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Python 3.14
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7836). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-10-28T16:11:13
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PR_kwDODunzps6wHK9e
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Add DICOM support
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supports #7804
Add support for the dicom file format.
This PR follows PR #7815 and PR #7325 closely.
Remarkable differences:
I made sure that we can load all of pydicom's test data, and encountered the `force=True` parameter that we explicitly support here. This allows to trying to load corrupted dicom files, we explicitly test this!
There is one dataset with all of dicom's test data on huggingface which can be loaded using this branch with the following script:
```python
from datasets import load_dataset
from datasets import Features, ClassLabel
from datasets.features import Dicom
features = Features({
"dicom": Dicom(force=True), # necessary to be able to load one corrupted file
"label": ClassLabel(num_classes=2)
})
ds = load_dataset("TobiasPitters/dicom-sample-dataset",
features=features)
error_count = 0
for idx, item in enumerate(ds["test"]):
dicom = item["dicom"]
try:
print(f"Type: {type(dicom)}")
if hasattr(dicom, 'PatientID'):
print(f"PatientID: {dicom.PatientID}")
if hasattr(dicom, 'StudyInstanceUID'):
print(f"StudyInstanceUID: {dicom.StudyInstanceUID}")
if hasattr(dicom, 'Modality'):
print(f"Modality: {dicom.Modality}")
except Exception as e:
error_count += 1
print(e)
print(f"Finished processing with {error_count} errors.")
```
todo:
- [ ] add docs (will do so soon)
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I_kwDODunzps7UHwYP
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Audio.cast_column() or Audio.decode_example() causes Colab kernel crash (std::bad_alloc)
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"Hi ! `datasets` v4 uses `torchcodec` for audio decoding (previous versions were using `soundfile`). What is your `torchcodec` version ? Can you try other versions of `torchcodec` and see if it works ?",
"When I install `datasets` with `pip install datasets[audio]` it install this version of `torchcodec`:\n```\nName: torchcodec\nVersion: 0.8.1\n```\nCan you please point to a working version of `torchcodec`?\n\nThanks for your help",
"I believe you simply need to make sure the torchcodec and torch versions work together. Here is how to fix it:\n\n```python\n!pip install -U torchcodec torch\n```",
"I am also encountering this same issue when i run `print(ug_court[\"train\"][0])` to view the features of the first row of my audio data",
"the problem still goes on to when i force training with seeing these features"
] | 2025-10-27T22:02:00
| 2025-10-30T12:03:24
| null |
NONE
| null | null | null | null |
### Describe the bug
When using the huggingface datasets.Audio feature to decode a local or remote (public HF dataset) audio file inside Google Colab, the notebook kernel crashes with std::bad_alloc (C++ memory allocation failure).
The crash happens even with a minimal code example and valid .wav file that can be read successfully using soundfile.
Here is a sample Collab notebook to reproduce the problem.
https://colab.research.google.com/drive/1nnb-GC5748Tux3xcYRussCGp2x-zM9Id?usp=sharing
code sample:
```
...
audio_dataset = audio_dataset.cast_column("audio", Audio(sampling_rate=16000))
# Accessing the first element crashes the Colab kernel
print(audio_dataset[0]["audio"])
```
Error log
```
WARNING what(): std::bad_alloc
terminate called after throwing an instance of 'std::bad_alloc'
```
Environment
Platform: Google Colab (Python 3.12.12)
datasets Version: 4.3.0
soundfile Version: 0.13.1
torchaudio Version: 2.8.0+cu126
Thanks in advance to help me on this error I get approx two weeks now after it was working before.
Regards
### Steps to reproduce the bug
https://colab.research.google.com/drive/1nnb-GC5748Tux3xcYRussCGp2x-zM9Id?usp=sharing
### Expected behavior
Loading the audio and decode it.
It should safely return:
{
"path": "path/filaname.wav",
"array": np.ndarray([...]),
"sampling_rate": 16000
}
### Environment info
Environment
Platform: Google Colab (Python 3.12.12)
datasets Version: 4.3.0
soundfile Version: 0.13.1
torchaudio Version: 2.8.0+cu126
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Fix: Properly render [!TIP] block in stream.shuffle documentation
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The second line starting with the bracket doesn't properly render on huggingface/docs. Added "> " to address it.
In the client documentation, the markdown 'TIP' paragraph in docs/stream#shuffle is not well executed, not as the other in the same page / while markdown is correctly considering it.
Documentation:
https://huggingface.co/docs/datasets/v4.3.0/en/stream#shuffle:~:text=%5B!TIP%5D%5BIterableDataset.shuffle()%5D(/docs/datasets/v4.3.0/en/package_reference/main_classes%23datasets.IterableDataset.shuffle)%20will%20also%20shuffle%20the%20order%20of%20the%20shards%20if%20the%20dataset%20is%20sharded%20into%20multiple%20files.
Github source:
https://github.com/huggingface/datasets/blob/main/docs/source/stream.mdx#:~:text=Casting%20only%20works%20if%20the%20original%20feature%20type%20and%20new%20feature%20type%20are%20compatible.%20For%20example%2C%20you%20can%20cast%20a%20column%20with%20the%20feature%20type%20Value(%27int32%27)%20to%20Value(%27bool%27)%20if%20the%20original%20column%20only%20contains%20ones%20and%20zeros.
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[DOCS][minor] TIPS paragraph not compiled in docs/stream
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CONTRIBUTOR
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In the client documentation, the markdown 'TIP' paragraph for paragraph in docs/stream#shuffle is not well executed — not as the other in the same page / while markdown is correctly considering it.
Documentation:
https://huggingface.co/docs/datasets/v4.3.0/en/stream#shuffle:~:text=%5B!TIP%5D%5BIterableDataset.shuffle()%5D(/docs/datasets/v4.3.0/en/package_reference/main_classes%23datasets.IterableDataset.shuffle)%20will%20also%20shuffle%20the%20order%20of%20the%20shards%20if%20the%20dataset%20is%20sharded%20into%20multiple%20files.
Github source:
https://github.com/huggingface/datasets/blob/main/docs/source/stream.mdx#:~:text=Casting%20only%20works%20if%20the%20original%20feature%20type%20and%20new%20feature%20type%20are%20compatible.%20For%20example%2C%20you%20can%20cast%20a%20column%20with%20the%20feature%20type%20Value(%27int32%27)%20to%20Value(%27bool%27)%20if%20the%20original%20column%20only%20contains%20ones%20and%20zeros.
