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gusornu

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isuee_gus

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I_kwDODunzps6Yt4Al

Datasets conflicts with fsspec 2024.9

open

NONE

### Describe the bug Installing both in latest versions are not possible `pip install "datasets==3.0.1" "fsspec==2024.9.0"` But using older version of datasets is ok `pip install "datasets==1.24.4" "fsspec==2024.9.0"` ### Steps to reproduce the bug `pip install "datasets==3.0.1" "fsspec==2024.9.0"` ### Expected behavior install both versions. ### Environment info debian 11. python 3.10.15

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I_kwDODunzps6Yt1mN

Audio preview in dataset viewer for audio array data without a path/filename

open

NONE

### Feature request Huggingface has quite a comprehensive set of guides for [audio datasets](https://huggingface.co/docs/datasets/en/audio_dataset). It seems, however, all these guides assume the audio array data to be decoded/inserted into a HF dataset always originates from individual files. The [Audio-dataclass](https://github.com/huggingface/datasets/blob/3.0.1/src/datasets/features/audio.py#L20) appears designed with this assumption in mind. Looking at its source code it returns a dictionary with the keys `path`, `array` and `sampling_rate`. However, sometimes users may have different pipelines where they themselves decode the audio array. This feature request has to do with wishing some clarification in guides on whether it is possible, and in such case how users can insert already decoded audio array data into datasets (pandas DataFrame, HF dataset or whatever) that are later saved as parquet, and still get a functioning audio preview in the dataset viewer. Do I perhaps need to write a tempfile of my audio array slice to wav and capture the bytes object with `io.BytesIO` and pass that to `Audio()`? ### Motivation I'm working with large audio datasets, and my pipeline reads (decodes) audio from larger files, and slices the relevant portions of audio from that larger file based on metadata I have available. The pipeline is designed this way to avoid having to store multiple copies of data, and to avoid having to store tens of millions of small files. I tried [test-uploading parquet files](https://huggingface.co/datasets/Lauler/riksdagen_test) where I store the audio array data of decoded slices of audio in an `audio` column with a dictionary with the keys `path`, `array` and `sampling_rate`. But I don't know the secret sauce of what the Huggingface Hub expects and requires to be able to display audio previews correctly. ### Your contribution I could contribute a tool agnostic guide of creating HF audio datasets directly as parquet to the HF documentation if there is an interest. Provided you help me figure out the secret sauce of what the dataset viewer expects to display the preview correctly.

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PR_kwDODunzps59VSrf

Pin multiprocess<0.70.1 to align with dill<0.3.9

closed

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Pin multiprocess<0.70.1 to align with dill<0.3.9. Note that multiprocess-0.70.1 requires dill-0.3.9: https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17 Fix #7186.

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I_kwDODunzps6YniY8

shard_data_sources() got an unexpected keyword argument 'worker_id'

open

NONE

### Describe the bug ``` [rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 238, in __iter__ [rank0]: for key_example in islice(self.generate_examples_fn(**gen_kwags), shard_example_idx_start, None): [rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/packaged_modules/generator/generator.py", line 32, in _generate_examples [rank0]: for idx, ex in enumerate(self.config.generator(**gen_kwargs)): [rank0]: File "/home/qinghao/workdir/doremi/doremi/dataloader.py", line 337, in take_data_generator [rank0]: for ex in ds: [rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1791, in __iter__ [rank0]: yield from self._iter_pytorch() [rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1704, in _iter_pytorch [rank0]: ex_iterable = ex_iterable.shard_data_sources(worker_id=worker_info.id, num_workers=worker_info.num_workers) [rank0]: TypeError: UpdatableRandomlyCyclingMultiSourcesExamplesIterable.shard_data_sources() got an unexpected keyword argument 'worker_id' ``` ### Steps to reproduce the bug IterableDataset cannot use ### Expected behavior can work on datasets==2.10, but will raise error for later versions. ### Environment info datasets==3.0.1

