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https://api.github.com/repos/huggingface/datasets/issues/7164 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7164/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7164/comments | https://api.github.com/repos/huggingface/datasets/issues/7164/events | https://github.com/huggingface/datasets/issues/7164 | 2,544,757,297 | I_kwDODunzps6Xreox | 7,164 | fsspec.exceptions.FSTimeoutError when downloading dataset | {
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"Hi ! If you check the dataset loading script [here](https://huggingface.co/datasets/openslr/librispeech_asr/blob/main/librispeech_asr.py) you'll see that it downloads the data from OpenSLR, and apparently their storage has timeout issues. It would be great to ultimately host the dataset on Hugging Face instead.\r\... | 2024-09-24T08:45:05Z | 2025-04-09T22:25:56Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
I am trying to download the `librispeech_asr` `clean` dataset, which results in a `FSTimeoutError` exception after downloading around 61% of the data.
### Steps to reproduce the bug
```
import datasets
datasets.load_dataset("librispeech_asr", "clean")
```
The output is as follows:
> Downloading data: 61%|βββββββββββββββ | 3.92G/6.39G [05:00<03:06, 13.2MB/s]Traceback (most recent call last):
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 56, in _runner
> result[0] = await coro
> ^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/implementations/http.py", line 262, in _get_file
> chunk = await r.content.read(chunk_size)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 393, in read
> await self._wait("read")
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 311, in _wait
> with self._timer:
> ^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/helpers.py", line 713, in __exit__
> raise asyncio.TimeoutError from None
> TimeoutError
>
> The above exception was the direct cause of the following exception:
>
> Traceback (most recent call last):
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/load_dataset.py", line 3, in <module>
> datasets.load_dataset("librispeech_asr", "clean")
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/load.py", line 2096, in load_dataset
> builder_instance.download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 924, in download_and_prepare
> self._download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1647, in _download_and_prepare
> super()._download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 977, in _download_and_prepare
> split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/2712a8f82f0d20807a56faadcd08734f9bdd24c850bb118ba21ff33ebff0432f/librispeech_asr.py", line 115, in _split_generators
> archive_path = dl_manager.download(_DL_URLS[self.config.name])
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 159, in download
> downloaded_path_or_paths = map_nested(
> ^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 512, in map_nested
> _single_map_nested((function, obj, batched, batch_size, types, None, True, None))
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 380, in _single_map_nested
> return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
> ^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 216, in _download_batched
> self._download_single(url_or_filename, download_config=download_config)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 225, in _download_single
> out = cached_path(url_or_filename, download_config=download_config)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 205, in cached_path
> output_path = get_from_cache(
> ^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 415, in get_from_cache
> fsspec_get(url, temp_file, storage_options=storage_options, desc=download_desc, disable_tqdm=disable_tqdm)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 334, in fsspec_get
> fs.get_file(path, temp_file.name, callback=callback)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 118, in wrapper
> return sync(self.loop, func, *args, **kwargs)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 101, in sync
> raise FSTimeoutError from return_result
> fsspec.exceptions.FSTimeoutError
> Downloading data: 61%|βββββββββββββββ | 3.92G/6.39G [05:00<03:09, 13.0MB/s]
### Expected behavior
Complete the download
### Environment info
Python version 3.12.6
Dependencies:
> dependencies = [
> "accelerate>=0.34.2",
> "datasets[audio]>=3.0.0",
> "ipython>=8.18.1",
> "librosa>=0.10.2.post1",
> "torch>=2.4.1",
> "torchaudio>=2.4.1",
> "transformers>=4.44.2",
> ]
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https://api.github.com/repos/huggingface/datasets/issues/6587 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6587/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6587/comments | https://api.github.com/repos/huggingface/datasets/issues/6587/events | https://github.com/huggingface/datasets/pull/6587 | 2,080,348,016 | PR_kwDODunzps5kAT_5 | 6,587 | Allow concatenation of datasets with mixed structs | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6587). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"friendly bump",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<... | 2024-01-13T15:33:20Z | 2024-02-15T15:20:06Z | 2024-02-08T14:38:32Z | CONTRIBUTOR | null | null | null | Fixes #6466
The idea is to do a recursive check for structs. PyArrow handles it well enough.
For a demo you can do:
```python
from datasets import Dataset, concatenate_datasets
ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]})
ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]})
print(concatenate_datasets([ds, ds2]).features)
print(concatenate_datasets([ds, ds2]).to_dict())
```
Now both the features and the rows are fixed.
I note that Sequence suffers from the same problem, so I can fix that in a future PR once this one is merged. | {
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https://api.github.com/repos/huggingface/datasets/issues/6510 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6510/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6510/comments | https://api.github.com/repos/huggingface/datasets/issues/6510/events | https://github.com/huggingface/datasets/pull/6510 | 2,046,928,742 | PR_kwDODunzps5iRyiV | 6,510 | Replace `list_files_info` with `list_repo_tree` in `push_to_hub` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6510). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"CI errors are unrelated to the changes, so I'm merging.",
"<details>\n<summary>Show b... | 2023-12-18T15:34:19Z | 2023-12-19T18:05:47Z | 2023-12-19T17:58:34Z | COLLABORATOR | null | null | null | Starting from `huggingface_hub` 0.20.0, `list_files_info` will be deprecated in favor of `list_repo_tree` (see https://github.com/huggingface/huggingface_hub/pull/1910) | {
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https://api.github.com/repos/huggingface/datasets/issues/5710 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5710/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5710/comments | https://api.github.com/repos/huggingface/datasets/issues/5710/events | https://github.com/huggingface/datasets/issues/5710 | 1,655,703,534 | I_kwDODunzps5isAfu | 5,710 | OSError: Memory mapping file failed: Cannot allocate memory | {
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"Hi! This error means that PyArrow's internal [`mmap`](https://man7.org/linux/man-pages/man2/mmap.2.html) call failed to allocate memory, which can be tricky to debug. Since this error is more related to PyArrow than us, I think it's best to report this issue in their [repo](https://github.com/apache/arrow) (they a... | 2023-04-05T14:11:26Z | 2023-04-20T17:16:40Z | 2023-04-20T17:16:40Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
Hello, I have a series of datasets each of 5 GB, 600 datasets in total. So together this makes 3TB.
When I trying to load all the 600 datasets into memory, I get the above error message.
Is this normal because I'm hitting the max size of memory mapping of the OS?
Thank you
```terminal
0_21/cache-e9c42499f65b1881.arrow
load_hf_datasets_from_disk: 82%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 494/600 [07:26<01:35, 1.11it/s]
Traceback (most recent call last):
File "example_load_genkalm_dataset.py", line 35, in <module>
multi_ds.post_process(max_node_num=args.max_node_num,max_seq_length=args.max_seq_length,delay=args.delay)
File "/home/geng/GenKaLM/src/dataloader/dataset.py", line 142, in post_process
genkalm_dataset = GenKaLM_Dataset.from_hf_dataset(path_or_name=ds_path, max_seq_length=self.max_seq_length,
File "/home/geng/GenKaLM/src/dataloader/dataset.py", line 47, in from_hf_dataset
hf_ds = load_from_disk(path_or_name)
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/load.py", line 1848, in load_from_disk
return Dataset.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1549, in load_from_disk
arrow_table = concat_tables(
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/table.py", line 1805, in concat_tables
tables = list(tables)
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1550, in <genexpr>
table_cls.from_file(Path(dataset_path, data_file["filename"]).as_posix())
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/table.py", line 1065, in from_file
table = _memory_mapped_arrow_table_from_file(filename)
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/table.py", line 50, in _memory_mapped_arrow_table_from_file
memory_mapped_stream = pa.memory_map(filename)
File "pyarrow/io.pxi", line 950, in pyarrow.lib.memory_map
File "pyarrow/io.pxi", line 911, in pyarrow.lib.MemoryMappedFile._open
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: Memory mapping file failed: Cannot allocate memory
```
### Steps to reproduce the bug
Sorry I can not provide a reproducible code as the data is stored on my server and it's too large to share.
### Expected behavior
I expect the 3TB of data can be fully mapped to memory
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-4.15.0-204-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.6
- PyArrow version: 11.0.0
- Pandas version: 1.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/6242 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6242/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6242/comments | https://api.github.com/repos/huggingface/datasets/issues/6242/events | https://github.com/huggingface/datasets/issues/6242 | 1,896,899,123 | I_kwDODunzps5xEGIz | 6,242 | Data alteration when loading dataset with unspecified inner sequence length | {
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"While this issue may seem specific, it led to a silent problem in my workflow that took days to diagnose. If this feature is not intended to be supported, an error should be raised when encountering this configuration to prevent such issues.",
"Thanks for reporting! This is a MRE:\r\n\r\n```python\r\nimport pyar... | 2023-09-14T16:12:45Z | 2023-09-19T17:53:18Z | 2023-09-19T17:53:18Z | MEMBER | null | null | {
"completed": 0,
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} | ### Describe the bug
When a dataset saved with a specified inner sequence length is loaded without specifying that length, the original data is altered and becomes inconsistent.
### Steps to reproduce the bug
```python
from datasets import Dataset, Features, Value, Sequence, load_dataset
# Repository ID
repo_id = "my_repo_id"
# Define features with a specific length of 3 for each inner sequence
specified_features = Features({"key": Sequence(Sequence(Value("float32"), length=3))})
# Create a dataset with the specified features
data = [
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
[[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]],
]
dataset = Dataset.from_dict({"key": data}, features=specified_features)
# Push the dataset to the hub
dataset.push_to_hub(repo_id)
# Define features without specifying the length
unspecified_features = Features({"key": Sequence(Sequence(Value("float32")))})
# Load the dataset from the hub with this new feature definition
dataset = load_dataset(f"qgallouedec/{repo_id}", split="train", features=unspecified_features)
# The obtained data is altered
print(dataset.to_dict()) # {'key': [[[1.0], [2.0]], [[3.0], [4.0]]]}
```
### Expected behavior
```python
print(dataset.to_dict()) # {'key': [[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]]}
```
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-6.2.0-32-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/4985 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4985/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4985/comments | https://api.github.com/repos/huggingface/datasets/issues/4985/events | https://github.com/huggingface/datasets/pull/4985 | 1,375,807,768 | PR_kwDODunzps4_F6kU | 4,985 | Prefer split patterns from directories over split patterns from filenames | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Can we merge this one since the issue this PR fixes was reported for the second time? I also think we don't need a test for this simple change.",
"@mariosasko sure! could you please approve it? ",
"Hi there @polinaeterna @mariosa... | 2022-09-16T11:20:40Z | 2022-11-02T11:54:28Z | 2022-09-29T08:07:49Z | CONTRIBUTOR | null | null | null | related to https://github.com/huggingface/datasets/issues/4895
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https://api.github.com/repos/huggingface/datasets/issues/7002 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7002/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7002/comments | https://api.github.com/repos/huggingface/datasets/issues/7002/events | https://github.com/huggingface/datasets/pull/7002 | 2,373,010,351 | PR_kwDODunzps5zhBld | 7,002 | Fix dump of bfloat16 torch tensor | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7002). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-06-25T15:38:09Z | 2024-06-25T16:10:16Z | 2024-06-25T15:51:52Z | MEMBER | null | null | null | close https://github.com/huggingface/datasets/issues/7000 | {
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https://api.github.com/repos/huggingface/datasets/issues/4571 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4571/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4571/comments | https://api.github.com/repos/huggingface/datasets/issues/4571/events | https://github.com/huggingface/datasets/issues/4571 | 1,284,883,289 | I_kwDODunzps5MlcNZ | 4,571 | move under the facebook org? | {
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"Related to https://github.com/huggingface/datasets/issues/4562#issuecomment-1166911751\r\n\r\nI'll assign @albertvillanova ",
"I'm just wondering why we don't have this dataset under:\r\n- the `facebook` namespace\r\n- or the canonical dataset `flores`: why does this only have 2 languages?",
"fwiw: the dataset... | 2022-06-26T11:19:09Z | 2023-09-25T12:05:18Z | null | MEMBER | null | null | {
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} | ### Link
https://huggingface.co/datasets/gsarti/flores_101
### Description
It seems like streaming isn't supported for this dataset:
```
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://dl.fbaipublicfiles.com/flores101/dataset/flores101_dataset.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
### Owner
No | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5485 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5485/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5485/comments | https://api.github.com/repos/huggingface/datasets/issues/5485/events | https://github.com/huggingface/datasets/pull/5485 | 1,563,002,829 | PR_kwDODunzps5I2ER2 | 5,485 | Add section in tutorial for IterableDataset | {
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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 | rea... | 2023-01-30T18:43:04Z | 2023-02-01T18:15:38Z | 2023-02-01T18:08:46Z | MEMBER | null | null | null | Introduces an `IterableDataset` and how to access it in the tutorial section. It also adds a brief next step section at the end to provide a path for users who want more explanation and a path for users who want something more practical and learn how to preprocess these dataset types. It'll complement the awesome new doc introduced in:
- #5410 | {
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https://api.github.com/repos/huggingface/datasets/issues/7046 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7046/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7046/comments | https://api.github.com/repos/huggingface/datasets/issues/7046/events | https://github.com/huggingface/datasets/pull/7046 | 2,405,485,582 | PR_kwDODunzps51N05n | 7,046 | Support librosa and numpy 2.0 for Python 3.10 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7046). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-07-12T12:42:47Z | 2024-07-12T13:04:40Z | 2024-07-12T12:58:17Z | MEMBER | null | null | null | Support librosa and numpy 2.0 for Python 3.10 by installing soxr 0.4.0b1 pre-release:
- https://github.com/dofuuz/python-soxr/releases/tag/v0.4.0b1
- https://github.com/dofuuz/python-soxr/issues/28 | {
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https://api.github.com/repos/huggingface/datasets/issues/7467 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7467/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7467/comments | https://api.github.com/repos/huggingface/datasets/issues/7467/events | https://github.com/huggingface/datasets/issues/7467 | 2,930,067,107 | I_kwDODunzps6upUaj | 7,467 | load_dataset with streaming hangs on parquet datasets | {
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"Hi ! The issue comes from `pyarrow`, I reported it here: https://github.com/apache/arrow/issues/45214 (feel free to comment / thumb up).\n\nAlternatively we can try to find something else than `ParquetFileFragment.to_batches()` to iterate on Parquet data and keep the option the pass `filters=`..."
] | 2025-03-18T23:33:54Z | 2025-03-25T10:28:04Z | null | NONE | null | null | {
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} | ### Describe the bug
When I try to load a dataset with parquet files (e.g. "bigcode/the-stack") the dataset loads, but python interpreter can't exit and hangs
### Steps to reproduce the bug
```python3
import datasets
print('Start')
dataset = datasets.load_dataset("bigcode/the-stack", data_dir="data/yaml", streaming=True, split="train")
it = iter(dataset)
next(it)
print('Finish')
```
The program prints finish but doesn't exit and hangs indefinitely.
I tried this on two different machines and several datasets.
### Expected behavior
The program exits successfully
### Environment info
datasets==3.4.1
Python 3.12.9.
MacOS and Ubuntu Linux | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5624 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5624/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5624/comments | https://api.github.com/repos/huggingface/datasets/issues/5624/events | https://github.com/huggingface/datasets/issues/5624 | 1,617,400,192 | I_kwDODunzps5gZ5GA | 5,624 | glue datasets returning -1 for test split | {
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"Hi @lithafnium, thanks for reporting.\r\n\r\nPlease note that you can use the \"Community\" tab in the corresponding dataset page to start any discussion: https://huggingface.co/datasets/glue/discussions\r\n\r\nIndeed this issue was already raised there (https://huggingface.co/datasets/glue/discussions/5) and answ... | 2023-03-09T14:47:18Z | 2023-03-09T16:49:29Z | 2023-03-09T16:49:29Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Downloading any dataset from GLUE has -1 as class labels for test split. Train and validation have regular 0/1 class labels. This is also present in the dataset card online.
### Steps to reproduce the bug
```
dataset = load_dataset("glue", "sst2")
for d in dataset:
# prints out -1
print(d["label"]
```
### Expected behavior
Expected behavior should be 0/1 instead of -1.
### Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17
- Python version: 3.8.16
- PyArrow version: 8.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/4961 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4961/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4961/comments | https://api.github.com/repos/huggingface/datasets/issues/4961/events | https://github.com/huggingface/datasets/issues/4961 | 1,368,124,033 | I_kwDODunzps5Ri-qB | 4,961 | fsspec 2022.8.2 breaks xopen in streaming mode | {
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"loading `fsspec==2022.7.1` fixes this issue, setup.py would need to be changed to prevent users from using the latest version of fsspec.",
"Opened [PR](https://github.com/huggingface/datasets/pull/4962) to address this.",
"Hi @DCNemesis, thanks for reporting.\r\n\r\nThat was a temporary issue in `fsspec` relea... | 2022-09-09T17:26:55Z | 2022-09-12T17:45:50Z | 2022-09-12T14:32:05Z | NONE | null | null | {
"completed": 0,
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} | ## Describe the bug
When fsspec 2022.8.2 is installed in your environment, xopen will prematurely close files, making streaming mode inoperable.
