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Guard against duplicate builder_kwargs/config_kwargs in load_dataset_…
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…builder (#4910 )
### What does this PR do?
Fixes edge case in `load_dataset_builder` by raising a `TypeError` if the same key exists in both `builder_kwargs` and `config_kwargs`.
### Implementation details
- Added a guard clause in `load_dataset_builder` to detect duplicate keys between `builder_kwargs` and `config_kwargs`
- Wrote a unit test in `tests/test_load_duplicate_keys.py` to verify the exception is raised correctly
### Fixes
Closes #4910
### Reviewers
@zach-huggingface
@SunMarc
Would appreciate your review if you have time - thanks!
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before release 4.0
(I also did minor improvements to `features` to not show their `id=None` in their `__repr__()`)
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`from_list` fails while `from_generator` works for large datasets
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NaT
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NONE
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### Describe the bug
I am constructing a large time series dataset and observed that first constructing a list of entries and then using `Dataset.from_list` led to a crash as the number of items became large. However, this is not a problem when using `Dataset.from_generator`.
### Steps to reproduce the bug
#### Snippet A (crashes)
```py
from tqdm.auto import tqdm
import numpy as np
import datasets
def data_generator():
for i in tqdm(range(10_000_000)):
length = np.random.randint(2048)
series = np.random.rand(length)
yield {"target": series, "item_id": str(i), "start": np.datetime64("2000", "ms")}
data_list = list(data_generator())
ds = datasets.Dataset.from_list(data_list)
```
The last line crashes with
```
ArrowInvalid: Value 2147483761 too large to fit in C integer type
```
#### Snippet B (works)
```py
from tqdm.auto import tqdm
import numpy as np
import datasets
def data_generator():
for i in tqdm(range(10_000_000)):
length = np.random.randint(2048)
series = np.random.rand(length)
yield {"target": series, "item_id": str(i), "start": np.datetime64("2000", "ms")}
ds = datasets.Dataset.from_generator(data_generator)
```
### Expected behavior
I expected both the approaches to work or to fail similarly.
### Environment info
```
- `datasets` version: 3.6.0
- Platform: Linux-6.8.0-1029-aws-x86_64-with-glibc2.35
- Python version: 3.11.11
- `huggingface_hub` version: 0.32.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2025.3.0
```
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fix: raise error when folder-based datasets are loaded without data_dir or data_files
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### Related Issues/PRs
<!-- Uncomment 'Resolve' if this PR can close the linked items. -->
<!-- Resolve --> #6152
---
### What changes are proposed in this pull request?
This PR adds an early validation step for folder-based datasets (like `audiofolder`) to prevent silent fallback behavior.
**Before this fix**:
- When `data_dir` or `data_files` were not provided, the loader defaulted to the current working directory.
- This caused unexpected behavior like:
- Long loading times
- Scanning unintended local files
**Now**:
- If both `data_dir` and `data_files` are missing, a `ValueError` is raised early with a helpful message.
---
### How is this PR tested?
- [x] Manual test via `load_dataset("audiofolder")` with missing `data_dir`
- [ ] Existing unit tests (should not break any)
- [ ] New tests (if needed, maintainers can guide)
---
### Does this PR require documentation update?
- [x] No. You can skip the rest of this section.
---
### Release Notes
#### Is this a user-facing change?
- [x] Yes. Give a description of this change to be included in the release notes for users.
> Adds early error handling for folder-based datasets when neither `data_dir` nor `data_files` is specified, avoiding unintended resolution to the current directory.
#### What component(s), interfaces, languages, and integrations does this PR affect?
Components:
- [x] `area/datasets`
- [x] `area/load`
---
<a name="release-note-category"></a>
#### How should the PR be classified in the release notes? Choose one:
- [x] `rn/bug-fix` - A user-facing bug fix worth mentioning in the release notes
---
#### Should this PR be included in the next patch release?
- [x] Yes (this PR will be cherry-picked and included in the next patch release)
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I_kwDODunzps67pDnF
| 7,617
|
Unwanted column padding in nested lists of dicts
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2025-06-16 13:43:31+00:00
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```python
from datasets import Dataset
dataset = Dataset.from_dict({
"messages": [
[
{"a": "...",},
{"b": "...",},
],
]
})
print(dataset[0])
```
What I get:
```
{'messages': [{'a': '...', 'b': None}, {'a': None, 'b': '...'}]}
```
What I want:
```
{'messages': [{'a': '...'}, {'b': '...'}]}
```
Is there an easy way to automatically remove these auto-filled null/none values?