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I_kwDODunzps6Ym3FN

pinning `dill<0.3.9` without pinning `multiprocess`

closed

NONE

### Describe the bug The [latest `multiprocess` release](https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17) requires `dill>=0.3.9` which causes issues when installing `datasets` without backtracking during package version resolution. Is it possible to add a pin for multiprocess so something like `multiprocess<=0.70.16` so that the `dill` version is compatible? ### Steps to reproduce the bug NA ### Expected behavior NA ### Environment info NA

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I_kwDODunzps6Yf77M

CI benchmarks are broken

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MEMBER

Since Aug 30, 2024, CI benchmarks are broken: https://github.com/huggingface/datasets/actions/runs/11108421214/job/30861323975 ``` {"level":"error","message":"Resource not accessible by integration","name":"HttpError","request":{"body":"{\"body\":\"<details>\\n<summary>Show benchmarks</summary>\\n\\nPyArrow==8.0.0\\n\\n<details>\\n<summary>Show updated benchmarks!</summary>\\n\\n### Benchmark: benchmark_array_xd.json\\n\\n| metric | read_batch_formatted_as_numpy after write_array2d | ... "headers":{"accept":"application/vnd.github.v3+json","authorization":"token [REDACTED]","content-type":"application/json; charset=utf-8","user-agent":"octokit-rest.js/18.0.0 octokit-core.js/3.6.0 Node.js/16.20.2 (linux; x64)"},"method":"POST","request":{"agent":{"_events":{},"_eventsCount":2,"cache": ... "response":{"data":{"documentation_url":"https://docs.github.com/rest/issues/comments#create-an-issue-comment","message":"Resource not accessible by integration","status":"403"}, ... "stack":"HttpError: Resource not accessible by integration\n at /usr/lib/node_modules/@dvcorg/cml/node_modules/@octokit/request/dist-node/index.js:86:21\n at processTicksAndRejections (node:internal/process/task_queues:96:5)\n at async Job.doExecute (/usr/lib/node_modules/@dvcorg/cml/node_modules/bottleneck/light.js:405:18)","status":403} ```

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PR_kwDODunzps59IPEf

Pin dill<0.3.9 to fix CI

closed

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Pin dill<0.3.9 to fix CI for deps-latest. Note that dill-0.3.9 was released yesterday Sep 29, 2024: - https://pypi.org/project/dill/0.3.9/ - https://github.com/uqfoundation/dill/releases/tag/0.3.9 Fix #7183.

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I_kwDODunzps6YZYE_

CI is broken for deps-latest

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See: https://github.com/huggingface/datasets/actions/runs/11106149906/job/30853879890 ``` =========================== short test summary info ============================ FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_caching_on_disk - AssertionError: Lists differ: [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}] != [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}] First differing element 1: {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'} {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'} [{'filename': '/tmp/tmp6xcyyjs4/dataset0.arrow'}, - {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}] ? ^^^^^ -------- + {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}] ? ++++++++++ ^^ + FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - AssertionError: Lists differ: [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}] != [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}] First differing element 0: {'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'} {'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'} - [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}] ? ^^ ----------- + [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}] ? +++++++++++ ^^ FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_regex - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ignores_line_definition_of_function - AssertionError: '52e56ee04ad92499' != '0a4f75cec280f634' - 52e56ee04ad92499 + 0a4f75cec280f634 FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ipython_function - AssertionError: 'a6bd2041ca63d6c0' != '517bf36b7eecdef5' - a6bd2041ca63d6c0 + 517bf36b7eecdef5 FAILED tests/test_fingerprint.py::HashingTest::test_hash_tiktoken_encoding - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_compiled_module - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_generator - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_tensor - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_set_doesnt_depend_on_order - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_set_stable - NameError: name 'log' is not defined ERROR tests/test_iterable_dataset.py::test_iterable_dataset_from_file - NameError: name 'log' is not defined = 11 failed, 2850 passed, 3 skipped, 23 warnings, 1 error in 191.06s (0:03:11) = ```