## Steps to reproduce the bug
```python
import datasets
data = datasets.load_dataset('MLCommons/ml_spoken_words', 'id_wav', split='train', streaming=True)
```
## Expected results
Dataset should load as iterator.
## Actual results
```
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) 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, **config_kwargs)
1737 # Return iterable dataset in case of streaming
1738 if streaming:
-> 1739 return builder_instance.as_streaming_dataset(split=split)
1740
1741 # Some datasets are already processed on the HF google storage
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1023 )
1024 self._check_manual_download(dl_manager)
-> 1025 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
1026 # By default, return all splits
1027 if split is None:
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _split_generators(self, dl_manager)
182 name=datasets.Split.TRAIN,
183 gen_kwargs={
--> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages],
185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in
186 self.config.languages] if not dl_manager.is_streaming else None,
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in <listcomp>(.0)
182 name=datasets.Split.TRAIN,
183 gen_kwargs={
--> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages],
185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in
186 self.config.languages] if not dl_manager.is_streaming else None,
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives(dl_manager, lang, format, split)
267 # for streaming case
268 def _download_audio_archives(dl_manager, lang, format, split):
--> 269 archives_paths = _download_audio_archives_paths(dl_manager, lang, format, split)
270 return [dl_manager.iter_archive(archive_path) for archive_path in archives_paths]
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives_paths(dl_manager, lang, format, split)
251 n_files_path = dl_manager.download(n_files_url)
252
--> 253 with open(n_files_path, "r", encoding="utf-8") as file:
254 n_files = int(file.read().strip()) # the file contains a number of archives
255
ValueError: I/O operation on closed file.
```
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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https://api.github.com/repos/huggingface/datasets/issues/7521 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7521/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7521/comments | https://api.github.com/repos/huggingface/datasets/issues/7521/events | https://github.com/huggingface/datasets/pull/7521 | 2,997,666,366 | PR_kwDODunzps6SvEZp | 7,521 | fix: Image Feature in Datasets Library Fails to Handle bytearray Objects from Spark DataFrames (#7517) | {
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"@lhoestq let me know if you prefer to change the spark iterator so it outputs `bytes`"
] | 2025-04-15T21:23:58Z | 2025-04-16T06:57:22Z | null | NONE | null | null | null | ## Task
Support bytes-like objects (bytes and bytearray) in Features classes
### Description
The `Features` classes only accept `bytes` objects for binary data, but not `bytearray`. This leads to errors when using `IterableDataset.from_spark()` with Spark DataFrames as they contain `bytearray` objects, even though both `bytes` and `bytearray` are valid [*bytes-like objects* in Python](https://docs.python.org/3/glossary.html#term-bytes-like-object).
### Changes
- Updated `Features` classes to accept both `bytes` and `bytearray` types for binary data fields.
### Reasoning
- `bytes` and `bytearray` serve the same purpose for binary data, with the only difference being mutability.
- Modifying the Spark iterator to convert `bytearray` to `bytes` would be a workaround, not a true fix. I think the correct solution is to accept all bytes-like objects as input.
- This approach is more robust and future-proof since Python 3.12+ provides a [standard way to check for buffer protocol](https://docs.python.org/3/c-api/buffer.html#bufferobjects).
### Testing
- Added tests to cover `bytearray` inputs for image features.
### Related Issues
- Fixes: #7517 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5721 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5721/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5721/comments | https://api.github.com/repos/huggingface/datasets/issues/5721/events | https://github.com/huggingface/datasets/issues/5721 | 1,659,680,682 | I_kwDODunzps5i7Leq | 5,721 | Calling datasets.load_dataset("text" ...) results in a wrong split. | {
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} | ### Describe the bug
When creating a text dataset, the training split should have the bulk of the examples by default. Currently, testing does.
### Steps to reproduce the bug
I have a folder with 18K text files in it. Each text file essentially consists in a document or article scraped from online. Calling the following codeL
```
folder_path = "/home/cyril/Downloads/llama_dataset"
data = datasets.load_dataset("text", data_dir=folder_path)
data.save_to_disk("/home/cyril/Downloads/data.hf")
data = datasets.load_from_disk("/home/cyril/Downloads/data.hf")
print(data)
```
Results in the following split:
```
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 2114
})
test: Dataset({
features: ['text'],
num_rows: 200882
})
validation: Dataset({
features: ['text'],
num_rows: 152
})
})
```
It seems to me like the train/test/validation splits are in the wrong order since test split >>>> train_split
### Expected behavior
Train split should have the bulk of the training examples.
### Environment info
datasets 2.11.0, python 3.10.6 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7125 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7125/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7125/comments | https://api.github.com/repos/huggingface/datasets/issues/7125/events | https://github.com/huggingface/datasets/pull/7125 | 2,485,912,246 | PR_kwDODunzps55Y4TM | 7,125 | Fix wrong SHA in CI tests of HubDatasetModuleFactoryWithParquetExport | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7125). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-08-26T05:09:35Z | 2024-08-26T05:33:15Z | 2024-08-26T05:27:09Z | MEMBER | null | null | null | Fix wrong SHA in CI tests of HubDatasetModuleFactoryWithParquetExport. | {
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https://api.github.com/repos/huggingface/datasets/issues/5682 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5682/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5682/comments | https://api.github.com/repos/huggingface/datasets/issues/5682/events | https://github.com/huggingface/datasets/issues/5682 | 1,646,000,571 | I_kwDODunzps5iG_m7 | 5,682 | ValueError when passing ignore_verifications | {
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```
ValueError: 'none' is not a valid VerificationMode
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5106 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5106/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5106/comments | https://api.github.com/repos/huggingface/datasets/issues/5106/events | https://github.com/huggingface/datasets/pull/5106 | 1,406,635,758 | PR_kwDODunzps5ArM6G | 5,106 | Fix task template reload from dict | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Just wondering if there might be other data classes default values missed that could cause an issue... Apart from feature-like classes and tasks, I don't see any others though...\r\n\r\nI think we're good ! `asdict` is used on the ... | 2022-10-12T18:33:49Z | 2022-10-13T09:59:07Z | 2022-10-13T09:56:51Z | MEMBER | null | null | null | Since #4926 the JSON dumps are simplified and it made task template dicts empty by default.
I fixed this by always including the task name which is needed to reload a task from a dict | {
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https://api.github.com/repos/huggingface/datasets/issues/4602 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4602/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4602/comments | https://api.github.com/repos/huggingface/datasets/issues/4602/events | https://github.com/huggingface/datasets/pull/4602 | 1,289,950,379 | PR_kwDODunzps46obqi | 4,602 | Upgrade setuptools in windows CI | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-06-30T10:48:41Z | 2023-09-24T10:05:10Z | 2022-06-30T12:46:17Z | MEMBER | null | null | null | The windows CI is currently flaky: some dependencies like aiobotocore, multiprocess and seqeval sometimes fail to install.
In particular it seems that building the wheels fail. Here is an example of logs
```
Building wheel for seqeval (setup.py): started
Running command 'C:\tools\miniconda3\envs\py37\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"'; __file__='"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6'
No parent package detected, impossible to derive `name`
running bdist_wheel
running build
running build_py
package init file 'seqeval\__init__.py' not found (or not a regular file)
package init file 'seqeval\metrics\__init__.py' not found (or not a regular file)
C:\tools\miniconda3\envs\py37\lib\site-packages\setuptools\command\install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
setuptools.SetuptoolsDeprecationWarning,
installing to build\bdist.win-amd64\wheel
running install
running install_lib
warning: install_lib: 'build\lib' does not exist -- no Python modules to install
running install_egg_info
running egg_info
creating UNKNOWN.egg-info
writing UNKNOWN.egg-info\PKG-INFO
writing dependency_links to UNKNOWN.egg-info\dependency_links.txt
writing top-level names to UNKNOWN.egg-info\top_level.txt
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
reading manifest file 'UNKNOWN.egg-info\SOURCES.txt'
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
Copying UNKNOWN.egg-info to build\bdist.win-amd64\wheel\.\UNKNOWN-0.0.0-py3.7.egg-info
running install_scripts
creating build\bdist.win-amd64\wheel\UNKNOWN-0.0.0.dist-info\WHEEL
creating 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6\UNKNOWN-0.0.0-py3-none-any.whl' and adding 'build\bdist.win-amd64\wheel' to it
adding 'UNKNOWN-0.0.0.dist-info/METADATA'
adding 'UNKNOWN-0.0.0.dist-info/WHEEL'
adding 'UNKNOWN-0.0.0.dist-info/top_level.txt'
adding 'UNKNOWN-0.0.0.dist-info/RECORD'
removing build\bdist.win-amd64\wheel
Building wheel for seqeval (setup.py): finished with status 'done'
Created wheel for seqeval: filename=UNKNOWN-0.0.0-py3-none-any.whl size=963 sha256=67eb93a6e1ff4796c5882a13f9fa25bb0d3d103796e2525f9cecf3b2ef26d4b1
Stored in directory: c:\users\circleci\appdata\local\pip\cache\wheels\05\96\ee\7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7
WARNING: Built wheel for seqeval is invalid: Wheel has unexpected file name: expected 'seqeval', got 'UNKNOWN'
```
hopefully this fixes the issue
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https://api.github.com/repos/huggingface/datasets/issues/5448 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5448/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5448/comments | https://api.github.com/repos/huggingface/datasets/issues/5448/events | https://github.com/huggingface/datasets/issues/5448 | 1,550,618,514 | I_kwDODunzps5cbI-S | 5,448 | Support fsspec 2023.1.0 in CI | {
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} | Once we find out the root cause of:
- #5445
we should revert the temporary pin on fsspec introduced by:
- #5447 | {
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https://api.github.com/repos/huggingface/datasets/issues/6686 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6686/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6686/comments | https://api.github.com/repos/huggingface/datasets/issues/6686/events | https://github.com/huggingface/datasets/issues/6686 | 2,147,795,103 | I_kwDODunzps6ABMCf | 6,686 | Question: Is there any way for uploading a large image dataset? | {
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"```\r\nimport pandas as pd\r\nfrom datasets import Dataset, Image\r\n\r\n# Read the CSV file\r\ndata = pd.read_csv(\"XXXX.csv\")\r\n\r\n# Create a Hugging Face Dataset\r\ndataset = Dataset.from_pandas(data)\r\ndataset = dataset.cast_column(\"file_name\", Image())\r\n\r\n# Upload to Hugging Face Hub (make sure auth... | 2024-02-21T22:07:21Z | 2024-05-02T03:44:59Z | null | NONE | null | null | {
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} | I am uploading an image dataset like this:
```
dataset = load_dataset(
"json",
data_files={"train": "data/custom_dataset/train.json", "validation": "data/custom_dataset/val.json"},
)
dataset = dataset.cast_column("images", Sequence(Image()))
dataset.push_to_hub("StanfordAIMI/custom_dataset", max_shard_size="1GB")
```
where it takes a long time in the `Map` process. Do you think I can use multi-processing to map all the image data to the memory first? For the `Map()` function, I can set `num_proc`. But for `push_to_hub` and `cast_column`, I can not find it.
Thanks in advance!
Best, | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4653 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4653/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4653/comments | https://api.github.com/repos/huggingface/datasets/issues/4653/events | https://github.com/huggingface/datasets/issues/4653 | 1,296,702,834 | I_kwDODunzps5NSh1y | 4,653 | Add Altlex dataset | {
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/altlex)."
] | 2022-07-07T02:23:02Z | 2022-07-14T02:12:39Z | 2022-07-14T02:12:39Z | NONE | null | null | {
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} | ## Adding a Dataset
- **Name:** *Altlex*
- **Description:** *Git repository for software associated with the 2016 ACL paper "Identifying Causal Relations Using Parallel Wikipedia Articles.β*
- **Paper:** *https://aclanthology.org/P16-1135.pdf*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/altlex.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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https://api.github.com/repos/huggingface/datasets/issues/6136 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6136/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6136/comments | https://api.github.com/repos/huggingface/datasets/issues/6136/events | https://github.com/huggingface/datasets/issues/6136 | 1,844,887,866 | I_kwDODunzps5t9sE6 | 6,136 | CI check_code_quality error: E721 Do not compare types, use `isinstance()` | {
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} | After latest release of `ruff` (https://pypi.org/project/ruff/0.0.284/), we get the following CI error:
```
src/datasets/utils/py_utils.py:689:12: E721 Do not compare types, use `isinstance()`
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https://api.github.com/repos/huggingface/datasets/issues/6271 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6271/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6271/comments | https://api.github.com/repos/huggingface/datasets/issues/6271/events | https://github.com/huggingface/datasets/issues/6271 | 1,920,420,295 | I_kwDODunzps5yd0nH | 6,271 | Overwriting Split overwrites data but not metadata, corrupting dataset | {
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"completed": 0,
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} | ### Describe the bug
I want to be able to overwrite/update/delete splits in my dataset. Currently the only way to do is to manually go into the dataset and delete the split. If I try to overwrite programmatically I end up in an error state and (somewhat) corrupting the dataset. Read below.
**Current Behavior**
When I push to an existing split I get this error:
`ValueError: Split complexRoofLocation_01Apr2023_to_31May2023test already present`
This seems to suggest that the library doesn't support overwriting splits.
**Potential Bug**
Whatβs strange is that datasets, despite the operation erroring out with the ValueError above, does, in fact, overwrite the split:
`Pushing dataset shards to the dataset hub: 100% [.....................] 1/1 [00:00<00:00, 55.04it/s]`
Even though you got an error message and your code fails, your dataset is now changed. That seems like a bug. Either don't change the dataset, or don't throw the error and allow the script to proceed.
Additional Bug
While it overwrites the split, it doesnβt overwrite the splitβs information. Because of this when you pull down the dataset you may end up getting a `NonMatchingSplitsSizesError` if the size of the dataset during the overwrite is different. For example, my original split had 5 rows, but on my overwrite, I only had 4. Then when I try to download the dataset, I get a `NonMatchingSplitsSizesError` because the dataset's data.json states thereβs 5 but only 4 exist in the split.
Expected Behavior
This corrupts the dataset rendering it unusable (until you take manual intervention). Either the library should let the overwrite happen (which it does but should also update the metadata) or it shouldnβt do anything.
### Steps to reproduce the bug
[Colab Notebook](https://colab.research.google.com/drive/1bqVkD06Ngs9MQNdSk_ygCG6y1UqXA4pC?usp=sharing)
### Expected behavior
The split should be overwritten and I should be able to use the new version of the dataset without issue.
### Environment info
- `datasets` version: 2.14.5
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.17.3
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/7126 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7126/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7126/comments | https://api.github.com/repos/huggingface/datasets/issues/7126/events | https://github.com/huggingface/datasets/pull/7126 | 2,485,939,495 | PR_kwDODunzps55Y-Ws | 7,126 | Disable implicit token in CI | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7126). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-08-26T05:29:46Z | 2024-08-26T06:05:01Z | 2024-08-26T05:59:15Z | MEMBER | null | null | null | Disable implicit token in CI.
This PR allows running CI tests locally without implicitly using the local user HF token. For example, run locally the tests in:
- #7124 | {
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https://api.github.com/repos/huggingface/datasets/issues/4798 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4798/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4798/comments | https://api.github.com/repos/huggingface/datasets/issues/4798/events | https://github.com/huggingface/datasets/pull/4798 | 1,330,699,942 | PR_kwDODunzps48wVEG | 4,798 | Shard generator | {
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"Hi, thanks!\r\n\r\n> I was using Hugging Face datasets to process some very large datasets and found that it would be quite handy to have a feature that will allow to \"split\" these large datasets into chunks with equal size\r\n\r\n`map`, the method we use for processing in `datasets`, already does that if `batch... | 2022-08-06T09:14:06Z | 2022-10-03T15:35:10Z | 2022-10-03T15:35:10Z | NONE | null | null | null | Hi everyone! I was using Hugging Face datasets to process some very large datasets and found that it would be quite handy to have a feature that will allow to "split" these large datasets into chunks with equal size. Even better - be able to run through these chunks one by one in simple and convenient way. So I decided to add the method called shard_generator() to the main Dataset class. It works similar to shard method but it returns a generator of datasets with equal size (defined by shard_size attribute).
Example:
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="validation")
>>> ds
Dataset({
features: ['text', 'label'],
num_rows: 1066
})
>>> next(ds.shard_generator(300))
Dataset({
features: ['text', 'label'],
num_rows: 300
})
```
I hope it can be helpful to someone. Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/5626 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5626/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5626/comments | https://api.github.com/repos/huggingface/datasets/issues/5626/events | https://github.com/huggingface/datasets/pull/5626 | 1,619,252,984 | PR_kwDODunzps5LyBT4 | 5,626 | Support streaming datasets with numpy.load | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-03-10T16:33:39Z | 2023-03-21T06:36:05Z | 2023-03-21T06:28:54Z | MEMBER | null | null | null | Support streaming datasets with `numpy.load`.