If not, I probably need a recursive none exclusion function, don't I?
Datasets 3.6.0
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PR_kwDODunzps6acSW7
| 7,616
|
Torchcodec decoding
|
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Closes #7607
## New signatures
### Audio
```python
Audio(sampling_rate: Optional[int] = None, mono: bool = True, decode: bool = True, stream_index: Optional[int] = None)
Audio.encode_example(self, value: Union[str, bytes, bytearray, dict, "AudioDecoder"]) -> dict
Audio.decode_example(self, value: dict, token_per_repo_id: Optional[dict[str, Union[str, bool, None]]] = None) -> "AudioDecoder":
```
### Video
```python
Video(decode: bool = True, stream_index: Optional[int] = None, dimension_order: Literal['NCHW', 'NHWC'] = 'NCHW', num_ffmpeg_threads: int = 1, device: Optional[Union[str, "torch.device"]] = 'cpu', seek_mode: Literal['exact', 'approximate'] = 'exact')
Video.encode_example(self, value: Union[str, bytes, bytearray, Example, np.ndarray, "VideoDecoder"]) -> Example:
Video.decode_example(self, value: Union[str, Example], token_per_repo_id: Optional[dict[str, Union[bool, str]]] = None, ) -> "VideoDecoder":
```
## Notes
Audio features constructor takes in 1 new optional param stream_index which is passed to the AudioDecoder constructor to select the stream index of a file.
Audio feature can now take in torchcodec.decoders.AudioDecoder as input to encode_example()
Audio feature decode_example() returns torchcodec.decoders.AudioDecoder
Video feature constructor takes in 5 new optional params stream_index, dimension_order, num_ffmpeg_threads, device, seek_mode all of which are passed to VideoDecoder constructor
Video feature decode_example() returns torchcodec.decoders.VideoDecoder
Video feature can now take in torchcodec.decoders.VideoDecoder as input to encode_example()
All test cases have been updated to reflect these changes
All documentation has also been updated to reflect these changes.
Both VideoDecoder and AudioDecoder when formatted with (np_formatter, tf_formatter, etc) will ignore the type and return themselves. Formatting test cases were updated accordingly to reflect this. (Pretty simple to make this not the case if we want though)
## Errors
This test case from `tests/packaged_modules/test_audiofolder.py`
```python
@require_librosa
@require_sndfile
@pytest.mark.parametrize("streaming", [False, True])
def test_data_files_with_metadata_and_archives(streaming, cache_dir, data_files_with_zip_archives):
audiofolder = AudioFolder(data_files=data_files_with_zip_archives, cache_dir=cache_dir)
audiofolder.download_and_prepare()
datasets = audiofolder.as_streaming_dataset() if streaming else audiofolder.as_dataset()
for split, data_files in data_files_with_zip_archives.items():
num_of_archives = len(data_files) # the metadata file is inside the archive
expected_num_of_audios = 2 * num_of_archives
assert split in datasets
dataset = list(datasets[split])
assert len(dataset) == expected_num_of_audios
# make sure each sample has its own audio (all arrays are different) and metadata
assert (
sum(np.array_equal(dataset[0]["audio"].get_all_samples().data.numpy(), example["audio"].get_all_samples().data.numpy()) for example in dataset[1:])
== 0
)
assert len({example["text"] for example in dataset}) == expected_num_of_audios
assert all(example["text"] is not None for example in dataset)
```
Fails now because AudioDecoder needs to access the files after the lines below are run, but there seems to be some context issues. The file the decoder is trying to read is closed before the decoder gets the chance to decode it.
