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PR_kwDODunzps6aWgr3
| 7,613 |
fix parallel push_to_hub in dataset_dict
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I_kwDODunzps67RaqZ
| 7,612 |
Provide an option of robust dataset iterator with error handling
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[] | 2025-06-13T00:40:48Z | 2025-06-13T00:42:00Z | null |
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### Feature request
Adding an option to skip corrupted data samples when __iter__ is called. Currently the datasets behavior is throwing errors if the data sample if corrupted and let user aware and handle the data corruption. When I tried to try-catch the error at user level, the iterator will raise StopIteration when I called next() again.
The way I try to do error handling is: (This doesn't work, unfortunately)
```
# Load the dataset with streaming enabled
dataset = load_dataset(
"pixparse/cc12m-wds", split="train", streaming=True
)
# Get an iterator from the dataset
iterator = iter(dataset)
while True:
try:
# Try to get the next example
example = next(iterator)
# Try to access and process the image
image = example["jpg"]
pil_image = Image.fromarray(np.array(image))
pil_image.verify() # Verify it's a valid image file
except StopIteration: # Code path 1
print("\nStopIteration was raised! Reach the end of dataset")
raise StopIteration
except Exception as e: # Code path 2
errors += 1
print("Error! Skip this sample")
cotinue
else:
successful += 1
```
This is because the `IterableDataset` already throws an error (reaches Code path 2). And if I continue call next(), it will hit Code path 1. This is because the inner iterator of `IterableDataset`([code](https://github.com/huggingface/datasets/blob/89bd1f971402acb62805ef110bc1059c38b1c8c6/src/datasets/iterable_dataset.py#L2242)) as been stopped, so calling next() on it will raise StopIteration.
So I can not skip the corrupted data sample in this way. Would also love to hear any suggestions about creating a robust dataloader.
Thanks for your help in advance!
### Motivation
## Public dataset corruption might be common
A lot of users would use public dataset, and the public dataset might contains some corrupted data, especially for dataset with image / video etc. I totally understand it's dataset owner and user's responsibility to ensure the data integrity / run data cleaning or preprocessing, but it would be easier for developers who would use the dataset
## Use cases
For example, a robust dataloader would be easy for users who want to try quick tests on different dataset, and chose one dataset which fits their needs. So user could use IterableDataloader with `stream=True` to use the dataset easily without downloading and removing corrupted data samples from the dataset.
### Your contribution
The error handling might not trivial and might need more careful design.
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I_kwDODunzps67PbcE
| 7,611 |
Code example for dataset.add_column() does not reflect correct way to use function
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[] | 2025-06-12T19:42:29Z | 2025-06-12T19:42:29Z | null |
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https://github.com/huggingface/datasets/blame/38d4d0e11e22fdbc4acf373d2421d25abeb43439/src/datasets/arrow_dataset.py#L5925C10-L5925C10
The example seems to suggest that dataset.add_column() can add column inplace, however, this is wrong -- it cannot. It returns a new dataset with the column added to it.
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I_kwDODunzps67PCcY
| 7,610 |
i cant confirm email
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[] | 2025-06-12T18:58:49Z | 2025-06-12T18:58:49Z | null |
NONE
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### Describe the bug
This is dificult, I cant confirm email because I'm not get any email!
I cant post forum because I cant confirm email!
I can send help desk because... no exist on web page.
paragraph 44
### Steps to reproduce the bug
rthjrtrt
### Expected behavior
ewtgfwetgf
### Environment info
sdgfswdegfwe
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| 3,140,373,128 |
PR_kwDODunzps6aOQ_g
| 7,609 |
Update `_dill.py` to use `co_linetable` for Python 3.10+ in place of `co_lnotab`
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"not 100% sure either, I tried removing unnecessary checks - let me know if they sound good to you otherwise I'll revert",
"I can't reproduce the warning anymore... 🤦🏻♂️\r\n"
] | 2025-06-12T13:47:01Z | 2025-06-12T14:56:11Z | null |
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Not 100% about this one, but it seems to be recommended.
```
/fsx/qgallouedec/miniconda3/envs/trl/lib/python3.12/site-packages/datasets/utils/_dill.py:385: DeprecationWarning: co_lnotab is deprecated, use co_lines instead.
```
Tests pass locally. And the warning is gone with this change.
https://peps.python.org/pep-0626/#backwards-compatibility
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PR_kwDODunzps6aEr6b
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Tests typing and fixes for push_to_hub
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todo:
- [x] fix TestPushToHub.test_push_dataset_dict_to_hub_iterable_num_proc
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Video and audio decoding with torchcodec
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"Good idea ! let me know if you have any question or if I can help",
"@lhoestq Almost finished, but I'm having trouble understanding this test case.\nThis is how it looks originally. The `map` function is called, and then `with_format` is called. According to the test case example[\"video\"] is supposed to be a VideoReader. However, according to the [docs](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.with_format) its supposed to be the type passed into `with_format` (numpy in this case). My implementation with VideoDecoder currently does the latter, is that correct, or should it be a VideoDecoder object instead?\n```\n@require_torchvision\ndef test_dataset_with_video_map_and_formatted(shared_datadir):\n from torchvision.io import VideoReader\n\n video_path = str(shared_datadir / \"test_video_66x50.mov\")\n data = {\"video\": [video_path]}\n features = Features({\"video\": Video()})\n dset = Dataset.from_dict(data, features=features)\n dset = dset.map(lambda x: x).with_format(\"numpy\")\n example = dset[0]\n assert isinstance(example[\"video\"], VideoReader)\n # assert isinstance(example[\"video\"][0], np.ndarray)\n\n # from bytes\n with open(video_path, \"rb\") as f:\n data = {\"video\": [f.read()]}\n dset = Dataset.from_dict(data, features=features)\n dset = dset.map(lambda x: x).with_format(\"numpy\")\n example = dset[0]\n assert isinstance(example[\"video\"], VideoReader)\n # assert isinstance(example[\"video\"][0], np.ndarray)\n\n```",
"Hi ! It's maybe more convenient for users to always have a VideoDecoder, since they might only access a few frames and not the full video. So IMO it's fine to always return a VideoDecoder (maybe later we can extend the VideoDecoder to return other types of tensors than numpy arrays though ? 👀 it's not crucial for now though)",
"@lhoestq ya that makes sense, looks like this functionality lives in `src/datasets/formatting`, where an exception is made for VideoReader objects to remain as themselves when being formatted. I'll make the necessary changes. ",
"@lhoestq I'm assuming this was also the case for torchaudio objects?",
"We're not using torchaudio but soundfile. But anyway we unfortunately decode full audio files instead of returning a Reader and it can be interesting to fix this. Currently it always returns a dict {\"array\": np.array(...), \"sampling_rate\": int(...)}, while it would be cool to return a reader with seek() and read() - like methods as for videos.\n\n(there is a way to make the audio change backward compatible anyway by allowing `reader[\"array\"]` to return the full array)",
"@lhoestq (sorry for the spam btw)\nLooks like there's a # TODO to have these returned as np.arrays instead. I'm curious why the authors didn't do it initially. Maybe a performance thing?\nThis is from `/src/datasets/formatting/np_formatter.py` line 70\n```\nif config.TORCHVISION_AVAILABLE and \"torchvision\" in sys.modules:\n from torchvision.io import VideoReader\n\n if isinstance(value, VideoReader):\n return value # TODO(QL): set output to np arrays ?\n```",
"Oh cool ya this is something that I could implement with torchcodec. I can add that to the PR as well.",
"> Looks like there's a # TODO to have these returned as np.arrays instead. I'm curious why the authors didn't do it initially. Maybe a performance thing?\n\nyea that was me, I focused on a simple logic to start with, since I knew there was torchcodec coming and maybe wasn't worth it at the time ^^\n\nbut anyway it's fine to start with a logic without formatting to start with and then iterate",
"Hey @lhoestq I ran into an error with this test case for the Audio feature\n\n```\n@require_sndfile\n@require_torchcodec\ndef test_dataset_with_audio_feature_map_is_decoded(shared_datadir):\n audio_path = str(shared_datadir / \"test_audio_44100.wav\")\n data = {\"audio\": [audio_path], \"text\": [\"Hello\"]}\n features = Features({\"audio\": Audio(), \"text\": Value(\"string\")})\n dset = Dataset.from_dict(data, features=features)\n\n def process_audio_sampling_rate_by_example(example):\n sample_rate = example[\"audio\"].get_all_samples().sample_rate\n example[\"double_sampling_rate\"] = 2 * sample_rate\n return example\n\n decoded_dset = dset.map(process_audio_sampling_rate_by_example)\n for item in decoded_dset.cast_column(\"audio\", Audio(decode=False)):\n assert item.keys() == {\"audio\", \"text\", \"double_sampling_rate\"}\n assert item[\"double_sampling_rate\"] == 88200\n\n def process_audio_sampling_rate_by_batch(batch):\n double_sampling_rates = []\n for audio in batch[\"audio\"]:\n double_sampling_rates.append(2 * audio.get_all_samples().sample_rate)\n batch[\"double_sampling_rate\"] = double_sampling_rates\n return batch\n\n decoded_dset = dset.map(process_audio_sampling_rate_by_batch, batched=True)\n for item in decoded_dset.cast_column(\"audio\", Audio(decode=False)):\n assert item.keys() == {\"audio\", \"text\", \"double_sampling_rate\"}\n assert item[\"double_sampling_rate\"] == 88200\n```\n\nthis is the error below\n```\nsrc/datasets/arrow_writer.py:626: in write_batch\n arrays.append(pa.array(typed_sequence))\n.....\nFAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_decoded - pyarrow.lib.ArrowInvalid: Could not convert <torchcodec.decoders._audio_decoder.AudioDecoder object at 0x138cdd810> with type AudioDecoder: did not recognize Python value type when inferring an Arrow data type\n```\n\nBy the way I copied the test case and ran it on the original implementation of the Video feature, which uses the torchvision backend and I got a similar error.\n```\ndef test_dataset_with_video_feature_map_is_decoded(shared_datadir):\n video_path = str(shared_datadir / \"test_video_66x50.mov\")\n data = {\"video\": [video_path], \"text\": [\"Hello\"]}\n features = Features({\"video\": Video(), \"text\": Value(\"string\")})\n dset = Dataset.from_dict(data, features=features)\n\n def process_audio_sampling_rate_by_example(example):\n metadata = example[\"video\"].get_metadata()\n example[\"double_fps\"] = 2 * metadata[\"video\"][\"fps\"][0]\n return example\n\n decoded_dset = dset.map(process_audio_sampling_rate_by_example)\n for item in decoded_dset.cast_column(\"video\", Video(decode=False)):\n assert item.keys() == {\"video\", \"text\", \"double_fps\"}\n assert item[\"double_fps\"] == 2 * 10 # prollly wont work past 2*10 is made up!! shouldn't pass\n\n def process_audio_sampling_rate_by_batch(batch):\n double_fps = []\n for video in batch[\"video\"]:\n double_fps.append(2 * video.metadata.begin_stream_seconds)\n batch[\"double_fps\"] = double_fps\n return batch\n\n decoded_dset = dset.map(process_audio_sampling_rate_by_batch, batched=True)\n for item in decoded_dset.cast_column(\"video\", Video(decode=False)):\n assert item.keys() == {\"video\", \"text\", \"double_fps\"}\n assert item[\"double_fps\"] == 2 * 10 # prollly wont work past this no reason it should\n```\n\nI was wondering if these error's are expected. They seem to be coming from the fact that the function `_cast_to_python_objects` in `src/datasets/features/features.py` doesn't handle VideoDecoders or AudioDecoders. I was able to fix it and get rid of the error by adding this to the bottom of the function\n```\n elif config.TORCHCODEC_AVAILABLE and \"torchcodec\" in sys.modules and isinstance(obj, VideoDecoder):\n v = Video()\n return v.encode_example(obj), True\n elif config.TORCHCODEC_AVAILABLE and \"torchcodec\" in sys.modules and isinstance(obj, AudioDecoder):\n a = Audio()\n return a.encode_example(obj), True\n```\nThis fixed it, but I just want to make sure I'm not adding things that are messing up the intended functionality.",
"This is the right fix ! :)",
"Btw I just remembered that we were using soundfile because it can support a wide range of audio formats, is it also the case for torchcodec ? including ogg, opus for example",
"Yes from what I understand torchcodec supports everything ffmpeg supports.",
"Okay just finished. However, I wasn't able to pass this test case:\n```python\n@require_torchcodec\n@require_sndfile\[email protected](\"streaming\", [False, True])\ndef test_load_dataset_with_audio_feature(streaming, jsonl_audio_dataset_path, shared_datadir):\n from torchcodec.decoders import AudioDecoder\n audio_path = str(shared_datadir / \"test_audio_44100.wav\")\n data_files = jsonl_audio_dataset_path\n features = Features({\"audio\": Audio(), \"text\": Value(\"string\")})\n dset = load_dataset(\"json\", split=\"train\", data_files=data_files, features=features, streaming=streaming)\n item = dset[0] if not streaming else next(iter(dset))\n assert item.keys() == {\"audio\", \"text\"}\n assert isinstance(item[\"audio\"], AudioDecoder)\n samples = item[\"audio\"].get_all_samples()\n assert samples.sample_rate == 44100\n assert samples.data.shape == (1, 202311)\n```\n\nIt returned this error\n```\nstreaming = False, jsonl_audio_dataset_path = '/private/var/folders/47/c7dlgs_n6lx8rtr8f5w5m1m00000gn/T/pytest-of-tytodd/pytest-103/data2/audio_dataset.jsonl'\nshared_datadir = PosixPath('/private/var/folders/47/c7dlgs_n6lx8rtr8f5w5m1m00000gn/T/pytest-of-tytodd/pytest-103/test_load_dataset_with_audio_f0/data')\n\n @require_torchcodec\n @require_sndfile\n @pytest.mark.parametrize(\"streaming\", [False, True])\n def test_load_dataset_with_audio_feature(streaming, jsonl_audio_dataset_path, shared_datadir):\n from torchcodec.decoders import AudioDecoder\n audio_path = str(shared_datadir / \"test_audio_44100.wav\")\n data_files = jsonl_audio_dataset_path\n features = Features({\"audio\": Audio(), \"text\": Value(\"string\")})\n> dset = load_dataset(\"json\", split=\"train\", data_files=data_files, features=features, streaming=streaming)\n\ntests/features/test_audio.py:686: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\nsrc/datasets/load.py:1418: in load_dataset\n builder_instance.download_and_prepare(\nsrc/datasets/builder.py:925: in download_and_prepare\n self._download_and_prepare(\nsrc/datasets/builder.py:1019: in _download_and_prepare\n verify_splits(self.info.splits, split_dict)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\n\nexpected_splits = {'train': SplitInfo(name='train', num_bytes=2351563, num_examples=10000, shard_lengths=None, dataset_name=None), 'validation': SplitInfo(name='validation', num_bytes=238418, num_examples=1000, shard_lengths=None, dataset_name=None)}\nrecorded_splits = {'train': SplitInfo(name='train', num_bytes=167, num_examples=1, shard_lengths=None, dataset_name='json')}\n\n def verify_splits(expected_splits: Optional[dict], recorded_splits: dict):\n if expected_splits is None:\n logger.info(\"Unable to verify splits sizes.\")\n return\n if len(set(expected_splits) - set(recorded_splits)) > 0:\n> raise ExpectedMoreSplitsError(str(set(expected_splits) - set(recorded_splits)))\nE datasets.exceptions.ExpectedMoreSplitsError: {'validation'}\n\nsrc/datasets/utils/info_utils.py:68: ExpectedMoreSplitsError\n```\n\nIt looks like this test case wasn't passing when I forked the repo, so I assume I didn't do anything to break it. I also added this case to `test_video.py`, and it fails there as well. If this looks good, I'll go ahead and submit the PR."
] | 2025-06-11T07:02:30Z | 2025-06-13T08:43:05Z | null |
NONE
| null | null | null | null |
### Feature request
Pytorch is migrating video processing to torchcodec and it's pretty cool. It would be nice to migrate both the audio and video features to use torchcodec instead of torchaudio/video.
### Motivation
My use case is I'm working on a multimodal AV model, and what's nice about torchcodec is I can extract the audio tensors directly from MP4 files. Also, I can easily resample video data to whatever fps I like on the fly. I haven't found an easy/efficient way to do this with torchvision.
### Your contribution
I’m modifying the Video dataclass to use torchcodec in place of the current backend, starting from a stable commit for a project I’m working on. If it ends up working well, I’m happy to open a PR on main.
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Add `num_proc=` to `.push_to_hub()` (Dataset and IterableDataset)
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7606). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-06-10T14:35:10Z | 2025-06-11T16:47:28Z | 2025-06-11T16:47:25Z |
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Make `push_to_hub` atomic (#7600)
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7605). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Hi ! unfortunately we can't allow atomic commits for commits with hundreds of files additions (HF would time out)\r\n\r\nMaybe an alternative would be to retry if there was a commit in between ? this could be the default behavior as well",
"Thanks for taking a look – much appreciated!\r\n\r\nI've verified that commits with up to 20,000 files don't time out and the commit time scales linearly with the number of operations enqueued. It took just under 2 minutes to complete (successfully) the 20k file commit.\r\n\r\nThe fundamental issue I'm trying to tackle here is dataset corruption: getting into a state where a dataset on the hub cannot be used when downloaded. Non-atomic commits won't get us there, I think. If, for example, 3 of 5 commits complete and the machine/process calling `push_to_hub` has a network, hardware, or other failure that prevents it from completing the rest of the commits (even with retries) we'll now have some pointer files pointing to the new data and others pointing to the old data => corrupted. While this may seem like an unlikely scenario, it's a regular occurrence at scale.\r\n\r\nIf you still feel strongly that atomic commits are not the right way to go, I can either set it to not be the default or remove it entirely from this PR.\r\n\r\nAs for retries, it's a good idea. In a non-atomic world, the logic gets more complicated:\r\n- keep an explicit queue of pending add/delete operations\r\n- chunkwise pop from queue and commit with `parent_commit` set to previous chunked commit hash\r\n- if `create_commit` fails:\r\n - re-fetch README and set `parent_commit` to latest hash for `revision`\r\n - re-generate dataset card content\r\n - swap old `CommitOperationAdd` with new one for README in the pending queue\r\n- resume chunkwise committing from the queue as above\r\n\r\nEntirely doable, but more involved than I signed up for with this PR.",
"Just to clarify – setting the `parent_commit` can be separated from making the commit atomic (which is what I'm suggesting by either atomic commits not the default or removing it from this PR). It's crucial to set the parent commit to avoid the read-modify-write race condition on the README schema."
] | 2025-06-09T22:29:38Z | 2025-06-11T15:56:58Z | null |
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Docs and more methods for IterableDataset: push_to_hub, to_parquet...
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to_csv, to_json, to_sql, to_pandas, to_polars, to_dict, to_list
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No TF in win tests
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Enhance error handling and input validation across multiple modules
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This PR improves the robustness and user experience by:
1. **Audio Module**:
- Added clear error messages when required fields ('path' or 'bytes') are missing in audio encoding
2. **DatasetDict**:
- Enhanced key access error messages to show available splits when an invalid key is accessed
3. **NonMutableDict**:
- Added input validation for the update() method to ensure proper mapping types
4. **Arrow Reader**:
- Improved error messages for small dataset percentage splits with suggestions for alternatives
5. **FaissIndex**:
- Strengthened input validation with descriptive error messages
- Added proper type checking and shape validation for search queries
These changes make the code more maintainable and user-friendly by providing actionable feedback when issues arise.
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`push_to_hub` is not concurrency safe (dataset schema corruption)
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"@lhoestq can you please take a look? I've submitted a PR that fixes this issue. Thanks.",
"Thanks for the ping ! As I said in https://github.com/huggingface/datasets/pull/7605 there is maybe a more general approach using retries :)"
] | 2025-06-07T17:28:56Z | 2025-06-11T14:14:04Z | null |
NONE
| null | null | null | null |
### Describe the bug
Concurrent processes modifying and pushing a dataset can overwrite each others' dataset card, leaving the dataset unusable.
Consider this scenario:
- we have an Arrow dataset
- there are `N` configs of the dataset
- there are `N` independent processes operating on each of the individual configs (e.g. adding a column, `new_col`)
- each process calls `push_to_hub` on their particular config when they're done processing
- all calls to `push_to_hub` succeed
- the `README.md` now has some configs with `new_col` added and some with `new_col` missing
Any attempt to load a config (using `load_dataset`) where `new_col` is missing will fail because of a schema mismatch between `README.md` and the Arrow files. Fixing the dataset requires updating `README.md` by hand with the correct schema for the affected config. In effect, `push_to_hub` is doing a `git push --force` (I found this behavior quite surprising).
We have hit this issue every time we run processing jobs over our datasets and have to fix corrupted schemas by hand.
Reading through the code, it seems that specifying a [`parent_commit`](https://github.com/huggingface/huggingface_hub/blob/v0.32.4/src/huggingface_hub/hf_api.py#L4587) hash around here https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L5794 would get us to a normal, non-forced git push, and avoid schema corruption. I'm not familiar enough with the code to know how to determine the commit hash from which the in-memory dataset card was loaded.