See: https://huggingface.co/datasets/qgallouedec/gia_dataset/discussions/1 | {
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https://api.github.com/repos/huggingface/datasets/issues/7528 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7528/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7528/comments | https://api.github.com/repos/huggingface/datasets/issues/7528/events | https://github.com/huggingface/datasets/issues/7528 | 3,006,433,485 | I_kwDODunzps6zMojN | 7,528 | Data Studio Error: Convert JSONL incorrectly | {
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"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Hi there,
I uploaded a dataset here https://huggingface.co/datasets/V-STaR-Bench/V-STaR, but I found that Data Studio incorrectly convert the "bboxes" value for the whole dataset. Therefore, anyone who downloaded the dataset via the API would get the wrong "bboxes" value in the data file.
Could you help me address the issue?
Many thanks,
### Steps to reproduce the bug
The JSONL file of [V_STaR_test_release.jsonl](https://huggingface.co/datasets/V-STaR-Bench/V-STaR/blob/main/V_STaR_test_release.jsonl) has the correct values of every "bboxes" for each sample.
But in the Data Studio, we can see that the values of "bboxes" have changed, and load the dataset via API will also get the wrong values.
### Expected behavior
Fix the bug to correctly download my dataset.
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.14.0-427.22.1.el9_4.x86_64-x86_64-with-glibc2.34
- Python version: 3.10.16
- `huggingface_hub` version: 0.29.3
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2023.10.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6910 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6910/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6910/comments | https://api.github.com/repos/huggingface/datasets/issues/6910/events | https://github.com/huggingface/datasets/pull/6910 | 2,307,570,084 | PR_kwDODunzps5wC2An | 6,910 | Fix wrong type hints in data_files | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6910). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-05-21T07:41:09Z | 2024-05-23T06:04:05Z | 2024-05-23T05:58:05Z | MEMBER | null | null | null | Fix wrong type hints in data_files introduced in:
- #6493 | {
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https://api.github.com/repos/huggingface/datasets/issues/4837 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4837/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4837/comments | https://api.github.com/repos/huggingface/datasets/issues/4837/events | https://github.com/huggingface/datasets/pull/4837 | 1,337,079,723 | PR_kwDODunzps49Fb6l | 4,837 | Add support for CSV metadata files to ImageFolder | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Cool thanks ! Maybe let's include this change after the refactoring from FolderBasedBuilder in #3963 to avoid dealing with too many unpleasant conflicts ?",
"@lhoestq I resolved the conflicts (AudioFolder also supports CSV metadata... | 2022-08-12T11:19:18Z | 2022-08-31T12:01:27Z | 2022-08-31T11:59:07Z | COLLABORATOR | null | null | null | Fix #4814 | {
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https://api.github.com/repos/huggingface/datasets/issues/5217 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5217/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5217/comments | https://api.github.com/repos/huggingface/datasets/issues/5217/events | https://github.com/huggingface/datasets/pull/5217 | 1,441,252,740 | PR_kwDODunzps5CetXs | 5,217 | Reword E2E training and inference tips in the vision guides | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-11-09T02:40:01Z | 2022-11-10T01:38:09Z | 2022-11-10T01:36:09Z | MEMBER | null | null | null | Reference: https://github.com/huggingface/datasets/pull/5188#discussion_r1012148730 | {
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https://api.github.com/repos/huggingface/datasets/issues/5172 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5172/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5172/comments | https://api.github.com/repos/huggingface/datasets/issues/5172/events | https://github.com/huggingface/datasets/issues/5172 | 1,425,523,114 | I_kwDODunzps5U98Gq | 5,172 | Inconsistency behavior between handling local file protocol and other FS protocols | {
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} | ### Describe the bug
These lines us used during load_from_disk:
```
if is_remote_filesystem(fs):
dest_dataset_dict_path = extract_path_from_uri(dataset_dict_path)
else:
fs = fsspec.filesystem("file")
dest_dataset_dict_path = dataset_dict_path
```
If a local FS is given, then it will the URL as the path name. If a remote Fs is given, then it will use the path of the URL. This is an inconsistent behavior when handling a file: when using remote FS, you must write a URL, but for local FS, even if you passed LocalFileSystem as `fs` you still can't use a `file://` URL. It will be recognized as a directory named `file:`.
### Steps to reproduce the bug
```
import fsspec.core
url = "hdfs:///somewhere/MNIST"
# url = "file:///somewhere/MNIST"
fs, path = fsspec.core.url_to_fs(url)
fs.ls(path) # this will always work
load_from_disk(path, fs) # only works for local FS
load_from_disk(url, fs) # only works for remote FS
```
### Expected behavior
one of `url` or `path` should always work
I think we extract path from given URL by using `fsspec.core.url_to_fs` instead of using `is_remote_filesystem` and `extract_path_from_uri` will fix this, since:
```
fsspec.core.url_to_fs("/somewhere/MNIST") -> LocalFs, '/somewhere/MNIST'
fsspec.core.url_to_fs("file:///somewhere/MNIST") -> LocalFs, '/somewhere/MNIST'
fsspec.core.url_to_fs("hdfs:///somewhere/MNIST") -> HDFS, '/somewhere/MNIST'
```
and
```
fsspec.core.url_to_fs("file:///somewhere/MNIST") == fsspec.core.url_to_fs("/somewhere/MNIST")
```
In theory, this wouldn't break anything, since giving local path and remote uri still works. It will only affect local URI (make it works too)
### Environment info
- `datasets` version: 2.5.1
- Platform: Linux-5.4.205.1**HIDDEN**
- Python version: 3.7.10
- PyArrow version: 8.0.0
- Pandas version: 1.2.4
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https://api.github.com/repos/huggingface/datasets/issues/5666 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5666/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5666/comments | https://api.github.com/repos/huggingface/datasets/issues/5666/events | https://github.com/huggingface/datasets/issues/5666 | 1,637,675,062 | I_kwDODunzps5hnPA2 | 5,666 | Support tensorflow 2.12.0 in CI | {
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} | Once we find out the root cause of:
- #5663
we should revert the temporary pin on tensorflow introduced by:
- #5664 | {
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https://api.github.com/repos/huggingface/datasets/issues/7305 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7305/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7305/comments | https://api.github.com/repos/huggingface/datasets/issues/7305/events | https://github.com/huggingface/datasets/issues/7305 | 2,715,907,267 | I_kwDODunzps6h4XTD | 7,305 | Build Documentation Test Fails Due to "Bad Credentials" Error | {
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} | [] | open | false | null | [] | null | [
"how were you able to fix this please?",
"> how were you able to fix this please?\r\n\r\nI was not able to fix this."
] | 2024-12-03T20:22:54Z | 2025-01-08T22:38:14Z | null | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
The `Build documentation / build / build_main_documentation (push)` job is consistently failing during the "Syncing repository" step. The error occurs when attempting to determine the default branch name, resulting in "Bad credentials" errors.
### Steps to reproduce the bug
1. Trigger the `build_main_documentation` job.
2. Observe the logs during the "Syncing repository" step.
### Expected behavior
The workflow should be able to retrieve the default branch name without encountering credential issues.
### Environment info
```plaintext
Syncing repository: huggingface/notebooks
Getting Git version info
Temporarily overriding HOME='/home/runner/work/_temp/00e62748-9940-4a4f-bbbc-eb2cda6d7ed6' before making global git config changes
Adding repository directory to the temporary git global config as a safe directory
/usr/bin/git config --global --add safe.directory /home/runner/work/datasets/datasets/notebooks
Initializing the repository
Disabling automatic garbage collection
Setting up auth
Determining the default branch
Retrieving the default branch name
Bad credentials - https://docs.github.com/rest
Waiting 20 seconds before trying again
Retrieving the default branch name
Bad credentials - https://docs.github.com/rest
Waiting 19 seconds before trying again
Retrieving the default branch name
Error: Bad credentials - https://docs.github.com/rest
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6894 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6894/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6894/comments | https://api.github.com/repos/huggingface/datasets/issues/6894/events | https://github.com/huggingface/datasets/issues/6894 | 2,292,840,226 | I_kwDODunzps6Iqfci | 6,894 | Better document defaults of to_json | {
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} | Better document defaults of `to_json`: the default format is [JSON-Lines](https://jsonlines.org/).
Related to:
- #6891 | {
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https://api.github.com/repos/huggingface/datasets/issues/4767 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4767/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4767/comments | https://api.github.com/repos/huggingface/datasets/issues/4767/events | https://github.com/huggingface/datasets/pull/4767 | 1,321,843,538 | PR_kwDODunzps48TCpI | 4,767 | Add 2.4.0 version added to docstrings | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-29T07:01:56Z | 2022-07-29T11:16:49Z | 2022-07-29T11:03:58Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7163 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7163/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7163/comments | https://api.github.com/repos/huggingface/datasets/issues/7163/events | https://github.com/huggingface/datasets/issues/7163 | 2,542,361,234 | I_kwDODunzps6XiVqS | 7,163 | Set explicit seed in iterable dataset ddp shuffling example | {
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"thanks for reporting !"
] | 2024-09-23T11:34:06Z | 2024-09-24T14:40:15Z | 2024-09-24T14:40:15Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
In the examples section of the iterable dataset docs https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.IterableDataset
the ddp example shuffles without seeding
```python
from datasets.distributed import split_dataset_by_node
ids = ds.to_iterable_dataset(num_shards=512)
ids = ids.shuffle(buffer_size=10_000) # will shuffle the shards order and use a shuffle buffer when you start iterating
ids = split_dataset_by_node(ds, world_size=8, rank=0) # will keep only 512 / 8 = 64 shards from the shuffled lists of shards when you start iterating
dataloader = torch.utils.data.DataLoader(ids, num_workers=4) # will assign 64 / 4 = 16 shards from this node's list of shards to each worker when you start iterating
for example in ids:
pass
```
This code would - I think - raise an error due to the lack of an explicit seed:
https://github.com/huggingface/datasets/blob/2eb4edb97e1a6af2ea62738ec58afbd3812fc66e/src/datasets/iterable_dataset.py#L1707-L1711
### Steps to reproduce the bug
Run example code
### Expected behavior
Add explicit seeding to example code
### Environment info
latest datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/6817 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6817/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6817/comments | https://api.github.com/repos/huggingface/datasets/issues/6817/events | https://github.com/huggingface/datasets/pull/6817 | 2,246,578,480 | PR_kwDODunzps5s1RAN | 6,817 | Support indexable objects in `Dataset.__getitem__` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6817). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-04-16T17:41:27Z | 2024-04-16T18:27:44Z | 2024-04-16T18:17:29Z | COLLABORATOR | null | null | null | As discussed in https://github.com/huggingface/datasets/pull/6816, this is needed to support objects that implement `__index__` such as `np.int64` in `Dataset.__getitem__`. | {
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https://api.github.com/repos/huggingface/datasets/issues/6036 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6036/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6036/comments | https://api.github.com/repos/huggingface/datasets/issues/6036/events | https://github.com/huggingface/datasets/pull/6036 | 1,805,138,898 | PR_kwDODunzps5ViKc4 | 6,036 | Deprecate search API | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-07-14T16:22:09Z | 2023-09-07T16:44:32Z | null | COLLABORATOR | null | null | null | The Search API only supports Faiss and ElasticSearch as vector stores, is somewhat difficult to maintain (e.g., it still doesn't support ElasticSeach 8.0, difficult testing, ...), does not have the best design (adds a bunch of methods to the `Dataset` class that are only useful after creating an index), the usage doesn't seem to be significant and is not integrated with the Hub. Since we have no plans/bandwidth to improve it and better alternatives such as `langchain` and `docarray` exist, I think it should be deprecated (and eventually removed).
If we decide to deprecate/remove it, the following usage instances need to be addressed:
* [Course](https://github.com/huggingface/course/blob/0018bb434204d9750a03592cb0d4e846093218d8/chapters/en/chapter5/6.mdx#L342 ) and [Blog](https://github.com/huggingface/blog/blob/4897c6f73d4492a0955ade503281711d01840e09/image-search-datasets.md?plain=1#L252) - calling the FAISS API directly should be OK in these instances as it's pretty simple to use for basic scenarios. Alternatively, we can use `langchain`, but this adds an extra dependency
* [Transformers](https://github.com/huggingface/transformers/blob/50726f9ea7afc6113da617f8f4ca1ab264a5e28a/src/transformers/models/rag/retrieval_rag.py#L183) - we can use the FAISS API directly and store the index as a separate attribute (and instead of building the `wiki_dpr` index each time the dataset is generated, we can generate it once and push it to the Hub repo, and then read it from there
cc @huggingface/datasets @LysandreJik for the opinion | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5060 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5060/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5060/comments | https://api.github.com/repos/huggingface/datasets/issues/5060/events | https://github.com/huggingface/datasets/issues/5060 | 1,395,382,940 | I_kwDODunzps5TK9qc | 5,060 | Unable to Use Custom Dataset Locally | {
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"Hi ! I opened a PR in your repo to fix this :)\r\nhttps://huggingface.co/datasets/zpn/pubchem_selfies/discussions/7\r\n\r\nbasically you need to use `open` for streaming to work properly",
"Thank you so much for this! Naive question, is this a feature of `open` or have you all overloaded it to be able to read fr... | 2022-10-03T21:55:16Z | 2022-10-06T14:29:18Z | 2022-10-06T14:29:17Z | CONTRIBUTOR | null | null | {
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} | ## Describe the bug
I have uploaded a [dataset](https://huggingface.co/datasets/zpn/pubchem_selfies) and followed the instructions from the [dataset_loader](https://huggingface.co/docs/datasets/dataset_script#download-data-files-and-organize-splits) tutorial. In that tutorial, it says
```
If the data files live in the same folder or repository of the dataset script,
you can just pass the relative paths to the files instead of URLs.
```
Accordingly, I put the [relative path](https://huggingface.co/datasets/zpn/pubchem_selfies/blob/main/pubchem_selfies.py#L76) to the data to be used. I was able to test the dataset and generate the metadata locally with `datasets-cli test path/to/<your-dataset-loading-script> --save_infos --all_configs`
However, if I try to load the data using `load_dataset`, I get the following error
```
with gzip.open(filepath, mode="rt") as f:
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 58, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 173, in __init__
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/zpn/pubchem_selfies/resolve/main/data/Compound_021000001_021500000/Compound_021000001_021500000_SELFIES.jsonl.gz'
```
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("zpn/pubchem_selfies", streaming=True)
>>> t = dataset["train"]
>>> for item in t:
...... print(item)
...... break
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 723, in __iter__
for key, example in self._iter():
File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 713, in _iter
yield from ex_iterable
File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 113, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/Users/zachnussbaum/.cache/huggingface/modules/datasets_modules/datasets/zpn--pubchem_selfies/d2571f35996765aea70fd3f3f8e3882d59c401fb738615c79282e2eb1d9f7a25/pubchem_selfies.py", line 475, in _generate_examples
with gzip.open(filepath, mode="rt") as f:
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 58, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 173, in __init__
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/zpn/pubchem_selfies/resolve/main/data/Compound_021000001_021500000/Compound_021000001_021500000_SELFIES.jsonl.gz'
````
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.1
- Platform: macOS-12.5.1-x86_64-i386-64bit
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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https://api.github.com/repos/huggingface/datasets/issues/6501 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6501/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6501/comments | https://api.github.com/repos/huggingface/datasets/issues/6501/events | https://github.com/huggingface/datasets/issues/6501 | 2,043,377,240 | I_kwDODunzps55y3ZY | 6,501 | OverflowError: value too large to convert to int32_t | {
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"completed": 0,
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} | ### Describe the bug
### Steps to reproduce the bug
just loading datasets
### Expected behavior
how can I fix it
### Environment info
pip install /mnt/cluster/zhangfan/study_info/LLaMA-Factory/peft-0.6.0-py3-none-any.whl
pip install huggingface_hub-0.19.4-py3-none-any.whl tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl transformers-4.36.1-py3-none-any.whl pyarrow_hotfix-0.6-py3-none-any.whl datasets-2.15.0-py3-none-any.whl tyro-0.5.18-py3-none-any.whl trl-0.7.4-py3-none-any.whl
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https://api.github.com/repos/huggingface/datasets/issues/6043 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6043/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6043/comments | https://api.github.com/repos/huggingface/datasets/issues/6043/events | https://github.com/huggingface/datasets/issues/6043 | 1,807,771,750 | I_kwDODunzps5rwGhm | 6,043 | Compression kwargs have no effect when saving datasets as csv | {
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"Hello @exs-avianello, I have reproduced the bug successfully and have understood the problem. But I am confused regarding this part of the statement, \"`pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`\".\r\n\r\nCan you please elaborate on it?\r\n\r\nThanks!",
"Hi @aryanxk02 ! Sure, what I... | 2023-07-17T13:19:21Z | 2023-07-22T17:34:18Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Attempting to save a dataset as a compressed csv file, the compression kwargs provided to `.to_csv()` that get piped to panda's `pandas.DataFrame.to_csv` do not have any effect - resulting in the dataset not getting compressed.