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resolves the ValueError: Unable to avoid copy while creating an array
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"## Also i have done some tests on real dataset\r\n\r\n### Tested with real datasets:\r\n- ✅ IMDB dataset with NumPy 1.26.4 and 2.3.4\r\n- ✅ Rotten Tomatoes dataset with NumPy 1.26.4 and 2.3.4\r\n- ✅ Artificial datasets with ClassLabel features\r\n\r\n### Results:\r\n- Stratified splits work correctly in both NumPy versions\r\n- Class balance is perfectly maintained (e.g., Rotten Tomatoes: 426:426 train, 107:107 test)\r\n- Non-stratified splits continue to work as expected\r\n- Backward compatibility with NumPy 1.x confirmed\r\n\r\nBelow are the RAW logs of testing - \r\n\r\n```\r\n(venv) F:\\Python\\Machine learning\\datasets>pip install \"numpy<2.0\"\r\nCollecting numpy<2.0\r\n Using cached numpy-1.26.4-cp311-cp311-win_amd64.whl.metadata (61 kB)\r\nUsing cached numpy-1.26.4-cp311-cp311-win_amd64.whl (15.8 MB)\r\nInstalling collected packages: numpy\r\n Attempting uninstall: numpy\r\n Found existing installation: numpy 2.3.4\r\n Uninstalling numpy-2.3.4:\r\n Successfully uninstalled numpy-2.3.4\r\nSuccessfully installed numpy-1.26.4\r\n\r\n[notice] A new release of pip is available: 24.0 -> 25.3\r\n[notice] To update, run: python.exe -m pip install --upgrade pip\r\n\r\n(venv) F:\\Python\\Machine learning\\datasets>python test_fix.py\r\nNumPy version: 1.26.4\r\n============================================================\r\n\r\n[Test 1] Testing with IMDB dataset...\r\nREADME.md: 7.81kB [00:00, 7.78MB/s]\r\nF:\\Python\\Machine learning\\datasets\\venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\arjun\\.cache\\huggingface\\hub\\datasets--imdb. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\r\nTo support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\r\n warnings.warn(message)\r\nXet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\r\ntrain-00000-of-00001.parquet: 100%|███████████████████████████████████████████████████████████████████████████████████| 21.0M/21.0M [00:06<00:00, 3.08MB/s]\r\nXet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\r\ntest-00000-of-00001.parquet: 100%|████████████████████████████████████████████████████████████████████████████████████| 20.5M/20.5M [00:07<00:00, 2.56MB/s]\r\nXet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\r\nunsupervised-00000-of-00001.parquet: 100%|████████████████████████████████████████████████████████████████████████████| 42.0M/42.0M [00:14<00:00, 2.84MB/s]\r\nGenerating train split: 100%|█████████████████████████████████████████████████████████████████████████████| 25000/25000 [00:00<00:00, 267643.40 examples/s]\r\nGenerating test split: 100%|██████████████████████████████████████████████████████████████████████████████| 25000/25000 [00:00<00:00, 324697.85 examples/s]\r\nGenerating unsupervised split: 100%|██████████████████████████████████████████████████████████████████████| 50000/50000 [00:00<00:00, 289202.11 examples/s]\r\nLoaded 1000 samples\r\n✅ IMDB SUCCESS!\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 800\r\n })\r\n test: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 200\r\n })\r\n})\r\nTrain class distribution: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\r\nTest class distribution: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\r\n\r\n============================================================\r\n\r\n[Test 2] Testing with Rotten Tomatoes dataset...\r\nREADME.md: 7.46kB [00:00, ?B/s]\r\nF:\\Python\\Machine learning\\datasets\\venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\arjun\\.cache\\huggingface\\hub\\datasets--rotten_tomatoes. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\r\nTo support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\r\n warnings.warn(message)\r\nXet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\r\ntrain.parquet: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 699k/699k [00:00<00:00, 3.46MB/s]\r\nXet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\r\nvalidation.parquet: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 90.0k/90.0k [00:00<00:00, 6.80MB/s]\r\nXet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\r\ntest.parquet: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 92.2k/92.2k [00:00<00:00, 5.85MB/s]\r\nGenerating train split: 100%|███████████████████████████████████████████████████████████████████████████████| 8530/8530 [00:00<00:00, 856082.82 examples/s]\r\nGenerating validation split: 100%|██████████████████████████████████████████████████████████████████████████| 1066/1066 [00:00<00:00, 531075.91 examples/s]\r\nGenerating test split: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 1066/1066 [00:00<?, ? examples/s]\r\nLoaded 1066 samples\r\n✅ Rotten Tomatoes SUCCESS!\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 852\r\n })\r\n test: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 214\r\n })\r\n})\r\n\r\nTrain: class_0=426, class_1=426\r\nTest: class_0=107, class_1=107\r\n\r\n============================================================\r\n\r\n[Test 3] Testing without stratification (sanity check)...\r\n✅ Non-stratified split SUCCESS!\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 80\r\n })\r\n test: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 20\r\n })\r\n})\r\n\r\n============================================================\r\nAll tests completed!\r\n\r\n```\r\n\r\n### Upgrading numpy for >= 2\r\n\r\n```\r\n(venv) F:\\Python\\Machine learning\\datasets>pip install \"numpy>=2.0\"\r\nCollecting numpy>=2.0\r\n Using cached numpy-2.3.4-cp311-cp311-win_amd64.whl.metadata (60 kB)\r\nUsing cached numpy-2.3.4-cp311-cp311-win_amd64.whl (13.1 MB)\r\nInstalling collected packages: numpy\r\n Attempting uninstall: numpy\r\n Found existing installation: numpy 1.26.4\r\n Uninstalling numpy-1.26.4:\r\n Successfully uninstalled numpy-1.26.4\r\nSuccessfully installed numpy-2.3.4\r\n\r\n[notice] A new release of pip is available: 24.0 -> 25.3\r\n[notice] To update, run: python.exe -m pip install --upgrade pip\r\n\r\n(venv) F:\\Python\\Machine learning\\datasets>python test_fix.py\r\nNumPy version: 2.3.4\r\n============================================================\r\n\r\n[Test 1] Testing with IMDB dataset...\r\nLoaded 1000 samples\r\n✅ IMDB SUCCESS!\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 800\r\n })\r\n test: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 200\r\n })\r\n})\r\nTrain class distribution: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\r\nTest class distribution: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\r\n\r\n============================================================\r\n\r\n[Test 2] Testing with Rotten Tomatoes dataset...\r\nLoaded 1066 samples\r\n✅ Rotten Tomatoes SUCCESS!\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 852\r\n })\r\n test: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 214\r\n })\r\n})\r\n\r\nTrain: class_0=426, class_1=426\r\nTest: class_0=107, class_1=107\r\n\r\n============================================================\r\n\r\n[Test 3] Testing without stratification (sanity check)...\r\n✅ Non-stratified split SUCCESS!\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 80\r\n })\r\n test: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 20\r\n })\r\n})\r\n\r\n============================================================\r\nAll tests completed!\r\n",
"[test_fix.py](https://github.com/user-attachments/files/23140682/test_fix.py)\r\n\r\nhere is the file I used for testing @lhoestq ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7831). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-10-25T08:13:54
| 2025-10-29T06:35:32
| 2025-10-28T16:10:43
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## Summary
Fixes #7818
This PR resolves the `ValueError: Unable to avoid copy while creating an array` error that occurs when using `train_test_split` with `stratify_by_column` parameter in NumPy 2.0+.
## Changes
- Wrapped the stratify column array access with `np.asarray()` in `arrow_dataset.py`
- This allows NumPy 2.0 to make a copy when the Arrow array is non-contiguous in memory
## Testing
- ✅ Tested with NumPy 2.3.4 - stratified splits work correctly
- ✅ Tested with NumPy 1.26.4 - backward compatibility maintained
- ✅ Verified class balance is preserved in stratified splits
- ✅ Non-stratified splits continue to work as expected
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fix ci compressionfs
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] | 2025-10-24T14:29:00
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I_kwDODunzps7TgxiV
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Memory leak / Large memory usage with num_workers = 0 and numerous dataset within DatasetDict
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"Thanks for the report, this is possibly related #7722 and #7694.\n\nCould you pls provide steps to reproduce this?",
"To overcome this issue right now I did simply reduce the size of the dataset and ended up running a for loop (my training has now a constant learning rate schedule). From what I understood, and I don't know if it's possible, the solution would be to tell the backend of `datasets` to leave x% of the memory free (including memory mapping). Can't release the data right now but I will and then allow to reproduce this issue. But it will involve to have some free TB of disk",
"@raphaelsty thanks for coming back to this. I assume you are running in streaming mode? That should prevent these errors but it looks like more people than just you have this problem, so a clearly reproducing example (including data + code) is highly appreciated."