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PR_kwDODunzps59GdLS

Support features in metadata configs

closed

MEMBER

Support features in metadata configs, like: ``` configs: - config_name: default features: - name: id dtype: int64 - name: name dtype: string - name: score dtype: float64 ``` This will allow to avoid inference of data types. Currently, we allow passing this information in the `dataset_info` (instead of `configs`) field, but this is not intuitive and it is not properly documented. TODO: - [ ] Document usage

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PR_kwDODunzps59Br4E

Fix datasets export to JSON

closed

CONTRIBUTOR

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I_kwDODunzps6YPq6O

Memory leak when wrapping datasets into PyTorch Dataset without explicit deletion

closed

NONE

### Describe the bug I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use. ### Steps to reproduce the bug Steps to reproduce: Create a PyTorch Dataset wrapper for 'nebula/cc12m': ```` from torch.utils.data import Dataset from tqdm import tqdm from datasets import load_dataset from torchvision import transforms Image.MAX_IMAGE_PIXELS = None class CC12M(Dataset): def __init__(self, path_or_name='nebula/cc12m', split='train', transform=None, single_caption=True): self.raw_dataset = load_dataset(path_or_name)[split] if transform is None: self.transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize( mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711] ) ]) else: self.transform = transforms.Compose(transform) self.single_caption = single_caption self.length = len(self.raw_dataset) def __len__(self): return self.length def __getitem__(self, index): item = self.raw_dataset[index] caption = item['txt'] with io.BytesIO(item['webp']) as buffer: image = Image.open(buffer).convert('RGB') if self.transform: image = self.transform(image) # del item # Uncomment this line to prevent the memory leak return image, caption ```` Iterate through the dataset without the del item line in __getitem__. Observe RAM usage increasing constantly. Add del item at the end of __getitem__: ``` def __getitem__(self, index): item = self.raw_dataset[index] caption = item['txt'] with io.BytesIO(item['webp']) as buffer: image = Image.open(buffer).convert('RGB') if self.transform: image = self.transform(image) del item # This line prevents the memory leak return image, caption ``` Iterate through the dataset again and observe that RAM usage remains stable. ### Expected behavior Expected behavior: RAM usage should remain stable during iteration without needing to explicitly delete items. Actual behavior: RAM usage constantly increases unless items are explicitly deleted after use ### Environment info - `datasets` version: 2.21.0 - Platform: Linux-4.18.0-513.5.1.el8_9.x86_64-x86_64-with-glibc2.28 - Python version: 3.12.4 - `huggingface_hub` version: 0.24.6 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1

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PR_kwDODunzps585Jcd

Support Python 3.11

closed

MEMBER

Support Python 3.11. Fix #7178.

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I_kwDODunzps6YIjPa

Support Python 3.11

closed

MEMBER

Support Python 3.11: https://peps.python.org/pep-0664/

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PR_kwDODunzps585Fx2

Fix release instructions

closed

MEMBER

Fix release instructions. During last release, I had to make this additional update.

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PR_kwDODunzps580hTn

fix grammar in fingerprint.py

open

CONTRIBUTOR

I see this error all the time and it was starting to get to me.

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I_kwDODunzps6YDIUZ

[FSTimeoutError] load_dataset

closed

NONE

### Describe the bug When using `load_dataset`to load [HuggingFaceM4/VQAv2](https://huggingface.co/datasets/HuggingFaceM4/VQAv2), I am getting `FSTimeoutError`. ### Error ``` TimeoutError: The above exception was the direct cause of the following exception: FSTimeoutError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/fsspec/asyn.py](https://klh9mr78js-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240924-060116_RC00_678132060#) in sync(loop, func, timeout, *args, **kwargs) 99 if isinstance(return_result, asyncio.TimeoutError): 100 # suppress asyncio.TimeoutError, raise FSTimeoutError --> 101 raise FSTimeoutError from return_result 102 elif isinstance(return_result, BaseException): 103 raise return_result FSTimeoutError: ``` It usually fails around 5-6 GB. <img width="847" alt="Screenshot 2024-09-26 at 9 10 19 PM" src="https://github.com/user-attachments/assets/ff91995a-fb55-4de6-8214-94025d6c8470"> ### Steps to reproduce the bug To reproduce it, run this in colab notebook: ``` !pip install -q -U datasets from datasets import load_dataset ds = load_dataset('HuggingFaceM4/VQAv2', split="train[:10%]") ``` ### Expected behavior It should download properly. ### Environment info Using Colab Notebook.

