.768363) | 0.141309 / 0.424275 (-0.282967) | 0.014462 / 0.007607 (0.006855) | 0.772274 / 0.226044 (0.546230) | 7.547357 / 2.268929 (5.278429) | 3.150178 / 55.444624 (-52.294446) | 2.500130 / 6.876477 (-4.376347) | 2.572036 / 2.142072 (0.429964) | 1.434498 / 4.805227 (-3.370729) | 0.257355 / 6.500664 (-6.243309) | 0.087491 / 0.075469 (0.012022) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.483899 / 1.841788 (-0.357889) | 17.990741 / 8.074308 (9.916433) | 20.398965 / 10.191392 (10.207573) | 0.239529 / 0.680424 (-0.440895) | 0.046118 / 0.534201 (-0.488083) | 0.528349 / 0.579283 (-0.050934) | 0.614333 / 0.434364 (0.179969) | 0.653621 / 0.540337 (0.113284) | 0.794654 / 1.386936 (-0.592282) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008732 / 0.011353 (-0.002621) | 0.006432 / 0.011008 (-0.004576) | 0.090811 / 0.038508 (0.052303) | 0.030154 / 0.023109 (0.007045) | 0.407885 / 0.275898 (0.131987) | 0.452457 / 0.323480 (0.128977) | 0.006966 / 0.007986 (-0.001020) | 0.006449 / 0.004328 (0.002120) | 0.094439 / 0.004250 (0.090188) | 0.050628 / 0.037052 (0.013576) | 0.401815 / 0.258489 (0.143326) | 0.451814 / 0.293841 (0.157973) | 0.047456 / 0.128546 (-0.081090) | 0.019019 / 0.075646 (-0.056628) | 0.112941 / 0.419271 (-0.306331) | 0.057677 / 0.043533 (0.014145) | 0.406160 / 0.255139 (0.151021) | 0.434469 / 0.283200 (0.151269) | 0.110515 / 0.141683 (-0.031167) | 1.601393 / 1.452155 (0.149238) | 1.745581 / 1.492716 (0.252865) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.280264 / 0.018006 (0.262258) | 0.630074 / 0.000490 (0.629585) | 0.006900 / 0.000200 (0.006700) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027338 / 0.037411 (-0.010073) | 0.114772 / 0.014526 (0.100246) | 0.130436 / 0.176557 (-0.046121) | 0.168990 / 0.737135 (-0.568145) | 0.135842 / 0.296338 (-0.160496) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.666739 / 0.215209 (0.451530) | 6.212953 / 2.077655 (4.135298) | 2.781716 / 1.504120 (1.277596) | 2.369975 / 1.541195 (0.828781) | 2.338807 / 1.468490 (0.870317) | 1.174138 / 4.584777 (-3.410639) | 5.420297 / 3.745712 (1.674585) | 4.972669 / 5.269862 (-0.297192) | 2.214294 / 4.565676 (-2.351382) | 0.135429 / 0.424275 (-0.288846) | 0.013877 / 0.007607 (0.006270) | 0.750805 / 0.226044 (0.524761) | 7.145429 / 2.268929 (4.876500) | 3.215081 / 55.444624 (-52.229544) | 2.598307 / 6.876477 (-4.278170) | 2.690479 / 2.142072 (0.548406) | 1.344673 / 4.805227 (-3.460554) | 0.241536 / 6.500664 (-6.259128) | 0.075544 / 0.075469 (0.000074) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.473595 / 1.841788 (-0.368192) | 17.372237 / 8.074308 (9.297929) | 18.586588 / 10.191392 (8.395196) | 0.209300 / 0.680424 (-0.471124) | 0.030878 / 0.534201 (-0.503323) | 0.509131 / 0.579283 (-0.070152) | 0.617884 / 0.434364 (0.183520) | 0.633721 / 0.540337 (0.093383) | 0.727624 / 1.386936 (-0.659312) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87f2062d47fdbec3fadf5b39bab0801f59c0f4a3 \"CML watermark\")\n",
"Took your last comments into account !\r\n\r\n> so maybe a better title for it would be \"Optimize processing\" (or \"Working with datasets at scale\" as I mentioned earlier on Slack)\r\n\r\nI think the content would be slightly different, e.g. focus more on multiprocessing/sharding or what data formats to use. This can be a complementary page IMO\r\n\r\n> PS: I think it would be a good idea to add links to the Guide pages for better discoverability and to somewhat \"justify their presence in the docs\" (from the tutorial/how-to pages to the guides; some guides are not referenced at all)\r\n\r\nAdded a link in the how-to stream page. We may want to include it in the tutorial at one point at well - right now none of the tutorials mention streaming",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009167 / 0.011353 (-0.002186) | 0.005345 / 0.011008 (-0.005663) | 0.098302 / 0.038508 (0.059794) | 0.035649 / 0.023109 (0.012540) | 0.295597 / 0.275898 (0.019699) | 0.358843 / 0.323480 (0.035364) | 0.008011 / 0.007986 (0.000025) | 0.004229 / 0.004328 (-0.000100) | 0.075123 / 0.004250 (0.070872) | 0.046098 / 0.037052 (0.009046) | 0.310581 / 0.258489 (0.052092) | 0.343230 / 0.293841 (0.049389) | 0.038318 / 0.128546 (-0.090229) | 0.011954 / 0.075646 (-0.063693) | 0.331056 / 0.419271 (-0.088216) | 0.052875 / 0.043533 (0.009342) | 0.302758 / 0.255139 (0.047619) | 0.340596 / 0.283200 (0.057396) | 0.113676 / 0.141683 (-0.028007) | 1.448272 / 1.452155 (-0.003883) | 1.498008 / 1.492716 (0.005291) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240524 / 0.018006 (0.222518) | 0.555823 / 0.000490 (0.555333) | 0.003143 / 0.000200 (0.002943) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027764 / 0.037411 (-0.009647) | 0.105006 / 0.014526 (0.090480) | 0.120550 / 0.176557 (-0.056007) | 0.167052 / 0.737135 (-0.570084) | 0.124521 / 0.296338 (-0.171818) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401758 / 0.215209 (0.186549) | 3.989629 / 2.077655 (1.911974) | 1.767307 / 1.504120 (0.263187) | 1.579451 / 1.541195 (0.038257) | 1.637642 / 1.468490 (0.169152) | 0.702524 / 4.584777 (-3.882253) | 3.714326 / 3.745712 (-0.031386) | 2.131829 / 5.269862 (-3.138033) | 1.487410 / 4.565676 (-3.078267) | 0.084901 / 0.424275 (-0.339374) | 0.012292 / 0.007607 (0.004685) | 0.505211 / 0.226044 (0.279166) | 5.074479 / 2.268929 (2.805551) | 2.243068 / 55.444624 (-53.201556) | 1.880199 / 6.876477 (-4.996278) | 2.003757 / 2.142072 (-0.138315) | 0.870719 / 4.805227 (-3.934508) | 0.167626 / 6.500664 (-6.333039) | 0.062024 / 0.075469 (-0.013445) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.192969 / 1.841788 (-0.648819) | 14.830812 / 8.074308 (6.756504) | 14.331178 / 10.191392 (4.139786) | 0.199222 / 0.680424 (-0.481202) | 0.029292 / 0.534201 (-0.504909) | 0.440427 / 0.579283 (-0.138857) | 0.437893 / 0.434364 (0.003529) | 0.547155 / 0.540337 (0.006818) | 0.645255 / 1.386936 (-0.741681) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007465 / 0.011353 (-0.003888) | 0.005386 / 0.011008 (-0.005622) | 0.073609 / 0.038508 (0.035100) | 0.033550 / 0.023109 (0.010440) | 0.341730 / 0.275898 (0.065832) | 0.371518 / 0.323480 (0.048038) | 0.005986 / 0.007986 (-0.001999) | 0.004264 / 0.004328 (-0.000065) | 0.073749 / 0.004250 (0.069498) | 0.051452 / 0.037052 (0.014399) | 0.347385 / 0.258489 (0.088896) | 0.392284 / 0.293841 (0.098444) | 0.036981 / 0.128546 (-0.091566) | 0.012431 / 0.075646 (-0.063216) | 0.086421 / 0.419271 (-0.332850) | 0.053014 / 0.043533 (0.009481) | 0.336660 / 0.255139 (0.081521) | 0.359155 / 0.283200 (0.075956) | 0.107666 / 0.141683 (-0.034017) | 1.424324 / 1.452155 (-0.027830) | 1.543027 / 1.492716 (0.050310) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260862 / 0.018006 (0.242855) | 0.552057 / 0.000490 (0.551567) | 0.000449 / 0.000200 (0.000249) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029184 / 0.037411 (-0.008227) | 0.108799 / 0.014526 (0.094274) | 0.125136 / 0.176557 (-0.051421) | 0.157436 / 0.737135 (-0.579699) | 0.126333 / 0.296338 (-0.170005) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424054 / 0.215209 (0.208845) | 4.227847 / 2.077655 (2.150192) | 2.051102 / 1.504120 (0.546983) | 1.848651 / 1.541195 (0.307457) | 1.922728 / 1.468490 (0.454238) | 0.705903 / 4.584777 (-3.878874) | 3.800977 / 3.745712 (0.055265) | 2.099345 / 5.269862 (-3.170517) | 1.342919 / 4.565676 (-3.222757) | 0.086128 / 0.424275 (-0.338147) | 0.012539 / 0.007607 (0.004932) | 0.528767 / 0.226044 (0.302723) | 5.299989 / 2.268929 (3.031061) | 2.534280 / 55.444624 (-52.910345) | 2.229532 / 6.876477 (-4.646945) | 2.326704 / 2.142072 (0.184632) | 0.838533 / 4.805227 (-3.966694) | 0.168446 / 6.500664 (-6.332218) | 0.065158 / 0.075469 (-0.010311) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.250091 / 1.841788 (-0.591697) | 14.988651 / 8.074308 (6.914343) | 13.655103 / 10.191392 (3.463711) | 0.165079 / 0.680424 (-0.515345) | 0.017829 / 0.534201 (-0.516372) | 0.425903 / 0.579283 (-0.153381) | 0.419771 / 0.434364 (-0.014593) | 0.534309 / 0.540337 (-0.006028) | 0.635563 / 1.386936 (-0.751373) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7d17ccc9b9dde2d94803b1305226c5a58d916c5 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010569 / 0.011353 (-0.000784) | 0.005790 / 0.011008 (-0.005218) | 0.118626 / 0.038508 (0.080118) | 0.040455 / 0.023109 (0.017346) | 0.342309 / 0.275898 (0.066411) | 0.411828 / 0.323480 (0.088349) | 0.008824 / 0.007986 (0.000839) | 0.005426 / 0.004328 (0.001098) | 0.088740 / 0.004250 (0.084489) | 0.050042 / 0.037052 (0.012990) | 0.352350 / 0.258489 (0.093861) | 0.396030 / 0.293841 (0.102189) | 0.043385 / 0.128546 (-0.085162) | 0.013805 / 0.075646 (-0.061841) | 0.396489 / 0.419271 (-0.022783) | 0.055667 / 0.043533 (0.012135) | 0.336165 / 0.255139 (0.081026) | 0.372912 / 0.283200 (0.089713) | 0.115343 / 0.141683 (-0.026340) | 1.656412 / 1.452155 (0.204257) | 1.708993 / 1.492716 (0.216277) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011650 / 0.018006 (-0.006357) | 0.444415 / 0.000490 (0.443926) | 0.003985 / 0.000200 (0.003785) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031718 / 0.037411 (-0.005693) | 0.119640 / 0.014526 (0.105114) | 0.138519 / 0.176557 (-0.038037) | 0.188847 / 0.737135 (-0.548288) | 0.137891 / 0.296338 (-0.158448) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.447540 / 0.215209 (0.232331) | 4.577189 / 2.077655 (2.499534) | 2.106992 / 1.504120 (0.602872) | 1.889631 / 1.541195 (0.348436) | 1.972256 / 1.468490 (0.503766) | 0.778209 / 4.584777 (-3.806568) | 4.430279 / 3.745712 (0.684567) | 2.401226 / 5.269862 (-2.868636) | 1.481251 / 4.565676 (-3.084425) | 0.094244 / 0.424275 (-0.330031) | 0.013961 / 0.007607 (0.006354) | 0.570962 / 0.226044 (0.344917) | 5.809224 / 2.268929 (3.540295) | 2.663290 / 55.444624 (-52.781334) | 2.201228 / 6.876477 (-4.675249) | 2.319240 / 2.142072 (0.177168) | 0.938340 / 4.805227 (-3.866887) | 0.185546 / 6.500664 (-6.315118) | 0.069087 / 0.075469 (-0.006382) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.448597 / 1.841788 (-0.393191) | 17.188573 / 8.074308 (9.114265) | 16.197532 / 10.191392 (6.006140) | 0.194064 / 0.680424 (-0.486360) | 0.033694 / 0.534201 (-0.500507) | 0.507585 / 0.579283 (-0.071699) | 0.505470 / 0.434364 (0.071106) | 0.623270 / 0.540337 (0.082932) | 0.729964 / 1.386936 (-0.656972) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008529 / 0.011353 (-0.002824) | 0.005705 / 0.011008 (-0.005304) | 0.085594 / 0.038508 (0.047086) | 0.038377 / 0.023109 (0.015268) | 0.384221 / 0.275898 (0.108323) | 0.414678 / 0.323480 (0.091199) | 0.006195 / 0.007986 (-0.001791) | 0.004549 / 0.004328 (0.000221) | 0.082710 / 0.004250 (0.078460) | 0.054899 / 0.037052 (0.017847) | 0.404017 / 0.258489 (0.145528) | 0.450309 / 0.293841 (0.156468) | 0.040620 / 0.128546 (-0.087926) | 0.013774 / 0.075646 (-0.061872) | 0.099231 / 0.419271 (-0.320041) | 0.057183 / 0.043533 (0.013650) | 0.390806 / 0.255139 (0.135667) | 0.419334 / 0.283200 (0.136134) | 0.116449 / 0.141683 (-0.025234) | 1.709124 / 1.452155 (0.256969) | 1.812769 / 1.492716 (0.320052) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225206 / 0.018006 (0.207199) | 0.440530 / 0.000490 (0.440040) | 0.002982 / 0.000200 (0.002782) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032256 / 0.037411 (-0.005155) | 0.127086 / 0.014526 (0.112560) | 0.138133 / 0.176557 (-0.038424) | 0.176168 / 0.737135 (-0.560968) | 0.146072 / 0.296338 (-0.150267) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474374 / 0.215209 (0.259165) | 4.785106 / 2.077655 (2.707452) | 2.319344 / 1.504120 (0.815225) | 2.075239 / 1.541195 (0.534045) | 2.179231 / 1.468490 (0.710741) | 0.832124 / 4.584777 (-3.752653) | 4.376302 / 3.745712 (0.630590) | 3.966837 / 5.269862 (-1.303024) | 1.820230 / 4.565676 (-2.745446) | 0.100692 / 0.424275 (-0.323583) | 0.014748 / 0.007607 (0.007141) | 0.568702 / 0.226044 (0.342657) | 5.771548 / 2.268929 (3.502619) | 2.747431 / 55.444624 (-52.697193) | 2.448482 / 6.876477 (-4.427994) | 2.497206 / 2.142072 (0.355133) | 0.960842 / 4.805227 (-3.844385) | 0.192855 / 6.500664 (-6.307809) | 0.072494 / 0.075469 (-0.002975) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.474542 / 1.841788 (-0.367245) | 17.344804 / 8.074308 (9.270496) | 15.336082 / 10.191392 (5.144690) | 0.200134 / 0.680424 (-0.480290) | 0.020728 / 0.534201 (-0.513473) | 0.488854 / 0.579283 (-0.090429) | 0.490781 / 0.434364 (0.056418) | 0.626288 / 0.540337 (0.085950) | 0.721130 / 1.386936 (-0.665806) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cd7877892aa48a2470b01f52013390c54aca8a49 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008542 / 0.011353 (-0.002811) | 0.004624 / 0.011008 (-0.006384) | 0.100749 / 0.038508 (0.062241) | 0.029587 / 0.023109 (0.006478) | 0.298680 / 0.275898 (0.022782) | 0.359659 / 0.323480 (0.036180) | 0.007001 / 0.007986 (-0.000984) | 0.003398 / 0.004328 (-0.000930) | 0.078654 / 0.004250 (0.074404) | 0.036440 / 0.037052 (-0.000612) | 0.313245 / 0.258489 (0.054756) | 0.342776 / 0.293841 (0.048936) | 0.033195 / 0.128546 (-0.095352) | 0.011500 / 0.075646 (-0.064146) | 0.323957 / 0.419271 (-0.095314) | 0.039878 / 0.043533 (-0.003655) | 0.298189 / 0.255139 (0.043050) | 0.325488 / 0.283200 (0.042289) | 0.087276 / 0.141683 (-0.054407) | 1.480846 / 1.452155 (0.028691) | 1.507016 / 1.492716 (0.014300) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.189570 / 0.018006 (0.171564) | 0.406407 / 0.000490 (0.405917) | 0.003062 / 0.000200 (0.002862) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022865 / 0.037411 (-0.014546) | 0.096103 / 0.014526 (0.081578) | 0.106462 / 0.176557 (-0.070094) | 0.140888 / 0.737135 (-0.596247) | 0.108172 / 0.296338 (-0.188167) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415951 / 0.215209 (0.200742) | 4.172187 / 2.077655 (2.094532) | 1.842210 / 1.504120 (0.338090) | 1.636997 / 1.541195 (0.095802) | 1.706078 / 1.468490 (0.237588) | 0.695825 / 4.584777 (-3.888952) | 3.337354 / 3.745712 (-0.408358) | 1.877880 / 5.269862 (-3.391982) | 1.153882 / 4.565676 (-3.411794) | 0.082923 / 0.424275 (-0.341352) | 0.012814 / 0.007607 (0.005207) | 0.521793 / 0.226044 (0.295748) | 5.275980 / 2.268929 (3.007051) | 2.279230 / 55.444624 (-53.165394) | 1.941777 / 6.876477 (-4.934700) | 1.981297 / 2.142072 (-0.160775) | 0.809669 / 4.805227 (-3.995558) | 0.148753 / 6.500664 (-6.351911) | 0.064909 / 0.075469 (-0.010560) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226757 / 1.841788 (-0.615031) | 13.717354 / 8.074308 (5.643046) | 12.925885 / 10.191392 (2.734493) | 0.137926 / 0.680424 (-0.542498) | 0.028788 / 0.534201 (-0.505413) | 0.396654 / 0.579283 (-0.182630) | 0.401931 / 0.434364 (-0.032432) | 0.460515 / 0.540337 (-0.079823) | 0.537903 / 1.386936 (-0.849033) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006757 / 0.011353 (-0.004596) | 0.004474 / 0.011008 (-0.006534) | 0.076571 / 0.038508 (0.038063) | 0.027580 / 0.023109 (0.004471) | 0.348231 / 0.275898 (0.072333) | 0.398403 / 0.323480 (0.074923) | 0.005089 / 0.007986 (-0.002897) | 0.004676 / 0.004328 (0.000347) | 0.076444 / 0.004250 (0.072194) | 0.038508 / 0.037052 (0.001456) | 0.348515 / 0.258489 (0.090026) | 0.401456 / 0.293841 (0.107615) | 0.031630 / 0.128546 (-0.096916) | 0.011698 / 0.075646 (-0.063949) | 0.085805 / 0.419271 (-0.333467) | 0.041962 / 0.043533 (-0.001570) | 0.343415 / 0.255139 (0.088276) | 0.383001 / 0.283200 (0.099801) | 0.090231 / 0.141683 (-0.051452) | 1.488114 / 1.452155 (0.035960) | 1.569039 / 1.492716 (0.076323) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.261751 / 0.018006 (0.243745) | 0.411354 / 0.000490 (0.410865) | 0.015103 / 0.000200 (0.014903) | 0.000262 / 0.000054 (0.000208) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025423 / 0.037411 (-0.011988) | 0.101334 / 0.014526 (0.086808) | 0.108835 / 0.176557 (-0.067722) | 0.143995 / 0.737135 (-0.593140) | 0.111751 / 0.296338 (-0.184588) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446507 / 0.215209 (0.231298) | 4.461543 / 2.077655 (2.383888) | 2.104648 / 1.504120 (0.600528) | 1.895900 / 1.541195 (0.354706) | 1.985481 / 1.468490 (0.516991) | 0.699029 / 4.584777 (-3.885748) | 3.371064 / 3.745712 (-0.374648) | 1.883445 / 5.269862 (-3.386416) | 1.166150 / 4.565676 (-3.399527) | 0.082639 / 0.424275 (-0.341636) | 0.012605 / 0.007607 (0.004998) | 0.544860 / 0.226044 (0.318815) | 5.513223 / 2.268929 (3.244294) | 2.570661 / 55.444624 (-52.873963) | 2.206066 / 6.876477 (-4.670411) | 2.256346 / 2.142072 (0.114273) | 0.801142 / 4.805227 (-4.004085) | 0.150412 / 6.500664 (-6.350252) | 0.067742 / 0.075469 (-0.007727) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303477 / 1.841788 (-0.538310) | 14.287767 / 8.074308 (6.213458) | 13.525563 / 10.191392 (3.334171) | 0.148202 / 0.680424 (-0.532222) | 0.016868 / 0.534201 (-0.517333) | 0.380729 / 0.579283 (-0.198555) | 0.388177 / 0.434364 (-0.046187) | 0.477410 / 0.540337 (-0.062927) | 0.569343 / 1.386936 (-0.817593) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c18b77113da3f2e31af0570ec119877ca2a390 \"CML watermark\")\n",
"> PS: I think it would be a good idea to add links to the Guide pages for better discoverability and to somewhat \"justify their presence in the docs\" (from the tutorial/how-to pages to the guides; some guides are not referenced at all)\r\n\r\nJust merged #5485, which references this new doc! Will look for other pages in the docs where it'd make sense to add them :)"
] | 2023-01-05T18:12:17 | 2023-02-01T18:11:45 | 2023-02-01T16:36:01 | MEMBER | null | false | {
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} | Added `ds.to_iterable()` to get an iterable dataset from a map-style arrow dataset.
It also has a `num_shards` argument to split the dataset before converting to an iterable dataset. Sharding is important to enable efficient shuffling and parallel loading of iterable datasets.
TODO:
- [x] tests
- [x] docs
Fix https://github.com/huggingface/datasets/issues/5265 | {
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https://api.github.com/repos/huggingface/datasets/issues/5409 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5409/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5409/comments | https://api.github.com/repos/huggingface/datasets/issues/5409/events | https://github.com/huggingface/datasets/pull/5409 | 1,520,374,219 | PR_kwDODunzps5Gs3nL | 5,409 | Fix deprecation warning when use_auth_token passed to download_and_prepare | {
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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) | 0.008627 / 0.011353 (-0.002726) | 0.004572 / 0.011008 (-0.006436) | 0.099653 / 0.038508 (0.061145) | 0.030010 / 0.023109 (0.006901) | 0.300492 / 0.275898 (0.024594) | 0.360443 / 0.323480 (0.036963) | 0.007125 / 0.007986 (-0.000860) | 0.003431 / 0.004328 (-0.000897) | 0.078103 / 0.004250 (0.073852) | 0.036884 / 0.037052 (-0.000168) | 0.312289 / 0.258489 (0.053800) | 0.345795 / 0.293841 (0.051954) | 0.034001 / 0.128546 (-0.094545) | 0.011405 / 0.075646 (-0.064242) | 0.321258 / 0.419271 (-0.098013) | 0.040591 / 0.043533 (-0.002942) | 0.301114 / 0.255139 (0.045975) | 0.337226 / 0.283200 (0.054027) | 0.088055 / 0.141683 (-0.053628) | 1.451892 / 1.452155 (-0.000263) | 1.494881 / 1.492716 (0.002164) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.186749 / 0.018006 (0.168743) | 0.414089 / 0.000490 (0.413600) | 0.002475 / 0.000200 (0.002275) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022413 / 0.037411 (-0.014999) | 0.097547 / 0.014526 (0.083021) | 0.104196 / 0.176557 (-0.072361) | 0.139819 / 0.737135 (-0.597316) | 0.108345 / 0.296338 (-0.187994) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424750 / 0.215209 (0.209541) | 4.261513 / 2.077655 (2.183859) | 2.150888 / 1.504120 (0.646768) | 1.935925 / 1.541195 (0.394730) | 1.867456 / 1.468490 (0.398966) | 0.694384 / 4.584777 (-3.890393) | 3.370539 / 3.745712 (-0.375173) | 1.886714 / 5.269862 (-3.383148) | 1.256542 / 4.565676 (-3.309135) | 0.082841 / 0.424275 (-0.341434) | 0.012344 / 0.007607 (0.004737) | 0.529801 / 0.226044 (0.303757) | 5.315438 / 2.268929 (3.046509) | 2.460517 / 55.444624 (-52.984107) | 2.261840 / 6.876477 (-4.614637) | 2.338710 / 2.142072 (0.196638) | 0.818433 / 4.805227 (-3.986794) | 0.150571 / 6.500664 (-6.350093) | 0.066524 / 0.075469 (-0.008945) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253086 / 1.841788 (-0.588702) | 13.862614 / 8.074308 (5.788306) | 14.145149 / 10.191392 (3.953757) | 0.165867 / 0.680424 (-0.514557) | 0.029269 / 0.534201 (-0.504932) | 0.397579 / 0.579283 (-0.181704) | 0.401113 / 0.434364 (-0.033251) | 0.463269 / 0.540337 (-0.077068) | 0.551494 / 1.386936 (-0.835442) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006610 / 0.011353 (-0.004743) | 0.004583 / 0.011008 (-0.006425) | 0.096680 / 0.038508 (0.058172) | 0.027352 / 0.023109 (0.004242) | 0.409292 / 0.275898 (0.133394) | 0.445790 / 0.323480 (0.122310) | 0.004987 / 0.007986 (-0.002999) | 0.003462 / 0.004328 (-0.000866) | 0.074472 / 0.004250 (0.070221) | 0.037875 / 0.037052 (0.000822) | 0.411496 / 0.258489 (0.153007) | 0.454721 / 0.293841 (0.160880) | 0.031884 / 0.128546 (-0.096662) | 0.011682 / 0.075646 (-0.063964) | 0.318831 / 0.419271 (-0.100441) | 0.041781 / 0.043533 (-0.001752) | 0.411247 / 0.255139 (0.156108) | 0.436215 / 0.283200 (0.153016) | 0.090021 / 0.141683 (-0.051662) | 1.492385 / 1.452155 (0.040231) | 1.565182 / 1.492716 (0.072465) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221263 / 0.018006 (0.203257) | 0.399074 / 0.000490 (0.398584) | 0.000405 / 0.000200 (0.000205) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025139 / 0.037411 (-0.012272) | 0.097952 / 0.014526 (0.083426) | 0.106078 / 0.176557 (-0.070479) | 0.143231 / 0.737135 (-0.593904) | 0.109177 / 0.296338 (-0.187161) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441668 / 0.215209 (0.226459) | 4.403247 / 2.077655 (2.325592) | 2.072749 / 1.504120 (0.568629) | 1.866248 / 1.541195 (0.325053) | 1.906418 / 1.468490 (0.437927) | 0.697234 / 4.584777 (-3.887543) | 3.412016 / 3.745712 (-0.333696) | 1.852572 / 5.269862 (-3.417289) | 1.168270 / 4.565676 (-3.397407) | 0.082132 / 0.424275 (-0.342144) | 0.013191 / 0.007607 (0.005584) | 0.548932 / 0.226044 (0.322888) | 5.503891 / 2.268929 (3.234962) | 2.539784 / 55.444624 (-52.904841) | 2.181292 / 6.876477 (-4.695184) | 2.242197 / 2.142072 (0.100125) | 0.804027 / 4.805227 (-4.001200) | 0.151649 / 6.500664 (-6.349015) | 0.067088 / 0.075469 (-0.008381) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296267 / 1.841788 (-0.545520) | 13.986484 / 8.074308 (5.912176) | 13.440705 / 10.191392 (3.249313) | 0.140787 / 0.680424 (-0.539637) | 0.017132 / 0.534201 (-0.517069) | 0.381899 / 0.579283 (-0.197384) | 0.385535 / 0.434364 (-0.048829) | 0.439957 / 0.540337 (-0.100380) | 0.532980 / 1.386936 (-0.853956) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2023-01-05T09:10:58 | 2023-01-06T11:06:16 | 2023-01-06T10:59:13 | MEMBER | null | false | {
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} | The `DatasetBuilder.download_and_prepare` argument `use_auth_token` was deprecated in:
- #5302
However, `use_auth_token` is still passed to `download_and_prepare` in our built-in `io` readers (csv, json, parquet,...).
This PR fixes it, so that no deprecation warning is raised.
Fix #5407. | {
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"Hi ! On macos I tried with\r\n- py 3.9.11\r\n- datasets 2.8.0\r\n- transformers 4.25.1\r\n- dill 0.3.4\r\n\r\nand I was able to hash `prepare_dataset` correctly:\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\nHasher.hash(prepare_dataset)\r\n```\r\n\r\nWhat version of transformers do you have ? Can you try to call `Hasher.hash` on the the tokenizer and the feature extractor to see which one can't be hashed ?",
"Thanks for your prompt reply.\r\n\r\nI update datasets version to 2.8.0 and the warning is gong."
] | 2023-01-05T01:59:59 | 2023-01-06T13:22:19 | 2023-01-06T13:22:18 | NONE | null | null | null | ### Describe the bug
I follow the [blog post](https://huggingface.co./blog/fine-tune-whisper#building-a-demo) to finetune a Cantonese transcribe model.
When using map function to prepare dataset, following warning pop out:
`common_voice = common_voice.map(prepare_dataset,
remove_columns=common_voice.column_names["train"], num_proc=1)`
> Parameter 'function'=<function prepare_dataset at 0x000001D1D9D79A60> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
I read https://github.com/huggingface/datasets/issues/4521 and https://github.com/huggingface/datasets/issues/3178 but cannot solve the issue.
### Steps to reproduce the bug
```python
from datasets import load_dataset, DatasetDict
common_voice = DatasetDict()
common_voice["train"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK",
split="train+validation")
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK",
split="test")
common_voice = common_voice.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes"])
from transformers import WhisperFeatureExtractor, WhisperTokenizer, WhisperProcessor
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="chinese", task="transcribe")
processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="chinese", task="transcribe")
from datasets import Audio
common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000))
def prepare_dataset(batch):
# load and resample audio data from 48 to 16kHz
audio = batch["audio"]
# compute log-Mel input features from input audio array
batch["input_features"] = feature_extractor(audio["array"],
sampling_rate=audio["sampling_rate"]).input_features[0]
# encode target text to label ids
batch["labels"] = tokenizer(batch["sentence"]).input_ids
return batch
common_voice = common_voice.map(prepare_dataset,
remove_columns=common_voice.column_names["train"], num_proc=1)
```
### Expected behavior
Should be no warning shown.
### Environment info
- `datasets` version: 2.7.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
- dill version: 0.3.4
- multiprocess version: 0.70.12.2 | {
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"Thanks for reporting @msummerfield. We are fixing it."
] | 2023-01-05T00:43:17 | 2023-01-06T10:59:14 | 2023-01-06T10:59:14 | NONE | null | null | null | ### Describe the bug
Calling `Datasets.from_sql()` generates a warning:
`.../site-packages/datasets/builder.py:712: FutureWarning: 'use_auth_token' was deprecated in version 2.7.1 and will be removed in 3.0.0. Pass 'use_auth_token' to the initializer/'load_dataset_builder' instead.`
### Steps to reproduce the bug
Any valid call to `Datasets.from_sql()` will produce the deprecation warning.
### Expected behavior
No warning.
The fix should be simply to remove the parameter `use_auth_token` from the call to `builder.download_and_prepare()` at line 43 of `io/sql.py` (it is set to `None` anyway, and is not needed).
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-4.15.0-169-generic-x86_64-with-glibc2.27
- Python version: 3.9.15
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
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"I still get this error on 2.9.0\r\n<img width=\"1925\" alt=\"image\" src=\"https://user-images.githubusercontent.com/7208470/215597359-2f253c76-c472-4612-8099-d3a74d16eb29.png\">\r\n",
"Hi ! I just tested locally and or colab and it works fine for 2.9 on `sst2`.\r\n\r\nAlso the code that is shown in your stack trace is not present in the 2.9 source code - so I'm wondering how you installed `datasets` that could cause this ? (you can check by searching for `[0:{label_ids[-1] + 1}]` in the [2.9 codebase](https://github.dev/huggingface/datasets/tree/b5672a956d5de864e6f5550e493527d962d6ae55) - it doesn't find anything)\r\n\r\nAnyway you can try uninstalling `datasets` and install it again",
"For what it's worth, I've also gotten this error on 2.9.0, and I've tried uninstalling an reinstalling\r\n\r\n\r\nI'm very new to this package (I was following this tutorial: https://huggingface.co./docs/transformers/training), so there's a good chance I was doing something wrong 😅 but thought I'd pass along the feedback",
"@ntrpnr @mtwichel Did you install `datasets` with conda ?\r\n\r\nI suspect that `datasets` 2.9 on conda still have this issue for some reason. When I install `datasets` with `pip` I don't have this error.",
"> @ntrpnr @mtwichel Did you install datasets with conda ?\r\n\r\nI did yeah, I wonder if that's the issue",
"I just checked on conda at https://anaconda.org/HuggingFace/datasets/files\r\n\r\nand everything looks fine, I got\r\n```python\r\n\r\nf\"ClassLabel expected a value for all label ids [0:{int(label_ids[-1]) + 1}] but some ids are missing.\"\r\n```\r\nas expected in features.py line 1760 (notice the \"int()\") to not have the TypeError.\r\n\r\nFrom where on conda did you install `datasets` ? You should use the `HuggingFace` official channel\r\n\r\nedit: the conda-forge one [here](https://anaconda.org/conda-forge/datasets/files) seems ok as well",
"Could you also try this in your notebook ? In case your python kernel doesn't match the `pip` environment in your shell\r\n```python\r\nimport datasets; datasets.__version__\r\n```\r\nand\r\n```\r\n!which python\r\n```\r\n```python\r\nimport sys; sys.executable\r\n```",
"Mmmm, just a potential clue:\r\n\r\nWhere are you running your Python code? Is it the Spyder IDE?\r\n\r\nI have recently seen some users reporting conflicting Python environments while using Spyder...\r\n\r\nMaybe related:\r\n- #5487",
"Other potential clue:\r\n- Had you already imported `datasets` before pip-updating it? You should first update datasets, before importing it. Otherwise, you need to restart the kernel after updating it.",
"I installed `datasets` with Conda using `conda install datasets` and got this issue.\r\n\r\nThen I tried to reinstall using\r\n`\r\nconda install -c huggingface -c conda-forge datasets\r\n`\r\nThe issue is now fixed.",
"I'm still getting this error on 2.13.0"
] | 2023-01-04T15:10:04 | 2023-06-21T18:45:38 | null | MEMBER | null | null | null | `datasets` 2.6.1 and 2.7.0 started to stop supporting datasets like IMDB, ConLL or MNIST datasets.
When loading a dataset using 2.6.1 or 2.7.0, you may this error when loading certain datasets:
```python
TypeError: can only concatenate str (not "int") to str
```
This is because we started to update the metadata of those datasets to a format that is not supported in 2.6.1 and 2.7.0
This change is required or those datasets won't be supported by the Hugging Face Hub.
Therefore if you encounter this error or if you're using `datasets` 2.6.1 or 2.7.0, we encourage you to update to a newer version.
For example, versions 2.6.2 and 2.7.1 patch this issue.
```python
pip install -U datasets
```
All the datasets affected are the ones with a ClassLabel feature type and YAML "dataset_info" metadata. More info [here](https://github.com/huggingface/datasets/issues/5275).
We apologize for the inconvenience. | {
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"Hi @Breakend,\r\n\r\nIndeed, the attribute `size_in_bytes` refers to the size of the entire dataset configuration, for all splits (size of downloaded files + Arrow files), not the specific split.\r\nThis is also the case for `download_size` (downloaded files) and `dataset_size` (Arrow files).\r\n\r\nThe size of the Arrow files for a specific split can be accessed: e.g. size of the \"test\" split only\r\n```python\r\nds[\"train\"].info.splits[\"test\"].num_bytes\r\n```\r\n\r\nI agree this is confusing and maybe we should improve it."
] | 2023-01-03T20:25:48 | 2023-01-04T09:22:59 | null | NONE | null | null | null | ### Describe the bug
Hi, it looks like whenever you pull a dataset and get size_in_bytes, it returns the same size for all splits (and that size is the combined size of all splits). It seems like this shouldn't be the intended behavior since it is misleading. Here's an example:
```
>>> from datasets import load_dataset
>>> x = load_dataset("glue", "wnli")
Found cached dataset glue (/Users/breakend/.cache/huggingface/datasets/glue/wnli/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1097.70it/s]
>>> x["train"].size_in_bytes
186159
>>> x["validation"].size_in_bytes
186159
>>> x["test"].size_in_bytes
186159
>>>
```
### Steps to reproduce the bug
```
>>> from datasets import load_dataset
>>> x = load_dataset("glue", "wnli")
>>> x["train"].size_in_bytes
186159
>>> x["validation"].size_in_bytes
186159
>>> x["test"].size_in_bytes
186159
```
### Expected behavior
The expected behavior is that it should return the separate sizes for all splits.
### Environment info
- `datasets` version: 2.7.1
- Platform: macOS-12.5-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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"Hi, I made my version : https://huggingface.co./datasets/tasksource/bigbench"
] | 2023-01-03T15:37:57 | 2023-02-09T20:30:26 | null | MEMBER | null | null | null | ### Feature request
Ideally, it would be nice to have a maintained PyPI package for `bigbench`.
### Motivation
We'd like to allow anyone to access, explore and use any task.
### Your contribution
@lhoestq has opened an issue in their repo:
- https://github.com/google/BIG-bench/issues/906 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Thanks for the docs fix for consistency.\r\n> \r\n> Again for consistency, it would be nice to make the same fix across all the docs, e.g.\r\n> \r\n> https://github.com/huggingface/datasets/blob/310cdddd1c43f9658de172b85b6509d07d5e31a1/docs/source/image_classification.mdx?plain=1#L41\r\n\r\nExcellent point!",
"@albertvillanova Should be all of them now :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008776 / 0.011353 (-0.002576) | 0.004534 / 0.011008 (-0.006474) | 0.101921 / 0.038508 (0.063413) | 0.029995 / 0.023109 (0.006886) | 0.307180 / 0.275898 (0.031282) | 0.371001 / 0.323480 (0.047521) | 0.007089 / 0.007986 (-0.000896) | 0.003474 / 0.004328 (-0.000855) | 0.079498 / 0.004250 (0.075248) | 0.036522 / 0.037052 (-0.000531) | 0.311729 / 0.258489 (0.053240) | 0.349861 / 0.293841 (0.056020) | 0.033815 / 0.128546 (-0.094731) | 0.011435 / 0.075646 (-0.064211) | 0.322924 / 0.419271 (-0.096347) | 0.040981 / 0.043533 (-0.002552) | 0.306174 / 0.255139 (0.051035) | 0.331979 / 0.283200 (0.048780) | 0.091293 / 0.141683 (-0.050389) | 1.480935 / 1.452155 (0.028780) | 1.522022 / 1.492716 (0.029306) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195053 / 0.018006 (0.177047) | 0.424898 / 0.000490 (0.424408) | 0.003869 / 0.000200 (0.003669) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024323 / 0.037411 (-0.013088) | 0.098061 / 0.014526 (0.083535) | 0.105770 / 0.176557 (-0.070787) | 0.145799 / 0.737135 (-0.591336) | 0.109109 / 0.296338 (-0.187230) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420434 / 0.215209 (0.205225) | 4.194781 / 2.077655 (2.117126) | 2.030498 / 1.504120 (0.526378) | 1.885314 / 1.541195 (0.344120) | 1.996485 / 1.468490 (0.527995) | 0.708540 / 4.584777 (-3.876237) | 3.400694 / 3.745712 (-0.345018) | 2.888704 / 5.269862 (-2.381157) | 1.578100 / 4.565676 (-2.987577) | 0.082150 / 0.424275 (-0.342125) | 0.012277 / 0.007607 (0.004669) | 0.527312 / 0.226044 (0.301268) | 5.289566 / 2.268929 (3.020637) | 2.369997 / 55.444624 (-53.074628) | 2.040365 / 6.876477 (-4.836112) | 2.298857 / 2.142072 (0.156785) | 0.808446 / 4.805227 (-3.996781) | 0.149355 / 6.500664 (-6.351309) | 0.065993 / 0.075469 (-0.009477) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.231829 / 1.841788 (-0.609959) | 13.874762 / 8.074308 (5.800454) | 13.464379 / 10.191392 (3.272987) | 0.151105 / 0.680424 (-0.529319) | 0.028689 / 0.534201 (-0.505512) | 0.398720 / 0.579283 (-0.180564) | 0.402108 / 0.434364 (-0.032256) | 0.463426 / 0.540337 (-0.076912) | 0.541919 / 1.386936 (-0.845017) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006979 / 0.011353 (-0.004373) | 0.004723 / 0.011008 (-0.006285) | 0.099172 / 0.038508 (0.060664) | 0.027970 / 0.023109 (0.004861) | 0.415096 / 0.275898 (0.139198) | 0.455916 / 0.323480 (0.132437) | 0.005950 / 0.007986 (-0.002036) | 0.003423 / 0.004328 (-0.000906) | 0.075512 / 0.004250 (0.071262) | 0.040894 / 0.037052 (0.003842) | 0.419810 / 0.258489 (0.161321) | 0.461913 / 0.293841 (0.168072) | 0.033014 / 0.128546 (-0.095532) | 0.011613 / 0.075646 (-0.064033) | 0.320983 / 0.419271 (-0.098289) | 0.049902 / 0.043533 (0.006369) | 0.426378 / 0.255139 (0.171239) | 0.445594 / 0.283200 (0.162394) | 0.098978 / 0.141683 (-0.042705) | 1.485724 / 1.452155 (0.033570) | 1.563978 / 1.492716 (0.071262) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232137 / 0.018006 (0.214131) | 0.432785 / 0.000490 (0.432296) | 0.006173 / 0.000200 (0.005973) | 0.000085 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024924 / 0.037411 (-0.012487) | 0.102878 / 0.014526 (0.088352) | 0.107976 / 0.176557 (-0.068581) | 0.143581 / 0.737135 (-0.593554) | 0.111644 / 0.296338 (-0.184694) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.490902 / 0.215209 (0.275693) | 4.914060 / 2.077655 (2.836405) | 2.569465 / 1.504120 (1.065345) | 2.346872 / 1.541195 (0.805677) | 2.412047 / 1.468490 (0.943557) | 0.704975 / 4.584777 (-3.879802) | 3.443669 / 3.745712 (-0.302043) | 3.172055 / 5.269862 (-2.097807) | 1.332152 / 4.565676 (-3.233525) | 0.083023 / 0.424275 (-0.341252) | 0.012699 / 0.007607 (0.005092) | 0.592511 / 0.226044 (0.366466) | 5.916376 / 2.268929 (3.647448) | 3.028472 / 55.444624 (-52.416152) | 2.691159 / 6.876477 (-4.185318) | 2.786132 / 2.142072 (0.644060) | 0.814045 / 4.805227 (-3.991182) | 0.156630 / 6.500664 (-6.344034) | 0.071330 / 0.075469 (-0.004139) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277936 / 1.841788 (-0.563852) | 14.331367 / 8.074308 (6.257059) | 13.685694 / 10.191392 (3.494302) | 0.138915 / 0.680424 (-0.541509) | 0.016844 / 0.534201 (-0.517357) | 0.390307 / 0.579283 (-0.188976) | 0.385207 / 0.434364 (-0.049157) | 0.448128 / 0.540337 (-0.092210) | 0.532609 / 1.386936 (-0.854327) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2023-01-03T14:26:32 | 2023-01-03T15:06:18 | 2023-01-03T14:59:01 | CONTRIBUTOR | null | false | {
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} | This PR updates a code example for consistency across the docs based on [feedback from this comment](https://github.com/huggingface/transformers/pull/20925/files/9fda31634d203a47d3212e4e8d43d3267faf9808#r1058769500):
"In terms of style we usually stay away from one-letter imports like this (even if the community uses them) as they are not always known by beginners and one letter is very undescriptive. Here it wouldn't change anything to use albumentations instead of A."