] | 2025-10-24T09:51:38
| 2025-10-27T19:37:21
| null |
NONE
| null | null | null | null |
### Describe the bug
Hi team, first off, I love the datasets library! 🥰
I'm encountering a potential memory leak / increasing memory usage when training a model on a very large DatasetDict.
Setup: I have a DatasetDict containing 362 distinct datasets, which sum up to ~2.8 billion rows.
Training Task: I'm performing contrastive learning with SentenceTransformer and Accelerate on a single node with 4 H100, which requires me to sample from only one dataset at a time.
Training Loop: At each training step, I sample ~16,000 examples from a single dataset, and then switch to a different dataset for the next step. I iterate through all 362 datasets this way.
Problem: The process's memory usage continuously increases over time, eventually causing a stale status where GPUs would stop working. It seems memory from previously sampled datasets isn't being released. I've set num_workers=0 for all experiments.
Chart 1: Standard DatasetDict The memory usage grows steadily until it make the training stale (RSS memory) <img width="773" height="719" alt="Image" src="https://github.com/user-attachments/assets/6606bef5-1153-4f2d-bf08-82da249d6e8d" />
Chart 2: IterableDatasetDict I also tried to use IterableDatasetDict and IterableDataset. The memory curve is "smoother," but the result is the same: it grows indefinitely and the training become stale. <img width="339" height="705" alt="Image" src="https://github.com/user-attachments/assets/ee90c1a1-6c3b-4135-9edc-90955cb1695a" />
Any feedback or guidance on how to manage this memory would be greatly appreciated!
### Steps to reproduce the bug
WIP, I'll add some code that manage to reproduce this error, but not straightforward.
### Expected behavior
The memory usage should remain relatively constant or plateau after a few steps. Memory used for sampling one dataset should be released before or during the sampling of the next dataset.
### Environment info
Python: 3.12
Datasets: 4.3.0
SentenceTransformers: 5.1.1
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picklable batch_fn
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Fix ArrowInvalid for large variables
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"@lhoestq : from what I see in the CI logs ( tests/test_arrow_dataset.py::test_map_int32_overflow \r\n/opt/hostedtoolcache/Python/3.9.23/x64/lib/python3.9/site-packages/multiprocess/resource_tracker.py:219: UserWarning: resource_tracker: There appear to be 24 leaked shared_memory objects to clean up at shutdown\r\n warnings.warn('resource_tracker: There appear to be %d '), see [here](https://github.com/huggingface/datasets/actions/runs/18720027796/job/53591652242#step:13:6530), the test crashes probably since it consumes too much memory, it works locally for me though. \r\n \r\n Shall we keep the test, remove ti, or skip it, or is there a specific pytest flag you have for slow/memory-consuming tests I can add, so that it gets skipped automatically on the CI but might be run locally?\r\n \r\n EDIT: I added the `high_memory` marker (see [here](https://github.com/huggingface/datasets/pull/7825/files#diff-c480dc36aca482db963c43ee66ba21705111f62f826352c96558ef5ed1619791R4789)), so this will not be run in the CI, which is not ideal but I guess the way if we want to keep the test"
] | 2025-10-22T14:42:47
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Fixes #7821
In addition to the solution proposed in the issue, I encountered we also need to support 64bit types when writing using the `RecordBatchStreamWriter`.
Not sure if we want to create such large objects in the CI, but this is the only way to test that the issue is fixed, therefore I added the unit test.
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Fix batch_size default description in `to_polars` docstrings.
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PR_kwDODunzps6uRkGa
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Fix random seed on shuffle and interleave_datasets
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7823). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Cool ! To avoid unwanted side effects it could be implemented for every class instead of using `hasattr`, and also return a new instance instead of modifying it in place. Some other functions work like that already like `shuffle_data_sources` and `shard_data_sources`.\r\n\r\nReturning a new object is actually quite important otherwise iterating on the dataset multiple times would shift the RNGs every time",
"> Cool ! To avoid unwanted side effects it could be implemented for every class instead of using `hasattr`, and also return a new instance instead of modifying it in place. Some other functions work like that already like `shuffle_data_sources` and `shard_data_sources`.\r\n> \r\n> Returning a new object is actually quite important otherwise iterating on the dataset multiple times would shift the RNGs every time\r\n\r\nThanks for the review. \r\n\r\nI managed to return instances of `_BaseExamplesIterable` instead of modifying inplace and this shifts the seed correctly even in the case of multiple iterations (see additional test). But I kept the `hasattr` for the reason that we have chained ex_iterables (so an ex_iterable can have other ex_iterables) and for the child ex_iterable it is not clear if they do have a generator attribute (e.g. `ArrowExamplesIterable`, `RebatchedArrowExamplesIterable`, `SelectColumnsIterable`, `StepExamplesIterable`, `SkipExamplesIterable`, `RepeatExamplesIterable` all don't have that). We could implement a generic method `shift_rngs` on the baseclass and then just return the ex_iterable and get rid of `hasattr` but I'd prefer the `hasattr` solution here. Let me now what you think about that.\r\n "
] | 2025-10-17T10:21:47
| 2025-10-24T14:21:22
| 2025-10-24T14:04:36
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closes #7567
Add `shift_rngs` method to `ExamplesIterable` that is called directly after sharding. If a generator is available (not the case for all subclasses) we update the seed of the generator by shifting by the worker_id.
~This is just the fix for `shuffle`, in the corresponding issue `interleave_datasets` is mentioned as well, which won't be fixed with this approach.~
EDIT: This is a fix for `shuffle` and `interleave_datasets`. Adding recursivity to `shift_rngs` solved `interleave_datasets` as well. Not sure though if this is completely safe or if we could destroy something with that. I don't think so but could be wrong and appreciate some guidance from the maintainers. I also checked, on a single_worker we are always handing over `index=0` so that case preserves the seed the user specified.
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Retry open hf file
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Fix this error
```python
File "/workdir/.venv/lib/python3.13/site-packages/datasets/utils/file_utils.py", line 934, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
File "/workdir/.venv/lib/python3.13/site-packages/fsspec/core.py", line 147, in open
return self.__enter__()
~~~~~~~~~~~~~~^^
File "/workdir/.venv/lib/python3.13/site-packages/fsspec/core.py", line 105, in __enter__
f = self.fs.open(self.path, mode=mode)
File "/workdir/.venv/lib/python3.13/site-packages/fsspec/spec.py", line 1338, in open
f = self._open(
path,
...<4 lines>...