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PR_kwDODunzps58wluR

Set dev version

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PR_kwDODunzps58wjjc

Release: 3.0.1

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PR_kwDODunzps58wNQ7

Add torchdata as a regular test dependency

closed

MEMBER

Add `torchdata` as a regular test dependency. Note that previously, `torchdata` was installed from their repo and current main branch (0.10.0.dev) requires Python>=3.9. Also note they made a recent release: 0.8.0 on Jul 31, 2024. Fix #7171.

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I_kwDODunzps6X-e2n

CI is broken: No solution found when resolving dependencies

closed

MEMBER

See: https://github.com/huggingface/datasets/actions/runs/11046967444/job/30687294297 ``` Run uv pip install --system -r additional-tests-requirements.txt --no-deps × No solution found when resolving dependencies: ╰─▶ Because the current Python version (3.8.18) does not satisfy Python>=3.9 and torchdata==0.10.0a0+1a98f21 depends on Python>=3.9, we can conclude that torchdata==0.10.0a0+1a98f21 cannot be used. And because only torchdata==0.10.0a0+1a98f21 is available and you require torchdata, we can conclude that your requirements are unsatisfiable. Error: Process completed with exit code 1. ```

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PR_kwDODunzps58mfF5

Support JSON lines with missing columns

closed

MEMBER

Support JSON lines with missing columns. Fix #7169. The implemented test raised: ``` datasets.table.CastError: Couldn't cast age: int64 to {'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)} because column names don't match ``` Related to: - #7160 - #7162

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I_kwDODunzps6XzoT8

JSON lines with missing columns raise CastError

closed

MEMBER

JSON lines with missing columns raise CastError: > CastError: Couldn't cast ... to ... because column names don't match Related to: - #7159 - #7161

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I_kwDODunzps6Xy7hn

sd1.5 diffusers controlnet training script gives new error

closed

NONE

### Describe the bug This will randomly pop up during training now ``` Traceback (most recent call last): File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1192, in <module> main(args) File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1041, in main for step, batch in enumerate(train_dataloader): File "/usr/local/lib/python3.11/dist-packages/accelerate/data_loader.py", line 561, in __iter__ next_batch = next(dataloader_iter) ^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 630, in __next__ data = self._next_data() ^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 673, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/_utils/fetch.py", line 50, in fetch data = self.dataset.__getitems__(possibly_batched_index) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2746, in __getitems__ batch = self.__getitem__(keys) ^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2742, in __getitem__ return self._getitem(key) ^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2727, in _getitem formatted_output = format_table( ^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 639, in format_table return formatter(pa_table, query_type=query_type) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 407, in __call__ return self.format_batch(pa_table) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 521, in format_batch batch = self.python_features_decoder.decode_batch(batch) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 228, in decode_batch return self.features.decode_batch(batch) if self.features else batch ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2084, in decode_batch [ File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2085, in <listcomp> decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id) File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 1403, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/features/image.py", line 188, in decode_example image.load() # to avoid "Too many open files" errors ``` ### Steps to reproduce the bug Train on diffusers sd1.5 controlnet example script This will pop up randomly, you can see in wandb below when i manually resume run everytime this error appears  ### Expected behavior Training to continue without above error ### Environment info - datasets version: 3.0.0 - Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35 - Python version: 3.11.9 - huggingface_hub version: 0.25.1 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - fsspec version: 2024.6.1 Training on 4090