```python
audiofolder.download_and_prepare()
datasets = audiofolder.as_streaming_dataset() if streaming else audiofolder.as_dataset()
```
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PR_kwDODunzps6aYp18
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|
remove unused code
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| 3,143,381,638
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PR_kwDODunzps6aYcbH
| 7,614
|
Lazy column
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Same as https://github.com/huggingface/datasets/pull/7564 but for `Dataset`, cc @TopCoder2K FYI
e.g. `ds[col]` now returns a lazy Column instead of a list
This way calling `ds[col][idx]` only loads the required data in memory
(bonus: also supports subfields access with `ds[col][subcol][idx]`)
the breaking change will be for the next major release, which also includes removal of dataset scripts support
close https://github.com/huggingface/datasets/issues/4180
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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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[] | null | 2
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2025-06-13 00:40:48+00:00
|
2025-06-24 16:52:30+00:00
|
NaT
|
NONE
| null | null | null | null |
### Feature request
Adding an option to skip corrupted data samples. 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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2025-06-12 19:42:29+00:00
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2025-07-17 13:14:18+00:00
|
2025-07-17 13:14:18+00:00
|
NONE
| null | null | null | null |
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
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|
i cant confirm email
|
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2025-06-12 18:58:49+00:00
|
2025-06-27 14:36:47+00:00
|
NaT
|
NONE
| null | null | null | null |
### 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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2025-06-12 13:47:01+00:00
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2025-06-16 12:14:10+00:00
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2025-06-16 12:14:08+00:00
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MEMBER
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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
| 7,608
|
Tests typing and fixes for push_to_hub
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MEMBER
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todo:
- [x] fix TestPushToHub.test_push_dataset_dict_to_hub_iterable_num_proc
|
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I_kwDODunzps6651RA
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Video and audio decoding with torchcodec
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2025-06-19 18:25:49+00:00
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CONTRIBUTOR
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### 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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Make `push_to_hub` atomic (#7600)
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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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I_kwDODunzps66ZsC2
| 7,600
|
`push_to_hub` is not concurrency safe (dataset schema corruption)
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2025-06-07 17:28:56+00:00
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2025-07-31 10:00:50+00:00
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2025-07-31 10:00:50+00:00
|
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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2025-06-06 18:59:00+00:00
|
2025-06-16 15:18:00+00:00
|
2025-06-16 15:18:00+00:00
|
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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2025-06-06 15:54:29+00:00
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2025-06-06 16:12:22+00:00
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2025-06-06 16:12:21+00:00
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MEMBER
| null | null | 0
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| 3,123,962,709
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I_kwDODunzps66M-NV
| 7,597
|
Download datasets from a private hub in 2025
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2025-06-06 07:55:19+00:00
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2025-06-13 13:46:00+00:00
|
2025-06-13 13:46:00+00:00
|
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
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Add albumentations to use dataset
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2025-06-05 20:39:46+00:00
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2025-06-17 18:38:08+00:00
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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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2025-06-05 15:29:32+00:00
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2025-06-06 16:12:37+00:00
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2025-06-06 16:12:36+00:00
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MEMBER
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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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2025-06-05 11:12:45+00:00
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2025-06-28 09:03:00+00:00
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NaT
|
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
| 7,593
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Fix broken link to albumentations
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2025-06-04 19:00:13+00:00
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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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2025-06-04 15:14:11+00:00
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MEMBER
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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
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Add num_proc parameter to push_to_hub
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2025-06-04 13:19:15+00:00
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2025-09-04 10:43:33+00:00
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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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`Sequence(Features(...))` causes PyArrow cast error in `load_dataset` despite correct schema.
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2025-05-29 22:53:36+00:00
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2025-07-19 22:45:08+00:00
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2025-07-19 22:45:08+00:00
|
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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PR_kwDODunzps6YKiyL
| 7,589
|
feat: use content defined chunking
|
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2025-05-29 18:19:41+00:00
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2025-09-09 13:45:24+00:00
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Use content defined chunking by default when writing parquet files.
- [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 released now.
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ValueError: Invalid pattern: '**' can only be an entire path component [Colab]
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2025-05-27 13:46:05+00:00
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2025-05-30 13:22:52+00:00
|
2025-05-30 01:26:30+00:00
|
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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close https://github.com/huggingface/datasets/issues/7583
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I_kwDODunzps64Qc5v
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help is appreciated
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[] | null | 1
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2025-05-26 14:00:42+00:00
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2025-05-26 18:21:57+00:00
|
NaT
|
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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PR_kwDODunzps6Xo-Tw
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Avoid multiple default config names
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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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I_kwDODunzps64MYyv
| 7,584
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Add LMDB format support
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2025-05-26 07:10:13+00:00
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2025-05-26 18:23:37+00:00
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NaT
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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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I_kwDODunzps64HjZt
| 7,583
|
load_dataset type stubs reject List[str] for split parameter, but runtime supports it
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2025-05-26 18:29:58+00:00
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2025-05-26 18:29:58+00:00
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NONE
| 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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CONTRIBUTOR
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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
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Requesting a specific split (eg: test) still downloads all (train, test, val) data when streaming=False.