**kwargs,
)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_file_system.py", line 275, in _open
return HfFileSystemFile(self, path, mode=mode, revision=revision, block_size=block_size, **kwargs)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_file_system.py", line 950, in __init__
self.resolved_path = fs.resolve_path(path, revision=revision)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_file_system.py", line 198, in resolve_path
repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_file_system.py", line 125, in _repo_and_revision_exist
self._api.repo_info(
~~~~~~~~~~~~~~~~~~~^
repo_id, revision=revision, repo_type=repo_type, timeout=constants.HF_HUB_ETAG_TIMEOUT
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_api.py", line 2864, in repo_info
return method(
repo_id,
...<4 lines>...
files_metadata=files_metadata,
)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_api.py", line 2721, in dataset_info
r = get_session().get(path, headers=headers, timeout=timeout, params=params)
File "/workdir/.venv/lib/python3.13/site-packages/requests/sessions.py", line 602, in get
return self.request("GET", url, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
File "/workdir/.venv/lib/python3.13/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
File "/workdir/.venv/lib/python3.13/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_http.py", line 95, in send
return super().send(request, *args, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workdir/.venv/lib/python3.13/site-packages/requests/adapters.py", line 690, in send
raise ReadTimeout(e, request=request)
requests.exceptions.ReadTimeout: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: e7e1ae72-54a0-4ce4-b011-144fb7a3fb06)')
```
which could also be related to
```python
File "/workdir/.venv/lib/python3.13/site-packages/datasets/utils/file_utils.py", line 1364, in _iter_from_urlpaths
raise FileNotFoundError(urlpath)
FileNotFoundError: hf://datasets/.../train-00013-of-00031.parquet
```
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I_kwDODunzps7R3N8r
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Building a dataset with large variable size arrays results in error ArrowInvalid: Value X too large to fit in C integer type
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open
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[
"Thanks for reporting ! You can fix this by specifying the output type explicitly and use `LargeList` which uses int64 for offsets:\n\n```python\nfeatures = Features({\"audio\": LargeList(Value(\"uint16\"))})\nds = ds.map(..., features=features)\n```\n\nIt would be cool to improve `list_of_pa_arrays_to_pyarrow_listarray()` to automatically use `LargeList` if the lists are longer than the int32 limit though. Contributions are welcome if you'd like to improve it"
] | 2025-10-16T08:45:17
| 2025-10-20T13:42:05
| null |
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
I used map to store raw audio waveforms of variable lengths in a column of a dataset the `map` call fails with ArrowInvalid: Value X too large to fit in C integer type.
```
Traceback (most recent call last):
Traceback (most recent call last):
File "...lib/python3.12/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "...lib/python3.12/site-packages/datasets/utils/py_utils.py", line 678, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
^^^^^^^^^^^^^^^^^^^^^^^^^
File "...lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3526, in _map_single
writer.write_batch(batch)
File "...lib/python3.12/site-packages/datasets/arrow_writer.py", line 605, in write_batch
arrays.append(pa.array(typed_sequence))
^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 252, in pyarrow.lib.array
File "pyarrow/array.pxi", line 114, in pyarrow.lib._handle_arrow_array_protocol
File "...lib/python3.12/site-packages/datasets/arrow_writer.py", line 225, in __arrow_array__
out = list_of_np_array_to_pyarrow_listarray(data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "...lib/python3.12/site-packages/datasets/features/features.py", line 1538, in list_of_np_array_to_pyarrow_listarray
return list_of_pa_arrays_to_pyarrow_listarray(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "...lib/python3.12/site-packages/datasets/features/features.py", line 1530, in list_of_pa_arrays_to_pyarrow_listarray
offsets = pa.array(offsets, type=pa.int32())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 362, in pyarrow.lib.array
File "pyarrow/array.pxi", line 87, in pyarrow.lib._ndarray_to_array
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Value 2148479376 too large to fit in C integer type
```
### Steps to reproduce the bug
Calling map on a dataset that returns a column with long 1d numpy arrays of variable length.
Example:
```python
# %%
import logging
import datasets
import pandas as pd
import numpy as np
# %%
def process_batch(batch, rank):
res = []
for _ in batch["id"]:
res.append(np.zeros((2**30)).astype(np.uint16))
return {"audio": res}
if __name__ == "__main__":
df = pd.DataFrame(
{
"id": list(range(400)),
}
)
ds = datasets.Dataset.from_pandas(df)
try:
from multiprocess import set_start_method
set_start_method("spawn")
except RuntimeError:
print("Spawn method already set, continuing...")
mapped_ds = ds.map(
process_batch,
batched=True,
batch_size=2,
with_rank=True,
num_proc=2,
cache_file_name="path_to_cache/tmp.arrow",
writer_batch_size=200,
remove_columns=ds.column_names,
# disable_nullable=True,
)
```
### Expected behavior
I think the offsets should be pa.int64() if needed and not forced to be `pa.int32()`
in https://github.com/huggingface/datasets/blob/3e13d30823f8ec498d56adbc18c6880a5463b313/src/datasets/features/features.py#L1535
### Environment info
- `datasets` version: 3.3.1
- Platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.35
- Python version: 3.12.9
- `huggingface_hub` version: 0.29.0
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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PR_kwDODunzps6t6suZ
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Keep hffs cache in workers when streaming
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] | 2025-10-15T15:51:28
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(and also reorder the hffs args to improve caching)
When using `DataLoader(iterable_dataset, num_workers=...)` the dataset is pickled and passed to the worker. However previously the resulting dataset would be in a process with an empty hffs cache. By keeping the cache attached to `IterableDataset`, the cached hffs instances are pickled with the dataset and re-populates the cache in the DataLoader workers
this requires https://github.com/huggingface/huggingface_hub/pull/3443 to work effectively though, otherwise the unpickled hffs cache would start empty
cc @andimarafioti @ltmeyer
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I_kwDODunzps7Ronme
| 7,819
|
Cannot download opus dataset
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[
"Hi ! it seems \"en-zh\" doesn't exist for this dataset\n\nYou can see the list of subsets here: https://huggingface.co/datasets/Helsinki-NLP/opus_books"
] | 2025-10-15T09:06:19
| 2025-10-20T13:45:16
| null |
NONE
| null | null | null | null |
When I tried to download opus_books using:
from datasets import load_dataset
dataset = load_dataset("Helsinki-NLP/opus_books")
I got the following errors:
FileNotFoundError: Couldn't find any data file at /workspace/Helsinki-NLP/opus_books. Couldn't find 'Helsinki-NLP/opus_books' on the Hugging Face Hub either: LocalEntryNotFoundError: An error happened while trying to locate the file on the Hub and we cannot find the requested files in the local cache. Please check your connection and try again or make sure your Internet connection is on.
I also tried:
dataset = load_dataset("opus_books", "en-zh")
and the errors remain the same. However, I can download "mlabonne/FineTome-100k" successfully.
My datasets is version 4.2.0
Any clues? Big thanks.
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I_kwDODunzps7RkDAC
| 7,818
|
train_test_split and stratify breaks with Numpy 2.0
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[
"I can't reproduce this. Could you pls provide an example with a public dataset/artificial dataset and show how you loaded that?\n\nThis works for me:\n\n```python\nimport numpy as np\nfrom datasets import Dataset, Features, ClassLabel, Value\n\ndata = {\"text\": [f\"sample_{i}\" for i in range(100)], \"label\": [i % 3 for i in range(100)]}\nfeatures = Features({\"text\": Value(\"string\"),\n \"label\": ClassLabel(names=[\"class_0\", \"class_1\", \"class_2\"])})\ndataset = Dataset.from_dict(data, features=features)\nsplits = dataset.train_test_split(test_size=0.2, stratify_by_column=\"label\")\nprint(f\"Success with numpy {np.__version__}\")\n```\nbut it also works for `numpy<2`",
"@davebulaval tried with numpy 2.3.4, and maybe i have successfully reproduced the bug!\n```\nValueError: Unable to avoid copy while creating an array as requested.\nIf using `np.array(obj, copy=False)` replace it with `np.asarray(obj)` to allow a copy when needed (no behavior change in NumPy 1.x).\nFor more details, see https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword.\n```\n\nAlso i downgraded to numpy 1.26.4\n```\n(hf-reproduce) F:\\Python\\Machine learning\\reproducing>python repro.py\nDatasetDict({\n train: Dataset({\n features: ['text', 'label'],\n num_rows: 16\n })\n test: Dataset({\n features: ['text', 'label'],\n num_rows: 4\n })\n})\n```",
"Also @CloseChoice The bug only appears in cases where the Arrow array cannot be represented as a contiguous NumPy array without copying.\n\nSo closing the discussion loop here - \n\nThe error occurs because `train_test_split(..., stratify_by_column=...)` attempts to convert\nan Arrow column to a NumPy array using `np.array(..., copy=False)`.\n\nIn NumPy <2.0 this silently allowed a copy if needed.\nIn NumPy ≥2.0 this raises:\nValueError: Unable to avoid copy while creating an array as requested.\n\nThis only happens when the Arrow column is not contiguous in memory, which explains\nwhy some datasets reproduce it and others do not."