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"`load_from_disk` must be used on datasets saved using `save_to_disk`: they correspond to fully serialized datasets including their state.\r\n\r\nOn the other hand, `download_and_prepare` just downloads the raw data and convert them to arrow (or parquet if you want). We are working on allowing you to reload a dataset saved on S3 with `download_and_prepare` using `load_dataset` in #5281 \r\n\r\nFor now I'd encourage you to keep using `save_to_disk`",
"Thanks, I'll follow that issue. \r\n\r\nI was following the [cloud storage](https://huggingface.co./docs/datasets/filesystems) docs section and perhaps I'm missing some part of the flow; start with `load_dataset_builder` + `download_and_prepare`. You say I need an explicit `save_to_disk` but what object needs to be saved? the builder? is that related to the other issue?",
"Right now `load_dataset_builder` + `download_and_prepare` is to be used with tools like dask or spark, but `load_dataset` will support private cloud storage soon as well so you'll be able to reload the dataset with `datasets`.\r\n\r\nRight now the only function that can load a dataset from a cloud storage is `load_from_disk`, that must be used with a dataset serialized with `save_to_disk`."
] | 2023-01-03T13:39:59 | 2023-01-04T17:23:57 | null | NONE | null | null | null | ### Describe the bug
Using `load_dataset_builder` to create a builder, run `download_and_prepare` do upload it to S3. However when trying to load it, there are missing `state.json` files. Complete example:
```python
from aiobotocore.session import AioSession as Session
from datasets import load_from_disk, load_datase, load_dataset_builder
import s3fs
storage_options = {"session": Session()}
fs = s3fs.S3FileSystem(**storage_options)
output_dir = "s3://bucket/imdb"
builder = load_dataset_builder("imdb")
builder.download_and_prepare(output_dir, storage_options=storage_options)
load_from_disk(output_dir, fs=fs) # ERROR
# [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json'
```
As a comparison, if you use the non lazy `load_dataset`, it works and the S3 folder has different structure + state.json files. Example:
```python
from aiobotocore.session import AioSession as Session
from datasets import load_from_disk, load_dataset, load_dataset_builder
import s3fs
storage_options = {"session": Session()}
fs = s3fs.S3FileSystem(**storage_options)
output_dir = "s3://bucket/imdb"
dataset = load_dataset("imdb",)
dataset.save_to_disk(output_dir, fs=fs)
load_from_disk(output_dir, fs=fs) # WORKS
```
You still want the 1st option for the laziness and the parquet conversion. Thanks!
### Steps to reproduce the bug
```python
from aiobotocore.session import AioSession as Session
from datasets import load_from_disk, load_datase, load_dataset_builder
import s3fs
storage_options = {"session": Session()}
fs = s3fs.S3FileSystem(**storage_options)
output_dir = "s3://bucket/imdb"
builder = load_dataset_builder("imdb")
builder.download_and_prepare(output_dir, storage_options=storage_options)
load_from_disk(output_dir, fs=fs) # ERROR
# [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json'
```
BTW, you need the AioSession as s3fs is now based on aiobotocore, see https://github.com/fsspec/s3fs/issues/385.
### Expected behavior
Expected to be able to load the dataset from S3.
### Environment info
```
s3fs 2022.11.0
s3transfer 0.6.0
datasets 2.8.0
aiobotocore 2.4.2
boto3 1.24.59
botocore 1.27.59
```
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5401). All of your documentation changes will be reflected on that endpoint.",
"Cool thanks !\r\n\r\nSpark DataFrame are usually quite big, and I believe here `from_spark` would load everything in the driver node's RAM, which is quite limiting. Same for `to_spark` which would load everything in the driver node's RAM before sending the data to the executor. Maybe we can mention this in the docstring ?\r\n\r\nTo transfer big datasets from/into the HF ecosystem using Spark maybe we can just make sure that `pyspark` can read/write to the HF Hub, and that `datasets` can read from HDFS/S3/etc.",
"Yes @lhoestq , consider this as a first integration of the Datasets library with Spark.\r\n- This PR implements the basic conversion between both.\r\n - And yes, we are using the Spark's `pandas` API (that uses `pyarrow` under the hood): everything is transferred to the driver.\r\n - Note that we are converting from/to a Datasets dataset: this is not distributed\r\n\r\nThe next step is to support the integration of the HF Hub with Spark, that I think should be done using `hffs`.",
"Thinking more about it I don't really see how those two methods help in practice, since one can already do `datasets` <-> pandas <-> spark and those two methods don't add value over this.\r\n\r\nHowever I think it can be good documentation to explain that it's possible to do it and it's super simple"
] | 2023-01-03T09:57:40 | 2023-01-05T14:21:33 | null | MEMBER | null | false | {
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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) | 0.008638 / 0.011353 (-0.002715) | 0.004565 / 0.011008 (-0.006444) | 0.098984 / 0.038508 (0.060476) | 0.030118 / 0.023109 (0.007009) | 0.321779 / 0.275898 (0.045881) | 0.366905 / 0.323480 (0.043426) | 0.006931 / 0.007986 (-0.001055) | 0.004728 / 0.004328 (0.000399) | 0.078358 / 0.004250 (0.074108) | 0.037755 / 0.037052 (0.000702) | 0.312694 / 0.258489 (0.054205) | 0.351781 / 0.293841 (0.057940) | 0.033266 / 0.128546 (-0.095280) | 0.011397 / 0.075646 (-0.064250) | 0.323501 / 0.419271 (-0.095771) | 0.040779 / 0.043533 (-0.002754) | 0.303533 / 0.255139 (0.048394) | 0.340940 / 0.283200 (0.057740) | 0.088701 / 0.141683 (-0.052982) | 1.472058 / 1.452155 (0.019904) | 1.529535 / 1.492716 (0.036818) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191803 / 0.018006 (0.173797) | 0.409773 / 0.000490 (0.409283) | 0.002704 / 0.000200 (0.002504) | 0.000217 / 0.000054 (0.000163) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023520 / 0.037411 (-0.013891) | 0.096967 / 0.014526 (0.082441) | 0.107911 / 0.176557 (-0.068646) | 0.146425 / 0.737135 (-0.590710) | 0.109025 / 0.296338 (-0.187314) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418565 / 0.215209 (0.203356) | 4.183429 / 2.077655 (2.105774) | 1.886534 / 1.504120 (0.382414) | 1.689015 / 1.541195 (0.147820) | 1.710757 / 1.468490 (0.242267) | 0.693211 / 4.584777 (-3.891566) | 3.380062 / 3.745712 (-0.365650) | 2.619910 / 5.269862 (-2.649952) | 1.457512 / 4.565676 (-3.108164) | 0.082421 / 0.424275 (-0.341854) | 0.012126 / 0.007607 (0.004519) | 0.525249 / 0.226044 (0.299205) | 5.244541 / 2.268929 (2.975613) | 2.305908 / 55.444624 (-53.138717) | 1.945298 / 6.876477 (-4.931178) | 2.015618 / 2.142072 (-0.126455) | 0.816746 / 4.805227 (-3.988481) | 0.148325 / 6.500664 (-6.352339) | 0.063939 / 0.075469 (-0.011530) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255790 / 1.841788 (-0.585998) | 13.433219 / 8.074308 (5.358911) | 13.916957 / 10.191392 (3.725565) | 0.153468 / 0.680424 (-0.526956) | 0.028722 / 0.534201 (-0.505479) | 0.398245 / 0.579283 (-0.181038) | 0.399067 / 0.434364 (-0.035296) | 0.457525 / 0.540337 (-0.082812) | 0.542391 / 1.386936 (-0.844545) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006411 / 0.011353 (-0.004942) | 0.004552 / 0.011008 (-0.006456) | 0.098036 / 0.038508 (0.059527) | 0.026532 / 0.023109 (0.003422) | 0.412270 / 0.275898 (0.136372) | 0.442771 / 0.323480 (0.119291) | 0.004891 / 0.007986 (-0.003094) | 0.003488 / 0.004328 (-0.000841) | 0.075437 / 0.004250 (0.071186) | 0.036228 / 0.037052 (-0.000824) | 0.413246 / 0.258489 (0.154757) | 0.453546 / 0.293841 (0.159705) | 0.031054 / 0.128546 (-0.097492) | 0.011589 / 0.075646 (-0.064058) | 0.318477 / 0.419271 (-0.100794) | 0.041075 / 0.043533 (-0.002457) | 0.411182 / 0.255139 (0.156043) | 0.436991 / 0.283200 (0.153792) | 0.086563 / 0.141683 (-0.055120) | 1.511948 / 1.452155 (0.059793) | 1.570925 / 1.492716 (0.078208) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200510 / 0.018006 (0.182504) | 0.403450 / 0.000490 (0.402960) | 0.000397 / 0.000200 (0.000197) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023950 / 0.037411 (-0.013461) | 0.097334 / 0.014526 (0.082808) | 0.105228 / 0.176557 (-0.071328) | 0.137699 / 0.737135 (-0.599436) | 0.107063 / 0.296338 (-0.189275) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474420 / 0.215209 (0.259211) | 4.748212 / 2.077655 (2.670557) | 2.407318 / 1.504120 (0.903198) | 2.198949 / 1.541195 (0.657755) | 2.220377 / 1.468490 (0.751887) | 0.704022 / 4.584777 (-3.880755) | 3.366128 / 3.745712 (-0.379584) | 1.839454 / 5.269862 (-3.430408) | 1.151183 / 4.565676 (-3.414493) | 0.082818 / 0.424275 (-0.341457) | 0.012765 / 0.007607 (0.005158) | 0.571913 / 0.226044 (0.345868) | 5.722544 / 2.268929 (3.453615) | 2.858279 / 55.444624 (-52.586346) | 2.513479 / 6.876477 (-4.362998) | 2.574227 / 2.142072 (0.432154) | 0.803282 / 4.805227 (-4.001945) | 0.150603 / 6.500664 (-6.350061) | 0.066594 / 0.075469 (-0.008875) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.301161 / 1.841788 (-0.540627) | 13.580745 / 8.074308 (5.506436) | 13.301551 / 10.191392 (3.110159) | 0.141424 / 0.680424 (-0.539000) | 0.016579 / 0.534201 (-0.517622) | 0.380726 / 0.579283 (-0.198557) | 0.383011 / 0.434364 (-0.051353) | 0.438717 / 0.540337 (-0.101620) | 0.527085 / 1.386936 (-0.859851) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2023-01-03T07:42:37 | 2023-01-06T10:42:44 | 2023-01-06T10:35:44 | MEMBER | null | false | {
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} | [] | closed | false | null | [] | null | [] | 2023-01-01T13:00:11 | 2023-01-02T07:21:52 | 2023-01-02T07:21:52 | NONE | null | null | null | ### Describe the bug
While trying to upload my image dataset of a CSV file type to huggingface by running the below code. The dataset consists of a little over 100k of image-caption pairs
### Steps to reproduce the bug
```
df = pd.read_csv('x.csv', encoding='utf-8-sig')
features = Features({
'link': Image(decode=True),
'caption': Value(dtype='string'),
})
#make sure u r logged in to HF
ds = Dataset.from_pandas(df, features=features)
ds.features
ds.push_to_hub("x/x")
```
I got the below error and It always stops at the same progress
```
100%|██████████| 4/4 [23:53<00:00, 358.48s/ba]
100%|██████████| 4/4 [24:37<00:00, 369.47s/ba]%|▍ | 1/22 [00:06<02:09, 6.16s/it]
100%|██████████| 4/4 [25:00<00:00, 375.15s/ba]%|▉ | 2/22 [25:54<2:36:15, 468.80s/it]
100%|██████████| 4/4 [24:53<00:00, 373.29s/ba]%|█▎ | 3/22 [51:01<4:07:07, 780.39s/it]
100%|██████████| 4/4 [24:01<00:00, 360.34s/ba]%|█▊ | 4/22 [1:17:00<5:04:07, 1013.74s/it]
100%|██████████| 4/4 [23:59<00:00, 359.91s/ba]%|██▎ | 5/22 [1:41:07<5:24:06, 1143.90s/it]
100%|██████████| 4/4 [24:16<00:00, 364.06s/ba]%|██▋ | 6/22 [2:05:14<5:29:15, 1234.74s/it]
100%|██████████| 4/4 [25:24<00:00, 381.10s/ba]%|███▏ | 7/22 [2:29:38<5:25:52, 1303.52s/it]
100%|██████████| 4/4 [25:24<00:00, 381.24s/ba]%|███▋ | 8/22 [2:56:02<5:23:46, 1387.58s/it]
100%|██████████| 4/4 [25:08<00:00, 377.23s/ba]%|████ | 9/22 [3:22:24<5:13:17, 1445.97s/it]
100%|██████████| 4/4 [24:11<00:00, 362.87s/ba]%|████▌ | 10/22 [3:48:24<4:56:02, 1480.19s/it]
100%|██████████| 4/4 [24:44<00:00, 371.11s/ba]%|█████ | 11/22 [4:12:42<4:30:10, 1473.66s/it]
100%|██████████| 4/4 [24:35<00:00, 368.81s/ba]%|█████▍ | 12/22 [4:37:34<4:06:29, 1478.98s/it]
100%|██████████| 4/4 [24:02<00:00, 360.67s/ba]%|█████▉ | 13/22 [5:03:24<3:45:04, 1500.45s/it]
100%|██████████| 4/4 [24:07<00:00, 361.78s/ba]%|██████▎ | 14/22 [5:27:33<3:17:59, 1484.97s/it]
100%|██████████| 4/4 [23:39<00:00, 354.85s/ba]%|██████▊ | 15/22 [5:51:48<2:52:10, 1475.82s/it]
Pushing dataset shards to the dataset hub: 73%|███████▎ | 16/22 [6:16:58<2:28:37, 1486.31s/it]Got disconnected from remote data host. Retrying in 5sec [1/20]
Got disconnected from remote data host. Retrying in 5sec [2/20]
Got disconnected from remote data host. Retrying in 5sec [3/20]
Got disconnected from remote data host. Retrying in 5sec [4/20]
Got disconnected from remote data host. Retrying in 5sec [5/20]
Got disconnected from remote data host. Retrying in 5sec [6/20]
Got disconnected from remote data host. Retrying in 5sec [7/20]
Got disconnected from remote data host. Retrying in 5sec [8/20]
Got disconnected from remote data host. Retrying in 5sec [9/20]
...
Got disconnected from remote data host. Retrying in 5sec [19/20]
Got disconnected from remote data host. Retrying in 5sec [20/20]
75%|███████▌ | 3/4 [24:47<08:15, 495.86s/ba]
Pushing dataset shards to the dataset hub: 73%|███████▎ | 16/22 [6:41:46<2:30:39, 1506.65s/it]
Output exceeds the size limit. Open the full output data in a text editor
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-1-dbf8530779e9> in <module>
16 ds.features
```
### Expected behavior
I was trying to upload an image dataset and expected it to be fully uploaded
### Environment info
- `datasets` version: 2.8.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.9
- PyArrow version: 10.0.1
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5398 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5398/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5398/comments | https://api.github.com/repos/huggingface/datasets/issues/5398/events | https://github.com/huggingface/datasets/issues/5398 | 1,514,425,231 | I_kwDODunzps5aREuP | 5,398 | Unpin pydantic | {
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} | [] | closed | false | null | [] | null | [] | 2022-12-30T10:37:31 | 2022-12-30T10:43:41 | 2022-12-30T10:43:41 | MEMBER | null | null | null | Once `pydantic` fixes their issue in their 1.10.3 version, unpin it.
See issue:
- #5394
See temporary fix:
- #5395 | {
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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) | 0.012922 / 0.011353 (0.001569) | 0.006568 / 0.011008 (-0.004440) | 0.139567 / 0.038508 (0.101059) | 0.039362 / 0.023109 (0.016253) | 0.444238 / 0.275898 (0.168340) | 0.529102 / 0.323480 (0.205622) | 0.010275 / 0.007986 (0.002290) | 0.006134 / 0.004328 (0.001805) | 0.107506 / 0.004250 (0.103255) | 0.047948 / 0.037052 (0.010896) | 0.460469 / 0.258489 (0.201980) | 0.516817 / 0.293841 (0.222976) | 0.058637 / 0.128546 (-0.069909) | 0.019516 / 0.075646 (-0.056130) | 0.464111 / 0.419271 (0.044839) | 0.062140 / 0.043533 (0.018607) | 0.445004 / 0.255139 (0.189865) | 0.460117 / 0.283200 (0.176917) | 0.116591 / 0.141683 (-0.025092) | 1.936834 / 1.452155 (0.484680) | 1.941837 / 1.492716 (0.449120) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284130 / 0.018006 (0.266124) | 0.588109 / 0.000490 (0.587619) | 0.004383 / 0.000200 (0.004183) | 0.000143 / 0.000054 (0.000089) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032984 / 0.037411 (-0.004427) | 0.132811 / 0.014526 (0.118285) | 0.150932 / 0.176557 (-0.025625) | 0.203759 / 0.737135 (-0.533377) | 0.149612 / 0.296338 (-0.146726) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.677666 / 0.215209 (0.462457) | 6.627611 / 2.077655 (4.549956) | 2.679526 / 1.504120 (1.175406) | 2.272536 / 1.541195 (0.731342) | 2.371179 / 1.468490 (0.902689) | 1.205282 / 4.584777 (-3.379495) | 5.733537 / 3.745712 (1.987825) | 3.165279 / 5.269862 (-2.104583) | 2.287918 / 4.565676 (-2.277759) | 0.144581 / 0.424275 (-0.279695) | 0.016812 / 0.007607 (0.009205) | 0.841719 / 0.226044 (0.615675) | 8.379119 / 2.268929 (6.110191) | 3.507169 / 55.444624 (-51.937456) | 2.756666 / 6.876477 (-4.119811) | 2.814091 / 2.142072 (0.672018) | 1.495835 / 4.805227 (-3.309392) | 0.253651 / 6.500664 (-6.247013) | 0.081258 / 0.075469 (0.005789) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.651586 / 1.841788 (-0.190202) | 19.039628 / 8.074308 (10.965320) | 21.269814 / 10.191392 (11.078421) | 0.241024 / 0.680424 (-0.439400) | 0.047975 / 0.534201 (-0.486225) | 0.563727 / 0.579283 (-0.015556) | 0.666808 / 0.434364 (0.232445) | 0.661065 / 0.540337 (0.120728) | 0.762884 / 1.386936 (-0.624052) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010141 / 0.011353 (-0.001212) | 0.006216 / 0.011008 (-0.004792) | 0.135491 / 0.038508 (0.096983) | 0.035439 / 0.023109 (0.012330) | 0.482789 / 0.275898 (0.206891) | 0.520673 / 0.323480 (0.197193) | 0.006358 / 0.007986 (-0.001627) | 0.005432 / 0.004328 (0.001104) | 0.094448 / 0.004250 (0.090197) | 0.048379 / 0.037052 (0.011326) | 0.509359 / 0.258489 (0.250870) | 0.539583 / 0.293841 (0.245742) | 0.054621 / 0.128546 (-0.073925) | 0.021382 / 0.075646 (-0.054265) | 0.435539 / 0.419271 (0.016267) | 0.060630 / 0.043533 (0.017097) | 0.469593 / 0.255139 (0.214454) | 0.507838 / 0.283200 (0.224639) | 0.112062 / 0.141683 (-0.029621) | 1.829694 / 1.452155 (0.377539) | 1.972266 / 1.492716 (0.479549) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291669 / 0.018006 (0.273663) | 0.590104 / 0.000490 (0.589614) | 0.000661 / 0.000200 (0.000461) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034933 / 0.037411 (-0.002479) | 0.134867 / 0.014526 (0.120341) | 0.138892 / 0.176557 (-0.037665) | 0.192619 / 0.737135 (-0.544516) | 0.153787 / 0.296338 (-0.142551) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.666762 / 0.215209 (0.451553) | 6.741736 / 2.077655 (4.664082) | 2.988712 / 1.504120 (1.484592) | 2.554823 / 1.541195 (1.013628) | 2.655651 / 1.468490 (1.187161) | 1.276603 / 4.584777 (-3.308174) | 5.827960 / 3.745712 (2.082247) | 5.046876 / 5.269862 (-0.222985) | 2.829775 / 4.565676 (-1.735902) | 0.151525 / 0.424275 (-0.272750) | 0.016504 / 0.007607 (0.008897) | 0.849749 / 0.226044 (0.623704) | 8.331675 / 2.268929 (6.062747) | 3.664529 / 55.444624 (-51.780096) | 2.976495 / 6.876477 (-3.899982) | 3.034737 / 2.142072 (0.892664) | 1.499036 / 4.805227 (-3.306191) | 0.261027 / 6.500664 (-6.239637) | 0.088306 / 0.075469 (0.012837) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.693506 / 1.841788 (-0.148282) | 18.939914 / 8.074308 (10.865605) | 20.685460 / 10.191392 (10.494068) | 0.218316 / 0.680424 (-0.462108) | 0.029010 / 0.534201 (-0.505191) | 0.565246 / 0.579283 (-0.014037) | 0.633573 / 0.434364 (0.199209) | 0.656895 / 0.540337 (0.116558) | 0.781975 / 1.386936 (-0.604961) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2022-12-30T10:22:09 | 2022-12-30T10:53:11 | 2022-12-30T10:43:40 | MEMBER | null | false | {
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} | Once pydantic-1.10.3 has been yanked, we can unpin it: https://pypi.org/project/pydantic/1.10.3/
See reply by pydantic team https://github.com/pydantic/pydantic/issues/4885#issuecomment-1367819807
```
v1.10.3 has been yanked.
```
in response to spacy request: https://github.com/pydantic/pydantic/issues/4885#issuecomment-1367810049
```
On behalf of spacy-related packages: would it be possible for you to temporarily yank v1.10.3?
To address this and be compatible with v1.10.4, we'd have to release new versions of a whole series of packages and nearly everyone (including me) is currently on vacation. Even if v1.10.4 is released with a fix, pip would still back off to v1.10.3 for spacy, etc. because of its current pins for typing_extensions. If it could instead back off to v1.10.2, we'd have a bit more breathing room to make the updates on our end.
```
Close #5398.
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"Hi ! If I'm not mistaken both `expected_checksums[url]` and `recorded_checksums[url]` are dictionaries with keys \"checksum\" and \"num_bytes\". So we need to check whether `expected_checksums[url] != recorded_checksums[url]` (or simply `expected_checksums[url][\"checksum\"] != recorded_checksums[url][\"checksum\"]`)\r\n\r\nBut in your fix you're checking `expected_checksums[url] != recorded_checksums[url]['checksum']`.\r\n\r\nSo I think it's fine to keep this as is",
"No, the issue is that there is comparison of sclar value and dictionary.",
"Acording to [`DatasetInfo`][1], we need specify a dictionary which maps a URL to a checksum as follows.\r\n\r\n```python\r\nCHECKSUMS = {\r\n URL: 'a5dc6bf63ea088ade6e98594bfa386f45211c38b2a3db3dd11b33bd530f3c481',\r\n}\r\n\r\nclass FancyDataset:\r\n def _info(self):\r\n return DatasetInfo(..., download_checksums=CHECKSUMS)\r\n```\r\n\r\nHowever, `load_dataset` fails with this checksum definition.\r\n\r\n[1]: https://github.com/huggingface/datasets/blob/main/src/datasets/info.py#L124-L125",
"I think it has to be formatted like this right now. Maybe the DatasetInfo doc is unclear and we can improve it\r\n```python\r\nCHECKSUMS = {\r\n URL: {\"checksum\": checksum, \"num_bytes\": num_bytes},\r\n}\r\n```",
"Right. I am not sure that this is a correct way to do it. People usually calculate sha256, md5, or whatever else but not size in bytes. Also, people use only some of checksum algorithms. This means that comparing dictionaries in `verify_checksums` is too strict (requires equality of all items) and raises compatibility issues in the future. Another issue is that a comparison of dictionaries assumes type constraints which imply type equality. \r\n\r\nSince almost noone uses checksums as far as I known, my PR suggests a minimal change to mitigate these issues except support of a specific checksum algorithm which is a separated feature and should be contributed in a separate PRs from my perspective.",
"Applying this change will break the verification code, since the `expected_checksums` is a dict with those two keys.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5396). All of your documentation changes will be reflected on that endpoint."
] | 2022-12-29T19:45:17 | 2023-02-13T11:11:22 | 2023-02-13T11:11:22 | CONTRIBUTOR | null | false | {
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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) | 0.012220 / 0.011353 (0.000867) | 0.005943 / 0.011008 (-0.005065) | 0.128223 / 0.038508 (0.089715) | 0.037352 / 0.023109 (0.014242) | 0.397143 / 0.275898 (0.121245) | 0.483935 / 0.323480 (0.160455) | 0.010279 / 0.007986 (0.002293) | 0.004842 / 0.004328 (0.000513) | 0.101403 / 0.004250 (0.097153) | 0.042935 / 0.037052 (0.005883) | 0.421642 / 0.258489 (0.163153) | 0.456328 / 0.293841 (0.162487) | 0.065639 / 0.128546 (-0.062907) | 0.019820 / 0.075646 (-0.055826) | 0.426090 / 0.419271 (0.006818) | 0.069583 / 0.043533 (0.026051) | 0.402662 / 0.255139 (0.147523) | 0.428826 / 0.283200 (0.145626) | 0.116760 / 0.141683 (-0.024923) | 1.806216 / 1.452155 (0.354061) | 1.852629 / 1.492716 (0.359913) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226555 / 0.018006 (0.208548) | 0.584693 / 0.000490 (0.584203) | 0.008612 / 0.000200 (0.008412) | 0.000205 / 0.000054 (0.000150) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028393 / 0.037411 (-0.009018) | 0.123355 / 0.014526 (0.108829) | 0.134423 / 0.176557 (-0.042133) | 0.188536 / 0.737135 (-0.548600) | 0.141595 / 0.296338 (-0.154743) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.589359 / 0.215209 (0.374150) | 5.974655 / 2.077655 (3.897001) | 2.465580 / 1.504120 (0.961460) | 2.007618 / 1.541195 (0.466424) | 2.078788 / 1.468490 (0.610298) | 1.216646 / 4.584777 (-3.368131) | 5.217516 / 3.745712 (1.471804) | 3.107188 / 5.269862 (-2.162674) | 2.251641 / 4.565676 (-2.314036) | 0.138640 / 0.424275 (-0.285635) | 0.015046 / 0.007607 (0.007439) | 0.780092 / 0.226044 (0.554048) | 7.749564 / 2.268929 (5.480635) | 3.080708 / 55.444624 (-52.363917) | 2.393897 / 6.876477 (-4.482579) | 2.387738 / 2.142072 (0.245665) | 1.458844 / 4.805227 (-3.346384) | 0.252476 / 6.500664 (-6.248188) | 0.076594 / 0.075469 (0.001125) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.540868 / 1.841788 (-0.300919) | 17.295684 / 8.074308 (9.221376) | 19.669300 / 10.191392 (9.477908) | 0.250315 / 0.680424 (-0.430109) | 0.045068 / 0.534201 (-0.489133) | 0.538840 / 0.579283 (-0.040443) | 0.584443 / 0.434364 (0.150079) | 0.614476 / 0.540337 (0.074138) | 0.729928 / 1.386936 (-0.657008) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009218 / 0.011353 (-0.002135) | 0.006261 / 0.011008 (-0.004747) | 0.125541 / 0.038508 (0.087033) | 0.034405 / 0.023109 (0.011296) | 0.468381 / 0.275898 (0.192483) | 0.503336 / 0.323480 (0.179856) | 0.006839 / 0.007986 (-0.001146) | 0.004724 / 0.004328 (0.000396) | 0.097875 / 0.004250 (0.093625) | 0.051278 / 0.037052 (0.014225) | 0.473323 / 0.258489 (0.214834) | 0.537392 / 0.293841 (0.243551) | 0.055588 / 0.128546 (-0.072958) | 0.021041 / 0.075646 (-0.054605) | 0.416952 / 0.419271 (-0.002320) | 0.070128 / 0.043533 (0.026595) | 0.465224 / 0.255139 (0.210085) | 0.504678 / 0.283200 (0.221478) | 0.112504 / 0.141683 (-0.029179) | 1.865865 / 1.452155 (0.413710) | 1.988296 / 1.492716 (0.495580) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.314170 / 0.018006 (0.296164) | 0.526726 / 0.000490 (0.526236) | 0.018691 / 0.000200 (0.018491) | 0.000128 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033772 / 0.037411 (-0.003639) | 0.124796 / 0.014526 (0.110270) | 0.134700 / 0.176557 (-0.041856) | 0.190595 / 0.737135 (-0.546541) | 0.143205 / 0.296338 (-0.153133) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.656708 / 0.215209 (0.441499) | 6.470503 / 2.077655 (4.392848) | 2.866430 / 1.504120 (1.362310) | 2.506846 / 1.541195 (0.965651) | 2.548669 / 1.468490 (1.080179) | 1.226695 / 4.584777 (-3.358082) | 5.117866 / 3.745712 (1.372153) | 3.032822 / 5.269862 (-2.237040) | 1.999152 / 4.565676 (-2.566524) | 0.142974 / 0.424275 (-0.281301) | 0.015011 / 0.007607 (0.007404) | 0.799729 / 0.226044 (0.573684) | 8.286313 / 2.268929 (6.017385) | 3.636482 / 55.444624 (-51.808142) | 2.888038 / 6.876477 (-3.988439) | 2.924982 / 2.142072 (0.782910) | 1.471996 / 4.805227 (-3.333231) | 0.257119 / 6.500664 (-6.243545) | 0.077294 / 0.075469 (0.001825) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.608290 / 1.841788 (-0.233497) | 17.599119 / 8.074308 (9.524811) | 18.917086 / 10.191392 (8.725694) | 0.236237 / 0.680424 (-0.444187) | 0.026061 / 0.534201 (-0.508140) | 0.527359 / 0.579283 (-0.051925) | 0.589176 / 0.434364 (0.154812) | 0.602310 / 0.540337 (0.061973) | 0.726756 / 1.386936 (-0.660180) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n",
"Issue reported to `pydantic`: \r\n- https://github.com/pydantic/pydantic/issues/4885\r\n\r\nFixing PR at `pydantic`:\r\n- https://github.com/pydantic/pydantic/pull/4886"
] | 2022-12-29T19:34:19 | 2022-12-30T06:36:57 | 2022-12-29T21:00:26 | MEMBER | null | false | {
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} | Temporarily pin `pydantic` until a permanent solution is found.
Fix #5394. | {
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https://api.github.com/repos/huggingface/datasets/issues/5394 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5394/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5394/comments | https://api.github.com/repos/huggingface/datasets/issues/5394/events | https://github.com/huggingface/datasets/issues/5394 | 1,513,976,229 | I_kwDODunzps5aPXGl | 5,394 | CI error: TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers' | {
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"I still getting the same error :\r\n\r\n`python -m spacy download fr_core_news_lg\r\n`.\r\n`import spacy`",
"@MFatnassi, this issue and the corresponding fix only affect our Continuous Integration testing environment.\r\n\r\nNote that `datasets` does not depend on `spacy`."
] | 2022-12-29T18:58:44 | 2022-12-30T10:40:51 | 2022-12-29T21:00:27 | MEMBER | null | null | null | ### Describe the bug
While installing the dependencies, the CI raises a TypeError:
```
Traceback (most recent call last):
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 183, in _run_module_as_main
mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 142, in _get_module_details
return _get_module_details(pkg_main_name, error)
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 109, in _get_module_details
__import__(pkg_name)
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/__init__.py", line 6, in <module>
from .errors import setup_default_warnings
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/errors.py", line 2, in <module>
from .compat import Literal
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/compat.py", line 3, in <module>
from thinc.util import copy_array
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/__init__.py", line 5, in <module>
from .config import registry
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/config.py", line 2, in <module>
import confection
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/confection/__init__.py", line 10, in <module>
from pydantic import BaseModel, create_model, ValidationError, Extra
File "pydantic/__init__.py", line 2, in init pydantic.__init__
File "pydantic/dataclasses.py", line 46, in init pydantic.dataclasses
# | None | Attribute is set to None. |
File "pydantic/main.py", line 121, in init pydantic.main
TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers'
```
See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565
### Steps to reproduce the bug
```shell
pip install .[tests,metrics-tests]
python -m spacy download en_core_web_sm
```
### Expected behavior
No error.
### Environment info
See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Thanks for the deprecation. Some minor suggested fixes below...\r\n> \r\n> Also note that the corresponding tests should be updated as well.\r\n\r\nThanks for the suggestions/typo fixes. I updated the failing test - passing locally now",
"Nice thanks !\r\n\r\nI believe you also need to update `_load_info` and `_save_info` in `builder.py` - they're still passing `fs=self._fs` instead of `storage_options=self._fs.storage_options`\r\n\r\nThis should remove the remaining warnings in the CI such as \r\n\r\n```python\r\ntests/test_builder.py::test_builder_with_filesystem_download_and_prepare_reload\r\ntests/test_load.py::test_load_dataset_local[False]\r\ntests/test_load.py::test_load_dataset_local[True]\r\ntests/test_load.py::test_load_dataset_zip_csv[csv_path-False]\r\ntests/test_load.py::test_load_dataset_then_move_then_reload\r\n /opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/info.py:344: FutureWarning: 'fs' was deprecated in favor of 'storage_options' in version 2.9.0 and will be removed in 3.0.0.\r\n You can remove this warning by passing 'storage_options=fs.storage_options' instead.\r\n```",
"re: docstring, I assume passing in `storage_options=s3.storage_options` is correct/necessary to pass the secrets?",
"what about \r\nhttps://github.com/huggingface/datasets/blob/5b793dd8c43bf6e85f165238becb3c64f6cd3ed0/src/datasets/filesystems/__init__.py#L43-L54\r\nleave as is? Is this function no longer necessary?",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008877 / 0.011353 (-0.002475) | 0.004725 / 0.011008 (-0.006283) | 0.100738 / 0.038508 (0.062230) | 0.030251 / 0.023109 (0.007141) | 0.301483 / 0.275898 (0.025585) | 0.374161 / 0.323480 (0.050681) | 0.007225 / 0.007986 (-0.000761) | 0.003654 / 0.004328 (-0.000674) | 0.078400 / 0.004250 (0.074149) | 0.035786 / 0.037052 (-0.001267) | 0.309744 / 0.258489 (0.051255) | 0.355834 / 0.293841 (0.061994) | 0.034344 / 0.128546 (-0.094202) | 0.011584 / 0.075646 (-0.064062) | 0.321462 / 0.419271 (-0.097810) | 0.041201 / 0.043533 (-0.002332) | 0.298808 / 0.255139 (0.043669) | 0.332626 / 0.283200 (0.049426) | 0.089131 / 0.141683 (-0.052552) | 1.477888 / 1.452155 (0.025734) | 1.530365 / 1.492716 (0.037649) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191647 / 0.018006 (0.173640) | 0.424339 / 0.000490 (0.423849) | 0.002941 / 0.000200 (0.002741) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023442 / 0.037411 (-0.013969) | 0.097264 / 0.014526 (0.082738) | 0.105655 / 0.176557 (-0.070901) | 0.145055 / 0.737135 (-0.592081) | 0.108750 / 0.296338 (-0.187588) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422925 / 0.215209 (0.207716) | 4.216022 / 2.077655 (2.138367) | 1.876441 / 1.504120 (0.372322) | 1.665115 / 1.541195 (0.123920) | 1.711105 / 1.468490 (0.242615) | 0.701820 / 4.584777 (-3.882957) | 3.389319 / 3.745712 (-0.356393) | 1.909868 / 5.269862 (-3.359994) | 1.270482 / 4.565676 (-3.295195) | 0.083680 / 0.424275 (-0.340595) | 0.012347 / 0.007607 (0.004740) | 0.531076 / 0.226044 (0.305031) | 5.344045 / 2.268929 (3.075117) | 2.310897 / 55.444624 (-53.133728) | 1.971953 / 6.876477 (-4.904524) | 2.113748 / 2.142072 (-0.028325) | 0.823766 / 4.805227 (-3.981462) | 0.150864 / 6.500664 (-6.349800) | 0.066263 / 0.075469 (-0.009206) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253190 / 1.841788 (-0.588598) | 13.757887 / 8.074308 (5.683579) | 13.888195 / 10.191392 (3.696803) | 0.137285 / 0.680424 (-0.543139) | 0.029151 / 0.534201 (-0.505050) | 0.387402 / 0.579283 (-0.191881) | 0.401673 / 0.434364 (-0.032691) | 0.450474 / 0.540337 (-0.089863) | 0.533757 / 1.386936 (-0.853179) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006919 / 0.011353 (-0.004434) | 0.004655 / 0.011008 (-0.006353) | 0.096946 / 0.038508 (0.058438) | 0.028697 / 0.023109 (0.005588) | 0.420020 / 0.275898 (0.144122) | 0.460193 / 0.323480 (0.136713) | 0.005189 / 0.007986 (-0.002796) | 0.003425 / 0.004328 (-0.000904) | 0.074900 / 0.004250 (0.070649) | 0.041844 / 0.037052 (0.004792) | 0.421538 / 0.258489 (0.163049) | 0.468497 / 0.293841 (0.174656) | 0.032573 / 0.128546 (-0.095973) | 0.011731 / 0.075646 (-0.063916) | 0.320221 / 0.419271 (-0.099050) | 0.042113 / 0.043533 (-0.001420) | 0.422757 / 0.255139 (0.167618) | 0.445372 / 0.283200 (0.162172) | 0.090300 / 0.141683 (-0.051383) | 1.458598 / 1.452155 (0.006443) | 1.550060 / 1.492716 (0.057344) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235489 / 0.018006 (0.217483) | 0.418207 / 0.000490 (0.417718) | 0.002511 / 0.000200 (0.002311) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025603 / 0.037411 (-0.011808) | 0.100237 / 0.014526 (0.085711) | 0.108617 / 0.176557 (-0.067939) | 0.148417 / 0.737135 (-0.588719) | 0.110163 / 0.296338 (-0.186176) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474804 / 0.215209 (0.259595) | 4.745370 / 2.077655 (2.667715) | 2.417819 / 1.504120 (0.913699) | 2.209892 / 1.541195 (0.668697) | 2.263296 / 1.468490 (0.794806) | 0.695537 / 4.584777 (-3.889240) | 3.381028 / 3.745712 (-0.364684) | 2.952271 / 5.269862 (-2.317591) | 1.507041 / 4.565676 (-3.058636) | 0.083334 / 0.424275 (-0.340941) | 0.012554 / 0.007607 (0.004947) | 0.578861 / 0.226044 (0.352817) | 5.795241 / 2.268929 (3.526313) | 2.858544 / 55.444624 (-52.586080) | 2.516270 / 6.876477 (-4.360207) | 2.557350 / 2.142072 (0.415278) | 0.801799 / 4.805227 (-4.003428) | 0.151579 / 6.500664 (-6.349085) | 0.068765 / 0.075469 (-0.006704) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.279935 / 1.841788 (-0.561853) | 14.049065 / 8.074308 (5.974757) | 13.972703 / 10.191392 (3.781311) | 0.140551 / 0.680424 (-0.539873) | 0.016831 / 0.534201 (-0.517370) | 0.383886 / 0.579283 (-0.195397) | 0.385661 / 0.434364 (-0.048703) | 0.444525 / 0.540337 (-0.095813) | 0.532197 / 1.386936 (-0.854739) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8d206848fb7afeafecf2a2581ca9a332bdedefa9 \"CML watermark\")\n"
] | 2022-12-28T15:33:17 | 2023-01-18T12:42:33 | 2023-01-18T12:35:32 | CONTRIBUTOR | null | false | {
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} | See #5385 for some discussion on this
The `fs=` arg was depcrecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in `2.8.0` (to be removed in `3.0.0`). There are a few other places where the `fs=` arg was still used (functions/methods in `datasets.info` and `datasets.load`). This PR adds a similar behavior, warnings and the `storage_options=` arg to these functions and methods.
One question: should the "deprecated" / "added" versions be `2.8.1` for the docs/warnings on these? Right now I'm going with "fs was deprecated in 2.8.0" but "storage_options= was added in 2.8.1" where appropriate.