### Steps to reproduce the bug
See above.
### Expected behavior
Concurrent edits to disjoint configs of a dataset should never corrupt the dataset schema.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.15.0-118-generic-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.2
- `fsspec` version: 2023.9.0
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I_kwDODunzps66TS2X
| 7,599 |
My already working dataset (when uploaded few months ago) now is ignoring metadata.jsonl
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"Maybe its been a recent update, but i can manage to load the metadata.jsonl separately from the images with:\n\n```\nmetadata = load_dataset(\"PRAIG/SMB\", split=\"train\", data_files=[\"*.jsonl\"])\nimages = load_dataset(\"PRAIG/SMB\", split=\"train\")\n```\nDo you know it this is an expected behaviour? This makes my dataset viewer to only load the images without the labeling of metadata.jsonl.\n\nThanks",
"Hi ! this is because we now expect the metadata file to be inside the directory named after the split \"train\" (this way each split can have its own metadata and can be loaded independently)\n\nYou can fix that by configuring it explicitly in the dataset's README.md header:\n\n```yaml\nconfigs:\n- config_name: default\n data_files:\n - split: train\n path:\n - \"train/**/*.png\"\n - \"metadata.jsonl\"\n```\n\n(or by moving the metadata.jsonl in train/ but in this case you also have to modify the content of the JSONL to fix the relative paths to the images)"
] | 2025-06-06T18:59:00Z | 2025-06-11T14:36:55Z | null |
NONE
| null | null | null | null |
### Describe the bug
Hi everyone, I uploaded my dataset https://huggingface.co/datasets/PRAIG/SMB a few months ago while I was waiting for a conference acceptance response. Without modifying anything in the dataset repository now the Dataset viewer is not rendering the metadata.jsonl annotations, neither it is being downloaded when using load_dataset. Can you please help? Thank you in advance.
### Steps to reproduce the bug
from datasets import load_dataset
ds = load_dataset("PRAIG/SMB")
ds = ds["train"]
### Expected behavior
It is expected to have all the metadata available in the jsonl file. Fields like: "score_id", "original_width", "original_height", "regions"... among others.
### Environment info
datasets==3.6.0, python 3.13.3 (but he problem is already in the huggingface dataset page)
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PR_kwDODunzps6ZaclZ
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fix string_to_dict usage for windows
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| 3,123,962,709 |
I_kwDODunzps66M-NV
| 7,597 |
Download datasets from a private hub in 2025
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"Hi ! First, and in the general case, Hugging Face does offer to host private datasets, and with a subscription you can even choose the region in which the repositories are hosted (US, EU)\n\nThen if you happen to have a private deployment, you can set the HF_ENDPOINT environment variable (same as in https://github.com/huggingface/transformers/issues/38634)",
"Thank you @lhoestq. Works as described!"
] | 2025-06-06T07:55:19Z | 2025-06-11T14:43:36Z | null |
NONE
| null | null | null | null |
### Feature request
In the context of a private hub deployment, customers would like to use load_dataset() to load datasets from their hub, not from the public hub. This doesn't seem to be configurable at the moment and it would be nice to add this feature.
The obvious workaround is to clone the repo first and then load it from local storage, but this adds an extra step. It'd be great to have the same experience regardless of where the hub is hosted.
This issue was raised before here: https://github.com/huggingface/datasets/issues/3679
@juliensimon
### Motivation
none
### Your contribution
none
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PR_kwDODunzps6ZRkEU
| 7,596 |
Add albumentations to use dataset
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CONTRIBUTOR
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1. Fixed broken link to the list of transforms in torchvison.
2. Extended section about video image augmentations with an example from Albumentations.
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PR_kwDODunzps6ZOaFl
| 7,595 |
Add `IterableDataset.push_to_hub()`
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Basic implementation, which writes one shard per input dataset shard.
This is to be improved later.
Close https://github.com/huggingface/datasets/issues/5665
PS: for image/audio datasets structured as actual image/audio files (not parquet), you can sometimes speed it up with `ds.decode(num_threads=...).push_to_hub(...)`
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I_kwDODunzps66A5-K
| 7,594 |
Add option to ignore keys/columns when loading a dataset from jsonl(or any other data format)
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"Good point, I'd be in favor of having the `columns` argument in `JsonConfig` (and the others) to align with `ParquetConfig` to let users choose which columns to load and ignore the rest",
"Is it possible to ignore columns when using parquet? ",
"Yes, you can pass `columns=...` to load_dataset to select which columns to load, and it is passed to `ParquetConfig` :)",
"Ok, i didn't know that. \nAnyway, it would be good to add this to others"
] | 2025-06-05T11:12:45Z | 2025-06-05T12:58:12Z | null |
NONE
| null | null | null | null |
### Feature request
Hi, I would like the option to ignore keys/columns when loading a dataset from files (e.g. jsonl).
### Motivation
I am working on a dataset which is built on jsonl. It seems the dataset is unclean and a column has different types in each row. I can't clean this or remove the column (It is not my data and it is too big for me to clean and save on my own hardware).
I would like the option to just ignore this column when using `load_dataset`, since i don't need it.
I tried to look if this is already possible but couldn't find a solution. if there is I would love some help. If it is not currently possible, I would love this feature
### Your contribution
I don't think I can help this time, unfortunately.
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PR_kwDODunzps6ZE34G
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Fix broken link to albumentations
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CONTRIBUTOR
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A few months back I rewrote all docs at [https://albumentations.ai/docs](https://albumentations.ai/docs), and some pages changed their links.
In this PR fixed link to the most recent doc in Albumentations about bounding boxes and it's format.
Fix a few typos in the doc as well.
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PR_kwDODunzps6ZC2so
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Remove scripts altogether
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TODO:
- [x] remplace fixtures based on script with no-script fixtures
- [x] windaube
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I_kwDODunzps651hpE
| 7,591 |
Add num_proc parameter to push_to_hub
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[] | 2025-06-04T13:19:15Z | 2025-06-04T13:19:23Z | null |
NONE
| null | null | null | null |
### Feature request
A number of processes parameter to the dataset.push_to_hub method
### Motivation
Shards are currently uploaded serially which makes it slow for many shards, uploading can be done in parallel and much faster
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I_kwDODunzps64339s
| 7,590 |
`Sequence(Features(...))` causes PyArrow cast error in `load_dataset` despite correct schema.
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"Hi @lhoestq \n\nCould you help confirm whether this qualifies as a bug?\n\nIt looks like the issue stems from how `Sequence(Features(...))` is interpreted as a plain struct during schema inference, which leads to a mismatch when casting with PyArrow (especially with nested structs inside lists). From the description, this seems like an inconsistency with expected behavior.\n\nIf confirmed, I’d be happy to take a shot at investigating and potentially submitting a fix.\n\nAlso looping in @AHS-uni — could you kindly share a minimal JSONL example that reproduces this?\n\nThanks!",
"Hello @Flink-ddd \n\nI updated the minimal example and included both JSON and JSONL minimal examples in the Colab notebook. \n\nHere is the minimal JSON file for convenience (can't upload JSONL files).\n\n[mini.json](https://github.com/user-attachments/files/20535145/mini.json)\n\nI've also found a number of issues which describe a similar problem:\n\n[7569](https://github.com/huggingface/datasets/issues/7569) (Open)\n[7137](https://github.com/huggingface/datasets/issues/7137) (Open)\n[7501](https://github.com/huggingface/datasets/issues/7501) (Closed)\n[2434](https://github.com/huggingface/datasets/issues/2434) (Closed)\n\nThe closed issues don't really address the problem (IMO). [7501](https://github.com/huggingface/datasets/issues/7501) provides a workaround (using a Python list instead of `Sequence`), but it seem precarious. ",
"Hi ! `Sequence({...})` corresponds to a struct of lists ([docs](https://huggingface.co/docs/datasets/v3.6.0/en/package_reference/main_classes#datasets.Features)). This come from Tensorflow Datasets.\n\nIf you want to use a list of structs, you should use `[{...}]`, e.g.\n\n```python\nitem = {\n \"id\": Value(\"string\"),\n \"data\": Value(\"string\"),\n}\n\nfeatures = Features({\n \"list\": [item],\n})\n```"
] | 2025-05-29T22:53:36Z | 2025-06-04T13:13:08Z | null |
NONE
| null | null | null | null |
### Description
When loading a dataset with a field declared as a list of structs using `Sequence(Features(...))`, `load_dataset` incorrectly infers the field as a plain `struct<...>` instead of a `list<struct<...>>`. This leads to the following error:
```
ArrowNotImplementedError: Unsupported cast from list<item: struct<id: string, data: string>> to struct using function cast_struct
```
This occurs even when the `features` schema is explicitly provided and the dataset format supports nested structures natively (e.g., JSON, JSONL).
---
### Minimal Reproduction
[Colab Link.](https://colab.research.google.com/drive/1FZPQy6TP3jVd4B3mYKyfQaWNuOAvljUq?usp=sharing)
#### Dataset
```python
data = [
{
"list": [
{"id": "example1", "data": "text"},
]
},
]
```
#### Schema
```python
from datasets import Features, Sequence, Value
item = Features({
"id": Value("string"),
"data": Value("string"),
})
features = Features({
"list": Sequence(item),
})
```
---
### Tested File Formats
The same schema was tested across different formats:
| Format | Method | Result |
| --------- | --------------------------- | ------------------- |
| JSONL | `load_dataset("json", ...)` | Arrow cast error |
| JSON | `load_dataset("json", ...)` | Arrow cast error |
| In-memory | `Dataset.from_list(...)` | Works as expected |
The issue seems not to be in the schema or the data, but in how `load_dataset()` handles the `Sequence(Features(...))` pattern when parsing from files (specifically JSON and JSONL).
---
### Expected Behavior
If `features` is explicitly defined as:
```python
Features({"list": Sequence(Features({...}))})
```
Then the data should load correctly across all backends — including from JSON and JSONL — without any Arrow casting errors. This works correctly when loading from memory via `Dataset.from_list`.
---
### Environment
* `datasets`: 3.6.0
* `pyarrow`: 20.0.0
* Python: 3.12.10
* OS: Ubuntu 24.04.2 LTS
* Notebook: \[Colab test notebook available]
---
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| 3,101,119,704 |
PR_kwDODunzps6YKiyL
| 7,589 |
feat: use content defined chunking
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7589). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-05-29T18:19:41Z | 2025-06-08T15:06:05Z | null |
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WIP:
- [x] set the parameters in `io.parquet.ParquetDatasetReader`
- [x] set the parameters in `arrow_writer.ParquetWriter`
It requires a new pyarrow pin ">=21.0.0" which is not yet released.
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I_kwDODunzps64auB5
| 7,588 |
ValueError: Invalid pattern: '**' can only be an entire path component [Colab]
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[
"Could you please run the following code snippet in your environment and share the exact output? This will help check for any compatibility issues within the env itself. \n\n```\nimport datasets\nimport huggingface_hub\nimport fsspec\n\nprint(\"datasets version:\", datasets.__version__)\nprint(\"huggingface_hub version:\", huggingface_hub.__version__)\nprint(\"fsspec version:\", fsspec.__version__)\n```",
"```bash\ndatasets version: 2.14.4\nhuggingface_hub version: 0.31.4\nfsspec version: 2025.3.2\n```",
"Version 2.14.4 is not the latest version available, in fact it is from August 08, 2023 (you can check here: https://pypi.org/project/datasets/#history)\n\nUse pip install datasets==3.6.0 to install a more recent version (from May 7, 2025)\n\nI also had the same problem with Colab, after updating to the latest version it was solved.\n\nI hope it helps",
"thank you @CleitonOERocha. it sure did help.\n\nupdating `datasets` to v3.6.0 and keeping `fsspec` on v2025.3.2 eliminates the issue.",
"Very helpful, thank you!"
] | 2025-05-27T13:46:05Z | 2025-05-30T13:22:52Z | 2025-05-30T01:26:30Z |
NONE
| null | null | null | null |
### Describe the bug
I have a dataset on HF [here](https://huggingface.co/datasets/kambale/luganda-english-parallel-corpus) that i've previously used to train a translation model [here](https://huggingface.co/kambale/pearl-11m-translate).
now i changed a few hyperparameters to increase number of tokens for the model, increase Transformer layers, and all
however, when i try to load the dataset, this error keeps coming up.. i have tried everything.. i have re-written the code a hundred times, and this keep coming up
### Steps to reproduce the bug
Imports:
```bash
!pip install datasets huggingface_hub fsspec
```
Python code:
```python
from datasets import load_dataset
HF_DATASET_NAME = "kambale/luganda-english-parallel-corpus"
# Load the dataset
try:
if not HF_DATASET_NAME or HF_DATASET_NAME == "YOUR_HF_DATASET_NAME":
raise ValueError(
"Please provide a valid Hugging Face dataset name."
)
dataset = load_dataset(HF_DATASET_NAME)
# Omitted code as the error happens on the line above
except ValueError as ve:
print(f"Configuration Error: {ve}")
raise
except Exception as e:
print(f"An error occurred while loading the dataset '{HF_DATASET_NAME}': {e}")
raise e
```
now, i have tried going through this [issue](https://github.com/huggingface/datasets/issues/6737) and nothing helps
### Expected behavior
loading the dataset successfully and perform splits (train, test, validation)
### Environment info
from the imports, i do not install specific versions of these libraries, so the latest or available version is installed
* `datasets` version: latest
* `Platform`: Google Colab
* `Hardware`: NVIDIA A100 GPU
* `Python` version: latest
* `huggingface_hub` version: latest
* `fsspec` version: latest
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load_dataset splits typing
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I_kwDODunzps64Qc5v
| 7,586 |
help is appreciated
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"how is this related to this repository ?"
] | 2025-05-26T14:00:42Z | 2025-05-26T18:21:57Z | null |
NONE
| null | null | null | null |
### Feature request
https://github.com/rajasekarnp1/neural-audio-upscaler/tree/main
### Motivation
ai model develpment and audio
### Your contribution
ai model develpment and audio
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| 3,091,227,921 |
PR_kwDODunzps6Xo-Tw
| 7,585 |
Avoid multiple default config names
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] | 2025-05-26T13:27:59Z | 2025-06-05T12:41:54Z | 2025-06-05T12:41:52Z |
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Fix duplicating default config names.
Currently, when calling `push_to_hub(set_default=True` with 2 different config names, both are set as default.
Moreover, this will generate an error next time we try to push another default config name, raised by `MetadataConfigs.get_default_config_name`:
https://github.com/huggingface/datasets/blob/da1db8a5b89fc0badaa0f571b36e122e52ae8c61/src/datasets/arrow_dataset.py#L5757
https://github.com/huggingface/datasets/blob/da1db8a5b89fc0badaa0f571b36e122e52ae8c61/src/datasets/utils/metadata.py#L186-L188
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| 3,090,255,023 |
I_kwDODunzps64MYyv
| 7,584 |
Add LMDB format support
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"Hi ! Can you explain what's your use case ? Is it about converting LMDB to Dataset objects (i.e. converting to Arrow) ?"
] | 2025-05-26T07:10:13Z | 2025-05-26T18:23:37Z | null |
NONE
| null | null | null | null |
### Feature request
Add LMDB format support for large memory-mapping files
### Motivation
Add LMDB format support for large memory-mapping files
### Your contribution
I'm trying to add it
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| 3,088,987,757 |
I_kwDODunzps64HjZt
| 7,583 |
load_dataset type stubs reject List[str] for split parameter, but runtime supports it
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| null | null | null | null |
### Describe the bug
The [load_dataset](https://huggingface.co/docs/datasets/v3.6.0/en/package_reference/loading_methods#datasets.load_dataset) method accepts a `List[str]` as the split parameter at runtime, however, the current type stubs restrict the split parameter to `Union[str, Split, None]`. This causes type checkers like Pylance to raise `reportArgumentType` errors when passing a list of strings, even though it works as intended at runtime.
### Steps to reproduce the bug
1. Use load_dataset with multiple splits e.g.:
```
from datasets import load_dataset
ds_train, ds_val, ds_test = load_dataset(
"Silly-Machine/TuPyE-Dataset",
"binary",
split=["train[:75%]", "train[75%:]", "test"]
)
```
2. Observe that code executes correctly at runtime and Pylance raises `Argument of type "List[str]" cannot be assigned to parameter "split" of type "str | Split | None"`
### Expected behavior
The type stubs for [load_dataset](https://huggingface.co/docs/datasets/v3.6.0/en/package_reference/loading_methods#datasets.load_dataset) should accept `Union[str, Split, List[str], None]` or more specific overloads for the split parameter to correctly represent runtime behavior.
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.39
- Python version: 3.12.7
- `huggingface_hub` version: 0.32.0
- PyArrow version: 20.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2025.3.0
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PR_kwDODunzps6XPIt7
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fix: Add embed_storage in Pdf feature
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] | 2025-05-22T14:06:29Z | 2025-05-22T14:17:38Z | 2025-05-22T14:17:36Z |
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Add missing `embed_storage` method in Pdf feature (Same as in Audio and Image)
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PR_kwDODunzps6XNpm0
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Add missing property on `RepeatExamplesIterable`
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Fixes #7561
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I_kwDODunzps63wr2D
| 7,580 |
Requesting a specific split (eg: test) still downloads all (train, test, val) data when streaming=False.
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"Hi ! There was a PR open to improve this: https://github.com/huggingface/datasets/pull/6832 \nbut it hasn't been continued so far.\n\nIt would be a cool improvement though !"
] | 2025-05-22T11:08:16Z | 2025-05-26T18:40:31Z | null |
NONE
| null | null | null | null |
### Describe the bug
When using load_dataset() from the datasets library (in load.py), specifying a particular split (e.g., split="train") still results in downloading data for all splits when streaming=False. This happens during the builder_instance.download_and_prepare() call.
This behavior leads to unnecessary bandwidth usage and longer download times, especially for large datasets, even if the user only intends to use a single split.
### Steps to reproduce the bug
dataset_name = "skbose/indian-english-nptel-v0"
dataset = load_dataset(dataset_name, token=hf_token, split="test")
### Expected behavior
Optimize the download logic so that only the required split is downloaded when streaming=False when a specific split is provided.
### Environment info
Dataset: skbose/indian-english-nptel-v0
Platform: M1 Apple Silicon
Python verison: 3.12.9
datasets>=3.5.0
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PR_kwDODunzps6XJerX
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Fix typos in PDF and Video documentation
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] | 2025-05-22T02:27:40Z | 2025-05-22T12:53:49Z | 2025-05-22T12:53:47Z |
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I_kwDODunzps63ocrM
| 7,577 |
arrow_schema is not compatible with list
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[
"Thanks for reporting, I'll look into it",
"Actually it looks like you just forgot parenthesis:\n\n```diff\n- f = datasets.Features({'x': list[datasets.Value(dtype='int32')]})\n+ f = datasets.Features({'x': list([datasets.Value(dtype='int32')])})\n```\n\nor simply using the `[ ]` syntax:\n\n```python\nf = datasets.Features({'x':[datasets.Value(dtype='int32')]})\n```\n\nI'm closing this issue if you don't mind",
"Ah is that what the syntax is? I don't think I was able to find an actual example of it so I assumed it was in the same way that you specify types eg. `list[int]`. This is good to know, thanks."
] | 2025-05-21T16:37:01Z | 2025-05-26T18:49:51Z | 2025-05-26T18:32:55Z |
NONE
| null | null | null | null |
### Describe the bug
```
import datasets
f = datasets.Features({'x': list[datasets.Value(dtype='int32')]})
f.arrow_schema
Traceback (most recent call last):
File "datasets/features/features.py", line 1826, in arrow_schema
return pa.schema(self.type).with_metadata({"huggingface": json.dumps(hf_metadata)})
^^^^^^^^^
File "datasets/features/features.py", line 1815, in type
return get_nested_type(self)
^^^^^^^^^^^^^^^^^^^^^
File "datasets/features/features.py", line 1252, in get_nested_type
return pa.struct(
^^^^^^^^^^
File "pyarrow/types.pxi", line 5406, in pyarrow.lib.struct
File "pyarrow/types.pxi", line 3890, in pyarrow.lib.field
File "pyarrow/types.pxi", line 5918, in pyarrow.lib.ensure_type
TypeError: DataType expected, got <class 'list'>
```
The following works
```
f = datasets.Features({'x': datasets.LargeList(datasets.Value(dtype='int32'))})
```
### Expected behavior
according to https://github.com/huggingface/datasets/blob/458f45a22c3cc9aea5f442f6f519333dcfeae9b9/src/datasets/features/features.py#L1765 python list should be a valid type specification for features
### Environment info
- `datasets` version: 3.5.1
- Platform: macOS-15.5-arm64-arm-64bit
- Python version: 3.12.9
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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PR_kwDODunzps6XEuMz
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Fix regex library warnings
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] | 2025-05-21T14:31:58Z | 2025-06-05T13:35:16Z | 2025-06-05T12:37:55Z |
CONTRIBUTOR
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# PR Summary
This small PR resolves the regex library warnings showing starting Python3.11:
```python
DeprecationWarning: 'count' is passed as positional argument
```
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[MINOR:TYPO] Update save_to_disk docstring
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CONTRIBUTOR
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r/hub/filesystem in save_to_disk
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| 3,079,641,072 |
I_kwDODunzps63j5fw
| 7,574 |
Missing multilingual directions in IWSLT2017 dataset's processing script
|
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[
"I have opened 2 PRs on the Hub: `https://huggingface.co/datasets/IWSLT/iwslt2017/discussions/7` and `https://huggingface.co/datasets/IWSLT/iwslt2017/discussions/8` to resolve this issue",
"cool ! I pinged the owners of the dataset on HF to merge your PRs :)"
] | 2025-05-21T09:53:17Z | 2025-05-26T18:36:38Z | null |
NONE
| null | null | null | null |
### Describe the bug
Hi,
Upon using `iwslt2017.py` in `IWSLT/iwslt2017` on the Hub for loading the datasets, I am unable to obtain the datasets for the language pairs `de-it`, `de-ro`, `de-nl`, `it-de`, `nl-de`, and `ro-de` using it. These 6 pairs do not show up when using `get_dataset_config_names()` to obtain the list of all the configs present in `IWSLT/iwslt2017`. This should not be the case since as mentioned in their original paper (please see https://aclanthology.org/2017.iwslt-1.1.pdf), the authors specify that "_this year we proposed the multilingual translation between any pair of languages from {Dutch, English, German, Italian, Romanian}..._" and because these datasets are indeed present in `data/2017-01-trnmted/texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.zip`.