] | 2025-10-15T00:01:19
| 2025-10-28T16:10:44
| 2025-10-28T16:10:44
|
NONE
| null | null | null | null |
### Describe the bug
As stated in the title, since Numpy changed in version >2.0 with copy, the stratify parameters break.
e.g. `all_dataset.train_test_split(test_size=0.2,stratify_by_column="label")` returns a Numpy error.
It works if you downgrade Numpy to a version lower than 2.0.
### Steps to reproduce the bug
1. Numpy > 2.0
2. `all_dataset.train_test_split(test_size=0.2,stratify_by_column="label")`
### Expected behavior
It returns a stratified split as per the results of Numpy < 2.0
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-6.8.0-85-generic-x86_64-with-glibc2.35
- Python version: 3.13.7
- Huggingface_hub version: 0.34.4
- PyArrow version: 19.0.0
- Pandas version: 2.3.2
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PR_kwDODunzps6tw-GG
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fix: better args passthrough for `_batch_setitems()`
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"@sghng There is a regression with python 3.13.8 when lm-eval is calling datasets load\r\n\r\n```python\r\nself = <test_llama3_2.TestLlama3_2 testMethod=test_llama3_2>\r\n\r\n def test_llama3_2(self):\r\n> self.quant_lm_eval()\r\n\r\ntests/models/test_llama3_2.py:35: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\ntests/models/model_test.py:773: in quant_lm_eval\r\n self.model, _ = self.quantModel(self.NATIVE_MODEL_ID, batch_size=self.QUANT_BATCH_SIZE, trust_remote_code=self.TRUST_REMOTE_CODE, dtype=self.TORCH_DTYPE)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\ntests/models/model_test.py:588: in quantModel\r\n reuse_candidates = self.perform_post_quant_validation(path, trust_remote_code=trust_remote_code)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\ntests/models/model_test.py:250: in perform_post_quant_validation\r\n arc_records[backend] = self.run_arc_challenge_eval(model, backend, trust_remote_code=trust_remote_code)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\ntests/models/model_test.py:217: in run_arc_challenge_eval\r\n task_results = self.lm_eval(\r\ntests/models/model_test.py:753: in lm_eval\r\n raise e\r\ntests/models/model_test.py:699: in lm_eval\r\n results = GPTQModel.eval(\r\ngptqmodel/models/auto.py:474: in eval\r\n results = simple_evaluate(\r\n../lm-evaluation-harness/lm_eval/utils.py:456: in _wrapper\r\n return fn(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^\r\n../lm-evaluation-harness/lm_eval/evaluator.py:283: in simple_evaluate\r\n task_dict = get_task_dict(\r\n../lm-evaluation-harness/lm_eval/tasks/__init__.py:635: in get_task_dict\r\n task_name_from_string_dict = task_manager.load_task_or_group(\r\n../lm-evaluation-harness/lm_eval/tasks/__init__.py:426: in load_task_or_group\r\n collections.ChainMap(\r\n../lm-evaluation-harness/lm_eval/tasks/__init__.py:428: in <lambda>\r\n lambda task: self._load_individual_task_or_group(task),\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n../lm-evaluation-harness/lm_eval/tasks/__init__.py:326: in _load_individual_task_or_group\r\n return _load_task(task_config, task=name_or_config)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n../lm-evaluation-harness/lm_eval/tasks/__init__.py:286: in _load_task\r\n task_object = ConfigurableTask(config=config)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n../lm-evaluation-harness/lm_eval/api/task.py:865: in __init__\r\n self.download(self.config.dataset_kwargs)\r\n../lm-evaluation-harness/lm_eval/api/task.py:997: in download\r\n self.dataset = datasets.load_dataset(\r\n../datasets/src/datasets/load.py:1397: in load_dataset\r\n builder_instance = load_dataset_builder(\r\n../datasets/src/datasets/load.py:1185: in load_dataset_builder\r\n builder_instance._use_legacy_cache_dir_if_possible(dataset_module)\r\n../datasets/src/datasets/builder.py:612: in _use_legacy_cache_dir_if_possible\r\n self._check_legacy_cache2(dataset_module) or self._check_legacy_cache() or None\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n../datasets/src/datasets/builder.py:485: in _check_legacy_cache2\r\n config_id = self.config.name + \"-\" + Hasher.hash({\"data_files\": self.config.data_files})\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n../datasets/src/datasets/fingerprint.py:188: in hash\r\n return cls.hash_bytes(dumps(value))\r\n ^^^^^^^^^^^^\r\n../datasets/src/datasets/utils/_dill.py:120: in dumps\r\n dump(obj, file)\r\n../datasets/src/datasets/utils/_dill.py:114: in dump\r\n Pickler(file, recurse=True).dump(obj)\r\n../vm313t/lib/python3.13t/site-packages/dill/_dill.py:428: in dump\r\n StockPickler.dump(self, obj)\r\n/usr/lib/python3.13/pickle.py:484: in dump\r\n self.save(obj)\r\n../datasets/src/datasets/utils/_dill.py:70: in save\r\n dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)\r\n../vm313t/lib/python3.13t/site-packages/dill/_dill.py:422: in save\r\n StockPickler.save(self, obj, save_persistent_id)\r\n/usr/lib/python3.13/pickle.py:558: in save\r\n f(self, obj) # Call unbound method with explicit self\r\n ^^^^^^^^^^^^\r\n../vm313t/lib/python3.13t/site-packages/dill/_dill.py:1262: in save_module_dict\r\n StockPickler.save_dict(pickler, obj)\r\n_ _ _ _ _ _ _ _ _`` _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\nself = <datasets.utils._dill.Pickler object at 0x4eaf175e390>\r\nobj = {'data_files': {'train': ['hf://datasets/allenai/ai2_arc@210d026faf9955653af8916fad021475a3f00453/ARC-Challenge/train-...//datasets/allenai/ai2_arc@210d026faf9955653af8916fad021475a3f00453/ARC-Challenge/validation-00000-of-00001.parquet']}}\r\n\r\n def save_dict(self, obj):\r\n if self.bin:\r\n self.write(EMPTY_DICT)\r\n else: # proto 0 -- can't use EMPTY_DICT\r\n self.write(MARK + DICT)\r\n \r\n self.memoize(obj)\r\n> self._batch_setitems(obj.items())\r\nE TypeError: Pickler._batch_setitems() missing 1 required positional argument: 'obj'\r\n\r\n/usr/lib/python3.13/pickle.py:990: TypeError\r\n```\r\n\r\nPython 3.13.8\r\n```python\r\n(vm313t) root@gpu-base:~/datasets# pip show dill datasets transformers lm-eval\r\nName: dill\r\nVersion: 0.4.0\r\nSummary: serialize all of Python\r\nHome-page: https://github.com/uqfoundation/dill\r\nAuthor: Mike McKerns\r\nAuthor-email: mmckerns@uqfoundation.org\r\nLicense: BSD-3-Clause\r\nLocation: /root/vm313t/lib/python3.13t/site-packages\r\nRequires: \r\nRequired-by: datasets, evaluate, GPTQModel, lm_eval, multiprocess\r\n---\r\nName: datasets\r\nVersion: 4.2.1.dev0 <-- this PR\r\nSummary: HuggingFace community-driven open-source library of datasets\r\nHome-page: https://github.com/huggingface/datasets\r\nAuthor: HuggingFace Inc.