A warning is raised if explicitly providing a `compression` kwarg, but no warnings are raised if relying on the defaults. This can lead to datasets secretly not getting compressed for users expecting the behaviour to match panda's `.to_csv()`, where the compression format is automatically inferred from the destination path suffix.
### Steps to reproduce the bug
```python
# dataset is not compressed (but at least a warning is emitted)
import datasets
dataset = datasets.load_dataset("rotten_tomatoes", split="train")
dataset.to_csv("uncompressed.csv")
print(os.path.getsize("uncompressed.csv")) # 1008607
dataset.to_csv("compressed.csv.gz", compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1})
print(os.path.getsize("compressed.csv.gz")) # 1008607
```
```shell
>>>
RuntimeWarning: compression has no effect when passing a non-binary object as input.
csv_str = batch.to_pandas().to_csv(
```
```python
# dataset is not compressed and no warnings are emitted
dataset.to_csv("compressed.csv.gz")
print(os.path.getsize("compressed.csv.gz")) # 1008607
# compare with
dataset.to_pandas().to_csv("pandas.csv.gz")
print(os.path.getsize("pandas.csv.gz")) # 418561
```
---
I think that this is because behind the scenes `pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`, but users that are providing a path-like to `datasets.Dataset.to_csv` are likely not to expect / know that - leading to a mismatch in their understanding of the expected behaviour of the `compression` kwarg.
### Expected behavior
The dataset to be saved as a compressed csv file when providing a `compression` kwarg, or when relying on the default `compression='infer'`
### Environment info
`datasets == 2.13.1`
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https://api.github.com/repos/huggingface/datasets/issues/5359 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5359/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5359/comments | https://api.github.com/repos/huggingface/datasets/issues/5359/events | https://github.com/huggingface/datasets/pull/5359 | 1,495,297,857 | PR_kwDODunzps5FYHWm | 5,359 | Raise error if ClassLabel names is not python list | {
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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... | 2022-12-13T23:04:06Z | 2022-12-22T16:35:49Z | 2022-12-22T16:32:49Z | CONTRIBUTOR | null | null | null | Checks type of names provided to ClassLabel to avoid easy and hard to debug errors (closes #5332 - see for discussion) | {
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https://api.github.com/repos/huggingface/datasets/issues/6653 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6653/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6653/comments | https://api.github.com/repos/huggingface/datasets/issues/6653/events | https://github.com/huggingface/datasets/pull/6653 | 2,126,831,929 | PR_kwDODunzps5mdv5S | 6,653 | Set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6653). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-02-09T10:12:02Z | 2024-02-09T10:18:20Z | 2024-02-09T10:12:12Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5354 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5354/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5354/comments | https://api.github.com/repos/huggingface/datasets/issues/5354/events | https://github.com/huggingface/datasets/issues/5354 | 1,492,174,125 | I_kwDODunzps5Y8MUt | 5,354 | Consider using "Sequence" instead of "List" | {
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"Hi! Linking a comment to provide more info on the issue: https://stackoverflow.com/a/39458225. This means we should replace all (most of) the occurrences of `List` with `Sequence` in function signatures.\r\n\r\n@tranhd95 Would you be interested in submitting a PR?",
"Hi all! I tried to reproduce this issue and d... | 2022-12-12T15:39:45Z | 2024-10-05T14:38:44Z | null | NONE | null | null | {
"completed": 0,
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} | ### Feature request
Hi, please consider using `Sequence` type annotation instead of `List` in function arguments such as in [`Dataset.from_parquet()`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L1088). It leads to type checking errors, see below.
**How to reproduce**
```py
list_of_filenames = ["foo.parquet", "bar.parquet"]
ds = Dataset.from_parquet(list_of_filenames)
```
**Expected mypy output:**
```
Success: no issues found
```
**Actual mypy output:**
```py
test.py:19: error: Argument 1 to "from_parquet" of "Dataset" has incompatible type "List[str]"; expected "Union[Union[str, bytes, PathLike[Any]], List[Union[str, bytes, PathLike[Any]]]]" [arg-type]
test.py:19: note: "List" is invariant -- see https://mypy.readthedocs.io/en/stable/common_issues.html#variance
test.py:19: note: Consider using "Sequence" instead, which is covariant
```
**Env:** mypy 0.991, Python 3.10.0, datasets 2.7.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5759 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5759/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5759/comments | https://api.github.com/repos/huggingface/datasets/issues/5759/events | https://github.com/huggingface/datasets/issues/5759 | 1,669,977,848 | I_kwDODunzps5jidb4 | 5,759 | Can I load in list of list of dict format? | {
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"Thanks for reporting, @LZY-the-boys.\r\n\r\nCould you please give more details about what is your intended dataset structure? What are the names of the columns and the value of each row?\r\n\r\nCurrently, the JSON-Lines format is supported:\r\n- Each line correspond to one row of the dataset\r\n- Each line is comp... | 2023-04-16T13:50:14Z | 2023-04-19T12:04:36Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Feature request
my jsonl dataset has following format:
```
[{'input':xxx, 'output':xxx},{'input:xxx,'output':xxx},...]
[{'input':xxx, 'output':xxx},{'input:xxx,'output':xxx},...]
```
I try to use `datasets.load_dataset('json', data_files=path)` or `datasets.Dataset.from_json`, it raises
```
File "site-packages/datasets/arrow_dataset.py", line 1078, in from_json
).read()
File "site-packages/datasets/io/json.py", line 59, in read
self.builder.download_and_prepare(
File "site-packages/datasets/builder.py", line 872, in download_and_prepare
self._download_and_prepare(
File "site-packages/datasets/builder.py", line 967, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "site-packages/datasets/builder.py", line 1749, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "site-packages/datasets/builder.py", line 1892, 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
```
### Motivation
I wanna use features like `Datasets.map` or `Datasets.shuffle`, so i need the dataset in memory to be `arrow_dataset.Datasets` format
### Your contribution
PR | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7238 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7238/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7238/comments | https://api.github.com/repos/huggingface/datasets/issues/7238/events | https://github.com/huggingface/datasets/issues/7238 | 2,598,409,993 | I_kwDODunzps6a4JcJ | 7,238 | incompatibily issue when using load_dataset with datasets==3.0.1 | {
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"Hi! I'm also getting the same issue - have you been able to find a solution to this? ",
"From what I remember, I stayed at the \"downgraded\" version of dataset (2.21.0)"
] | 2024-10-18T21:25:23Z | 2024-12-09T09:49:32Z | null | NONE | null | null | {
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} | ### Describe the bug
There is a bug when using load_dataset with dataset version at 3.0.1 .
Please see below in the "steps to reproduce the bug".
To resolve the bug, I had to downgrade to version 2.21.0
OS: Ubuntu 24 (AWS instance)
Python: same bug under 3.12 and 3.10
The error I had was:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/load.py", line 2096, in load_dataset
builder_instance.download_and_prepare(
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 924, in download_and_prepare
self._download_and_prepare(
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 1647, in _download_and_prepare
super()._download_and_prepare(
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 977, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_6_0/cb17afd34f5799f97e8f48398748f83006335b702bd785f9880797838d541b81/common_voice_6_0.py", line 159, in _split_generators
archive_path = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_manager.py", line 150, in download
download_config = self.download_config.copy()
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_config.py", line 73, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
TypeError: DownloadConfig.__init__() got an unexpected keyword argument 'ignore_url_params'
### Steps to reproduce the bug
1. install dataset with ```pip install datasets --upgrade```
2. launch python; from datasets import loaad_dataset
3. run load_dataset("mozilla-foundation/common_voice_6_0")
4. exit python
5. uninstall datasets; then ```pip install datasets==2.21.0```
6. launch python; from datasets import loaad_dataset
7. run load_dataset("mozilla-foundation/common_voice_6_0")
8. Everything runs great now
### Expected behavior
Be able to download a dataset without error
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 3.0.1
- Platform: Linux-6.8.0-1017-aws-x86_64-with-glibc2.39
- Python version: 3.12.4
- `huggingface_hub` version: 0.26.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6764 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6764/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6764/comments | https://api.github.com/repos/huggingface/datasets/issues/6764/events | https://github.com/huggingface/datasets/issues/6764 | 2,215,767,119 | I_kwDODunzps6EEexP | 6,764 | load_dataset can't work with symbolic links | {
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} | ### Feature request
Enable the `load_dataset` function to load local datasets with symbolic links.
E.g, this dataset can be loaded:
βββ example_dataset/
β βββ data/
β β βββ train/
β β β βββ file0
β β β βββ file1
β β βββ dev/
β β β βββ file2
β β β βββ file3
β βββ metadata.csv
while this dataset can't:
βββ example_dataset_symlink/
β βββ data/
β β βββ train/
β β β βββ sym0 -> file0
β β β βββ sym1 -> file1
β β βββ dev/
β β β βββ sym2 -> file2
β β β βββ sym3 -> file3
β βββ metadata.csv
I have created an example dataset in order to reproduce the problem:
1. Unzip `example_dataset.zip`.
2. Run `no_symlink.sh`. Training should start without issues.
3. Run `symlink.sh`. You will see that all four examples will be in train split, instead of having two examples in train and two examples in dev. The script won't load the correct audio files.
[example_dataset.zip](https://github.com/huggingface/datasets/files/14807053/example_dataset.zip)
### Motivation
I have a very large dataset locally. Instead of initiating training on the entire dataset, I need to start training on smaller subsets of the data. Due to the purpose of the experiments I am running, I will need to create many smaller datasets with overlapping data. Instead of copying the all the files for each subset, I would prefer copying symbolic links of the data. This way, the memory usage would not significantly increase beyond the initial dataset size.
Advantages of this approach:
- It would leave a smaller memory footprint on the hard drive
- Creating smaller datasets would be much faster
### Your contribution
I would gladly contribute, if this is something useful to the community. It seems like a simple change of code, something like `file_path = os.path.realpath(file_path)` should be added before loading the files. If anyone has insights on how to incorporate this functionality, I would greatly appreciate your knowledge and input. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5191 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5191/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5191/comments | https://api.github.com/repos/huggingface/datasets/issues/5191/events | https://github.com/huggingface/datasets/pull/5191 | 1,433,191,658 | PR_kwDODunzps5CD0Qp | 5,191 | Make torch.Tensor and spacy models cacheable | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-11-02T13:56:18Z | 2022-11-02T17:20:48Z | 2022-11-02T17:18:42Z | COLLABORATOR | null | null | null | Override `Pickler.save` to implement deterministic reduction (lazily registered; inspired by https://github.com/uqfoundation/dill/blob/master/dill/_dill.py#L343) functions for `torch.Tensor` and spaCy models.
Fix https://github.com/huggingface/datasets/issues/5170, fix https://github.com/huggingface/datasets/issues/3178
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https://api.github.com/repos/huggingface/datasets/issues/7180 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7180/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7180/comments | https://api.github.com/repos/huggingface/datasets/issues/7180/events | https://github.com/huggingface/datasets/issues/7180 | 2,554,244,750 | I_kwDODunzps6YPq6O | 7,180 | Memory leak when wrapping datasets into PyTorch Dataset without explicit deletion | {
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"> I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.\r\n\r\nDatasets are memory mapped so they work like SWAP memory. In particular as long as you have RAM available the data... | 2024-09-28T14:00:47Z | 2024-09-30T12:07:56Z | 2024-09-30T12:07:56Z | NONE | null | null | {
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} | ### Describe the bug
I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.
### Steps to reproduce the bug
Steps to reproduce:
Create a PyTorch Dataset wrapper for 'nebula/cc12m':
````
from torch.utils.data import Dataset
from tqdm import tqdm
from datasets import load_dataset
from torchvision import transforms
Image.MAX_IMAGE_PIXELS = None
class CC12M(Dataset):
def __init__(self, path_or_name='nebula/cc12m', split='train', transform=None, single_caption=True):
self.raw_dataset = load_dataset(path_or_name)[split]
if transform is None:
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711]
)
])
else:
self.transform = transforms.Compose(transform)
self.single_caption = single_caption
self.length = len(self.raw_dataset)
def __len__(self):
return self.length
def __getitem__(self, index):
item = self.raw_dataset[index]
caption = item['txt']
with io.BytesIO(item['webp']) as buffer:
image = Image.open(buffer).convert('RGB')
if self.transform:
image = self.transform(image)
# del item # Uncomment this line to prevent the memory leak
return image, caption
````
Iterate through the dataset without the del item line in __getitem__.
Observe RAM usage increasing constantly.
Add del item at the end of __getitem__:
```
def __getitem__(self, index):
item = self.raw_dataset[index]
caption = item['txt']
with io.BytesIO(item['webp']) as buffer:
image = Image.open(buffer).convert('RGB')
if self.transform:
image = self.transform(image)
del item # This line prevents the memory leak
return image, caption
```
Iterate through the dataset again and observe that RAM usage remains stable.
### Expected behavior
Expected behavior:
RAM usage should remain stable during iteration without needing to explicitly delete items.
Actual behavior:
RAM usage constantly increases unless items are explicitly deleted after use
### Environment info
- `datasets` version: 2.21.0
- Platform: Linux-4.18.0-513.5.1.el8_9.x86_64-x86_64-with-glibc2.28
- Python version: 3.12.4
- `huggingface_hub` version: 0.24.6
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
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https://api.github.com/repos/huggingface/datasets/issues/6439 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6439/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6439/comments | https://api.github.com/repos/huggingface/datasets/issues/6439/events | https://github.com/huggingface/datasets/issues/6439 | 2,002,916,514 | I_kwDODunzps53YhSi | 6,439 | Download + preparation speed of datasets.load_dataset is 20x slower than huggingface hub snapshot and manual loding | {
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} | ### Describe the bug
I am working with a dataset I am trying to publish.
The path is Antreas/TALI.
It's a fairly large dataset, and contains images, video, audio and text.
I have been having multiple problems when the dataset is being downloaded using the load_dataset function -- even with 64 workers taking more than 7 days to process.
With snapshot download it takes 12 hours, and that includes the dataset preparation done using load_dataset and passing the dataset parquet file paths.
Find the script I am using below:
```python
import multiprocessing as mp
import pathlib
from typing import Optional
import datasets
from rich import print
from tqdm import tqdm
def download_dataset_via_hub(
dataset_name: str,
dataset_download_path: pathlib.Path,
num_download_workers: int = mp.cpu_count(),
):
import huggingface_hub as hf_hub
download_folder = hf_hub.snapshot_download(
repo_id=dataset_name,
repo_type="dataset",
cache_dir=dataset_download_path,
resume_download=True,
max_workers=num_download_workers,
ignore_patterns=[],
)
return pathlib.Path(download_folder) / "data"
def load_dataset_via_hub(
dataset_download_path: pathlib.Path,
num_download_workers: int = mp.cpu_count(),
dataset_name: Optional[str] = None,
):
from dataclasses import dataclass, field
from datasets import ClassLabel, Features, Image, Sequence, Value
dataset_path = download_dataset_via_hub(
dataset_download_path=dataset_download_path,
num_download_workers=num_download_workers,
dataset_name=dataset_name,
)
# Building a list of file paths for validation set
train_files = [
file.as_posix()
for file in pathlib.Path(dataset_path).glob("*.parquet")
if "train" in file.as_posix()
]
val_files = [
file.as_posix()
for file in pathlib.Path(dataset_path).glob("*.parquet")
if "val" in file.as_posix()
]
test_files = [
file.as_posix()
for file in pathlib.Path(dataset_path).glob("*.parquet")
if "test" in file.as_posix()
]
print(
f"Found {len(test_files)} files for testing set, {len(train_files)} for training set and {len(val_files)} for validation set"
)
data_files = {
"test": test_files,
"val": val_files,
"train": train_files,
}
features = Features(
{
"image": Image(
decode=True
), # Set `decode=True` if you want to decode the images, otherwise `decode=False`
"image_url": Value("string"),
"item_idx": Value("int64"),
"wit_features": Sequence(
{
"attribution_passes_lang_id": Value("bool"),
"caption_alt_text_description": Value("string"),
"caption_reference_description": Value("string"),
"caption_title_and_reference_description": Value("string"),
"context_page_description": Value("string"),
"context_section_description": Value("string"),
"hierarchical_section_title": Value("string"),
"is_main_image": Value("bool"),
"language": Value("string"),
"page_changed_recently": Value("bool"),
"page_title": Value("string"),
"page_url": Value("string"),
"section_title": Value("string"),
}
),
"wit_idx": Value("int64"),
"youtube_title_text": Value("string"),
"youtube_description_text": Value("string"),
"youtube_video_content": Value("binary"),
"youtube_video_starting_time": Value("string"),
"youtube_subtitle_text": Value("string"),
"youtube_video_size": Value("int64"),
"youtube_video_file_path": Value("string"),
}
)
dataset = datasets.load_dataset(
"parquet" if dataset_name is None else dataset_name,
data_files=data_files,
features=features,
num_proc=1,
cache_dir=dataset_download_path / "cache",
)
return dataset
if __name__ == "__main__":
dataset_cache = pathlib.Path("/disk/scratch_fast0/tali/")
dataset = load_dataset_via_hub(dataset_cache, dataset_name="Antreas/TALI")[
"test"
]
for sample in tqdm(dataset):
print(list(sample.keys()))
```
Also, streaming this dataset has been a very painfully slow process. Streaming the train set takes 15m to start, and streaming the test and val sets takes 3 hours to start!