Best Regards,
Anand
### Steps to reproduce the bug
Check the output of `get_dataset_config_names("IWSLT/iwslt2017", trust_remote_code=True)`: only 24 language pairs are present and the following 6 config names are absent: `iwslt2017-de-it`, `iwslt2017-de-ro`, `iwslt2017-de-nl`, `iwslt2017-it-de`, `iwslt2017-nl-de`, and `iwslt2017-ro-de`.
### Expected behavior
The aforementioned 6 language pairs should also be present and hence, all these 6 language pairs' IWSLT2017 datasets must also be available for further use.
I would suggest removing `de` from the `BI_LANGUAGES` list and moving it over to the `MULTI_LANGUAGES` list instead in `iwslt2017.py` to account for all the 6 missing language pairs (the same `de-en` dataset is present in both `data/2017-01-trnmted/texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.zip` and `data/2017-01-trnted/texts/de/en/de-en.zip` but the `de-ro`, `de-nl`, `it-de`, `nl-de`, and `ro-de` datasets are only present in `data/2017-01-trnmted/texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.zip`: so, its unclear why the following comment: _`# XXX: Artificially removed DE from here, as it also exists within bilingual data`_ has been added as `L71` in `iwslt2017.py`). The `README.md` file in `IWSLT/iwslt2017`must then be re-created using `datasets-cli test path/to/iwslt2017.py --save_info --all_configs` to pass all split size verification checks for the 6 new language pairs which were previously non-existent.
### Environment info
- `datasets` version: 3.5.0
- Platform: Linux-6.8.0-56-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.30.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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| 3,076,415,382 |
I_kwDODunzps63Xl-W
| 7,573 |
No Samsum dataset
|
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[
"According to the following https://huggingface.co/posts/seawolf2357/424129432408590, as of now the dataset seems to be inaccessible.\n\n@IgorKasianenko, would https://huggingface.co/datasets/knkarthick/samsum suffice for your purpose?\n",
"Thanks @SP1029 for the update!\nThat will work for now, using it as replacement. Is there a officially recommended way to maintain the CC licensed dataset under the organization account? \nFeel free to close this issue"
] | 2025-05-20T09:54:35Z | 2025-06-09T08:58:24Z | null |
NONE
| null | null | null | null |
### Describe the bug
https://huggingface.co/datasets/Samsung/samsum dataset not found error 404
Originated from https://github.com/meta-llama/llama-cookbook/issues/948
### Steps to reproduce the bug
go to website https://huggingface.co/datasets/Samsung/samsum
see the error
also downloading it with python throws
```
Couldn't find 'Samsung/samsum' on the Hugging Face Hub either: FileNotFoundError: Samsung/samsum@f00baf5a7d4abfec6820415493bcb52c587788e6/samsum.py (repository not found)
```
### Expected behavior
Dataset exists
### Environment info
```
- `datasets` version: 3.2.0
- Platform: macOS-15.4.1-arm64-arm-64bit
- Python version: 3.12.2
- `huggingface_hub` version: 0.26.5
- PyArrow version: 16.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
```
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| 3,074,529,251 |
PR_kwDODunzps6WwsZB
| 7,572 |
Fixed typos
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[
"@lhoestq, mentioning in case you haven't seen this PR. The contribution is very small and easy to check :)"
] | 2025-05-19T17:16:59Z | 2025-06-05T12:25:42Z | 2025-06-05T12:25:41Z |
CONTRIBUTOR
| null | null | 0 |
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More info: [comment](https://github.com/huggingface/datasets/pull/7564#issuecomment-2863391781).
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| 3,074,116,942 |
PR_kwDODunzps6WvRqi
| 7,571 |
fix string_to_dict test
|
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7571). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-05-19T14:49:23Z | 2025-05-19T14:52:24Z | 2025-05-19T14:49:28Z |
MEMBER
| null | null | 0 |
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| 3,065,966,529 |
I_kwDODunzps62vu_B
| 7,570 |
Dataset lib seems to broke after fssec lib update
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[
"Hi, can you try updating `datasets` ? Colab still installs `datasets` 2.x by default, instead of 3.x\n\nIt would be cool to also report this to google colab, they have a GitHub repo for this IIRC",
"@lhoestq I have updated it to `datasets==3.6.0` and now there's an entirely different issue on colab while locally its fine. \n\n```\n/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \nThe secret `HF_TOKEN` does not exist in your Colab secrets.\nTo authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\nYou will be able to reuse this secret in all of your notebooks.\nPlease note that authentication is recommended but still optional to access public models or datasets.\n warnings.warn(\nREADME.md: 100%\n 2.88k/2.88k [00:00<00:00, 166kB/s]\nsuno.jsonl.zst: 100%\n 221M/221M [00:05<00:00, 48.6MB/s]\nGenerating train split: \n 18633/0 [00:01<00:00, 13018.92 examples/s]\n---------------------------------------------------------------------------\nTypeError Traceback (most recent call last)\n[/usr/local/lib/python3.11/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)\n 1870 try:\n-> 1871 writer.write_table(table)\n 1872 except CastError as cast_error:\n\n17 frames\nTypeError: Couldn't cast array of type\nstruct<id: string, type: string, infill: bool, source: string, continue_at: double, infill_dur_s: double, infill_end_s: double, infill_start_s: double, include_future_s: double, include_history_s: double, infill_context_end_s: double, infill_context_start_s: int64>\nto\n{'id': Value(dtype='string', id=None), 'type': Value(dtype='string', id=None), 'infill': Value(dtype='bool', id=None), 'source': Value(dtype='string', id=None), 'continue_at': Value(dtype='float64', id=None), 'include_history_s': Value(dtype='float64', id=None)}\n\nThe above exception was the direct cause of the following exception:\n\nDatasetGenerationError Traceback (most recent call last)\n[/usr/local/lib/python3.11/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)\n 1896 if isinstance(e, DatasetGenerationError):\n 1897 raise\n-> 1898 raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\n 1899 \n 1900 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)\n\nDatasetGenerationError: An error occurred while generating the dataset\n```",
"@lhoestq opps sorry the dataset was in .zst which was causing this error rather than being a datasets library fault. After upgrading dataset version Colab is working fine. "
] | 2025-05-15T11:45:06Z | 2025-06-13T00:44:27Z | 2025-06-13T00:44:27Z |
NONE
| null | null | null | null |
### Describe the bug
I am facing an issue since today where HF's dataset is acting weird and in some instances failure to recognise a valid dataset entirely, I think it is happening due to recent change in `fsspec` lib as using this command fixed it for me in one-time: `!pip install -U datasets huggingface_hub fsspec`
### Steps to reproduce the bug
from datasets import load_dataset
def download_hf():
dataset_name = input("Enter the dataset name: ")
subset_name = input("Enter subset name: ")
ds = load_dataset(dataset_name, name=subset_name)
for split in ds:
ds[split].to_pandas().to_csv(f"{subset_name}.csv", index=False)
download_hf()
### Expected behavior
```
Downloading readme: 100%
1.55k/1.55k [00:00<00:00, 121kB/s]
Downloading data files: 100%
1/1 [00:00<00:00, 2.06it/s]
Downloading data: 0%| | 0.00/54.2k [00:00<?, ?B/s]
Downloading data: 100%|██████████| 54.2k/54.2k [00:00<00:00, 121kB/s]
Extracting data files: 100%
1/1 [00:00<00:00, 35.17it/s]
Generating test split:
140/0 [00:00<00:00, 2628.62 examples/s]
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
[<ipython-input-2-12ab305b0e77>](https://localhost:8080/#) in <cell line: 0>()
8 ds[split].to_pandas().to_csv(f"{subset_name}.csv", index=False)
9
---> 10 download_hf()
2 frames
[/usr/local/lib/python3.11/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)
1171 is_local = not is_remote_filesystem(self._fs)
1172 if not is_local:
-> 1173 raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")
1174 if not os.path.exists(self._output_dir):
1175 raise FileNotFoundError(
NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.
```
OR
```
Traceback (most recent call last):
File "e:\Fuck\download-data\mcq_dataset.py", line 10, in <module>
download_hf()
File "e:\Fuck\download-data\mcq_dataset.py", line 6, in download_hf
ds = load_dataset(dataset_name, name=subset_name)
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py", line 2606, in load_dataset
builder_instance = load_dataset_builder(
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py", line 2277, in load_dataset_builder
dataset_module = dataset_module_factory(
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py", line 1917, in dataset_module_factory
raise e1 from None
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py", line 1867, in dataset_module_factory
raise DatasetNotFoundError(f"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.") from e
datasets.exceptions.DatasetNotFoundError: Dataset 'dataset repo_id' doesn't exist on the Hub or cannot be accessed.
```
### Environment info
colab and 3.10 local system
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| 3,061,234,054 |
I_kwDODunzps62drmG
| 7,569 |
Dataset creation is broken if nesting a dict inside a dict inside a list
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[
"Hi ! That's because Séquence is a type that comes from tensorflow datasets and inverts lists and focus when doing Séquence(dict).\n\nInstead you should use a list. In your case\n```python\nfeatures = Features({\n \"a\": [{\"b\": {\"c\": Value(\"string\")}}]\n})\n```",
"Hi,\n\nThanks for the swift reply! Could you quickly clarify a couple of points?\n\n1. Is there any benefit in using Sequence over normal lists? Especially for longer lists (in my case, up to 256 entries)\n2. When exactly can I use Sequence? If there is a maximum of one level of dictionaries inside, then it's always fine?\n3. When creating the data in the generator, do I need to swap lists and dicts manually, or does that happen automatically?\n\nAlso, the documentation does not seem to mention this limitation of the Sequence type anywhere and encourages users to use it [here](https://huggingface.co/docs/datasets/en/about_dataset_features). In fact, I did not even know that just using a Python list was an option. Maybe the documentation can be improved to mention the limitations of Sequence and highlight that lists can be used instead.\n\nThanks a lot in advance!\n\nBest,\nTim"
] | 2025-05-13T21:06:45Z | 2025-05-20T19:25:15Z | null |
NONE
| null | null | null | null |
### Describe the bug
Hey,
I noticed that the creation of datasets with `Dataset.from_generator` is broken if dicts and lists are nested in a certain way and a schema is being passed. See below for details.
Best,
Tim
### Steps to reproduce the bug
Runing this code:
```python
from datasets import Dataset, Features, Sequence, Value
def generator():
yield {
"a": [{"b": {"c": 0}}],
}
features = Features(
{
"a": Sequence(
feature={
"b": {
"c": Value("int32"),
},
},
length=1,
)
}
)
dataset = Dataset.from_generator(generator, features=features)
```
leads to
```
Generating train split: 1 examples [00:00, 540.85 examples/s]
Traceback (most recent call last):
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 1635, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
^^^^^^^^^^^^^^^^^
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/arrow_writer.py", line 657, in finalize
self.write_examples_on_file()
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/arrow_writer.py", line 510, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/arrow_writer.py", line 629, in write_batch
pa_table = pa.Table.from_arrays(arrays, schema=schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 4851, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 1608, in pyarrow.lib._sanitize_arrays
File "pyarrow/array.pxi", line 399, in pyarrow.lib.asarray
File "pyarrow/array.pxi", line 1004, in pyarrow.lib.Array.cast
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/pyarrow/compute.py", line 405, in cast
return call_function("cast", [arr], options, memory_pool)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/_compute.pyx", line 598, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 393, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from fixed_size_list<item: struct<c: int32>>[1] to struct using function cast_struct
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/user/test/tools/hf_test2.py", line 23, in <module>
dataset = Dataset.from_generator(generator, features=features)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 1114, in from_generator
).read()
^^^^^^
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/io/generator.py", line 49, in read
self.builder.download_and_prepare(
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 1649, in _download_and_prepare
super()._download_and_prepare(
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 1487, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 1644, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
Process finished with exit code 1
```
### Expected behavior
I expected this code not to lead to an error.
I have done some digging and figured out that the problem seems to be the `get_nested_type` function in `features.py`, which, for whatever reason, flips Sequences and dicts whenever it encounters a dict inside of a sequence. This seems to be necessary, as disabling that flip leads to another error. However, by keeping that flip enabled for the highest level and disabling it for all subsequent levels, I was able to work around this problem. Specifically, by patching `get_nested_type` as follows, it works on the given example (emphasis on the `level` parameter I added):
```python
def get_nested_type(schema: FeatureType, level=0) -> pa.DataType:
"""
get_nested_type() converts a datasets.FeatureType into a pyarrow.DataType, and acts as the inverse of
generate_from_arrow_type().
It performs double-duty as the implementation of Features.type and handles the conversion of
datasets.Feature->pa.struct
"""
# Nested structures: we allow dict, list/tuples, sequences
if isinstance(schema, Features):
return pa.struct(
{key: get_nested_type(schema[key], level = level + 1) for key in schema}
) # Features is subclass of dict, and dict order is deterministic since Python 3.6
elif isinstance(schema, dict):
return pa.struct(
{key: get_nested_type(schema[key], level = level + 1) for key in schema}
) # however don't sort on struct types since the order matters
elif isinstance(schema, (list, tuple)):
if len(schema) != 1:
raise ValueError("When defining list feature, you should just provide one example of the inner type")
value_type = get_nested_type(schema[0], level = level + 1)
return pa.list_(value_type)
elif isinstance(schema, LargeList):
value_type = get_nested_type(schema.feature, level = level + 1)
return pa.large_list(value_type)
elif isinstance(schema, Sequence):
value_type = get_nested_type(schema.feature, level = level + 1)
# We allow to reverse list of dict => dict of list for compatibility with tfds
if isinstance(schema.feature, dict) and level == 1:
data_type = pa.struct({f.name: pa.list_(f.type, schema.length) for f in value_type})
else:
data_type = pa.list_(value_type, schema.length)
return data_type
# Other objects are callable which returns their data type (ClassLabel, Array2D, Translation, Arrow datatype creation methods)
return schema()
```
I have honestly no idea what I am doing here, so this might produce other issues for different inputs.
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-6.8.0-59-generic-x86_64-with-glibc2.35
- Python version: 3.11.11
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
Also tested it with 3.5.0, same result.
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| 3,060,515,257 |
I_kwDODunzps62a8G5
| 7,568 |
`IterableDatasetDict.map()` call removes `column_names` (in fact info.features)
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[
"Hi ! IterableDataset doesn't know what's the output of the function you pass to map(), so it's not possible to know in advance the features of the output dataset.\n\nThere is a workaround though: either do `ds = ds.map(..., features=features)`, or you can do `ds = ds._resolve_features()` which iterates on the first rows to infer the dataset features.",
"Thank you. I understand that “IterableDataset doesn't know what's the output of the function”—that’s true, but:\n\nUnfortunately, the workaround you proposed **doesn’t solve** the problem. `ds.map()` is called multiple times by third-party code (i.e. `SFTTrainer`). To apply your approach, I would have to modify external library code. That’s why I decided to patch the _class_ rather than update `dataset` _objects_ (in fact, updating the object after `map()` was my initial approach, but then I realized I’m not the only one mapping an already-mapped dataset.)\n\nAs a user, I expected that after mapping I would get a new dataset with the correct column names. If, for some reason, that can’t be the default behavior, I would expect an argument—i.e. `auto_resolve_features: bool = False` — to control how my dataset is mapped if following mapping operation are called.\n\nIt’s also problematic that `column_names` are tied to `features`, which is even more confusing and forces you to inspect the source code to understand what’s going on.\n\n**New version of workaround:**\n```python\ndef patch_iterable_dataset_map():\n _orig_map = IterableDataset.map\n\n def _patched_map(self, *args, **kwargs):\n ds = _orig_map(self, *args, **kwargs)\n return ds._resolve_features()\n\n IterableDataset.map = _patched_map\n```",
"I see, maybe `.resolve_features()` should be called by default in this case in the SFTTrainer ? (or pass `features=` if the data processing always output the same features)\n\nWe can even support a new parameter `features=\"infer\"` if it would be comfortable to not use internal methods in SFTTrainer",
"I think most straightforward solution would be to reinitialize `features` from data after mapping if `feature` argument is not passed. I hink it is more intuitive behavior than just cleaning features. There is also problem in usage `.resolve_features()` in this context. I observed that it leads to `_head()` method execution and it then causes that 5 batches from dataset are iterated (`_head()` defaults to 5 batches). \nI'm not sure how it influences whole process. Are those 5 batches (in my case it's 5000 rows) used only to find `features`. Does final training/eval process \"see\" this items? How it affects IterableDataset state (current position)?",
"I checked the source code and while it indeed iterates on the first 5 rows. As a normal iteration, it does record the state in case you call `.state_dict()`, but it doesn't change the starting state. The starting state is always the beginning of the dataset, unless it is explicitly set with `.load_state_dict()`. To be clear, if you iterate on the dataset after `._resolve_features()`, it will start from the beginning of the dataset (or from a state you manually pass using `.load_state_dict()`)"
] | 2025-05-13T15:45:42Z | 2025-05-19T12:09:48Z | null |
NONE
| null | null | null | null |
When calling `IterableDatasetDict.map()`, each split’s `IterableDataset.map()` is invoked without a `features` argument. While omitting the argument isn’t itself incorrect, the implementation then sets `info.features = features`, which destroys the original `features` content. Since `IterableDataset.column_names` relies on `info.features`, it ends up broken (`None`).
**Reproduction**
1. Define an IterableDatasetDict with a non-None features schema.
2. my_iterable_dataset_dict contains "text" column.
3. Call:
```Python
new_dict = my_iterable_dataset_dict.map(
function=my_fn,
with_indices=False,
batched=True,
batch_size=16,
)
```
4. Observe
```Python
new_dict["train"].info.features # {'text': Value(dtype='string', id=None)}
new_dict["train"].column_names # ['text']
```
5. Call:
```Python
new_dict = my_iterable_dataset_dict.map(
function=my_fn,
with_indices=False,
batched=True,
batch_size=16,
remove_columns=["foo"]
)
```
6. Observe:
```Python
new_dict["train"].info.features # → None
new_dict["train"].column_names # → None
```
5. Internally, in dataset_dict.py this loop omits features ([code](https://github.com/huggingface/datasets/blob/b9efdc64c3bfb8f21f8a4a22b21bddd31ecd5a31/src/datasets/dataset_dict.py#L2047C5-L2056C14)):
```Python
for split, dataset in self.items():
dataset_dict[split] = dataset.map(
function=function,
with_indices=with_indices,
input_columns=input_columns,
batched=batched,
batch_size=batch_size,
drop_last_batch=drop_last_batch,
remove_columns=remove_columns,
fn_kwargs=fn_kwargs,
# features omitted → defaults to None
)
```
7. Then inside IterableDataset.map() ([code](https://github.com/huggingface/datasets/blob/b9efdc64c3bfb8f21f8a4a22b21bddd31ecd5a31/src/datasets/iterable_dataset.py#L2619C1-L2622C37)) correct `info.features` is replaced by features which is None:
```Python
info = self.info.copy()
info.features = features # features is None here
return IterableDataset(..., info=info, ...)
```
**Suggestion**
It looks like this replacement was added intentionally but maybe should be done only if `features` is `not None`.
**Workarround:**
`SFTTrainer` calls `dataset.map()` several times and then fails on `NoneType` when iterating `dataset.column_names`.
I decided to write this patch - works form me.