@mariosasko | {
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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) | 0.011196 / 0.011353 (-0.000157) | 0.006039 / 0.011008 (-0.004969) | 0.122497 / 0.038508 (0.083989) | 0.043884 / 0.023109 (0.020774) | 0.372982 / 0.275898 (0.097084) | 0.444229 / 0.323480 (0.120749) | 0.009489 / 0.007986 (0.001503) | 0.004612 / 0.004328 (0.000284) | 0.093921 / 0.004250 (0.089670) | 0.052698 / 0.037052 (0.015646) | 0.372327 / 0.258489 (0.113838) | 0.426586 / 0.293841 (0.132745) | 0.046755 / 0.128546 (-0.081792) | 0.014848 / 0.075646 (-0.060799) | 0.410474 / 0.419271 (-0.008798) | 0.058206 / 0.043533 (0.014674) | 0.367051 / 0.255139 (0.111912) | 0.389950 / 0.283200 (0.106750) | 0.120857 / 0.141683 (-0.020826) | 1.795195 / 1.452155 (0.343040) | 1.823938 / 1.492716 (0.331222) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215199 / 0.018006 (0.197192) | 0.482420 / 0.000490 (0.481930) | 0.001834 / 0.000200 (0.001634) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034483 / 0.037411 (-0.002928) | 0.135503 / 0.014526 (0.120977) | 0.149991 / 0.176557 (-0.026565) | 0.198482 / 0.737135 (-0.538653) | 0.153556 / 0.296338 (-0.142783) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.504492 / 0.215209 (0.289283) | 4.950949 / 2.077655 (2.873294) | 2.251186 / 1.504120 (0.747067) | 2.049195 / 1.541195 (0.508000) | 2.123325 / 1.468490 (0.654835) | 0.865651 / 4.584777 (-3.719126) | 4.652297 / 3.745712 (0.906585) | 4.417260 / 5.269862 (-0.852602) | 2.362390 / 4.565676 (-2.203287) | 0.098845 / 0.424275 (-0.325430) | 0.014675 / 0.007607 (0.007068) | 0.608048 / 0.226044 (0.382003) | 6.063863 / 2.268929 (3.794935) | 2.753041 / 55.444624 (-52.691583) | 2.340961 / 6.876477 (-4.535516) | 2.511934 / 2.142072 (0.369862) | 0.989297 / 4.805227 (-3.815930) | 0.195770 / 6.500664 (-6.304894) | 0.076027 / 0.075469 (0.000558) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.479617 / 1.841788 (-0.362170) | 18.917860 / 8.074308 (10.843552) | 18.219594 / 10.191392 (8.028202) | 0.218494 / 0.680424 (-0.461930) | 0.037207 / 0.534201 (-0.496994) | 0.571543 / 0.579283 (-0.007741) | 0.527884 / 0.434364 (0.093520) | 0.658661 / 0.540337 (0.118324) | 0.755449 / 1.386936 (-0.631487) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008762 / 0.011353 (-0.002591) | 0.006019 / 0.011008 (-0.004989) | 0.118756 / 0.038508 (0.080248) | 0.039584 / 0.023109 (0.016474) | 0.400127 / 0.275898 (0.124229) | 0.468114 / 0.323480 (0.144634) | 0.006771 / 0.007986 (-0.001215) | 0.004689 / 0.004328 (0.000360) | 0.087274 / 0.004250 (0.083023) | 0.055548 / 0.037052 (0.018496) | 0.419901 / 0.258489 (0.161412) | 0.459516 / 0.293841 (0.165675) | 0.044197 / 0.128546 (-0.084349) | 0.014162 / 0.075646 (-0.061484) | 0.409634 / 0.419271 (-0.009638) | 0.058668 / 0.043533 (0.015135) | 0.404758 / 0.255139 (0.149619) | 0.431562 / 0.283200 (0.148363) | 0.122361 / 0.141683 (-0.019322) | 1.726597 / 1.452155 (0.274442) | 1.798977 / 1.492716 (0.306260) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250831 / 0.018006 (0.232825) | 0.489811 / 0.000490 (0.489321) | 0.000490 / 0.000200 (0.000290) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035666 / 0.037411 (-0.001745) | 0.134899 / 0.014526 (0.120374) | 0.153156 / 0.176557 (-0.023401) | 0.202409 / 0.737135 (-0.534726) | 0.157350 / 0.296338 (-0.138989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522464 / 0.215209 (0.307254) | 5.204449 / 2.077655 (3.126794) | 2.617410 / 1.504120 (1.113290) | 2.406246 / 1.541195 (0.865052) | 2.494487 / 1.468490 (1.025997) | 0.834923 / 4.584777 (-3.749854) | 4.794186 / 3.745712 (1.048474) | 2.617939 / 5.269862 (-2.651922) | 1.648310 / 4.565676 (-2.917367) | 0.109785 / 0.424275 (-0.314490) | 0.015217 / 0.007607 (0.007610) | 0.682970 / 0.226044 (0.456926) | 6.853894 / 2.268929 (4.584966) | 3.277150 / 55.444624 (-52.167475) | 2.832502 / 6.876477 (-4.043975) | 2.984874 / 2.142072 (0.842802) | 1.005307 / 4.805227 (-3.799921) | 0.200623 / 6.500664 (-6.300041) | 0.076852 / 0.075469 (0.001383) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.556656 / 1.841788 (-0.285131) | 19.088978 / 8.074308 (11.014669) | 16.946406 / 10.191392 (6.755014) | 0.204419 / 0.680424 (-0.476004) | 0.021456 / 0.534201 (-0.512745) | 0.523603 / 0.579283 (-0.055680) | 0.530067 / 0.434364 (0.095703) | 0.604058 / 0.540337 (0.063721) | 0.731531 / 1.386936 (-0.655405) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2022-12-28T11:44:53 | 2023-01-03T15:36:14 | 2023-01-03T15:27:31 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/5391 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5391/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5391/comments | https://api.github.com/repos/huggingface/datasets/issues/5391/events | https://github.com/huggingface/datasets/issues/5391 | 1,510,350,400 | I_kwDODunzps5aBh5A | 5,391 | Whisper Event - RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 [2:52:21<00:00, 10.34s/it] | {
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"Hey @catswithbats! Super sorry for the late reply! This is happening because there is data with label length (504) that exceeds the model's max length (448). \r\n\r\nThere are two options here:\r\n1. Increase the model's `max_length` parameter: \r\n```python\r\nmodel.config.max_length = 512\r\n```\r\n2. Filter data with labels longer than max length: https://discuss.huggingface.co/t/open-to-the-community-whisper-fine-tuning-event/26681/21?u=sanchit-gandhi\r\n\r\nNote that the datasets repo is reserved for issues directly related to the HF datasets library. Issues related to custom fine-tuning implementations are more applicable to the HF Forum: https://discuss.huggingface.co. You're more likely to get a response by posting your issue in the most applicable place and boost the chance of someone sharing a working solution!",
"@sanchit-gandhi Thank you for all your work on this topic.\r\n\r\nI'm finding that changing the `max_length` value does not make this error go away."
] | 2022-12-25T15:17:14 | 2023-07-21T14:29:47 | 2023-07-21T14:29:47 | NONE | null | null | null | Done in a VM with a GPU (Ubuntu) following the [Whisper Event - PYTHON](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event#python-script) instructions.
Attempted using [RuntimeError: he size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 - WEB](https://discuss.huggingface.co/t/trainer-runtimeerror-the-size-of-tensor-a-462-must-match-the-size-of-tensor-b-448-at-non-singleton-dimension-1/26010/10 ) - another person experiencing the same issue. But could not resolve the issue with the google/fleurs data. __Not clear what can be modified in the PY code to resolve the input data size mismatch, as the training data is already very small__.
Tried posting on Discord, @sanchit-gandhi and @vaibhavs10. Was hoping that the event is over and some input/help is now available. [Hugging Face - whisper-small-amet](https://huggingface.co./drmeeseeks/whisper-small-amet).
The paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) am_et is a low resource language (Table E), with the WER results ranging from 120-229, based on model size. (Whisper small WER=120.2).
# ---> Initial Training Output
/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
warnings.warn(
[INFO|trainer.py:1641] 2022-12-18 05:23:28,799 >> ***** Running training *****
[INFO|trainer.py:1642] 2022-12-18 05:23:28,799 >> Num examples = 446
[INFO|trainer.py:1643] 2022-12-18 05:23:28,799 >> Num Epochs = 72
[INFO|trainer.py:1644] 2022-12-18 05:23:28,799 >> Instantaneous batch size per device = 16
[INFO|trainer.py:1645] 2022-12-18 05:23:28,799 >> Total train batch size (w. parallel, distributed & accumulation) = 32
[INFO|trainer.py:1646] 2022-12-18 05:23:28,799 >> Gradient Accumulation steps = 2
[INFO|trainer.py:1647] 2022-12-18 05:23:28,800 >> Total optimization steps = 1000
[INFO|trainer.py:1648] 2022-12-18 05:23:28,801 >> Number of trainable parameters = 241734912
# ---> Error
14% 9/65 [07:07<48:34, 52.04s/it][INFO|configuration_utils.py:523] 2022-12-18 05:03:07,941 >> Generate config GenerationConfig {
"begin_suppress_tokens": [
220,
50257
],
"bos_token_id": 50257,
"decoder_start_token_id": 50258,
"eos_token_id": 50257,
"max_length": 448,
"pad_token_id": 50257,
"transformers_version": "4.26.0.dev0",
"use_cache": false
}
Traceback (most recent call last):
File "run_speech_recognition_seq2seq_streaming.py", line 629, in <module>
main()
File "run_speech_recognition_seq2seq_streaming.py", line 578, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1534, in train
return inner_training_loop(
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1859, in _inner_training_loop
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2122, in _maybe_log_save_evaluate
metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 78, in evaluate
return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2818, in evaluate
output = eval_loop(
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 3000, in evaluation_loop
loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 213, in prediction_step
outputs = model(**inputs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1197, in forward
outputs = self.model(
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1066, in forward
decoder_outputs = self.decoder(
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 873, in forward
hidden_states = inputs_embeds + positions
RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1
100% 1000/1000 [2:52:21<00:00, 10.34s/it]
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https://api.github.com/repos/huggingface/datasets/issues/5390 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5390/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5390/comments | https://api.github.com/repos/huggingface/datasets/issues/5390/events | https://github.com/huggingface/datasets/issues/5390 | 1,509,357,553 | I_kwDODunzps5Z9vfx | 5,390 | Error when pushing to the CI hub | {
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"Hmmm, git bisect tells me that the behavior is the same since https://github.com/huggingface/datasets/commit/67e65c90e9490810b89ee140da11fdd13c356c9c (3 Oct), i.e. https://github.com/huggingface/datasets/pull/4926",
"Maybe related to the discussions in https://github.com/huggingface/datasets/pull/5196",
"Maybe the current version of moonlanding in Hub CI is the issue.\r\n\r\nI relaunched tests that were working two days ago: now they are failing. https://github.com/huggingface/datasets-server/commit/746414449cae4b311733f8a76e5b3b4ca73b38a9 for example\r\n\r\ncc @huggingface/moon-landing ",
"Hi! I don't think this has anything to do with `datasets`. Hub CI seems to be the culprit - the identical failure can be found in [this](https://github.com/huggingface/datasets/pull/5389) PR (with unrelated changes) opened today.",
"OK! Thanks for looking at it. Closing then."
] | 2022-12-23T13:36:37 | 2022-12-23T20:29:02 | 2022-12-23T20:29:02 | CONTRIBUTOR | null | null | null | ### Describe the bug
Note that it's a special case where the Hub URL is "https://hub-ci.huggingface.co", which does not appear if we do the same on the Hub (https://huggingface.co.).
The call to `dataset.push_to_hub(` fails:
```
Pushing dataset shards to the dataset hub: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.93s/it]
Traceback (most recent call last):
File "reproduce_hubci.py", line 16, in <module>
dataset.push_to_hub(repo_id=repo_id, private=False, token=USER_TOKEN, embed_external_files=True)
File "/home/slesage/hf/datasets/src/datasets/arrow_dataset.py", line 5025, in push_to_hub
HfApi(endpoint=config.HF_ENDPOINT).upload_file(
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1346, in upload_file
raise err
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1337, in upload_file
r.raise_for_status()
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_DATASETS_SERVER_USER__/bug-16718047265472/upload/main/README.md
```
### Steps to reproduce the bug
```python
# reproduce.py
from datasets import Dataset
import time
USER = "__DUMMY_DATASETS_SERVER_USER__"
USER_TOKEN = "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"
dataset = Dataset.from_dict({"a": [1, 2, 3]})
repo_id = f"{USER}/bug-{int(time.time() * 10e3)}"
dataset.push_to_hub(repo_id=repo_id, private=False, token=USER_TOKEN, embed_external_files=True)
```
```bash
$ HF_ENDPOINT="https://hub-ci.huggingface.co" python reproduce.py
```
### Expected behavior
No error and the dataset should be uploaded to the Hub with the README file (which generates the error).
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.0-1026-aws-x86_64-with-glibc2.35
- Python version: 3.9.15
- PyArrow version: 7.0.0
- Pandas version: 1.5.2
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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) | 0.008935 / 0.011353 (-0.002417) | 0.004582 / 0.011008 (-0.006426) | 0.100950 / 0.038508 (0.062442) | 0.030305 / 0.023109 (0.007196) | 0.299759 / 0.275898 (0.023861) | 0.378577 / 0.323480 (0.055097) | 0.007834 / 0.007986 (-0.000152) | 0.003399 / 0.004328 (-0.000930) | 0.078568 / 0.004250 (0.074318) | 0.037990 / 0.037052 (0.000938) | 0.313025 / 0.258489 (0.054536) | 0.359543 / 0.293841 (0.065702) | 0.033631 / 0.128546 (-0.094916) | 0.011681 / 0.075646 (-0.063966) | 0.324542 / 0.419271 (-0.094729) | 0.041014 / 0.043533 (-0.002519) | 0.302884 / 0.255139 (0.047745) | 0.337059 / 0.283200 (0.053859) | 0.089403 / 0.141683 (-0.052280) | 1.491262 / 1.452155 (0.039108) | 1.521626 / 1.492716 (0.028910) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.172627 / 0.018006 (0.154621) | 0.419406 / 0.000490 (0.418917) | 0.001974 / 0.000200 (0.001775) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023598 / 0.037411 (-0.013814) | 0.098127 / 0.014526 (0.083601) | 0.105611 / 0.176557 (-0.070946) | 0.142612 / 0.737135 (-0.594523) | 0.121687 / 0.296338 (-0.174651) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418512 / 0.215209 (0.203303) | 4.173099 / 2.077655 (2.095444) | 1.865900 / 1.504120 (0.361780) | 1.664053 / 1.541195 (0.122858) | 1.726289 / 1.468490 (0.257799) | 0.693214 / 4.584777 (-3.891563) | 3.499982 / 3.745712 (-0.245730) | 1.894278 / 5.269862 (-3.375583) | 1.178214 / 4.565676 (-3.387463) | 0.082391 / 0.424275 (-0.341884) | 0.012486 / 0.007607 (0.004878) | 0.532190 / 0.226044 (0.306145) | 5.286612 / 2.268929 (3.017684) | 2.316680 / 55.444624 (-53.127944) | 1.964020 / 6.876477 (-4.912457) | 2.016457 / 2.142072 (-0.125616) | 0.812290 / 4.805227 (-3.992937) | 0.149102 / 6.500664 (-6.351562) | 0.064215 / 0.075469 (-0.011254) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281919 / 1.841788 (-0.559869) | 14.107509 / 8.074308 (6.033201) | 13.892369 / 10.191392 (3.700977) | 0.146164 / 0.680424 (-0.534260) | 0.028740 / 0.534201 (-0.505460) | 0.395218 / 0.579283 (-0.184066) | 0.406321 / 0.434364 (-0.028043) | 0.460880 / 0.540337 (-0.079458) | 0.545975 / 1.386936 (-0.840961) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006797 / 0.011353 (-0.004556) | 0.004522 / 0.011008 (-0.006486) | 0.098440 / 0.038508 (0.059932) | 0.027722 / 0.023109 (0.004613) | 0.423995 / 0.275898 (0.148097) | 0.456164 / 0.323480 (0.132684) | 0.005156 / 0.007986 (-0.002830) | 0.003439 / 0.004328 (-0.000889) | 0.075307 / 0.004250 (0.071057) | 0.039599 / 0.037052 (0.002547) | 0.423671 / 0.258489 (0.165181) | 0.463841 / 0.293841 (0.170001) | 0.032473 / 0.128546 (-0.096073) | 0.011674 / 0.075646 (-0.063972) | 0.320548 / 0.419271 (-0.098723) | 0.041618 / 0.043533 (-0.001915) | 0.426133 / 0.255139 (0.170994) | 0.443018 / 0.283200 (0.159819) | 0.091103 / 0.141683 (-0.050579) | 1.468758 / 1.452155 (0.016604) | 1.532695 / 1.492716 (0.039978) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255314 / 0.018006 (0.237308) | 0.422982 / 0.000490 (0.422492) | 0.015405 / 0.000200 (0.015205) | 0.000103 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025260 / 0.037411 (-0.012152) | 0.102062 / 0.014526 (0.087537) | 0.108161 / 0.176557 (-0.068395) | 0.144205 / 0.737135 (-0.592930) | 0.111686 / 0.296338 (-0.184653) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.482633 / 0.215209 (0.267424) | 4.824777 / 2.077655 (2.747123) | 2.488626 / 1.504120 (0.984506) | 2.285410 / 1.541195 (0.744215) | 2.336793 / 1.468490 (0.868303) | 0.701894 / 4.584777 (-3.882883) | 3.506908 / 3.745712 (-0.238804) | 3.399789 / 5.269862 (-1.870072) | 1.536359 / 4.565676 (-3.029317) | 0.083621 / 0.424275 (-0.340655) | 0.012702 / 0.007607 (0.005094) | 0.581259 / 0.226044 (0.355215) | 5.829640 / 2.268929 (3.560711) | 2.932201 / 55.444624 (-52.512424) | 2.577175 / 6.876477 (-4.299301) | 2.621782 / 2.142072 (0.479710) | 0.812074 / 4.805227 (-3.993153) | 0.152840 / 6.500664 (-6.347824) | 0.067982 / 0.075469 (-0.007487) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.274915 / 1.841788 (-0.566873) | 14.345800 / 8.074308 (6.271492) | 14.242475 / 10.191392 (4.051083) | 0.143636 / 0.680424 (-0.536788) | 0.016824 / 0.534201 (-0.517377) | 0.376449 / 0.579283 (-0.202834) | 0.394219 / 0.434364 (-0.040145) | 0.435368 / 0.540337 (-0.104969) | 0.518393 / 1.386936 (-0.868544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#187e4faa978fef267a055f6988564f922e51eaa4 \"CML watermark\")\n",
"I also fixed the rest of the links that point to the markdown files. \r\n\r\nPS: the CI failures are unrelated ",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008641 / 0.011353 (-0.002712) | 0.004560 / 0.011008 (-0.006448) | 0.100559 / 0.038508 (0.062051) | 0.029744 / 0.023109 (0.006635) | 0.300580 / 0.275898 (0.024682) | 0.359100 / 0.323480 (0.035620) | 0.007016 / 0.007986 (-0.000970) | 0.003393 / 0.004328 (-0.000936) | 0.078649 / 0.004250 (0.074399) | 0.038138 / 0.037052 (0.001086) | 0.307730 / 0.258489 (0.049241) | 0.347678 / 0.293841 (0.053837) | 0.033630 / 0.128546 (-0.094917) | 0.011452 / 0.075646 (-0.064194) | 0.320903 / 0.419271 (-0.098369) | 0.042659 / 0.043533 (-0.000874) | 0.298886 / 0.255139 (0.043747) | 0.324371 / 0.283200 (0.041171) | 0.092582 / 0.141683 (-0.049101) | 1.490017 / 1.452155 (0.037863) | 1.512825 / 1.492716 (0.020109) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.178965 / 0.018006 (0.160958) | 0.420001 / 0.000490 (0.419512) | 0.002686 / 0.000200 (0.002486) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023568 / 0.037411 (-0.013843) | 0.097027 / 0.014526 (0.082502) | 0.104721 / 0.176557 (-0.071836) | 0.148757 / 0.737135 (-0.588378) | 0.110849 / 0.296338 (-0.185489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415034 / 0.215209 (0.199825) | 4.155249 / 2.077655 (2.077594) | 1.837027 / 1.504120 (0.332907) | 1.627754 / 1.541195 (0.086559) | 1.687958 / 1.468490 (0.219468) | 0.699542 / 4.584777 (-3.885235) | 3.376707 / 3.745712 (-0.369005) | 2.900778 / 5.269862 (-2.369083) | 1.556168 / 4.565676 (-3.009508) | 0.082438 / 0.424275 (-0.341837) | 0.012339 / 0.007607 (0.004732) | 0.524952 / 0.226044 (0.298907) | 5.269852 / 2.268929 (3.000924) | 2.278770 / 55.444624 (-53.165854) | 1.917987 / 6.876477 (-4.958490) | 1.955000 / 2.142072 (-0.187072) | 0.821169 / 4.805227 (-3.984058) | 0.149019 / 6.500664 (-6.351645) | 0.064604 / 0.075469 (-0.010865) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.199768 / 1.841788 (-0.642020) | 13.760897 / 8.074308 (5.686589) | 13.911550 / 10.191392 (3.720158) | 0.161727 / 0.680424 (-0.518697) | 0.028615 / 0.534201 (-0.505586) | 0.393917 / 0.579283 (-0.185366) | 0.392524 / 0.434364 (-0.041840) | 0.451763 / 0.540337 (-0.088574) | 0.536880 / 1.386936 (-0.850056) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006407 / 0.011353 (-0.004946) | 0.004420 / 0.011008 (-0.006588) | 0.097244 / 0.038508 (0.058736) | 0.027114 / 0.023109 (0.004005) | 0.412512 / 0.275898 (0.136614) | 0.448189 / 0.323480 (0.124709) | 0.005831 / 0.007986 (-0.002155) | 0.005423 / 0.004328 (0.001095) | 0.076051 / 0.004250 (0.071801) | 0.038828 / 0.037052 (0.001776) | 0.414586 / 0.258489 (0.156097) | 0.457196 / 0.293841 (0.163355) | 0.031615 / 0.128546 (-0.096931) | 0.011542 / 0.075646 (-0.064104) | 0.316967 / 0.419271 (-0.102304) | 0.041278 / 0.043533 (-0.002254) | 0.411371 / 0.255139 (0.156232) | 0.436376 / 0.283200 (0.153177) | 0.090212 / 0.141683 (-0.051471) | 1.461831 / 1.452155 (0.009677) | 1.606515 / 1.492716 (0.113799) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221453 / 0.018006 (0.203447) | 0.404140 / 0.000490 (0.403650) | 0.000422 / 0.000200 (0.000222) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024588 / 0.037411 (-0.012824) | 0.098604 / 0.014526 (0.084078) | 0.113682 / 0.176557 (-0.062874) | 0.141141 / 0.737135 (-0.595994) | 0.110069 / 0.296338 (-0.186270) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.477267 / 0.215209 (0.262058) | 4.775086 / 2.077655 (2.697431) | 2.445449 / 1.504120 (0.941329) | 2.242220 / 1.541195 (0.701025) | 2.303542 / 1.468490 (0.835051) | 0.693448 / 4.584777 (-3.891329) | 3.413319 / 3.745712 (-0.332393) | 3.052734 / 5.269862 (-2.217127) | 1.434075 / 4.565676 (-3.131602) | 0.082429 / 0.424275 (-0.341846) | 0.012594 / 0.007607 (0.004987) | 0.584259 / 0.226044 (0.358214) | 5.865098 / 2.268929 (3.596169) | 2.926301 / 55.444624 (-52.518324) | 2.572555 / 6.876477 (-4.303921) | 2.608584 / 2.142072 (0.466512) | 0.805029 / 4.805227 (-4.000198) | 0.151247 / 6.500664 (-6.349417) | 0.067142 / 0.075469 (-0.008327) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285454 / 1.841788 (-0.556334) | 14.296425 / 8.074308 (6.222117) | 14.147278 / 10.191392 (3.955886) | 0.151698 / 0.680424 (-0.528726) | 0.016876 / 0.534201 (-0.517325) | 0.383302 / 0.579283 (-0.195981) | 0.388461 / 0.434364 (-0.045902) | 0.438286 / 0.540337 (-0.102051) | 0.525249 / 1.386936 (-0.861687) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2a3b2f04f1fd62249ac43c534761ce151ad5c269 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008677 / 0.011353 (-0.002676) | 0.004863 / 0.011008 (-0.006145) | 0.096606 / 0.038508 (0.058098) | 0.034004 / 0.023109 (0.010895) | 0.296362 / 0.275898 (0.020464) | 0.323445 / 0.323480 (-0.000035) | 0.007341 / 0.007986 (-0.000644) | 0.005518 / 0.004328 (0.001189) | 0.073584 / 0.004250 (0.069334) | 0.041471 / 0.037052 (0.004419) | 0.302183 / 0.258489 (0.043694) | 0.339369 / 0.293841 (0.045528) | 0.037375 / 0.128546 (-0.091171) | 0.011827 / 0.075646 (-0.063819) | 0.330723 / 0.419271 (-0.088549) | 0.048751 / 0.043533 (0.005218) | 0.298370 / 0.255139 (0.043231) | 0.317781 / 0.283200 (0.034582) | 0.097488 / 0.141683 (-0.044195) | 1.456242 / 1.452155 (0.004088) | 1.530149 / 1.492716 (0.037433) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207053 / 0.018006 (0.189046) | 0.438165 / 0.000490 (0.437675) | 0.001161 / 0.000200 (0.000961) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025353 / 0.037411 (-0.012059) | 0.105536 / 0.014526 (0.091010) | 0.116122 / 0.176557 (-0.060434) | 0.151605 / 0.737135 (-0.585530) | 0.121777 / 0.296338 (-0.174561) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402780 / 0.215209 (0.187571) | 4.017882 / 2.077655 (1.940227) | 1.813111 / 1.504120 (0.308991) | 1.620000 / 1.541195 (0.078805) | 1.649186 / 1.468490 (0.180696) | 0.687523 / 4.584777 (-3.897254) | 3.712595 / 3.745712 (-0.033117) | 2.038535 / 5.269862 (-3.231326) | 1.414794 / 4.565676 (-3.150882) | 0.083357 / 0.424275 (-0.340918) | 0.012032 / 0.007607 (0.004425) | 0.502899 / 0.226044 (0.276854) | 5.038914 / 2.268929 (2.769985) | 2.250476 / 55.444624 (-53.194148) | 1.919954 / 6.876477 (-4.956523) | 1.930928 / 2.142072 (-0.211144) | 0.826634 / 4.805227 (-3.978593) | 0.161599 / 6.500664 (-6.339066) | 0.061356 / 0.075469 (-0.014113) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.228998 / 1.841788 (-0.612790) | 14.587914 / 8.074308 (6.513606) | 14.237514 / 10.191392 (4.046122) | 0.190913 / 0.680424 (-0.489510) | 0.029104 / 0.534201 (-0.505097) | 0.436160 / 0.579283 (-0.143123) | 0.431464 / 0.434364 (-0.002900) | 0.511670 / 0.540337 (-0.028668) | 0.609046 / 1.386936 (-0.777890) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006980 / 0.011353 (-0.004373) | 0.005260 / 0.011008 (-0.005748) | 0.095288 / 0.038508 (0.056780) | 0.032465 / 0.023109 (0.009356) | 0.410799 / 0.275898 (0.134901) | 0.423814 / 0.323480 (0.100334) | 0.005533 / 0.007986 (-0.002452) | 0.005764 / 0.004328 (0.001436) | 0.070713 / 0.004250 (0.066462) | 0.048193 / 0.037052 (0.011141) | 0.405742 / 0.258489 (0.147253) | 0.458773 / 0.293841 (0.164932) | 0.036415 / 0.128546 (-0.092131) | 0.012192 / 0.075646 (-0.063454) | 0.330655 / 0.419271 (-0.088617) | 0.055945 / 0.043533 (0.012412) | 0.407497 / 0.255139 (0.152358) | 0.421496 / 0.283200 (0.138296) | 0.106285 / 0.141683 (-0.035398) | 1.459837 / 1.452155 (0.007683) | 1.573147 / 1.492716 (0.080431) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205776 / 0.018006 (0.187770) | 0.441523 / 0.000490 (0.441033) | 0.003073 / 0.000200 (0.002873) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029207 / 0.037411 (-0.008205) | 0.110295 / 0.014526 (0.095770) | 0.130233 / 0.176557 (-0.046324) | 0.157489 / 0.737135 (-0.579647) | 0.125374 / 0.296338 (-0.170965) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440942 / 0.215209 (0.225733) | 4.389647 / 2.077655 (2.311992) | 2.234883 / 1.504120 (0.730763) | 2.029510 / 1.541195 (0.488315) | 2.082503 / 1.468490 (0.614013) | 0.698046 / 4.584777 (-3.886731) | 3.769127 / 3.745712 (0.023415) | 2.058511 / 5.269862 (-3.211351) | 1.324302 / 4.565676 (-3.241375) | 0.085695 / 0.424275 (-0.338580) | 0.012122 / 0.007607 (0.004515) | 0.552406 / 0.226044 (0.326362) | 5.527073 / 2.268929 (3.258145) | 2.711354 / 55.444624 (-52.733270) | 2.328848 / 6.876477 (-4.547629) | 2.340750 / 2.142072 (0.198678) | 0.846300 / 4.805227 (-3.958927) | 0.167465 / 6.500664 (-6.333199) | 0.063419 / 0.075469 (-0.012050) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.262452 / 1.841788 (-0.579336) | 15.043537 / 8.074308 (6.969229) | 14.212563 / 10.191392 (4.021171) | 0.170229 / 0.680424 (-0.510194) | 0.017696 / 0.534201 (-0.516505) | 0.423194 / 0.579283 (-0.156089) | 0.430908 / 0.434364 (-0.003456) | 0.491733 / 0.540337 (-0.048604) | 0.599267 / 1.386936 (-0.787669) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2a3b2f04f1fd62249ac43c534761ce151ad5c269 \"CML watermark\")\n",
"Program enthusiastic "
] | 2022-12-23T13:26:31 | 2023-01-25T19:00:43 | 2023-01-24T16:33:38 | CONTRIBUTOR | null | false | {
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} | Fix https://github.com/huggingface/datasets/issues/5387, fix https://github.com/huggingface/datasets/issues/4566 | {
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https://api.github.com/repos/huggingface/datasets/issues/5388 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5388/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5388/comments | https://api.github.com/repos/huggingface/datasets/issues/5388/events | https://github.com/huggingface/datasets/issues/5388 | 1,509,042,348 | I_kwDODunzps5Z8iis | 5,388 | Getting Value Error while loading a dataset.. | {
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"Hi! I can't reproduce this error locally (Mac) or in Colab. What version of `datasets` are you using?",
"Hi [mariosasko](https://github.com/mariosasko), the datasets version is '2.8.0'.",
"@valmetisrinivas you get that error because you imported `datasets` (and thus `fsspec`) before installing `zstandard`.\r\n\r\nPlease, restart your Colab runtime and execute the install commands before importing `datasets`:\r\n```python\r\n!pip install datasets\r\n!pip install zstandard\r\n\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\r\n \"json\",\r\n data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\r\n split=\"train\",\r\n streaming=True,\r\n)\r\nnext(iter(ds))\r\n```",
"> @valmetisrinivas you get that error because you imported `datasets` (and thus `fsspec`) before installing `zstandard`.\r\n> \r\n> Please, restart your Colab runtime and execute the install commands before importing `datasets`:\r\n> \r\n> ```python\r\n> !pip install datasets\r\n> !pip install zstandard\r\n> \r\n> from datasets import load_dataset\r\n> \r\n> ds = load_dataset(\r\n> \"json\",\r\n> data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\r\n> split=\"train\",\r\n> streaming=True,\r\n> )\r\n> next(iter(ds))\r\n> ```\r\n\r\nI guess that was the problem, importing datasets before the installation of zstandard. Thank you for the feedback. "
] | 2022-12-23T08:16:43 | 2022-12-29T08:36:33 | 2022-12-27T17:59:09 | NONE | null | null | null | ### Describe the bug
I am trying to load a dataset using Hugging Face Datasets load_dataset method. I am getting the value error as show below. Can someone help with this? I am using Windows laptop and Google Colab notebook.
```
WARNING:datasets.builder:Using custom data configuration default-a1d9e8eaedd958cd
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-12-5b4fdcb8e6d5>](https://localhost:8080/#) in <module>
6 )
7
----> 8 next(iter(law_dataset_streamed))
17 frames
[/usr/local/lib/python3.8/dist-packages/fsspec/core.py](https://localhost:8080/#) in get_compression(urlpath, compression)
485 compression = infer_compression(urlpath)
486 if compression is not None and compression not in compr:
--> 487 raise ValueError("Compression type %s not supported" % compression)
488 return compression
489
ValueError: Compression type zstd not supported
```
### Steps to reproduce the bug
```
!pip install zstandard
from datasets import load_dataset
lds = load_dataset(
"json",
data_files="https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst",
split="train",
streaming=True,
)
```
### Expected behavior
I expect an iterable object as the output 'lds' to be created.
### Environment info
Windows laptop with Google Colab notebook | {
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https://api.github.com/repos/huggingface/datasets/issues/5387 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5387/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5387/comments | https://api.github.com/repos/huggingface/datasets/issues/5387/events | https://github.com/huggingface/datasets/issues/5387 | 1,508,740,177 | I_kwDODunzps5Z7YxR | 5,387 | Missing documentation page : improve-performance | {
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"Hi! Our documentation builder does not support links to sections, hence the bug. This is the link it should point to https://huggingface.co./docs/datasets/v2.8.0/en/cache#improve-performance."
] | 2022-12-23T01:12:57 | 2023-01-24T16:33:40 | 2023-01-24T16:33:40 | NONE | null | null | null | ### Describe the bug
Trying to access https://huggingface.co./docs/datasets/v2.8.0/en/package_reference/cache#improve-performance, the page is missing.
The link is in here : https://huggingface.co./docs/datasets/v2.8.0/en/package_reference/loading_methods#datasets.load_dataset.keep_in_memory
### Steps to reproduce the bug
Access the page and see it's missing.
### Expected behavior
Not missing page
### Environment info
Doesn't matter | {
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"Hi! \r\n\r\nThis behavior stems from the fact that we don't always embed image bytes in the underlying arrow table, which can lead to bad size estimation (we use the first 1000 table rows to [estimate](https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L4627) the external file size). We plan to address this in the next major release by always embedding external bytes. In the meantime, you can either shuffle the dataset with `.shuffle().flatten_indices()` to make the estimation more precise or embed the bytes in the table like so:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\nformat = ds.format\r\nds = ds.with_format(\"arrow\")\r\nds = ds.map(embed_table_storage, batched=True)\r\nds = ds.with_format(**format)\r\n...\r\nds.push_to_hub(...)\r\n```",
"Embedding the bytes worked like charm. Thanks @mariosasko!"
] | 2022-12-22T21:50:58 | 2022-12-26T23:45:51 | 2022-12-26T23:45:51 | NONE | null | null | null | ### Describe the bug
`max_shard_size` parameter for `datasets.push_to_hub()` works unreliably with large files, generating shard files that are way past the specified limit.
In my private dataset, which contains unprocessed images of all sizes (up to `~100MB` per file), I've encountered cases where `max_shard_size='100MB'` results in shard files that are `>2GB` in size. Setting `max_shard_size` to another value, such as `1GB` or `500MB` does not fix this problem.
**The real problem is this:** When the shard file size grows too big, the entire dataset breaks because of #4721 and ultimately https://issues.apache.org/jira/browse/ARROW-5030. Since `max_shard_size` does not let one accurately control the size of the shard files, it becomes very easy to build a large dataset without any warnings that it will be broken -- even when you think you are mitigating this problem by setting `max_shard_size`.
```
File " /path/to/sd-test-suite-v1/venv/lib/site-packages/datasets/builder.py", line 1763, in _prepare_split_single
for _, table in generator:
File " /path/to/sd-test-suite-v1/venv/lib/site-packages/datasets/packaged_modules/parquet/parquet.py", line 69, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1323, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
```
### Steps to reproduce the bug
1. Clone [example repo](https://github.com/salieri/hf-dataset-shard-size-bug)
2. Follow steps in [README.md](https://github.com/salieri/hf-dataset-shard-size-bug/blob/main/README.md)
3. After uploading the dataset, you will see that the shard file size varies between `30MB` and `200MB` -- way beyond the `max_shard_size='75MB'` limit (example: `train-00003-of-00131...` is `155MB` in [here](https://huggingface.co./datasets/slri/shard-size-test/tree/main/data))
(Note that this example repo does not generate shard files that are so large that they would trigger #4721)
### Expected behavior
The shard file size should remain below or equal to `max_shard_size`.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.10.157-139.675.amzn2.aarch64-aarch64-with-glibc2.17
- Python version: 3.7.15
- PyArrow version: 10.0.1
- Pandas version: 1.3.5 | {
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"Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR? ",
"> Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR?\r\n\r\nYeah I can do that sometime next week. Should the storage_options be a new arg here? I’ll look around for anywhere else where fs is an arg.",
"Closed by #5393."
] | 2022-12-22T21:00:45 | 2023-01-23T10:50:05 | 2023-01-23T10:50:04 | CONTRIBUTOR | null | null | null | ### Describe the bug
The `fs=` argument was deprecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in favor of automagically figuring it out via fsspec:
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L1339-L1340
Is there a reason the same thing shouldn't also apply to `datasets.load.load_from_disk()` as well ?
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/load.py#L1779
### Steps to reproduce the bug
n/a
### Expected behavior
n/a
### Environment info
n/a | {
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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) | 0.010576 / 0.011353 (-0.000777) | 0.006010 / 0.011008 (-0.004998) | 0.109375 / 0.038508 (0.070867) | 0.037780 / 0.023109 (0.014670) | 0.381552 / 0.275898 (0.105654) | 0.446039 / 0.323480 (0.122559) | 0.009004 / 0.007986 (0.001019) | 0.005653 / 0.004328 (0.001324) | 0.087027 / 0.004250 (0.082776) | 0.040346 / 0.037052 (0.003293) | 0.398827 / 0.258489 (0.140338) | 0.407281 / 0.293841 (0.113440) | 0.051723 / 0.128546 (-0.076824) | 0.020254 / 0.075646 (-0.055392) | 0.376841 / 0.419271 (-0.042430) | 0.055505 / 0.043533 (0.011972) | 0.383464 / 0.255139 (0.128325) | 0.436130 / 0.283200 (0.152930) | 0.117403 / 0.141683 (-0.024280) | 1.569016 / 1.452155 (0.116862) | 1.889831 / 1.492716 (0.397115) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.297962 / 0.018006 (0.279956) | 0.683699 / 0.000490 (0.683210) | 0.000918 / 0.000200 (0.000718) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026742 / 0.037411 (-0.010669) | 0.125293 / 0.014526 (0.110768) | 0.128769 / 0.176557 (-0.047787) | 0.179447 / 0.737135 (-0.557688) | 0.142032 / 0.296338 (-0.154306) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.588389 / 0.215209 (0.373180) | 5.943514 / 2.077655 (3.865859) | 2.631163 / 1.504120 (1.127043) | 1.865446 / 1.541195 (0.324252) | 2.055610 / 1.468490 (0.587120) | 1.090288 / 4.584777 (-3.494489) | 5.457151 / 3.745712 (1.711439) | 5.645614 / 5.269862 (0.375752) | 2.849492 / 4.565676 (-1.716184) | 0.140447 / 0.424275 (-0.283828) | 0.015421 / 0.007607 (0.007813) | 0.735528 / 0.226044 (0.509484) | 7.394097 / 2.268929 (5.125169) | 3.219714 / 55.444624 (-52.224911) | 2.504134 / 6.876477 (-4.372342) | 2.524291 / 2.142072 (0.382219) | 1.452776 / 4.805227 (-3.352452) | 0.256142 / 6.500664 (-6.244522) | 0.093809 / 0.075469 (0.018340) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.570046 / 1.841788 (-0.271742) | 17.360385 / 8.074308 (9.286077) | 20.750595 / 10.191392 (10.559203) | 0.218486 / 0.680424 (-0.461938) | 0.048527 / 0.534201 (-0.485674) | 0.549568 / 0.579283 (-0.029715) | 0.633993 / 0.434364 (0.199629) | 0.632585 / 0.540337 (0.092248) | 0.712817 / 1.386936 (-0.674119) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010524 / 0.011353 (-0.000829) | 0.006307 / 0.011008 (-0.004701) | 0.129671 / 0.038508 (0.091162) | 0.038952 / 0.023109 (0.015842) | 0.421936 / 0.275898 (0.146038) | 0.489911 / 0.323480 (0.166431) | 0.007661 / 0.007986 (-0.000325) | 0.005430 / 0.004328 (0.001102) | 0.091851 / 0.004250 (0.087600) | 0.059755 / 0.037052 (0.022703) | 0.449810 / 0.258489 (0.191321) | 0.519498 / 0.293841 (0.225657) | 0.061644 / 0.128546 (-0.066902) | 0.018950 / 0.075646 (-0.056696) | 0.399149 / 0.419271 (-0.020122) | 0.067670 / 0.043533 (0.024137) | 0.441091 / 0.255139 (0.185952) | 0.459327 / 0.283200 (0.176128) | 0.122476 / 0.141683 (-0.019207) | 1.760129 / 1.452155 (0.307974) | 1.767945 / 1.492716 (0.275228) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.276675 / 0.018006 (0.258669) | 0.606798 / 0.000490 (0.606308) | 0.000449 / 0.000200 (0.000249) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027762 / 0.037411 (-0.009649) | 0.108330 / 0.014526 (0.093805) | 0.134714 / 0.176557 (-0.041843) | 0.175666 / 0.737135 (-0.561470) | 0.134917 / 0.296338 (-0.161421) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.676756 / 0.215209 (0.461547) | 6.746519 / 2.077655 (4.668864) | 2.660869 / 1.504120 (1.156750) | 2.273688 / 1.541195 (0.732494) | 2.392580 / 1.468490 (0.924090) | 1.127848 / 4.584777 (-3.456929) | 5.356499 / 3.745712 (1.610787) | 2.933006 / 5.269862 (-2.336855) | 1.872877 / 4.565676 (-2.692799) | 0.139504 / 0.424275 (-0.284771) | 0.013501 / 0.007607 (0.005894) | 0.749888 / 0.226044 (0.523843) | 8.157031 / 2.268929 (5.888103) | 3.627751 / 55.444624 (-51.816874) | 2.713152 / 6.876477 (-4.163324) | 2.934585 / 2.142072 (0.792512) | 1.376398 / 4.805227 (-3.428829) | 0.251537 / 6.500664 (-6.249127) | 0.083995 / 0.075469 (0.008526) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.635446 / 1.841788 (-0.206342) | 18.435807 / 8.074308 (10.361498) | 21.395291 / 10.191392 (11.203899) | 0.247238 / 0.680424 (-0.433186) | 0.030503 / 0.534201 (-0.503698) | 0.553096 / 0.579283 (-0.026187) | 0.597583 / 0.434364 (0.163219) | 0.594135 / 0.540337 (0.053797) | 0.673815 / 1.386936 (-0.713122) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2022-12-22T16:15:30 | 2023-01-13T16:10:15 | 2023-01-13T16:00:52 | CONTRIBUTOR | null | false | {
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"Another example is that `IterableDataset.map` does not have `fn_kwargs`, among other arguments. It makes it harder to convert code from Dataset to IterableDataset.",
"Hi! `fn_kwargs` was added to `IterableDataset.map` in `datasets 2.5.0`, so please update your installation (`pip install -U datasets`) to use it.\r\n\r\nRegarding `column_names`, I agree we should add this property to `IterableDataset`. In the meantime, you can use `list(dataset.features.keys())` instead.",
"Thanks! That's great news.\n\nOn Thu, Dec 22, 2022, 07:48 Mario Šaško ***@***.***> wrote:\n\n> Hi! fn_kwargs was added to IterableDataset.map in datasets 2.5.0, so\n> please update your installation (pip install -U datasets) to use it.\n>\n> Regarding column_names, I agree we should add this property to\n> IterableDataset. In the meantime, you can use\n> list(dataset.features.keys()) instead.\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5383#issuecomment-1362993633>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AAHD6N2EQUFEOUFDW3VHSILWORZ45ANCNFSM6AAAAAATGKWVGM>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n",
"I'm marking this issue as a \"good first issue\", as it makes sense to have `IterableDataset.column_names` in the API. Besides the case when `features` are `None` (e.g., `features` are `None` after `map`), in which we can also return `column_names` as `None`, adding this property should be straightforward,",
"Hi @mariosasko, I can work on this if that's ok?",
"Yes! I've assigned you the issue."
] | 2022-12-22T05:27:02 | 2023-03-13T19:03:33 | 2023-03-13T19:03:33 | NONE | null | null | null | ### Describe the bug
The documentation on [Stream](https://huggingface.co./docs/datasets/v1.18.2/stream.html) seems to imply that IterableDataset behaves just like a Dataset. However, examples like
```
dataset.map(augment_data, batched=True, remove_columns=dataset.column_names, ...)