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NaT
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NONE
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### 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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I_kwDODunzps63ocrM
| 7,577
|
arrow_schema is not compatible with list
|
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2025-05-21 16:37:01+00:00
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2025-05-26 18:49:51+00:00
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2025-05-26 18:32:55+00:00
|
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
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PR_kwDODunzps6XEuMz
| 7,576
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Fix regex library warnings
|
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2025-05-21 14:31:58+00:00
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2025-06-05 13:35:16+00:00
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2025-06-05 12:37:55+00:00
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CONTRIBUTOR
| null | null | 0
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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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PR_kwDODunzps6XD9gM
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[MINOR:TYPO] Update save_to_disk docstring
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2025-05-21 13:22:24+00:00
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2025-06-05 12:39:13+00:00
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CONTRIBUTOR
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r/hub/filesystem in save_to_disk
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I_kwDODunzps63j5fw
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Missing multilingual directions in IWSLT2017 dataset's processing script
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2025-05-21 09:53:17+00:00
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2025-05-26 18:36:38+00:00
|
NaT
|
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
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- 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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2025-05-20 09:54:35+00:00
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2025-07-21 18:34:34+00:00
|
2025-06-18 12:52:23+00:00
|
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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PR_kwDODunzps6WwsZB
| 7,572
|
Fixed typos
|
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2025-05-19 17:16:59+00:00
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2025-06-05 12:25:42+00:00
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2025-06-05 12:25:41+00:00
|
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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2025-05-19 14:49:23+00:00
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2025-05-19 14:52:24+00:00
|
2025-05-19 14:49:28+00:00
|
MEMBER
| null | null | 0
|
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I_kwDODunzps62vu_B
| 7,570
|
Dataset lib seems to broke after fssec lib update
|
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2025-05-15 11:45:06+00:00
|
2025-06-13 00:44:27+00:00
|
2025-06-13 00:44:27+00:00
|
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
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I_kwDODunzps62drmG
| 7,569
|
Dataset creation is broken if nesting a dict inside a dict inside a list
|
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2025-05-13 21:06:45+00:00
|
2025-05-20 19:25:15+00:00
|
NaT
|
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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|
[] |
open
| false
| null |
[] | null | 6
|
2025-05-13 15:45:42+00:00
|
2025-06-30 09:33:47+00:00
|
NaT
|
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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open
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[] | null | 7
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2025-05-12 22:38:27+00:00
|
2025-06-29 06:53:59+00:00
|
NaT
|
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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open
| false
| null |
[] | null | 4
|
2025-05-11 23:05:54+00:00
|
2025-06-23 17:56:02+00:00
|
NaT
|
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://%20jwashington@ap.org/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
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add check if repo exists for dataset uploading
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2025-05-09 10:27:00+00:00
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2025-09-11 05:00:10+00:00
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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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CONTRIBUTOR
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Refers to [this issue](https://github.com/huggingface/datasets/issues/7381).
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PR_kwDODunzps6VS0QL
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set dev version
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release: 3.6.0
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NotImplementedError: <class 'datasets.iterable_dataset.RepeatExamplesIterable'> doesn't implement num_shards yet
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NONE
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### 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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fix decoding tests
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fix aiohttp import
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PR_kwDODunzps6VR1gN
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fix regression
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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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PR_kwDODunzps6VRenr
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check for empty _formatting
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2025-05-07 13:22:37+00:00
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CONTRIBUTOR
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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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| 3,043,615,210
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PR_kwDODunzps6VJlTR
| 7,556
|
Add `--merge-pull-request` option for `convert_to_parquet`
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2025-05-06 18:05:05+00:00
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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
| 7,554
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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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2025-05-07 14:53:44+00:00
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NONE
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### 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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duplicate
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| 3,042,953,907
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PR_kwDODunzps6VHUNW
| 7,553
|
Rebatch arrow iterables before formatted iterable
|
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2025-05-06 13:59:58+00:00
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2025-05-07 13:17:41+00:00
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close https://github.com/huggingface/datasets/issues/7538 and https://github.com/huggingface/datasets/issues/7475
|
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| 3,040,258,084
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PR_kwDODunzps6U-BUv
| 7,552
|
Enable xet in push to hub
|
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2025-05-05 17:02:09+00:00
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2025-05-06 12:42:51+00:00
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2025-05-06 12:42:48+00:00
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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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| 3,038,114,928
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I_kwDODunzps61FfRw
| 7,551
|
Issue with offline mode and partial dataset cached
|
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2025-05-04 16:49:37+00:00
|
2025-05-13 03:18:43+00:00
|
NaT
|
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
| 7,550
|
disable aiohttp depend for python 3.13t free-threading compat
|
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2025-05-03 00:28:18+00:00
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2025-05-03 00:28:24+00:00
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2025-05-03 00:28:24+00:00
|
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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2025-05-02 15:18:07+00:00
|
2025-05-02 15:37:05+00:00
|
NaT
|
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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open
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| null |
[] | null | 7
|
2025-05-02 09:20:09+00:00
|
2025-05-12 15:11:32+00:00
|
NaT
|
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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Avoid global umask for setting file mode.