```python
def patch_iterable_dataset_map():
_orig_map = IterableDataset.map
def _patched_map(self, *args, **kwargs):
if "features" not in kwargs or kwargs["features"] is None:
kwargs["features"] = self.info.features
return _orig_map(self, *args, **kwargs)
IterableDataset.map = _patched_map
```
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| 3,058,308,538 |
I_kwDODunzps62ShW6
| 7,567 |
interleave_datasets seed with multiple workers
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[
"Hi ! It's already the case IIRC: the effective seed looks like `seed + worker_id`. Do you have a reproducible example ?",
"here is an example with shuffle\n\n```\nimport itertools\nimport datasets\nimport multiprocessing\nimport torch.utils.data\n\n\ndef gen(shard):\n worker_info = torch.utils.data.get_worker_info()\n for i in range(10):\n yield {'value': i, 'worker_id': worker_info.id}\n\n\ndef main():\n ds = datasets.IterableDataset.from_generator(gen, gen_kwargs={'shard': list(range(8))})\n ds = ds.shuffle(buffer_size=100, seed=1234)\n dataloader = torch.utils.data.DataLoader(ds, batch_size=None, num_workers=8)\n for i, ex in enumerate(itertools.islice(dataloader, 50)):\n print(i, ex)\n\n\nif __name__ == '__main__':\n multiprocessing.set_start_method('spawn')\n main()\n```\n\n```\npython test.py\n0 {'value': 8, 'worker_id': 0}\n1 {'value': 8, 'worker_id': 1}\n2 {'value': 8, 'worker_id': 2}\n3 {'value': 8, 'worker_id': 3}\n4 {'value': 8, 'worker_id': 4}\n5 {'value': 8, 'worker_id': 5}\n6 {'value': 8, 'worker_id': 6}\n7 {'value': 8, 'worker_id': 7}\n8 {'value': 9, 'worker_id': 0}\n9 {'value': 9, 'worker_id': 1}\n10 {'value': 9, 'worker_id': 2}\n11 {'value': 9, 'worker_id': 3}\n12 {'value': 9, 'worker_id': 4}\n13 {'value': 9, 'worker_id': 5}\n14 {'value': 9, 'worker_id': 6}\n15 {'value': 9, 'worker_id': 7}\n16 {'value': 5, 'worker_id': 0}\n17 {'value': 5, 'worker_id': 1}\n18 {'value': 5, 'worker_id': 2}\n19 {'value': 5, 'worker_id': 3}\n```",
"With `interleave_datasets`\n\n```\nimport itertools\nimport datasets\nimport multiprocessing\nimport torch.utils.data\n\n\ndef gen(shard, value):\n while True:\n yield {'value': value}\n\n\ndef main():\n ds = [\n datasets.IterableDataset.from_generator(gen, gen_kwargs={'shard': list(range(8)), 'value': i})\n for i in range(10)\n ]\n ds = datasets.interleave_datasets(ds, probabilities=[1 / len(ds)] * len(ds), seed=1234)\n dataloader = torch.utils.data.DataLoader(ds, batch_size=None, num_workers=8)\n for i, ex in enumerate(itertools.islice(dataloader, 50)):\n print(i, ex)\n\n\nif __name__ == '__main__':\n multiprocessing.set_start_method('spawn')\n main()\n```\n\n```\npython test.py\n0 {'value': 9}\n1 {'value': 9}\n2 {'value': 9}\n3 {'value': 9}\n4 {'value': 9}\n5 {'value': 9}\n6 {'value': 9}\n7 {'value': 9}\n8 {'value': 3}\n9 {'value': 3}\n10 {'value': 3}\n11 {'value': 3}\n12 {'value': 3}\n13 {'value': 3}\n14 {'value': 3}\n15 {'value': 3}\n16 {'value': 9}\n17 {'value': 9}\n18 {'value': 9}\n19 {'value': 9}\n20 {'value': 9}\n21 {'value': 9}\n22 {'value': 9}\n23 {'value': 9}\n```",
"Same results after updating to datasets 3.6.0.",
"Ah my bad, `shuffle()` uses a global effective seed which is something like `seed + epoch`, which is used to do the same shards shuffle in each worker so that each worker have a non-overlapping set of shards:\n\nhttps://github.com/huggingface/datasets/blob/b9efdc64c3bfb8f21f8a4a22b21bddd31ecd5a31/src/datasets/iterable_dataset.py#L2102-L2111\n\nI think we should take into account the `worker_id` in a local seed for the buffer right after this line:\n\nhttps://github.com/huggingface/datasets/blob/b9efdc64c3bfb8f21f8a4a22b21bddd31ecd5a31/src/datasets/iterable_dataset.py#L2151-L2153\n\nlike adding a new step that would propagate in the examples iterables or something like that:\n\n```python\nex_iterable = ex_iterable.shift_rngs(value=worker_id)\n```\n\nis this something you'd like to explore ? contributions on this subject are very welcome",
"Potentially, but busy. If anyone wants to take this up please feel free to, otherwise I may or may not revisit when I have free time.\n\nFor what it's worth I got around this with\n\n```\n\nclass SeedGeneratorWithWorkerIterable(iterable_dataset._BaseExamplesIterable):\n \"\"\"ExamplesIterable that seeds the rng with worker id.\"\"\"\n\n def __init__(\n self,\n ex_iterable: iterable_dataset._BaseExamplesIterable,\n generator: np.random.Generator,\n rank: int = 0,\n ):\n \"\"\"Constructor.\"\"\"\n super().__init__()\n self.ex_iterable = ex_iterable\n self.generator = generator\n self.rank = rank\n\n def _init_state_dict(self) -> dict:\n self._state_dict = self.ex_iterable._init_state_dict()\n return self._state_dict\n\n def __iter__(self):\n \"\"\"Data iterator.\"\"\"\n effective_seed = copy.deepcopy(self.generator).integers(0, 1 << 63) - self.rank\n effective_seed = (1 << 63) + effective_seed if effective_seed < 0 else effective_seed\n generator = np.random.default_rng(effective_seed)\n self.ex_iterable = self.ex_iterable.shuffle_data_sources(generator)\n if self._state_dict:\n self._state_dict = self.ex_iterable._init_state_dict()\n yield from iter(self.ex_iterable)\n\n def shuffle_data_sources(self, generator):\n \"\"\"Shuffle data sources.\"\"\"\n ex_iterable = self.ex_iterable.shuffle_data_sources(generator)\n return SeedGeneratorWithWorkerIterable(ex_iterable, generator=generator, rank=self.rank)\n\n def shard_data_sources(self, num_shards: int, index: int, contiguous=True): # noqa: FBT002\n \"\"\"Shard data sources.\"\"\"\n ex_iterable = self.ex_iterable.shard_data_sources(num_shards, index, contiguous=contiguous)\n return SeedGeneratorWithWorkerIterable(ex_iterable, generator=self.generator, rank=index)\n\n @property\n def is_typed(self):\n return self.ex_iterable.is_typed\n\n @property\n def features(self):\n return self.ex_iterable.features\n\n @property\n def num_shards(self) -> int:\n \"\"\"Number of shards.\"\"\"\n return self.ex_iterable.num_shards\n```"
] | 2025-05-12T22:38:27Z | 2025-05-15T20:39:37Z | null |
NONE
| null | null | null | null |
### Describe the bug
Using interleave_datasets with multiple dataloader workers and a seed set causes the same dataset sampling order across all workers.
Should the seed be modulated with the worker id?
### Steps to reproduce the bug
See above
### Expected behavior
See above
### Environment info
- `datasets` version: 3.5.1
- Platform: macOS-15.4.1-arm64-arm-64bit
- Python version: 3.12.9
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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I_kwDODunzps62G9zw
| 7,566 |
terminate called without an active exception; Aborted (core dumped)
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[] | 2025-05-11T23:05:54Z | 2025-05-11T23:05:54Z | null |
NONE
| null | null | null | null |
### Describe the bug
I use it as in the tutorial here: https://huggingface.co/docs/datasets/stream, and it ends up with abort.
### Steps to reproduce the bug
1. `pip install datasets`
2.
```
$ cat main.py
#!/usr/bin/env python3
from datasets import load_dataset
dataset = load_dataset('HuggingFaceFW/fineweb', split='train', streaming=True)
print(next(iter(dataset)))
```
3. `chmod +x main.py`
```
$ ./main.py
README.md: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 43.1k/43.1k [00:00<00:00, 7.04MB/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25868/25868 [00:05<00:00, 4859.26it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25868/25868 [00:00<00:00, 54773.56it/s]
{'text': "How AP reported in all formats from tornado-stricken regionsMarch 8, 2012\nWhen the first serious bout of tornadoes of 2012 blew through middle America in the middle of the night, they touched down in places hours from any AP bureau. Our closest video journalist was Chicago-based Robert Ray, who dropped his plans to travel to Georgia for Super Tuesday, booked several flights to the cities closest to the strikes and headed for the airport. He’d decide once there which flight to take.\nHe never got on board a plane. Instead, he ended up driving toward Harrisburg, Ill., where initial reports suggested a town was destroyed. That decision turned out to be a lucky break for the AP. Twice.\nRay was among the first journalists to arrive and he confirmed those reports -- in all formats. He shot powerful video, put victims on the phone with AP Radio and played back sound to an editor who transcribed the interviews and put the material on text wires. He then walked around the devastation with the Central Regional Desk on the line, talking to victims with the phone held so close that editors could transcribe his interviews in real time.\nRay also made a dramatic image of a young girl who found a man’s prosthetic leg in the rubble, propped it up next to her destroyed home and spray-painted an impromptu sign: “Found leg. Seriously.”\nThe following day, he was back on the road and headed for Georgia and a Super Tuesday date with Newt Gingrich’s campaign. The drive would take him through a stretch of the South that forecasters expected would suffer another wave of tornadoes.\nTo prevent running into THAT storm, Ray used his iPhone to monitor Doppler radar, zooming in on extreme cells and using Google maps to direct himself to safe routes. And then the journalist took over again.\n“When weather like that occurs, a reporter must seize the opportunity to get the news out and allow people to see, hear and read the power of nature so that they can take proper shelter,” Ray says.\nSo Ray now started to use his phone to follow the storms. He attached a small GoPro camera to his steering wheel in case a tornado dropped down in front of the car somewhere, and took video of heavy rain and hail with his iPhone. Soon, he spotted a tornado and the chase was on. He followed an unmarked emergency vehicle to Cleveland, Tenn., where he was first on the scene of the storm's aftermath.\nAgain, the tornadoes had struck in locations that were hours from the nearest AP bureau. Damage and debris, as well as a wickedly violent storm that made travel dangerous, slowed our efforts to get to the news. That wasn’t a problem in Tennessee, where our customers were well served by an all-formats report that included this text story.\n“CLEVELAND, Tenn. (AP) _ Fierce wind, hail and rain lashed Tennessee for the second time in three days, and at least 15 people were hospitalized Friday in the Chattanooga area.”\nThe byline? Robert Ray.\nFor being adept with technology, chasing after news as it literally dropped from the sky and setting a standard for all-formats reporting that put the AP ahead on the most competitive news story of the day, Ray wins this week’s $300 Best of the States prize.\n© 2013 The Associated Press. All rights reserved. Terms and conditions apply. See AP.org for details.", 'id': '<urn:uuid:d66bc6fe-8477-4adf-b430-f6a558ccc8ff>', 'dump': 'CC-MAIN-2013-20', 'url': 'http://%[email protected]/Content/Press-Release/2012/How-AP-reported-in-all-formats-from-tornado-stricken-regions', 'date': '2013-05-18T05:48:54Z', 'file_path': 's3://commoncrawl/crawl-data/CC-MAIN-2013-20/segments/1368696381249/warc/CC-MAIN-20130516092621-00000-ip-10-60-113-184.ec2.internal.warc.gz', 'language': 'en', 'language_score': 0.9721424579620361, 'token_count': 717}
terminate called without an active exception
Aborted (core dumped)
```
### Expected behavior
I'm not a proficient Python user, so it might be my own error, but even in that case, the error message should be better.
### Environment info
`Successfully installed datasets-3.6.0 dill-0.3.8 hf-xet-1.1.0 huggingface-hub-0.31.1 multiprocess-0.70.16 requests-2.32.3 xxhash-3.5.0`
```
$ cat /etc/lsb-release
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=22.04
DISTRIB_CODENAME=jammy
DISTRIB_DESCRIPTION="Ubuntu 22.04.4 LTS"
```
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PR_kwDODunzps6VkFBm
| 7,565 |
add check if repo exists for dataset uploading
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7565). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@lhoestq Can you review, please? I don't think that errors in CI are related to my changes"
] | 2025-05-09T10:27:00Z | 2025-06-09T14:39:23Z | null |
NONE
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Currently, I'm reuploading datasets for [`MTEB`](https://github.com/embeddings-benchmark/mteb/). Some of them have many splits (more than 20), and I'm encountering the error:
`Too many requests for https://huggingface.co/datasets/repo/create`.
It seems that this issue occurs because the dataset tries to recreate itself every time a split is uploaded. To resolve this, I've added a check to ensure that if the dataset already exists, it won't attempt to recreate it.
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PR_kwDODunzps6VczLS
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Implementation of iteration over values of a column in an IterableDataset object
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[
"A couple of questions:\r\n1. I've noticed two strange things: 1) \"Around 80% of the final dataset is made of the `en_dataset`\" in https://huggingface.co/docs/datasets/stream, 2) \"Click on \"Pull request\" to send your to the project maintainers\" in https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md Are `en_dataset` and \"your [???]\" typos? If so, I can fix them in this PR.\r\n2. Should I update https://huggingface.co/docs/datasets/stream or https://huggingface.co/docs/datasets/access#iterabledataset to include the new feature?",
"Great ! and chained indexing was easy indeed, thanks :)\r\n\r\nregarding your questions:\r\n\r\n> I've noticed two strange things: 1) \"Around 80% of the final dataset is made of the en_dataset\" in https://huggingface.co/docs/datasets/stream, 2) \"Click on \"Pull request\" to send your to the project maintainers\" in https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md Are en_dataset and \"your [???]\" typos? If so, I can fix them in this PR.\r\n\r\nOh good catch, both should be fixed indeed. Feel free to open a new PR for those docs fixes\r\n\r\n> Should I update https://huggingface.co/docs/datasets/stream or https://huggingface.co/docs/datasets/access#iterabledataset to include the new feature?\r\n\r\nYep good idea, I think in both places, since /stream is supposed to be exhaustive, and /access already mentions accessing a specific column for `Dataset`",
"@lhoestq, thank you for the answers!\r\n\r\n> Yep good idea, I think in both places, since /stream is supposed to be exhaustive, and /access already mentions accessing a specific column for Dataset\r\n\r\n👍, I'll try to add something.\r\n\r\nBy the way, do you have any ideas about why the CI pipelines have failed? Essentially, I've already encountered these problems [here](https://github.com/huggingface/datasets/issues/7381#issuecomment-2863421974).\r\nI think `check_code_quality` has failed due to the usage of `pre-commit`. The problem seems to be the old version of the ruff hook. I've tried `v0.11.8` (the one that was installed with `pip install -e \".[quality]\"`) and `pre-commit` seems to work like `make style` now. However, I don't have any ideas about `pyav` since I don't know what it is...",
"I've updated /stream and /access, please check the style and clarity. By the way, I would like to add `IterableDataset.skip` near `IterableDataset.take` to mimic [slicing](https://huggingface.co/docs/datasets/access/#slicing). What do you think?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7564). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-05-08T14:59:22Z | 2025-05-19T12:15:02Z | 2025-05-19T12:15:02Z |
CONTRIBUTOR
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Refers to [this issue](https://github.com/huggingface/datasets/issues/7381).
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NONE
| null | null | null | null |
### Describe the bug
When using `.repeat()` on an `IterableDataset`, this error gets thrown. There is [this thread](https://discuss.huggingface.co/t/making-an-infinite-iterabledataset/146192/5) that seems to imply the fix is trivial, but I don't know anything about this codebase, so I'm opening this issue rather than attempting to open a PR.
### Steps to reproduce the bug
1. Create an `IterableDataset`.
2. Call `.repeat(None)` on it.
3. Wrap it in a pytorch `DataLoader`
4. Iterate over it.
### Expected behavior
This should work normally.
### Environment info
datasets: 3.5.0
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reported in https://github.com/huggingface/datasets/pull/7557 (I just reorganized the condition)
wanted to apply this change to the original PR but github didn't let me apply it directly - merging this one instead
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check for empty _formatting
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"Thanks for reporting and for the fix ! I tried to reorganize the condition in your PR but didn't get the right permission so. I ended up merging https://github.com/huggingface/datasets/pull/7558 directly so I can make a release today - I hope you don't mind"
] | 2025-05-07T13:22:37Z | 2025-05-07T13:57:12Z | 2025-05-07T13:57:12Z |
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Fixes a regression from #7553 breaking shuffling of iterable datasets
<img width="884" alt="Screenshot 2025-05-07 at 9 16 52 AM" src="https://github.com/user-attachments/assets/d2f43c5f-4092-4efe-ac31-a32cbd025fe3" />
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Add `--merge-pull-request` option for `convert_to_parquet`
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"This is ready for a review, happy to make any changes. The main question for maintainers is how this should interact with #7555. If my suggestion there is accepted, this PR can be kept as is. If not, more changes are required to merge all the PR parts."
] | 2025-05-06T18:05:05Z | 2025-05-07T17:41:16Z | null |
NONE
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Closes #7527
Note that this implementation **will only merge the last PR in the case that they get split up by `push_to_hub`**. See https://github.com/huggingface/datasets/discussions/7555 for more details.
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I_kwDODunzps61Yd20
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datasets downloads and generates all splits, even though a single split is requested (for dataset with loading script)
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"Hi ! there has been some effort on allowing to download only a subset of splits in https://github.com/huggingface/datasets/pull/6832 but no one has been continuing this work so far. This would be a welcomed contribution though\n\nAlso note that loading script are often unoptimized, and we recommend using datasets in standard formats like Parquet instead.\n\nBtw there is a CLI tool to convert a loading script to parquet:\n\n```\ndatasets-cli convert_to_parquet <dataset-name> --trust_remote_code\n```",
"Closing in favor of #6832 "
] | 2025-05-06T14:43:38Z | 2025-05-07T14:53:45Z | 2025-05-07T14:53:44Z |
NONE
| null | null | null | null |
### Describe the bug
`datasets` downloads and generates all splits, even though a single split is requested. [This](https://huggingface.co/datasets/jordiae/exebench) is the dataset in question. It uses a loading script. I am not 100% sure that this is a bug, because maybe with loading scripts `datasets` must actually process all the splits? But I thought loading scripts were designed to avoid this.
### Steps to reproduce the bug
See [this notebook](https://colab.research.google.com/drive/14kcXp_hgcdj-kIzK0bCG6taE-CLZPVvq?usp=sharing)
Or:
```python
from datasets import load_dataset
dataset = load_dataset('jordiae/exebench', split='test_synth', trust_remote_code=True)
```
### Expected behavior
I expected only the `test_synth` split to be downloaded and processed.
### Environment info
- `datasets` version: 3.5.1
- Platform: Linux-6.1.123+-x86_64-with-glibc2.35
- Python version: 3.11.12
- `huggingface_hub` version: 0.30.2
- PyArrow version: 18.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2025.3.0
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Rebatch arrow iterables before formatted iterable
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"@lhoestq Our CI found an issue with this changeset causing a regression with shuffling iterable datasets \r\n<img width=\"884\" alt=\"Screenshot 2025-05-07 at 9 16 52 AM\" src=\"https://github.com/user-attachments/assets/bf7d9c7e-cc14-47da-8da6-d1a345992d7c\" />\r\n"
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close https://github.com/huggingface/datasets/issues/7538 and https://github.com/huggingface/datasets/issues/7475
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Enable xet in push to hub
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] | 2025-05-05T17:02:09Z | 2025-05-06T12:42:51Z | 2025-05-06T12:42:48Z |
MEMBER
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follows https://github.com/huggingface/huggingface_hub/pull/3035
related to https://github.com/huggingface/datasets/issues/7526
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I_kwDODunzps61FfRw
| 7,551 |
Issue with offline mode and partial dataset cached
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[
"It seems the problem comes from builder.py / create_config_id()\n\nOn the first call, when the cache is empty we have\n```\nconfig_kwargs = {'data_files': {'train': ['hf://datasets/uonlp/CulturaX@6a8734bc69fefcbb7735f4f9250f43e4cd7a442e/fr/fr_part_00038.parquet']}}\n```\nleading to config_id beeing 'default-2935e8cdcc21c613'\n\nthen, on the second call, \n```\nconfig_kwargs = {'data_files': 'fr/fr_part_00038.parquet'}\n```\nthus explaining why the hash is not the same, despite having the same parameter when calling load_dataset : data_files=\"fr/fr_part_00038.parquet\"",
"Same behavior with version 3.5.1",
"Same issue when loading `google/IndicGenBench_flores_in` with `dataset==2.21.0` and `dataset==3.6.0` .",
"\n\n\n> It seems the problem comes from builder.py / create_config_id()\n> \n> On the first call, when the cache is empty we have\n> \n> ```\n> config_kwargs = {'data_files': {'train': ['hf://datasets/uonlp/CulturaX@6a8734bc69fefcbb7735f4f9250f43e4cd7a442e/fr/fr_part_00038.parquet']}}\n> ```\n> \n> leading to config_id beeing 'default-2935e8cdcc21c613'\n> \n> then, on the second call,\n> \n> ```\n> config_kwargs = {'data_files': 'fr/fr_part_00038.parquet'}\n> ```\n> \n> thus explaining why the hash is not the same, despite having the same parameter when calling load_dataset : data_files=\"fr/fr_part_00038.parquet\"\n\n\nI have identified that the issue indeed lies in the `data_files` within `config_kwargs`. \nThe format and prefix of `data_files` differ depending on whether `HF_HUB_OFFLINE` is set, leading to different final `config_id` values. \nWhen I use other datasets without passing the `data_files` parameter, this issue does not occur.\n\nA possible solution might be to standardize the formatting of `data_files` within the `create_config_id` function."