```
will not work because `.column_names` does not exist on IterableDataset. I cannot find any clear explanation on why this is not available, is it an oversight? We do have `iterable_ds.features` available.
### Steps to reproduce the bug
See above
### Expected behavior
Dataset and IterableDataset would be expected to have the same interface, with any differences noted in the documentation.
### Environment info
n/a | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Could you review this small PR @albertvillanova ? :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011200 / 0.011353 (-0.000153) | 0.006156 / 0.011008 (-0.004852) | 0.119072 / 0.038508 (0.080564) | 0.042616 / 0.023109 (0.019507) | 0.348329 / 0.275898 (0.072431) | 0.418550 / 0.323480 (0.095070) | 0.009302 / 0.007986 (0.001316) | 0.004596 / 0.004328 (0.000267) | 0.090111 / 0.004250 (0.085860) | 0.053341 / 0.037052 (0.016289) | 0.361234 / 0.258489 (0.102745) | 0.400427 / 0.293841 (0.106586) | 0.045601 / 0.128546 (-0.082945) | 0.013806 / 0.075646 (-0.061841) | 0.393178 / 0.419271 (-0.026094) | 0.056809 / 0.043533 (0.013276) | 0.344090 / 0.255139 (0.088951) | 0.370610 / 0.283200 (0.087410) | 0.125728 / 0.141683 (-0.015955) | 1.671931 / 1.452155 (0.219776) | 1.703143 / 1.492716 (0.210427) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226534 / 0.018006 (0.208527) | 0.496487 / 0.000490 (0.495998) | 0.002235 / 0.000200 (0.002035) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031298 / 0.037411 (-0.006113) | 0.137740 / 0.014526 (0.123214) | 0.153497 / 0.176557 (-0.023059) | 0.204201 / 0.737135 (-0.532934) | 0.162324 / 0.296338 (-0.134014) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.475922 / 0.215209 (0.260712) | 4.682344 / 2.077655 (2.604689) | 2.107387 / 1.504120 (0.603267) | 1.884792 / 1.541195 (0.343597) | 2.003180 / 1.468490 (0.534690) | 0.810212 / 4.584777 (-3.774564) | 4.631047 / 3.745712 (0.885334) | 4.467606 / 5.269862 (-0.802256) | 2.334196 / 4.565676 (-2.231480) | 0.099713 / 0.424275 (-0.324562) | 0.014732 / 0.007607 (0.007125) | 0.604587 / 0.226044 (0.378543) | 5.951679 / 2.268929 (3.682751) | 2.704761 / 55.444624 (-52.739863) | 2.280695 / 6.876477 (-4.595781) | 2.279489 / 2.142072 (0.137417) | 0.962474 / 4.805227 (-3.842753) | 0.195279 / 6.500664 (-6.305385) | 0.071503 / 0.075469 (-0.003966) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.558037 / 1.841788 (-0.283751) | 17.722140 / 8.074308 (9.647832) | 16.229016 / 10.191392 (6.037624) | 0.177148 / 0.680424 (-0.503276) | 0.034162 / 0.534201 (-0.500039) | 0.513945 / 0.579283 (-0.065338) | 0.533542 / 0.434364 (0.099178) | 0.672457 / 0.540337 (0.132119) | 0.762390 / 1.386936 (-0.624546) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009739 / 0.011353 (-0.001613) | 0.006095 / 0.011008 (-0.004914) | 0.105968 / 0.038508 (0.067460) | 0.046229 / 0.023109 (0.023120) | 0.449156 / 0.275898 (0.173258) | 0.462182 / 0.323480 (0.138702) | 0.006981 / 0.007986 (-0.001004) | 0.004867 / 0.004328 (0.000539) | 0.082142 / 0.004250 (0.077891) | 0.058652 / 0.037052 (0.021600) | 0.454542 / 0.258489 (0.196052) | 0.494910 / 0.293841 (0.201069) | 0.047159 / 0.128546 (-0.081387) | 0.014677 / 0.075646 (-0.060969) | 0.370819 / 0.419271 (-0.048452) | 0.064603 / 0.043533 (0.021070) | 0.441514 / 0.255139 (0.186375) | 0.442802 / 0.283200 (0.159603) | 0.138603 / 0.141683 (-0.003080) | 1.692810 / 1.452155 (0.240655) | 1.894596 / 1.492716 (0.401880) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.281681 / 0.018006 (0.263675) | 0.532693 / 0.000490 (0.532203) | 0.005484 / 0.000200 (0.005284) | 0.000156 / 0.000054 (0.000102) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032994 / 0.037411 (-0.004417) | 0.134614 / 0.014526 (0.120088) | 0.142286 / 0.176557 (-0.034270) | 0.187220 / 0.737135 (-0.549916) | 0.144897 / 0.296338 (-0.151441) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.519536 / 0.215209 (0.304327) | 5.214429 / 2.077655 (3.136775) | 2.612575 / 1.504120 (1.108455) | 2.369085 / 1.541195 (0.827891) | 2.503157 / 1.468490 (1.034667) | 0.834827 / 4.584777 (-3.749950) | 4.586789 / 3.745712 (0.841077) | 4.472605 / 5.269862 (-0.797257) | 2.314471 / 4.565676 (-2.251205) | 0.095817 / 0.424275 (-0.328458) | 0.014086 / 0.007607 (0.006478) | 0.605875 / 0.226044 (0.379831) | 6.153143 / 2.268929 (3.884214) | 3.187456 / 55.444624 (-52.257169) | 2.755377 / 6.876477 (-4.121100) | 2.777118 / 2.142072 (0.635046) | 0.967285 / 4.805227 (-3.837942) | 0.199202 / 6.500664 (-6.301462) | 0.075979 / 0.075469 (0.000510) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.481758 / 1.841788 (-0.360030) | 18.053769 / 8.074308 (9.979461) | 15.558780 / 10.191392 (5.367388) | 0.226135 / 0.680424 (-0.454288) | 0.021668 / 0.534201 (-0.512533) | 0.562618 / 0.579283 (-0.016666) | 0.518183 / 0.434364 (0.083819) | 0.628580 / 0.540337 (0.088243) | 0.740368 / 1.386936 (-0.646568) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4e4d46eec24c36799c0efcc1b7231f597039c497 \"CML watermark\")\n"
] | 2022-12-20T15:52:44 | 2023-01-26T09:51:13 | 2023-01-26T09:42:45 | MEMBER | null | false | {
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} | this way we can know the cause of the disconnect
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"Hi! This error can happen if there is a local file/folder with the same name as the requested dataset. And to avoid it, rename the local file/folder.\r\n\r\nSoon, it will be possible to explicitly request a Hub dataset as follows:https://github.com/huggingface/datasets/issues/5228#issuecomment-1313494020"
] | 2022-12-20T12:40:14 | 2023-02-15T16:24:57 | 2023-02-15T16:24:57 | NONE | null | null | null | ### Describe the bug
When trying to load `the_pile` dataset from the library, I get a `FileNotFound` error.
### Steps to reproduce the bug
Steps to reproduce:
Run:
```
from datasets import load_dataset
dataset = load_dataset("the_pile")
```
I get the output:
"name": "FileNotFoundError",
"message": "Unable to resolve any data file that matches '['**']' at /storage/store/work/lgrinszt/memorization/the_pile with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'BLP', 'BMP', 'DIB', 'BUFR', 'CUR', 'PCX', 'DCX', 'DDS', 'PS', 'EPS', 'FIT', 'FITS', 'FLI', 'FLC', 'FTC', 'FTU', 'GBR', 'GIF', 'GRIB', 'H5', 'HDF', 'PNG', 'APNG', 'JP2', 'J2K', 'JPC', 'JPF', 'JPX', 'J2C', 'ICNS', 'ICO', 'IM', 'IIM', 'TIF', 'TIFF', 'JFIF', 'JPE', 'JPG', 'JPEG', 'MPG', 'MPEG', 'MSP', 'PCD', 'PXR', 'PBM', 'PGM', 'PPM', 'PNM', 'PSD', 'BW', 'RGB', 'RGBA', 'SGI', 'RAS', 'TGA', 'ICB', 'VDA', 'VST', 'WEBP', 'WMF', 'EMF', 'XBM', 'XPM', 'aiff', 'au', 'avr', 'caf', 'flac', 'htk', 'svx', 'mat4', 'mat5', 'mpc2k', 'ogg', 'paf', 'pvf', 'raw', 'rf64', 'sd2', 'sds', 'ircam', 'voc', 'w64', 'wav', 'nist', 'wavex', 'wve', 'xi', 'mp3', 'opus', 'AIFF', 'AU', 'AVR', 'CAF', 'FLAC', 'HTK', 'SVX', 'MAT4', 'MAT5', 'MPC2K', 'OGG', 'PAF', 'PVF', 'RAW', 'RF64', 'SD2', 'SDS', 'IRCAM', 'VOC', 'W64', 'WAV', 'NIST', 'WAVEX', 'WVE', 'XI', 'MP3', 'OPUS', 'zip']"
### Expected behavior
`the_pile` dataset should be dowloaded.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-4.15.0-112-generic-x86_64-with-glibc2.27
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5380 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5380/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5380/comments | https://api.github.com/repos/huggingface/datasets/issues/5380/events | https://github.com/huggingface/datasets/issues/5380 | 1,504,404,043 | I_kwDODunzps5Zq2JL | 5,380 | Improve dataset `.skip()` speed in streaming mode | {
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"Hi! I agree `skip` can be inefficient to use in the current state.\r\n\r\nTo make it fast, we could use \"statistics\" stored in Parquet metadata and read only the chunks needed to form a dataset. \r\n\r\nAnd thanks to the \"datasets-server\" project, which aims to store the Parquet versions of the Hub datasets (only the smaller datasets are covered currently), this solution can also be applied to datasets stored in formats other than Parquet. (cc @severo)",
"@mariosasko do the current parquet files created by the datasets-server already have the required \"statistics\"? If not, please open an issue on https://github.com/huggingface/datasets-server with some details to make sure we implement it.",
"Yes, nothing has to be changed on the datasets-server side. What I mean by \"statistics\" is that we can use the \"row_group\" metadata embedded in a Parquet file (by default) to fetch the requested rows more efficiently.",
"Glad to see the feature could be of interest. \r\n\r\nI'm sure there are many possible ways to implement this feature. I don't know enough about the datasets-server, but I guess that it is not instantaneous, in the sense that user-owned private datasets might need hours or days until they are ported to the datasets-server (if at all), which could be cumbersome. Having optionally that information in the `dataset_infos.json` file would make it easier for users to control the skip process a bit.",
"re: statistics:\r\n\r\n- https://arrow.apache.org/docs/python/generated/pyarrow.parquet.FileMetaData.html\r\n- https://arrow.apache.org/docs/python/generated/pyarrow.parquet.RowGroupMetaData.html\r\n\r\n```python\r\n>>> import pyarrow.parquet as pq\r\n>>> import hffs\r\n>>> fs = hffs.HfFileSystem(\"glue\", repo_type=\"dataset\", revision=\"refs/convert/parquet\")\r\n>>> metadata = pq.read_metadata(\"ax/glue-test.parquet\", filesystem=fs)\r\n>>> metadata\r\n<pyarrow._parquet.FileMetaData object at 0x7f4537cec400>\r\n created_by: parquet-cpp-arrow version 7.0.0\r\n num_columns: 4\r\n num_rows: 1104\r\n num_row_groups: 2\r\n format_version: 1.0\r\n serialized_size: 2902\r\n>>> metadata.row_group(0)\r\n<pyarrow._parquet.RowGroupMetaData object at 0x7f45564bcbd0>\r\n num_columns: 4\r\n num_rows: 1000\r\n total_byte_size: 164474\r\n>>> metadata.row_group(1)\r\n<pyarrow._parquet.RowGroupMetaData object at 0x7f455005c400>\r\n num_columns: 4\r\n num_rows: 104\r\n total_byte_size: 13064\r\n```",
"> user-owned private datasets might need hours or days until they are ported to the datasets-server (if at all)\r\n\r\nprivate datasets are not supported yet (https://github.com/huggingface/datasets-server/issues/39)",
"@versae `Dataset.push_to_hub` writes shards in Parquet, so this solution would also work for such datasets (immediately after the push). ",
"@mariosasko that is right. However, there are still a good amount of datasets for which the shards are created manually. In our very specific case, we create medium-sized datasets (rarely over 100-200GB) of both text and audio, we prepare the shards by hand and then upload then. It would be great to have immediate access to this download skipping feature for them too.",
"From looking at Arrow's source, it seems Parquet stores metadata at the end, which means one needs to iterate over a Parquet file's data before accessing its metadata. We could mimic Dask to address this \"limitation\" and write metadata in a `_metadata`/`_common_metadata` file in `to_parquet`/`push_to_hub`, which we could then use to optimize reads (if present). Plus, it's handy that PyArrow can also parse these metadata files.",
"So if Parquet metadata needs to be in its own file anyway, why not implement this skipping feature by storing the example counts per shard in `dataset_infos.json`? That would allow:\r\n- Support both private and public datasets\r\n- Immediate access to the feature upon uploading of shards\r\n- Use any dataset, not only those uploaded using `.push_to_hub()`\r\n\r\nA proper Parquet metadata file could still be created and \"overwrite\" the `dataset_infos.json` info in the datasets-server."
] | 2022-12-20T11:25:23 | 2023-03-08T10:47:12 | null | CONTRIBUTOR | null | null | null | ### Feature request
Add extra information to the `dataset_infos.json` file to include the number of samples/examples in each shard, for example in a new field `num_examples` alongside `num_bytes`. The `.skip()` function could use this information to ignore the download of a shard when in streaming mode, which AFAICT it should speed up the skipping process.
### Motivation
When resuming from a checkpoint after a crashed run, using `dataset.skip()` is very convenient to recover the exact state of the data and to not train again over the same examples (assuming same seed, no shuffling). However, I have noticed that for audio datasets in streaming mode this is very costly in terms of time, as shards need to be downloaded every time before skipping the right number of examples.
### Your contribution
I took a look already at the code, but it seems a change like this is way deeper than I am able to manage, as it touches the library in several parts. I could give it a try but might need some guidance on the internals. | {
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https://api.github.com/repos/huggingface/datasets/issues/5379 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5379/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5379/comments | https://api.github.com/repos/huggingface/datasets/issues/5379/events | https://github.com/huggingface/datasets/pull/5379 | 1,504,010,639 | PR_kwDODunzps5F1r2k | 5,379 | feat: depth estimation dataset guide. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for the changes, looks good to me!",
"@stevhliu I have pushed some quality improvements both in terms of code and content. Would you be able to re-review? ",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008325 / 0.011353 (-0.003028) | 0.004432 / 0.011008 (-0.006576) | 0.099794 / 0.038508 (0.061286) | 0.029469 / 0.023109 (0.006360) | 0.306554 / 0.275898 (0.030656) | 0.367373 / 0.323480 (0.043893) | 0.007532 / 0.007986 (-0.000454) | 0.003310 / 0.004328 (-0.001018) | 0.077453 / 0.004250 (0.073203) | 0.034836 / 0.037052 (-0.002216) | 0.311696 / 0.258489 (0.053207) | 0.349683 / 0.293841 (0.055842) | 0.033089 / 0.128546 (-0.095457) | 0.011339 / 0.075646 (-0.064307) | 0.321699 / 0.419271 (-0.097573) | 0.040213 / 0.043533 (-0.003320) | 0.304741 / 0.255139 (0.049602) | 0.331569 / 0.283200 (0.048369) | 0.090397 / 0.141683 (-0.051285) | 1.526001 / 1.452155 (0.073847) | 1.558863 / 1.492716 (0.066146) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.179446 / 0.018006 (0.161440) | 0.416308 / 0.000490 (0.415818) | 0.002390 / 0.000200 (0.002190) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023641 / 0.037411 (-0.013770) | 0.096672 / 0.014526 (0.082147) | 0.104330 / 0.176557 (-0.072227) | 0.146338 / 0.737135 (-0.590797) | 0.108278 / 0.296338 (-0.188060) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420194 / 0.215209 (0.204985) | 4.196981 / 2.077655 (2.119326) | 1.861206 / 1.504120 (0.357086) | 1.658748 / 1.541195 (0.117554) | 1.704309 / 1.468490 (0.235819) | 0.691639 / 4.584777 (-3.893138) | 3.346303 / 3.745712 (-0.399409) | 1.932962 / 5.269862 (-3.336900) | 1.299395 / 4.565676 (-3.266281) | 0.081869 / 0.424275 (-0.342406) | 0.012415 / 0.007607 (0.004808) | 0.530805 / 0.226044 (0.304761) | 5.293486 / 2.268929 (3.024558) | 2.328327 / 55.444624 (-53.116297) | 1.964956 / 6.876477 (-4.911521) | 2.002793 / 2.142072 (-0.139280) | 0.813380 / 4.805227 (-3.991847) | 0.150030 / 6.500664 (-6.350634) | 0.065194 / 0.075469 (-0.010275) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259421 / 1.841788 (-0.582367) | 13.667796 / 8.074308 (5.593488) | 13.819121 / 10.191392 (3.627729) | 0.136718 / 0.680424 (-0.543706) | 0.028510 / 0.534201 (-0.505691) | 0.402246 / 0.579283 (-0.177037) | 0.405279 / 0.434364 (-0.029085) | 0.467185 / 0.540337 (-0.073153) | 0.554213 / 1.386936 (-0.832723) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006738 / 0.011353 (-0.004615) | 0.004616 / 0.011008 (-0.006393) | 0.096978 / 0.038508 (0.058470) | 0.027750 / 0.023109 (0.004640) | 0.411505 / 0.275898 (0.135607) | 0.441796 / 0.323480 (0.118316) | 0.005073 / 0.007986 (-0.002913) | 0.003360 / 0.004328 (-0.000968) | 0.074445 / 0.004250 (0.070194) | 0.040654 / 0.037052 (0.003602) | 0.414277 / 0.258489 (0.155788) | 0.448665 / 0.293841 (0.154824) | 0.032346 / 0.128546 (-0.096200) | 0.011533 / 0.075646 (-0.064114) | 0.317349 / 0.419271 (-0.101923) | 0.041934 / 0.043533 (-0.001599) | 0.409102 / 0.255139 (0.153963) | 0.429977 / 0.283200 (0.146777) | 0.089459 / 0.141683 (-0.052224) | 1.518127 / 1.452155 (0.065973) | 1.569902 / 1.492716 (0.077186) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232648 / 0.018006 (0.214642) | 0.413751 / 0.000490 (0.413261) | 0.000404 / 0.000200 (0.000204) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025468 / 0.037411 (-0.011943) | 0.098195 / 0.014526 (0.083669) | 0.108882 / 0.176557 (-0.067674) | 0.150059 / 0.737135 (-0.587076) | 0.110742 / 0.296338 (-0.185597) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445326 / 0.215209 (0.230117) | 4.449200 / 2.077655 (2.371545) | 2.098939 / 1.504120 (0.594819) | 1.861207 / 1.541195 (0.320012) | 1.901385 / 1.468490 (0.432894) | 0.695287 / 4.584777 (-3.889490) | 3.461775 / 3.745712 (-0.283938) | 2.998566 / 5.269862 (-2.271296) | 1.555036 / 4.565676 (-3.010641) | 0.082789 / 0.424275 (-0.341486) | 0.012772 / 0.007607 (0.005165) | 0.564855 / 0.226044 (0.338811) | 5.631049 / 2.268929 (3.362120) | 2.543771 / 55.444624 (-52.900854) | 2.194378 / 6.876477 (-4.682099) | 2.267168 / 2.142072 (0.125095) | 0.803330 / 4.805227 (-4.001898) | 0.151336 / 6.500664 (-6.349328) | 0.067015 / 0.075469 (-0.008454) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.298422 / 1.841788 (-0.543366) | 13.933637 / 8.074308 (5.859329) | 13.570848 / 10.191392 (3.379456) | 0.150787 / 0.680424 (-0.529637) | 0.016911 / 0.534201 (-0.517290) | 0.384771 / 0.579283 (-0.194512) | 0.397505 / 0.434364 (-0.036858) | 0.450931 / 0.540337 (-0.089406) | 0.534501 / 1.386936 (-0.852435) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n",
"@lhoestq @nateraw made some changes as per the comments. PTAL and approve as necessary. ",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009037 / 0.011353 (-0.002316) | 0.004970 / 0.011008 (-0.006038) | 0.099223 / 0.038508 (0.060715) | 0.034935 / 0.023109 (0.011826) | 0.297027 / 0.275898 (0.021129) | 0.352861 / 0.323480 (0.029382) | 0.007558 / 0.007986 (-0.000427) | 0.003903 / 0.004328 (-0.000425) | 0.075663 / 0.004250 (0.071413) | 0.042577 / 0.037052 (0.005524) | 0.307182 / 0.258489 (0.048693) | 0.344237 / 0.293841 (0.050396) | 0.041438 / 0.128546 (-0.087108) | 0.012159 / 0.075646 (-0.063487) | 0.333771 / 0.419271 (-0.085501) | 0.047847 / 0.043533 (0.004314) | 0.290797 / 0.255139 (0.035658) | 0.320517 / 0.283200 (0.037318) | 0.098334 / 0.141683 (-0.043349) | 1.446187 / 1.452155 (-0.005968) | 1.495506 / 1.492716 (0.002789) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203704 / 0.018006 (0.185698) | 0.441325 / 0.000490 (0.440835) | 0.001173 / 0.000200 (0.000973) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026694 / 0.037411 (-0.010718) | 0.103819 / 0.014526 (0.089294) | 0.116377 / 0.176557 (-0.060179) | 0.158280 / 0.737135 (-0.578856) | 0.119797 / 0.296338 (-0.176541) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405723 / 0.215209 (0.190514) | 4.047633 / 2.077655 (1.969979) | 1.805652 / 1.504120 (0.301532) | 1.611382 / 1.541195 (0.070187) | 1.663117 / 1.468490 (0.194627) | 0.692589 / 4.584777 (-3.892188) | 3.689970 / 3.745712 (-0.055742) | 2.089760 / 5.269862 (-3.180101) | 1.450576 / 4.565676 (-3.115101) | 0.085276 / 0.424275 (-0.338999) | 0.012042 / 0.007607 (0.004434) | 0.513159 / 0.226044 (0.287115) | 5.123235 / 2.268929 (2.854306) | 2.281864 / 55.444624 (-53.162761) | 1.926170 / 6.876477 (-4.950307) | 2.035093 / 2.142072 (-0.106979) | 0.857457 / 4.805227 (-3.947770) | 0.166088 / 6.500664 (-6.334576) | 0.062115 / 0.075469 (-0.013354) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197776 / 1.841788 (-0.644012) | 14.674452 / 8.074308 (6.600144) | 14.275990 / 10.191392 (4.084598) | 0.170848 / 0.680424 (-0.509576) | 0.028613 / 0.534201 (-0.505588) | 0.438650 / 0.579283 (-0.140633) | 0.439323 / 0.434364 (0.004959) | 0.515090 / 0.540337 (-0.025247) | 0.614216 / 1.386936 (-0.772720) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007159 / 0.011353 (-0.004194) | 0.005142 / 0.011008 (-0.005866) | 0.096953 / 0.038508 (0.058445) | 0.033036 / 0.023109 (0.009927) | 0.391790 / 0.275898 (0.115892) | 0.427120 / 0.323480 (0.103640) | 0.005691 / 0.007986 (-0.002294) | 0.004848 / 0.004328 (0.000519) | 0.072258 / 0.004250 (0.068008) | 0.049017 / 0.037052 (0.011965) | 0.387267 / 0.258489 (0.128778) | 0.437112 / 0.293841 (0.143272) | 0.036360 / 0.128546 (-0.092186) | 0.012249 / 0.075646 (-0.063397) | 0.336246 / 0.419271 (-0.083025) | 0.048777 / 0.043533 (0.005244) | 0.397872 / 0.255139 (0.142733) | 0.399768 / 0.283200 (0.116568) | 0.101283 / 0.141683 (-0.040400) | 1.443999 / 1.452155 (-0.008156) | 1.575496 / 1.492716 (0.082779) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220952 / 0.018006 (0.202946) | 0.442220 / 0.000490 (0.441730) | 0.000406 / 0.000200 (0.000206) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028626 / 0.037411 (-0.008786) | 0.109929 / 0.014526 (0.095403) | 0.120989 / 0.176557 (-0.055568) | 0.157377 / 0.737135 (-0.579758) | 0.125522 / 0.296338 (-0.170816) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436565 / 0.215209 (0.221356) | 4.380771 / 2.077655 (2.303117) | 2.200003 / 1.504120 (0.695883) | 2.013289 / 1.541195 (0.472094) | 2.052658 / 1.468490 (0.584168) | 0.703706 / 4.584777 (-3.881071) | 3.823289 / 3.745712 (0.077577) | 2.064882 / 5.269862 (-3.204980) | 1.330834 / 4.565676 (-3.234842) | 0.085945 / 0.424275 (-0.338330) | 0.012511 / 0.007607 (0.004904) | 0.544171 / 0.226044 (0.318127) | 5.476059 / 2.268929 (3.207130) | 2.695586 / 55.444624 (-52.749039) | 2.330239 / 6.876477 (-4.546238) | 2.429290 / 2.142072 (0.287218) | 0.843154 / 4.805227 (-3.962073) | 0.169334 / 6.500664 (-6.331330) | 0.064261 / 0.075469 (-0.011209) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268344 / 1.841788 (-0.573444) | 14.934342 / 8.074308 (6.860034) | 13.555389 / 10.191392 (3.363997) | 0.142725 / 0.680424 (-0.537699) | 0.017891 / 0.534201 (-0.516310) | 0.424833 / 0.579283 (-0.154450) | 0.420035 / 0.434364 (-0.014329) | 0.491009 / 0.540337 (-0.049329) | 0.586953 / 1.386936 (-0.799983) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n",
"Merging this PR with approvals from @stevhliu @lhoestq. ",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008586 / 0.011353 (-0.002767) | 0.004659 / 0.011008 (-0.006350) | 0.100343 / 0.038508 (0.061835) | 0.029861 / 0.023109 (0.006751) | 0.301090 / 0.275898 (0.025192) | 0.369528 / 0.323480 (0.046048) | 0.006920 / 0.007986 (-0.001065) | 0.003513 / 0.004328 (-0.000815) | 0.078514 / 0.004250 (0.074263) | 0.035285 / 0.037052 (-0.001767) | 0.311257 / 0.258489 (0.052768) | 0.353995 / 0.293841 (0.060154) | 0.033733 / 0.128546 (-0.094813) | 0.011489 / 0.075646 (-0.064157) | 0.323095 / 0.419271 (-0.096176) | 0.040808 / 0.043533 (-0.002725) | 0.301779 / 0.255139 (0.046640) | 0.348517 / 0.283200 (0.065318) | 0.086962 / 0.141683 (-0.054721) | 1.496270 / 1.452155 (0.044115) | 1.514260 / 1.492716 (0.021544) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.189502 / 0.018006 (0.171496) | 0.419326 / 0.000490 (0.418837) | 0.002160 / 0.000200 (0.001960) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023669 / 0.037411 (-0.013742) | 0.096574 / 0.014526 (0.082048) | 0.105970 / 0.176557 (-0.070587) | 0.148531 / 0.737135 (-0.588605) | 0.109948 / 0.296338 (-0.186391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424968 / 0.215209 (0.209759) | 4.246292 / 2.077655 (2.168637) | 1.911062 / 1.504120 (0.406943) | 1.700733 / 1.541195 (0.159538) | 1.760756 / 1.468490 (0.292266) | 0.696966 / 4.584777 (-3.887811) | 3.372320 / 3.745712 (-0.373392) | 2.886281 / 5.269862 (-2.383581) | 1.553082 / 4.565676 (-3.012594) | 0.082835 / 0.424275 (-0.341440) | 0.012688 / 0.007607 (0.005081) | 0.536352 / 0.226044 (0.310308) | 5.382510 / 2.268929 (3.113582) | 2.365664 / 55.444624 (-53.078960) | 1.995631 / 6.876477 (-4.880845) | 2.073865 / 2.142072 (-0.068207) | 0.819109 / 4.805227 (-3.986118) | 0.150278 / 6.500664 (-6.350386) | 0.065201 / 0.075469 (-0.010268) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239835 / 1.841788 (-0.601953) | 13.911847 / 8.074308 (5.837539) | 13.500433 / 10.191392 (3.309041) | 0.137153 / 0.680424 (-0.543271) | 0.028451 / 0.534201 (-0.505750) | 0.394659 / 0.579283 (-0.184625) | 0.404915 / 0.434364 (-0.029449) | 0.458944 / 0.540337 (-0.081394) | 0.542288 / 1.386936 (-0.844648) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006791 / 0.011353 (-0.004562) | 0.004590 / 0.011008 (-0.006419) | 0.098697 / 0.038508 (0.060189) | 0.027634 / 0.023109 (0.004525) | 0.344383 / 0.275898 (0.068485) | 0.385607 / 0.323480 (0.062127) | 0.005413 / 0.007986 (-0.002573) | 0.003447 / 0.004328 (-0.000881) | 0.077268 / 0.004250 (0.073018) | 0.041823 / 0.037052 (0.004770) | 0.342904 / 0.258489 (0.084414) | 0.399371 / 0.293841 (0.105530) | 0.032668 / 0.128546 (-0.095879) | 0.011598 / 0.075646 (-0.064048) | 0.319973 / 0.419271 (-0.099299) | 0.041760 / 0.043533 (-0.001773) | 0.340510 / 0.255139 (0.085371) | 0.377929 / 0.283200 (0.094730) | 0.090889 / 0.141683 (-0.050793) | 1.496068 / 1.452155 (0.043913) | 1.574884 / 1.492716 (0.082168) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230489 / 0.018006 (0.212483) | 0.425234 / 0.000490 (0.424745) | 0.000406 / 0.000200 (0.000206) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024650 / 0.037411 (-0.012761) | 0.102706 / 0.014526 (0.088180) | 0.108017 / 0.176557 (-0.068539) | 0.143645 / 0.737135 (-0.593490) | 0.110556 / 0.296338 (-0.185782) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468038 / 0.215209 (0.252829) | 4.670514 / 2.077655 (2.592860) | 2.446620 / 1.504120 (0.942500) | 2.241255 / 1.541195 (0.700060) | 2.286409 / 1.468490 (0.817919) | 0.698923 / 4.584777 (-3.885854) | 3.401121 / 3.745712 (-0.344592) | 1.892399 / 5.269862 (-3.377462) | 1.163101 / 4.565676 (-3.402575) | 0.082567 / 0.424275 (-0.341708) | 0.012662 / 0.007607 (0.005055) | 0.571262 / 0.226044 (0.345218) | 5.731740 / 2.268929 (3.462812) | 2.879649 / 55.444624 (-52.564975) | 2.533846 / 6.876477 (-4.342631) | 2.654789 / 2.142072 (0.512717) | 0.811345 / 4.805227 (-3.993882) | 0.152495 / 6.500664 (-6.348169) | 0.067748 / 0.075469 (-0.007721) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267852 / 1.841788 (-0.573935) | 14.114920 / 8.074308 (6.040612) | 14.355403 / 10.191392 (4.164011) | 0.150393 / 0.680424 (-0.530031) | 0.016855 / 0.534201 (-0.517346) | 0.378710 / 0.579283 (-0.200573) | 0.385380 / 0.434364 (-0.048984) | 0.439054 / 0.540337 (-0.101284) | 0.524343 / 1.386936 (-0.862593) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2022-12-20T05:32:11 | 2023-01-13T12:30:31 | 2023-01-13T12:23:34 | MEMBER | null | false | {
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} | This PR adds a guide for prepping datasets for depth estimation.