### Steps to reproduce the bug
1. Run the code I provided to get a sense of how fast snapshot + manual is
2. Run datasets.load_dataset("Antreas/TALI") to get a sense of the speed of that OP.
3. You should now have an appreciation of how long these things take.
### Expected behavior
The load dataset function should be at least as fast as the huggingface snapshot download function in terms of downloading dataset files. Not 20 times slower.
### Environment info
- `datasets` version: 2.14.5
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.13
- Huggingface_hub version: 0.17.3
- PyArrow version: 13.0.0
- Pandas version: 2.1.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4744 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4744/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4744/comments | https://api.github.com/repos/huggingface/datasets/issues/4744/events | https://github.com/huggingface/datasets/issues/4744 | 1,317,822,345 | I_kwDODunzps5OjF-J | 4,744 | Remove instructions to generate dummy data from our docs | {
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"gi... | null | [
"Note that for me personally, conceptually all the dummy data (even for \"canonical\" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones)",
"I totally agree: next step should be rethinking if dummy data makes sense for canonical dataset... | 2022-07-26T07:32:58Z | 2022-08-02T23:50:30Z | 2022-08-02T23:50:30Z | MEMBER | null | null | {
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} | In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI
- for datasets on the Hub:
- they do not pass any CI test requiring dummy data
- there are no instructions on how they can test their dataset locally using the dummy data
- the generation of the dummy data assumes our GitHub directory structure:
- the dummy data will be generated under `./datasets/<dataset_name>/dummy` even if locally there is no `./datasets` directory (which is the usual case). See issue:
- #4742
CC: @stevhliu | {
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https://api.github.com/repos/huggingface/datasets/issues/4586 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4586/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4586/comments | https://api.github.com/repos/huggingface/datasets/issues/4586/events | https://github.com/huggingface/datasets/pull/4586 | 1,287,105,636 | PR_kwDODunzps46e9xB | 4,586 | Host pn_summary data on the Hub instead of Google Drive | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-06-28T10:05:05Z | 2022-06-28T14:52:56Z | 2022-06-28T14:42:03Z | MEMBER | null | null | null | Fix #4581. | {
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https://api.github.com/repos/huggingface/datasets/issues/5436 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5436/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5436/comments | https://api.github.com/repos/huggingface/datasets/issues/5436/events | https://github.com/huggingface/datasets/pull/5436 | 1,536,633,173 | PR_kwDODunzps5Hjh4v | 5,436 | Revert container image pin in CI benchmarks | {
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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 | rea... | 2023-01-17T15:59:50Z | 2023-01-18T09:05:49Z | 2023-01-18T06:29:06Z | CONTRIBUTOR | null | null | null | 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/7537 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7537/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7537/comments | https://api.github.com/repos/huggingface/datasets/issues/7537/events | https://github.com/huggingface/datasets/issues/7537 | 3,018,792,966 | I_kwDODunzps6z7yAG | 7,537 | `datasets.map(..., num_proc=4)` multi-processing fails | {
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} | The following code fails in python 3.11+
```python
tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"])
```
Error log:
```bash
Traceback (most recent call last):
File "/usr/local/lib/python3.12/dist-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/usr/local/lib/python3.12/dist-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.12/dist-packages/multiprocess/pool.py", line 114, in worker
task = get()
^^^^^
File "/usr/local/lib/python3.12/dist-packages/multiprocess/queues.py", line 371, in get
return _ForkingPickler.loads(res)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/dill/_dill.py", line 327, in loads
return load(file, ignore, **kwds)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/dill/_dill.py", line 313, in load
return Unpickler(file, ignore=ignore, **kwds).load()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/dill/_dill.py", line 525, in load
obj = StockUnpickler.load(self)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/dill/_dill.py", line 659, in _create_code
if len(args) == 16: return CodeType(*args)
^^^^^^^^^^^^^^^
TypeError: code() argument 13 must be str, not int
```
After upgrading dill to the latest 0.4.0 with "pip install --upgrade dill", it can pass. So it seems that there is a compatibility issue between dill 0.3.4 and python 3.11+, because python 3.10 works fine.
Is the dill deterministic issue mentioned in https://github.com/huggingface/datasets/blob/main/setup.py#L117) still valid? Any plan to unpin?
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https://api.github.com/repos/huggingface/datasets/issues/7272 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7272/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7272/comments | https://api.github.com/repos/huggingface/datasets/issues/7272/events | https://github.com/huggingface/datasets/pull/7272 | 2,627,223,390 | PR_kwDODunzps6AirL2 | 7,272 | fix conda release worlflow | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7272). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-31T15:56:19Z | 2024-10-31T15:58:35Z | 2024-10-31T15:57:29Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5693 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5693/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5693/comments | https://api.github.com/repos/huggingface/datasets/issues/5693/events | https://github.com/huggingface/datasets/pull/5693 | 1,649,934,749 | PR_kwDODunzps5NYdPS | 5,693 | [docs] Split pattern search order | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-03-31T19:51:38Z | 2023-04-03T18:43:30Z | 2023-04-03T18:29:58Z | MEMBER | null | null | null | This PR addresses #5681 about the order of split patterns π€ Datasets searches for when generating dataset splits. | {
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https://api.github.com/repos/huggingface/datasets/issues/4968 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4968/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4968/comments | https://api.github.com/repos/huggingface/datasets/issues/4968/events | https://github.com/huggingface/datasets/pull/4968 | 1,369,312,877 | PR_kwDODunzps4-wKkw | 4,968 | Support streaming compguesswhat dataset | {
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] | 2022-09-12T05:42:24Z | 2022-09-12T08:00:06Z | 2022-09-12T07:58:06Z | MEMBER | null | null | null | Support streaming `compguesswhat` dataset.
Fix #3191. | {
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https://api.github.com/repos/huggingface/datasets/issues/6921 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6921/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6921/comments | https://api.github.com/repos/huggingface/datasets/issues/6921/events | https://github.com/huggingface/datasets/pull/6921 | 2,318,394,398 | PR_kwDODunzps5wn4Dz | 6,921 | Support fsspec 2024.5.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6921). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-05-27T07:00:59Z | 2024-05-27T08:07:16Z | 2024-05-27T08:01:08Z | MEMBER | null | null | null | Support fsspec 2024.5.0. | {
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https://api.github.com/repos/huggingface/datasets/issues/6177 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6177/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6177/comments | https://api.github.com/repos/huggingface/datasets/issues/6177/events | https://github.com/huggingface/datasets/pull/6177 | 1,865,490,962 | PR_kwDODunzps5Ytky- | 6,177 | Use object detection images from `huggingface/documentation-images` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-08-24T16:16:09Z | 2023-08-25T16:30:00Z | 2023-08-25T16:21:17Z | COLLABORATOR | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6071 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6071/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6071/comments | https://api.github.com/repos/huggingface/datasets/issues/6071/events | https://github.com/huggingface/datasets/issues/6071 | 1,821,990,749 | I_kwDODunzps5smV9d | 6,071 | storage_options provided to load_dataset not fully piping through since datasets 2.14.0 | {
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"Hi ! Thanks for reporting, I opened a PR to fix this\r\n\r\nWhat filesystem are you using ?",
"Hi @lhoestq ! Thank you so much π \r\n\r\nIt's a bit of a custom setup, but in practice I am using a [pyarrow.fs.S3FileSystem](https://arrow.apache.org/docs/python/generated/pyarrow.fs.S3FileSystem.html) (wrapped in a... | 2023-07-26T09:37:20Z | 2023-07-27T12:42:58Z | 2023-07-27T12:42:58Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
Since the latest release of `datasets` (`2.14.0`), custom filesystem `storage_options` passed to `load_dataset()` do not seem to propagate through all the way - leading to problems if loading data files that need those options to be set.
I think this is because of the new `_prepare_path_and_storage_options()` (https://github.com/huggingface/datasets/pull/6028), which returns the right `storage_options` to use given a path and a `DownloadConfig` - but which might not be taking into account the extra `storage_options` explicitly provided e.g. through `load_dataset()`
### Steps to reproduce the bug
```python
import fsspec
import pandas as pd
import datasets
# Generate mock parquet file
data_files = "demo.parquet"
pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}).to_parquet(data_files)
_storage_options = {"x": 1, "y": 2}
fs = fsspec.filesystem("file", **_storage_options)
dataset = datasets.load_dataset(
"parquet",
data_files=data_files,
storage_options=fs.storage_options
)
```
Looking at the `storage_options` resolved here:
https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L331
they end up being `{}`, instead of propagating through the `storage_options` that were provided to `load_dataset` (`fs.storage_options`). As these then get used for the filesystem operation a few lines below
https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L339
the call will fail if the user-provided `storage_options` were needed.
---
A temporary workaround that seemed to work locally to bypass the problem was to bundle a duplicate of the `storage_options` into the `download_config`, so that they make their way all the way to `_prepare_path_and_storage_options()` and get extracted correctly:
```python
dataset = datasets.load_dataset(
"parquet",
data_files=data_files,
storage_options=fs.storage_options,
download_config=datasets.DownloadConfig(storage_options={fs.protocol: fs.storage_options}),
)
```
### Expected behavior
`storage_options` provided to `load_dataset` take effect in all backend filesystem operations.
### Environment info
datasets==2.14.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6919 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6919/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6919/comments | https://api.github.com/repos/huggingface/datasets/issues/6919/events | https://github.com/huggingface/datasets/issues/6919 | 2,315,618,993 | I_kwDODunzps6KBYqx | 6,919 | Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple> | {
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} | ### Describe the bug
I wrote a notebook to load an existing dataset, process it, and upload as a private dataset using `dataset.push_to_hub(...)` at the end. The push to hub is failing with:
```
ValueError: Invalid metadata in README.md.
- Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python[/tuple](http://192.168.1.128:8888/tuple)> (50:11)
47 | - 4
48 | - 4
49 | - 8
50 | - !!binary |
----------------^
51 | TwAAAA==
52 | '1': !!python[/object/apply](http://192.168.1.128:8888/object/apply):nump ...
```
My dataset has a `train` and `validation` dataset. These are the features:
```
{'c1': Value(dtype='string', id=None),
'c2': Value(dtype='string', id=None),
'c3': [{'value': Value(dtype='string', id=None),
'start': Value(dtype='int64', id=None),
'end': Value(dtype='int64', id=None),
'label': Value(dtype='string', id=None)}],
'c4': Value(dtype='string', id=None),
'c5': Value(dtype='string', id=None),
'c6': Value(dtype='string', id=None),
'c7': Value(dtype='string', id=None),
'c8': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None),
'c9': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),
'c10': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),
'labels': Sequence(feature=ClassLabel(names=['O', 'B-ABC', 'I-ABC', ...], id=None), length=-1, id=None),
'c12': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
```
This used to work until I decided to cast the `labels` feature to a `Sequence(ClassLabel(...))` type with:
```
ds['train'] = ds['train'].cast_column("labels", Sequence(ClassLabel(names=list(labels))))
ds['validation'] = ds['validation'].cast_column("labels", Sequence(ClassLabel(names=list(labels))))
```
### Steps to reproduce the bug
1. Start with any token classification dataset.
2. Add a `labels` column with data such as `[0,0,0,12,13,13,13,0,0]`.
3. Cast the label column from `Sequence` to `Sequence(ClassLabel))` with:
```
labels = ['O', 'B-TEST', 'I-TEST']
ds = ds.cast_column("labels", Sequence(ClassLabel(names=labels)))
```
4. Push to hub with `ds.push_to_hub("me/awesome-stuff-dataset")`
### Expected behavior
I expected `push_to_hub` to successfully push my dataset to the hub without error.
### Environment info
Python 3.11.9
datasets==2.19.1
transformers==4.41.1
PyYAML==6.0.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6897 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6897/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6897/comments | https://api.github.com/repos/huggingface/datasets/issues/6897/events | https://github.com/huggingface/datasets/issues/6897 | 2,293,428,243 | I_kwDODunzps6IsvAT | 6,897 | datasets template guide :: issue in documentation YAML | {
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"Hello, @bghira.\r\n\r\nThanks for reporting. Please note that the text originating the error is not supposed to be valid YAML: it contains the instructions to generate the actual YAML content, that should replace the instructions comment.\r\n\r\nOn the other hand, I agree that it is not nice to have that YAML erro... | 2024-05-13T17:33:59Z | 2024-05-16T14:28:17Z | 2024-05-16T14:28:17Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
There is a YAML error at the top of the page, and I don't think it's supposed to be there
### Steps to reproduce the bug
1. Browse to [this tutorial document](https://github.com/huggingface/datasets/blob/main/templates/README_guide.md)
2. Observe a big red error at the top
3. The rest of the document remains functional
### Expected behavior
I think the YAML block should be displayed or ignored.
### Environment info
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https://api.github.com/repos/huggingface/datasets/issues/6782 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6782/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6782/comments | https://api.github.com/repos/huggingface/datasets/issues/6782/events | https://github.com/huggingface/datasets/issues/6782 | 2,228,081,955 | I_kwDODunzps6EzdUj | 6,782 | Image cast_storage very slow for arrays (e.g. numpy, tensors) | {
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"This may be a solution that only changes `cast_storage` of `Image`.\r\nHowever, I'm not totally sure that the assumptions hold that are made about the `ListArray`.\r\n\r\n```python\r\nelif pa.types.is_list(storage.type):\r\n from .features import Array3DExtensionType\r\n\r\n def get_shapes(arr):\r\n s... | 2024-04-05T13:46:54Z | 2024-04-10T14:36:13Z | null | CONTRIBUTOR | null | null | {
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} | Update: see comments below
### Describe the bug
Operations that save an image from a path are very slow.
I believe the reason for this is that the image data (`numpy`) is converted into `pyarrow` format but then back to python using `.pylist()` before being converted to a numpy array again.
`pylist` is already slow but used on a multi-dimensional numpy array such as an image it takes a very long time.
From the trace below we can see that `__arrow_array__` takes a long time.
It is currently also called in `get_inferred_type`, this should be removable #6781 but doesn't change the underyling issue.
The conversion to `pyarrow` and back also leads to the `numpy` array having type `int64` which causes a warning message because the image type excepts `uint8`.
However, originally the `numpy` image array was in `uint8`.
### Steps to reproduce the bug
```python
from PIL import Image
import numpy as np
import datasets
import cProfile
image = Image.fromarray(np.random.randint(0, 255, (2048, 2048, 3), dtype=np.uint8))
image.save("test_image.jpg")
ds = datasets.Dataset.from_dict(
{"image": ["test_image.jpg"]},
features=datasets.Features({"image": datasets.Image(decode=True)}),
)
# load as numpy array, e.g. for further processing with map
# same result as map returning numpy arrays
ds.set_format("numpy")
cProfile.run("ds.map(writer_batch_size=1, load_from_cache_file=False)", "restats")
```
```bash
Fri Apr 5 14:56:17 2024 restats
66817 function calls (64992 primitive calls) in 33.382 seconds
Ordered by: cumulative time
List reduced from 1073 to 20 due to restriction <20>
ncalls tottime percall cumtime percall filename:lineno(function)
46/1 0.000 0.000 33.382 33.382 {built-in method builtins.exec}
1 0.000 0.000 33.382 33.382 <string>:1(<module>)
1 0.000 0.000 33.382 33.382 arrow_dataset.py:594(wrapper)
1 0.000 0.000 33.382 33.382 arrow_dataset.py:551(wrapper)
1 0.000 0.000 33.379 33.379 arrow_dataset.py:2916(map)
4 0.000 0.000 33.327 8.332 arrow_dataset.py:3277(_map_single)
1 0.000 0.000 33.311 33.311 arrow_writer.py:465(write)
2 0.000 0.000 33.311 16.656 arrow_writer.py:423(write_examples_on_file)
1 0.000 0.000 33.311 33.311 arrow_writer.py:527(write_batch)
2 14.484 7.242 33.260 16.630 arrow_writer.py:161(__arrow_array__)
1 0.001 0.001 16.438 16.438 arrow_writer.py:121(get_inferred_type)
1 0.000 0.000 14.398 14.398 threading.py:637(wait)
1 0.000 0.000 14.398 14.398 threading.py:323(wait)
8 14.398 1.800 14.398 1.800 {method 'acquire' of '_thread.lock' objects}
4/2 0.000 0.000 4.337 2.169 table.py:1800(wrapper)
2 0.000 0.000 4.337 2.169 table.py:1950(cast_array_to_feature)
2 0.475 0.238 4.337 2.169 image.py:209(cast_storage)
9 2.583 0.287 2.583 0.287 {built-in method numpy.array}
2 0.000 0.000 1.284 0.642 image.py:319(encode_np_array)
2 0.000 0.000 1.246 0.623 image.py:301(image_to_bytes)
```
### Expected behavior
The `numpy` image data should be passed through as it will be directly consumed by `pillow` to convert it to bytes.