] | 2025-05-04T16:49:37Z | 2025-05-13T03:18:43Z | null |
NONE
| null | null | null | null |
### Describe the bug
Hi,
a issue related to #4760 here when loading a single file from a dataset, unable to access it in offline mode afterwards
### Steps to reproduce the bug
```python
import os
# os.environ["HF_HUB_OFFLINE"] = "1"
os.environ["HF_TOKEN"] = "xxxxxxxxxxxxxx"
import datasets
dataset_name = "uonlp/CulturaX"
data_files = "fr/fr_part_00038.parquet"
ds = datasets.load_dataset(dataset_name, split='train', data_files=data_files)
print(f"Dataset loaded : {ds}")
```
Once the file has been cached, I rerun with the HF_HUB_OFFLINE activated an get this error :
```
ValueError: Couldn't find cache for uonlp/CulturaX for config 'default-1e725f978350254e'
Available configs in the cache: ['default-2935e8cdcc21c613']
```
### Expected behavior
Should be able to access the previously cached files
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.4.0-215-generic-x86_64-with-glibc2.31
- Python version: 3.12.0
- `huggingface_hub` version: 0.27.0
- PyArrow version: 19.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
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PR_kwDODunzps6UzksN
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disable aiohttp depend for python 3.13t free-threading compat
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[] | 2025-05-03T00:28:18Z | 2025-05-03T00:28:24Z | 2025-05-03T00:28:24Z |
NONE
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I_kwDODunzps60-dWP
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TypeError: Couldn't cast array of type string to null on webdataset format dataset
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[
"seems to get fixed by explicitly adding `dataset_infos.json` like this\n\n```json\n{\n \"default\": {\n \"description\": \"Image dataset with tags and ratings\",\n \"citation\": \"\",\n \"homepage\": \"\",\n \"license\": \"\",\n \"features\": {\n \"image\": {\n \"dtype\": \"image\",\n \"_type\": \"Image\"\n },\n \"json\": {\n \"id\": {\n \"dtype\": \"int32\",\n \"_type\": \"Value\"\n },\n \"width\": {\n \"dtype\": \"int32\",\n \"_type\": \"Value\"\n },\n \"height\": {\n \"dtype\": \"int32\",\n \"_type\": \"Value\"\n },\n \"rating\": {\n \"feature\": {\n \"dtype\": \"string\",\n \"_type\": \"Value\"\n },\n \"_type\": \"Sequence\"\n },\n \"general_tags\": {\n \"feature\": {\n \"dtype\": \"string\",\n \"_type\": \"Value\"\n },\n \"_type\": \"Sequence\"\n },\n \"character_tags\": {\n \"feature\": {\n \"dtype\": \"string\",\n \"_type\": \"Value\"\n },\n \"_type\": \"Sequence\"\n }\n }\n },\n \"builder_name\": \"webdataset\",\n \"config_name\": \"default\",\n \"version\": {\n \"version_str\": \"1.0.0\",\n \"description\": null,\n \"major\": 1,\n \"minor\": 0,\n \"patch\": 0\n }\n }\n}\n\n```\n\nwill close this issue if no further issues found"
] | 2025-05-02T15:18:07Z | 2025-05-02T15:37:05Z | null |
NONE
| null | null | null | null |
### Describe the bug
```python
from datasets import load_dataset
dataset = load_dataset("animetimm/danbooru-wdtagger-v4-w640-ws-30k")
```
got
```
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/arrow_writer.py", line 626, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 255, in pyarrow.lib.array
File "pyarrow/array.pxi", line 117, in pyarrow.lib._handle_arrow_array_protocol
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/arrow_writer.py", line 258, in __arrow_array__
out = cast_array_to_feature(
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/table.py", line 1798, in wrapper
return func(array, *args, **kwargs)
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/table.py", line 2006, in cast_array_to_feature
arrays = [
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/table.py", line 2007, in <listcomp>
_c(array.field(name) if name in array_fields else null_array, subfeature)
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/table.py", line 1798, in wrapper
return func(array, *args, **kwargs)
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/table.py", line 2066, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/table.py", line 1798, in wrapper
return func(array, *args, **kwargs)
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/table.py", line 2103, in cast_array_to_feature
return array_cast(
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/table.py", line 1798, in wrapper
return func(array, *args, **kwargs)
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/table.py", line 1949, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/load.py", line 2084, in load_dataset
builder_instance.download_and_prepare(
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare
super()._download_and_prepare(
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/builder.py", line 1487, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/ubuntu/miniconda3/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
```
`datasets==3.5.1` whats wrong
its inner json structure is like
```yaml
features:
- name: "image"
dtype: "image"
- name: "json.id"
dtype: "string"
- name: "json.width"
dtype: "int32"
- name: "json.height"
dtype: "int32"
- name: "json.rating"
sequence:
dtype: "string"
- name: "json.general_tags"
sequence:
dtype: "string"
- name: "json.character_tags"
sequence:
dtype: "string"
```
i'm 100% sure all the jsons satisfies the abovementioned format.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("animetimm/danbooru-wdtagger-v4-w640-ws-30k")
```
### Expected behavior
load the dataset successfully, with the abovementioned json format and webp images
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 3.5.1
- Platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.35
- Python version: 3.10.16
- `huggingface_hub` version: 0.30.2
- PyArrow version: 20.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2025.3.0
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I_kwDODunzps607xrT
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Python 3.13t (free threads) Compat
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[
"Update: `datasets` use `aiohttp` for data streaming and from what I understand data streaming is useful for large datasets that do not fit in memory and/or multi-modal datasets like image/audio where you only what the actual binary bits to fed in as needed. \n\nHowever, there are also many cases where aiohttp will never be used. Text datasets that are not huge, relative to machine spec, and non-multi-modal datasets. \n\nGetting `aiohttp` fixed for `free threading` appeals to be a large task that is not going to be get done in a quick manner. It may be faster to make `aiohttp` optional and not forced build. Otherwise, testing python 3.13t is going to be a painful install. \n\nI have created a fork/branch that temp disables aiohttp import so non-streaming usage of datasets can be tested under python 3.13.t:\n\nhttps://github.com/Qubitium/datasets/tree/disable-aiohttp-depend",
"We are mostly relying on `huggingface_hub` which uses `requests` to stream files from Hugging Face, so maybe we can move aiohttp to optional dependencies now. Would it solve your issue ? Btw what do you think of `datasets` in the free-threading setting ?",
"> We are mostly relying on `huggingface_hub` which uses `requests` to stream files from Hugging Face, so maybe we can move aiohttp to optional dependencies now. Would it solve your issue ? Btw what do you think of `datasets` in the free-threading setting ?\n\nI am testing transformers + dataset (simple text dataset usage) + GPTQModel for quantization and there were no issues encountered with python 3.13t but my test-case is the base-bare minimal test-case since dataset is not sharded, fully in-memory, text-only, small, not used for training. \n\nOn the technical side, dataset is almost always 100% read-only so there should be zero locking issues but I have not checked the dataset internals so there may be cases where streaming, sharding, and/or cases where datset memory/states are updated needs a per dataset `threading.lock`. \n\nSo yes, making `aiohttp` optional will definitely solve my issue. There is also a companion (datasets and tokenizers usually go hand-in-hand) issue with `Tokenizers` as well but that's simple enough with package version update: https://github.com/huggingface/tokenizers/pull/1774\n",
"Ok I see ! Anyway feel free to edit the setup.py to move aiohttp to optional (tests) dependencies and open a PR, we can run the CI to see if it's ok as a change",
"actually there is https://github.com/huggingface/datasets/pull/7294/ already, let's see if we can merge it",
"wouldn't it be the good reason to switch to `httpx`? 😄 (would require slightly more work, short term agree with https://github.com/huggingface/datasets/issues/7548#issuecomment-2854405923)",
"I made `aiohttp` optional in `datasets` 3.6.0 :)\n\n`datasets` doesn't use it directly anyway, it's only used when someone wants to download files from HTTP URLs outside of HF"
] | 2025-05-02T09:20:09Z | 2025-05-12T15:11:32Z | null |
NONE
| null | null | null | null |
### Describe the bug
Cannot install `datasets` under `python 3.13t` due to dependency on `aiohttp` and aiohttp cannot be built for free-threading python.
The `free threading` support issue in `aiothttp` is active since August 2024! Ouch.
https://github.com/aio-libs/aiohttp/issues/8796#issue-2475941784
`pip install dataset`
```bash
(vm313t) root@gpu-base:~/GPTQModel# pip install datasets
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='pypi.org', port=443): Read timed out. (read timeout=15)")': /simple/datasets/
Collecting datasets
Using cached datasets-3.5.1-py3-none-any.whl.metadata (19 kB)
Requirement already satisfied: filelock in /root/vm313t/lib/python3.13t/site-packages (from datasets) (3.18.0)
Requirement already satisfied: numpy>=1.17 in /root/vm313t/lib/python3.13t/site-packages (from datasets) (2.2.5)
Collecting pyarrow>=15.0.0 (from datasets)
Using cached pyarrow-20.0.0-cp313-cp313t-manylinux_2_28_x86_64.whl.metadata (3.3 kB)
Collecting dill<0.3.9,>=0.3.0 (from datasets)
Using cached dill-0.3.8-py3-none-any.whl.metadata (10 kB)
Collecting pandas (from datasets)
Using cached pandas-2.2.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (89 kB)
Requirement already satisfied: requests>=2.32.2 in /root/vm313t/lib/python3.13t/site-packages (from datasets) (2.32.3)
Requirement already satisfied: tqdm>=4.66.3 in /root/vm313t/lib/python3.13t/site-packages (from datasets) (4.67.1)
Collecting xxhash (from datasets)
Using cached xxhash-3.5.0-cp313-cp313t-linux_x86_64.whl
Collecting multiprocess<0.70.17 (from datasets)
Using cached multiprocess-0.70.16-py312-none-any.whl.metadata (7.2 kB)
Collecting fsspec<=2025.3.0,>=2023.1.0 (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets)
Using cached fsspec-2025.3.0-py3-none-any.whl.metadata (11 kB)
Collecting aiohttp (from datasets)
Using cached aiohttp-3.11.18.tar.gz (7.7 MB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Requirement already satisfied: huggingface-hub>=0.24.0 in /root/vm313t/lib/python3.13t/site-packages (from datasets) (0.30.2)
Requirement already satisfied: packaging in /root/vm313t/lib/python3.13t/site-packages (from datasets) (25.0)
Requirement already satisfied: pyyaml>=5.1 in /root/vm313t/lib/python3.13t/site-packages (from datasets) (6.0.2)
Collecting aiohappyeyeballs>=2.3.0 (from aiohttp->datasets)
Using cached aiohappyeyeballs-2.6.1-py3-none-any.whl.metadata (5.9 kB)
Collecting aiosignal>=1.1.2 (from aiohttp->datasets)
Using cached aiosignal-1.3.2-py2.py3-none-any.whl.metadata (3.8 kB)
Collecting attrs>=17.3.0 (from aiohttp->datasets)
Using cached attrs-25.3.0-py3-none-any.whl.metadata (10 kB)
Collecting frozenlist>=1.1.1 (from aiohttp->datasets)
Using cached frozenlist-1.6.0-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (16 kB)
Collecting multidict<7.0,>=4.5 (from aiohttp->datasets)
Using cached multidict-6.4.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.3 kB)
Collecting propcache>=0.2.0 (from aiohttp->datasets)
Using cached propcache-0.3.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (10 kB)
Collecting yarl<2.0,>=1.17.0 (from aiohttp->datasets)
Using cached yarl-1.20.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (72 kB)
Requirement already satisfied: idna>=2.0 in /root/vm313t/lib/python3.13t/site-packages (from yarl<2.0,>=1.17.0->aiohttp->datasets) (3.10)
Requirement already satisfied: typing-extensions>=3.7.4.3 in /root/vm313t/lib/python3.13t/site-packages (from huggingface-hub>=0.24.0->datasets) (4.13.2)
Requirement already satisfied: charset-normalizer<4,>=2 in /root/vm313t/lib/python3.13t/site-packages (from requests>=2.32.2->datasets) (3.4.1)
Requirement already satisfied: urllib3<3,>=1.21.1 in /root/vm313t/lib/python3.13t/site-packages (from requests>=2.32.2->datasets) (2.4.0)
Requirement already satisfied: certifi>=2017.4.17 in /root/vm313t/lib/python3.13t/site-packages (from requests>=2.32.2->datasets) (2025.4.26)
Collecting python-dateutil>=2.8.2 (from pandas->datasets)
Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)
Collecting pytz>=2020.1 (from pandas->datasets)
Using cached pytz-2025.2-py2.py3-none-any.whl.metadata (22 kB)
Collecting tzdata>=2022.7 (from pandas->datasets)
Using cached tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)
Collecting six>=1.5 (from python-dateutil>=2.8.2->pandas->datasets)
Using cached six-1.17.0-py2.py3-none-any.whl.metadata (1.7 kB)
Using cached datasets-3.5.1-py3-none-any.whl (491 kB)
Using cached dill-0.3.8-py3-none-any.whl (116 kB)
Using cached fsspec-2025.3.0-py3-none-any.whl (193 kB)
Using cached multiprocess-0.70.16-py312-none-any.whl (146 kB)
Using cached multidict-6.4.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (220 kB)
Using cached yarl-1.20.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (404 kB)
Using cached aiohappyeyeballs-2.6.1-py3-none-any.whl (15 kB)
Using cached aiosignal-1.3.2-py2.py3-none-any.whl (7.6 kB)
Using cached attrs-25.3.0-py3-none-any.whl (63 kB)
Using cached frozenlist-1.6.0-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (385 kB)
Using cached propcache-0.3.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (282 kB)
Using cached pyarrow-20.0.0-cp313-cp313t-manylinux_2_28_x86_64.whl (42.2 MB)
Using cached pandas-2.2.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.9 MB)
Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB)
Using cached pytz-2025.2-py2.py3-none-any.whl (509 kB)
Using cached six-1.17.0-py2.py3-none-any.whl (11 kB)
Using cached tzdata-2025.2-py2.py3-none-any.whl (347 kB)
Building wheels for collected packages: aiohttp
Building wheel for aiohttp (pyproject.toml) ... error
error: subprocess-exited-with-error
× Building wheel for aiohttp (pyproject.toml) did not run successfully.
│ exit code: 1
╰─> [156 lines of output]
*********************
* Accelerated build *
*********************
/tmp/pip-build-env-wjqi8_7w/overlay/lib/python3.13t/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!
********************************************************************************
Please consider removing the following classifiers in favor of a SPDX license expression:
License :: OSI Approved :: Apache Software License
See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
********************************************************************************
!!
self._finalize_license_expression()
running bdist_wheel
running build
running build_py
creating build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/typedefs.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/http_parser.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/client_reqrep.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/client_ws.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_app.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/http_websocket.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/resolver.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/tracing.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/http_writer.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/http_exceptions.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/log.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/__init__.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_runner.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/worker.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/connector.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/client_exceptions.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_middlewares.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/tcp_helpers.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_response.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_server.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_request.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_urldispatcher.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_exceptions.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/formdata.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/streams.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/multipart.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_routedef.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_ws.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/payload.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/client_proto.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_log.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/base_protocol.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/payload_streamer.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/http.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_fileresponse.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/test_utils.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/client.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/cookiejar.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/compression_utils.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/hdrs.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/helpers.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/pytest_plugin.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/web_protocol.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/abc.py -> build/lib.linux-x86_64-cpython-313t/aiohttp
creating build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket
copying aiohttp/_websocket/__init__.py -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket
copying aiohttp/_websocket/writer.py -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket
copying aiohttp/_websocket/models.py -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket
copying aiohttp/_websocket/reader.py -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket
copying aiohttp/_websocket/reader_c.py -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket
copying aiohttp/_websocket/helpers.py -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket
copying aiohttp/_websocket/reader_py.py -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket
running egg_info
writing aiohttp.egg-info/PKG-INFO
writing dependency_links to aiohttp.egg-info/dependency_links.txt
writing requirements to aiohttp.egg-info/requires.txt
writing top-level names to aiohttp.egg-info/top_level.txt
reading manifest file 'aiohttp.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'aiohttp' anywhere in distribution
warning: no files found matching '*.pyi' anywhere in distribution
warning: no previously-included files matching '*.pyc' found anywhere in distribution
warning: no previously-included files matching '*.pyd' found anywhere in distribution
warning: no previously-included files matching '*.so' found anywhere in distribution
warning: no previously-included files matching '*.lib' found anywhere in distribution
warning: no previously-included files matching '*.dll' found anywhere in distribution
warning: no previously-included files matching '*.a' found anywhere in distribution
warning: no previously-included files matching '*.obj' found anywhere in distribution
warning: no previously-included files found matching 'aiohttp/*.html'
no previously-included directories found matching 'docs/_build'
adding license file 'LICENSE.txt'
writing manifest file 'aiohttp.egg-info/SOURCES.txt'
copying aiohttp/_cparser.pxd -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/_find_header.pxd -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/_headers.pxi -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/_http_parser.pyx -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/_http_writer.pyx -> build/lib.linux-x86_64-cpython-313t/aiohttp
copying aiohttp/py.typed -> build/lib.linux-x86_64-cpython-313t/aiohttp
creating build/lib.linux-x86_64-cpython-313t/aiohttp/.hash
copying aiohttp/.hash/_cparser.pxd.hash -> build/lib.linux-x86_64-cpython-313t/aiohttp/.hash
copying aiohttp/.hash/_find_header.pxd.hash -> build/lib.linux-x86_64-cpython-313t/aiohttp/.hash
copying aiohttp/.hash/_http_parser.pyx.hash -> build/lib.linux-x86_64-cpython-313t/aiohttp/.hash
copying aiohttp/.hash/_http_writer.pyx.hash -> build/lib.linux-x86_64-cpython-313t/aiohttp/.hash
copying aiohttp/.hash/hdrs.py.hash -> build/lib.linux-x86_64-cpython-313t/aiohttp/.hash
copying aiohttp/_websocket/mask.pxd -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket
copying aiohttp/_websocket/mask.pyx -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket
copying aiohttp/_websocket/reader_c.pxd -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket
creating build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket/.hash
copying aiohttp/_websocket/.hash/mask.pxd.hash -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket/.hash
copying aiohttp/_websocket/.hash/mask.pyx.hash -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket/.hash
copying aiohttp/_websocket/.hash/reader_c.pxd.hash -> build/lib.linux-x86_64-cpython-313t/aiohttp/_websocket/.hash
running build_ext
building 'aiohttp._websocket.mask' extension
creating build/temp.linux-x86_64-cpython-313t/aiohttp/_websocket
x86_64-linux-gnu-gcc -fno-strict-overflow -Wsign-compare -DNDEBUG -g -O2 -Wall -g -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security -fcf-protection -fPIC -I/root/vm313t/include -I/usr/include/python3.13t -c aiohttp/_websocket/mask.c -o build/temp.linux-x86_64-cpython-313t/aiohttp/_websocket/mask.o
aiohttp/_websocket/mask.c:1864:80: error: unknown type name ‘__pyx_vectorcallfunc’; did you mean ‘vectorcallfunc’?
1864 | static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw);
| ^~~~~~~~~~~~~~~~~~~~
| vectorcallfunc
aiohttp/_websocket/mask.c: In function ‘__pyx_f_7aiohttp_10_websocket_4mask__websocket_mask_cython’:
aiohttp/_websocket/mask.c:2905:3: warning: ‘Py_OptimizeFlag’ is deprecated [-Wdeprecated-declarations]
2905 | if (unlikely(__pyx_assertions_enabled())) {
| ^~
In file included from /usr/include/python3.13t/Python.h:76,
from aiohttp/_websocket/mask.c:16:
/usr/include/python3.13t/cpython/pydebug.h:13:37: note: declared here
13 | Py_DEPRECATED(3.12) PyAPI_DATA(int) Py_OptimizeFlag;
| ^~~~~~~~~~~~~~~
aiohttp/_websocket/mask.c: At top level:
aiohttp/_websocket/mask.c:4846:69: error: unknown type name ‘__pyx_vectorcallfunc’; did you mean ‘vectorcallfunc’?
4846 | static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
| ^~~~~~~~~~~~~~~~~~~~
| vectorcallfunc
aiohttp/_websocket/mask.c:4891:80: error: unknown type name ‘__pyx_vectorcallfunc’; did you mean ‘vectorcallfunc’?