PR to add documentation images is up here: https://huggingface.co./datasets/huggingface/documentation-images/discussions/22 | {
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https://api.github.com/repos/huggingface/datasets/issues/5378 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5378/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5378/comments | https://api.github.com/repos/huggingface/datasets/issues/5378/events | https://github.com/huggingface/datasets/issues/5378 | 1,503,887,508 | I_kwDODunzps5Zo4CU | 5,378 | The dataset "the_pile", subset "enron_emails" , load_dataset() failure | {
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"Thanks for reporting @shaoyuta. We are investigating it.\r\n\r\nWe are transferring the issue to \"the_pile\" Community tab on the Hub: https://huggingface.co./datasets/the_pile/discussions/4"
] | 2022-12-20T02:19:13 | 2022-12-20T07:52:54 | 2022-12-20T07:52:54 | NONE | null | null | null | ### Describe the bug
When run
"datasets.load_dataset("the_pile","enron_emails")" failure
### Steps to reproduce the bug
Run below code in python cli:
>>> import datasets
>>> datasets.load_dataset("the_pile","enron_emails")
### Expected behavior
Load dataset "the_pile", "enron_emails" successfully.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.7.1
- Platform: Linux-5.15.0-53-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- PyArrow version: 10.0.0
- Pandas version: 1.4.3
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https://api.github.com/repos/huggingface/datasets/issues/5377 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5377/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5377/comments | https://api.github.com/repos/huggingface/datasets/issues/5377/events | https://github.com/huggingface/datasets/pull/5377 | 1,503,477,833 | PR_kwDODunzps5Fz5lw | 5,377 | Add a parallel implementation of to_tf_dataset() | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Failing because the test server uses Py3.7 but the `SharedMemory` features require Py3.8! I forgot we still support 3.7 for another couple of months. I'm not sure exactly how to proceed, whether I should leave this PR until then, or just gate the feature behind a version check and skip the tests until the Python version catches up.",
"I haven't played with `NumpyMultiprocessingGenerator` so I can't really help here, but this sounds promising :) Otherwise I think it's also fine to allow `num_workers` only for py>=3.8 for now. You can skip the test on 3.7 and make sure to raise an informative error if someone wants to use `num_workers` with 3.7",
"Lots of comments here - I'll reply to the specific code comments underneath them, but in response to the general comments:\r\n\r\n@gante: I think this approach is much more performant than a `multiprocessing.Pool`. The reason is that when results are returned from a process `Pool`, the returned Python objects are pickled by the child processes, sent down a pipe and unpickled by the parent process. This creates a huge single-process bottleneck as the parent has to unpickle lots of large NumPy arrays, which is quite slow.\r\n\r\nWhen you use a `SharedMemory` approach, the data is just **there** for the parent process - the child and the parent are writing to exactly the same array in memory, and no pickling or unpickling occurs. This means the parent can just immediately copy the array (which is much faster than unpickling) and yield it to `tf.data`. We're taking advantage of the fact that we know the data is just big NumPy arrays and we don't need the full generality of `pickle`.\r\n\r\n@lhoestq: Sounds good! I'll add a clear error and skip the tests on Py<=3.7.",
"Also, an extra technicality, just for information in case anyone looks at this PR later: Recent versions of Python allow [pickled objects to store out-of-band data](https://peps.python.org/pep-0574/). This allows for very efficient zero-copy unpickling of objects like NumPy arrays, with the unpickled object having a view on the same memory as the original. \r\n\r\nHowever, this explicitly does **not** work when the object is unpickled by a different process than the one that created it. For this to work you must explicitly allocate shared memory and create the array there, which pickle cannot handle for you. As a result, if you just benchmark unpickling vs copying of NumPy arrays it can seem like unpickling is very fast - but this is only true when the pickle was created in the unpickling process!",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008666 / 0.011353 (-0.002687) | 0.004624 / 0.011008 (-0.006384) | 0.099247 / 0.038508 (0.060739) | 0.029766 / 0.023109 (0.006657) | 0.303347 / 0.275898 (0.027449) | 0.370022 / 0.323480 (0.046542) | 0.007128 / 0.007986 (-0.000857) | 0.003446 / 0.004328 (-0.000883) | 0.076670 / 0.004250 (0.072420) | 0.038892 / 0.037052 (0.001840) | 0.313035 / 0.258489 (0.054546) | 0.350503 / 0.293841 (0.056662) | 0.033732 / 0.128546 (-0.094815) | 0.011644 / 0.075646 (-0.064003) | 0.323295 / 0.419271 (-0.095977) | 0.040336 / 0.043533 (-0.003196) | 0.302253 / 0.255139 (0.047114) | 0.337199 / 0.283200 (0.053999) | 0.089454 / 0.141683 (-0.052229) | 1.624906 / 1.452155 (0.172752) | 1.546187 / 1.492716 (0.053470) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184614 / 0.018006 (0.166608) | 0.427397 / 0.000490 (0.426907) | 0.003342 / 0.000200 (0.003142) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023684 / 0.037411 (-0.013727) | 0.100095 / 0.014526 (0.085569) | 0.104996 / 0.176557 (-0.071560) | 0.144719 / 0.737135 (-0.592416) | 0.110759 / 0.296338 (-0.185579) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421108 / 0.215209 (0.205899) | 4.214094 / 2.077655 (2.136440) | 1.906231 / 1.504120 (0.402111) | 1.698000 / 1.541195 (0.156806) | 1.744856 / 1.468490 (0.276366) | 0.693671 / 4.584777 (-3.891106) | 3.362522 / 3.745712 (-0.383190) | 1.878470 / 5.269862 (-3.391392) | 1.167563 / 4.565676 (-3.398113) | 0.082455 / 0.424275 (-0.341820) | 0.012261 / 0.007607 (0.004654) | 0.525196 / 0.226044 (0.299152) | 5.257553 / 2.268929 (2.988624) | 2.298286 / 55.444624 (-53.146339) | 1.956106 / 6.876477 (-4.920371) | 2.006308 / 2.142072 (-0.135764) | 0.811069 / 4.805227 (-3.994158) | 0.150368 / 6.500664 (-6.350296) | 0.065699 / 0.075469 (-0.009771) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224516 / 1.841788 (-0.617272) | 13.619084 / 8.074308 (5.544776) | 14.096666 / 10.191392 (3.905274) | 0.151068 / 0.680424 (-0.529356) | 0.028819 / 0.534201 (-0.505382) | 0.402071 / 0.579283 (-0.177212) | 0.408647 / 0.434364 (-0.025717) | 0.466605 / 0.540337 (-0.073733) | 0.547094 / 1.386936 (-0.839842) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006935 / 0.011353 (-0.004418) | 0.004590 / 0.011008 (-0.006419) | 0.099398 / 0.038508 (0.060890) | 0.028145 / 0.023109 (0.005036) | 0.426582 / 0.275898 (0.150684) | 0.465712 / 0.323480 (0.142233) | 0.005254 / 0.007986 (-0.002731) | 0.004956 / 0.004328 (0.000627) | 0.075616 / 0.004250 (0.071365) | 0.039871 / 0.037052 (0.002819) | 0.428859 / 0.258489 (0.170370) | 0.470839 / 0.293841 (0.176998) | 0.032150 / 0.128546 (-0.096396) | 0.011778 / 0.075646 (-0.063868) | 0.322358 / 0.419271 (-0.096913) | 0.041974 / 0.043533 (-0.001559) | 0.427459 / 0.255139 (0.172320) | 0.446685 / 0.283200 (0.163485) | 0.092000 / 0.141683 (-0.049683) | 1.509231 / 1.452155 (0.057076) | 1.578950 / 1.492716 (0.086234) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.168047 / 0.018006 (0.150041) | 0.418993 / 0.000490 (0.418503) | 0.002855 / 0.000200 (0.002655) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025652 / 0.037411 (-0.011759) | 0.100141 / 0.014526 (0.085616) | 0.107293 / 0.176557 (-0.069264) | 0.142857 / 0.737135 (-0.594278) | 0.110933 / 0.296338 (-0.185406) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.477556 / 0.215209 (0.262347) | 4.777951 / 2.077655 (2.700296) | 2.461885 / 1.504120 (0.957765) | 2.252307 / 1.541195 (0.711112) | 2.307983 / 1.468490 (0.839493) | 0.697570 / 4.584777 (-3.887207) | 3.370323 / 3.745712 (-0.375389) | 3.131333 / 5.269862 (-2.138529) | 1.594839 / 4.565676 (-2.970838) | 0.082333 / 0.424275 (-0.341942) | 0.012574 / 0.007607 (0.004967) | 0.583704 / 0.226044 (0.357660) | 5.817675 / 2.268929 (3.548746) | 2.927054 / 55.444624 (-52.517570) | 2.582929 / 6.876477 (-4.293548) | 2.634275 / 2.142072 (0.492202) | 0.806407 / 4.805227 (-3.998821) | 0.151438 / 6.500664 (-6.349226) | 0.067429 / 0.075469 (-0.008040) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267011 / 1.841788 (-0.574776) | 13.989515 / 8.074308 (5.915207) | 14.087968 / 10.191392 (3.896576) | 0.142130 / 0.680424 (-0.538293) | 0.017201 / 0.534201 (-0.517000) | 0.383394 / 0.579283 (-0.195889) | 0.381921 / 0.434364 (-0.052443) | 0.439169 / 0.540337 (-0.101168) | 0.524215 / 1.386936 (-0.862721) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#be2ebc8f3cfeb532c933be2443094603bafcab04 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008489 / 0.011353 (-0.002864) | 0.004617 / 0.011008 (-0.006391) | 0.102035 / 0.038508 (0.063527) | 0.029850 / 0.023109 (0.006741) | 0.296789 / 0.275898 (0.020891) | 0.367270 / 0.323480 (0.043790) | 0.006934 / 0.007986 (-0.001052) | 0.004923 / 0.004328 (0.000595) | 0.079150 / 0.004250 (0.074900) | 0.036884 / 0.037052 (-0.000169) | 0.305747 / 0.258489 (0.047258) | 0.348510 / 0.293841 (0.054669) | 0.034074 / 0.128546 (-0.094472) | 0.011650 / 0.075646 (-0.063997) | 0.324226 / 0.419271 (-0.095045) | 0.041763 / 0.043533 (-0.001770) | 0.300887 / 0.255139 (0.045748) | 0.333393 / 0.283200 (0.050193) | 0.093838 / 0.141683 (-0.047844) | 1.499801 / 1.452155 (0.047646) | 1.505988 / 1.492716 (0.013272) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198610 / 0.018006 (0.180604) | 0.407380 / 0.000490 (0.406891) | 0.000367 / 0.000200 (0.000167) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022858 / 0.037411 (-0.014554) | 0.095727 / 0.014526 (0.081202) | 0.104014 / 0.176557 (-0.072543) | 0.138764 / 0.737135 (-0.598371) | 0.105860 / 0.296338 (-0.190478) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416352 / 0.215209 (0.201143) | 4.150007 / 2.077655 (2.072352) | 1.878727 / 1.504120 (0.374607) | 1.678978 / 1.541195 (0.137783) | 1.711990 / 1.468490 (0.243500) | 0.691722 / 4.584777 (-3.893055) | 3.386466 / 3.745712 (-0.359246) | 1.835730 / 5.269862 (-3.434132) | 1.149975 / 4.565676 (-3.415702) | 0.081914 / 0.424275 (-0.342362) | 0.012238 / 0.007607 (0.004631) | 0.522945 / 0.226044 (0.296900) | 5.251793 / 2.268929 (2.982864) | 2.306907 / 55.444624 (-53.137717) | 1.968400 / 6.876477 (-4.908076) | 1.981154 / 2.142072 (-0.160919) | 0.810126 / 4.805227 (-3.995101) | 0.147876 / 6.500664 (-6.352788) | 0.064042 / 0.075469 (-0.011428) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.199150 / 1.841788 (-0.642637) | 13.913473 / 8.074308 (5.839165) | 14.079132 / 10.191392 (3.887740) | 0.137387 / 0.680424 (-0.543037) | 0.028456 / 0.534201 (-0.505745) | 0.394162 / 0.579283 (-0.185122) | 0.402051 / 0.434364 (-0.032313) | 0.461944 / 0.540337 (-0.078394) | 0.542648 / 1.386936 (-0.844288) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006393 / 0.011353 (-0.004960) | 0.004599 / 0.011008 (-0.006409) | 0.097389 / 0.038508 (0.058881) | 0.027719 / 0.023109 (0.004610) | 0.341060 / 0.275898 (0.065162) | 0.379604 / 0.323480 (0.056124) | 0.004955 / 0.007986 (-0.003030) | 0.003369 / 0.004328 (-0.000959) | 0.075390 / 0.004250 (0.071139) | 0.038518 / 0.037052 (0.001466) | 0.347085 / 0.258489 (0.088596) | 0.393468 / 0.293841 (0.099627) | 0.031482 / 0.128546 (-0.097064) | 0.011585 / 0.075646 (-0.064061) | 0.317969 / 0.419271 (-0.101302) | 0.041389 / 0.043533 (-0.002144) | 0.343812 / 0.255139 (0.088673) | 0.371047 / 0.283200 (0.087848) | 0.090020 / 0.141683 (-0.051663) | 1.461690 / 1.452155 (0.009536) | 1.552458 / 1.492716 (0.059741) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188691 / 0.018006 (0.170684) | 0.415635 / 0.000490 (0.415145) | 0.005285 / 0.000200 (0.005085) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024695 / 0.037411 (-0.012716) | 0.098939 / 0.014526 (0.084413) | 0.108472 / 0.176557 (-0.068085) | 0.152635 / 0.737135 (-0.584501) | 0.109947 / 0.296338 (-0.186391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471975 / 0.215209 (0.256766) | 4.716437 / 2.077655 (2.638782) | 2.420148 / 1.504120 (0.916028) | 2.219864 / 1.541195 (0.678669) | 2.238647 / 1.468490 (0.770157) | 0.697628 / 4.584777 (-3.887149) | 3.530720 / 3.745712 (-0.214993) | 3.327354 / 5.269862 (-1.942508) | 1.665877 / 4.565676 (-2.899800) | 0.082650 / 0.424275 (-0.341625) | 0.012593 / 0.007607 (0.004986) | 0.576109 / 0.226044 (0.350065) | 5.744691 / 2.268929 (3.475762) | 2.863473 / 55.444624 (-52.581152) | 2.529616 / 6.876477 (-4.346861) | 2.562802 / 2.142072 (0.420730) | 0.805631 / 4.805227 (-3.999597) | 0.150788 / 6.500664 (-6.349876) | 0.065743 / 0.075469 (-0.009726) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.295134 / 1.841788 (-0.546654) | 14.096046 / 8.074308 (6.021738) | 13.901399 / 10.191392 (3.710007) | 0.127481 / 0.680424 (-0.552943) | 0.016666 / 0.534201 (-0.517535) | 0.381819 / 0.579283 (-0.197464) | 0.382629 / 0.434364 (-0.051735) | 0.439354 / 0.540337 (-0.100984) | 0.527662 / 1.386936 (-0.859274) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fe2ad43f59e65d39f2f2ce7442c76990493deb7 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008509 / 0.011353 (-0.002844) | 0.004523 / 0.011008 (-0.006485) | 0.100616 / 0.038508 (0.062108) | 0.029573 / 0.023109 (0.006464) | 0.306414 / 0.275898 (0.030516) | 0.377034 / 0.323480 (0.053554) | 0.007621 / 0.007986 (-0.000365) | 0.003335 / 0.004328 (-0.000993) | 0.078598 / 0.004250 (0.074348) | 0.036902 / 0.037052 (-0.000150) | 0.318146 / 0.258489 (0.059657) | 0.355626 / 0.293841 (0.061785) | 0.033441 / 0.128546 (-0.095105) | 0.011552 / 0.075646 (-0.064094) | 0.322973 / 0.419271 (-0.096299) | 0.040564 / 0.043533 (-0.002968) | 0.306451 / 0.255139 (0.051312) | 0.337591 / 0.283200 (0.054392) | 0.086822 / 0.141683 (-0.054861) | 1.484601 / 1.452155 (0.032447) | 1.542777 / 1.492716 (0.050061) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201711 / 0.018006 (0.183705) | 0.418387 / 0.000490 (0.417898) | 0.002753 / 0.000200 (0.002553) | 0.000263 / 0.000054 (0.000209) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023016 / 0.037411 (-0.014395) | 0.097313 / 0.014526 (0.082787) | 0.103435 / 0.176557 (-0.073122) | 0.142665 / 0.737135 (-0.594470) | 0.107397 / 0.296338 (-0.188942) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422739 / 0.215209 (0.207530) | 4.220126 / 2.077655 (2.142471) | 1.865447 / 1.504120 (0.361327) | 1.649647 / 1.541195 (0.108453) | 1.711655 / 1.468490 (0.243165) | 0.704269 / 4.584777 (-3.880508) | 3.407390 / 3.745712 (-0.338322) | 1.929224 / 5.269862 (-3.340638) | 1.281225 / 4.565676 (-3.284452) | 0.082924 / 0.424275 (-0.341351) | 0.012588 / 0.007607 (0.004981) | 0.531025 / 0.226044 (0.304980) | 5.339441 / 2.268929 (3.070512) | 2.298969 / 55.444624 (-53.145656) | 1.952145 / 6.876477 (-4.924332) | 2.034754 / 2.142072 (-0.107318) | 0.823672 / 4.805227 (-3.981555) | 0.151465 / 6.500664 (-6.349199) | 0.066663 / 0.075469 (-0.008807) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.258981 / 1.841788 (-0.582807) | 13.791640 / 8.074308 (5.717332) | 14.001514 / 10.191392 (3.810122) | 0.149805 / 0.680424 (-0.530619) | 0.028614 / 0.534201 (-0.505587) | 0.400266 / 0.579283 (-0.179017) | 0.405891 / 0.434364 (-0.028473) | 0.471903 / 0.540337 (-0.068435) | 0.563656 / 1.386936 (-0.823280) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006751 / 0.011353 (-0.004601) | 0.004665 / 0.011008 (-0.006343) | 0.098362 / 0.038508 (0.059854) | 0.027451 / 0.023109 (0.004342) | 0.421859 / 0.275898 (0.145961) | 0.458089 / 0.323480 (0.134609) | 0.004885 / 0.007986 (-0.003101) | 0.003459 / 0.004328 (-0.000870) | 0.075871 / 0.004250 (0.071621) | 0.036591 / 0.037052 (-0.000462) | 0.423307 / 0.258489 (0.164818) | 0.467040 / 0.293841 (0.173199) | 0.031837 / 0.128546 (-0.096710) | 0.011604 / 0.075646 (-0.064042) | 0.321132 / 0.419271 (-0.098140) | 0.041806 / 0.043533 (-0.001727) | 0.421653 / 0.255139 (0.166514) | 0.445896 / 0.283200 (0.162696) | 0.087998 / 0.141683 (-0.053685) | 1.475818 / 1.452155 (0.023664) | 1.559487 / 1.492716 (0.066770) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203096 / 0.018006 (0.185090) | 0.401381 / 0.000490 (0.400892) | 0.004037 / 0.000200 (0.003837) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023757 / 0.037411 (-0.013654) | 0.099919 / 0.014526 (0.085393) | 0.108384 / 0.176557 (-0.068173) | 0.143780 / 0.737135 (-0.593355) | 0.111528 / 0.296338 (-0.184811) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.475896 / 0.215209 (0.260686) | 4.754567 / 2.077655 (2.676912) | 2.444986 / 1.504120 (0.940866) | 2.231055 / 1.541195 (0.689860) | 2.283646 / 1.468490 (0.815156) | 0.701303 / 4.584777 (-3.883474) | 3.381597 / 3.745712 (-0.364115) | 1.878714 / 5.269862 (-3.391148) | 1.171566 / 4.565676 (-3.394111) | 0.083106 / 0.424275 (-0.341169) | 0.012575 / 0.007607 (0.004967) | 0.582570 / 0.226044 (0.356526) | 5.813677 / 2.268929 (3.544748) | 2.908578 / 55.444624 (-52.536046) | 2.548459 / 6.876477 (-4.328017) | 2.581211 / 2.142072 (0.439139) | 0.807925 / 4.805227 (-3.997302) | 0.153516 / 6.500664 (-6.347148) | 0.068763 / 0.075469 (-0.006706) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249595 / 1.841788 (-0.592193) | 14.208573 / 8.074308 (6.134265) | 14.179174 / 10.191392 (3.987781) | 0.156005 / 0.680424 (-0.524419) | 0.017045 / 0.534201 (-0.517156) | 0.377414 / 0.579283 (-0.201869) | 0.395291 / 0.434364 (-0.039073) | 0.444642 / 0.540337 (-0.095695) | 0.531626 / 1.386936 (-0.855311) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#52888645daa6854928474df6308bd997c8878ced \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008871 / 0.011353 (-0.002482) | 0.004616 / 0.011008 (-0.006392) | 0.100910 / 0.038508 (0.062402) | 0.030381 / 0.023109 (0.007272) | 0.304636 / 0.275898 (0.028737) | 0.384258 / 0.323480 (0.060778) | 0.007019 / 0.007986 (-0.000966) | 0.004262 / 0.004328 (-0.000066) | 0.077082 / 0.004250 (0.072832) | 0.035235 / 0.037052 (-0.001817) | 0.318293 / 0.258489 (0.059804) | 0.356578 / 0.293841 (0.062737) | 0.033568 / 0.128546 (-0.094978) | 0.011583 / 0.075646 (-0.064063) | 0.322442 / 0.419271 (-0.096830) | 0.041941 / 0.043533 (-0.001592) | 0.310469 / 0.255139 (0.055330) | 0.335626 / 0.283200 (0.052427) | 0.088195 / 0.141683 (-0.053487) | 1.466778 / 1.452155 (0.014623) | 1.512459 / 1.492716 (0.019743) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184126 / 0.018006 (0.166120) | 0.413392 / 0.000490 (0.412902) | 0.002191 / 0.000200 (0.001992) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023426 / 0.037411 (-0.013985) | 0.096240 / 0.014526 (0.081715) | 0.105908 / 0.176557 (-0.070648) | 0.146331 / 0.737135 (-0.590804) | 0.107441 / 0.296338 (-0.188898) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420018 / 0.215209 (0.204809) | 4.198129 / 2.077655 (2.120474) | 1.998726 / 1.504120 (0.494606) | 1.870410 / 1.541195 (0.329215) | 1.925160 / 1.468490 (0.456670) | 0.688790 / 4.584777 (-3.895987) | 3.430629 / 3.745712 (-0.315083) | 2.875616 / 5.269862 (-2.394246) | 1.566269 / 4.565676 (-2.999408) | 0.082431 / 0.424275 (-0.341844) | 0.012409 / 0.007607 (0.004802) | 0.536178 / 0.226044 (0.310134) | 5.342918 / 2.268929 (3.073989) | 2.410814 / 55.444624 (-53.033811) | 2.056518 / 6.876477 (-4.819958) | 2.240148 / 2.142072 (0.098075) | 0.804848 / 4.805227 (-4.000379) | 0.147325 / 6.500664 (-6.353340) | 0.064217 / 0.075469 (-0.011252) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285725 / 1.841788 (-0.556063) | 13.909739 / 8.074308 (5.835431) | 14.025774 / 10.191392 (3.834382) | 0.142413 / 0.680424 (-0.538011) | 0.028390 / 0.534201 (-0.505811) | 0.402345 / 0.579283 (-0.176939) | 0.404341 / 0.434364 (-0.030023) | 0.463055 / 0.540337 (-0.077282) | 0.556811 / 1.386936 (-0.830125) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006557 / 0.011353 (-0.004795) | 0.004668 / 0.011008 (-0.006340) | 0.098839 / 0.038508 (0.060331) | 0.027618 / 0.023109 (0.004508) | 0.409338 / 0.275898 (0.133440) | 0.444048 / 0.323480 (0.120568) | 0.004881 / 0.007986 (-0.003105) | 0.003434 / 0.004328 (-0.000895) | 0.076497 / 0.004250 (0.072247) | 0.038932 / 0.037052 (0.001880) | 0.411419 / 0.258489 (0.152930) | 0.451167 / 0.293841 (0.157326) | 0.031649 / 0.128546 (-0.096897) | 0.011691 / 0.075646 (-0.063955) | 0.321586 / 0.419271 (-0.097685) | 0.041984 / 0.043533 (-0.001549) | 0.407717 / 0.255139 (0.152578) | 0.434687 / 0.283200 (0.151487) | 0.086419 / 0.141683 (-0.055264) | 1.491755 / 1.452155 (0.039601) | 1.569081 / 1.492716 (0.076364) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231746 / 0.018006 (0.213739) | 0.412271 / 0.000490 (0.411781) | 0.000403 / 0.000200 (0.000203) | 0.000063 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024264 / 0.037411 (-0.013147) | 0.100478 / 0.014526 (0.085952) | 0.107065 / 0.176557 (-0.069491) | 0.140724 / 0.737135 (-0.596412) | 0.110631 / 0.296338 (-0.185707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472476 / 0.215209 (0.257267) | 4.738919 / 2.077655 (2.661265) | 2.438049 / 1.504120 (0.933929) | 2.237855 / 1.541195 (0.696660) | 2.282885 / 1.468490 (0.814395) | 0.690420 / 4.584777 (-3.894357) | 3.426487 / 3.745712 (-0.319225) | 1.842443 / 5.269862 (-3.427418) | 1.154466 / 4.565676 (-3.411210) | 0.082166 / 0.424275 (-0.342109) | 0.012309 / 0.007607 (0.004701) | 0.574730 / 0.226044 (0.348686) | 5.737566 / 2.268929 (3.468638) | 2.882405 / 55.444624 (-52.562220) | 2.540276 / 6.876477 (-4.336201) | 2.552356 / 2.142072 (0.410283) | 0.796413 / 4.805227 (-4.008815) | 0.152705 / 6.500664 (-6.347959) | 0.068273 / 0.075469 (-0.007196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244423 / 1.841788 (-0.597365) | 13.827750 / 8.074308 (5.753442) | 14.074083 / 10.191392 (3.882691) | 0.140291 / 0.680424 (-0.540133) | 0.017337 / 0.534201 (-0.516864) | 0.389314 / 0.579283 (-0.189969) | 0.390914 / 0.434364 (-0.043450) | 0.450333 / 0.540337 (-0.090004) | 0.543860 / 1.386936 (-0.843076) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2cdcddc51d3cda24c2d79ad137af9e55d0a38044 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009490 / 0.011353 (-0.001863) | 0.005211 / 0.011008 (-0.005798) | 0.100884 / 0.038508 (0.062376) | 0.035834 / 0.023109 (0.012725) | 0.293623 / 0.275898 (0.017724) | 0.378118 / 0.323480 (0.054638) | 0.008106 / 0.007986 (0.000120) | 0.005339 / 0.004328 (0.001010) | 0.076311 / 0.004250 (0.072061) | 0.045954 / 0.037052 (0.008902) | 0.308163 / 0.258489 (0.049674) | 0.353470 / 0.293841 (0.059629) | 0.038539 / 0.128546 (-0.090008) | 0.012174 / 0.075646 (-0.063472) | 0.334875 / 0.419271 (-0.084396) | 0.048602 / 0.043533 (0.005069) | 0.295803 / 0.255139 (0.040664) | 0.318894 / 0.283200 (0.035695) | 0.105487 / 0.141683 (-0.036195) | 1.433628 / 1.452155 (-0.018526) | 1.466843 / 1.492716 (-0.025873) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203426 / 0.018006 (0.185419) | 0.456877 / 0.000490 (0.456387) | 0.001452 / 0.000200 (0.001252) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028308 / 0.037411 (-0.009103) | 0.108965 / 0.014526 (0.094439) | 0.119552 / 0.176557 (-0.057005) | 0.156371 / 0.737135 (-0.580765) | 0.124141 / 0.296338 (-0.172197) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400183 / 0.215209 (0.184973) | 3.990983 / 2.077655 (1.913329) | 1.806729 / 1.504120 (0.302609) | 1.611944 / 1.541195 (0.070750) | 1.740019 / 1.468490 (0.271529) | 0.699600 / 4.584777 (-3.885177) | 3.868711 / 3.745712 (0.122999) | 3.249758 / 5.269862 (-2.020103) | 1.832213 / 4.565676 (-2.733463) | 0.085282 / 0.424275 (-0.338993) | 0.012726 / 0.007607 (0.005119) | 0.509385 / 0.226044 (0.283341) | 5.066913 / 2.268929 (2.797984) | 2.325710 / 55.444624 (-53.118914) | 1.962238 / 6.876477 (-4.914239) | 2.017576 / 2.142072 (-0.124496) | 0.839444 / 4.805227 (-3.965783) | 0.166936 / 6.500664 (-6.333728) | 0.064546 / 0.075469 (-0.010923) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196396 / 1.841788 (-0.645392) | 15.077063 / 8.074308 (7.002755) | 14.268103 / 10.191392 (4.076711) | 0.163782 / 0.680424 (-0.516642) | 0.028794 / 0.534201 (-0.505407) | 0.440564 / 0.579283 (-0.138719) | 0.439826 / 0.434364 (0.005463) | 0.514786 / 0.540337 (-0.025551) | 0.603353 / 1.386936 (-0.783583) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007874 / 0.011353 (-0.003479) | 0.005347 / 0.011008 (-0.005661) | 0.099461 / 0.038508 (0.060953) | 0.034010 / 0.023109 (0.010901) | 0.384650 / 0.275898 (0.108752) | 0.423827 / 0.323480 (0.100347) | 0.006201 / 0.007986 (-0.001784) | 0.004212 / 0.004328 (-0.000117) | 0.074354 / 0.004250 (0.070104) | 0.051675 / 0.037052 (0.014623) | 0.392488 / 0.258489 (0.133999) | 0.425828 / 0.293841 (0.131987) | 0.037444 / 0.128546 (-0.091103) | 0.012388 / 0.075646 (-0.063258) | 0.334482 / 0.419271 (-0.084789) | 0.050715 / 0.043533 (0.007182) | 0.378323 / 0.255139 (0.123184) | 0.395450 / 0.283200 (0.112250) | 0.108403 / 0.141683 (-0.033280) | 1.426803 / 1.452155 (-0.025352) | 1.532417 / 1.492716 (0.039701) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219989 / 0.018006 (0.201982) | 0.454101 / 0.000490 (0.453611) | 0.000407 / 0.000200 (0.000207) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030590 / 0.037411 (-0.006822) | 0.113483 / 0.014526 (0.098957) | 0.122603 / 0.176557 (-0.053954) | 0.161031 / 0.737135 (-0.576104) | 0.128039 / 0.296338 (-0.168300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430458 / 0.215209 (0.215249) | 4.286594 / 2.077655 (2.208940) | 2.056666 / 1.504120 (0.552546) | 1.861142 / 1.541195 (0.319948) | 1.937185 / 1.468490 (0.468695) | 0.701881 / 4.584777 (-3.882896) | 3.970144 / 3.745712 (0.224432) | 2.107118 / 5.269862 (-3.162744) | 1.351561 / 4.565676 (-3.214115) | 0.085470 / 0.424275 (-0.338805) | 0.012366 / 0.007607 (0.004759) | 0.525212 / 0.226044 (0.299168) | 5.301553 / 2.268929 (3.032625) | 2.593862 / 55.444624 (-52.850763) | 2.287315 / 6.876477 (-4.589161) | 2.368249 / 2.142072 (0.226176) | 0.855656 / 4.805227 (-3.949571) | 0.167846 / 6.500664 (-6.332818) | 0.064521 / 0.075469 (-0.010948) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237008 / 1.841788 (-0.604779) | 15.784303 / 8.074308 (7.709995) | 14.613081 / 10.191392 (4.421689) | 0.161012 / 0.680424 (-0.519412) | 0.017928 / 0.534201 (-0.516273) | 0.423905 / 0.579283 (-0.155378) | 0.428316 / 0.434364 (-0.006048) | 0.500226 / 0.540337 (-0.040112) | 0.606725 / 1.386936 (-0.780211) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#08473e2ee66acb7e6f82d3591bb9b03924a661ed \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008874 / 0.011353 (-0.002479) | 0.004581 / 0.011008 (-0.006428) | 0.100180 / 0.038508 (0.061672) | 0.029990 / 0.023109 (0.006880) | 0.301616 / 0.275898 (0.025718) | 0.343662 / 0.323480 (0.020183) | 0.007111 / 0.007986 (-0.000875) | 0.003428 / 0.004328 (-0.000900) | 0.078031 / 0.004250 (0.073780) | 0.037332 / 0.037052 (0.000279) | 0.301977 / 0.258489 (0.043488) | 0.345581 / 0.293841 (0.051740) | 0.034305 / 0.128546 (-0.094241) | 0.011660 / 0.075646 (-0.063986) | 0.322289 / 0.419271 (-0.096982) | 0.041488 / 0.043533 (-0.002045) | 0.301612 / 0.255139 (0.046473) | 0.328174 / 0.283200 (0.044974) | 0.085561 / 0.141683 (-0.056122) | 1.482114 / 1.452155 (0.029959) | 1.556194 / 1.492716 (0.063478) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.186989 / 0.018006 (0.168983) | 0.421499 / 0.000490 (0.421009) | 0.001193 / 0.000200 (0.000993) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023551 / 0.037411 (-0.013861) | 0.099868 / 0.014526 (0.085343) | 0.105233 / 0.176557 (-0.071324) | 0.141628 / 0.737135 (-0.595507) | 0.109004 / 0.296338 (-0.187335) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415189 / 0.215209 (0.199979) | 4.145716 / 2.077655 (2.068061) | 1.837917 / 1.504120 (0.333797) | 1.635043 / 1.541195 (0.093848) | 1.683299 / 1.468490 (0.214809) | 0.688538 / 4.584777 (-3.896239) | 3.412628 / 3.745712 (-0.333084) | 1.877456 / 5.269862 (-3.392405) | 1.154129 / 4.565676 (-3.411547) | 0.081850 / 0.424275 (-0.342425) | 0.012309 / 0.007607 (0.004702) | 0.522830 / 0.226044 (0.296785) | 5.238685 / 2.268929 (2.969756) | 2.277840 / 55.444624 (-53.166784) | 1.941787 / 6.876477 (-4.934690) | 1.999688 / 2.142072 (-0.142385) | 0.807590 / 4.805227 (-3.997637) | 0.148157 / 6.500664 (-6.352507) | 0.064898 / 0.075469 (-0.010571) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253859 / 1.841788 (-0.587929) | 13.676097 / 8.074308 (5.601789) | 14.237837 / 10.191392 (4.046444) | 0.137178 / 0.680424 (-0.543246) | 0.028971 / 0.534201 (-0.505230) | 0.400380 / 0.579283 (-0.178903) | 0.409990 / 0.434364 (-0.024374) | 0.462552 / 0.540337 (-0.077786) | 0.552153 / 1.386936 (-0.834783) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006831 / 0.011353 (-0.004522) | 0.004627 / 0.011008 (-0.006381) | 0.099883 / 0.038508 (0.061375) | 0.028072 / 0.023109 (0.004962) | 0.343556 / 0.275898 (0.067658) | 0.386792 / 0.323480 (0.063312) | 0.005080 / 0.007986 (-0.002906) | 0.003508 / 0.004328 (-0.000820) | 0.077803 / 0.004250 (0.073552) | 0.040038 / 0.037052 (0.002985) | 0.345089 / 0.258489 (0.086600) | 0.396078 / 0.293841 (0.102238) | 0.032241 / 0.128546 (-0.096305) | 0.011711 / 0.075646 (-0.063935) | 0.320531 / 0.419271 (-0.098740) | 0.043658 / 0.043533 (0.000125) | 0.344696 / 0.255139 (0.089557) | 0.389847 / 0.283200 (0.106648) | 0.092328 / 0.141683 (-0.049355) | 1.477290 / 1.452155 (0.025136) | 1.548698 / 1.492716 (0.055982) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236073 / 0.018006 (0.218067) | 0.422113 / 0.000490 (0.421624) | 0.000431 / 0.000200 (0.000231) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024738 / 0.037411 (-0.012673) | 0.100546 / 0.014526 (0.086020) | 0.107550 / 0.176557 (-0.069006) | 0.146056 / 0.737135 (-0.591079) | 0.112665 / 0.296338 (-0.183674) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.490259 / 0.215209 (0.275050) | 4.907994 / 2.077655 (2.830339) | 2.547175 / 1.504120 (1.043055) | 2.344419 / 1.541195 (0.803224) | 2.403985 / 1.468490 (0.935495) | 0.696011 / 4.584777 (-3.888766) | 3.442426 / 3.745712 (-0.303286) | 1.878702 / 5.269862 (-3.391159) | 1.158280 / 4.565676 (-3.407396) | 0.082300 / 0.424275 (-0.341975) | 0.012513 / 0.007607 (0.004906) | 0.602696 / 0.226044 (0.376651) | 6.014592 / 2.268929 (3.745663) | 3.014466 / 55.444624 (-52.430159) | 2.669376 / 6.876477 (-4.207101) | 2.724485 / 2.142072 (0.582412) | 0.799795 / 4.805227 (-4.005432) | 0.151220 / 6.500664 (-6.349444) | 0.067486 / 0.075469 (-0.007983) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281265 / 1.841788 (-0.560523) | 14.362284 / 8.074308 (6.287976) | 14.313690 / 10.191392 (4.122298) | 0.142870 / 0.680424 (-0.537554) | 0.017206 / 0.534201 (-0.516995) | 0.380084 / 0.579283 (-0.199199) | 0.388161 / 0.434364 (-0.046203) | 0.442617 / 0.540337 (-0.097721) | 0.528487 / 1.386936 (-0.858449) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#452b7f8ae78967dc662f5436e751233d46c62e78 \"CML watermark\")\n",
"@lhoestq @amyeroberts @gante I did a substantial rewrite and all tests are passing now (Windows seems to time out or something and I can't figure out why - not sure if that's related to this PR!). I also confirmed tests are passing locally with Py==3.10. \r\n\r\nAside from incorporating everyone's comments, I also made a context manager to create and handle shared memory - this ensures that shared memory is cleaned up even if execution is interrupted. Also, shared memory names include a UUID string now to avoid collisions. Finally, string arrays are now split up into fixed-width character arrays in the workers so that they can be passed through shared memory, and the parent process reconstructs them into string arrays.",
"Update: `test_arrow_dataset.py` ran fine in this branch on my Windows machine (Py 3.10), so I have no idea what's up with those tests",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008852 / 0.011353 (-0.002500) | 0.004545 / 0.011008 (-0.006464) | 0.099814 / 0.038508 (0.061306) | 0.030314 / 0.023109 (0.007205) | 0.310426 / 0.275898 (0.034528) | 0.366893 / 0.323480 (0.043413) | 0.007183 / 0.007986 (-0.000802) | 0.003476 / 0.004328 (-0.000853) | 0.077566 / 0.004250 (0.073315) | 0.038269 / 0.037052 (0.001217) | 0.319133 / 0.258489 (0.060644) | 0.352399 / 0.293841 (0.058558) | 0.033847 / 0.128546 (-0.094700) | 0.011568 / 0.075646 (-0.064078) | 0.321355 / 0.419271 (-0.097917) | 0.040719 / 0.043533 (-0.002814) | 0.304812 / 0.255139 (0.049673) | 0.329512 / 0.283200 (0.046312) | 0.088045 / 0.141683 (-0.053638) | 1.514182 / 1.452155 (0.062027) | 1.529459 / 1.492716 (0.036742) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216749 / 0.018006 (0.198743) | 0.409909 / 0.000490 (0.409419) | 0.002790 / 0.000200 (0.002590) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023390 / 0.037411 (-0.014021) | 0.095955 / 0.014526 (0.081430) | 0.104749 / 0.176557 (-0.071807) | 0.143414 / 0.737135 (-0.593721) | 0.109011 / 0.296338 (-0.187328) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420410 / 0.215209 (0.205201) | 4.185745 / 2.077655 (2.108090) | 1.910207 / 1.504120 (0.406087) | 1.679330 / 1.541195 (0.138135) | 1.727134 / 1.468490 (0.258644) | 0.692379 / 4.584777 (-3.892398) | 3.358731 / 3.745712 (-0.386982) | 2.914657 / 5.269862 (-2.355205) | 1.506083 / 4.565676 (-3.059594) | 0.081922 / 0.424275 (-0.342353) | 0.012691 / 0.007607 (0.005084) | 0.530942 / 0.226044 (0.304897) | 5.357642 / 2.268929 (3.088714) | 2.387347 / 55.444624 (-53.057277) | 2.030001 / 6.876477 (-4.846476) | 2.026405 / 2.142072 (-0.115667) | 0.809406 / 4.805227 (-3.995821) | 0.149003 / 6.500664 (-6.351661) | 0.066910 / 0.075469 (-0.008559) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.278160 / 1.841788 (-0.563627) | 13.632742 / 8.074308 (5.558434) | 13.995537 / 10.191392 (3.804145) | 0.136507 / 0.680424 (-0.543917) | 0.028817 / 0.534201 (-0.505384) | 0.394842 / 0.579283 (-0.184441) | 0.399526 / 0.434364 (-0.034838) | 0.459174 / 0.540337 (-0.081163) | 0.536877 / 1.386936 (-0.850059) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006814 / 0.011353 (-0.004539) | 0.004456 / 0.011008 (-0.006552) | 0.098386 / 0.038508 (0.059878) | 0.028124 / 0.023109 (0.005015) | 0.409004 / 0.275898 (0.133106) | 0.446746 / 0.323480 (0.123266) | 0.005108 / 0.007986 (-0.002877) | 0.004807 / 0.004328 (0.000479) | 0.075751 / 0.004250 (0.071500) | 0.039297 / 0.037052 (0.002244) | 0.413198 / 0.258489 (0.154709) | 0.452124 / 0.293841 (0.158283) | 0.032534 / 0.128546 (-0.096012) | 0.011689 / 0.075646 (-0.063957) | 0.325465 / 0.419271 (-0.093806) | 0.041347 / 0.043533 (-0.002185) | 0.411489 / 0.255139 (0.156350) | 0.447120 / 0.283200 (0.163920) | 0.093058 / 0.141683 (-0.048625) | 1.489903 / 1.452155 (0.037748) | 1.580771 / 1.492716 (0.088055) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192619 / 0.018006 (0.174613) | 0.399201 / 0.000490 (0.398711) | 0.002894 / 0.000200 (0.002694) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025120 / 0.037411 (-0.012292) | 0.100126 / 0.014526 (0.085600) | 0.108669 / 0.176557 (-0.067887) | 0.148687 / 0.737135 (-0.588448) | 0.112286 / 0.296338 (-0.184052) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438866 / 0.215209 (0.223657) | 4.382418 / 2.077655 (2.304764) | 2.106450 / 1.504120 (0.602330) | 1.885105 / 1.541195 (0.343910) | 1.922948 / 1.468490 (0.454458) | 0.693145 / 4.584777 (-3.891632) | 3.378206 / 3.745712 (-0.367506) | 1.867295 / 5.269862 (-3.402566) | 1.164999 / 4.565676 (-3.400678) | 0.081918 / 0.424275 (-0.342357) | 0.012225 / 0.007607 (0.004618) | 0.547114 / 0.226044 (0.321069) | 5.454208 / 2.268929 (3.185279) | 2.532112 / 55.444624 (-52.912512) | 2.192573 / 6.876477 (-4.683904) | 2.225364 / 2.142072 (0.083291) | 0.797165 / 4.805227 (-4.008062) | 0.151185 / 6.500664 (-6.349480) | 0.067512 / 0.075469 (-0.007957) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303905 / 1.841788 (-0.537883) | 14.107678 / 8.074308 (6.033370) | 14.147630 / 10.191392 (3.956238) | 0.156597 / 0.680424 (-0.523827) | 0.017037 / 0.534201 (-0.517164) | 0.383202 / 0.579283 (-0.196081) | 0.385340 / 0.434364 (-0.049024) | 0.443338 / 0.540337 (-0.097000) | 0.542345 / 1.386936 (-0.844591) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#38228533a03767aab713a3806aac0e8503668c68 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009982 / 0.011353 (-0.001371) | 0.005327 / 0.011008 (-0.005681) | 0.099092 / 0.038508 (0.060584) | 0.035824 / 0.023109 (0.012715) | 0.303258 / 0.275898 (0.027360) | 0.335379 / 0.323480 (0.011899) | 0.008192 / 0.007986 (0.000207) | 0.004242 / 0.004328 (-0.000087) | 0.076277 / 0.004250 (0.072026) | 0.043851 / 0.037052 (0.006799) | 0.307750 / 0.258489 (0.049261) | 0.348459 / 0.293841 (0.054618) | 0.038943 / 0.128546 (-0.089604) | 0.012128 / 0.075646 (-0.063519) | 0.334143 / 0.419271 (-0.085128) | 0.047865 / 0.043533 (0.004332) | 0.300909 / 0.255139 (0.045770) | 0.320879 / 0.283200 (0.037680) | 0.103812 / 0.141683 (-0.037871) | 1.468646 / 1.452155 (0.016491) | 1.557660 / 1.492716 (0.064944) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244108 / 0.018006 (0.226102) | 0.554895 / 0.000490 (0.554405) | 0.005311 / 0.000200 (0.005111) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028771 / 0.037411 (-0.008640) | 0.108133 / 0.014526 (0.093608) | 0.120098 / 0.176557 (-0.056458) | 0.159815 / 0.737135 (-0.577320) | 0.125437 / 0.296338 (-0.170901) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397675 / 0.215209 (0.182466) | 3.975839 / 2.077655 (1.898184) | 1.797803 / 1.504120 (0.293683) | 1.612517 / 1.541195 (0.071322) | 1.659086 / 1.468490 (0.190596) | 0.679822 / 4.584777 (-3.904955) | 3.688321 / 3.745712 (-0.057391) | 2.155285 / 5.269862 (-3.114576) | 1.466453 / 4.565676 (-3.099223) | 0.084102 / 0.424275 (-0.340173) | 0.012074 / 0.007607 (0.004467) | 0.503744 / 0.226044 (0.277699) | 5.075599 / 2.268929 (2.806670) | 2.312149 / 55.444624 (-53.132476) | 1.975028 / 6.876477 (-4.901449) | 2.069554 / 2.142072 (-0.072519) | 0.828329 / 4.805227 (-3.976898) | 0.162816 / 6.500664 (-6.337849) | 0.063813 / 0.075469 (-0.011656) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.173327 / 1.841788 (-0.668461) | 15.281584 / 8.074308 (7.207276) | 14.450851 / 10.191392 (4.259459) | 0.165621 / 0.680424 (-0.514802) | 0.028779 / 0.534201 (-0.505422) | 0.438483 / 0.579283 (-0.140800) | 0.438477 / 0.434364 (0.004113) | 0.517703 / 0.540337 (-0.022634) | 0.615119 / 1.386936 (-0.771817) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007013 / 0.011353 (-0.004340) | 0.005272 / 0.011008 (-0.005736) | 0.097203 / 0.038508 (0.058695) | 0.033103 / 0.023109 (0.009994) | 0.380203 / 0.275898 (0.104305) | 0.414868 / 0.323480 (0.091388) | 0.006326 / 0.007986 (-0.001659) | 0.005433 / 0.004328 (0.001104) | 0.074299 / 0.004250 (0.070049) | 0.049418 / 0.037052 (0.012366) | 0.388771 / 0.258489 (0.130282) | 0.435169 / 0.293841 (0.141328) | 0.036170 / 0.128546 (-0.092377) | 0.012452 / 0.075646 (-0.063195) | 0.331215 / 0.419271 (-0.088056) | 0.048577 / 0.043533 (0.005044) | 0.381491 / 0.255139 (0.126352) | 0.396731 / 0.283200 (0.113531) | 0.106435 / 0.141683 (-0.035248) | 1.446437 / 1.452155 (-0.005718) | 1.542337 / 1.492716 (0.049621) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216714 / 0.018006 (0.198707) | 0.562460 / 0.000490 (0.561970) | 0.003636 / 0.000200 (0.003436) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028726 / 0.037411 (-0.008686) | 0.111993 / 0.014526 (0.097467) | 0.125325 / 0.176557 (-0.051232) | 0.157779 / 0.737135 (-0.579356) | 0.130633 / 0.296338 (-0.165705) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440520 / 0.215209 (0.225311) | 4.396283 / 2.077655 (2.318628) | 2.204714 / 1.504120 (0.700594) | 2.011667 / 1.541195 (0.470473) | 2.050518 / 1.468490 (0.582028) | 0.695204 / 4.584777 (-3.889573) | 3.779699 / 3.745712 (0.033987) | 2.096064 / 5.269862 (-3.173798) | 1.325446 / 4.565676 (-3.240230) | 0.085315 / 0.424275 (-0.338960) | 0.012178 / 0.007607 (0.004570) | 0.550478 / 0.226044 (0.324434) | 5.471872 / 2.268929 (3.202943) | 2.687147 / 55.444624 (-52.757478) | 2.348465 / 6.876477 (-4.528011) | 2.409700 / 2.142072 (0.267628) | 0.839468 / 4.805227 (-3.965760) | 0.167030 / 6.500664 (-6.333635) | 0.063243 / 0.075469 (-0.012226) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257347 / 1.841788 (-0.584441) | 15.157821 / 8.074308 (7.083512) | 14.646381 / 10.191392 (4.454989) | 0.185550 / 0.680424 (-0.494874) | 0.018441 / 0.534201 (-0.515760) | 0.423330 / 0.579283 (-0.155954) | 0.426204 / 0.434364 (-0.008160) | 0.498985 / 0.540337 (-0.041352) | 0.608432 / 1.386936 (-0.778504) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f96e349ec5665e1e4135b5a108ba5db227bd3b1 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010856 / 0.011353 (-0.000497) | 0.005897 / 0.011008 (-0.005111) | 0.117826 / 0.038508 (0.079317) | 0.041899 / 0.023109 (0.018790) | 0.353804 / 0.275898 (0.077906) | 0.431021 / 0.323480 (0.107541) | 0.009288 / 0.007986 (0.001303) | 0.004556 / 0.004328 (0.000227) | 0.089344 / 0.004250 (0.085094) | 0.052224 / 0.037052 (0.015172) | 0.373242 / 0.258489 (0.114753) | 0.420667 / 0.293841 (0.126826) | 0.044191 / 0.128546 (-0.084355) | 0.014083 / 0.075646 (-0.061564) | 0.400373 / 0.419271 (-0.018898) | 0.056119 / 0.043533 (0.012586) | 0.363302 / 0.255139 (0.108163) | 0.382073 / 0.283200 (0.098873) | 0.118646 / 0.141683 (-0.023037) | 1.696576 / 1.452155 (0.244422) | 1.756518 / 1.492716 (0.263802) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216388 / 0.018006 (0.198382) | 0.485732 / 0.000490 (0.485242) | 0.004012 / 0.000200 (0.003812) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032095 / 0.037411 (-0.005316) | 0.128954 / 0.014526 (0.114429) | 0.137564 / 0.176557 (-0.038993) | 0.184315 / 0.737135 (-0.552820) | 0.144707 / 0.296338 (-0.151631) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472792 / 0.215209 (0.257583) | 4.723044 / 2.077655 (2.645390) | 2.115075 / 1.504120 (0.610955) | 1.898993 / 1.541195 (0.357798) | 1.972894 / 1.468490 (0.504404) | 0.807210 / 4.584777 (-3.777567) | 4.493139 / 3.745712 (0.747427) | 2.501053 / 5.269862 (-2.768808) | 1.686121 / 4.565676 (-2.879556) | 0.099545 / 0.424275 (-0.324730) | 0.014360 / 0.007607 (0.006753) | 0.596235 / 0.226044 (0.370191) | 5.944285 / 2.268929 (3.675357) | 2.654944 / 55.444624 (-52.789681) | 2.281451 / 6.876477 (-4.595026) | 2.448407 / 2.142072 (0.306334) | 1.000512 / 4.805227 (-3.804716) | 0.196413 / 6.500664 (-6.304251) | 0.075810 / 0.075469 (0.000341) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.435707 / 1.841788 (-0.406081) | 17.931070 / 8.074308 (9.856762) | 16.635522 / 10.191392 (6.444130) | 0.189119 / 0.680424 (-0.491304) | 0.034392 / 0.534201 (-0.499809) | 0.519041 / 0.579283 (-0.060242) | 0.516159 / 0.434364 (0.081795) | 0.601180 / 0.540337 (0.060843) | 0.713180 / 1.386936 (-0.673756) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008741 / 0.011353 (-0.002612) | 0.006102 / 0.011008 (-0.004906) | 0.114787 / 0.038508 (0.076279) | 0.039610 / 0.023109 (0.016501) | 0.451730 / 0.275898 (0.175832) | 0.488820 / 0.323480 (0.165340) | 0.006979 / 0.007986 (-0.001006) | 0.006458 / 0.004328 (0.002130) | 0.086505 / 0.004250 (0.082254) | 0.057684 / 0.037052 (0.020632) | 0.451354 / 0.258489 (0.192865) | 0.523143 / 0.293841 (0.229302) | 0.043224 / 0.128546 (-0.085323) | 0.014671 / 0.075646 (-0.060975) | 0.398030 / 0.419271 (-0.021241) | 0.063650 / 0.043533 (0.020117) | 0.448324 / 0.255139 (0.193185) | 0.476560 / 0.283200 (0.193361) | 0.125772 / 0.141683 (-0.015911) | 1.801051 / 1.452155 (0.348896) | 1.872736 / 1.492716 (0.380020) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256146 / 0.018006 (0.238139) | 0.486915 / 0.000490 (0.486425) | 0.000513 / 0.000200 (0.000313) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035242 / 0.037411 (-0.002170) | 0.134322 / 0.014526 (0.119797) | 0.144786 / 0.176557 (-0.031770) | 0.188786 / 0.737135 (-0.548349) | 0.151737 / 0.296338 (-0.144602) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.506047 / 0.215209 (0.290838) | 5.028253 / 2.077655 (2.950598) | 2.393070 / 1.504120 (0.888950) | 2.157847 / 1.541195 (0.616652) | 2.229412 / 1.468490 (0.760922) | 0.828973 / 4.584777 (-3.755804) | 4.741470 / 3.745712 (0.995758) | 4.048118 / 5.269862 (-1.221744) | 2.573818 / 4.565676 (-1.991859) | 0.101019 / 0.424275 (-0.323256) | 0.014640 / 0.007607 (0.007033) | 0.632591 / 0.226044 (0.406546) | 6.289153 / 2.268929 (4.020224) | 2.977261 / 55.444624 (-52.467363) | 2.554396 / 6.876477 (-4.322081) | 2.619446 / 2.142072 (0.477374) | 0.988376 / 4.805227 (-3.816851) | 0.196895 / 6.500664 (-6.303769) | 0.076355 / 0.075469 (0.000886) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.493570 / 1.841788 (-0.348218) | 18.422758 / 8.074308 (10.348449) | 17.007352 / 10.191392 (6.815960) | 0.191903 / 0.680424 (-0.488521) | 0.020974 / 0.534201 (-0.513227) | 0.500573 / 0.579283 (-0.078710) | 0.489381 / 0.434364 (0.055017) | 0.580765 / 0.540337 (0.040428) | 0.698907 / 1.386936 (-0.688029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa9baa268a6d285ab0a61cc37413392c94cfe2e8 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008979 / 0.011353 (-0.002374) | 0.004497 / 0.011008 (-0.006511) | 0.102227 / 0.038508 (0.063719) | 0.031302 / 0.023109 (0.008193) | 0.298488 / 0.275898 (0.022590) | 0.372589 / 0.323480 (0.049109) | 0.007261 / 0.007986 (-0.000725) | 0.003542 / 0.004328 (-0.000786) | 0.078503 / 0.004250 (0.074253) | 0.039474 / 0.037052 (0.002422) | 0.310991 / 0.258489 (0.052502) | 0.353245 / 0.293841 (0.059404) | 0.033798 / 0.128546 (-0.094749) | 0.011634 / 0.075646 (-0.064012) | 0.321141 / 0.419271 (-0.098131) | 0.041264 / 0.043533 (-0.002268) | 0.300900 / 0.255139 (0.045761) | 0.326255 / 0.283200 (0.043055) | 0.092477 / 0.141683 (-0.049205) | 1.478921 / 1.452155 (0.026766) | 1.514915 / 1.492716 (0.022198) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184415 / 0.018006 (0.166408) | 0.428986 / 0.000490 (0.428497) | 0.002590 / 0.000200 (0.002390) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023730 / 0.037411 (-0.013681) | 0.099846 / 0.014526 (0.085320) | 0.107075 / 0.176557 (-0.069482) | 0.147475 / 0.737135 (-0.589661) | 0.111802 / 0.296338 (-0.184537) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413704 / 0.215209 (0.198494) | 4.144498 / 2.077655 (2.066843) | 1.855900 / 1.504120 (0.351780) | 1.647958 / 1.541195 (0.106763) | 1.712437 / 1.468490 (0.243947) | 0.688382 / 4.584777 (-3.896395) | 3.432136 / 3.745712 (-0.313576) | 2.837211 / 5.269862 (-2.432651) | 1.519004 / 4.565676 (-3.046672) | 0.082429 / 0.424275 (-0.341846) | 0.012610 / 0.007607 (0.005003) | 0.525078 / 0.226044 (0.299034) | 5.272932 / 2.268929 (3.004003) | 2.340482 / 55.444624 (-53.104143) | 2.007372 / 6.876477 (-4.869104) | 2.060567 / 2.142072 (-0.081506) | 0.806476 / 4.805227 (-3.998752) | 0.149421 / 6.500664 (-6.351243) | 0.066252 / 0.075469 (-0.009218) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235078 / 1.841788 (-0.606710) | 13.870758 / 8.074308 (5.796450) | 14.104582 / 10.191392 (3.913190) | 0.159375 / 0.680424 (-0.521049) | 0.029233 / 0.534201 (-0.504968) | 0.392184 / 0.579283 (-0.187099) | 0.407909 / 0.434364 (-0.026455) | 0.458757 / 0.540337 (-0.081581) | 0.547681 / 1.386936 (-0.839255) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007194 / 0.011353 (-0.004159) | 0.004578 / 0.011008 (-0.006431) | 0.098936 / 0.038508 (0.060428) | 0.029639 / 0.023109 (0.006530) | 0.347241 / 0.275898 (0.071343) | 0.378838 / 0.323480 (0.055358) | 0.005632 / 0.007986 (-0.002353) | 0.003469 / 0.004328 (-0.000860) | 0.075536 / 0.004250 (0.071285) | 0.043301 / 0.037052 (0.006249) | 0.348091 / 0.258489 (0.089602) | 0.388595 / 0.293841 (0.094754) | 0.033512 / 0.128546 (-0.095034) | 0.011754 / 0.075646 (-0.063892) | 0.321003 / 0.419271 (-0.098268) | 0.044634 / 0.043533 (0.001101) | 0.346688 / 0.255139 (0.091549) | 0.366346 / 0.283200 (0.083147) | 0.093650 / 0.141683 (-0.048033) | 1.509913 / 1.452155 (0.057759) | 1.596414 / 1.492716 (0.103698) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230466 / 0.018006 (0.212459) | 0.417106 / 0.000490 (0.416617) | 0.000959 / 0.000200 (0.000759) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025581 / 0.037411 (-0.011830) | 0.105246 / 0.014526 (0.090720) | 0.108997 / 0.176557 (-0.067560) | 0.144342 / 0.737135 (-0.592794) | 0.113911 / 0.296338 (-0.182427) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.479608 / 0.215209 (0.264399) | 4.766081 / 2.077655 (2.688426) | 2.446597 / 1.504120 (0.942477) | 2.228278 / 1.541195 (0.687083) | 2.289943 / 1.468490 (0.821453) | 0.703146 / 4.584777 (-3.881631) | 3.414150 / 3.745712 (-0.331562) | 2.957730 / 5.269862 (-2.312132) | 1.531524 / 4.565676 (-3.034152) | 0.083449 / 0.424275 (-0.340826) | 0.012684 / 0.007607 (0.005077) | 0.587622 / 0.226044 (0.361578) | 5.888791 / 2.268929 (3.619863) | 2.884200 / 55.444624 (-52.560424) | 2.543739 / 6.876477 (-4.332737) | 2.596245 / 2.142072 (0.454173) | 0.813070 / 4.805227 (-3.992157) | 0.152706 / 6.500664 (-6.347958) | 0.069257 / 0.075469 (-0.006212) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.302945 / 1.841788 (-0.538842) | 14.484051 / 8.074308 (6.409743) | 14.216143 / 10.191392 (4.024751) | 0.154537 / 0.680424 (-0.525886) | 0.016909 / 0.534201 (-0.517292) | 0.389433 / 0.579283 (-0.189850) | 0.393280 / 0.434364 (-0.041084) | 0.446884 / 0.540337 (-0.093453) | 0.534394 / 1.386936 (-0.852542) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bcdeb952c57c5f22643061d49d16014a7b6426a \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008822 / 0.011353 (-0.002530) | 0.004826 / 0.011008 (-0.006182) | 0.102710 / 0.038508 (0.064202) | 0.030353 / 0.023109 (0.007244) | 0.297224 / 0.275898 (0.021326) | 0.371861 / 0.323480 (0.048381) | 0.007266 / 0.007986 (-0.000720) | 0.003632 / 0.004328 (-0.000696) | 0.079960 / 0.004250 (0.075710) | 0.036908 / 0.037052 (-0.000144) | 0.309582 / 0.258489 (0.051093) | 0.350108 / 0.293841 (0.056267) | 0.034280 / 0.128546 (-0.094266) | 0.011739 / 0.075646 (-0.063907) | 0.323217 / 0.419271 (-0.096054) | 0.043491 / 0.043533 (-0.000042) | 0.298454 / 0.255139 (0.043315) | 0.326735 / 0.283200 (0.043535) | 0.093955 / 0.141683 (-0.047728) | 1.494313 / 1.452155 (0.042159) | 1.562104 / 1.492716 (0.069388) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.182796 / 0.018006 (0.164790) | 0.420133 / 0.000490 (0.419643) | 0.002537 / 0.000200 (0.002337) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023143 / 0.037411 (-0.014269) | 0.098560 / 0.014526 (0.084034) | 0.105060 / 0.176557 (-0.071496) | 0.140269 / 0.737135 (-0.596866) | 0.109120 / 0.296338 (-0.187219) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419907 / 0.215209 (0.204698) | 4.196179 / 2.077655 (2.118524) | 1.887663 / 1.504120 (0.383543) | 1.686232 / 1.541195 (0.145037) | 1.741741 / 1.468490 (0.273251) | 0.696222 / 4.584777 (-3.888555) | 3.400250 / 3.745712 (-0.345462) | 1.875058 / 5.269862 (-3.394803) | 1.159466 / 4.565676 (-3.406211) | 0.082520 / 0.424275 (-0.341755) | 0.012408 / 0.007607 (0.004801) | 0.525212 / 0.226044 (0.299168) | 5.283691 / 2.268929 (3.014762) | 2.314487 / 55.444624 (-53.130138) | 1.966212 / 6.876477 (-4.910265) | 2.023458 / 2.142072 (-0.118615) | 0.808896 / 4.805227 (-3.996331) | 0.148973 / 6.500664 (-6.351691) | 0.065378 / 0.075469 (-0.010091) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223833 / 1.841788 (-0.617955) | 14.053651 / 8.074308 (5.979343) | 14.072165 / 10.191392 (3.880773) | 0.156006 / 0.680424 (-0.524418) | 0.028665 / 0.534201 (-0.505536) | 0.392099 / 0.579283 (-0.187184) | 0.401460 / 0.434364 (-0.032904) | 0.462184 / 0.540337 (-0.078153) | 0.540459 / 1.386936 (-0.846477) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006907 / 0.011353 (-0.004446) | 0.004585 / 0.011008 (-0.006423) | 0.099027 / 0.038508 (0.060519) | 0.028317 / 0.023109 (0.005208) | 0.421068 / 0.275898 (0.145170) | 0.450712 / 0.323480 (0.127233) | 0.005229 / 0.007986 (-0.002756) | 0.004873 / 0.004328 (0.000545) | 0.077374 / 0.004250 (0.073124) | 0.042530 / 0.037052 (0.005477) | 0.417392 / 0.258489 (0.158903) | 0.462605 / 0.293841 (0.168764) | 0.032195 / 0.128546 (-0.096351) | 0.011777 / 0.075646 (-0.063870) | 0.321927 / 0.419271 (-0.097344) | 0.041999 / 0.043533 (-0.001533) | 0.419402 / 0.255139 (0.164263) | 0.437179 / 0.283200 (0.153979) | 0.089549 / 0.141683 (-0.052134) | 1.469525 / 1.452155 (0.017370) | 1.586407 / 1.492716 (0.093691) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209533 / 0.018006 (0.191526) | 0.413886 / 0.000490 (0.413396) | 0.003357 / 0.000200 (0.003157) | 0.000121 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026133 / 0.037411 (-0.011278) | 0.103128 / 0.014526 (0.088602) | 0.110604 / 0.176557 (-0.065952) | 0.153055 / 0.737135 (-0.584080) | 0.112257 / 0.296338 (-0.184081) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471281 / 0.215209 (0.256072) | 4.708361 / 2.077655 (2.630706) | 2.572681 / 1.504120 (1.068561) | 2.370536 / 1.541195 (0.829341) | 2.456010 / 1.468490 (0.987520) | 0.694173 / 4.584777 (-3.890603) | 3.434511 / 3.745712 (-0.311201) | 1.877169 / 5.269862 (-3.392693) | 1.158387 / 4.565676 (-3.407289) | 0.081849 / 0.424275 (-0.342426) | 0.012176 / 0.007607 (0.004569) | 0.581736 / 0.226044 (0.355692) | 5.803173 / 2.268929 (3.534245) | 3.040003 / 55.444624 (-52.404621) | 2.704698 / 6.876477 (-4.171779) | 2.760138 / 2.142072 (0.618065) | 0.802557 / 4.805227 (-4.002671) | 0.151397 / 6.500664 (-6.349268) | 0.068308 / 0.075469 (-0.007161) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.304062 / 1.841788 (-0.537725) | 14.364809 / 8.074308 (6.290501) | 14.192131 / 10.191392 (4.000739) | 0.150025 / 0.680424 (-0.530399) | 0.017020 / 0.534201 (-0.517181) | 0.389235 / 0.579283 (-0.190048) | 0.387557 / 0.434364 (-0.046807) | 0.454636 / 0.540337 (-0.085702) | 0.558182 / 1.386936 (-0.828754) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#663e5eddca188abbb37e2f803846f02fe4ca0d9b \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008519 / 0.011353 (-0.002834) | 0.004538 / 0.011008 (-0.006470) | 0.102066 / 0.038508 (0.063558) | 0.029700 / 0.023109 (0.006591) | 0.304573 / 0.275898 (0.028675) | 0.366232 / 0.323480 (0.042752) | 0.007154 / 0.007986 (-0.000832) | 0.003497 / 0.004328 (-0.000831) | 0.079119 / 0.004250 (0.074868) | 0.036088 / 0.037052 (-0.000964) | 0.311076 / 0.258489 (0.052587) | 0.352205 / 0.293841 (0.058364) | 0.033706 / 0.128546 (-0.094840) | 0.011657 / 0.075646 (-0.063990) | 0.324024 / 0.419271 (-0.095247) | 0.040777 / 0.043533 (-0.002756) | 0.302661 / 0.255139 (0.047522) | 0.329091 / 0.283200 (0.045891) | 0.086774 / 0.141683 (-0.054909) | 1.485874 / 1.452155 (0.033720) | 1.535726 / 1.492716 (0.043009) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194284 / 0.018006 (0.176277) | 0.412875 / 0.000490 (0.412385) | 0.003348 / 0.000200 (0.003148) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022432 / 0.037411 (-0.014979) | 0.095008 / 0.014526 (0.080482) | 0.103268 / 0.176557 (-0.073288) | 0.140121 / 0.737135 (-0.597014) | 0.106619 / 0.296338 (-0.189719) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414786 / 0.215209 (0.199577) | 4.146345 / 2.077655 (2.068690) | 1.873703 / 1.504120 (0.369583) | 1.673498 / 1.541195 (0.132303) | 1.716993 / 1.468490 (0.248502) | 0.692098 / 4.584777 (-3.892679) | 3.380991 / 3.745712 (-0.364721) | 1.846811 / 5.269862 (-3.423050) | 1.159617 / 4.565676 (-3.406059) | 0.081867 / 0.424275 (-0.342408) | 0.012371 / 0.007607 (0.004764) | 0.526228 / 0.226044 (0.300184) | 5.273139 / 2.268929 (3.004211) | 2.327147 / 55.444624 (-53.117477) | 1.968366 / 6.876477 (-4.908111) | 2.018053 / 2.142072 (-0.124019) | 0.816098 / 4.805227 (-3.989130) | 0.149438 / 6.500664 (-6.351226) | 0.065000 / 0.075469 (-0.010469) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244408 / 1.841788 (-0.597380) | 13.774354 / 8.074308 (5.700046) | 14.178923 / 10.191392 (3.987531) | 0.150032 / 0.680424 (-0.530392) | 0.029736 / 0.534201 (-0.504465) | 0.399134 / 0.579283 (-0.180149) | 0.404214 / 0.434364 (-0.030150) | 0.462096 / 0.540337 (-0.078242) | 0.542256 / 1.386936 (-0.844680) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006776 / 0.011353 (-0.004577) | 0.004586 / 0.011008 (-0.006422) | 0.097658 / 0.038508 (0.059150) | 0.027627 / 0.023109 (0.004517) | 0.423794 / 0.275898 (0.147896) | 0.447443 / 0.323480 (0.123963) | 0.005099 / 0.007986 (-0.002886) | 0.004846 / 0.004328 (0.000517) | 0.075135 / 0.004250 (0.070884) | 0.038068 / 0.037052 (0.001016) | 0.420999 / 0.258489 (0.162510) | 0.460368 / 0.293841 (0.166527) | 0.032107 / 0.128546 (-0.096439) | 0.011775 / 0.075646 (-0.063871) | 0.323854 / 0.419271 (-0.095418) | 0.045538 / 0.043533 (0.002005) | 0.420949 / 0.255139 (0.165810) | 0.441906 / 0.283200 (0.158706) | 0.091955 / 0.141683 (-0.049728) | 1.523736 / 1.452155 (0.071581) | 1.587865 / 1.492716 (0.095148) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.263297 / 0.018006 (0.245290) | 0.416170 / 0.000490 (0.415680) | 0.023161 / 0.000200 (0.022961) | 0.000243 / 0.000054 (0.000188) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024000 / 0.037411 (-0.013412) | 0.097787 / 0.014526 (0.083262) | 0.106884 / 0.176557 (-0.069672) | 0.140861 / 0.737135 (-0.596274) | 0.108228 / 0.296338 (-0.188111) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.477222 / 0.215209 (0.262013) | 4.774729 / 2.077655 (2.697074) | 2.451575 / 1.504120 (0.947455) | 2.251255 / 1.541195 (0.710060) | 2.281154 / 1.468490 (0.812664) | 0.699394 / 4.584777 (-3.885383) | 3.421575 / 3.745712 (-0.324137) | 2.704713 / 5.269862 (-2.565148) | 1.508464 / 4.565676 (-3.057212) | 0.082199 / 0.424275 (-0.342076) | 0.012586 / 0.007607 (0.004979) | 0.588783 / 0.226044 (0.362739) | 5.878434 / 2.268929 (3.609505) | 2.927422 / 55.444624 (-52.517202) | 2.574357 / 6.876477 (-4.302120) | 2.603626 / 2.142072 (0.461554) | 0.804706 / 4.805227 (-4.000521) | 0.152919 / 6.500664 (-6.347745) | 0.069316 / 0.075469 (-0.006153) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280025 / 1.841788 (-0.561763) | 13.968407 / 8.074308 (5.894099) | 13.874506 / 10.191392 (3.683114) | 0.154711 / 0.680424 (-0.525713) | 0.016827 / 0.534201 (-0.517374) | 0.377775 / 0.579283 (-0.201508) | 0.393035 / 0.434364 (-0.041329) | 0.439405 / 0.540337 (-0.100932) | 0.528135 / 1.386936 (-0.858801) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#00b27a59b8af9075967b800e3b0f1de8616aa0ce \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009035 / 0.011353 (-0.002318) | 0.004518 / 0.011008 (-0.006490) | 0.102077 / 0.038508 (0.063569) | 0.030169 / 0.023109 (0.007060) | 0.297713 / 0.275898 (0.021815) | 0.364976 / 0.323480 (0.041496) | 0.007079 / 0.007986 (-0.000906) | 0.003438 / 0.004328 (-0.000890) | 0.079667 / 0.004250 (0.075416) | 0.035890 / 0.037052 (-0.001162) | 0.306065 / 0.258489 (0.047576) | 0.352133 / 0.293841 (0.058292) | 0.033800 / 0.128546 (-0.094746) | 0.011613 / 0.075646 (-0.064034) | 0.322917 / 0.419271 (-0.096354) | 0.040973 / 0.043533 (-0.002560) | 0.300896 / 0.255139 (0.045757) | 0.331540 / 0.283200 (0.048341) | 0.089579 / 0.141683 (-0.052103) | 1.466755 / 1.452155 (0.014600) | 1.522120 / 1.492716 (0.029404) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.193172 / 0.018006 (0.175166) | 0.408878 / 0.000490 (0.408389) | 0.001586 / 0.000200 (0.001386) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023496 / 0.037411 (-0.013915) | 0.098046 / 0.014526 (0.083520) | 0.104599 / 0.176557 (-0.071957) | 0.139054 / 0.737135 (-0.598081) | 0.111163 / 0.296338 (-0.185175) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417374 / 0.215209 (0.202165) | 4.145808 / 2.077655 (2.068153) | 1.847101 / 1.504120 (0.342981) | 1.637207 / 1.541195 (0.096012) | 1.676906 / 1.468490 (0.208416) | 0.689851 / 4.584777 (-3.894926) | 3.402099 / 3.745712 (-0.343614) | 1.896808 / 5.269862 (-3.373054) | 1.257876 / 4.565676 (-3.307801) | 0.081744 / 0.424275 (-0.342531) | 0.012206 / 0.007607 (0.004599) | 0.524830 / 0.226044 (0.298786) | 5.251344 / 2.268929 (2.982416) | 2.277907 / 55.444624 (-53.166717) | 1.933985 / 6.876477 (-4.942491) | 2.038500 / 2.142072 (-0.103573) | 0.808696 / 4.805227 (-3.996532) | 0.149488 / 6.500664 (-6.351176) | 0.065323 / 0.075469 (-0.010146) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.204294 / 1.841788 (-0.637493) | 13.696526 / 8.074308 (5.622218) | 13.947195 / 10.191392 (3.755802) | 0.136812 / 0.680424 (-0.543611) | 0.028625 / 0.534201 (-0.505576) | 0.397662 / 0.579283 (-0.181621) | 0.403423 / 0.434364 (-0.030941) | 0.465288 / 0.540337 (-0.075049) | 0.551919 / 1.386936 (-0.835017) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006467 / 0.011353 (-0.004886) | 0.004562 / 0.011008 (-0.006447) | 0.097514 / 0.038508 (0.059006) | 0.027471 / 0.023109 (0.004362) | 0.425504 / 0.275898 (0.149606) | 0.458856 / 0.323480 (0.135376) | 0.004816 / 0.007986 (-0.003169) | 0.003264 / 0.004328 (-0.001065) | 0.074947 / 0.004250 (0.070697) | 0.037147 / 0.037052 (0.000095) | 0.429513 / 0.258489 (0.171024) | 0.463971 / 0.293841 (0.170130) | 0.031638 / 0.128546 (-0.096908) | 0.011545 / 0.075646 (-0.064101) | 0.320261 / 0.419271 (-0.099010) | 0.041570 / 0.043533 (-0.001963) | 0.424809 / 0.255139 (0.169670) | 0.447158 / 0.283200 (0.163959) | 0.088418 / 0.141683 (-0.053265) | 1.492242 / 1.452155 (0.040087) | 1.545523 / 1.492716 (0.052807) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217865 / 0.018006 (0.199859) | 0.399925 / 0.000490 (0.399436) | 0.004853 / 0.000200 (0.004653) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024275 / 0.037411 (-0.013137) | 0.098249 / 0.014526 (0.083723) | 0.107110 / 0.176557 (-0.069446) | 0.143870 / 0.737135 (-0.593265) | 0.108796 / 0.296338 (-0.187542) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470856 / 0.215209 (0.255647) | 4.687921 / 2.077655 (2.610266) | 2.448631 / 1.504120 (0.944511) | 2.247748 / 1.541195 (0.706553) | 2.287713 / 1.468490 (0.819223) | 0.687534 / 4.584777 (-3.897243) | 3.421099 / 3.745712 (-0.324613) | 2.977280 / 5.269862 (-2.292582) | 1.274837 / 4.565676 (-3.290839) | 0.081611 / 0.424275 (-0.342664) | 0.012603 / 0.007607 (0.004996) | 0.574600 / 0.226044 (0.348556) | 5.802826 / 2.268929 (3.533898) | 2.913178 / 55.444624 (-52.531446) | 2.589486 / 6.876477 (-4.286991) | 2.630004 / 2.142072 (0.487932) | 0.790087 / 4.805227 (-4.015140) | 0.150019 / 6.500664 (-6.350645) | 0.067346 / 0.075469 (-0.008123) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266521 / 1.841788 (-0.575267) | 13.818770 / 8.074308 (5.744462) | 13.872277 / 10.191392 (3.680885) | 0.147375 / 0.680424 (-0.533049) | 0.016837 / 0.534201 (-0.517363) | 0.376421 / 0.579283 (-0.202862) | 0.400236 / 0.434364 (-0.034128) | 0.436623 / 0.540337 (-0.103714) | 0.527173 / 1.386936 (-0.859763) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f347cf8443aa35401ba6a4159600b92bc6a156b \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009341 / 0.011353 (-0.002012) | 0.005188 / 0.011008 (-0.005820) | 0.101831 / 0.038508 (0.063323) | 0.035141 / 0.023109 (0.012032) | 0.299324 / 0.275898 (0.023426) | 0.334749 / 0.323480 (0.011269) | 0.007958 / 0.007986 (-0.000027) | 0.005482 / 0.004328 (0.001153) | 0.077070 / 0.004250 (0.072820) | 0.044733 / 0.037052 (0.007680) | 0.310398 / 0.258489 (0.051909) | 0.347925 / 0.293841 (0.054084) | 0.038141 / 0.128546 (-0.090405) | 0.012135 / 0.075646 (-0.063512) | 0.333799 / 0.419271 (-0.085472) | 0.048881 / 0.043533 (0.005348) | 0.301336 / 0.255139 (0.046197) | 0.314592 / 0.283200 (0.031393) | 0.103635 / 0.141683 (-0.038048) | 1.437321 / 1.452155 (-0.014833) | 1.598781 / 1.492716 (0.106065) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248911 / 0.018006 (0.230905) | 0.528932 / 0.000490 (0.528442) | 0.002495 / 0.000200 (0.002295) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027903 / 0.037411 (-0.009509) | 0.106716 / 0.014526 (0.092190) | 0.122650 / 0.176557 (-0.053907) | 0.162481 / 0.737135 (-0.574654) | 0.126402 / 0.296338 (-0.169937) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.352819 / 0.215209 (0.137610) | 3.522761 / 2.077655 (1.445106) | 1.576761 / 1.504120 (0.072641) | 1.411631 / 1.541195 (-0.129563) | 1.449689 / 1.468490 (-0.018801) | 0.608987 / 4.584777 (-3.975790) | 3.705121 / 3.745712 (-0.040592) | 2.085071 / 5.269862 (-3.184790) | 1.308653 / 4.565676 (-3.257024) | 0.083763 / 0.424275 (-0.340512) | 0.011957 / 0.007607 (0.004350) | 0.502182 / 0.226044 (0.276137) | 5.008829 / 2.268929 (2.739900) | 2.244687 / 55.444624 (-53.199937) | 1.891411 / 6.876477 (-4.985065) | 1.940789 / 2.142072 (-0.201284) | 0.825966 / 4.805227 (-3.979261) | 0.165267 / 6.500664 (-6.335397) | 0.063020 / 0.075469 (-0.012449) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196707 / 1.841788 (-0.645081) | 14.236877 / 8.074308 (6.162569) | 14.872954 / 10.191392 (4.681562) | 0.168560 / 0.680424 (-0.511864) | 0.029038 / 0.534201 (-0.505163) | 0.440192 / 0.579283 (-0.139091) | 0.437021 / 0.434364 (0.002657) | 0.519612 / 0.540337 (-0.020725) | 0.612013 / 1.386936 (-0.774923) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007170 / 0.011353 (-0.004183) | 0.005303 / 0.011008 (-0.005705) | 0.098503 / 0.038508 (0.059995) | 0.032573 / 0.023109 (0.009463) | 0.398203 / 0.275898 (0.122305) | 0.446075 / 0.323480 (0.122595) | 0.005712 / 0.007986 (-0.002274) | 0.004165 / 0.004328 (-0.000164) | 0.074273 / 0.004250 (0.070023) | 0.049587 / 0.037052 (0.012534) | 0.399458 / 0.258489 (0.140969) | 0.459167 / 0.293841 (0.165327) | 0.036063 / 0.128546 (-0.092483) | 0.012394 / 0.075646 (-0.063253) | 0.332559 / 0.419271 (-0.086713) | 0.048499 / 0.043533 (0.004967) | 0.404044 / 0.255139 (0.148905) | 0.410462 / 0.283200 (0.127262) | 0.104104 / 0.141683 (-0.037579) | 1.488141 / 1.452155 (0.035986) | 1.535517 / 1.492716 (0.042801) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292976 / 0.018006 (0.274970) | 0.569139 / 0.000490 (0.568649) | 0.000553 / 0.000200 (0.000353) | 0.000063 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030144 / 0.037411 (-0.007267) | 0.098699 / 0.014526 (0.084173) | 0.114437 / 0.176557 (-0.062120) | 0.156657 / 0.737135 (-0.580478) | 0.117449 / 0.296338 (-0.178890) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441921 / 0.215209 (0.226712) | 4.413090 / 2.077655 (2.335435) | 2.190458 / 1.504120 (0.686338) | 2.008919 / 1.541195 (0.467724) | 2.049657 / 1.468490 (0.581167) | 0.691751 / 4.584777 (-3.893026) | 3.767524 / 3.745712 (0.021812) | 3.395564 / 5.269862 (-1.874297) | 1.633480 / 4.565676 (-2.932196) | 0.084880 / 0.424275 (-0.339395) | 0.012133 / 0.007607 (0.004526) | 0.555372 / 0.226044 (0.329327) | 5.522820 / 2.268929 (3.253892) | 2.723331 / 55.444624 (-52.721293) | 2.337583 / 6.876477 (-4.538894) | 2.368746 / 2.142072 (0.226674) | 0.830127 / 4.805227 (-3.975100) | 0.166239 / 6.500664 (-6.334425) | 0.064279 / 0.075469 (-0.011190) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.123421 / 1.841788 (-0.718367) | 14.413392 / 8.074308 (6.339084) | 12.865143 / 10.191392 (2.673751) | 0.132198 / 0.680424 (-0.548226) | 0.016138 / 0.534201 (-0.518063) | 0.380760 / 0.579283 (-0.198523) | 0.387223 / 0.434364 (-0.047141) | 0.445574 / 0.540337 (-0.094764) | 0.535658 / 1.386936 (-0.851278) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a89564d3d17b5960db2435662cb9c49f8ad7488a \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008316 / 0.011353 (-0.003037) | 0.004503 / 0.011008 (-0.006505) | 0.100565 / 0.038508 (0.062057) | 0.030388 / 0.023109 (0.007279) | 0.304417 / 0.275898 (0.028519) | 0.369655 / 0.323480 (0.046175) | 0.007796 / 0.007986 (-0.000190) | 0.003450 / 0.004328 (-0.000878) | 0.078694 / 0.004250 (0.074443) | 0.038068 / 0.037052 (0.001016) | 0.316353 / 0.258489 (0.057864) | 0.352344 / 0.293841 (0.058503) | 0.033271 / 0.128546 (-0.095276) | 0.011427 / 0.075646 (-0.064220) | 0.322367 / 0.419271 (-0.096904) | 0.041497 / 0.043533 (-0.002036) | 0.305876 / 0.255139 (0.050737) | 0.332279 / 0.283200 (0.049079) | 0.086719 / 0.141683 (-0.054964) | 1.488367 / 1.452155 (0.036212) | 1.528943 / 1.492716 (0.036227) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.171072 / 0.018006 (0.153066) | 0.421048 / 0.000490 (0.420558) | 0.003622 / 0.000200 (0.003422) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022632 / 0.037411 (-0.014779) | 0.095304 / 0.014526 (0.080778) | 0.106254 / 0.176557 (-0.070302) | 0.138437 / 0.737135 (-0.598698) | 0.107258 / 0.296338 (-0.189080) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423201 / 0.215209 (0.207992) | 4.208397 / 2.077655 (2.130742) | 1.899800 / 1.504120 (0.395680) | 1.682782 / 1.541195 (0.141587) | 1.708840 / 1.468490 (0.240350) | 0.694492 / 4.584777 (-3.890285) | 3.380369 / 3.745712 (-0.365344) | 1.851731 / 5.269862 (-3.418130) | 1.151615 / 4.565676 (-3.414061) | 0.082446 / 0.424275 (-0.341829) | 0.012483 / 0.007607 (0.004876) | 0.533688 / 0.226044 (0.307643) | 5.373434 / 2.268929 (3.104505) | 2.346403 / 55.444624 (-53.098221) | 1.978505 / 6.876477 (-4.897971) | 2.005875 / 2.142072 (-0.136198) | 0.820785 / 4.805227 (-3.984442) | 0.150728 / 6.500664 (-6.349936) | 0.065761 / 0.075469 (-0.009708) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244550 / 1.841788 (-0.597237) | 13.219096 / 8.074308 (5.144788) | 13.960463 / 10.191392 (3.769071) | 0.135572 / 0.680424 (-0.544852) | 0.028746 / 0.534201 (-0.505455) | 0.393082 / 0.579283 (-0.186201) | 0.402852 / 0.434364 (-0.031512) | 0.461191 / 0.540337 (-0.079147) | 0.543500 / 1.386936 (-0.843436) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006316 / 0.011353 (-0.005037) | 0.004394 / 0.011008 (-0.006615) | 0.096478 / 0.038508 (0.057970) | 0.026965 / 0.023109 (0.003855) | 0.340371 / 0.275898 (0.064473) | 0.368334 / 0.323480 (0.044854) | 0.004744 / 0.007986 (-0.003242) | 0.004652 / 0.004328 (0.000324) | 0.074479 / 0.004250 (0.070228) | 0.036358 / 0.037052 (-0.000694) | 0.342968 / 0.258489 (0.084479) | 0.383675 / 0.293841 (0.089834) | 0.031439 / 0.128546 (-0.097107) | 0.011529 / 0.075646 (-0.064117) | 0.319560 / 0.419271 (-0.099711) | 0.041370 / 0.043533 (-0.002163) | 0.342594 / 0.255139 (0.087455) | 0.363237 / 0.283200 (0.080038) | 0.087316 / 0.141683 (-0.054367) | 1.468690 / 1.452155 (0.016535) | 1.553974 / 1.492716 (0.061257) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198366 / 0.018006 (0.180360) | 0.401581 / 0.000490 (0.401091) | 0.000400 / 0.000200 (0.000200) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023150 / 0.037411 (-0.014261) | 0.097797 / 0.014526 (0.083271) | 0.106198 / 0.176557 (-0.070359) | 0.139599 / 0.737135 (-0.597536) | 0.108361 / 0.296338 (-0.187978) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472962 / 0.215209 (0.257753) | 4.702688 / 2.077655 (2.625033) | 2.401002 / 1.504120 (0.896882) | 2.193857 / 1.541195 (0.652663) | 2.219188 / 1.468490 (0.750697) | 0.689993 / 4.584777 (-3.894784) | 3.369409 / 3.745712 (-0.376304) | 1.824801 / 5.269862 (-3.445061) | 1.150815 / 4.565676 (-3.414862) | 0.082197 / 0.424275 (-0.342078) | 0.012287 / 0.007607 (0.004679) | 0.581963 / 0.226044 (0.355918) | 5.786943 / 2.268929 (3.518015) | 2.871235 / 55.444624 (-52.573389) | 2.516009 / 6.876477 (-4.360468) | 2.535669 / 2.142072 (0.393597) | 0.804733 / 4.805227 (-4.000494) | 0.150545 / 6.500664 (-6.350119) | 0.066964 / 0.075469 (-0.008505) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285431 / 1.841788 (-0.556356) | 14.097108 / 8.074308 (6.022800) | 13.821497 / 10.191392 (3.630105) | 0.141922 / 0.680424 (-0.538502) | 0.016964 / 0.534201 (-0.517237) | 0.374784 / 0.579283 (-0.204500) | 0.381034 / 0.434364 (-0.053330) | 0.435487 / 0.540337 (-0.104850) | 0.521894 / 1.386936 (-0.865042) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#462000c2b12a11f1fc26853e842d3f6e40287737 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009486 / 0.011353 (-0.001867) | 0.005363 / 0.011008 (-0.005645) | 0.101008 / 0.038508 (0.062500) | 0.036355 / 0.023109 (0.013246) | 0.290575 / 0.275898 (0.014677) | 0.391634 / 0.323480 (0.068154) | 0.009085 / 0.007986 (0.001099) | 0.005780 / 0.004328 (0.001451) | 0.077848 / 0.004250 (0.073598) | 0.049062 / 0.037052 (0.012009) | 0.310900 / 0.258489 (0.052411) | 0.358224 / 0.293841 (0.064383) | 0.038838 / 0.128546 (-0.089708) | 0.012244 / 0.075646 (-0.063402) | 0.333701 / 0.419271 (-0.085570) | 0.048021 / 0.043533 (0.004488) | 0.289584 / 0.255139 (0.034445) | 0.317556 / 0.283200 (0.034356) | 0.109807 / 0.141683 (-0.031876) | 1.465966 / 1.452155 (0.013811) | 1.526341 / 1.492716 (0.033625) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246221 / 0.018006 (0.228215) | 0.580659 / 0.000490 (0.580169) | 0.000627 / 0.000200 (0.000427) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028352 / 0.037411 (-0.009059) | 0.110569 / 0.014526 (0.096043) | 0.126456 / 0.176557 (-0.050100) | 0.163633 / 0.737135 (-0.573503) | 0.128252 / 0.296338 (-0.168087) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397271 / 0.215209 (0.182062) | 3.975336 / 2.077655 (1.897682) | 1.786957 / 1.504120 (0.282837) | 1.598468 / 1.541195 (0.057273) | 1.645299 / 1.468490 (0.176809) | 0.686221 / 4.584777 (-3.898556) | 3.753184 / 3.745712 (0.007472) | 2.089505 / 5.269862 (-3.180356) | 1.325799 / 4.565676 (-3.239878) | 0.084608 / 0.424275 (-0.339667) | 0.012343 / 0.007607 (0.004736) | 0.509951 / 0.226044 (0.283907) | 5.092102 / 2.268929 (2.823174) | 2.297551 / 55.444624 (-53.147073) | 1.938177 / 6.876477 (-4.938300) | 2.012448 / 2.142072 (-0.129625) | 0.835206 / 4.805227 (-3.970021) | 0.166373 / 6.500664 (-6.334291) | 0.063996 / 0.075469 (-0.011473) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212936 / 1.841788 (-0.628851) | 15.067370 / 8.074308 (6.993062) | 14.165214 / 10.191392 (3.973822) | 0.157041 / 0.680424 (-0.523383) | 0.029612 / 0.534201 (-0.504589) | 0.440006 / 0.579283 (-0.139277) | 0.439165 / 0.434364 (0.004801) | 0.524970 / 0.540337 (-0.015368) | 0.608305 / 1.386936 (-0.778631) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007433 / 0.011353 (-0.003920) | 0.005310 / 0.011008 (-0.005698) | 0.097194 / 0.038508 (0.058686) | 0.033265 / 0.023109 (0.010156) | 0.369908 / 0.275898 (0.094010) | 0.411508 / 0.323480 (0.088028) | 0.006000 / 0.007986 (-0.001986) | 0.005647 / 0.004328 (0.001319) | 0.075597 / 0.004250 (0.071347) | 0.051951 / 0.037052 (0.014899) | 0.378469 / 0.258489 (0.119980) | 0.424849 / 0.293841 (0.131008) | 0.036700 / 0.128546 (-0.091846) | 0.012535 / 0.075646 (-0.063111) | 0.333197 / 0.419271 (-0.086074) | 0.049046 / 0.043533 (0.005513) | 0.381845 / 0.255139 (0.126706) | 0.397846 / 0.283200 (0.114646) | 0.109152 / 0.141683 (-0.032531) | 1.432407 / 1.452155 (-0.019748) | 1.555509 / 1.492716 (0.062793) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265433 / 0.018006 (0.247427) | 0.559590 / 0.000490 (0.559100) | 0.000492 / 0.000200 (0.000292) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029748 / 0.037411 (-0.007663) | 0.110490 / 0.014526 (0.095964) | 0.124125 / 0.176557 (-0.052431) | 0.160089 / 0.737135 (-0.577046) | 0.128755 / 0.296338 (-0.167583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443976 / 0.215209 (0.228767) | 4.416960 / 2.077655 (2.339305) | 2.239408 / 1.504120 (0.735288) | 2.055341 / 1.541195 (0.514147) | 2.093479 / 1.468490 (0.624988) | 0.688846 / 4.584777 (-3.895930) | 3.797526 / 3.745712 (0.051814) | 3.578137 / 5.269862 (-1.691725) | 2.015073 / 4.565676 (-2.550603) | 0.084126 / 0.424275 (-0.340149) | 0.012581 / 0.007607 (0.004974) | 0.549774 / 0.226044 (0.323730) | 5.492185 / 2.268929 (3.223256) | 2.739851 / 55.444624 (-52.704773) | 2.371091 / 6.876477 (-4.505386) | 2.400178 / 2.142072 (0.258105) | 0.831227 / 4.805227 (-3.974001) | 0.166156 / 6.500664 (-6.334508) | 0.063901 / 0.075469 (-0.011568) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.236127 / 1.841788 (-0.605660) | 15.236884 / 8.074308 (7.162576) | 14.434351 / 10.191392 (4.242959) | 0.163725 / 0.680424 (-0.516699) | 0.018009 / 0.534201 (-0.516192) | 0.430612 / 0.579283 (-0.148671) | 0.420426 / 0.434364 (-0.013938) | 0.497062 / 0.540337 (-0.043275) | 0.590924 / 1.386936 (-0.796012) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#63377dc53fc94f19bc2b0bbfb118a90d01a1d020 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010862 / 0.011353 (-0.000491) | 0.005741 / 0.011008 (-0.005267) | 0.111911 / 0.038508 (0.073403) | 0.042316 / 0.023109 (0.019207) | 0.347665 / 0.275898 (0.071767) | 0.377335 / 0.323480 (0.053855) | 0.009400 / 0.007986 (0.001414) | 0.006814 / 0.004328 (0.002486) | 0.087194 / 0.004250 (0.082943) | 0.046878 / 0.037052 (0.009826) | 0.348920 / 0.258489 (0.090430) | 0.393347 / 0.293841 (0.099507) | 0.044212 / 0.128546 (-0.084334) | 0.013925 / 0.075646 (-0.061722) | 0.386076 / 0.419271 (-0.033195) | 0.054195 / 0.043533 (0.010662) | 0.358486 / 0.255139 (0.103347) | 0.360132 / 0.283200 (0.076932) | 0.109783 / 0.141683 (-0.031900) | 1.679875 / 1.452155 (0.227720) | 1.794379 / 1.492716 (0.301663) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221927 / 0.018006 (0.203921) | 0.487352 / 0.000490 (0.486863) | 0.003494 / 0.000200 (0.003294) | 0.000091 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032201 / 0.037411 (-0.005210) | 0.125861 / 0.014526 (0.111335) | 0.133905 / 0.176557 (-0.042652) | 0.183319 / 0.737135 (-0.553817) | 0.142646 / 0.296338 (-0.153693) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442720 / 0.215209 (0.227511) | 4.602619 / 2.077655 (2.524964) | 2.050214 / 1.504120 (0.546094) | 1.837968 / 1.541195 (0.296773) | 1.961199 / 1.468490 (0.492709) | 0.793426 / 4.584777 (-3.791351) | 4.472078 / 3.745712 (0.726366) | 2.364903 / 5.269862 (-2.904959) | 1.515076 / 4.565676 (-3.050600) | 0.103087 / 0.424275 (-0.321188) | 0.014676 / 0.007607 (0.007068) | 0.576887 / 0.226044 (0.350843) | 5.785525 / 2.268929 (3.516596) | 2.765231 / 55.444624 (-52.679393) | 2.365364 / 6.876477 (-4.511113) | 2.448335 / 2.142072 (0.306262) | 0.978726 / 4.805227 (-3.826501) | 0.191417 / 6.500664 (-6.309247) | 0.073295 / 0.075469 (-0.002174) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.378995 / 1.841788 (-0.462792) | 16.583655 / 8.074308 (8.509347) | 14.944731 / 10.191392 (4.753339) | 0.168916 / 0.680424 (-0.511508) | 0.035272 / 0.534201 (-0.498928) | 0.489729 / 0.579283 (-0.089554) | 0.496231 / 0.434364 (0.061867) | 0.576218 / 0.540337 (0.035880) | 0.673558 / 1.386936 (-0.713378) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008104 / 0.011353 (-0.003249) | 0.005179 / 0.011008 (-0.005829) | 0.103908 / 0.038508 (0.065400) | 0.034661 / 0.023109 (0.011552) | 0.398119 / 0.275898 (0.122221) | 0.411765 / 0.323480 (0.088286) | 0.006016 / 0.007986 (-0.001970) | 0.005637 / 0.004328 (0.001308) | 0.073662 / 0.004250 (0.069412) | 0.052411 / 0.037052 (0.015359) | 0.391826 / 0.258489 (0.133337) | 0.455217 / 0.293841 (0.161376) | 0.039924 / 0.128546 (-0.088622) | 0.013390 / 0.075646 (-0.062256) | 0.390319 / 0.419271 (-0.028953) | 0.054312 / 0.043533 (0.010779) | 0.395492 / 0.255139 (0.140353) | 0.446324 / 0.283200 (0.163124) | 0.116461 / 0.141683 (-0.025222) | 1.502163 / 1.452155 (0.050008) | 1.731541 / 1.492716 (0.238825) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282612 / 0.018006 (0.264606) | 0.503170 / 0.000490 (0.502680) | 0.005307 / 0.000200 (0.005107) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029071 / 0.037411 (-0.008340) | 0.123831 / 0.014526 (0.109306) | 0.133284 / 0.176557 (-0.043272) | 0.172029 / 0.737135 (-0.565106) | 0.140639 / 0.296338 (-0.155700) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.496812 / 0.215209 (0.281603) | 4.958915 / 2.077655 (2.881260) | 2.559188 / 1.504120 (1.055068) | 2.262434 / 1.541195 (0.721240) | 2.371126 / 1.468490 (0.902636) | 0.780150 / 4.584777 (-3.804627) | 4.417060 / 3.745712 (0.671348) | 2.401909 / 5.269862 (-2.867953) | 1.527943 / 4.565676 (-3.037733) | 0.100074 / 0.424275 (-0.324201) | 