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2025-05-01 22:24:24+00:00
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2025-05-06 13:05:00+00:00
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2025-05-06 13:05:00+00:00
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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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2025-05-01 14:43:47+00:00
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2025-05-13 13:30:09+00:00
|
2025-05-13 13:29:53+00:00
|
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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[] | null | 0
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2025-04-30 15:16:33+00:00
|
2025-04-30 15:16:33+00:00
|
NaT
|
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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2025-04-29 04:39:44+00:00
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2025-05-05 14:42:49+00:00
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2025-05-05 14:42:49+00:00
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CONTRIBUTOR
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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
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The memory-disk mapping failure issue of the map function（resolved, but there are some suggestions.）
|
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2025-04-29 03:04:59+00:00
|
2025-04-30 02:22:17+00:00
|
2025-04-30 02:22:17+00:00
|
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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release: 3.5.1
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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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PR_kwDODunzps6UFQ0W
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Fix IterableDataset state_dict shard_example_idx counting
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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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`IterableDataset` drops samples when resuming from a checkpoint
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COLLABORATOR
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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
| 7,537
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`datasets.map(..., num_proc=4)` multi-processing fails
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2025-04-25 01:53:47+00:00
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2025-05-06 13:12:08+00:00
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NaT
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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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2025-04-24 20:52:45+00:00
|
2025-05-06 13:05:01+00:00
|
2025-05-06 13:05:01+00:00
|
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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open
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[] | null | 1
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2025-04-24 19:44:28+00:00
|
2025-05-19 14:51:29+00:00
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NaT
|
NONE
| null | null | 0
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Change dill version to >=0.3.9,<0.4.5 and check for errors
| null |
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| 3,017,259,407
|
I_kwDODunzps6z17mP
| 7,534
|
TensorFlow RaggedTensor Support (batch-level)
|
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open
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[] | null | 4
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2025-04-24 13:14:52+00:00
|
2025-06-30 17:03:39+00:00
|
NaT
|
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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PR_kwDODunzps6TpraJ
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Add custom fingerprint support to `from_generator`
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2025-04-23 19:31:35+00:00
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2025-09-15 19:36:34+00:00
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NaT
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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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PR_kwDODunzps6TW8Ss
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Document the HF_DATASETS_CACHE environment variable in the datasets cache documentation
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2025-04-22 00:23:13+00:00
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CONTRIBUTOR
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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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2025-04-21 17:29:20+00:00
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2025-06-29 06:20:45+00:00
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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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2025-04-21 03:08:38+00:00
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2025-04-22 07:49:52+00:00
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2025-04-22 07:49:52+00:00
|
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
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|
audio folder builder cannot detect custom split name
|
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2025-04-20 16:53:21+00:00
|
2025-04-20 16:53:21+00:00
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NaT
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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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Data Studio Error: Convert JSONL incorrectly
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2025-04-19 13:21:44+00:00
|
2025-05-06 13:18:38+00:00
|
NaT
|
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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I_kwDODunzps6zIFw2
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Auto-merge option for `convert-to-parquet`
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2025-04-18 16:03:22+00:00
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2025-07-18 19:09:03+00:00
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2025-07-18 19:09:03+00:00
|
CONTRIBUTOR
| 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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I_kwDODunzps6zHk1Q
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Faster downloads/uploads with Xet storage
|
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2025-04-18 14:46:42+00:00
|
2025-05-12 12:09:09+00:00
|
NaT
|
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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PR_kwDODunzps6TBOH1
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|
Fix indexing in split commit messages
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2025-04-17 17:06:26+00:00
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2025-04-28 14:26:27+00:00
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2025-04-28 14:26:27+00:00
|
CONTRIBUTOR
| null | null | 0
|
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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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PR_kwDODunzps6S99KB
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correct use with polars example
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2025-04-17 10:19:19+00:00
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2025-04-28 13:48:34+00:00
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2025-04-28 13:48:33+00:00
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CONTRIBUTOR
| null | null | 0
|
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PR_kwDODunzps6S1r8w
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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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2025-04-16 02:37:33+00:00
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CONTRIBUTOR
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Fixes #7515
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PR_kwDODunzps6SvEZp
| 7,521
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fix: Image Feature in Datasets Library Fails to Handle bytearray Objects from Spark DataFrames (#7517)
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CONTRIBUTOR
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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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2025-04-15 19:39:01+00:00
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2025-04-19 18:47:46+00:00
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| 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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