4891 | static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
| ^~~~~~~~~~~~~~~~~~~~
| vectorcallfunc
aiohttp/_websocket/mask.c: In function ‘__Pyx_CyFunction_CallAsMethod’:
aiohttp/_websocket/mask.c:5580:6: error: unknown type name ‘__pyx_vectorcallfunc’; did you mean ‘vectorcallfunc’?
5580 | __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc);
| ^~~~~~~~~~~~~~~~~~~~
| vectorcallfunc
aiohttp/_websocket/mask.c:1954:45: warning: initialization of ‘int’ from ‘vectorcallfunc’ {aka ‘struct _object * (*)(struct _object *, struct _object * const*, long unsigned int, struct _object *)’} makes integer from pointer without a cast [-Wint-conversion]
1954 | #define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall)
| ^
aiohttp/_websocket/mask.c:5580:32: note: in expansion of macro ‘__Pyx_CyFunction_func_vectorcall’
5580 | __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
aiohttp/_websocket/mask.c:5583:16: warning: implicit declaration of function ‘__Pyx_PyVectorcall_FastCallDict’ [-Wimplicit-function-declaration]
5583 | return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
aiohttp/_websocket/mask.c:5583:16: warning: returning ‘int’ from a function with return type ‘PyObject *’ {aka ‘struct _object *’} makes pointer from integer without a cast [-Wint-conversion]
5583 | return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
error: command '/usr/bin/x86_64-linux-gnu-gcc' failed with exit code 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for aiohttp
Failed to build aiohttp
ERROR: Failed to build installable wheels for some pyproject.toml based projects (aiohttp)
```
### Steps to reproduce the bug
See above
### Expected behavior
Install
### Environment info
Ubuntu 24.04
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https://api.github.com/repos/huggingface/datasets/issues/7548/timeline
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PR_kwDODunzps6UsTuF
| 7,547 |
Avoid global umask for setting file mode.
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7547). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-05-01T22:24:24Z | 2025-05-06T13:05:00Z | 2025-05-06T13:05:00Z |
CONTRIBUTOR
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This PR updates the method for setting the permissions on `cache_path` after calling `shutil.move`. The call to `shutil.move` may not preserve permissions if the source and destination are on different filesystems. Reading and resetting umask can cause race conditions, so directly read what permissions were set for the `temp_file` instead.
This fixes https://github.com/huggingface/datasets/issues/7536.
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I_kwDODunzps6013H6
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Large memory use when loading large datasets to a ZFS pool
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"Hi ! datasets are memory mapped from disk, so they don't fill out your RAM. Not sure what's the source of your memory issue.\n\nWhat kind of system are you using ? and what kind of disk ?",
"Well, the fact of the matter is that my RAM is getting filled out by running the given example, as shown in [this video](https://streamable.com/usb0ql).\n\nMy system is a GPU server running Ubuntu. The disk is a SATA SSD attached to the server using a backplane. It is formatted with ZFS, mounted in /cache, and my HF_HOME is set to /cache/hf\n\nI really need this fixed, so I am more than willing to test out various suggestions you might have, or write a PR if we can figure out what is going on.",
"I'm not super familiar with ZFS, but it looks like it loads the data in memory when the files are memory mapped, which is an issue.\n\nMaybe it's a caching mechanism ? Since `datasets` accesses every memory mapped file to read a small part (the metadata of the arrow record batches), maybe ZFS brings the whole files in memory for quicker subsequent reads. This is an antipattern when it comes to lazy loading datasets of that size though",
"This is the answer.\n\nI tried changing my HF_HOME to an NFS share, and no RAM is then consumed loading the dataset.\n\nI will try to see if I can find a way to configure the ZFS pool to not cache the files (disabling the ARC/primary cache didn't work), and if I do write the solution in this issue. If I can't I guess I have to reformat my cache drive."
] | 2025-05-01T14:43:47Z | 2025-05-13T13:30:09Z | 2025-05-13T13:29:53Z |
NONE
| null | null | null | null |
### Describe the bug
When I load large parquet based datasets from the hub like `MLCommons/peoples_speech` using `load_dataset`, all my memory (500GB) is used and isn't released after loading, meaning that the process is terminated by the kernel if I try to load an additional dataset. This makes it impossible to train models using multiple large datasets.
### Steps to reproduce the bug
`uv run --with datasets==3.5.1 python`
```python
from datasets import load_dataset
load_dataset('MLCommons/peoples_speech', 'clean')
load_dataset('mozilla-foundation/common_voice_17_0', 'en')
```
### Expected behavior
I would expect that a lot less than 500GB of RAM would be required to load the dataset, or at least that the RAM usage would be cleared as soon as the dataset is loaded (and thus reside as a memory mapped file) such that other datasets can be loaded.
### Environment info
I am currently using the latest datasets==3.5.1 but I have had the same problem with multiple other versions.
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Networked Pull Through Cache
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[] | 2025-04-30T15:16:33Z | 2025-04-30T15:16:33Z | null |
NONE
| null | null | null | null |
### Feature request
Introduce a HF_DATASET_CACHE_NETWORK_LOCATION configuration (e.g. an environment variable) together with a companion network cache service.
Enable a three-tier cache lookup for datasets:
1. Local on-disk cache
2. Configurable network cache proxy
3. Official Hugging Face Hub
### Motivation
- Distributed training & ephemeral jobs: In high-performance or containerized clusters, relying solely on a local disk cache either becomes a streaming bottleneck or incurs a heavy cold-start penalty as each job must re-download datasets.
- Traffic & cost reduction: A pull-through network cache lets multiple consumers share a common cache layer, reducing duplicate downloads from the Hub and lowering egress costs.
- Better streaming adoption: By offloading repeat dataset pulls to a locally managed cache proxy, streaming workloads can achieve higher throughput and more predictable latency.
- Proven pattern: Similar proxy-cache solutions (e.g. Harbor’s Proxy Cache for Docker images) have demonstrated reliability and performance at scale: https://goharbor.io/docs/2.1.0/administration/configure-proxy-cache/
### Your contribution
I’m happy to draft the initial PR for adding HF_DATASET_CACHE_NETWORK_LOCATION support in datasets and sketch out a minimal cache-service prototype.
I have limited bandwidth so I would be looking for collaborators if anyone else is interested.
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PR_kwDODunzps6UR4Nn
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Add try_original_type to DatasetDict.map
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7544). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Sure! I just committed the changes",
"@lhoestq \r\nLet me know if there are other things to do before merge or other places to add `try_original_type` argument "
] | 2025-04-29T04:39:44Z | 2025-05-05T14:42:49Z | 2025-05-05T14:42:49Z |
CONTRIBUTOR
| null | null | 0 |
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This PR resolves #7472 for DatasetDict
The previously merged PR #7483 added `try_original_type` to ArrowDataset, but DatasetDict misses `try_original_type`
Cc: @lhoestq
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I_kwDODunzps60alX6
| 7,543 |
The memory-disk mapping failure issue of the map function（resolved, but there are some suggestions.）
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[] | 2025-04-29T03:04:59Z | 2025-04-30T02:22:17Z | 2025-04-30T02:22:17Z |
NONE
| null | null | null | null |
### Describe the bug
## bug
When the map function processes a large dataset, it temporarily stores the data in a cache file on the disk. After the data is stored, the memory occupied by it is released. Therefore, when using the map function to process a large-scale dataset, only a dataset space of the size of `writer_batch_size` will be occupied in memory.
However, I found that the map function does not actually reduce memory usage when I used it. At first, I thought there was a bug in the program, causing a memory leak—meaning the memory was not released after the data was stored in the cache. But later, I used a Linux command to check for recently modified files during program execution and found that no new files were created or modified. This indicates that the program did not store the dataset in the disk cache.
## bug solved
After modifying the parameters of the map function multiple times, I discovered the `cache_file_name` parameter. By changing it, the cache file can be stored in the specified directory. After making this change, I noticed that the cache file appeared. Initially, I found this quite incredible, but then I wondered if the cache file might have failed to be stored in a certain folder. This could be related to the fact that I don't have root privileges.
So, I delved into the source code of the map function to find out where the cache file would be stored by default. Eventually, I found the function `def _get_cache_file_path(self, fingerprint):`, which automatically generates the storage path for the cache file. The output was as follows: `/tmp/hf_datasets-j5qco9ug/cache-f2830487643b9cc2.arrow`. My hypothesis was confirmed: the lack of root privileges indeed prevented the cache file from being stored, which in turn prevented the release of memory. Therefore, changing the storage location to a folder where I have write access resolved the issue.
### Steps to reproduce the bug
my code
`train_data = train_data.map(process_fun, remove_columns=['image_name', 'question_type', 'concern', 'question', 'candidate_answers', 'answer'])`
### Expected behavior
Although my bug has been resolved, it still took me nearly a week to search for relevant information and debug the program. However, if a warning or error message about insufficient cache file write permissions could be provided during program execution, I might have been able to identify the cause more quickly. Therefore, I hope this aspect can be improved. I am documenting this bug here so that friends who encounter similar issues can solve their problems in a timely manner.
### Environment info
python: 3.10.15
datasets: 3.5.0
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support pyarrow 20
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fix
```
TypeError: ArrayExtensionArray.to_pylist() got an unexpected keyword argument 'maps_as_pydicts'
```
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Fix IterableDataset state_dict shard_example_idx counting
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"Hi ! FYI I made a PR to fix https://github.com/huggingface/datasets/issues/7538 and it also fixed https://github.com/huggingface/datasets/issues/7475, so if I'm not mistaken this PR is not needed anymore"
] | 2025-04-27T20:41:18Z | 2025-05-06T14:24:25Z | 2025-05-06T14:24:24Z |
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# Fix IterableDataset's state_dict shard_example_idx reporting
## Description
This PR fixes issue #7475 where the `shard_example_idx` value in `IterableDataset`'s `state_dict()` always equals the number of samples in a shard, even if only a few examples have been consumed.
The issue is in the `_iter_arrow` method of the `ArrowExamplesIterable` class where it updates the `shard_example_idx` state by the full length of the batch (`len(pa_table)`) even when we're only partway through processing the examples.
## Changes
Modified the `_iter_arrow` method of `ArrowExamplesIterable` to:
1. Track the actual number of examples processed
2. Only increment the `shard_example_idx` by the number of examples actually yielded
3. Handle partial batches correctly
## How to Test
I've included a simple test case that demonstrates the fix:
```python
from datasets import Dataset
# Create a test dataset
ds = Dataset.from_dict({"a": range(6)}).to_iterable_dataset(num_shards=1)
# Iterate through part of the dataset
for idx, example in enumerate(ds):
print(example)
if idx == 2: # Stop after 3 examples (0, 1, 2)
state_dict = ds.state_dict()
print("Checkpoint state_dict:", state_dict)
break
# Before the fix, the output would show shard_example_idx: 6
# After the fix, it shows shard_example_idx: 3, correctly reflecting the 3 processed examples
```
## Implementation Details
1. Added logic to track the number of examples actually seen in the current shard
2. Modified the state update to only count examples actually yielded
3. Improved handling of partial batches and skipped examples
This fix ensures that checkpointing and resuming works correctly with exactly the expected number of examples, rather than skipping ahead to the end of the batch.
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I_kwDODunzps60M5e4
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`IterableDataset` drops samples when resuming from a checkpoint
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"Thanks for reporting ! I fixed the issue using RebatchedArrowExamplesIterable before the formatted iterable"
] | 2025-04-27T19:34:49Z | 2025-05-06T14:04:05Z | 2025-05-06T14:03:42Z |
COLLABORATOR
| null | null | null | null |
When resuming from a checkpoint, `IterableDataset` will drop samples if `num_shards % world_size == 0` and the underlying example supports `iter_arrow` and needs to be formatted.
In that case, the `FormattedExamplesIterable` fetches a batch of samples from the child iterable's `iter_arrow` and yields them one by one (after formatting). However, the child increments the `shard_example_idx` counter (in its `iter_arrow`) before returning the batch for the whole batch size, which leads to a portion of samples being skipped if the iteration (of the parent iterable) is stopped mid-batch.
Perhaps one way to avoid this would be by signalling the child iterable which samples (within the chunk) are processed by the parent and which are not, so that it can adjust the `shard_example_idx` counter accordingly. This would also mean the chunk needs to be sliced when resuming, but this is straightforward to implement.
The following is a minimal reproducer of the bug:
```python
from datasets import Dataset
from datasets.distributed import split_dataset_by_node
ds = Dataset.from_dict({"n": list(range(24))})
ds = ds.to_iterable_dataset(num_shards=4)
world_size = 4
rank = 0
ds_rank = split_dataset_by_node(ds, rank, world_size)
it = iter(ds_rank)
examples = []
for idx, example in enumerate(it):
examples.append(example)
if idx == 2:
state_dict = ds_rank.state_dict()
break
ds_rank.load_state_dict(state_dict)
it_resumed = iter(ds_rank)
examples_resumed = examples[:]
for example in it:
examples.append(example)
for example in it_resumed:
examples_resumed.append(example)
print("ORIGINAL ITER EXAMPLES:", examples)
print("RESUMED ITER EXAMPLES:", examples_resumed)
```
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I_kwDODunzps6z7yAG
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`datasets.map(..., num_proc=4)` multi-processing fails
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[
"related: https://github.com/huggingface/datasets/issues/7510\n\nwe need to do more tests to see if latest `dill` is deterministic"
] | 2025-04-25T01:53:47Z | 2025-05-06T13:12:08Z | null |
NONE
| null | null | null | null |
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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I_kwDODunzps6z6YTN
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[Errno 13] Permission denied: on `.incomplete` file
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[
"It must be an issue with umask being used by multiple threads indeed. Maybe we can try to make a thread safe function to apply the umask (using filelock for example)",
"> It must be an issue with umask being used by multiple threads indeed. Maybe we can try to make a thread safe function to apply the umask (using filelock for example)\n\n@lhoestq is this something which can go in a 3.5.1 release?",
"Yes for sure",
"@lhoestq - can you take a look at https://github.com/huggingface/datasets/pull/7547/?"
] | 2025-04-24T20:52:45Z | 2025-05-06T13:05:01Z | 2025-05-06T13:05:01Z |
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
When downloading a dataset, we frequently hit the below Permission Denied error. This looks to happen (at least) across datasets in HF, S3, and GCS.
It looks like the `temp_file` being passed [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L412) can sometimes be created with `000` permissions leading to the permission denied error (the user running the code is still the owner of the file). Deleting that particular file and re-running the code with 0 changes will usually succeed.
Is there some race condition happening with the [umask](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L416), which is process global, and the [file creation](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L404)?
```
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.venv/lib/python3.12/site-packages/datasets/load.py:2084: in load_dataset
builder_instance.download_and_prepare(
.venv/lib/python3.12/site-packages/datasets/builder.py:925: in download_and_prepare
self._download_and_prepare(
.venv/lib/python3.12/site-packages/datasets/builder.py:1649: in _download_and_prepare
super()._download_and_prepare(
.venv/lib/python3.12/site-packages/datasets/builder.py:979: in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
.venv/lib/python3.12/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py:120: in _split_generators
downloaded_files = dl_manager.download(files)
.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:159: in download
downloaded_path_or_paths = map_nested(
.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py:514: in map_nested
_single_map_nested((function, obj, batched, batch_size, types, None, True, None))
.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py:382: in _single_map_nested
return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:206: in _download_batched
return thread_map(
.venv/lib/python3.12/site-packages/tqdm/contrib/concurrent.py:69: in thread_map
return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
.venv/lib/python3.12/site-packages/tqdm/contrib/concurrent.py:51: in _executor_map
return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))
.venv/lib/python3.12/site-packages/tqdm/std.py:1181: in __iter__
for obj in iterable:
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:619: in result_iterator
yield _result_or_cancel(fs.pop())
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:317: in _result_or_cancel
return fut.result(timeout)
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:449: in result
return self.__get_result()
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:401: in __get_result
raise self._exception
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/thread.py:59: in run
result = self.fn(*self.args, **self.kwargs)
.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:229: in _download_single
out = cached_path(url_or_filename, download_config=download_config)
.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:206: in cached_path
output_path = get_from_cache(
.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:412: in get_from_cache
fsspec_get(url, temp_file, storage_options=storage_options, desc=download_desc, disable_tqdm=disable_tqdm)
.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:331: in fsspec_get
fs.get_file(path, temp_file.name, callback=callback)
.venv/lib/python3.12/site-packages/fsspec/asyn.py:118: in wrapper
return sync(self.loop, func, *args, **kwargs)
.venv/lib/python3.12/site-packages/fsspec/asyn.py:103: in sync
raise return_result
.venv/lib/python3.12/site-packages/fsspec/asyn.py:56: in _runner
result[0] = await coro
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <s3fs.core.S3FileSystem object at 0x7f27c18b2e70>
rpath = '<my-bucket>/<my-prefix>/img_1.jpg'
lpath = '/home/runner/_work/_temp/hf_cache/downloads/6c97983efa4e24e534557724655df8247a0bd04326cdfc4a95b638c11e78222d.incomplete'
callback = <datasets.utils.file_utils.TqdmCallback object at 0x7f27c00cdbe0>
version_id = None, kwargs = {}
_open_file = <function S3FileSystem._get_file.<locals>._open_file at 0x7f27628d1120>
body = <StreamingBody at 0x7f276344fa80 for ClientResponse at 0x7f27c015fce0>
content_length = 521923, failed_reads = 0, bytes_read = 0
async def _get_file(
self, rpath, lpath, callback=_DEFAULT_CALLBACK, version_id=None, **kwargs
):
if os.path.isdir(lpath):
return
bucket, key, vers = self.split_path(rpath)
async def _open_file(range: int):
kw = self.req_kw.copy()
if range:
kw["Range"] = f"bytes={range}-"
resp = await self._call_s3(
"get_object",
Bucket=bucket,
Key=key,
**version_id_kw(version_id or vers),
**kw,
)
return resp["Body"], resp.get("ContentLength", None)
body, content_length = await _open_file(range=0)
callback.set_size(content_length)
failed_reads = 0
bytes_read = 0
try:
> with open(lpath, "wb") as f0:
E PermissionError: [Errno 13] Permission denied: '/home/runner/_work/_temp/hf_cache/downloads/6c97983efa4e24e534557724655df8247a0bd04326cdfc4a95b638c11e78222d.incomplete'
.venv/lib/python3.12/site-packages/s3fs/core.py:1355: PermissionError
```
### Steps to reproduce the bug
I believe this is a race condition and cannot reliably re-produce it, but it happens fairly frequently in our GitHub Actions tests and can also be re-produced (with lesser frequency) on cloud VMs.
### Expected behavior
The dataset loads properly with no permission denied error.
### Environment info
- `datasets` version: 3.5.0
- Platform: Linux-5.10.0-34-cloud-amd64-x86_64-with-glibc2.31
- Python version: 3.12.10
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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| 3,018,289,872 |
PR_kwDODunzps6T0lm3
| 7,535 |
Change dill version in requirements
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7535). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-04-24T19:44:28Z | 2025-05-19T14:51:29Z | null |
NONE
| null | null | 0 |
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Change dill version to >=0.3.9,<0.4.5 and check for errors
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| 3,017,259,407 |
I_kwDODunzps6z17mP
| 7,534 |
TensorFlow RaggedTensor Support (batch-level)
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[
"Keras doesn't support other inputs other than tf.data.Dataset objects ? it's a bit painful to have to support and maintain this kind of integration\n\nIs there a way to use a `datasets.Dataset` with outputs formatted as tensors / ragged tensors instead ? like in https://huggingface.co/docs/datasets/use_with_tensorflow#dataset-format",
"I'll give it a try when I get the time. But quite sure I already tested the `with_format` approach.\n\nKeras when using TF as backend converts the datasets into `tf.data.Dataset`, much like you do."
] | 2025-04-24T13:14:52Z | 2025-05-08T14:13:47Z | null |
NONE
| null | null | null | null |
### Feature request
Hi,
Currently datasets does not support RaggedTensor output on batch-level.
When building a Object Detection Dataset (with TensorFlow) I need to enable RaggedTensors as that's how BBoxes & classes are expected from the Keras Model POV.
Currently there's a error thrown saying that "Nested Data is not supported".
It'd be very helpful if this was fixed! :)
### Motivation
Enabling Object Detection pipelines for TensorFlow.
### Your contribution
With guidance I'd happily help making the PR.
The current implementation with DataCollator and later enforcing `np.array` is the problematic part (at the end of `np_get_batch` in `tf_utils.py`). As `numpy` don't support "Raggednes"
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Add custom fingerprint support to `from_generator`
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[
"This is great !\r\n\r\nWhat do you think of passing `config_id=` directly to the builder instead of just the suffix ? This would be a power user argument though, or for internal use. And in from_generator the new argument can be `fingerprint=` as in `Dataset.__init__()`\r\n\r\nThe `config_id` can be defined using something like `config_id = \"default-fingerprint=\" + fingerprint`\r\n\r\nI feel ike this could make the Dataset API more coherent if we avoid introducing a new argument while we can juste use `fingerprint=`"
] | 2025-04-23T19:31:35Z | 2025-06-10T10:13:00Z | null |
NONE
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This PR adds `dataset_id_suffix` parameter to 'Dataset.from_generator' function.