As an example one can replace `list_of_np_array_to_pyarrow_listarray(data)` in `__arrow_array__` with just `out = data` as a test.
We have to change `cast_storage` of the `Image` feature so it handles the passed through data (& if to handle type before)
```python
bytes_array = pa.array(
[encode_np_array(arr)["bytes"] if arr is not None else None for arr in storage],
type=pa.binary(),
)
```
Leading to the following:
```bash
Fri Apr 5 15:44:27 2024 restats
66419 function calls (64595 primitive calls) in 0.937 seconds
Ordered by: cumulative time
List reduced from 1023 to 20 due to restriction <20>
ncalls tottime percall cumtime percall filename:lineno(function)
47/1 0.000 0.000 0.935 0.935 {built-in method builtins.exec}
2/1 0.000 0.000 0.935 0.935 <string>:1(<module>)
2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:594(wrapper)
2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:551(wrapper)
2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:2916(map)
4 0.000 0.000 0.933 0.233 arrow_dataset.py:3277(_map_single)
1 0.000 0.000 0.883 0.883 arrow_writer.py:466(write)
2 0.000 0.000 0.883 0.441 arrow_writer.py:424(write_examples_on_file)
1 0.000 0.000 0.882 0.882 arrow_writer.py:528(write_batch)
2 0.000 0.000 0.877 0.439 arrow_writer.py:161(__arrow_array__)
4/2 0.000 0.000 0.877 0.439 table.py:1800(wrapper)
2 0.000 0.000 0.877 0.439 table.py:1950(cast_array_to_feature)
2 0.009 0.005 0.877 0.439 image.py:209(cast_storage)
2 0.000 0.000 0.868 0.434 image.py:335(encode_np_array)
2 0.000 0.000 0.856 0.428 image.py:317(image_to_bytes)
2 0.000 0.000 0.822 0.411 Image.py:2376(save)
2 0.000 0.000 0.822 0.411 PngImagePlugin.py:1233(_save)
2 0.000 0.000 0.822 0.411 ImageFile.py:517(_save)
2 0.000 0.000 0.821 0.411 ImageFile.py:545(_encode_tile)
589 0.803 0.001 0.803 0.001 {method 'encode' of 'ImagingEncoder' objects}
```
This is of course only a test as it passes through all `numpy` arrays irrespective of if they should be an image.
Also I guess `cast_storage` is meant for casting `pyarrow` storage exclusively.
Converting to `pyarrow` array seems like a good solution as it also handles `pytorch` tensors etc., maybe there is a more efficient way to create a PIL image from a `pyarrow` array?
Not sure how this should be handled but I would be happy to help if there is a good solution.
### Environment info
- `datasets` version: 2.18.1.dev0
- Platform: Linux-6.7.11-200.fc39.x86_64-x86_64-with-glibc2.38
- Python version: 3.12.2
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2024.3.1 | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-11-02T09:48:58Z | 2023-11-02T10:22:33Z | 2023-11-02T10:11:19Z | MEMBER | null | null | null | Temporarily pin `pyarrow` < 14.0.0 until permanent solution is found.
Hot fix #6374. | {
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https://api.github.com/repos/huggingface/datasets/issues/4538 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4538/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4538/comments | https://api.github.com/repos/huggingface/datasets/issues/4538/events | https://github.com/huggingface/datasets/issues/4538 | 1,279,409,786 | I_kwDODunzps5MQj56 | 4,538 | Dataset Viewer issue for Pile of Law | {
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"Hi @Breakend, yes β we'll propose a solution today",
"Thanks so much, I appreciate it!",
"Thanks so much for adding the docs. I was able to successfully hide the viewer using the \r\n```\r\nviewer: false\r\n```\r\nflag in the README.md of the dataset. I'm closing the issue because this is resolved. Thanks agai... | 2022-06-22T02:48:40Z | 2022-06-27T07:30:23Z | 2022-06-26T22:26:22Z | NONE | null | null | {
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} | ### Link
https://huggingface.co/datasets/pile-of-law/pile-of-law
### Description
Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information?
Thanks so much!
### Owner
Yes | {
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"Thanks for reporting, @lewtun.\r\n\r\nAfter investigation, it seems that the server https://gitlab.lip6.fr does not allow HTTP Range requests.\r\n\r\nWe are trying to find a workaround..."
] | 2022-06-26T20:24:17Z | 2022-07-21T12:40:01Z | 2022-07-21T12:40:01Z | MEMBER | null | null | {
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} | ### Link
https://huggingface.co/datasets/mlsum/viewer/de/train
### Description
There's seems to be a problem with the download / streaming of this dataset:
```
Server error
Status code: 400
Exception: BadZipFile
Message: File is not a zip file
```
### Owner
No | {
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https://api.github.com/repos/huggingface/datasets/issues/6622 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6622/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6622/comments | https://api.github.com/repos/huggingface/datasets/issues/6622/events | https://github.com/huggingface/datasets/issues/6622 | 2,103,780,697 | I_kwDODunzps59ZSVZ | 6,622 | multi-GPU map does not work | {
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"This should now be fixed by https://github.com/huggingface/datasets/pull/6550 and updated with https://github.com/huggingface/datasets/pull/6646\r\n\r\nFeel free to re-open if you're still having issues :)"
] | 2024-01-27T20:06:08Z | 2024-02-08T11:18:21Z | 2024-02-08T11:18:21Z | NONE | null | null | {
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} | ### Describe the bug
Here is the code for single-GPU processing: https://pastebin.com/bfmEeK2y
Here is the code for multi-GPU processing: https://pastebin.com/gQ7i5AQy
Here is the video showing that the multi-GPU mapping does not work as expected (there are so many things wrong here, it's better to watch the 3-minute video than explain here):
https://youtu.be/RNbdPkSppc4
### Steps to reproduce the bug
-
### Expected behavior
-
### Environment info
x2 RTX A4000 | {
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https://api.github.com/repos/huggingface/datasets/issues/4593 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4593/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4593/comments | https://api.github.com/repos/huggingface/datasets/issues/4593/events | https://github.com/huggingface/datasets/pull/4593 | 1,288,067,699 | PR_kwDODunzps46iIkn | 4,593 | Fix error message when using load_from_disk to load DatasetDict | {
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Issue: When `datasets.load_from_disk` is wrongly used to load a `DatasetDict`, the error message suggests using `datasets.load_from_disk`, which is the same function that generated the error.
Fix: The appropriate function which should be suggested instead is `datasets.dataset_dict.load_from_disk`.
Changes: Change the suggestion to say "Please use `datasets.dataset_dict.load_from_disk` instead." | {
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https://api.github.com/repos/huggingface/datasets/issues/5080 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5080/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5080/comments | https://api.github.com/repos/huggingface/datasets/issues/5080/events | https://github.com/huggingface/datasets/issues/5080 | 1,398,849,565 | I_kwDODunzps5TYMAd | 5,080 | Use hfh for caching | {
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"There is some discussion in https://github.com/huggingface/huggingface_hub/pull/1088 if it can help :)"
] | 2022-10-06T05:51:58Z | 2022-10-06T14:26:05Z | null | MEMBER | null | null | {
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} | ## Is your feature request related to a problem?
As previously discussed in our meeting with @Wauplin and agreed on our last datasets team sync meeting, I'm investigating how `datasets` can use `hfh` for caching.
## Describe the solution you'd like
Due to the peculiarities of the `datasets` cache, I would propose adopting `hfh` caching system in stages.
First, we could easily start using `hfh` caching for:
- dataset Python scripts
- dataset READMEs
- dataset infos JSON files (now deprecated)
Second, we could also use `hfh` caching for data files downloaded from the Hub.
Further investigation is needed for:
- files downloaded from non-Hub hosts
- extracted files from downloaded archive/compressed files
- generated Arrow files
## Additional context
Docs about the `hfh` caching system:
- [Manage huggingface_hub cache-system](https://huggingface.co/docs/huggingface_hub/main/en/how-to-cache)
- [Cache-system reference](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/cache)
The `transformers` library has already adopted `hfh` for caching. See:
- huggingface/transformers#18438
- huggingface/transformers#18857
- huggingface/transformers#18966
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https://api.github.com/repos/huggingface/datasets/issues/6545 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6545/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6545/comments | https://api.github.com/repos/huggingface/datasets/issues/6545/events | https://github.com/huggingface/datasets/issues/6545 | 2,060,789,507 | I_kwDODunzps561ScD | 6,545 | `image` column not automatically inferred if image dataset only contains 1 image | {
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} | ### Describe the bug
By default, the standard Image Dataset maps out `file_name` to `image` when loading an Image Dataset.
However, if the dataset contains only 1 image, this does not take place
### Steps to reproduce the bug
Input
(dataset with one image `multimodalart/repro_1_image`)
```py
from datasets import load_dataset
dataset = load_dataset("multimodalart/repro_1_image")
dataset
```
Output:
```py
DatasetDict({
train: Dataset({
features: ['file_name', 'prompt'],
num_rows: 1
})
})
```
Input
(dataset with 2+ images `multimodalart/repro_2_image`)
```py
from datasets import load_dataset
dataset = load_dataset("multimodalart/repro_2_image")
dataset
```
Output:
```py
DatasetDict({
train: Dataset({
features: ['image', 'prompt'],
num_rows: 2
})
})
```
### Expected behavior
Expected to map `file_name` β `image` for all dataset sizes, including 1.
### Environment info
Both latest main and 2.16.0 | {
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"cc @SBrandeis",
"I think this has been addressed.\r\n\r\nPlease open a new issue if you are still not getting notified."
] | 2023-05-23T17:27:46Z | 2023-07-21T13:55:37Z | 2023-07-21T13:55:36Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
When a user access requests are enabled, and new requests are set to Manual Review, the dataset owner should be notified of the pending requests. However, instead, currently nothing happens, and so the dataset request can go unanswered for quite some time until the owner happens to check that particular dataset's Settings pane.
### Steps to reproduce the bug
1. Enable a dataset's user access requests
2. Set to Manual Review
3. Ask another HF user to request access to the dataset
4. Dataset owner is not notified
### Expected behavior
The dataset owner should receive some kind of notification, perhaps in their HF site inbox, or by email, when a dataset access request is made and manual review is enabled.
### Environment info
n/a | {
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"Hi ! We could definitely a type that holds the categories and uses a DictionaryType storage. There's a ClassLabel type that is similar with a 'names' parameter (similar to a id2label in deep learning frameworks) that uses an integer array as storage.\r\n\r\nIt can be added in `features.py`. Here are some pointers:... | 2023-04-04T09:45:35Z | 2024-06-07T12:20:43Z | 2024-06-07T12:20:43Z | NONE | null | null | {
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} | ### Feature request
Huggingface datasets does not seem to support categorical / dictionary data types for Parquet as of now. There seems to be a `TODO` in the code for this feature but no implementation yet. Below you can find sample code to reproduce the error that is currently thrown when attempting to read a Parquet file with categorical columns:
```python
import pandas as pd
import pyarrow.parquet as pq
from datasets import load_dataset
# Create categorical sample DataFrame
df = pd.DataFrame({'type': ['foo', 'bar']}).astype('category')
df.to_parquet('data.parquet')
# Read back as pyarrow table
table = pq.read_table('data.parquet')
print(table.schema)
# type: dictionary<values=string, indices=int32, ordered=0>
# Load with huggingface datasets
load_dataset('parquet', data_files='data.parquet')
```
Error:
```
Traceback (most recent call last):
File ".venv/lib/python3.10/site-packages/datasets/builder.py", line 1875, in _prepare_split_single
writer.write_table(table)
File ".venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 566, in write_table
self._build_writer(inferred_schema=pa_table.schema)
File ".venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 379, in _build_writer
inferred_features = Features.from_arrow_schema(inferred_schema)
File ".venv/lib/python3.10/site-packages/datasets/features/features.py", line 1622, in from_arrow_schema
obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema}
File ".venv/lib/python3.10/site-packages/datasets/features/features.py", line 1622, in <dictcomp>
obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema}
File ".venv/lib/python3.10/site-packages/datasets/features/features.py", line 1361, in generate_from_arrow_type
raise NotImplementedError # TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table
NotImplementedError
```
### Motivation
Categorical data types, as offered by Pandas and implemented with the `DictionaryType` dtype in `pyarrow` can significantly reduce dataset size and are a handy way to turn textual features into numerical representations and back. Lack of support in Huggingface datasets greatly reduces compatibility with a common Pandas / Parquet feature.
### Your contribution
I could provide a PR. However, it would be nice to have an initial complexity estimate from one of the core developers first. | {
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"Thanks for reporting, @finiteautomata.\r\n\r\nWe are investigating it. ",
"There is a bug in `datasets`. You can see our proposed fix:\r\n- #6309 "
] | 2023-10-16T20:11:27Z | 2023-10-18T13:50:36Z | 2023-10-18T13:50:36Z | NONE | null | null | {
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} | ### Describe the bug
I'm trying to load [piuba-bigdata/articles_and_comments] and I'm stumbling with this error on `2.14.5`. However, this works on `2.10.0`.
### Steps to reproduce the bug
[Colab link](https://colab.research.google.com/drive/1SAftFMQnFE708ikRnJJHIXZV7R5IBOCE#scrollTo=r2R2ipCCDmsg)
```python
Downloading readme: 100%
1.19k/1.19k [00:00<00:00, 30.9kB/s]
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
[<ipython-input-2-807c3583d297>](https://localhost:8080/#) in <cell line: 3>()
1 from datasets import load_dataset
2
----> 3 load_dataset("piuba-bigdata/articles_and_comments", split="train")
2 frames
[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
2127
2128 # Create a dataset builder
-> 2129 builder_instance = load_dataset_builder(
2130 path=path,
2131 name=name,
[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs)
1813 download_config = download_config.copy() if download_config else DownloadConfig()
1814 download_config.storage_options.update(storage_options)
-> 1815 dataset_module = dataset_module_factory(
1816 path,
1817 revision=revision,
[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1506 raise e1 from None
1507 if isinstance(e1, FileNotFoundError):
-> 1508 raise FileNotFoundError(
1509 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
1510 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
FileNotFoundError: Couldn't find a dataset script at /content/piuba-bigdata/articles_and_comments/articles_and_comments.py or any data file in the same directory. Couldn't find 'piuba-bigdata/articles_and_comments' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in piuba-bigdata/articles_and_comments.
```
### Expected behavior
It should load normally.
### Environment info
```
- `datasets` version: 2.14.5
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.18.0
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
``` | {
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} | ### Feature request
Running .map and filter functions with `num_procs` consecutively instantiates several multiprocessing pools iteratively.
As instantiating a Pool is very resource intensive it can be a bottleneck to performing iteratively filtering.
My ideas:
1. There should be an option to declare `persistent_workers` similar to pytorch DataLoader. Downside would be that would be complex to determine the correct resource allocation and deallocation of the pool. i.e. the dataset can outlive the utility of the pool.
2. Provide a pool as an argument. Downside would be the expertise required by the user. Upside, is that there is better resource management.
### Motivation
Is really slow to iteratively perform map and filter operations on a dataset.
### Your contribution
If approved I could integrate it. I would need to know what method would be most suitable to implement from the two options above. | null | {
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"You can probably copy the dataset in your HF account and rename the files (without having to download them to your disk). Or alternatively feel free to open a Pull Request to this dataset with the renamed file",
"Thank you, that will help me work around this problem"
] | 2025-02-10T17:46:31Z | 2025-02-11T13:42:32Z | 2025-02-11T13:42:30Z | NONE | null | null | {
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} | ### Describe the bug
I'm on Windows and i'm trying to load a datasets but i'm having title error because files in the repository are named with charactere like < >which can't be in a name file. Could it be possible to load this datasets but removing those charactere ?
### Steps to reproduce the bug
load_dataset("CATMuS/medieval") on Windows
### Expected behavior
Making the function to erase the forbidden character to allow loading the datasets who have those characters.