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I_kwDODunzps6Xy64u

Error Mapping on sd3, sdxl and upcoming flux controlnet training scripts in diffusers

closed

NONE

### Describe the bug ``` Map: 6%|██████ | 8000/138120 [19:27<5:16:36, 6.85 examples/s] Traceback (most recent call last): File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1416, in <module> main(args) File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1132, in main train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 560, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3035, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3461, in _map_single writer.write_batch(batch) File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 567, in write_batch self.write_table(pa_table, writer_batch_size) File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 579, in write_table pa_table = pa_table.combine_chunks() ^^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow/table.pxi", line 4387, in pyarrow.lib.Table.combine_chunks 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.ArrowInvalid: offset overflow while concatenating arrays Traceback (most recent call last): File "/usr/local/bin/accelerate", line 8, in <module> sys.exit(main()) ^^^^^^ File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/accelerate_cli.py", line 48, in main args.func(args) File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 1174, in launch_command simple_launcher(args) File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 769, in simple_launcher ``` ### Steps to reproduce the bug The dataset has no problem training on sd1.5 controlnet train script ### Expected behavior Script not randomly erroing with error above ### Environment info - `datasets` version: 3.0.0 - Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35 - Python version: 3.11.9 - `huggingface_hub` version: 0.25.1 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1 training on A100

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PR_kwDODunzps58h8pd

fix docstring code example for distributed shuffle

closed

MEMBER

close https://github.com/huggingface/datasets/issues/7163

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PR_kwDODunzps58fva1

fix increase_load_count

closed

MEMBER

it was failing since 3.0 and therefore not updating download counts on HF or in our dashboard

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I_kwDODunzps6Xreox

fsspec.exceptions.FSTimeoutError when downloading dataset

open

NONE

### Describe the bug I am trying to download the `librispeech_asr` `clean` dataset, which results in a `FSTimeoutError` exception after downloading around 61% of the data. ### Steps to reproduce the bug ``` import datasets datasets.load_dataset("librispeech_asr", "clean") ``` The output is as follows: > Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:06, 13.2MB/s]Traceback (most recent call last): > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 56, in _runner > result[0] = await coro > ^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/implementations/http.py", line 262, in _get_file > chunk = await r.content.read(chunk_size) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 393, in read > await self._wait("read") > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 311, in _wait > with self._timer: > ^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/helpers.py", line 713, in __exit__ > raise asyncio.TimeoutError from None > TimeoutError > > The above exception was the direct cause of the following exception: > > Traceback (most recent call last): > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/load_dataset.py", line 3, in <module> > datasets.load_dataset("librispeech_asr", "clean") > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/load.py", line 2096, in load_dataset > builder_instance.download_and_prepare( > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 924, in download_and_prepare > self._download_and_prepare( > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1647, in _download_and_prepare > super()._download_and_prepare( > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 977, in _download_and_prepare > split_generators = self._split_generators(dl_manager, **split_generators_kwargs) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/Users/Timon/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/2712a8f82f0d20807a56faadcd08734f9bdd24c850bb118ba21ff33ebff0432f/librispeech_asr.py", line 115, in _split_generators > archive_path = dl_manager.download(_DL_URLS[self.config.name]) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 159, in download > downloaded_path_or_paths = map_nested( > ^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 512, in map_nested > _single_map_nested((function, obj, batched, batch_size, types, None, True, None)) > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 380, in _single_map_nested > return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)] > ^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 216, in _download_batched > self._download_single(url_or_filename, download_config=download_config) > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 225, in _download_single > out = cached_path(url_or_filename, download_config=download_config) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 205, in cached_path > output_path = get_from_cache( > ^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 415, in get_from_cache > fsspec_get(url, temp_file, storage_options=storage_options, desc=download_desc, disable_tqdm=disable_tqdm) > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 334, in fsspec_get > fs.get_file(path, temp_file.name, callback=callback) > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 118, in wrapper > return sync(self.loop, func, *args, **kwargs) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 101, in sync > raise FSTimeoutError from return_result > fsspec.exceptions.FSTimeoutError > Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:09, 13.0MB/s] ### Expected behavior Complete the download ### Environment info Python version 3.12.6 Dependencies: > dependencies = [ > "accelerate>=0.34.2", > "datasets[audio]>=3.0.0", > "ipython>=8.18.1", > "librosa>=0.10.2.post1", > "torch>=2.4.1", > "torchaudio>=2.4.1", > "transformers>=4.44.2", > ] MacOS 14.6.1 (23G93)