`Dataset.from_generator` function passes all of its arguments to `BuilderConfig.create_config_id`, including generator function itself. `BuilderConfig.create_config_id` function tries to hash all the args, which can take a large amount of time or even cause MemoryError if the dataset processed in a generator function is large enough.
This PR allows user to pass a custom fingerprint (`dataset_id_suffix`) to be used as a suffix in a dataset name instead of the one generated by hashing the args.
This PR is a possible solution of #7513
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Document the HF_DATASETS_CACHE environment variable in the datasets cache documentation
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7532). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Your clarification in your comment at https://github.com/huggingface/datasets/issues/7480#issuecomment-2833640084 sounds great, would you like to update this PR to include it ?",
"Hi @lhoestq, I’ve updated the documentation to reflect the clarifications discussed in #7480. Let me know if anything else is needed!\r\n"
] | 2025-04-22T00:23:13Z | 2025-05-06T15:54:38Z | 2025-05-06T15:54:38Z |
CONTRIBUTOR
| null | null | 0 |
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This pull request updates the Datasets documentation to include the `HF_DATASETS_CACHE` environment variable. While the current documentation only mentions `HF_HOME` for overriding the default cache directory, `HF_DATASETS_CACHE` is also a supported and useful option for specifying a custom cache location for datasets stored in Arrow format.
This addition is based on the discussion in (https://github.com/huggingface/datasets/issues/7457), where users noted the absence of this variable in the documentation despite its functionality. The update adds a new section to `cache.mdx` that explains how to use `HF_DATASETS_CACHE` with an example.
This change aims to improve clarity and help users better manage their cache directories when working in shared environments or with limited local storage.
Closes #7457.
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I_kwDODunzps6zWGXH
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Deepspeed reward training hangs at end of training with Dataset.from_list
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"Hi ! How big is the dataset ? if you load it using `from_list`, the dataset lives in memory and has to be copied to every gpu process, which can be slow.\n\nIt's fasted if you load it from JSON files from disk, because in that case the dataset in converted to Arrow and loaded from disk using memory mapping. Memory mapping allows to quickly reload the dataset in other processes.\n\nMaybe we can change `from_list` and other methods to always use the disk though, instead of loading in memory, WDYT ?"
] | 2025-04-21T17:29:20Z | 2025-05-06T13:30:41Z | null |
NONE
| null | null | null | null |
There seems to be a weird interaction between Deepspeed, the Dataset.from_list method and trl's RewardTrainer. On a multi-GPU setup (10 A100s), training always hangs at the very end of training until it times out. The training itself works fine until the end of training and running the same script with Deepspeed on a single GPU works without hangig. The issue persisted across a wide range of Deepspeed configs and training arguments. The issue went away when storing the exact same dataset as a JSON and using `dataset = load_dataset("json", ...)`. Here is my training script:
```python
import pickle
import os
import random
import warnings
import torch
from datasets import load_dataset, Dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from trl import RewardConfig, RewardTrainer, ModelConfig
####################################### Reward model #################################################
# Explicitly set arguments
model_name_or_path = "Qwen/Qwen2.5-1.5B"
output_dir = "Qwen2-0.5B-Reward-LoRA"
per_device_train_batch_size = 2
num_train_epochs = 5
gradient_checkpointing = True
learning_rate = 1.0e-4
logging_steps = 25
eval_strategy = "steps"
eval_steps = 50
max_length = 2048
torch_dtype = "auto"
trust_remote_code = False
model_args = ModelConfig(
model_name_or_path=model_name_or_path,
model_revision=None,
trust_remote_code=trust_remote_code,
torch_dtype=torch_dtype,
lora_task_type="SEQ_CLS", # Make sure task type is seq_cls
)
training_args = RewardConfig(
output_dir=output_dir,
per_device_train_batch_size=per_device_train_batch_size,
num_train_epochs=num_train_epochs,
gradient_checkpointing=gradient_checkpointing,
learning_rate=learning_rate,
logging_steps=logging_steps,
eval_strategy=eval_strategy,
eval_steps=eval_steps,
max_length=max_length,
gradient_checkpointing_kwargs=dict(use_reentrant=False),
center_rewards_coefficient = 0.01,
fp16=False,
bf16=True,
save_strategy="no",
dataloader_num_workers=0,
# deepspeed="./configs/deepspeed_config.json",
)
################
# Model & Tokenizer
################
model_kwargs = dict(
revision=model_args.model_revision,
use_cache=False if training_args.gradient_checkpointing else True,
torch_dtype=model_args.torch_dtype,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, use_fast=True
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path, num_labels=1, trust_remote_code=model_args.trust_remote_code, **model_kwargs
)
# Align padding tokens between tokenizer and model
model.config.pad_token_id = tokenizer.pad_token_id
# If post-training a base model, use ChatML as the default template
if tokenizer.chat_template is None:
model, tokenizer = setup_chat_format(model, tokenizer)
if model_args.use_peft and model_args.lora_task_type != "SEQ_CLS":
warnings.warn(
"You are using a `task_type` that is different than `SEQ_CLS` for PEFT. This will lead to silent bugs"
" Make sure to pass --lora_task_type SEQ_CLS when using this script with PEFT.",
UserWarning,
)
##############
# Load dataset
##############
with open('./prefs.pkl', 'rb') as fh:
loaded_data = pickle.load(fh)
random.shuffle(loaded_data)
dataset = []
for a_wins, a, b in loaded_data:
if a_wins == 0:
a, b = b, a
dataset.append({'chosen': a, 'rejected': b})
dataset = Dataset.from_list(dataset)
# Split the dataset into training and evaluation sets
train_eval_split = dataset.train_test_split(test_size=0.15, shuffle=True, seed=42)
# Access the training and evaluation datasets
train_dataset = train_eval_split['train']
eval_dataset = train_eval_split['test']
##########
# Training
##########
trainer = RewardTrainer(
model=model,
processing_class=tokenizer,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
)
trainer.train()
```
Replacing `dataset = Dataset.from_list(dataset)` with
```python
with open('./prefs.json', 'w') as fh:
json.dump(dataset, fh)
dataset = load_dataset("json", data_files="./prefs.json", split='train')
```
resolves the issue.
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I_kwDODunzps6zQhVT
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How to solve "Spaces stuck in Building" problems
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[
"I'm facing the same issue—Space stuck in \"Building\" even after restart and Factory rebuild. Any fix?\n",
"> I'm facing the same issue—Space stuck in \"Building\" even after restart and Factory rebuild. Any fix?\n\nAlso see https://github.com/huggingface/huggingface_hub/issues/3019",
"I'm facing the same issue. The build fails with the same error, and restarting won't help. Is there a fix or ETA? "
] | 2025-04-21T03:08:38Z | 2025-04-22T07:49:52Z | 2025-04-22T07:49:52Z |
NONE
| null | null | null | null |
### Describe the bug
Public spaces may stuck in Building after restarting, error log as follows:
build error
Unexpected job error
ERROR: failed to push spaces-registry.huggingface.tech/spaces/*:cpu-*-*: unexpected status from HEAD request to https://spaces-registry.huggingface.tech/v2/spaces/*/manifests/cpu-*-*: 401 Unauthorized
### Steps to reproduce the bug
Restart space / Factory rebuild cannot avoid it
### Expected behavior
Fix this problem
### Environment info
no requirements.txt can still happen
python gradio spaces
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I_kwDODunzps6zPP55
| 7,529 |
audio folder builder cannot detect custom split name
|
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[] | 2025-04-20T16:53:21Z | 2025-04-20T16:53:21Z | null |
NONE
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### Describe the bug
when using audio folder builder (`load_dataset("audiofolder", data_dir="/path/to/folder")`), it cannot detect custom split name other than train/validation/test
### Steps to reproduce the bug
i have the following folder structure
```
my_dataset/
├── train/
│ ├── lorem.wav
│ ├── …
│ └── metadata.csv
├── test/
│ ├── ipsum.wav
│ ├── …
│ └── metadata.csv
├── validation/
│ ├── dolor.wav
│ ├── …
│ └── metadata.csv
└── custom/
├── sit.wav
├── …
└── metadata.csv
```
using `ds = load_dataset("audiofolder", data_dir="/path/to/my_dataset")`
### Expected behavior
i got `ds` with only 3 splits train/validation/test, whenever i rename train/validation/test folder it also disappear if i re-create `ds`
### Environment info
- `datasets` version: 3.5.0
- Platform: Windows-11-10.0.26100-SP0
- Python version: 3.12.8
- `huggingface_hub` version: 0.30.2
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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I_kwDODunzps6zMojN
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Data Studio Error: Convert JSONL incorrectly
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"Hi ! Your JSONL file is incompatible with Arrow / Parquet. Indeed in Arrow / Parquet every dict should have the same keys, while in your dataset the bboxes have varying keys.\n\nThis causes the Data Studio to treat the bboxes as if each row was missing the keys from other rows.\n\nFeel free to take a look at the docs on object segmentation to see how to format a dataset with bboxes: https://huggingface.co/docs/datasets/object_detection"
] | 2025-04-19T13:21:44Z | 2025-05-06T13:18:38Z | null |
NONE
| null | null | null | null |
### 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
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Auto-merge option for `convert-to-parquet`
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"Alternatively, there could be an option to switch from submitting PRs to just committing changes directly to `main`.",
"Why not, I'd be in favor of `--merge-pull-request` to call `HfApi().merge_pull_request()` at the end of the conversion :) feel free to open a PR if you'd like",
"#self-assign"
] | 2025-04-18T16:03:22Z | 2025-05-07T12:47:02Z | null |
NONE
| null | null | null | null |
### Feature request
Add a command-line option, e.g. `--auto-merge-pull-request` that enables automatic merging of the commits created by the `convert-to-parquet` tool.
### Motivation
Large datasets may result in dozens of PRs due to the splitting mechanism. Each of these has to be manually accepted via the website.
### Your contribution
Happy to look into submitting a PR if this is of interest to maintainers.
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Faster downloads/uploads with Xet storage
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[] | 2025-04-18T14:46:42Z | 2025-05-12T12:09:09Z | null |
MEMBER
| null | null | null | null |
## Xet is out !
Over the past few weeks, Hugging Face’s [Xet Team](https://huggingface.co/xet-team) took a major step forward by [migrating the first Model and Dataset repositories off LFS and to Xet storage](https://huggingface.co/posts/jsulz/911431940353906).
See more information on the HF blog: https://huggingface.co/blog/xet-on-the-hub
You can already enable Xet on Hugging Face account to benefit from faster downloads and uploads :)
We finalized an official integration with the `huggingface_hub` library that means you get the benefits of Xet without any significant changes to your current workflow.
## Previous versions of `datasets`
For older versions of `datasets` you might see this warning in `push_to_hub()`:
```
Uploading files as bytes or binary IO objects is not supported by Xet Storage.
```
This means the `huggingface_hub` + Xet integration isn't enabled for your version of `datasets`.
You can fix this by updating to `datasets>=3.6.0` and `huggingface_hub>=0.31.0`
```
pip install -U datasets huggingface_hub
```
## The future
Stay tuned for more Xet optimizations, especially on [Xet-optimized Parquet](https://huggingface.co/blog/improve_parquet_dedupe)
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Fix indexing in split commit messages
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[
"Hi ! this is expected and is coherent with other naming conventions in `datasets` such as parquet shards naming"
] | 2025-04-17T17:06:26Z | 2025-04-28T14:26:27Z | 2025-04-28T14:26:27Z |
NONE
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When a large commit is split up, it seems the commit index in the message is zero-based while the total number is one-based. I came across this running `convert-to-parquet` and was wondering why there was no `6-of-6` commit. This PR fixes that by adding one to the commit index, so both are one-based.
Current behavior:
<img width="463" alt="Screenshot 2025-04-17 at 1 00 17 PM" src="https://github.com/user-attachments/assets/7f3d389e-cb92-405d-a3c2-f2b1cdf0cb79" />
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correct use with polars example
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[] | 2025-04-17T10:19:19Z | 2025-04-28T13:48:34Z | 2025-04-28T13:48:33Z |
CONTRIBUTOR
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mention av in video docs
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PR_kwDODunzps6SwwXW
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Preserve formatting in concatenated IterableDataset
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Fixes #7515
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PR_kwDODunzps6SvEZp
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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`",
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] | 2025-04-15T21:23:58Z | 2025-05-07T14:17:29Z | 2025-05-07T14:17:29Z |
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## 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
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I_kwDODunzps6yqQfc
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Update items in the dataset without `map`
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"Hello!\n\nHave you looked at `Dataset.shard`? [Docs](https://huggingface.co/docs/datasets/en/process#shard)\n\nUsing this method you could break your dataset in N shards. Apply `map` on each shard and concatenate them back."
] | 2025-04-15T19:39:01Z | 2025-04-19T18:47:46Z | null |
NONE
| null | null | null | null |
### Feature request
I would like to be able to update items in my dataset without affecting all rows. At least if there was a range option, I would be able to process those items, save the dataset, and then continue.
If I am supposed to split the dataset first, that is not clear, since the docs suggest that any of those functions returns a new object, so I don't think I can do that.
### Motivation
I am applying an extremely time-consuming function to each item in my `Dataset`. Unfortunately, datasets only supports updating values via `map`, so if my computer dies in the middle of this long-running process, I lose all progress. This is far from ideal. I would like to use `datasets` throughout this processing, but this limitation is now forcing me to write my own dataset format just to do this intermediary operation.
It would be less intuitive but I suppose I could split and then concatenate the dataset before saving? But this feels very inefficient.
### Your contribution
I can test the feature.
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pdf docs fixes
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close https://github.com/huggingface/datasets/issues/7494
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num_proc parallelization works only for first ~10s.
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"Hi, can you check if the processes are still alive ? It's a bit weird because `datasets` does check if processes crash and return an error in that case",
"Thank you for reverting quickly. I digged a bit, and realized my disk's IOPS is also limited - which is causing this. will check further and report if it's an issue of hf datasets' side or mine. "
] | 2025-04-15T11:44:03Z | 2025-04-15T13:12:13Z | null |
NONE
| null | null | null | null |
### Describe the bug
When I try to load an already downloaded dataset with num_proc=64, the speed is very high for the first 10-20 seconds acheiving 30-40K samples / s, and 100% utilization for all cores but it soon drops to <= 1000 with almost 0% utilization for most cores.
### Steps to reproduce the bug
```
// download dataset with cli
!huggingface-cli download --repo-type dataset timm/imagenet-1k-wds --max-workers 32
from datasets import load_dataset
ds = load_dataset("timm/imagenet-1k-wds", num_proc=64)
```
### Expected behavior
100% core utilization throughout.
### Environment info
Azure A100-80GB, 16 cores VM
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I_kwDODunzps6ylPNd
| 7,517 |
Image Feature in Datasets Library Fails to Handle bytearray Objects from Spark DataFrames
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[
"Hi ! The `Image()` type accepts either\n- a `bytes` object containing the image bytes\n- a `str` object containing the image path\n- a `PIL.Image` object\n\nbut it doesn't support `bytearray`, maybe you can convert to `bytes` beforehand ?",
"Hi @lhoestq, \nconverting to bytes is certainly possible and would work around the error. However, the core issue is that `Dataset` and `IterableDataset` behave differently with the features.\n\nI’d be happy to work on a fix for this issue.",
"I see, that's an issue indeed. Feel free to ping me if I can help with reviews or any guidance\n\nIf it can help, the code that takes a Spark DataFrame and iterates on the rows for `IterableDataset` is here: \n\nhttps://github.com/huggingface/datasets/blob/6a96bf313085d7538a999b929a550e14e1d406c9/src/datasets/packaged_modules/spark/spark.py#L49-L53",
"#self-assign"
] | 2025-04-15T11:29:17Z | 2025-05-07T14:17:30Z | 2025-05-07T14:17:30Z |
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
When using `IterableDataset.from_spark()` with a Spark DataFrame containing image data, the `Image` feature class fails to properly process this data type, causing an `AttributeError: 'bytearray' object has no attribute 'get'`
### Steps to reproduce the bug
1. Create a Spark DataFrame with a column containing image data as bytearray objects
2. Define a Feature schema with an Image feature
3. Create an IterableDataset using `IterableDataset.from_spark()`
4. Attempt to iterate through the dataset
```
from pyspark.sql import SparkSession
from datasets import Dataset, IterableDataset, Features, Image, Value
# initialize spark
spark = SparkSession.builder.appName("MinimalRepro").getOrCreate()
# create spark dataframe
data = [(0, open("image.png", "rb").read())]
df = spark.createDataFrame(data, "idx: int, image: binary")
# convert to dataset
features = Features({"idx": Value("int64"), "image": Image()})
ds = Dataset.from_spark(df, features=features)
ds_iter = IterableDataset.from_spark(df, features=features)
# iterate
print(next(iter(ds)))
print(next(iter(ds_iter)))
```
### Expected behavior
The features should work on `IterableDataset` the same way they work on `Dataset`
### Environment info
- `datasets` version: 3.5.0
- Platform: macOS-15.3.2-arm64-arm-64bit
- Python version: 3.12.7
- `huggingface_hub` version: 0.30.2
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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unsloth/DeepSeek-R1-Distill-Qwen-32B server error
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[] | 2025-04-15T09:26:53Z | 2025-04-15T09:57:26Z | 2025-04-15T09:57:26Z |
NONE
| null | null | null | null |
### Describe the bug
hfhubhttperror: 500 server error: internal server error for url: https://huggingface.co/api/models/unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit/commits/main (request id: root=1-67fe23fa-3a2150eb444c2a823c388579;de3aed68-c397-4da5-94d4-6565efd3b919) internal error - we're working hard to fix this as soon as possible!
### Steps to reproduce the bug
unsloth/DeepSeek-R1-Distill-Qwen-32B server error
### Expected behavior
Network repair
### Environment info
The web side is also unavailable
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| 2,995,082,418 |
I_kwDODunzps6yhVSy
| 7,515 |
`concatenate_datasets` does not preserve Pytorch format for IterableDataset
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[
"Hi ! Oh indeed it would be cool to return the same format in that case. Would you like to submit a PR ? The function that does the concatenation is here:\n\nhttps://github.com/huggingface/datasets/blob/90e5bf8a8599b625d6103ee5ac83b98269991141/src/datasets/iterable_dataset.py#L3375-L3380",
"Thank you for the pointer, @lhoestq ! See #7522 "
] | 2025-04-15T04:36:34Z | 2025-05-19T15:07:38Z | 2025-05-19T15:07:38Z |
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
When concatenating datasets with `concatenate_datasets`, I would expect the resulting combined dataset to be in the same format as the inputs (assuming it's consistent). This is indeed the behavior when combining `Dataset`, but not when combining `IterableDataset`. Specifically, when applying `concatenate_datasets` to a list of `IterableDataset` in Pytorch format (i.e. using `.with_format(Pytorch)`), the output `IterableDataset` is not in Pytorch format.
### Steps to reproduce the bug
```
import datasets
ds = datasets.Dataset.from_dict({"a": [1,2,3]})
iterable_ds = ds.to_iterable_dataset()
datasets.concatenate_datasets([ds.with_format("torch")]) # <- this preserves Pytorch format
datasets.concatenate_datasets([iterable_ds.with_format("torch")]) # <- this does NOT preserves Pytorch format
```
### Expected behavior
Pytorch format should be preserved when combining IterableDataset in Pytorch format.
### Environment info
datasets==3.5.0, Python 3.11.11, torch==2.2.2
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PR_kwDODunzps6Sk7Et
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Do not hash `generator` in `BuilderConfig.create_config_id`
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[] | 2025-04-15T01:26:43Z | 2025-04-23T11:55:55Z | 2025-04-15T16:27:51Z |
NONE
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`Dataset.from_generator` function passes all of its arguments to `BuilderConfig.create_config_id`, including generator function itself. `BuilderConfig.create_config_id` function tries to hash all the args, and hashing a `generator` can take a large amount of time or even cause MemoryError if the dataset processed in a generator function is large enough.
Maybe we should pop generator from `config_kwargs_to_add_to_suffix` before hashing to avoid it.
There is a more detailed description of the problem this PR solves in #7513
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MemoryError while creating dataset from generator
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open
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[] | null |
[
"Upd: created a PR that can probably solve the problem: #7514",
"Hi ! We need to take the generator into account for the cache. The generator is hashed to make the dataset fingerprint used by the cache. This way you can reload the Dataset from the cache without regenerating in subsequent `from_generator` calls.\n\nMaybe instead of removing generator from the hasher input, we can let users pass their own Dataset fingerprint to `from_generator`, and if it's specified we don't need to hash anything",
"Upd: I successfully generated a dataset from my large geospatial data with `generator` excluded from hashing and saved it to disk without running into memory errors. So, it looks like there are no other bottlenecks in dataset generation in my case\n\nMaybe letting users pass their own fingerprint to skip hashing can be a great solution to that issue!",
"@lhoestq I tried to implement user-defined dataset fingerprint in #7533 . Am I doing it right?"