### Environment info
- `datasets` version: 3.2.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.12.2
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5627 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5627/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5627/comments | https://api.github.com/repos/huggingface/datasets/issues/5627/events | https://github.com/huggingface/datasets/issues/5627 | 1,619,336,609 | I_kwDODunzps5ghR2h | 5,627 | Unable to load AutoTrain-generated dataset from the hub | {
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} | [] | open | false | null | [] | null | [
"The AutoTrain format is not supported right now. I think it would require a dedicated dataset builder",
"Okay, good to know. Thanks for the reply. For now I will just have to\nmanage the split manually before training, because I canβt find any way of\npulling out file indices or file names from the autogenerated... | 2023-03-10T17:25:58Z | 2023-03-11T15:44:42Z | null | NONE | null | null | {
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} | ### Describe the bug
DatasetGenerationError: An error occurred while generating the dataset -> ValueError: Couldn't cast ... because column names don't match
```
ValueError: Couldn't cast
_data_files: list<item: struct<filename: string>>
child 0, item: struct<filename: string>
child 0, filename: string
_fingerprint: string
_format_columns: list<item: string>
child 0, item: string
_format_kwargs: struct<>
_format_type: null
_indexes: struct<>
_output_all_columns: bool
_split: null
to
{'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}}
because column names don't match
```
### Steps to reproduce the bug
Steps to reproduce:
1. `pip install datasets==2.10.1`
2. Attempt to load (private dataset). Note that I'm authenticated via ` huggingface-cli login`
```
from datasets import load_dataset
# load dataset
dataset = "ijmiller2/autotrain-data-betterbin-vision-10000"
dataset = load_dataset(dataset)
```
Here's the full traceback:
```Downloading and preparing dataset json/ijmiller2--autotrain-data-betterbin-vision-10000 to /Users/ian/.cache/huggingface/datasets/ijmiller2___json/ijmiller2--autotrain-data-betterbin-vision-10000-2eae034a9ff8a1a9/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...
Downloading data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 2383.80it/s]
Extracting data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 505.95it/s]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1874, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1868 writer = writer_class(
1869 features=writer._features,
1870 path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
1871 storage_options=self._fs.storage_options,
1872 embed_local_files=embed_local_files,
1873 )
-> 1874 writer.write_table(table)
1875 num_examples_progress_update += len(table)
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/arrow_writer.py:568, in ArrowWriter.write_table(self, pa_table, writer_batch_size)
567 pa_table = pa_table.combine_chunks()
--> 568 pa_table = table_cast(pa_table, self._schema)
569 if self.embed_local_files:
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2312, in table_cast(table, schema)
2311 if table.schema != schema:
-> 2312 return cast_table_to_schema(table, schema)
2313 elif table.schema.metadata != schema.metadata:
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2270, in cast_table_to_schema(table, schema)
2269 if sorted(table.column_names) != sorted(features):
-> 2270 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match")
2271 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
ValueError: Couldn't cast
_data_files: list<item: struct<filename: string>>
child 0, item: struct<filename: string>
child 0, filename: string
_fingerprint: string
_format_columns: list<item: string>
child 0, item: string
_format_kwargs: struct<>
_format_type: null
_indexes: struct<>
_output_all_columns: bool
_split: null
to
{'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}}
because column names don't match
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Input In [8], in <cell line: 6>()
4 # load dataset
5 dataset = "ijmiller2/autotrain-data-betterbin-vision-10000"
----> 6 dataset = load_dataset(dataset)
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/load.py:1782, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1779 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1781 # Download and prepare data
-> 1782 builder_instance.download_and_prepare(
1783 download_config=download_config,
1784 download_mode=download_mode,
1785 verification_mode=verification_mode,
1786 try_from_hf_gcs=try_from_hf_gcs,
1787 num_proc=num_proc,
1788 )
1790 # Build dataset for splits
1791 keep_in_memory = (
1792 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1793 )
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:872, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_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)
870 if num_proc is not None:
871 prepare_split_kwargs["num_proc"] = num_proc
--> 872 self._download_and_prepare(
873 dl_manager=dl_manager,
874 verification_mode=verification_mode,
875 **prepare_split_kwargs,
876 **download_and_prepare_kwargs,
877 )
878 # Sync info
879 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:967, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
963 split_dict.add(split_generator.split_info)
965 try:
966 # Prepare split will record examples associated to the split
--> 967 self._prepare_split(split_generator, **prepare_split_kwargs)
968 except OSError as e:
969 raise OSError(
970 "Cannot find data file. "
971 + (self.manual_download_instructions or "")
972 + "\nOriginal error:\n"
973 + str(e)
974 ) from None
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1749, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1747 job_id = 0
1748 with pbar:
-> 1749 for job_id, done, content in self._prepare_split_single(
1750 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1751 ):
1752 if done:
1753 result = content
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1892, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1891 e = e.__context__
-> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
I'm ultimately trying to generate my own performance metrics on validation data (before putting an endpoint into production) and so was hoping to load all or at least the validation subset from the hub.
I'm expecting the `load_dataset()` function to work as shown in the documentation [here](https://huggingface.co/docs/datasets/loading#hugging-face-hub):
```python
dataset = load_dataset(
"lhoestq/custom_squad",
revision="main" # tag name, or branch name, or commit hash
)
```
### Environment info
- `datasets` version: 2.10.1
- Platform: macOS-13.2.1-arm64-arm-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5926 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5926/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5926/comments | https://api.github.com/repos/huggingface/datasets/issues/5926/events | https://github.com/huggingface/datasets/issues/5926 | 1,743,922,028 | I_kwDODunzps5n8iNs | 5,926 | Uncaught exception when generating the splits from a dataset that miss data | {
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"Thanks for reporting, @severo.\r\n\r\nThis is a known issue with `fsspec`:\r\n- #5862\r\n- https://github.com/fsspec/filesystem_spec/issues/1265"
] | 2023-06-06T13:51:01Z | 2023-06-07T07:53:16Z | null | COLLABORATOR | null | null | {
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} | ### Describe the bug
Dataset https://huggingface.co/datasets/blog_authorship_corpus has an issue with its hosting platform, since https://drive.google.com/u/0/uc?id=1cGy4RNDV87ZHEXbiozABr9gsSrZpPaPz&export=download returns 404 error.
But when trying to generate the split names, we get an exception which is now correctly caught.
Seen originally in https://github.com/huggingface/datasets-server/blob/adbdcd6710ffed4e2eb2e4cd905b5e0dff530a15/services/worker/src/worker/job_runners/config/parquet_and_info.py#L435
### Steps to reproduce the bug
```python
>>> from datasets import StreamingDownloadManager, load_dataset_builder
>>> builder = load_dataset_builder(path="blog_authorship_corpus")
Downloading builder script: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 5.60k/5.60k [00:00<00:00, 23.1MB/s]
Downloading metadata: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2.81k/2.81k [00:00<00:00, 14.7MB/s]
Downloading readme: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 7.30k/7.30k [00:00<00:00, 30.8MB/s]
>>> dl_manager = StreamingDownloadManager(base_path=builder.base_path)
>>> builder._split_generators(dl_manager)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/blog_authorship_corpus/6f5d78241afd8313111956f877a57db7a0e9fc6718255dc85df0928197feb683/blog_authorship_corpus.py", line 79, in _split_generators
data = dl_manager.download_and_extract(_DATA_URL)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1087, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1039, in extract
urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 435, in map_nested
return function(data_struct)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1044, in _extract
protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 433, in _get_extraction_protocol
with fsspec.open(urlpath, **kwargs) as f:
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 439, in open
return open_files(
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 194, in __getitem__
out = super().__getitem__(item)
IndexError: list index out of range
```
### Expected behavior
We should have an Exception raised by the datasets library.
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.19.0-1026-aws-x86_64-with-glibc2.35
- Python version: 3.9.15
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5122 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5122/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5122/comments | https://api.github.com/repos/huggingface/datasets/issues/5122/events | https://github.com/huggingface/datasets/pull/5122 | 1,410,732,403 | PR_kwDODunzps5A4rWn | 5,122 | Add warning | {
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"As mentioned in https://github.com/huggingface/datasets/issues/5105 I think we just need to keep the existing files instead of deleting them.\r\nThe `dataset_info.json` file contains the split names anyway, so we know which files belong to the dataset, and which ones don't."
] | 2022-10-17T01:30:37Z | 2022-11-05T12:23:53Z | 2022-11-05T12:23:53Z | NONE | null | null | null | Fixes: #5105
I think removing the directory with warning is a better solution for this issue. Because if we decide to keep existing files in directory, then we should deal with the case providing same directory for several datasets! Which we know is not possible since `dataset_info.json` exists in that directory. | {
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https://api.github.com/repos/huggingface/datasets/issues/6869 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6869/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6869/comments | https://api.github.com/repos/huggingface/datasets/issues/6869/events | https://github.com/huggingface/datasets/issues/6869 | 2,280,048,297 | I_kwDODunzps6H5sap | 6,869 | Download is broken for dict of dicts: FileNotFoundError | {
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} | It seems there is a bug when downloading a dict of dicts of URLs introduced by:
- #6794
## Steps to reproduce the bug:
```python
from datasets import DownloadManager
dl_manager = DownloadManager()
paths = dl_manager.download({"train": {"frr": "hf://datasets/wikimedia/wikipedia/20231101.frr/train-00000-of-00001.parquet"}})
```
Stack trace:
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-7-0e0d76d25b09> in <module>
----> 1 paths = dl_manager.download({"train": {"frr": "hf://datasets/wikimedia/wikipedia/20231101.frr/train-00000-of-00001.parquet"}})
.../huggingface/datasets/src/datasets/download/download_manager.py in download(self, url_or_urls)
255 start_time = datetime.now()
256 with stack_multiprocessing_download_progress_bars():
--> 257 downloaded_path_or_paths = map_nested(
258 download_func,
259 url_or_urls,
.../huggingface/datasets/src/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc)
506 batch_size = max(len(iterable) // num_proc + int(len(iterable) % num_proc > 0), 1)
507 iterable = list(iter_batched(iterable, batch_size))
--> 508 mapped = [
509 _single_map_nested((function, obj, batched, batch_size, types, None, True, None))
510 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc)
.../huggingface/datasets/src/datasets/utils/py_utils.py in <listcomp>(.0)
507 iterable = list(iter_batched(iterable, batch_size))
508 mapped = [
--> 509 _single_map_nested((function, obj, batched, batch_size, types, None, True, None))
510 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc)
511 ]
.../huggingface/datasets/src/datasets/utils/py_utils.py in _single_map_nested(args)
375 and all(not isinstance(v, types) for v in data_struct)
376 ):
--> 377 return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
378
379 # Reduce logging to keep things readable in multiprocessing with tqdm
.../huggingface/datasets/src/datasets/utils/py_utils.py in <listcomp>(.0)
375 and all(not isinstance(v, types) for v in data_struct)
376 ):
--> 377 return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
378
379 # Reduce logging to keep things readable in multiprocessing with tqdm
.../huggingface/datasets/src/datasets/download/download_manager.py in _download_batched(self, url_or_filenames, download_config)
311 )
312 else:
--> 313 return [
314 self._download_single(url_or_filename, download_config=download_config)
315 for url_or_filename in url_or_filenames
.../huggingface/datasets/src/datasets/download/download_manager.py in <listcomp>(.0)
312 else:
313 return [
--> 314 self._download_single(url_or_filename, download_config=download_config)
315 for url_or_filename in url_or_filenames
316 ]
.../huggingface/datasets/src/datasets/download/download_manager.py in _download_single(self, url_or_filename, download_config)
321 # append the relative path to the base_path
322 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 323 out = cached_path(url_or_filename, download_config=download_config)
324 out = tracked_str(out)
325 out.set_origin(url_or_filename)
.../huggingface/datasets/src/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
220 elif is_local_path(url_or_filename):
221 # File, but it doesn't exist.
--> 222 raise FileNotFoundError(f"Local file {url_or_filename} doesn't exist")
223 else:
224 # Something unknown
FileNotFoundError: Local file .../huggingface/datasets/{'frr': 'hf:/datasets/wikimedia/wikipedia/20231101.frr/train-00000-of-00001.parquet'} doesn't exist
```
Related to:
- #6850
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https://api.github.com/repos/huggingface/datasets/issues/7032 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7032/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7032/comments | https://api.github.com/repos/huggingface/datasets/issues/7032/events | https://github.com/huggingface/datasets/pull/7032 | 2,395,531,699 | PR_kwDODunzps50sJTq | 7,032 | Register `.zstd` extension for zstd-compressed files | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7032). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@albertvillanova hm I don't know tbh, it's just that \"mlfoundations/dclm-baseline-1.0\... | 2024-07-08T12:39:50Z | 2024-07-12T15:07:03Z | 2024-07-12T15:07:03Z | CONTRIBUTOR | null | null | null | For example, https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0 dataset files have `.zstd` extension which is currently ignored (only `.zst` is registered). | {
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https://api.github.com/repos/huggingface/datasets/issues/6721 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6721/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6721/comments | https://api.github.com/repos/huggingface/datasets/issues/6721/events | https://github.com/huggingface/datasets/issues/6721 | 2,173,931,714 | I_kwDODunzps6Bk5DC | 6,721 | Hi,do you know how to load the dataset from local file now? | {
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"\r\n@Gera001\r\n# Loading Dataset from Local Files Using π€Hugging Face.\r\n\r\nTo load a dataset from local files using the Hugging Face datasets library, you can use the `load_dataset` function.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files={'train': 'path/to/train.c... | 2024-03-07T13:58:40Z | 2024-03-31T08:09:25Z | null | NONE | null | null | {
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} | Hi, if I want to load the dataset from local file, then how to specify the configuration name?
_Originally posted by @WHU-gentle in https://github.com/huggingface/datasets/issues/2976#issuecomment-1333455222_
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https://api.github.com/repos/huggingface/datasets/issues/5741 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5741/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5741/comments | https://api.github.com/repos/huggingface/datasets/issues/5741/events | https://github.com/huggingface/datasets/pull/5741 | 1,665,860,919 | PR_kwDODunzps5OM9nZ | 5,741 | Fix CI warnings | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-13T07:17:02Z | 2023-04-13T09:48:10Z | 2023-04-13T09:40:50Z | MEMBER | null | null | null | Fix warnings in our CI tests. | {
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https://api.github.com/repos/huggingface/datasets/issues/6272 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6272/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6272/comments | https://api.github.com/repos/huggingface/datasets/issues/6272/events | https://github.com/huggingface/datasets/issues/6272 | 1,920,831,487 | I_kwDODunzps5yfY__ | 6,272 | Duplicate `data_files` when named `<split>/<split>.parquet` | {
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"Also reported in https://github.com/huggingface/datasets/issues/6259",
"I think it's best to drop duplicates with a `set` (as a temporary fix) and improve the patterns when/if https://github.com/fsspec/filesystem_spec/pull/1382 gets merged. @lhoestq Do you have some other ideas?",
"Alternatively we could just... | 2023-10-01T15:43:56Z | 2024-03-15T15:22:05Z | 2024-03-15T15:22:05Z | MEMBER | null | null | {
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} | e.g. with `u23429/stock_1_minute_ticker`
```ipython
In [1]: from datasets import *
In [2]: b = load_dataset_builder("u23429/stock_1_minute_ticker")
Downloading readme: 100%|ββββββββββββββββββββββββββ| 627/627 [00:00<00:00, 246kB/s]
In [3]: b.config.data_files
Out[3]:
{NamedSplit('train'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet'],
NamedSplit('validation'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet'],
NamedSplit('test'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet']}
```
This bug issue is present in the current `datasets` 2.14.5 and also on `main` even after https://github.com/huggingface/datasets/pull/6244 cc @mariosasko | {
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"Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:\r\n```python\r\nfrom datasets import load_dataset\r\nimport torch\r\n\r\ndataset = load_dataset(\"lambdalabs/pokemon-blip-captions\", split='t... | 2023-02-16T16:08:51Z | 2023-02-22T10:30:30Z | 2023-02-21T13:03:57Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
When dataset contains a 0-dim tensor, formatting.py raises a following error and fails.
```bash
Traceback (most recent call last):
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 501, in format_row
return _unnest(formatted_batch)
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in _unnest
return {key: array[0] for key, array in py_dict.items()}
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in <dictcomp>
return {key: array[0] for key, array in py_dict.items()}
IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
```
### Steps to reproduce the bug
Load whichever dataset and add transform method to add 0-dim tensor. Or create/find a dataset containing 0-dim tensor. E.g.
```python
from datasets import load_dataset
import torch
dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train')
def t(batch):
return {"test": torch.tensor(1)}
dataset.set_transform(t)
d_0 = dataset[0]
```
### Expected behavior
Extractor will correctly get a row from the dataset, even if it contains 0-dim tensor.