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I_kwDODunzps6XiVqS

Set explicit seed in iterable dataset ddp shuffling example

closed

CONTRIBUTOR

### Describe the bug In the examples section of the iterable dataset docs https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.IterableDataset the ddp example shuffles without seeding ```python from datasets.distributed import split_dataset_by_node ids = ds.to_iterable_dataset(num_shards=512) ids = ids.shuffle(buffer_size=10_000) # will shuffle the shards order and use a shuffle buffer when you start iterating ids = split_dataset_by_node(ds, world_size=8, rank=0) # will keep only 512 / 8 = 64 shards from the shuffled lists of shards when you start iterating dataloader = torch.utils.data.DataLoader(ids, num_workers=4) # will assign 64 / 4 = 16 shards from this node's list of shards to each worker when you start iterating for example in ids: pass ``` This code would - I think - raise an error due to the lack of an explicit seed: https://github.com/huggingface/datasets/blob/2eb4edb97e1a6af2ea62738ec58afbd3812fc66e/src/datasets/iterable_dataset.py#L1707-L1711 ### Steps to reproduce the bug Run example code ### Expected behavior Add explicit seeding to example code ### Environment info latest datasets

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PR_kwDODunzps58WlX5

Support JSON lines with empty struct

closed

MEMBER

Support JSON lines with empty struct. Fix #7161. Related to: - #7160

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I_kwDODunzps6Xg2nb

JSON lines with empty struct raise ArrowTypeError

closed

MEMBER

JSON lines with empty struct raise ArrowTypeError: struct fields don't match or are in the wrong order See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5 > ArrowTypeError: struct fields don't match or are in the wrong order: Input fields: struct<> output fields: struct<pov_count: int64, update_count: int64, citation_needed_count: int64> Related to: - #7159

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PR_kwDODunzps58VDhq

Support JSON lines with missing struct fields

closed

MEMBER

Support JSON lines with missing struct fields. Fix #7159. The implemented test raised: ``` TypeError: Couldn't cast array of type struct<age: int64> to {'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)} ```

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I_kwDODunzps6XgcqN

JSON lines with missing struct fields raise TypeError: Couldn't cast array

closed

MEMBER

JSON lines with missing struct fields raise TypeError: Couldn't cast array of type. See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5 One would expect that the struct missing fields are added with null values.

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PR_kwDODunzps58Tuw9

google colab ex

open

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PR_kwDODunzps58P-1R

Fix zero proba interleave datasets

closed

MEMBER

fix https://github.com/huggingface/datasets/issues/7147

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I_kwDODunzps6XW5Fp

interleave_datasets resets shuffle state

open

NONE

### Describe the bug ``` import datasets import torch.utils.data def gen(shards): yield {"shards": shards} def main(): dataset = datasets.IterableDataset.from_generator( gen, gen_kwargs={'shards': list(range(25))} ) dataset = dataset.shuffle(buffer_size=1) dataset = datasets.interleave_datasets( [dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted" ) dataloader = torch.utils.data.DataLoader( dataset, batch_size=8, num_workers=8, ) for i, batch in enumerate(dataloader): print(batch) if i >= 10: break if __name__ == "__main__": main() ``` ### Steps to reproduce the bug Run the script, it will output ``` {'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]} {'shards': [tensor([ 1, 9, 17, 1, 9, 17, 1, 9])]} {'shards': [tensor([ 2, 10, 18, 2, 10, 18, 2, 10])]} {'shards': [tensor([ 3, 11, 19, 3, 11, 19, 3, 11])]} {'shards': [tensor([ 4, 12, 20, 4, 12, 20, 4, 12])]} {'shards': [tensor([ 5, 13, 21, 5, 13, 21, 5, 13])]} {'shards': [tensor([ 6, 14, 22, 6, 14, 22, 6, 14])]} {'shards': [tensor([ 7, 15, 23, 7, 15, 23, 7, 15])]} {'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]} {'shards': [tensor([17, 1, 9, 17, 1, 9, 17, 1])]} {'shards': [tensor([18, 2, 10, 18, 2, 10, 18, 2])]} ``` ### Expected behavior The shards should be shuffled. ### Environment info - `datasets` version: 3.0.0 - Platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.25.0 - PyArrow version: 17.0.0 - Pandas version: 2.0.3 - `fsspec` version: 2023.6.0