0.014853 / 0.007607 (0.007246) | 0.630192 / 0.226044 (0.404147) | 6.409685 / 2.268929 (4.140757) | 3.224718 / 55.444624 (-52.219906) | 2.795301 / 6.876477 (-4.081176) | 2.927205 / 2.142072 (0.785132) | 0.989537 / 4.805227 (-3.815690) | 0.199775 / 6.500664 (-6.300889) | 0.076725 / 0.075469 (0.001256) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.433504 / 1.841788 (-0.408284) | 17.117134 / 8.074308 (9.042825) | 16.606367 / 10.191392 (6.414975) | 0.165653 / 0.680424 (-0.514771) | 0.020818 / 0.534201 (-0.513383) | 0.496782 / 0.579283 (-0.082501) | 0.473895 / 0.434364 (0.039531) | 0.576796 / 0.540337 (0.036459) | 0.703272 / 1.386936 (-0.683664) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6627fb6f2639ac3b1435b3386545612db038a42e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012501 / 0.011353 (0.001148) | 0.006437 / 0.011008 (-0.004571) | 0.129387 / 0.038508 (0.090878) | 0.035847 / 0.023109 (0.012737) | 0.339243 / 0.275898 (0.063345) | 0.423274 / 0.323480 (0.099794) | 0.008489 / 0.007986 (0.000503) | 0.004596 / 0.004328 (0.000268) | 0.103322 / 0.004250 (0.099071) | 0.043570 / 0.037052 (0.006517) | 0.357004 / 0.258489 (0.098515) | 0.426511 / 0.293841 (0.132670) | 0.062923 / 0.128546 (-0.065623) | 0.021168 / 0.075646 (-0.054478) | 0.387485 / 0.419271 (-0.031787) | 0.059745 / 0.043533 (0.016213) | 0.341101 / 0.255139 (0.085962) | 0.365530 / 0.283200 (0.082331) | 0.102110 / 0.141683 (-0.039573) | 1.729408 / 1.452155 (0.277253) | 1.759510 / 1.492716 (0.266794) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187065 / 0.018006 (0.169059) | 0.499685 / 0.000490 (0.499196) | 0.004677 / 0.000200 (0.004478) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025827 / 0.037411 (-0.011584) | 0.113780 / 0.014526 (0.099255) | 0.146060 / 0.176557 (-0.030496) | 0.158169 / 0.737135 (-0.578966) | 0.136133 / 0.296338 (-0.160206) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.608421 / 0.215209 (0.393211) | 5.907395 / 2.077655 (3.829741) | 2.193140 / 1.504120 (0.689021) | 1.870315 / 1.541195 (0.329120) | 1.885660 / 1.468490 (0.417170) | 1.227637 / 4.584777 (-3.357140) | 5.319242 / 3.745712 (1.573530) | 2.991595 / 5.269862 (-2.278267) | 2.043906 / 4.565676 (-2.521771) | 0.151829 / 0.424275 (-0.272447) | 0.018974 / 0.007607 (0.011367) | 0.778035 / 0.226044 (0.551991) | 7.705796 / 2.268929 (5.436868) | 2.990156 / 55.444624 (-52.454468) | 2.372643 / 6.876477 (-4.503834) | 2.240847 / 2.142072 (0.098775) | 1.407209 / 4.805227 (-3.398018) | 0.242336 / 6.500664 (-6.258328) | 0.069847 / 0.075469 (-0.005622) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.445817 / 1.841788 (-0.395970) | 16.059632 / 8.074308 (7.985324) | 18.541971 / 10.191392 (8.350579) | 0.237830 / 0.680424 (-0.442594) | 0.041060 / 0.534201 (-0.493141) | 0.496765 / 0.579283 (-0.082518) | 0.609666 / 0.434364 (0.175302) | 0.584614 / 0.540337 (0.044277) | 0.680858 / 1.386936 (-0.706078) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009037 / 0.011353 (-0.002315) | 0.005961 / 0.011008 (-0.005047) | 0.127204 / 0.038508 (0.088696) | 0.030664 / 0.023109 (0.007555) | 0.417968 / 0.275898 (0.142070) | 0.515316 / 0.323480 (0.191836) | 0.006549 / 0.007986 (-0.001436) | 0.004456 / 0.004328 (0.000128) | 0.083715 / 0.004250 (0.079464) | 0.043701 / 0.037052 (0.006648) | 0.521153 / 0.258489 (0.262664) | 0.565456 / 0.293841 (0.271615) | 0.055298 / 0.128546 (-0.073248) | 0.018103 / 0.075646 (-0.057544) | 0.403990 / 0.419271 (-0.015282) | 0.060162 / 0.043533 (0.016629) | 0.486383 / 0.255139 (0.231244) | 0.470342 / 0.283200 (0.187142) | 0.102269 / 0.141683 (-0.039414) | 1.643241 / 1.452155 (0.191086) | 1.763850 / 1.492716 (0.271133) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185602 / 0.018006 (0.167596) | 0.489163 / 0.000490 (0.488674) | 0.000426 / 0.000200 (0.000226) | 0.000086 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026689 / 0.037411 (-0.010722) | 0.111520 / 0.014526 (0.096994) | 0.119838 / 0.176557 (-0.056719) | 0.153698 / 0.737135 (-0.583437) | 0.130969 / 0.296338 (-0.165370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616170 / 0.215209 (0.400961) | 6.219702 / 2.077655 (4.142048) | 2.533554 / 1.504120 (1.029434) | 2.256009 / 1.541195 (0.714815) | 2.217617 / 1.468490 (0.749127) | 1.156920 / 4.584777 (-3.427857) | 5.175759 / 3.745712 (1.430046) | 2.848419 / 5.269862 (-2.421442) | 1.943864 / 4.565676 (-2.621813) | 0.138342 / 0.424275 (-0.285933) | 0.013140 / 0.007607 (0.005533) | 0.782105 / 0.226044 (0.556060) | 7.602003 / 2.268929 (5.333075) | 3.629577 / 55.444624 (-51.815047) | 2.713849 / 6.876477 (-4.162628) | 2.663888 / 2.142072 (0.521816) | 1.418381 / 4.805227 (-3.386847) | 0.250649 / 6.500664 (-6.250015) | 0.073564 / 0.075469 (-0.001905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.483739 / 1.841788 (-0.358049) | 16.386204 / 8.074308 (8.311896) | 20.685262 / 10.191392 (10.493870) | 0.237084 / 0.680424 (-0.443340) | 0.039097 / 0.534201 (-0.495104) | 0.525399 / 0.579283 (-0.053884) | 0.587541 / 0.434364 (0.153177) | 0.566605 / 0.540337 (0.026268) | 0.677384 / 1.386936 (-0.709552) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3b67d42733dabb15ce4997c8324f8e047ce12bd \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.014050 / 0.011353 (0.002697) | 0.005981 / 0.011008 (-0.005028) | 0.126307 / 0.038508 (0.087799) | 0.035400 / 0.023109 (0.012290) | 0.387821 / 0.275898 (0.111923) | 0.462785 / 0.323480 (0.139305) | 0.009427 / 0.007986 (0.001441) | 0.005081 / 0.004328 (0.000753) | 0.097273 / 0.004250 (0.093023) | 0.044699 / 0.037052 (0.007647) | 0.396025 / 0.258489 (0.137536) | 0.450137 / 0.293841 (0.156296) | 0.055660 / 0.128546 (-0.072886) | 0.022710 / 0.075646 (-0.052936) | 0.443784 / 0.419271 (0.024513) | 0.065756 / 0.043533 (0.022223) | 0.379350 / 0.255139 (0.124211) | 0.396783 / 0.283200 (0.113583) | 0.114088 / 0.141683 (-0.027594) | 1.856834 / 1.452155 (0.404679) | 1.839292 / 1.492716 (0.346576) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206748 / 0.018006 (0.188742) | 0.517711 / 0.000490 (0.517222) | 0.008302 / 0.000200 (0.008102) | 0.000494 / 0.000054 (0.000440) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033987 / 0.037411 (-0.003424) | 0.131067 / 0.014526 (0.116542) | 0.155539 / 0.176557 (-0.021018) | 0.188598 / 0.737135 (-0.548537) | 0.156000 / 0.296338 (-0.140338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.641413 / 0.215209 (0.426204) | 6.156680 / 2.077655 (4.079025) | 2.428858 / 1.504120 (0.924738) | 2.086195 / 1.541195 (0.545000) | 2.109604 / 1.468490 (0.641114) | 1.209426 / 4.584777 (-3.375351) | 5.139398 / 3.745712 (1.393686) | 3.041337 / 5.269862 (-2.228524) | 2.294809 / 4.565676 (-2.270868) | 0.142206 / 0.424275 (-0.282069) | 0.015167 / 0.007607 (0.007560) | 0.816269 / 0.226044 (0.590224) | 7.953931 / 2.268929 (5.685002) | 3.201793 / 55.444624 (-52.242832) | 2.448620 / 6.876477 (-4.427857) | 2.521670 / 2.142072 (0.379597) | 1.484094 / 4.805227 (-3.321133) | 0.255069 / 6.500664 (-6.245595) | 0.076031 / 0.075469 (0.000561) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.590951 / 1.841788 (-0.250836) | 17.661353 / 8.074308 (9.587045) | 21.097837 / 10.191392 (10.906445) | 0.229265 / 0.680424 (-0.451159) | 0.042618 / 0.534201 (-0.491583) | 0.535942 / 0.579283 (-0.043342) | 0.590195 / 0.434364 (0.155831) | 0.623985 / 0.540337 (0.083648) | 0.742637 / 1.386936 (-0.644299) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009264 / 0.011353 (-0.002088) | 0.008798 / 0.011008 (-0.002210) | 0.122208 / 0.038508 (0.083700) | 0.034835 / 0.023109 (0.011726) | 0.462618 / 0.275898 (0.186720) | 0.505632 / 0.323480 (0.182152) | 0.006320 / 0.007986 (-0.001665) | 0.005383 / 0.004328 (0.001054) | 0.091229 / 0.004250 (0.086979) | 0.045828 / 0.037052 (0.008775) | 0.477507 / 0.258489 (0.219018) | 0.539616 / 0.293841 (0.245775) | 0.061913 / 0.128546 (-0.066633) | 0.019390 / 0.075646 (-0.056257) | 0.420016 / 0.419271 (0.000745) | 0.065958 / 0.043533 (0.022425) | 0.468603 / 0.255139 (0.213464) | 0.486246 / 0.283200 (0.203046) | 0.107924 / 0.141683 (-0.033759) | 1.843614 / 1.452155 (0.391459) | 1.988159 / 1.492716 (0.495442) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247043 / 0.018006 (0.229037) | 0.515580 / 0.000490 (0.515090) | 0.005630 / 0.000200 (0.005430) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030674 / 0.037411 (-0.006737) | 0.130783 / 0.014526 (0.116258) | 0.147669 / 0.176557 (-0.028888) | 0.175656 / 0.737135 (-0.561479) | 0.138317 / 0.296338 (-0.158022) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.727119 / 0.215209 (0.511909) | 6.848208 / 2.077655 (4.770553) | 3.121418 / 1.504120 (1.617298) | 2.701799 / 1.541195 (1.160604) | 2.749179 / 1.468490 (1.280689) | 1.312058 / 4.584777 (-3.272719) | 5.400562 / 3.745712 (1.654850) | 3.058142 / 5.269862 (-2.211719) | 2.076361 / 4.565676 (-2.489316) | 0.142169 / 0.424275 (-0.282106) | 0.014340 / 0.007607 (0.006733) | 0.853534 / 0.226044 (0.627490) | 8.734484 / 2.268929 (6.465556) | 3.968130 / 55.444624 (-51.476495) | 3.118032 / 6.876477 (-3.758444) | 3.078757 / 2.142072 (0.936684) | 1.460694 / 4.805227 (-3.344533) | 0.261858 / 6.500664 (-6.238806) | 0.081089 / 0.075469 (0.005620) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.611473 / 1.841788 (-0.230315) | 17.660545 / 8.074308 (9.586237) | 20.526023 / 10.191392 (10.334631) | 0.223320 / 0.680424 (-0.457103) | 0.027939 / 0.534201 (-0.506261) | 0.542704 / 0.579283 (-0.036579) | 0.563826 / 0.434364 (0.129462) | 0.639936 / 0.540337 (0.099599) | 0.755974 / 1.386936 (-0.630962) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#942141e13ba2be853e2231d9edbfa38044e2632d \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008776 / 0.011353 (-0.002577) | 0.004532 / 0.011008 (-0.006476) | 0.100373 / 0.038508 (0.061865) | 0.029706 / 0.023109 (0.006597) | 0.304374 / 0.275898 (0.028476) | 0.337223 / 0.323480 (0.013743) | 0.007021 / 0.007986 (-0.000965) | 0.003420 / 0.004328 (-0.000908) | 0.077754 / 0.004250 (0.073504) | 0.034411 / 0.037052 (-0.002642) | 0.302926 / 0.258489 (0.044437) | 0.342654 / 0.293841 (0.048813) | 0.034528 / 0.128546 (-0.094018) | 0.011926 / 0.075646 (-0.063721) | 0.322971 / 0.419271 (-0.096301) | 0.041384 / 0.043533 (-0.002149) | 0.306433 / 0.255139 (0.051294) | 0.332293 / 0.283200 (0.049093) | 0.084972 / 0.141683 (-0.056711) | 1.493426 / 1.452155 (0.041271) | 1.570446 / 1.492716 (0.077729) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.189090 / 0.018006 (0.171084) | 0.433904 / 0.000490 (0.433414) | 0.001323 / 0.000200 (0.001124) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023531 / 0.037411 (-0.013880) | 0.097774 / 0.014526 (0.083248) | 0.106383 / 0.176557 (-0.070174) | 0.139158 / 0.737135 (-0.597977) | 0.109443 / 0.296338 (-0.186896) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419078 / 0.215209 (0.203869) | 4.182657 / 2.077655 (2.105002) | 1.887276 / 1.504120 (0.383156) | 1.679542 / 1.541195 (0.138347) | 1.718035 / 1.468490 (0.249545) | 0.692628 / 4.584777 (-3.892149) | 3.361354 / 3.745712 (-0.384358) | 1.928583 / 5.269862 (-3.341278) | 1.317291 / 4.565676 (-3.248386) | 0.081799 / 0.424275 (-0.342476) | 0.012318 / 0.007607 (0.004711) | 0.525927 / 0.226044 (0.299883) | 5.285905 / 2.268929 (3.016977) | 2.317524 / 55.444624 (-53.127100) | 1.966478 / 6.876477 (-4.909998) | 2.054869 / 2.142072 (-0.087204) | 0.807579 / 4.805227 (-3.997649) | 0.149854 / 6.500664 (-6.350810) | 0.065285 / 0.075469 (-0.010184) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180516 / 1.841788 (-0.661271) | 13.889734 / 8.074308 (5.815426) | 14.076163 / 10.191392 (3.884771) | 0.156276 / 0.680424 (-0.524148) | 0.029187 / 0.534201 (-0.505013) | 0.403859 / 0.579283 (-0.175424) | 0.404998 / 0.434364 (-0.029366) | 0.471467 / 0.540337 (-0.068871) | 0.564526 / 1.386936 (-0.822410) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006739 / 0.011353 (-0.004614) | 0.004644 / 0.011008 (-0.006364) | 0.097326 / 0.038508 (0.058818) | 0.027728 / 0.023109 (0.004619) | 0.413537 / 0.275898 (0.137639) | 0.452012 / 0.323480 (0.128532) | 0.005346 / 0.007986 (-0.002639) | 0.003338 / 0.004328 (-0.000991) | 0.075670 / 0.004250 (0.071420) | 0.038825 / 0.037052 (0.001772) | 0.415612 / 0.258489 (0.157123) | 0.454680 / 0.293841 (0.160839) | 0.031866 / 0.128546 (-0.096680) | 0.011616 / 0.075646 (-0.064031) | 0.319527 / 0.419271 (-0.099745) | 0.041283 / 0.043533 (-0.002250) | 0.412046 / 0.255139 (0.156907) | 0.435244 / 0.283200 (0.152044) | 0.088400 / 0.141683 (-0.053283) | 1.478125 / 1.452155 (0.025970) | 1.553677 / 1.492716 (0.060960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229919 / 0.018006 (0.211913) | 0.415446 / 0.000490 (0.414956) | 0.000386 / 0.000200 (0.000186) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024365 / 0.037411 (-0.013046) | 0.098225 / 0.014526 (0.083699) | 0.106674 / 0.176557 (-0.069883) | 0.144755 / 0.737135 (-0.592380) | 0.109221 / 0.296338 (-0.187117) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457665 / 0.215209 (0.242456) | 4.597849 / 2.077655 (2.520195) | 2.171275 / 1.504120 (0.667155) | 1.945547 / 1.541195 (0.404352) | 2.014043 / 1.468490 (0.545553) | 0.699732 / 4.584777 (-3.885045) | 3.420711 / 3.745712 (-0.325001) | 3.298702 / 5.269862 (-1.971159) | 1.390324 / 4.565676 (-3.175353) | 0.082668 / 0.424275 (-0.341607) | 0.012556 / 0.007607 (0.004949) | 0.550406 / 0.226044 (0.324361) | 5.501060 / 2.268929 (3.232132) | 2.659841 / 55.444624 (-52.784783) | 2.243443 / 6.876477 (-4.633034) | 2.266006 / 2.142072 (0.123934) | 0.806295 / 4.805227 (-3.998933) | 0.151399 / 6.500664 (-6.349265) | 0.067048 / 0.075469 (-0.008421) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291404 / 1.841788 (-0.550384) | 14.164728 / 8.074308 (6.090419) | 13.980219 / 10.191392 (3.788827) | 0.140599 / 0.680424 (-0.539824) | 0.016880 / 0.534201 (-0.517321) | 0.379073 / 0.579283 (-0.200210) | 0.385770 / 0.434364 (-0.048594) | 0.442516 / 0.540337 (-0.097822) | 0.533569 / 1.386936 (-0.853367) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#29fa15df972353f51fc434cf8eceb574b60a415f \"CML watermark\")\n",
"Tests seem to be failing for unrelated reasons.",
"Tests are failing because of a bug on the Hub side - this is being fixed :)\r\n\r\nlmk once the TF documentation page is updated and we can merge !",
"@lhoestq Docs updated!"
] | 2022-12-19T19:40:27 | 2023-01-25T16:28:44 | 2023-01-25T16:21:40 | MEMBER | null | false | {
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} | Hey all! Here's a first draft of the PR to add a multiprocessing implementation for `to_tf_dataset()`. It worked in some quick testing for me, but obviously I need to do some much more rigorous testing/benchmarking, and add some proper library tests.
The core idea is that we do everything using `multiprocessing` and `numpy`, and just wrap a `tf.data.Dataset` around the output. We could also rewrite the existing single-threaded implementation based on this code, which might simplify it a bit.
Checklist:
- [X] Add initial draft
- [x] Check that it works regardless of whether the `collate_fn` or dataset returns `tf` or `np` arrays
- [x] Check that it works with `tf.string` return data
- [x] Check indices are correctly reshuffled each epoch
- [x] Make sure workers don't try to initialize a GPU device!!
- [x] Check `fit()` with multiple epochs works fine and that the progress bar is correct
- [x] Check there are no memory leaks or zombie processes
- [x] Benchmark performance
- [x] Tweak params for dataset inference - can we speed things up there a bit?
- [x] Add tests to the library
- [x] Add a PR to `transformers` to expose the `num_workers` argument via `prepare_tf_dataset` (will merge after this one is released)
- [x] Stop TF console spam!! (almost)
- [x] Add a method for creating SHM that doesn't crash if it was left and still linked
- [x] Add a barrier for Py <= 3.7 because it doesn't support SharedMemory
- [x] Support string dtypes by converting them into fixed-width character arrays | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5376). All of your documentation changes will be reflected on that endpoint."
] | 2022-12-19T10:56:56 | 2022-12-19T11:01:55 | 2022-12-19T10:57:16 | MEMBER | null | false | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-12-19T10:48:26 | 2022-12-19T10:55:43 | 2022-12-19T10:53:15 | MEMBER | null | false | {
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"The data files are hosted on HF at https://huggingface.co./datasets/allenai/c4/tree/main\r\n\r\nYou have 200 runs streaming the same files in parallel. So this is probably a Hub limitation. Maybe rate limiting ? cc @julien-c \r\n\r\nMaybe you can also try to reduce the number of HTTP requests by increasing the block size of each request. This can be done by increasing `DEFAULT_BLOCK_SIZE` in `fsspec.implementations.http`. Default is `5 * 2**20` (5MiB)\r\n\r\nAnyway maybe it's just better to save the dataset locally in that case ?",
"you don't get an HTTP error code or something in your stack trace? Kinda hard to debug with this info",
"You could try to re-run using this `datasets` branch: [raise-err-when-disconnect](https://github.com/huggingface/datasets/compare/raise-err-when-disconnect?expand=1)\r\nIt should raise the fsspec error",
"The weird thing is that I already have it saved locally & it seems to indeed be using the cached one 🧐 ; I'm also using offline mode, so I don't think it has something to do with the Hub.\r\n```\r\nWARNING:datasets.load:Using the latest cached version of the module from /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01 (last modified on Mon Dec 12 10:45:02 2022) since it couldn't be found locally at c4.\r\n```\r\n\r\n",
"No, you passed `streaming=True` so it streams the data from the Hub.\r\nThis message just shows that you use the cached version of the `c4` **module**, aka the python script that is run to generate the examples from the raw data files.\r\n\r\nMaybe the offline mode should also disable `fsspec`/`aiohttp` HTTP calls in `datasets` and not just the `requests` ones.",
"> This message just shows that you use the cached version of the c4 module\r\n\r\nAh my bad you're right about the module, but it's also using the downloaded & cached c4 dataset. There's no internet during the runs so it wouldn't work otherwise",
"You don't have internet, therefore you get an error while trying to stream ;)"
] | 2022-12-18T11:38:58 | 2023-07-24T15:23:07 | 2023-07-24T15:23:07 | CONTRIBUTOR | null | null | null | ### Describe the bug
`streaming_download_manager` seems to disconnect if too many runs access the same underlying dataset 🧐
The code works fine for me if I have ~100 runs in parallel, but disconnects once scaling to 200.
Possibly related:
- https://github.com/huggingface/datasets/pull/3100
- https://github.com/huggingface/datasets/pull/3050
### Steps to reproduce the bug
Running
```python
c4 = datasets.load_dataset("c4", "en", split="train", streaming=True).skip(args.start).take(args.end-args.start)
df = pd.DataFrame(c4, index=None)
```
with different start & end arguments on 200 CPUs in parallel yields:
```
WARNING:datasets.load:Using the latest cached version of the module from /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01 (last modified on Mon Dec 12 10:45:02 2022) since it couldn't be found locally at c4.
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [1/20]
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╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/dec-2022-tasky/inference │
│ _c4.py:68 in <module> │
│ │
│ 65 │ model.eval() │
│ 66 │ │
│ 67 │ c4 = datasets.load_dataset("c4", "en", split="train", streaming=Tru │
│ ❱ 68 │ df = pd.DataFrame(c4, index=None) │
│ 69 │ texts = df["text"].to_list() │
│ 70 │ preds = batch_inference(texts, batch_size=args.batch_size) │
│ 71 │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/site-packages/pandas/core/frame.p │
│ y:684 in __init__ │
│ │
│ 681 │ │ # For data is list-like, or Iterable (will consume into list │
│ 682 │ │ elif is_list_like(data): │
│ 683 │ │ │ if not isinstance(data, (abc.Sequence, ExtensionArray)): │
│ ❱ 684 │ │ │ │ data = list(data) │
│ 685 │ │ │ if len(data) > 0: │
│ 686 │ │ │ │ if is_dataclass(data[0]): │
│ 687 │ │ │ │ │ data = dataclasses_to_dicts(data) │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:751 in __iter__ │
│ │
│ 748 │ │ yield from ex_iterable.shard_data_sources(shard_idx) │
│ 749 │ │
│ 750 │ def __iter__(self): │
│ ❱ 751 │ │ for key, example in self._iter(): │
│ 752 │ │ │ if self.features: │
│ 753 │ │ │ │ # `IterableDataset` automatically fills missing colum │
│ 754 │ │ │ │ # This is done with `_apply_feature_types`. │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:741 in _iter │
│ │
│ 738 │ │ │ ex_iterable = self._ex_iterable.shuffle_data_sources(self │
│ 739 │ │ else: │
│ 740 │ │ │ ex_iterable = self._ex_iterable │
│ ❱ 741 │ │ yield from ex_iterable │
│ 742 │ │
│ 743 │ def _iter_shard(self, shard_idx: int): │
│ 744 │ │ if self._shuffling: │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:617 in __iter__ │
│ │
│ 614 │ │ self.n = n │
│ 615 │ │
│ 616 │ def __iter__(self): │
│ ❱ 617 │ │ yield from islice(self.ex_iterable, self.n) │
│ 618 │ │
│ 619 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │
│ 620 │ │ """Doesn't shuffle the wrapped examples iterable since it wou │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:594 in __iter__ │
│ │
│ 591 │ │
│ 592 │ def __iter__(self): │
│ 593 │ │ #ex_iterator = iter(self.ex_iterable) │
│ ❱ 594 │ │ yield from islice(self.ex_iterable, self.n, None) │
│ 595 │ │ #for _ in range(self.n): │
│ 596 │ │ # next(ex_iterator) │
│ 597 │ │ #yield from islice(ex_iterator, self.n, None) │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:106 in __iter__ │
│ │
│ 103 │ │ self.kwargs = kwargs │
│ 104 │ │
│ 105 │ def __iter__(self): │
│ ❱ 106 │ │ yield from self.generate_examples_fn(**self.kwargs) │
│ 107 │ │
│ 108 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │
│ 109 │ │ return ShardShuffledExamplesIterable(self.generate_examples_f │
│ │
│ /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/d │
│ f532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01/c4.py:89 in │
│ _generate_examples │
│ │
│ 86 │ │ for filepath in filepaths: │
│ 87 │ │ │ logger.info("generating examples from = %s", filepath) │
│ 88 │ │ │ with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8" │
│ ❱ 89 │ │ │ │ for line in f: │
│ 90 │ │ │ │ │ if line: │
│ 91 │ │ │ │ │ │ example = json.loads(line) │
│ 92 │ │ │ │ │ │ yield id_, example │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:313 in read1 │
│ │
│ 310 │ │ │
│ 311 │ │ if size < 0: │
│ 312 │ │ │ size = io.DEFAULT_BUFFER_SIZE │
│ ❱ 313 │ │ return self._buffer.read1(size) │
│ 314 │ │
│ 315 │ def peek(self, n): │
│ 316 │ │ self._check_not_closed() │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/_compression.py:68 in readinto │
│ │
│ 65 │ │
│ 66 │ def readinto(self, b): │
│ 67 │ │ with memoryview(b) as view, view.cast("B") as byte_view: │
│ ❱ 68 │ │ │ data = self.read(len(byte_view)) │
│ 69 │ │ │ byte_view[:len(data)] = data │
│ 70 │ │ return len(data) │
│ 71 │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:493 in read │
│ │
│ 490 │ │ │ │ self._new_member = False │
│ 491 │ │ │ │
│ 492 │ │ │ # Read a chunk of data from the file │
│ ❱ 493 │ │ │ buf = self._fp.read(io.DEFAULT_BUFFER_SIZE) │
│ 494 │ │ │ │
│ 495 │ │ │ uncompress = self._decompressor.decompress(buf, size) │
│ 496 │ │ │ if self._decompressor.unconsumed_tail != b"": │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:96 in read │
│ │
│ 93 │ │ │ read = self._read │
│ 94 │ │ │ self._read = None │
│ 95 │ │ │ return self._buffer[read:] + \ │
│ ❱ 96 │ │ │ │ self.file.read(size-self._length+read) │
│ 97 │ │
│ 98 │ def prepend(self, prepend=b''): │
│ 99 │ │ if self._read is None: │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/download/streaming_download_manager.py: │
│ 365 in read_with_retries │
│ │
│ 362 │ │ │ │ ) │
│ 363 │ │ │ │ time.sleep(config.STREAMING_READ_RETRY_INTERVAL) │
│ 364 │ │ else: │
│ ❱ 365 │ │ │ raise ConnectionError("Server Disconnected") │
│ 366 │ │ return out │
│ 367 │ │
│ 368 │ file_obj.read = read_with_retries │
╰──────────────────────────────────────────────────────────────────────────────╯
ConnectionError: Server Disconnected
```
### Expected behavior
There should be no disconnect I think.
### Environment info
```
datasets=2.7.0
Python 3.9.12
``` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-12-17T17:23:52 | 2022-12-18T21:43:31 | 2022-12-18T21:40:21 | CONTRIBUTOR | null | false | {
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} | Was hoping to find a way to speed up the skipping as I'm running into bottlenecks skipping 100M examples on C4 (it takes 12 hours to skip), but didn't find anything better than this small change :(
Maybe there's a way to directly skip whole shards to speed it up? 🧐 | {
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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) | 0.009517 / 0.011353 (-0.001835) | 0.005210 / 0.011008 (-0.005798) | 0.098916 / 0.038508 (0.060408) | 0.036123 / 0.023109 (0.013014) | 0.301564 / 0.275898 (0.025666) | 0.358086 / 0.323480 (0.034606) | 0.008159 / 0.007986 (0.000174) | 0.004122 / 0.004328 (-0.000206) | 0.075899 / 0.004250 (0.071648) | 0.046082 / 0.037052 (0.009030) | 0.302871 / 0.258489 (0.044382) | 0.351162 / 0.293841 (0.057321) | 0.038215 / 0.128546 (-0.090331) | 0.012026 / 0.075646 (-0.063620) | 0.330988 / 0.419271 (-0.088284) | 0.048351 / 0.043533 (0.004818) | 0.291840 / 0.255139 (0.036701) | 0.320387 / 0.283200 (0.037187) | 0.105018 / 0.141683 (-0.036665) | 1.447158 / 1.452155 (-0.004997) | 1.491205 / 1.492716 (-0.001511) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250870 / 0.018006 (0.232863) | 0.562974 / 0.000490 (0.562484) | 0.001789 / 0.000200 (0.001589) | 0.000252 / 0.000054 (0.000197) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028208 / 0.037411 (-0.009203) | 0.110897 / 0.014526 (0.096371) | 0.120394 / 0.176557 (-0.056163) | 0.164980 / 0.737135 (-0.572156) | 0.126283 / 0.296338 (-0.170056) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397922 / 0.215209 (0.182713) | 3.969233 / 2.077655 (1.891578) | 1.766422 / 1.504120 (0.262302) | 1.577503 / 1.541195 (0.036308) | 1.672344 / 1.468490 (0.203854) | 0.695708 / 4.584777 (-3.889069) | 3.770763 / 3.745712 (0.025051) | 3.369592 / 5.269862 (-1.900269) | 1.851122 / 4.565676 (-2.714554) | 0.084063 / 0.424275 (-0.340212) | 0.012156 / 0.007607 (0.004549) | 0.534639 / 0.226044 (0.308594) | 5.021955 / 2.268929 (2.753027) | 2.215438 / 55.444624 (-53.229186) | 1.890459 / 6.876477 (-4.986018) | 2.071361 / 2.142072 (-0.070712) | 0.834623 / 4.805227 (-3.970604) | 0.165588 / 6.500664 (-6.335076) | 0.064336 / 0.075469 (-0.011133) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.205651 / 1.841788 (-0.636136) | 14.916871 / 8.074308 (6.842563) | 14.559495 / 10.191392 (4.368103) | 0.166889 / 0.680424 (-0.513535) | 0.028645 / 0.534201 (-0.505556) | 0.433634 / 0.579283 (-0.145649) | 0.429849 / 0.434364 (-0.004515) | 0.508617 / 0.540337 (-0.031720) | 0.595261 / 1.386936 (-0.791675) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007696 / 0.011353 (-0.003657) | 0.005434 / 0.011008 (-0.005574) | 0.099234 / 0.038508 (0.060725) | 0.033904 / 0.023109 (0.010795) | 0.379181 / 0.275898 (0.103283) | 0.401858 / 0.323480 (0.078379) | 0.006257 / 0.007986 (-0.001729) | 0.004406 / 0.004328 (0.000077) | 0.073174 / 0.004250 (0.068923) | 0.056033 / 0.037052 (0.018981) | 0.379375 / 0.258489 (0.120886) | 0.425928 / 0.293841 (0.132087) | 0.037476 / 0.128546 (-0.091071) | 0.012520 / 0.075646 (-0.063127) | 0.364975 / 0.419271 (-0.054297) | 0.049341 / 0.043533 (0.005808) | 0.370519 / 0.255139 (0.115380) | 0.390585 / 0.283200 (0.107385) | 0.113339 / 0.141683 (-0.028344) | 1.460575 / 1.452155 (0.008421) | 1.564951 / 1.492716 (0.072235) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246217 / 0.018006 (0.228210) | 0.554358 / 0.000490 (0.553869) | 0.000451 / 0.000200 (0.000251) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029557 / 0.037411 (-0.007855) | 0.110472 / 0.014526 (0.095946) | 0.122652 / 0.176557 (-0.053904) | 0.159396 / 0.737135 (-0.577739) | 0.128852 / 0.296338 (-0.167486) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.447927 / 0.215209 (0.232718) | 4.448292 / 2.077655 (2.370637) | 2.228874 / 1.504120 (0.724754) | 2.030231 / 1.541195 (0.489036) | 2.116417 / 1.468490 (0.647927) | 0.702713 / 4.584777 (-3.882064) | 3.774063 / 3.745712 (0.028351) | 3.521662 / 5.269862 (-1.748200) | 1.476700 / 4.565676 (-3.088976) | 0.084921 / 0.424275 (-0.339354) | 0.012862 / 0.007607 (0.005255) | 0.559142 / 0.226044 (0.333098) | 5.512233 / 2.268929 (3.243305) | 2.750024 / 55.444624 (-52.694600) | 2.388845 / 6.876477 (-4.487632) | 2.541786 / 2.142072 (0.399714) | 0.842256 / 4.805227 (-3.962971) | 0.168088 / 6.500664 (-6.332576) | 0.064211 / 0.075469 (-0.011258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239001 / 1.841788 (-0.602787) | 15.286345 / 8.074308 (7.212036) | 13.883981 / 10.191392 (3.692589) | 0.186212 / 0.680424 (-0.494212) | 0.018305 / 0.534201 (-0.515896) | 0.420459 / 0.579283 (-0.158824) | 0.421039 / 0.434364 (-0.013325) | 0.487348 / 0.540337 (-0.052989) | 0.587730 / 1.386936 (-0.799206) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2022-12-17T12:58:52 | 2023-01-06T11:50:58 | 2023-01-06T11:43:37 | MEMBER | null | false | {
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} | This PR fixes `xpandas_read_excel`:
- Support passing a path string, besides a file-like object
- Support passing `use_auth_token`
- First assumes the host server supports HTTP range requests; only if a ValueError is thrown (Cannot seek streaming HTTP file), then it preserves previous behavior (see [#3355](https://github.com/huggingface/datasets/pull/3355)).
Fix https://huggingface.co./datasets/bigbio/meqsum/discussions/1
Fix:
- https://github.com/bigscience-workshop/biomedical/issues/801
Related to:
- #3355 | {
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"Ccing @nazneenrajani @lvwerra @osanseviero "
] | 2022-12-17T12:35:13 | 2022-12-20T06:21:41 | null | MEMBER | null | null | null | ### Name
ImageNet-C
### Paper
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
### Data
https://github.com/hendrycks/robustness
### Motivation
It's a known fact that vision models are brittle when they meet with slightly corrupted and perturbed data. This is also correlated to the robustness aspects of vision models.
Researchers use different benchmark datasets to evaluate the robustness aspects of vision models. ImageNet-C is one of them.
Having this dataset in 🤗 Datasets would allow researchers to evaluate and study the robustness aspects of vision models. Since the metric associated with these evaluations is top-1 accuracy, researchers should be able to easily take advantage of the evaluation benchmarks on the Hub and perform comprehensive reporting.
ImageNet-C is a large dataset. Once it's in, it can act as a reference and we can also reach out to the authors of the other robustness benchmark datasets in vision, such as ObjectNet, WILDS, Metashift, etc. These datasets cater to different aspects. For example, ObjectNet is related to assessing how well a model performs under sub-population shifts.
Related thread: https://huggingface.slack.com/archives/C036H4A5U8Z/p1669994598060499 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Alright all the tests are passing - this is ready for review",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.015146 / 0.011353 (0.003793) | 0.006683 / 0.011008 (-0.004326) | 0.125994 / 0.038508 (0.087486) | 0.041345 / 0.023109 (0.018235) | 0.378609 / 0.275898 (0.102711) | 0.483139 / 0.323480 (0.159659) | 0.009669 / 0.007986 (0.001684) | 0.005143 / 0.004328 (0.000814) | 0.092015 / 0.004250 (0.087765) | 0.052728 / 0.037052 (0.015676) | 0.397166 / 0.258489 (0.138677) | 0.465820 / 0.293841 (0.171979) | 0.051025 / 0.128546 (-0.077521) | 0.018451 / 0.075646 (-0.057196) | 0.397311 / 0.419271 (-0.021960) | 0.054842 / 0.043533 (0.011309) | 0.391203 / 0.255139 (0.136064) | 0.412743 / 0.283200 (0.129543) | 0.111356 / 0.141683 (-0.030327) | 1.697526 / 1.452155 (0.245372) | 1.795017 / 1.492716 (0.302301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253737 / 0.018006 (0.235731) | 0.583071 / 0.000490 (0.582581) | 0.005958 / 0.000200 (0.005758) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030397 / 0.037411 (-0.007014) | 0.112242 / 0.014526 (0.097716) | 0.138807 / 0.176557 (-0.037749) | 0.209820 / 0.737135 (-0.527316) | 0.139530 / 0.296338 (-0.156808) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.574111 / 0.215209 (0.358902) | 5.623713 / 2.077655 (3.546058) | 2.416880 / 1.504120 (0.912760) | 1.951013 / 1.541195 (0.409819) | 2.124565 / 1.468490 (0.656075) | 1.268854 / 4.584777 (-3.315923) | 5.942368 / 3.745712 (2.196656) | 5.413814 / 5.269862 (0.143952) | 2.931638 / 4.565676 (-1.634038) | 0.135070 / 0.424275 (-0.289205) | 0.014290 / 0.007607 (0.006683) | 0.708384 / 0.226044 (0.482340) | 7.487994 / 2.268929 (5.219065) | 3.074210 / 55.444624 (-52.370414) | 2.380583 / 6.876477 (-4.495893) | 2.522298 / 2.142072 (0.380226) | 1.336741 / 4.805227 (-3.468486) | 0.236761 / 6.500664 (-6.263903) | 0.076592 / 0.075469 (0.001123) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.629415 / 1.841788 (-0.212373) | 19.000640 / 8.074308 (10.926332) | 21.474058 / 10.191392 (11.282666) | 0.231227 / 0.680424 (-0.449197) | 0.046213 / 0.534201 (-0.487988) | 0.565703 / 0.579283 (-0.013580) | 0.662956 / 0.434364 (0.228592) | 0.656475 / 0.540337 (0.116137) | 0.762534 / 1.386936 (-0.624402) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010952 / 0.011353 (-0.000400) | 0.006259 / 0.011008 (-0.004749) | 0.132430 / 0.038508 (0.093922) | 0.037920 / 0.023109 (0.014811) | 0.483565 / 0.275898 (0.207667) | 0.528190 / 0.323480 (0.204710) | 0.008116 / 0.007986 (0.000130) | 0.006768 / 0.004328 (0.002440) | 0.100520 / 0.004250 (0.096270) | 0.055208 / 0.037052 (0.018155) | 0.484672 / 0.258489 (0.226183) | 0.556937 / 0.293841 (0.263096) | 0.057938 / 0.128546 (-0.070609) | 0.020821 / 0.075646 (-0.054826) | 0.430735 / 0.419271 (0.011464) | 0.066317 / 0.043533 (0.022785) | 0.496652 / 0.255139 (0.241513) | 0.502004 / 0.283200 (0.218804) | 0.125403 / 0.141683 (-0.016280) | 1.833396 / 1.452155 (0.381241) | 1.974517 / 1.492716 (0.481800) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269198 / 0.018006 (0.251191) | 0.620314 / 0.000490 (0.619824) | 0.000535 / 0.000200 (0.000335) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032373 / 0.037411 (-0.005039) | 0.130043 / 0.014526 (0.115517) | 0.146217 / 0.176557 (-0.030339) | 0.200187 / 0.737135 (-0.536948) | 0.152839 / 0.296338 (-0.143499) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.677478 / 0.215209 (0.462268) | 6.678856 / 2.077655 (4.601201) | 3.025870 / 1.504120 (1.521750) | 2.678196 / 1.541195 (1.137001) | 2.740640 / 1.468490 (1.272150) | 1.237163 / 4.584777 (-3.347614) | 5.752621 / 3.745712 (2.006908) | 3.170435 / 5.269862 (-2.099427) | 2.049174 / 4.565676 (-2.516502) | 0.147663 / 0.424275 (-0.276612) | 0.016107 / 0.007607 (0.008500) | 0.849666 / 0.226044 (0.623621) | 8.395212 / 2.268929 (6.126283) | 3.741120 / 55.444624 (-51.703505) | 3.102926 / 6.876477 (-3.773550) | 3.233655 / 2.142072 (1.091583) | 1.520349 / 4.805227 (-3.284878) | 0.267159 / 6.500664 (-6.233505) | 0.083646 / 0.075469 (0.008177) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.640458 / 1.841788 (-0.201330) | 19.043169 / 8.074308 (10.968861) | 22.786126 / 10.191392 (12.594734) | 0.218040 / 0.680424 (-0.462384) | 0.032948 / 0.534201 (-0.501253) | 0.569574 / 0.579283 (-0.009710) | 0.658746 / 0.434364 (0.224382) | 0.650501 / 0.540337 (0.110164) | 0.730588 / 1.386936 (-0.656348) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n",
"just added a note :)",
"Hi @lhoestq ,\r\nCan you please throw some light on the following statement\r\n`If the dataset has a number of shards that is a factor of world_size (i.e. if dataset.n_shards % world_size == 0), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of world_size, skipping the other examples.`\r\n\r\nLet's assume I have 127 parquet files and world_size is 4. I was not able to fully comprehend the above statement\r\nWhat does this statement mean?\r\n`each node keeps 1 example out of world_size, skipping the other examples.`\r\nThank you!",
"If you have 128 parquet files, then `dataset.n_shards % world_size == 0`. In this case each worker can take care of 32 parquet files.\r\n\r\nOn the other hand if you have `dataset.n_shards % world_size != 0` (in your case 127 files), then we can't assign the same number of files to each worker. This is an issue because it may under-utilize your GPU at the end of your training since some workers will take longer to iterate on the dataset than others.\r\n\r\nTherefore in this case, all the workers take care of the 127 parquet files but workers will skip examples to not end up with duplicates. That's what \"each node keeps 1 example out of world_size, skipping the other examples\" means, and in your case it implies:\r\n- rank=0 will read the samples with idx=0, 4, 8 etc.\r\n- rank=1 will read the samples with idx=1, 5, 9 etc.\r\n- rank=2 will read the samples with idx=2, 6, 10 etc.\r\n- rank=3 will read the samples with idx=3, 7, 11 etc.",
"Thanks a lot @lhoestq , this helps!",
"Hi, in the case above, if we use `keep_in_memory=True` for `Dataset`, then we still need to read in n times the dataset if we use DDP on n GPUs (1 node), right? That means we need n times the memory. Is there any way to only load the data once, to save memory?",
"`Dataset` objects are memory mapped from disk so they use almost no RAM (only the current batch)\r\n\r\nAlso they are perfectly sharded using `split_dataset_by_node` so it's going to be read exactly once in total using DDP.\r\nYou can also achieve the same thing using a DistributedSampler in pytorch for DDP instead of using `split_dataset_by_node`.",
"Hi, please correct if I mistake anything: \r\n1. `Dataset` with `keep_in_memory=True` would explicitly pre-load the data into memory, instead of reading from disk via the memory map for every batch. The former way should be faster than the latter.\r\n2. When using DDP, before sending the `Dataset` object into `split_dataset_by_node` or incorporate it with `DistributedSampler`, every process still needs to pre-load the entire data into memory (when `keep_in_memory=True`) and then select the chunked indices from the loaded data. \r\n\r\nGenerally, the dilemma I'm facing is:\r\nSuppose we have a data around 120GB, and we want to use `DistributedLengthGroupedSampler` to optimize batching. When using DDP and `keep_in_memory=True`, every process loads 120GB which is not acceptable. For now, I turned off `keep_in_memory` and try to increase the number of workers for `DataLoader` to get better pipelining. \r\n\r\n**But is it possible to load 120GB once into 4 * A100 (which has around 4*120GB memory) and make each process read from this shared data from memory? Theoretically, maybe it should be faster?** ",
"Feel free to ask your questions on the [forum](https://discuss.huggingface.co/c/datasets/10) if you don't mind, this way the discussions may be useful to other people ;) "
] | 2022-12-16T17:43:47 | 2023-07-25T12:00:31 | 2023-01-16T13:33:32 | MEMBER | null | false | {
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"merged_at": "2023-01-16T13:33:32"
} | To split your dataset across your training nodes, you can use the new [`datasets.distributed.split_dataset_by_node`]:
```python
import os
from datasets.distributed import split_dataset_by_node
ds = split_dataset_by_node(ds, rank=int(os.environ["RANK"]), world_size=int(os.environ["WORLD_SIZE"]))
```
This works for both map-style datasets and iterable datasets.