### Environment info
`datasets==2.8.0`, but it looks like it is also applicable to main branch version (as of 16th February) | {
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https://api.github.com/repos/huggingface/datasets/issues/5887 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5887/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5887/comments | https://api.github.com/repos/huggingface/datasets/issues/5887/events | https://github.com/huggingface/datasets/issues/5887 | 1,722,166,382 | I_kwDODunzps5mpixu | 5,887 | HuggingsFace dataset example give error | {
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"Nice catch @donhuvy, that's because some models don't need the `token_type_ids`, as in this case, as the example is using `distilbert-base-cased`, and according to the DistilBert documentation at https://huggingface.co/transformers/v3.0.2/model_doc/distilbert.html, `DistilBert doesnβt have token_type_ids, you donβ... | 2023-05-23T14:09:05Z | 2023-07-25T14:01:01Z | 2023-07-25T14:01:00Z | NONE | null | null | {
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} | ### Describe the bug
### Steps to reproduce the bug
Use link as reference document written https://colab.research.google.com/github/huggingface/datasets/blob/main/notebooks/Overview.ipynb#scrollTo=biqDH9vpvSVz
```python
# Now let's train our model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.train().to(device)
for i, batch in enumerate(dataloader):
batch.to(device)
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
model.zero_grad()
print(f'Step {i} - loss: {loss:.3}')
if i > 5:
break
```
Error
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-44-7040b885f382>](https://localhost:8080/#) in <cell line: 5>()
5 for i, batch in enumerate(dataloader):
6 batch.to(device)
----> 7 outputs = model(**batch)
8 loss = outputs.loss
9 loss.backward()
[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs)
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
TypeError: DistilBertForQuestionAnswering.forward() got an unexpected keyword argument 'token_type_ids'
```
https://github.com/huggingface/datasets/assets/1328316/5d8b1d61-9337-4d59-8423-4f37f834c156
### Expected behavior
Run success on Google Colab (free)
### Environment info
Windows 11 x64, Google Colab free (my Google Drive just empty about 200 MB, but I don't think it cause problem) | {
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} | As reported in https://github.com/huggingface/datasets/issues/5360
If `seed=None` in `.shuffle()`, shuffled datasets don't use the same shuffling seed across nodes.
Because of that, the lists of shards is not shuffled the same way across nodes, and therefore some shards may be assigned to multiple nodes instead of exactly one.
This can happen only when you have a number of shards that is a factor of the number of nodes.
The current workaround is to always set a `seed` in `.shuffle()` | {
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https://api.github.com/repos/huggingface/datasets/issues/6611 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6611/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6611/comments | https://api.github.com/repos/huggingface/datasets/issues/6611/events | https://github.com/huggingface/datasets/issues/6611 | 2,096,004,858 | I_kwDODunzps587n76 | 6,611 | `load_from_disk` with large dataset from S3 runs into `botocore.exceptions.ClientError` | {
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} | ### Describe the bug
When loading a large dataset (>1000GB) from S3 I run into the following error:
```
Traceback (most recent call last):
File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 113, in _error_wrapper
return await func(*args, **kwargs)
File "/home/alp/.local/lib/python3.10/site-packages/aiobotocore/client.py", line 383, in _make_api_call
raise error_class(parsed_response, operation_name)
botocore.exceptions.ClientError: An error occurred (RequestTimeTooSkewed) when calling the GetObject operation: The difference between the request time and the current time is too large.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/alp/phoneme-classification.monorepo/aws_sagemaker/data_processing/inspect_final_dataset.py", line 13, in <module>
dataset = load_from_disk("s3://speech-recognition-processed-data/whisper/de/train_data/", storage_options=storage_options)
File "/home/alp/.local/lib/python3.10/site-packages/datasets/load.py", line 1902, in load_from_disk
return Dataset.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)
File "/home/alp/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1686, in load_from_disk
fs.download(src_dataset_path, [dest_dataset_path.as](http://dest_dataset_path.as/)_posix(), recursive=True)
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1480, in download
return self.get(rpath, lpath, recursive=recursive, **kwargs)
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper
return sync(self.loop, func, *args, **kwargs)
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync
raise return_result
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner
result[0] = await coro
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 604, in _get
return await _run_coros_in_chunks(
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 257, in _run_coros_in_chunks
await asyncio.gather(*chunk, return_exceptions=return_exceptions),
File "/usr/lib/python3.10/asyncio/tasks.py", line 408, in wait_for
return await fut
File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1193, in _get_file
body, content_length = await _open_file(range=0)
File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1184, in _open_file
resp = await self._call_s3(
File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 348, in _call_s3
return await _error_wrapper(
File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 140, in _error_wrapper
raise err
PermissionError: The difference between the request time and the current time is too large.
```
The usual problem for this error is that the time on my local machine is out of sync with the current time. However, this is not the case here. I checked the time and even reset it with no success. See resources here:
- https://stackoverflow.com/questions/4770635/s3-error-the-difference-between-the-request-time-and-the-current-time-is-too-la
- https://stackoverflow.com/questions/25964491/aws-s3-upload-fails-requesttimetooskewed
The error does not appear when loading a smaller dataset (e.g. our test set) from the same s3 path.
### Steps to reproduce the bug
1. Create large dataset
2. Try loading it from s3 using:
```
dataset = load_from_disk("s3://...", storage_options=storage_options)
```
### Expected behavior
Load dataset without running into this error.
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.19.3
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6240 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6240/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6240/comments | https://api.github.com/repos/huggingface/datasets/issues/6240/events | https://github.com/huggingface/datasets/issues/6240 | 1,895,723,888 | I_kwDODunzps5w_nNw | 6,240 | Dataloader stuck on multiple GPUs | {
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"What type of dataset are you using in this script? `torch.utils.data.Dataset` or `datasets.Dataset`? Please share the `datasets` package version if it's the latter. Otherwise, it's better to move this issue to the `accelerate` repo.",
"Very sorry, I thought I had a repo in `accelerate!`\r\nI will close this issu... | 2023-09-14T05:30:30Z | 2023-09-14T23:54:42Z | 2023-09-14T23:54:42Z | NONE | null | null | {
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} | ### Describe the bug
I am trying to get CLIP to fine-tuning with my code.
When I tried to run it on multiple GPUs using accelerate, I encountered the following phenomenon.
- Validation dataloader stuck in 2nd epoch only on multi-GPU
Specifically, when the "for inputs in valid_loader:" process is finished, it does not proceed to the next step. train_loader process is completed. Also, both train and valid are working correctly in the first epoch.
The accelerate command at that time is as follows.
`accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...`
- This will not happen when single GPU is used.
`CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...`
- Setting num_workers=0 in dataloader did not change the result.
### Steps to reproduce the bug
1. The codes for fine-tuning the regular CLIP were updated for accelerate.
2. Run the code with the accelerate command as `accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...` and the above problem will occur.
3. CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...` , it works fine.
### Expected behavior
It Should end normally as if it was run on a single GPU.
### Environment info
Since `datasets-cli env` did not work, the environment is described below.
- OS: Ubuntu 22.04 with Docker
- Docker: 24.0.5, build ced0996
- Python: 3.10.12
- torch==2.0.1
- accelerate==0.21.0
- transformers==4.33.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/6477 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6477/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6477/comments | https://api.github.com/repos/huggingface/datasets/issues/6477/events | https://github.com/huggingface/datasets/pull/6477 | 2,028,022,374 | PR_kwDODunzps5hRq_N | 6,477 | Fix PermissionError on Windows CI | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6477). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2023-12-06T08:34:53Z | 2023-12-06T09:24:11Z | 2023-12-06T09:17:52Z | MEMBER | null | null | null | Fix #6476. | {
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https://api.github.com/repos/huggingface/datasets/issues/4901 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4901/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4901/comments | https://api.github.com/repos/huggingface/datasets/issues/4901/events | https://github.com/huggingface/datasets/pull/4901 | 1,352,438,915 | PR_kwDODunzps494FNX | 4,901 | Raise ManualDownloadError from get_dataset_config_info | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-08-26T15:45:56Z | 2022-08-30T10:42:21Z | 2022-08-30T10:40:04Z | MEMBER | null | null | null | This PRs raises a specific `ManualDownloadError` when `get_dataset_config_info` is called for a dataset that requires manual download.
Related to:
- #4898
CC: @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/5107 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5107/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5107/comments | https://api.github.com/repos/huggingface/datasets/issues/5107/events | https://github.com/huggingface/datasets/pull/5107 | 1,406,736,710 | PR_kwDODunzps5ArjCZ | 5,107 | Multiprocessed dataset builder | {
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"I would also like to add a test, but am not sure whether it should go into `test_builder` (more natural imo) or `test_load` (which already contains a lot of the things I have to import to run my current testing setup). For reference, what I run to test that it works looks like:\r\n\r\n```\r\nimport os\r\nfrom path... | 2022-10-12T19:59:17Z | 2022-12-01T15:37:09Z | 2022-11-09T17:11:43Z | CONTRIBUTOR | null | null | null | This PR adds the multiprocessing part of #2650 (but not the caching of already-computed arrow files). On the other side, loading of sharded arrow files still needs to be implemented (sharded parquet files can already be loaded). | {
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https://api.github.com/repos/huggingface/datasets/issues/7313 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7313/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7313/comments | https://api.github.com/repos/huggingface/datasets/issues/7313/events | https://github.com/huggingface/datasets/issues/7313 | 2,726,240,634 | I_kwDODunzps6ifyF6 | 7,313 | Cannot create a dataset with relative audio path | {
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"Hello ! when you `cast_column` you need the paths to be absolute paths or relative paths to your working directory, not the original dataset directory.\r\n\r\nThough I'd recommend structuring your dataset as an AudioFolder which automatically links a metadata.jsonl or csv to the audio files via relative paths **wi... | 2024-12-09T07:34:20Z | 2025-04-19T07:13:08Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Hello! I want to create a dataset of parquet files, with audios stored as separate .mp3 files. However, it says "No such file or directory" (see the reproducing code).
### Steps to reproduce the bug
Creating a dataset
```
from pathlib import Path
from datasets import Dataset, load_dataset, Audio
Path('my_dataset/audio').mkdir(parents=True, exist_ok=True)
Path('my_dataset/audio/file.mp3').touch(exist_ok=True)
Dataset.from_list(
[{'audio': {'path': 'audio/file.mp3'}}]
).to_parquet('my_dataset/data.parquet')
```
Result:
```
# my_dataset
# βββ audio
# β βββ file.mp3
# βββ data.parquet
```
Trying to load the dataset
```
dataset = (
load_dataset('my_dataset', split='train')
.cast_column('audio', Audio(sampling_rate=16_000))
)
dataset[0]
>>> FileNotFoundError: [Errno 2] No such file or directory: 'audio/file.mp3'
```
### Expected behavior
I expect the dataset to load correctly.
I've found 2 workarounds, but they are not very good:
1. I can specify an absolute path to the audio, however, when I move the folder or upload to HF it will stop working.
2. I can set `'path': 'file.mp3'`, and load with `load_dataset('my_dataset', data_dir='audio')` - it seems to work, but does this mean that anyone from Hugging Face who wants to use this dataset should also pass the `data_dir` argument, otherwise it won't work?
### Environment info
datasets 3.1.0, Ubuntu 24.04.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4747 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4747/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4747/comments | https://api.github.com/repos/huggingface/datasets/issues/4747/events | https://github.com/huggingface/datasets/pull/4747 | 1,318,586,932 | PR_kwDODunzps48IWKj | 4,747 | Shard parquet in `download_and_prepare` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"This is ready for review cc @mariosasko :) please let me know what you think !"
] | 2022-07-26T18:05:01Z | 2022-09-15T13:43:55Z | 2022-09-15T13:41:26Z | MEMBER | null | null | null | Following https://github.com/huggingface/datasets/pull/4724 (needs to be merged first)
It's good practice to shard parquet files to enable parallelism with spark/dask/etc.
I added the `max_shard_size` parameter to `download_and_prepare` (default to 500MB for parquet, and None for arrow).
```python
from datasets import *
output_dir = "./output_dir" # also supports "s3://..."
builder = load_dataset_builder("squad")
builder.download_and_prepare(output_dir, file_format="parquet", max_shard_size="5MB")
```
### Implementation details
The examples are written to a parquet file until `ParquetWriter._num_bytes > max_shard_size`. When this happens, a new writer is instantiated to start writing the next shard. At the end, all the shards are renamed to include the total number of shards in their names: `{builder.name}-{split}-{shard_id:05d}-of-{num_shards:05d}.parquet`
I also added the `MAX_SHARD_SIZE` config variable (default to 500MB)
TODO:
- [x] docstrings
- [x] docs
- [x] tests
cc @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/5700 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5700/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5700/comments | https://api.github.com/repos/huggingface/datasets/issues/5700/events | https://github.com/huggingface/datasets/pull/5700 | 1,652,527,530 | PR_kwDODunzps5Ng6g_ | 5,700 | fix: fix wrong modification of the 'cache_file_name' -related paramet⦠| {
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"Have you tried to set the cache file names if `keep_in_memory`is True ?\r\n\r\n```diff\r\n- if self.cache_files:\r\n+ if self.cache_files and not keep_in_memory:\r\n```\r\n\r\nThis way it doesn't change the indice cache arguments and leave them as `None`",
"@lhoestq \r\nRegarding what you suggest:\r\nThe thing i... | 2023-04-03T18:05:26Z | 2023-04-06T17:17:27Z | null | NONE | null | null | null | β¦ers values in 'train_test_split' + fix bad interaction between 'keep_in_memory' and 'cache_file_name' -related parameters (#5699) | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4650 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4650/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4650/comments | https://api.github.com/repos/huggingface/datasets/issues/4650/events | https://github.com/huggingface/datasets/issues/4650 | 1,296,680,037 | I_kwDODunzps5NScRl | 4,650 | Add SPECTER dataset | {
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/SPECTER)"
] | 2022-07-07T01:41:32Z | 2022-07-14T02:07:49Z | null | NONE | null | null | {
"completed": 0,
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} | ## Adding a Dataset
- **Name:** *SPECTER*
- **Description:** *SPECTER: Document-level Representation Learning using Citation-informed Transformers*
- **Paper:** *https://doi.org/10.18653/v1/2020.acl-main.207*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/specter_train_triples.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/5620 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5620/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5620/comments | https://api.github.com/repos/huggingface/datasets/issues/5620/events | https://github.com/huggingface/datasets/pull/5620 | 1,613,460,520 | PR_kwDODunzps5LefAf | 5,620 | Bump pyarrow to 8.0.0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==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 | rea... | 2023-03-07T13:31:53Z | 2023-03-08T14:01:27Z | 2023-03-08T13:54:22Z | MEMBER | null | null | null | Fix those for Pandas 2.0 (tested [here](https://github.com/huggingface/datasets/actions/runs/4346221280/jobs/7592010397) with pandas==2.0.0.rc0):
```python
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_in_memory - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
===== 2 failed, 2137 passed, 18 skipped, 32 warnings in 212.76s (0:03:32) ======
```
EDIT: also for performance - with 8.0 we can use `.to_reader()` | {
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https://api.github.com/repos/huggingface/datasets/issues/6636 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6636/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6636/comments | https://api.github.com/repos/huggingface/datasets/issues/6636/events | https://github.com/huggingface/datasets/pull/6636 | 2,110,781,097 | PR_kwDODunzps5lm4zI | 6,636 | Faster column validation and reordering | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6636). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Thanks @mariosasko, I made the changes. However, I did some tests with `map` and I stil... | 2024-01-31T19:08:28Z | 2024-02-07T19:39:00Z | 2024-02-06T23:03:38Z | CONTRIBUTOR | null | null | null | I work with bioinformatics data and often these tables have thousands and even tens of thousands of features. These tables are also accompanied by metadata that I do not want to pass in the model. When I perform `set_format('pt', columns=large_column_list)` , it can take several minutes before it finishes. The culprit is when the following check is performed: `any(col not in self._data.column_names for col in columns)`. Replacing this by `set(columns) - (self._data.column_names)` is more efficient. | {
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https://api.github.com/repos/huggingface/datasets/issues/5728 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5728/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5728/comments | https://api.github.com/repos/huggingface/datasets/issues/5728/events | https://github.com/huggingface/datasets/issues/5728 | 1,661,925,932 | I_kwDODunzps5jDvos | 5,728 | The order of data split names is nondeterministic | {
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```
FAILED tests/test_data_files.py::test_get_data_files_patterns[data_file_per_split4] - AssertionError: assert ['random', 'train'] == ['train', 'random']
At index 0 diff: 'random' != 'train'
Full diff:
- ['train', 'random']
+ ['random', 'train']
```
I have checked locally and found out that the data split order is nondeterministic.
This is caused by the use of `set` for sharded splits. | {
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https://api.github.com/repos/huggingface/datasets/issues/5838 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5838/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5838/comments | https://api.github.com/repos/huggingface/datasets/issues/5838/events | https://github.com/huggingface/datasets/issues/5838 | 1,703,210,848 | I_kwDODunzps5lhO9g | 5,838 | Streaming support for `load_from_disk` | {
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"As the name says, `load_from_disk` load the data from your disk. If the data is hosted on S3, it is first downloaded locally and then loaded from your disk.\r\n\r\nThere is a discussion on streaming data from S3 here though: #5281 ",
"@lhoestq \r\nThanks for your comment. I have checked out the discussion before... | 2023-05-10T06:25:22Z | 2024-10-28T14:19:44Z | 2023-05-12T09:37:45Z | NONE | null | null | {
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} | ### Feature request
Support for streaming datasets stored in object stores in `load_from_disk`.
### Motivation
The `load_from_disk` function supports fetching datasets stored in object stores such as `s3`. In many cases, the datasets that are stored in object stores are very large and being able to stream the data from the buckets becomes essential.
### Your contribution
I'd be happy to contribute this feature if I could get the guidance on how to do so. | {
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