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I_kwDODunzps6XBE6O

Dataset viewer not working! Failure due to more than 32 splits.

closed

NONE

Hello guys, I have a dataset and I didn't know I couldn't upload more than 32 splits. Now, my dataset viewer is not working. I don't have the dataset locally on my node anymore and recreating would take a week. And I have to publish the dataset coming Monday. I read about the practice, how I can resolve it and avoid this issue in the future. But, at the moment I need a hard fix for two of my datasets. And I don't want to mess or change anything and allow everyone in public to see the dataset and interact with it. Can you please help me? https://huggingface.co/datasets/laion/Wikipedia-X https://huggingface.co/datasets/laion/Wikipedia-X-Full

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PR_kwDODunzps572hBd

Support ndjson data files

closed

MEMBER

Support `ndjson` (Newline Delimited JSON) data files. Fix #7153.

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I_kwDODunzps6W90lL

Support data files with .ndjson extension

closed

MEMBER

### Feature request Support data files with `.ndjson` extension. ### Motivation We already support data files with `.jsonl` extension. ### Your contribution I am opening a PR.

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PR_kwDODunzps57kyY4

Align filename prefix splitting with WebDataset library

closed

MEMBER

Align filename prefix splitting with WebDataset library. This PR uses the same `base_plus_ext` function as the one used by the `webdataset` library. Fix #7150. Related to #7144.

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I_kwDODunzps6Wp6zn

WebDataset loader splits keys differently than WebDataset library

closed

MEMBER

As reported by @ragavsachdeva (see discussion here: https://github.com/huggingface/datasets/pull/7144#issuecomment-2348307792), our webdataset loader is not aligned with the `webdataset` library when splitting keys from filenames. For example, we get a different key splitting for filename `/some/path/22.0/1.1.png`: - datasets library: `/some/path/22` and `0/1.1.png` - webdataset library: `/some/path/22.0/1`, `1.png` ```python import webdataset as wds wds.tariterators.base_plus_ext("/some/path/22.0/1.1.png") # ('/some/path/22.0/1', '1.png') ```

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I_kwDODunzps6WeMYo

Datasets Unknown Keyword Argument Error - task_templates

closed

NONE

### Describe the bug Issue ```python from datasets import load_dataset examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>) ``` Gives error ``` TypeError: DatasetInfo.__init__() got an unexpected keyword argument 'task_templates' ``` A simple downgrade to lower `datasets v 2.21.0` solves it. ### Steps to reproduce the bug 1. `pip install datsets` 2. ```python from datasets import load_dataset examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>) ``` ### Expected behavior Should load the dataset correctly. ### Environment info - Datasets version `3.0.0` - `transformers` version: 4.45.0.dev0 - Platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.35 - Python version: 3.12.4 - Huggingface_hub version: 0.24.6 - Safetensors version: 0.4.5 - Accelerate version: 0.35.0.dev0 - Accelerate config: not found - PyTorch version (GPU?): 2.4.1+cu121 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: Yes