The dataset is split for the node at rank `rank` in a pool of nodes of size `world_size`.
For map-style datasets:
Each node is assigned a chunk of data, e.g. rank 0 is given the first chunk of the dataset.
For iterable datasets:
If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`),
then the shards are evenly assigned across the nodes, which is the most optimized.
Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples.
This can also be combined with a `torch.utils.data.DataLoader` if you want each node to use multiple workers to load the data.
This also supports shuffling. At each epoch, the iterable dataset shards are reshuffled across all the nodes - you just have to call `iterable_ds.set_epoch(epoch_number)`.
TODO:
- [x] docs for usage in PyTorch
- [x] unit tests
- [x] integration tests with torch.distributed.launch
Related to https://github.com/huggingface/transformers/issues/20770
Close https://github.com/huggingface/datasets/issues/5360 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-12-16T14:28:47 | 2022-12-16T16:28:08 | 2022-12-16T16:25:12 | CONTRIBUTOR | null | false | {
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} | Align the `remove_columns` behavior and input dict mutation in `map` with the behavior before https://github.com/huggingface/datasets/pull/5252. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-12-15T22:04:12 | 2022-12-15T22:27:53 | 2022-12-15T22:24:50 | MEMBER | null | false | {
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} | This was introduced in https://github.com/huggingface/datasets/pull/5252 and causing the transformers CI to break: https://app.circleci.com/pipelines/github/huggingface/transformers/53886/workflows/522faf2e-a053-454c-94f8-a617fde33393/jobs/648597
Basically this code should return a dataset with only one column:
```python
from datasets import *
ds = Dataset.from_dict({"a": range(5)})
def f(x):
x["b"] = x["a"]
return x
ds = ds.map(f, remove_columns=["a"])
assert ds.column_names == ["b"]
``` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-12-15T14:23:05 | 2022-12-15T14:44:47 | 2022-12-15T14:41:45 | MEMBER | null | false | {
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} | fix typing and ExamplesIterable.shard_data_sources | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi, thanks for working on this! \r\n\r\nI agree that the current type-casting (always cast to `np.uint8` as Tensorflow Datasets does) is a bit too harsh. However, not all dtypes are supported in `Image.fromarray` (e.g. np.int64), so we need to treat these with special care (e.g. downcast to the closest supported dtype, maybe with warnings to let the user know what's happening).\r\n\r\nPS: To avoid the CI failures, we need to handle two more instances of the cast to `np.uint8` (both are in the `image.py` file).",
"I've made some changes to the PR.\r\n\r\nNow the encoding procedure behaves as follows:\r\n* for multi-channel arrays: if their dtype is `int`/`uint`, cast to np.uint8 (the only supported dtype for multi-channel arrays), throw an error otherwise\r\n* if the array dtype is of valid kind (\"u\", \"i\", \"f\", ...):\r\n * don't do anything if Pillow natively supports it\r\n * otherwise, downcast until it becomes compatible with Pillow\r\n* raise an error if nothing from above is true",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009537 / 0.011353 (-0.001816) | 0.004946 / 0.011008 (-0.006062) | 0.100552 / 0.038508 (0.062043) | 0.035119 / 0.023109 (0.012009) | 0.295989 / 0.275898 (0.020091) | 0.361326 / 0.323480 (0.037846) | 0.007608 / 0.007986 (-0.000378) | 0.004151 / 0.004328 (-0.000177) | 0.077301 / 0.004250 (0.073050) | 0.042921 / 0.037052 (0.005869) | 0.304804 / 0.258489 (0.046315) | 0.345934 / 0.293841 (0.052093) | 0.038987 / 0.128546 (-0.089559) | 0.012055 / 0.075646 (-0.063591) | 0.334035 / 0.419271 (-0.085236) | 0.052679 / 0.043533 (0.009146) | 0.291700 / 0.255139 (0.036561) | 0.335423 / 0.283200 (0.052223) | 0.107002 / 0.141683 (-0.034680) | 1.516780 / 1.452155 (0.064625) | 1.514137 / 1.492716 (0.021420) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014719 / 0.018006 (-0.003287) | 0.545251 / 0.000490 (0.544761) | 0.004719 / 0.000200 (0.004519) | 0.000275 / 0.000054 (0.000220) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026633 / 0.037411 (-0.010779) | 0.106911 / 0.014526 (0.092385) | 0.120258 / 0.176557 (-0.056299) | 0.156196 / 0.737135 (-0.580940) | 0.123132 / 0.296338 (-0.173207) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398018 / 0.215209 (0.182809) | 3.973992 / 2.077655 (1.896337) | 1.776436 / 1.504120 (0.272316) | 1.579036 / 1.541195 (0.037841) | 1.643345 / 1.468490 (0.174855) | 0.692408 / 4.584777 (-3.892369) | 3.757243 / 3.745712 (0.011531) | 3.226212 / 5.269862 (-2.043649) | 1.797845 / 4.565676 (-2.767831) | 0.085878 / 0.424275 (-0.338398) | 0.012451 / 0.007607 (0.004844) | 0.509755 / 0.226044 (0.283711) | 5.029035 / 2.268929 (2.760107) | 2.255507 / 55.444624 (-53.189117) | 1.892868 / 6.876477 (-4.983609) | 1.900017 / 2.142072 (-0.242055) | 0.853965 / 4.805227 (-3.951263) | 0.167268 / 6.500664 (-6.333396) | 0.062796 / 0.075469 (-0.012673) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.183361 / 1.841788 (-0.658427) | 15.103797 / 8.074308 (7.029489) | 14.112931 / 10.191392 (3.921539) | 0.167234 / 0.680424 (-0.513190) | 0.029487 / 0.534201 (-0.504713) | 0.444121 / 0.579283 (-0.135162) | 0.437821 / 0.434364 (0.003457) | 0.544900 / 0.540337 (0.004562) | 0.642142 / 1.386936 (-0.744794) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007078 / 0.011353 (-0.004275) | 0.004983 / 0.011008 (-0.006026) | 0.097106 / 0.038508 (0.058598) | 0.033747 / 0.023109 (0.010637) | 0.382030 / 0.275898 (0.106132) | 0.410193 / 0.323480 (0.086713) | 0.006658 / 0.007986 (-0.001327) | 0.005358 / 0.004328 (0.001029) | 0.073878 / 0.004250 (0.069628) | 0.049292 / 0.037052 (0.012240) | 0.384053 / 0.258489 (0.125564) | 0.427826 / 0.293841 (0.133985) | 0.036780 / 0.128546 (-0.091766) | 0.012469 / 0.075646 (-0.063178) | 0.332989 / 0.419271 (-0.086283) | 0.059531 / 0.043533 (0.015998) | 0.378431 / 0.255139 (0.123292) | 0.402672 / 0.283200 (0.119473) | 0.110782 / 0.141683 (-0.030901) | 1.484570 / 1.452155 (0.032416) | 1.608081 / 1.492716 (0.115365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232356 / 0.018006 (0.214350) | 0.545648 / 0.000490 (0.545158) | 0.003113 / 0.000200 (0.002913) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028138 / 0.037411 (-0.009273) | 0.110786 / 0.014526 (0.096260) | 0.123615 / 0.176557 (-0.052941) | 0.165773 / 0.737135 (-0.571362) | 0.126401 / 0.296338 (-0.169937) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440518 / 0.215209 (0.225309) | 4.393821 / 2.077655 (2.316166) | 2.295479 / 1.504120 (0.791359) | 2.116679 / 1.541195 (0.575485) | 2.215561 / 1.468490 (0.747071) | 0.722343 / 4.584777 (-3.862434) | 3.783360 / 3.745712 (0.037647) | 3.302242 / 5.269862 (-1.967620) | 1.681535 / 4.565676 (-2.884142) | 0.085738 / 0.424275 (-0.338537) | 0.012373 / 0.007607 (0.004766) | 0.540499 / 0.226044 (0.314455) | 5.384915 / 2.268929 (3.115986) | 2.766346 / 55.444624 (-52.678279) | 2.451994 / 6.876477 (-4.424483) | 2.505720 / 2.142072 (0.363647) | 0.833006 / 4.805227 (-3.972221) | 0.168206 / 6.500664 (-6.332458) | 0.064971 / 0.075469 (-0.010498) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253499 / 1.841788 (-0.588289) | 15.381840 / 8.074308 (7.307532) | 13.519493 / 10.191392 (3.328101) | 0.165559 / 0.680424 (-0.514865) | 0.017682 / 0.534201 (-0.516519) | 0.422248 / 0.579283 (-0.157035) | 0.422750 / 0.434364 (-0.011614) | 0.524546 / 0.540337 (-0.015792) | 0.626956 / 1.386936 (-0.759980) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d9a8d8af0961c473103516dd018e2d34d23cea02 \"CML watermark\")\n"
] | 2022-12-15T13:17:50 | 2023-01-26T18:46:45 | 2023-01-26T18:39:36 | CONTRIBUTOR | null | false | {
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} | Currently images that are provided as ndarrays, but not in `uint8` format are going to loose data. Namely, for example in a depth image where the data is in float32 format, the type-casting to uint8 will basically make the whole image blank.
`PIL.Image.fromarray` [does support mode `F`](https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes).
although maybe some further metadata could be supplied via the [Image](https://huggingface.co./docs/datasets/v2.7.1/en/package_reference/main_classes#datasets.Image) object. | {
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https://api.github.com/repos/huggingface/datasets/issues/5364 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5364/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5364/comments | https://api.github.com/repos/huggingface/datasets/issues/5364/events | https://github.com/huggingface/datasets/pull/5364 | 1,498,360,628 | PR_kwDODunzps5Fiss1 | 5,364 | Support for writing arrow files directly with BeamWriter | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5364). All of your documentation changes will be reflected on that endpoint.",
"Deleting `BeamPipeline` and `upload_local_to_remote` would break the existing Beam scripts, so I reverted this change.\r\n\r\nFrom what I understand, we need these components in our scripts for the pattern:\r\n```python\r\nif not pipeline.is_local():\r\n dl_manager.ship_files_with_pipeline()\r\n```\r\n\r\nI plan to address this in a subsequent PR by (implicitly) downloading the files directly to the remote storage of the non-local runners.",
"I got `AttributeError: 'Pipeline' object has no attribute 'is_local'` when running\r\n```python\r\nload_dataset(\"wikipedia\", language=\"af\", date=\"20230101\", beam_runner=\"DirectRunner\")\r\n```\r\n```python\r\n~/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py in _split_generators(self, dl_manager, pipeline)\r\n 965 # Use dictionary since testing mock always returns the same result.\r\n 966 downloaded_files = dl_manager.download({\"xml\": xml_urls})\r\n--> 967 if not pipeline.is_local():\r\n 968 downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline)\r\n 969 \r\n\r\nAttributeError: 'Pipeline' object has no attribute 'is_local'\r\n```",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010649 / 0.011353 (-0.000704) | 0.006116 / 0.011008 (-0.004892) | 0.115568 / 0.038508 (0.077060) | 0.041704 / 0.023109 (0.018595) | 0.360459 / 0.275898 (0.084561) | 0.425679 / 0.323480 (0.102200) | 0.008992 / 0.007986 (0.001006) | 0.006321 / 0.004328 (0.001993) | 0.090223 / 0.004250 (0.085973) | 0.049877 / 0.037052 (0.012824) | 0.382447 / 0.258489 (0.123958) | 0.406567 / 0.293841 (0.112726) | 0.045138 / 0.128546 (-0.083409) | 0.014203 / 0.075646 (-0.061444) | 0.388897 / 0.419271 (-0.030375) | 0.057176 / 0.043533 (0.013644) | 0.358729 / 0.255139 (0.103590) | 0.386086 / 0.283200 (0.102887) | 0.119221 / 0.141683 (-0.022462) | 1.731574 / 1.452155 (0.279419) | 1.744103 / 1.492716 (0.251386) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230380 / 0.018006 (0.212373) | 0.493690 / 0.000490 (0.493201) | 0.005150 / 0.000200 (0.004950) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030771 / 0.037411 (-0.006641) | 0.123196 / 0.014526 (0.108671) | 0.134097 / 0.176557 (-0.042459) | 0.190442 / 0.737135 (-0.546693) | 0.138416 / 0.296338 (-0.157923) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.469763 / 0.215209 (0.254554) | 4.682847 / 2.077655 (2.605192) | 2.076717 / 1.504120 (0.572597) | 1.843721 / 1.541195 (0.302527) | 1.923486 / 1.468490 (0.454996) | 0.817680 / 4.584777 (-3.767097) | 4.482409 / 3.745712 (0.736697) | 3.898695 / 5.269862 (-1.371167) | 2.078291 / 4.565676 (-2.487386) | 0.100285 / 0.424275 (-0.323990) | 0.014761 / 0.007607 (0.007154) | 0.611261 / 0.226044 (0.385217) | 5.926919 / 2.268929 (3.657990) | 2.685080 / 55.444624 (-52.759544) | 2.232179 / 6.876477 (-4.644298) | 2.305576 / 2.142072 (0.163504) | 0.993729 / 4.805227 (-3.811498) | 0.194491 / 6.500664 (-6.306173) | 0.074176 / 0.075469 (-0.001293) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.388592 / 1.841788 (-0.453196) | 17.146945 / 8.074308 (9.072636) | 15.989570 / 10.191392 (5.798178) | 0.200147 / 0.680424 (-0.480277) | 0.034009 / 0.534201 (-0.500192) | 0.517531 / 0.579283 (-0.061753) | 0.533966 / 0.434364 (0.099602) | 0.637024 / 0.540337 (0.096687) | 0.749166 / 1.386936 (-0.637770) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008240 / 0.011353 (-0.003113) | 0.006139 / 0.011008 (-0.004869) | 0.112258 / 0.038508 (0.073750) | 0.039001 / 0.023109 (0.015891) | 0.449467 / 0.275898 (0.173569) | 0.483422 / 0.323480 (0.159942) | 0.006176 / 0.007986 (-0.001810) | 0.006340 / 0.004328 (0.002012) | 0.083105 / 0.004250 (0.078855) | 0.047002 / 0.037052 (0.009950) | 0.458564 / 0.258489 (0.200075) | 0.513704 / 0.293841 (0.219863) | 0.041359 / 0.128546 (-0.087188) | 0.014515 / 0.075646 (-0.061131) | 0.392599 / 0.419271 (-0.026673) | 0.055222 / 0.043533 (0.011690) | 0.446956 / 0.255139 (0.191817) | 0.469194 / 0.283200 (0.185994) | 0.118212 / 0.141683 (-0.023471) | 1.682647 / 1.452155 (0.230492) | 1.780076 / 1.492716 (0.287360) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259124 / 0.018006 (0.241117) | 0.507559 / 0.000490 (0.507069) | 0.001080 / 0.000200 (0.000880) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031969 / 0.037411 (-0.005442) | 0.126997 / 0.014526 (0.112471) | 0.139593 / 0.176557 (-0.036963) | 0.182735 / 0.737135 (-0.554400) | 0.145871 / 0.296338 (-0.150468) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.530894 / 0.215209 (0.315685) | 5.284979 / 2.077655 (3.207324) | 2.592886 / 1.504120 (1.088766) | 2.407202 / 1.541195 (0.866007) | 2.434079 / 1.468490 (0.965589) | 0.829382 / 4.584777 (-3.755395) | 4.481710 / 3.745712 (0.735998) | 3.912280 / 5.269862 (-1.357581) | 1.962291 / 4.565676 (-2.603386) | 0.101840 / 0.424275 (-0.322435) | 0.014528 / 0.007607 (0.006921) | 0.639956 / 0.226044 (0.413911) | 6.414685 / 2.268929 (4.145756) | 3.240290 / 55.444624 (-52.204334) | 2.795208 / 6.876477 (-4.081269) | 2.912122 / 2.142072 (0.770050) | 0.992188 / 4.805227 (-3.813039) | 0.200701 / 6.500664 (-6.299964) | 0.074235 / 0.075469 (-0.001234) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.455075 / 1.841788 (-0.386712) | 17.186669 / 8.074308 (9.112361) | 15.404357 / 10.191392 (5.212965) | 0.168267 / 0.680424 (-0.512157) | 0.020774 / 0.534201 (-0.513427) | 0.502603 / 0.579283 (-0.076680) | 0.506500 / 0.434364 (0.072136) | 0.624245 / 0.540337 (0.083907) | 0.735529 / 1.386936 (-0.651407) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2022-12-15T12:38:05 | 2023-01-25T15:49:25 | null | CONTRIBUTOR | null | true | {
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"Thanks for reporting, @shaoyuta.\r\n\r\nWe have checked and yes, apparently there is an issue with the server hosting the data of the \"enron_emails\" subset of \"the_pile\" dataset: http://eaidata.bmk.sh/data/enron_emails.jsonl.zst\r\nIt seems to be down: The connection has timed out.\r\n\r\nPlease note that at the Hugging Face Hub, we are not hosting their data for this dataset, but only a script that downloads the data from their servers. We are updating the data URL to one in another server.\r\n\r\nIn the meantime, please note that you can train your model in the entire \"the_pile\" dataset, by passing the \"all\" config (instead of the \"enron_emails\" one).",
"We have transferred this issue to the corresponding dataset Community tab: https://huggingface.co./datasets/the_pile/discussions/2\r\n\r\nPlease, follow the updates there."
] | 2022-12-15T01:23:03 | 2022-12-15T07:45:54 | 2022-12-15T07:45:53 | NONE | null | null | null | ### Describe the bug
Run model "GPT-J" with dataset "the_pile" fail.
The fail out is as below:
Looks like which is due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst" unreachable .
### Steps to reproduce the bug
Steps to reproduce this issue:
git clone https://github.com/huggingface/transformers
cd transformers
python examples/pytorch/language-modeling/run_clm.py --model_name_or_path EleutherAI/gpt-j-6B --dataset_name the_pile --dataset_config_name enron_emails --do_eval --output_dir /tmp/output --overwrite_output_dir
### Expected behavior
This issue looks like due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst " couldn't be reached.
Is there another way to download the dataset "the_pile" ?
Is there another way to cache the dataset "the_pile" but not let the hg to download it when runtime ?
### Environment info
huggingface_hub version: 0.11.1
Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Python version: 3.9.12
Running in iPython ?: No
Running in notebook ?: No
Running in Google Colab ?: No
Token path ?: /home/taosy/.huggingface/token
Has saved token ?: False
Configured git credential helpers:
FastAI: N/A
Tensorflow: N/A
Torch: N/A
Jinja2: N/A
Graphviz: N/A
Pydot: N/A | {
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"You can try something like this ?\r\n```python\r\ndef mapper_function(batch):\r\n return {\"concatenated_audio\": [np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]])]}\r\n\r\ndataset = dataset.map(\r\n mapper_function,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n```",
"Thanks for the snippet!\r\n\r\nOne more question. I wonder why those two mappers are working so different that one taking 4 sec while other taking over 1 min :\r\n\r\n```python\r\n%%time\r\ndef mapper_function1(batch):\r\n # list_audio\r\n return {\r\n \"audio\": [\r\n {\r\n \"array\": np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]]),\r\n \"sampling_rate\": 16_000,\r\n }\r\n ]\r\n }\r\n\r\ndataset.map(\r\n mapper_function1,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n\r\n# 100%\r\n# 135/135 [01:13<00:00, 1.93ba/s]\r\n# CPU times: user 1min 10s, sys: 3.21 s, total: 1min 13s\r\n# Wall time: 1min 13s\r\n# Dataset({\r\n# features: ['audio'],\r\n# num_rows: 135\r\n# })\r\n\r\n# --------------------------------\r\n%%time\r\ndef mapper_function2(batch):\r\n # list_audio\r\n return {\"audio\": [np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]])]}\r\n\r\ndataset.map(\r\n mapper_function2,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n\r\n# 100%\r\n# 135/135 [00:03<00:00, 40.69ba/s]\r\n# CPU times: user 1.88 s, sys: 1.48 s, total: 3.36 s\r\n# Wall time: 4.8 s\r\n# Dataset({\r\n# features: ['audio'],\r\n# num_rows: 135\r\n# })\r\n```\r\n",
"In the first one you get a dataset with an Audio type, and in the second one you get a dataset with a sequence of floats type.\r\n\r\nThe Audio type encodes the data as WAV to save disk space, so it takes more time to create.\r\nThe Audio type is automatically inferred because you modify the column \"audio\" which was already an Audio type. If you name it to something else, type inference will use a type struct with array and sampling rate fields."
] | 2022-12-14T18:13:55 | 2023-07-21T14:30:51 | 2023-07-21T14:30:51 | NONE | null | null | null | ### Describe the bug
I am trying to do concatenate audios in a dataset e.g. `google/fleurs`.
```python
print(dataset)
# Dataset({
# features: ['path', 'audio'],
# num_rows: 24
# })
def mapper_function(batch):
# to merge every 3 audio
# np.concatnate(audios[i: i+3]) for i in range(i, len(batch), 3)
dataset = dataset.map(mapper_function, batch=True, batch_size=24)
print(dataset)
# Expected output:
# Dataset({
# features: ['path', 'audio'],
# num_rows: 8
# })
```
I tried to construct `result={}` dictionary inside the mapper function, I just found it will not work because it needs `byte` also needed :((
I'd appreciate if your share any use cases similar to my problem or any solutions really. Thanks!
cc: @lhoestq
### Steps to reproduce the bug
1. load audio dataset
2. try to merge every k audios and return as one
### Expected behavior
Merged dataset with a fewer rows. If we merge every 3 rows, then `n // 3` number of examples.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5 | {
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"If you use huggingface trainer, you will find the trainer has wrapped a `IterableDatasetShard` to avoid duplication.\r\nSee:\r\nhttps://github.com/huggingface/transformers/blob/dfd818420dcbad68e05a502495cf666d338b2bfb/src/transformers/trainer.py#L835\r\n",
"If you want to support it by datasets natively, maybe we also need to change the code in `transformers` ?",
"Opened https://github.com/huggingface/transformers/issues/20770 to discuss this :)",
"Maybe something like this then ?\r\n```python\r\nfrom datasets.distributed import split_dataset_by_node\r\nds = split_dataset_by_node(ds, rank=rank, world_size=world_size)\r\n```\r\n\r\nFor map-style datasets the implementation is trivial (it can simply use `.shard()`).\r\n\r\nFor iterable datasets we would need to implement a new ExamplesIterable that would only iterate on a subset of the (possibly shuffled and re-shuffled after each epoch) list of shards, based on the rank and world size.",
"My plan is to skip examples by default to not end up with duplicates.\r\n\r\nAnd if a dataset has a number of shards that is a factor of the world size, then I'd make it more optimized by distributing the shards evenly across nodes instead.",
"Opened a PR here: https://github.com/huggingface/datasets/pull/5369\r\n\r\nfeel free to play with it and share your feedbacks :)",
"@lhoestq I add shuffle after split_dataset_by_node, duplicated data still exist. \r\nFor example, we have a directory named `mock_pretraining_data`, which has three files, `part-00000`, `part-00002`,`part-00002`. \r\nText in `part-00000` is like this: \r\n{\"id\": 0}\r\n{\"id\": 1}\r\n{\"id\": 2}\r\n{\"id\": 3}\r\n{\"id\": 4}\r\n{\"id\": 5}\r\n{\"id\": 6}\r\n{\"id\": 7}\r\n{\"id\": 8}\r\n{\"id\": 9}\r\n\r\nand `part-00001`\r\n{\"id\": 10}\r\n{\"id\": 11}\r\n{\"id\": 12}\r\n{\"id\": 13}\r\n{\"id\": 14}\r\n{\"id\": 15}\r\n{\"id\": 16}\r\n{\"id\": 17}\r\n{\"id\": 18}\r\n{\"id\": 19}\r\n\r\nand `part-00002`\r\n{\"id\": 20}\r\n{\"id\": 21}\r\n{\"id\": 22}\r\n{\"id\": 23}\r\n{\"id\": 24}\r\n{\"id\": 25}\r\n{\"id\": 26}\r\n{\"id\": 27}\r\n{\"id\": 28}\r\n{\"id\": 29}\r\n\r\nAnd code in `test_dist.py` like this,\r\n```python\r\nimport torch\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom datasets import load_dataset\r\nimport os\r\nfrom transformers import AutoTokenizer, NezhaForPreTraining\r\nfrom transformers import AdamW, get_linear_schedule_with_warmup\r\nimport torch.nn.functional as F\r\nimport torch.nn as nn\r\nimport torch.distributed as dist\r\nfrom datasets.distributed import split_dataset_by_node\r\nfrom torch.nn.parallel import DistributedDataParallel as DDP\r\n\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = '5,6,7'\r\n\r\ndist.init_process_group(\"nccl\")\r\nlocal_rank = int(os.environ['LOCAL_RANK'])\r\nworld_size = torch.distributed.get_world_size()\r\ndevice = torch.device('cuda', local_rank)\r\ndata_dir = './'\r\n\r\ndef load_trainset(train_path):\r\n dataset = load_dataset('json', data_dir=os.path.join(data_dir, train_path), split='train', streaming=True)\r\n return dataset\r\n\r\ndef collate_fn(examples):\r\n input_ids = []\r\n for example in examples:\r\n input_ids.append(example['id'])\r\n return torch.LongTensor(input_ids).to(device)\r\n\r\n\r\ndataset = load_trainset('mock_pretraining_data')\r\ndataset = split_dataset_by_node(dataset, rank=local_rank, world_size=world_size).shuffle(buffer_size=512)\r\n# train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)\r\nbatch_size = 3\r\nprint('batch_size: {}'.format(batch_size))\r\ntrain_dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=collate_fn)\r\n\r\nfor x in train_dataloader:\r\n print({'rank': local_rank, 'id': x})\r\n```\r\nrun `python -m torch.distributed.launch --nproc_per_node=3 test_dist.py`\r\nThe output is\r\n```\r\n{'rank': 1, 'id': tensor([12, 15, 14], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([16, 10, 18], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([17, 13, 19], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([11], device='cuda:1')}\r\n{'rank': 0, 'id': tensor([0, 2, 9], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([4, 8, 1], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([5, 3, 6], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([7], device='cuda:0')}\r\n{'rank': 2, 'id': tensor([13, 15, 14], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([19, 17, 18], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([12, 16, 11], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([10], device='cuda:2')}\r\n```\r\n`part-00001` is loaded twice, `part-00002` isn't loaded.\r\n\r\nIf I run `python -m torch.distributed.launch --nproc_per_node=2 test_dist.py`\r\nThe output is weirder，many numbers appear twice\r\n```\r\n{'rank': 1, 'id': tensor([26, 8, 13], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([22, 19, 20], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([12, 28, 11], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([24, 2, 14], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([ 6, 27, 3], device='cuda:1')}\r\n{'rank': 0, 'id': tensor([ 8, 25, 1], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([20, 4, 12], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([14, 29, 5], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([ 7, 18, 23], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([19, 17, 11], device='cuda:0')}\r\n``` ",
"Hi ! Thanks for reporting, you need to pass `seed=` to `shuffle()` or the processes won't use the same seed to shuffle the shards order before assigning each shard to a node.\r\n\r\nThe issue is that the workers are not using the same seed to shuffle the shards before splitting the shards list by node.",
"Opened https://github.com/huggingface/datasets/issues/5696",
"I have the same issue\r\n```\r\nds['train'] = load_dataset(streaming=True)\r\nds['train'] = split_dataset_by_node(ds['train'], rank=int(os.environ[\"RANK\"]), world_size=int(os.environ[\"WORLD_SIZE\"]))\r\nvectorized_datasets = ds.map(\r\n prepare_dataset,\r\n remove_columns=raw_datasets_features,\r\n).with_format(\"torch\")\r\n\r\nvectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].shuffle(\r\n buffer_size=500,\r\n seed=42,\r\n)\r\n\r\ndef prepare_dataset(batch):\r\n ....\r\n print(f\"sentence: {batch['sentence']}, target_text: {batch['target_text']}\")\r\n return batch\r\n```\r\nWhen using split_dataset_by_node(), the data being read is indeed different for each GPU ID.\r\n\r\n```\r\ntrainer = Trainer(\r\n model=model,\r\n data_collator=data_collator,\r\n args=training_args,\r\n compute_metrics=compute_metrics,\r\n train_dataset=vectorized_datasets[\"train\"] if training_args.do_train else None,\r\n eval_dataset=vectorized_datasets[\"eval\"] if training_args.do_eval else None,\r\n tokenizer=processor,\r\n callbacks=[ShuffleCallback()],\r\n )\r\n...\r\ntrain_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n```\r\nHowever, when I execute trainer.train(), the data being read is different from what I expected.\r\nBecause I print the batch value in prepare_dataset() , I observe that the data is the same for each GPU ID.\r\n\r\nHow should I handle this issue?\r\n\r\n\r\n",
"There are two ways an iterable dataset can be split by node:\r\n1. if the number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU\r\n2. otherwise, each GPU iterate on the data and at the end keeps 1 sample out of n(GPUs) - skipping the others.\r\n\r\nIn case 2. it's therefore possible to have the same examples passed to `prepare_dataset` for each GPU.\r\n\r\nThis doesn't sound optimized though, because it runs the preprocessing on samples that won't be used in the end.\r\n\r\nCould you open a new issue so that we can discuss about this and find a solution ?"
] | 2022-12-14T16:06:19 | 2023-06-15T09:51:13 | 2023-01-16T13:33:33 | MEMBER | null | null | null | As mentioned in https://github.com/huggingface/datasets/issues/3423, when using PyTorch DDP the dataset ends up with duplicated data. We already check for the PyTorch `worker_info` for single node, but we should also check for `torch.distributed.get_world_size()` and `torch.distributed.get_rank()` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for your proposed fix, @freddyheppell.\r\n\r\nCurrently the CI fails because in a test we pass a `tuple` instead of a `list`. I would say we should accept `tuple` as a valid input type as well...\r\n\r\nWhat about checking for `Sequence` instead?",
"Fixed that @albertvillanova, can you approve CI again please? Had some issues related to Pytorch .so files when running tests on my M1 mac, so wasn't able to test locally first. Have got them working on my desktop now though."
] | 2022-12-13T23:04:06 | 2022-12-22T16:35:49 | 2022-12-22T16:32:49 | CONTRIBUTOR | null | false | {
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} | Checks type of names provided to ClassLabel to avoid easy and hard to debug errors (closes #5332 - see for discussion) | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@mariosasko Sorry, I didn't check tests/style after doing a merge from the Git UI last week. Thx for fixing. \r\n\r\nFYI I'm getting \"Only those with [write access](https://docs.github.com/articles/what-are-the-different-access-permissions) to this repository can merge pull requests.\" so it seems somebody else needs to merge this.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008816 / 0.011353 (-0.002536) | 0.004691 / 0.011008 (-0.006317) | 0.100039 / 0.038508 (0.061531) | 0.035422 / 0.023109 (0.012313) | 0.312600 / 0.275898 (0.036702) | 0.378684 / 0.323480 (0.055204) | 0.007593 / 0.007986 (-0.000392) | 0.005183 / 0.004328 (0.000855) | 0.078040 / 0.004250 (0.073790) | 0.041845 / 0.037052 (0.004793) | 0.325251 / 0.258489 (0.066762) | 0.363459 / 0.293841 (0.069618) | 0.038006 / 0.128546 (-0.090540) | 0.011911 / 0.075646 (-0.063735) | 0.335020 / 0.419271 (-0.084251) | 0.048765 / 0.043533 (0.005233) | 0.305913 / 0.255139 (0.050774) | 0.337620 / 0.283200 (0.054420) | 0.101867 / 0.141683 (-0.039816) | 1.450091 / 1.452155 (-0.002064) | 1.437303 / 1.492716 (-0.055413) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225650 / 0.018006 (0.207644) | 0.492480 / 0.000490 (0.491990) | 0.002857 / 0.000200 (0.002658) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026231 / 0.037411 (-0.011180) | 0.105479 / 0.014526 (0.090953) | 0.118438 / 0.176557 (-0.058119) | 0.167313 / 0.737135 (-0.569822) | 0.119416 / 0.296338 (-0.176923) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396233 / 0.215209 (0.181024) | 3.943325 / 2.077655 (1.865671) | 1.778864 / 1.504120 (0.274744) | 1.587957 / 1.541195 (0.046763) | 1.615404 / 1.468490 (0.146914) | 0.709427 / 4.584777 (-3.875350) | 3.823310 / 3.745712 (0.077598) | 3.461376 / 5.269862 (-1.808486) | 1.888330 / 4.565676 (-2.677346) | 0.086910 / 0.424275 (-0.337365) | 0.012215 / 0.007607 (0.004608) | 0.504877 / 0.226044 (0.278833) | 5.051513 / 2.268929 (2.782584) | 2.249389 / 55.444624 (-53.195235) | 1.890949 / 6.876477 (-4.985528) | 2.015584 / 2.142072 (-0.126489) | 0.862313 / 4.805227 (-3.942914) | 0.166295 / 6.500664 (-6.334369) | 0.061131 / 0.075469 (-0.014338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.201804 / 1.841788 (-0.639984) | 14.589425 / 8.074308 (6.515117) | 13.855522 / 10.191392 (3.664130) | 0.193406 / 0.680424 (-0.487018) | 0.028614 / 0.534201 (-0.505587) | 0.439857 / 0.579283 (-0.139426) | 0.443330 / 0.434364 (0.008966) | 0.514078 / 0.540337 (-0.026259) | 0.608245 / 1.386936 (-0.778691) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007087 / 0.011353 (-0.004265) | 0.005024 / 0.011008 (-0.005985) | 0.096852 / 0.038508 (0.058344) | 0.032870 / 0.023109 (0.009761) | 0.397790 / 0.275898 (0.121892) | 0.420717 / 0.323480 (0.097237) | 0.005552 / 0.007986 (-0.002434) | 0.003742 / 0.004328 (-0.000586) | 0.074788 / 0.004250 (0.070537) | 0.048030 / 0.037052 (0.010977) | 0.398520 / 0.258489 (0.140031) | 0.460919 / 0.293841 (0.167078) | 0.037652 / 0.128546 (-0.090894) | 0.012249 / 0.075646 (-0.063397) | 0.333077 / 0.419271 (-0.086194) | 0.052364 / 0.043533 (0.008831) | 0.394358 / 0.255139 (0.139219) | 0.414193 / 0.283200 (0.130994) | 0.103569 / 0.141683 (-0.038114) | 1.499208 / 1.452155 (0.047053) | 1.619481 / 1.492716 (0.126764) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229476 / 0.018006 (0.211470) | 0.448670 / 0.000490 (0.448180) | 0.000399 / 0.000200 (0.000199) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027550 / 0.037411 (-0.009862) | 0.109180 / 0.014526 (0.094654) | 0.118372 / 0.176557 (-0.058185) | 0.153136 / 0.737135 (-0.583999) | 0.122689 / 0.296338 (-0.173650) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445163 / 0.215209 (0.229954) | 4.426350 / 2.077655 (2.348695) | 2.194902 / 1.504120 (0.690782) | 2.019049 / 1.541195 (0.477854) | 2.032795 / 1.468490 (0.564305) | 0.700752 / 4.584777 (-3.884025) | 3.797616 / 3.745712 (0.051903) | 2.046414 / 5.269862 (-3.223447) | 1.345037 / 4.565676 (-3.220639) | 0.085389 / 0.424275 (-0.338886) | 0.012824 / 0.007607 (0.005217) | 0.553875 / 0.226044 (0.327831) | 5.550252 / 2.268929 (3.281323) | 2.702822 / 55.444624 (-52.741803) | 2.346257 / 6.876477 (-4.530220) | 2.410772 / 2.142072 (0.268699) | 0.848271 / 4.805227 (-3.956957) | 0.170787 / 6.500664 (-6.329877) | 0.064344 / 0.075469 (-0.011125) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266222 / 1.841788 (-0.575566) | 14.501194 / 8.074308 (6.426886) | 13.413678 / 10.191392 (3.222286) | 0.589048 / 0.680424 (-0.091375) | 0.018246 / 0.534201 (-0.515955) | 0.425221 / 0.579283 (-0.154062) | 0.425900 / 0.434364 (-0.008464) | 0.494023 / 0.540337 (-0.046314) | 0.604324 / 1.386936 (-0.782612) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2022-12-13T22:35:51 | 2023-01-05T16:46:31 | 2023-01-05T15:59:51 | CONTRIBUTOR | null | false | {
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} | Invoking `{load,save}_from_dict` results in resource leak warnings, this should fix.
Introduces no significant logic changes. | {
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https://api.github.com/repos/huggingface/datasets/issues/5357 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5357/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5357/comments | https://api.github.com/repos/huggingface/datasets/issues/5357/events | https://github.com/huggingface/datasets/pull/5357 | 1,495,029,602 | PR_kwDODunzps5FXNyR | 5,357 | Support torch dataloader without torch formatting | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Need some more time to fix the tests, especially with pickle",
"> And I actually don't quite understand the idea - what's the motivation behind making only IterableDataset compatible with torch DataLoader without setting the format explicitly?\r\n\r\nSetting the format to pytorch = set the output types of the dataset to be pytorch tensors. However sometimes your dataset is not made of tensors but you still want to be able to use a pytorch DataLoader",
"A bit more context. \r\n\r\nThe arrow-backed `Dataset` supports `DataLoader(ds)` (even if the format is not \"torch\"), and we want to be able to do the same with `IterableDataset` for consistency. However, this is when the PyTorch internals come into play - an iterable dataset needs to be an instance of `torch.utils.data.IterableDataset` due to [this](https://github.com/pytorch/pytorch/blob/abc54f93145830b502400faa92bec86e05422fbd/torch/utils/data/dataloader.py#L276) check (notice there is no check for the map-style version). Hence the explicit subclassing in this PR.",
"Exactly :) Btw I just took your comments into account @polinaeterna , so feel free to review again",
"@lhoestq just checking, does this change still preserve the fix to the \"data duplicate when setting num_works > 1 with streaming data\" issue from before?\r\n\r\nhttps://github.com/huggingface/datasets/issues/3423",
"Yes :)"
] | 2022-12-13T19:39:24 | 2023-01-04T12:45:40 | 2022-12-15T19:15:54 | MEMBER | null | false | {
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} | In https://github.com/huggingface/datasets/pull/5084 we make the torch formatting consistent with the map-style datasets formatting: a torch formatted iterable dataset will yield torch tensors.
The previous behavior of the torch formatting for iterable dataset was simply to make the iterable dataset inherit from `torch.utils.data.Dataset` to make it work in a torch DataLoader. However ideally an unformatted dataset should also work with a DataLoader. To fix that, `datasets.IterableDataset` should inherit from `torch.utils.data.IterableDataset`.
Since we don't want to import torch on startup, I created this PR to dynamically make the `datasets.IterableDataset` class inherit form the torch one when a `datasets.IterableDataset` is instantiated and if PyTorch is available.
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("c4", "en", streaming=True, split="train")
>>> import torch.utils.data
>>> isinstance(ds, torch.utils.data.IterableDataset)
True
>>> dataloader = torch.utils.data.DataLoader(ds, batch_size=32, num_workers=4)
>>> for example in dataloader:
...: ...
``` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-12-13T18:54:09 | 2022-12-14T17:25:58 | 2022-12-14T17:22:16 | MEMBER | null | false | {
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} | This PR cleans the `Filesystems` and `Logging` docstrings. | {
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