] | 2025-04-15T01:02:02Z | 2025-04-23T19:37:08Z | null |
NONE
| null | null | null | null |
### Describe the bug
# TL:DR
`Dataset.from_generator` function passes all of its arguments to `BuilderConfig.create_config_id`, including `generator` function itself. `BuilderConfig.create_config_id` function tries to hash all the args, which can take a large amount of time or even cause MemoryError if the dataset processed in a generator function is large enough.
Maybe we should pop `generator` from `config_kwargs_to_add_to_suffix` before hashing to avoid it.
# Full description
I have a pretty large spatial imagery dataset that is generated from two xbatcher.BatchGenerators via custom `dataset_generator` function that looks like this if simplified:
```
def dataset_generator():
for index in samples:
data_dict = {
"key": index,
"x": x_batches[index].data,
"y": y_batches[index].data,
}
yield data_dict
```
Then I use `datasets.Dataset.from_generator` to generate the dataset itself.
```
# Create dataset
ds = datasets.Dataset.from_generator(
dataset_generator,
features=feat,
cache_dir=(output / ".cache"),
)
```
It works nicely with pretty small data, but if the dataset is huge and barely fits in memory, it crashes with memory error:
<details>
<summary>Full stack trace</summary>
```
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\remote_sensing_processor\segmentation\semantic\tiles.py:248](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/remote_sensing_processor/segmentation/semantic/tiles.py#line=247), in generate_tiles(x, y, output, tile_size, shuffle, split, x_dtype, y_dtype, x_nodata, y_nodata)
245 yield data_dict
247 # Create dataset
--> 248 ds = datasets.Dataset.from_generator(
249 dataset_generator,
250 features=feat,
251 cache_dir=(output / ".cache"),
252 )
254 # Save dataset
255 ds.save_to_disk(output / name)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\arrow_dataset.py:1105](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/arrow_dataset.py#line=1104), in Dataset.from_generator(generator, features, cache_dir, keep_in_memory, gen_kwargs, num_proc, split, **kwargs)
1052 """Create a Dataset from a generator.
1053
1054 Args:
(...) 1101 ```
1102 """
1103 from .io.generator import GeneratorDatasetInputStream
-> 1105 return GeneratorDatasetInputStream(
1106 generator=generator,
1107 features=features,
1108 cache_dir=cache_dir,
1109 keep_in_memory=keep_in_memory,
1110 gen_kwargs=gen_kwargs,
1111 num_proc=num_proc,
1112 split=split,
1113 **kwargs,
1114 ).read()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\io\generator.py:29](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/io/generator.py#line=28), in GeneratorDatasetInputStream.__init__(self, generator, features, cache_dir, keep_in_memory, streaming, gen_kwargs, num_proc, split, **kwargs)
9 def __init__(
10 self,
11 generator: Callable,
(...) 19 **kwargs,
20 ):
21 super().__init__(
22 features=features,
23 cache_dir=cache_dir,
(...) 27 **kwargs,
28 )
---> 29 self.builder = Generator(
30 cache_dir=cache_dir,
31 features=features,
32 generator=generator,
33 gen_kwargs=gen_kwargs,
34 split=split,
35 **kwargs,
36 )
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\builder.py:343](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/builder.py#line=342), in DatasetBuilder.__init__(self, cache_dir, dataset_name, config_name, hash, base_path, info, features, token, repo_id, data_files, data_dir, storage_options, writer_batch_size, **config_kwargs)
341 config_kwargs["data_dir"] = data_dir
342 self.config_kwargs = config_kwargs
--> 343 self.config, self.config_id = self._create_builder_config(
344 config_name=config_name,
345 custom_features=features,
346 **config_kwargs,
347 )
349 # prepare info: DatasetInfo are a standardized dataclass across all datasets
350 # Prefill datasetinfo
351 if info is None:
352 # TODO FOR PACKAGED MODULES IT IMPORTS DATA FROM src/packaged_modules which doesn't make sense
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\builder.py:604](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/builder.py#line=603), in DatasetBuilder._create_builder_config(self, config_name, custom_features, **config_kwargs)
598 builder_config._resolve_data_files(
599 base_path=self.base_path,
600 download_config=DownloadConfig(token=self.token, storage_options=self.storage_options),
601 )
603 # compute the config id that is going to be used for caching
--> 604 config_id = builder_config.create_config_id(
605 config_kwargs,
606 custom_features=custom_features,
607 )
608 is_custom = (config_id not in self.builder_configs) and config_id != "default"
609 if is_custom:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\builder.py:187](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/builder.py#line=186), in BuilderConfig.create_config_id(self, config_kwargs, custom_features)
185 suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
186 else:
--> 187 suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
189 if custom_features is not None:
190 m = Hasher()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\fingerprint.py:188](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/fingerprint.py#line=187), in Hasher.hash(cls, value)
186 @classmethod
187 def hash(cls, value: Any) -> str:
--> 188 return cls.hash_bytes(dumps(value))
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:109](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=108), in dumps(obj)
107 """Pickle an object to a string."""
108 file = BytesIO()
--> 109 dump(obj, file)
110 return file.getvalue()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:103](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=102), in dump(obj, file)
101 def dump(obj, file):
102 """Pickle an object to a file."""
--> 103 Pickler(file, recurse=True).dump(obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:420](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=419), in Pickler.dump(self, obj)
418 def dump(self, obj): #NOTE: if settings change, need to update attributes
419 logger.trace_setup(self)
--> 420 StockPickler.dump(self, obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:484](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=483), in _Pickler.dump(self, obj)
482 if self.proto >= 4:
483 self.framer.start_framing()
--> 484 self.save(obj)
485 self.write(STOP)
486 self.framer.end_framing()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1985](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1984), in save_function(pickler, obj)
1982 if state_dict:
1983 state = state, state_dict
-> 1985 _save_with_postproc(pickler, (_create_function, (
1986 obj.__code__, globs, obj.__name__, obj.__defaults__,
1987 closure
1988 ), state), obj=obj, postproc_list=postproc_list)
1990 # Lift closure cell update to earliest function (#458)
1991 if _postproc:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1117](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1116), in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list)
1115 continue
1116 else:
-> 1117 pickler.save_reduce(*reduction)
1118 # pop None created by calling preprocessing step off stack
1119 pickler.write(POP)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:690](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=689), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
688 else:
689 save(func)
--> 690 save(args)
691 write(REDUCE)
693 if obj is not None:
694 # If the object is already in the memo, this means it is
695 # recursive. In this case, throw away everything we put on the
696 # stack, and fetch the object back from the memo.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:905](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=904), in _Pickler.save_tuple(self, obj)
903 if n <= 3 and self.proto >= 2:
904 for element in obj:
--> 905 save(element)
906 # Subtle. Same as in the big comment below.
907 if id(obj) in memo:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
[... skipping similar frames: Pickler.save at line 70 (1 times), Pickler.save at line 414 (1 times)]
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:905](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=904), in _Pickler.save_tuple(self, obj)
903 if n <= 3 and self.proto >= 2:
904 for element in obj:
--> 905 save(element)
906 # Subtle. Same as in the big comment below.
907 if id(obj) in memo:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:905](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=904), in _Pickler.save_tuple(self, obj)
903 if n <= 3 and self.proto >= 2:
904 for element in obj:
--> 905 save(element)
906 # Subtle. Same as in the big comment below.
907 if id(obj) in memo:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:690](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=689), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
688 else:
689 save(func)
--> 690 save(args)
691 write(REDUCE)
693 if obj is not None:
694 # If the object is already in the memo, this means it is
695 # recursive. In this case, throw away everything we put on the
696 # stack, and fetch the object back from the memo.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:920](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=919), in _Pickler.save_tuple(self, obj)
918 write(MARK)
919 for element in obj:
--> 920 save(element)
922 if id(obj) in memo:
923 # Subtle. d was not in memo when we entered save_tuple(), so
924 # the process of saving the tuple's elements must have saved
(...) 928 # could have been done in the "for element" loop instead, but
929 # recursive tuples are a rare thing.
930 get = self.get(memo[id(obj)][0])
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1019](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1018), in _Pickler._batch_setitems(self, items)
1017 k, v = tmp[0]
1018 save(k)
-> 1019 save(v)
1020 write(SETITEM)
1021 # else tmp is empty, and we're done
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
[... skipping similar frames: Pickler.save at line 70 (1 times), Pickler.save at line 414 (1 times)]
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
[... skipping similar frames: Pickler.save at line 70 (1 times), Pickler.save at line 414 (1 times)]
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:920](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=919), in _Pickler.save_tuple(self, obj)
918 write(MARK)
919 for element in obj:
--> 920 save(element)
922 if id(obj) in memo:
923 # Subtle. d was not in memo when we entered save_tuple(), so
924 # the process of saving the tuple's elements must have saved
(...) 928 # could have been done in the "for element" loop instead, but
929 # recursive tuples are a rare thing.
930 get = self.get(memo[id(obj)][0])
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:809](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=808), in _Pickler.save_bytes(self, obj)
806 self.save_reduce(codecs.encode,
807 (str(obj, 'latin1'), 'latin1'), obj=obj)
808 return
--> 809 self._save_bytes_no_memo(obj)
810 self.memoize(obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:797](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=796), in _Pickler._save_bytes_no_memo(self, obj)
795 self._write_large_bytes(BINBYTES8 + pack("<Q", n), obj)
796 elif n >= self.framer._FRAME_SIZE_TARGET:
--> 797 self._write_large_bytes(BINBYTES + pack("<I", n), obj)
798 else:
799 self.write(BINBYTES + pack("<I", n) + obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:254](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=253), in _Framer.write_large_bytes(self, header, payload)
247 # Perform direct write of the header and payload of the large binary
248 # object. Be careful not to concatenate the header and the payload
249 # prior to calling 'write' as we do not want to allocate a large
250 # temporary bytes object.
251 # We intentionally do not insert a protocol 4 frame opcode to make
252 # it possible to optimize file.read calls in the loader.
253 write(header)
--> 254 write(payload)
MemoryError:
```
</details>
Memory error is an expected type of error in such case, but when I started digging down, I found out that it occurs in a kinda unexpected place - in `create_config_id` function. It tries to hash `config_kwargs_to_add_to_suffix`, including generator function itself.
I modified the `BuilderConfig.create_config_id` code like this to check which values are hashed and how much time it takes to hash them and ran it on a toy dataset:
```
print(config_kwargs_to_add_to_suffix)
start_time = time.time()
if all(isinstance(v, (str, bool, int, float)) for v in config_kwargs_to_add_to_suffix.values()):
suffix = ",".join(
str(k) + "=" + urllib.parse.quote_plus(str(v)) for k, v in config_kwargs_to_add_to_suffix.items()
)
if len(suffix) > 32: # hash if too long
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
else:
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
end_time = time.time()
print(f"Execution time: {end_time - start_time:.4f} seconds")
print(suffix)
```
In my case the content of `config_kwargs_to_add_to_suffix` was like this:
```
{'features': {'key': Value(dtype='int64', id=None), 'x': Array3D(shape=(44, 128, 128), dtype='float32', id=None), 'y_class': Array2D(shape=(128, 128), dtype='int32', id=None)}, 'gen_kwargs': None, 'generator': <function generate_tiles.<locals>.dataset_generator at 0x00000139D10D7920>, 'split': NamedSplit('train')}
```
Also I noticed that hashing took a significant amount of time - 43.1482 seconds, while the overall function execution (with data loading, batching and saving dataset) took 2min 45s. The output of `create_config_id` is just a dataset id, so, it is inappropirately large amount of time.
But when I added `config_kwargs_to_add_to_suffix.pop("generator", None)`, the hashing took only 0.0060 seconds.
Maybe we shouldn't hash the generator function, as it can be really computationally and memory expensive.
### Steps to reproduce the bug
This is a simplified example of a workflow I used to generate dataset. But I think that you can use almost any workflow to reproduce that bug.
```
import pystac
import pystac_client
import planetary_computer
import numpy as np
import xarray as xr
import rioxarray as rxr
import dask
import xbatcher
import datasets
# Loading a dataset, in our case - single Landsat image
catalog = pystac_client.Client.open(
"https://planetarycomputer.microsoft.com/api/stac/v1",
modifier=planetary_computer.sign_inplace,
)
brazil = [-60.2, -3.31]
time_of_interest = "2021-06-01/2021-08-31"
search = catalog.search(collections=["landsat-c2-l2"], intersects={"type": "Point", "coordinates": brazil}, datetime=time_of_interest)
items = search.item_collection()
item = min(items, key=lambda item: pystac.extensions.eo.EOExtension.ext(item).cloud_cover)
# Getting x data
bands = []
for band in ["red", "green", "blue", "nir08", "coastal", "swir16", "swir22", "lwir11"]:
with rxr.open_rasterio(item.assets[band].href, chunks=True, lock=True) as raster:
raster = raster.to_dataset('band')
#print(raster)
raster = raster.rename({1: band})
bands.append(raster)
x = xr.merge(bands).squeeze().to_array("band").persist()
# Getting y data
with rxr.open_rasterio(item.assets['qa_pixel'].href, chunks=True, lock=True) as raster:
y = raster.squeeze().persist()
# Setting up batches generators
x_batches = xbatcher.BatchGenerator(ds=x, input_dims={"x": 256, "y": 256})
y_batches = xbatcher.BatchGenerator(ds=y, input_dims={"x": 256, "y": 256})
# Filtering samples that contain only nodata
samples = list(range(len(x_batches)))
samples_filtered = []
for i in samples:
if not np.array_equal(np.unique(x_batches[i]), np.array([0.])) and not np.array_equal(np.unique(y_batches[i]), np.array([0])):
samples_filtered.append(i)
samples = samples_filtered
np.random.shuffle(samples)
# Setting up features
feat = {
"key": datasets.Value(dtype="int64"),
"x": datasets.Array3D(dtype="float32", shape=(4, 256, 256)),
"y": datasets.Array2D(dtype="int32", shape=(256, 256))
}
feat = datasets.Features(feat)
# Setting up a generator
def dataset_generator():
for index in samples:
data_dict = {
"key": index,
"x": x_batches[index].data,
"y": y_batches[index].data,
}
yield data_dict
# Create dataset
ds = datasets.Dataset.from_generator(
dataset_generator,
features=feat,
cache_dir="temp/cache",
)
```
Please, try adding `config_kwargs_to_add_to_suffix.pop("generator", None)` to `BuilderConfig.create_config_id` and then measuring how much time it takes to run
```
if all(isinstance(v, (str, bool, int, float)) for v in config_kwargs_to_add_to_suffix.values()):
suffix = ",".join(
str(k) + "=" + urllib.parse.quote_plus(str(v)) for k, v in config_kwargs_to_add_to_suffix.items()
)
if len(suffix) > 32: # hash if too long
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
else:
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
```
code block with and without `config_kwargs_to_add_to_suffix.pop("generator", None)`
In my case the difference was 3.3828 seconds without popping generator function and 0.0010 seconds with popping.
### Expected behavior
Much faster hashing and no MemoryErrors
### Environment info
- `datasets` version: 3.5.0
- Platform: Windows-11-10.0.26100-SP0
- Python version: 3.12.9
- `huggingface_hub` version: 0.30.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.12.0
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| 2,994,043,544 |
I_kwDODunzps6ydXqY
| 7,512 |
.map() fails if function uses pyvista
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[
"I found a similar (?) issue in https://github.com/huggingface/datasets/issues/6435, where someone had issues with forks and CUDA. According to https://huggingface.co/docs/datasets/main/en/process#multiprocessing we should do \n\n```\nfrom multiprocess import set_start_method\nset_start_method(\"spawn\")\n```\n\nto avoid the fork. The updated code\n\n```python\nimport numpy as np\nimport pyvista as pv\nimport datasets\nimport multiprocess\n\ndata = [{\"coords\": np.random.rand(5, 3)} for _ in range(3)]\n\ndef render_point(example):\n plotter = pv.Plotter(off_screen=True)\n cloud = pv.PolyData(example[\"coords\"])\n plotter.add_mesh(cloud)\n img = plotter.screenshot(return_img=True)\n return {\"image\": img}\n\n\n# breaks if num_proc>1\nmultiprocess.set_start_method(\"spawn\")\nds = datasets.Dataset.from_list(data).map(render_point, num_proc=2)\n```\n\ninstead fails with `TypeError: fork_exec() takes exactly 23 arguments (21 given)` which also seems like a bug to me."
] | 2025-04-14T19:43:02Z | 2025-04-14T20:01:53Z | null |
NONE
| null | null | null | null |
### Describe the bug
Using PyVista inside a .map() produces a crash with `objc[78796]: +[NSResponder initialize] may have been in progress in another thread when fork() was called. We cannot safely call it or ignore it in the fork() child process. Crashing instead. Set a breakpoint on objc_initializeAfterForkError to debug.`
### Steps to reproduce the bug
Run
```python
import numpy as np
import pyvista as pv
import datasets
data = [{"coords": np.random.rand(5, 3)} for _ in range(3)]
def render_point(example):
plotter = pv.Plotter(off_screen=True)
cloud = pv.PolyData(example["coords"])
plotter.add_mesh(cloud)
img = plotter.screenshot(return_img=True)
return {"image": img}
# breaks if num_proc>1
ds = datasets.Dataset.from_list(data).map(render_point, num_proc=2)
```
### Expected behavior
It should work. Just like when I use a process pool to make it work.
```python
import numpy as np
import pyvista as pv
import multiprocessing
data = [{"coords": np.random.rand(5, 3)} for _ in range(3)]
def render_point(example):
plotter = pv.Plotter(off_screen=True)
cloud = pv.PolyData(example["coords"])
plotter.add_mesh(cloud)
img = plotter.screenshot(return_img=True)
return {"image": img}
if __name__ == "__main__":
with multiprocessing.Pool(processes=2) as pool:
results = pool.map(render_point, data)
print(results[0]["image"].shape)
```
### Environment info
- `datasets` version: 3.3.2
- Platform: macOS-15.3.2-arm64-arm-64bit
- Python version: 3.11.10
- `huggingface_hub` version: 0.28.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.10.0
And then I suppose pyvista info is good to have.
```python
import pyvista as pv
print(pv.Report())
```
gives
--------------------------------------------------------------------------------
Date: Mon Apr 14 21:42:08 2025 CEST
OS : Darwin (macOS 15.3.2)
CPU(s) : 10
Machine : arm64
Architecture : 64bit
RAM : 32.0 GiB
Environment : IPython
File system : apfs
GPU Vendor : Apple
GPU Renderer : Apple M1 Max
GPU Version : 4.1 Metal - 89.3
MathText Support : True
Python 3.11.10 (main, Oct 7 2024, 23:25:27) [Clang 18.1.8 ]
pyvista : 0.44.2
vtk : 9.4.0
numpy : 2.2.2
matplotlib : 3.10.0
scooby : 0.10.0
pooch : 1.8.2
pillow : 11.1.0
imageio : 2.36.1
PyQt5 : 5.15.11
IPython : 8.30.0
scipy : 1.14.1
tqdm : 4.67.1
jupyterlab : 4.3.5
nest_asyncio : 1.6.0
--------------------------------------------------------------------------------
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Incompatibile dill version (0.3.9) in datasets 2.18.0 - 3.5.0
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[
"Hi ! We can bump `dill` to 0.3.9 if we make sure it's deterministic and doesn't break the caching mechanism in `datasets`.\n\nWould you be interested in opening a PR ? Then we can run the CI to see if it works",
"Hi!. Yeah I can do it. Should I make any changes besides dill versions?",
"There are probably some usage of internal functions from `dill` that we'll need to update in `datasets`\n\nIf you run `pytest tests/test_fingerprint.py` you should already have a good idea of what works and what doesn't.\nBut feel free to open a PR anyway, this way we can run the full CI and see the results\n",
"Hi, sorry for no response from my side. I will try to do it today.",
"Created pull request: [LINK](https://github.com/huggingface/datasets/pull/7535)\nTried to run tests by using command you have send and got few errors:\n\n",
"Thanks for running the test ! So it appears we have two issues to fix:\n1. 'log' is not defined: it seems an internal `dill` function has disappeared, so we should adapt the `datasets` code that was using it\n2. there are some hashes mismatches, which means `dill` doesn't seem to output the same dump when passed the same ipython function twice, or the same function but located at a different line in a python file"
] | 2025-04-14T07:22:44Z | 2025-05-19T14:54:04Z | null |
NONE
| null | null | null | null |
### Describe the bug
Datasets 2.18.0 - 3.5.0 has a dependency on dill < 0.3.9. This causes errors with dill >= 0.3.9.
Could you please take a look into it and make it compatible?
### Steps to reproduce the bug
1. Install setuptools >= 2.18.0
2. Install dill >=0.3.9
3. Run pip check
4. Output:
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
datasets 2.18.0 requires dill<0.3.9,>=0.3.0, but you have dill 0.3.9 which is incompatible.
### Expected behavior
Pip install both libraries without any errors
### Environment info
Datasets version: 2.18 - 3.5
Python: 3.11
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