\r\nAuthor-email: thomas@huggingface.co\r\nLicense: Apache 2.0\r\nLocation: /root/vm313t/lib/python3.13t/site-packages\r\nEditable project location: /root/datasets\r\nRequires: dill, filelock, fsspec, httpx, huggingface-hub, multiprocess, numpy, packaging, pandas, pyarrow, pyyaml, requests, tqdm, xxhash\r\nRequired-by: evaluate, GPTQModel, lm_eval\r\n---\r\nName: transformers\r\nVersion: 4.57.1\r\nSummary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\r\nHome-page: https://github.com/huggingface/transformers\r\nAuthor: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\r\nAuthor-email: transformers@huggingface.co\r\nLicense: Apache 2.0 License\r\nLocation: /root/vm313t/lib/python3.13t/site-packages\r\nRequires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\r\nRequired-by: GPTQModel, lm_eval, peft, tokenicer\r\n---\r\nName: lm_eval\r\nVersion: 0.4.9.1\r\nSummary: A framework for evaluating language models\r\nHome-page: https://github.com/EleutherAI/lm-evaluation-harness\r\nAuthor: \r\nAuthor-email: EleutherAI <contact@eleuther.ai>\r\nLicense: MIT\r\nLocation: /root/vm313t/lib/python3.13t/site-packages\r\nEditable project location: /root/lm-evaluation-harness\r\nRequires: accelerate, datasets, dill, evaluate, jsonlines, more_itertools, numexpr, peft, pybind11, pytablewriter, rouge-score, sacrebleu, scikit-learn, sqlitedict, torch, tqdm-multiprocess, transformers, word2number, zstandard\r\nRequired-by: \r\n```",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7817). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@lhoestq Using `*args` works well. In fact we might as well pass through `**kwargs` as well to make the intent clearer.\r\n\r\nIt also appears to me that there could some other changes.\r\n\r\n```python\r\ndef _batch_setitems(self, items, obj):\r\n if self._legacy_no_dict_keys_sorting:\r\n return super()._batch_setitems(items)\r\n```\r\n\r\nPython dictionaries are insertion ordered official since Python 3.7, which came to end-of-life in mid 2023 already. I tried installing `datasets` for Python 3.8 and it resolves to version `3.1`, so I think it's safe to drop this in latest version.\r\n\r\n```python\r\n dill.Pickler._batch_setitems(self, items)\r\n```\r\n\r\nThis line should simply be `return super()._batch_setitems(items)`, since we're already extending from `dill.Pickler`. (not having `return` in the original version is probably a bug)"
] | 2025-10-14T22:51:51
| 2025-10-28T17:32:12
| 2025-10-27T17:08:25
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In Python 3.14, there's a change in the signature of `_Pickler._batch_setitems`.
It's changed to:
```python
# pickle.py
def _batch_setitems(self, items, obj):
# Helper to batch up SETITEMS sequences; proto >= 1 only
save = self.save
write = self.write
```
To accomodate this, in `dill`, we have this compatibility code:
```python
if sys.hexversion < 0x30E00A1:
pickler._batch_setitems(iter(source.items()))
else:
pickler._batch_setitems(iter(source.items()), obj=obj)
```
Thus, the datasets package will emit this error
```
│ /Users/sghuang/mamba/envs/ds/lib/python3.14/site-packages/dill/_dill.py:1262 in save_module_dict │
│ │
│ 1259 │ │ if is_dill(pickler, child=False) and pickler._session: │
│ 1260 │ │ │ # we only care about session the first pass thru │
│ 1261 │ │ │ pickler._first_pass = False │
│ ❱ 1262 │ │ StockPickler.save_dict(pickler, obj) │
│ 1263 │ │ logger.trace(pickler, "# D2") │
│ 1264 │ return │
│ 1265 │
│ │
│ /Users/sghuang/mamba/envs/ds/lib/python3.14/pickle.py:1133 in save_dict │
│ │
│ 1130 │ │ print(f"Line number: {inspect.getsourcelines(method)[1]}") │
│ 1131 │ │ print(f"Full path: {inspect.getmodule(method)}") │
│ 1132 │ │ print(f"Class: {method.__qualname__}") │
│ ❱ 1133 │ │ self._batch_setitems(obj.items(), obj) │
│ 1134 │ │
│ 1135 │ dispatch[dict] = save_dict │
│ 1136 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: Pickler._batch_setitems() takes 2 positional arguments but 3 were given
[NOTE] when serializing datasets.table.InMemoryTable state
[NOTE] when serializing datasets.table.InMemoryTable object
```
To fix it, we update the signature of the `_batch_setitems` method defined in `utils/_dill.py`.
This fix should be backward compatible, since the compatibility is handled by `dill`.
This should close #7813.
Similar to https://github.com/joblib/joblib/issues/1658.
Related to https://github.com/uqfoundation/dill/pull/724.
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I_kwDODunzps7RWBMe
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disable_progress_bar() not working as expected
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[
"@xianbaoqian ",
"Closing this one since it's a Xet issue."
] | 2025-10-14T03:25:39
| 2025-10-14T23:49:26
| 2025-10-14T23:49:26
|
NONE
| null | null | null | null |
### Describe the bug
Hi,
I'm trying to load a dataset on Kaggle TPU image. There is some known compat issue with progress bar on Kaggle, so I'm trying to disable the progress bar globally. This does not work as you can see in [here](https://www.kaggle.com/code/windmaple/hf-datasets-issue).
In contract, disabling progress bar for snapshot_download() works as expected as in [here](https://www.kaggle.com/code/windmaple/snapshot-download-error).
### Steps to reproduce the bug
See this [notebook](https://www.kaggle.com/code/windmaple/hf-datasets-issue).
There is sth. wrong with `shell_paraent`.
### Expected behavior
The downloader should disable progress bar and move forward w/ no error.
### Environment info
The latest version as I did:
!pip install -U datasets ipywidgets ipykernel
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PR_kwDODunzps6tiDIT
| 7,815
|
Add nifti support
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7815). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Btw I couldn't resist but share your PR with the community online on twitter already, I hope this is fine !",
"> Btw I couldn't resist but share your PR with the community online on twitter already, I hope this is fine !\r\n\r\nWow, that was quick! Thanks, already liked your comment, I appreciate it!"
] | 2025-10-13T20:07:32
| 2025-10-24T14:32:03
| 2025-10-24T13:22:18
|
CONTRIBUTOR
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Add support for NIfTI.
supports #7804
This PR follows https://github.com/huggingface/datasets/pull/7325 very closely
I am a bit unsure what we need to add to the `document_dataset.mdx` and `document_load.mdx`. I should probably create a dataset on the hub first to create this guide instead of copy+pasting from PDF.
Open todos:
- [x] create nifti dataset on the hub
- ~[ ] update `document_dataset.mdx` and `document_load.mdx`~
EDIT:
I tested with two datasets I created on the hub:
- https://huggingface.co/datasets/TobiasPitters/test-nifti-unzipped
- https://huggingface.co/datasets/TobiasPitters/test-nifti
for zipped (file extension `.nii.gz` and unzipped `.nii`) files and both seem to work fine. Also tested loading locally and that seems to work as well.
Here is the scriptsthat I ran against the hub:
```python
from pathlib import Path
from datasets import load_dataset
import nibabel as nib
dataset = load_dataset(
"TobiasPitters/test-nifti-unzipped",
split="test" # Load as single Dataset, not DatasetDict
)
print("length dataset unzipped:", len(dataset))
for item in dataset:
isinstance(item["nifti"], nib.nifti1.Nifti1Image)
dataset = load_dataset(
"TobiasPitters/test-nifti",
split="train" # Load as single Dataset, not DatasetDict
)
print("length dataset zipped:", len(dataset))
for item in dataset:
isinstance(item["nifti"], nib.nifti1.Nifti1Image)
```
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PR_kwDODunzps6tfJCm
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|
Allow streaming hdf5 files
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7814). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-10-13T15:25:44
| 2025-10-13T15:28:51
| 2025-10-13T15:28:49
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Add streaming support after https://github.com/huggingface/datasets/pull/7690, cc @klamike :)
## Details
in `datasets` loaders, `open()` is extended to work with files that are on disk but also on HF. Files on HF are streamed using HTTP range requests using the `HfFileSystem` implementation in the `huggingface_hub` library.
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Caching does not work when using python3.14
|
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[
"https://github.com/uqfoundation/dill/issues/725",
"@intexcor does #7817 fix your problem?"
] | 2025-10-10T15:36:46
| 2025-10-27T17:08:26
| 2025-10-27T17:08:26
|
NONE
| null | null | null | null |
### Describe the bug
Traceback (most recent call last):
File "/workspace/ctn.py", line 8, in <module>
ds = load_dataset(f"naver-clova-ix/synthdog-{lang}") # или "synthdog-zh" для китайского
File "/workspace/.venv/lib/python3.14/site-packages/datasets/load.py", line 1397, in load_dataset
builder_instance = load_dataset_builder(
path=path,
...<10 lines>...
**config_kwargs,
)
File "/workspace/.venv/lib/python3.14/site-packages/datasets/load.py", line 1185, in load_dataset_builder
builder_instance._use_legacy_cache_dir_if_possible(dataset_module)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/builder.py", line 612, in _use_legacy_cache_dir_if_possible
self._check_legacy_cache2(dataset_module) or self._check_legacy_cache() or None
~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/builder.py", line 485, in _check_legacy_cache2
config_id = self.config.name + "-" + Hasher.hash({"data_files": self.config.data_files})
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/fingerprint.py", line 188, in hash
return cls.hash_bytes(dumps(value))
~~~~~^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/utils/_dill.py", line 120, in dumps
dump(obj, file)
~~~~^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/utils/_dill.py", line 114, in dump
Pickler(file, recurse=True).dump(obj)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/dill/_dill.py", line 428, in dump
StockPickler.dump(self, obj)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^
File "/usr/lib/python3.14/pickle.py", line 498, in dump
self.save(obj)
~~~~~~~~~^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/utils/_dill.py", line 70, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/dill/_dill.py", line 422, in save
StockPickler.save(self, obj, save_persistent_id)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.14/pickle.py", line 572, in save
f(self, obj) # Call unbound method with explicit self
~^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/dill/_dill.py", line 1262, in save_module_dict
StockPickler.save_dict(pickler, obj)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^
File "/usr/lib/python3.14/pickle.py", line 1064, in save_dict
self._batch_setitems(obj.items(), obj)
~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^
TypeError: Pickler._batch_setitems() takes 2 positional arguments but 3 were given
### Steps to reproduce the bug
ds_train = ds["train"].map(lambda x: {**x, "lang": lang})
### Expected behavior
Fixed bugs
### Environment info
- `datasets` version: 4.2.0
- Platform: Linux-6.8.0-85-generic-x86_64-with-glibc2.39
- Python version: 3.14.0
- `huggingface_hub` version: 0.35.3
- PyArrow version: 21.0.0
- Pandas version: 2.3.3
- `fsspec` version: 2025.9.0
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docs: document_dataset PDFs & OCR
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[] | 2025-10-09T23:31:41
| 2025-10-20T14:03:53
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CONTRIBUTOR
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Use acronyms consistently across document_dataset docs.
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SIGSEGV when Python exits due to near null deref
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[
"The issue seems to come from `dill` which is a `datasets` dependency, e.g. this segfaults:\n\n```python\nimport dill\nfrom tqdm import tqdm\nprogress_bar = tqdm(total=(1000), unit='cols', desc='cols ')\nprogress_bar.update(1)\n```\n\n`tqdm` seems to segfault when `dill` is imported. I only found this about segfault but it's maybe not related https://github.com/tqdm/tqdm/issues/1678 ?",
"After more investigation it seems to be because of it imports `__main__`. This segfaults:\n\n```python\nimport __main__\nfrom tqdm import tqdm\nprogress_bar = tqdm(total=(1000), unit='cols', desc='cols ')\nprogress_bar.update(1)\n```\n\nI opened an issue at https://github.com/tqdm/tqdm/issues/1687",
"Here is a workaround. You can run your code as long as the progress bar is closed before exiting.\n\n```python\nfrom datasets import load_dataset\nfrom tqdm import tqdm\n\nprogress_bar = tqdm(total=(1000), unit='cols', desc='cols ')\nprogress_bar.update(1)\nprogress_bar.close() # avoids the segfault\n```",
"https://github.com/tqdm/tqdm/issues/1687#issuecomment-3392457094"
] | 2025-10-09T22:00:11
| 2025-10-10T22:09:24
| null |
NONE
| null | null | null | null |
### Describe the bug
When I run the following python script using datasets I get a segfault.
```python
from datasets import load_dataset
from tqdm import tqdm
progress_bar = tqdm(total=(1000), unit='cols', desc='cols ')
progress_bar.update(1)
```
```
% lldb -- python3 crashmin.py
(lldb) target create "python3"
Current executable set to '/Users/ian/bug/venv/bin/python3' (arm64).
(lldb) settings set -- target.run-args "crashmin.py"
(lldb) r
Process 8095 launched: '/Users/ian/bug/venv/bin/python3' (arm64)
Process 8095 stopped
* thread #2, stop reason = exec
frame #0: 0x0000000100014b30 dyld`_dyld_start
dyld`_dyld_start:
-> 0x100014b30 <+0>: mov x0, sp
0x100014b34 <+4>: and sp, x0, #0xfffffffffffffff0
0x100014b38 <+8>: mov x29, #0x0 ; =0
Target 0: (Python) stopped.
(lldb) c
Process 8095 resuming
cols : 0% 0/1000 [00:00<?, ?cols/s]Process 8095 stopped
* thread #2, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x10)
frame #0: 0x0000000101783454 _datetime.cpython-313-darwin.so`delta_new + 188
_datetime.cpython-313-darwin.so`delta_new:
-> 0x101783454 <+188>: ldr x3, [x20, #0x10]
0x101783458 <+192>: adrp x0, 10
0x10178345c <+196>: add x0, x0, #0x6fc ; "seconds"
Target 0: (Python) stopped.
(lldb) bt
* thread #2, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x10)
* frame #0: 0x0000000101783454 _datetime.cpython-313-darwin.so`delta_new + 188
frame #1: 0x0000000100704b60 Python`type_call + 96
frame #2: 0x000000010067ba34 Python`_PyObject_MakeTpCall + 120
frame #3: 0x00000001007aae3c Python`_PyEval_EvalFrameDefault + 30236
frame #4: 0x000000010067c900 Python`PyObject_CallOneArg + 112
frame #5: 0x000000010070f0a0 Python`slot_tp_finalize + 116
frame #6: 0x000000010070c3b4 Python`subtype_dealloc + 788
frame #7: 0x00000001006c378c Python`insertdict + 756
frame #8: 0x00000001006db2b0 Python`_PyModule_ClearDict + 660
frame #9: 0x000000010080a9a8 Python`finalize_modules + 1772
frame #10: 0x0000000100809a44 Python`_Py_Finalize + 264
frame #11: 0x0000000100837630 Python`Py_RunMain + 252
frame #12: 0x0000000100837ef8 Python`pymain_main + 304
frame #13: 0x0000000100837f98 Python`Py_BytesMain + 40
frame #14: 0x000000019cfcc274 dyld`start + 2840
(lldb) register read x20
x20 = 0x0000000000000000
(lldb)
```
### Steps to reproduce the bug
Run the script above, and observe the segfault.
### Expected behavior
No segfault
### Environment info
```
% pip freeze datasets | grep -i datasets
datasets==4.2.0
(venv) 0 ~/bug 14:58:06
% pip freeze tqdm | grep -i tqdm
tqdm==4.67.1
(venv) 0 ~/bug 14:58:16
% python --version
Python 3.13.7
```
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fix conda deps
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7810). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-10-09T16:32:04
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Set dev version
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release: 4.2.0
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PR_kwDODunzps6s7c_U
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typo
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| 2025-10-09T16:06:58
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add an s to be consistent with pandas' on_bad_lines
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PR_kwDODunzps6s6gnr
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|
Parquet: add `on_bad_file` argument to error/warn/skip bad files
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```python
from datasets import load_dataset
on_bad_file = "error" # default
# on_bad_file = "warn" # warn and skip bad file
# on_bad_file = "skip" # skip bad file
ds = load_dataset(parquet_dataset_id, on_bad_file=on_bad_file)
```
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PR_kwDODunzps6s52Ew
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Less api calls when resolving data_files
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There ~10 unnecessary `/api/datasets/user/dataset/revision`calls due to multithreading in data files resolution.
I disabled multithreading, which was actually not useful anymore since `HfFileSystem` has been using caching for a while now.
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I_kwDODunzps7Qh2bE
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Support scientific data formats
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[
"Please add the support for `Zarr`! That's what we use in the Bioimaging community. It is crucial, because raw upload of a *single* bio image can take _terrabytes in memory_!\n\nThe python library would be `bioio` or `zarr`:\n- [ ] Zarr: `bioio` or `zarr`\n\nSee a [Zarr example](https://ome.github.io/ome-ngff-validator/?source=https://uk1s3.embassy.ebi.ac.uk/bia-integrator-data/S-BIAD845/796b9fb8-f4ec-4c4b-bfc3-5cb00ccf19fe/796b9fb8-f4ec-4c4b-bfc3-5cb00ccf19fe.zarr)\n\ncc @joshmoore",
"@stefanches7 `zarr` is already usable with the hf hub as an array store. See this example from the [docs](https://huggingface.co/docs/huggingface_hub/en/guides/hf_file_system):\n\n```python\nimport numpy as np\nimport zarr\n\nembeddings = np.random.randn(50000, 1000).astype(\"float32\")\n\n# Write an array to a repo\nwith zarr.open_group(\"hf://my-username/my-model-repo/array-store\", mode=\"w\") as root:\n foo = root.create_group(\"embeddings\")\n foobar = foo.zeros('experiment_0', shape=(50000, 1000), chunks=(10000, 1000), dtype='f4')\n foobar[:] = embeddings\n\n# Read an array from a repo\nwith zarr.open_group(\"hf://my-username/my-model-repo/array-store\", mode=\"r\") as root:\n first_row = root[\"embeddings/experiment_0\"][0]\n```\n\nIs there additional functionality that would not be covered by this?",
"@cakiki I think some tiling capabilities, as well as metadata / labels handling. Consult ome-zarr doc here: https://ome-zarr.readthedocs.io/en/stable/python.html\nVisualization would be the cherry on the top. \n\ncc @joshmoore @lubianat @St3V0Bay: curious what you think",
"zarr-specific dataset viewer would be very cool",
"A support for BIDS it would be perfect, I think it's possible to do all the biosinal can be done with mne. There's a cool community for decoding brain signals, and now with EMG. The new META bracelet EMG is saving things in BIDS.\n\nI can help to interface, coding and try to make this happen. I am available at hugging face discord with the username aristimunha, if some 1-to-1 discuss it would be necessary :)",
"@lhoestq , @cakiki , do you think we can make this happen?",
"If you give me the OK, I'll create the PR to make everything for a Biosignal Reader logic, I already studied the nilabel PR :)"
] | 2025-10-09T10:18:24
| 2025-10-30T21:24:55
| null |
MEMBER
| null | null | null | null |
List of formats and libraries we can use to load the data in `datasets`:
- [ ] DICOMs: pydicom
- [x] NIfTIs: nibabel
- [ ] WFDB: wfdb
cc @zaRizk7 for viz
Feel free to comment / suggest other formats and libs you'd like to see or to share your interest in one of the mentioned format
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More Parquet streaming docs
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] | 2025-10-09T09:39:11
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I_kwDODunzps7Qduon
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[Docs] Missing documentation for `Dataset.from_dict`
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"I'd like to work on this documentation issue.",
"Hi I'd like to work on this. I can see the docstring is already in the code. \nCould you confirm:\n1. Is this still available?\n2. Should I add this to the main_classes.md file, or is there a specific \n documentation file I should update?\n3. Are there any formatting guidelines I should follow?\n\nI'm new to contributing but eager to learn!"
] | 2025-10-09T02:54:41
| 2025-10-19T16:09:33
| null |
NONE
| null | null | null | null |
Documentation link: https://huggingface.co/docs/datasets/en/package_reference/main_classes
Link to method (docstring present): https://github.com/huggingface/datasets/blob/6f2502c5a026caa89839713f6f7c8b958e5e83eb/src/datasets/arrow_dataset.py#L1029
The docstring is present for the function, but seems missing from the official documentation for the `Dataset` class on HuggingFace.
The method in question:
```python
@classmethod
def from_dict(
cls,
mapping: dict,
features: Optional[Features] = None,
info: Optional[DatasetInfo] = None,
split: Optional[NamedSplit] = None,
) -> "Dataset":
"""
Convert `dict` to a `pyarrow.Table` to create a [`Dataset`].
Important: a dataset created with from_dict() lives in memory
and therefore doesn't have an associated cache directory.
This may change in the future, but in the meantime if you
want to reduce memory usage you should write it back on disk
and reload using e.g. save_to_disk / load_from_disk.
Args:
mapping (`Mapping`):
Mapping of strings to Arrays or Python lists.
features ([`Features`], *optional*):
Dataset features.
info (`DatasetInfo`, *optional*):
Dataset information, like description, citation, etc.
split (`NamedSplit`, *optional*):
Name of the dataset split.
Returns:
[`Dataset`]
"""
```
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Add parquet scan options and docs
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] | 2025-10-08T18:04:52
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I added scan options, useful to control buffering and caching when streaming and docs, including how to select a subset of columns and apply filters
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