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I_kwDODunzps6WbqRF

Bug: Error when downloading mteb/mtop_domain

closed

NONE

### Describe the bug When downloading the dataset "mteb/mtop_domain", ran into the following error: ``` Traceback (most recent call last): File "/share/project/xzy/test/test_download.py", line 3, in <module> data = load_dataset("mteb/mtop_domain", "en", trust_remote_code=True) File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2606, in load_dataset builder_instance = load_dataset_builder( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2277, in load_dataset_builder dataset_module = dataset_module_factory( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1923, in dataset_module_factory raise e1 from None File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1896, in dataset_module_factory ).get_module() File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1507, in get_module local_path = self.download_loading_script() File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1467, in download_loading_script return cached_path(file_path, download_config=download_config) File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 211, in cached_path output_path = get_from_cache( File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 689, in get_from_cache fsspec_get( File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 395, in fsspec_get fs.get_file(path, temp_file.name, callback=callback) File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 648, in get_file http_get( File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 578, in http_get raise EnvironmentError( OSError: Consistency check failed: file should be of size 2191 but has size 2190 ((…)ets/mteb/mtop_domain@main/mtop_domain.py). We are sorry for the inconvenience. Please retry with `force_download=True`. If the issue persists, please let us know by opening an issue on https://github.com/huggingface/huggingface_hub. ``` Try to download through HF datasets directly but got the same error as above. ```python from datasets import load_dataset data = load_dataset("mteb/mtop_domain", "en") ``` ### Steps to reproduce the bug ```python from datasets import load_dataset data = load_dataset("mteb/mtop_domain", "en", force_download=True) ``` With and without `force_download=True` both ran into the same error. ### Expected behavior Should download the dataset successfully. ### Environment info - datasets version: 2.21.0 - huggingface-hub version: 0.24.6

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I_kwDODunzps6WY-Z5

IterableDataset strange deadlock

closed

NONE

### Describe the bug ``` import datasets import torch.utils.data num_shards = 1024 def gen(shards): for shard in shards: if shard < 25: yield {"shard": shard} def main(): dataset = datasets.IterableDataset.from_generator( gen, gen_kwargs={"shards": list(range(num_shards))}, ) dataset = dataset.shuffle(buffer_size=1) dataset = datasets.interleave_datasets( [dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted" ) dataset = dataset.shuffle(buffer_size=1) dataloader = torch.utils.data.DataLoader( dataset, batch_size=8, num_workers=8, ) for i, batch in enumerate(dataloader): print(batch) if i >= 10: break print() if __name__ == "__main__": for _ in range(100): main() ``` ### Steps to reproduce the bug Running the script above, at some point it will freeze. - Changing `num_shards` from 1024 to 25 avoids the issue - Commenting out the final shuffle avoids the issue - Commenting out the interleave_datasets call avoids the issue As an aside, if you comment out just the final shuffle, the output from interleave_datasets is not shuffled at all even though there's the shuffle before it. So something about that shuffle config is not being propagated to interleave_datasets. ### Expected behavior The script should not freeze. ### Environment info - `datasets` version: 3.0.0 - Platform: macOS-14.6.1-arm64-arm-64bit - Python version: 3.12.5 - `huggingface_hub` version: 0.24.7 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1 I observed this with 2.21.0 initially, then tried upgrading to 3.0.0 and could still repro.

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PR_kwDODunzps57KqRV

Set dev version

closed

MEMBER

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PR_kwDODunzps57Kjjc

Release: 3.0.0

closed

MEMBER

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PR_kwDODunzps57JLmb

Fix key error in webdataset

closed

NONE

I was running into ``` example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]} KeyError: 'png' ``` The issue is that a filename may have multiple "." e.g. `22.05.png`. Changing `split` to `rsplit` fixes it. Related https://github.com/huggingface/datasets/issues/6880

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PR_kwDODunzps56xCm6

Modify add_column() to optionally accept a FeatureType as param

closed

CONTRIBUTOR

Fix #7142. **Before (Add + Cast)**: ``` from datasets import load_dataset, Value ds = load_dataset("rotten_tomatoes", split="test") lst = [i for i in range(len(ds))] ds = ds.add_column("new_col", lst) # Assigns int64 to new_col by default print(ds.features) ds = ds.cast_column("new_col", Value(dtype="uint16", id=None)) print(ds.features) ``` **Before (Numpy Workaround)**: ``` from datasets import load_dataset import numpy as np ds = load_dataset("rotten_tomatoes", split="test") lst = [i for i in range(len(ds))] ds = ds.add_column("new_col", np.array(lst, dtype=np.uint16)) print(ds.features) ``` **After**: ``` from datasets import load_dataset, Value ds = load_dataset("rotten_tomatoes", split="test") lst = [i for i in range(len(ds))] val = Value(dtype="uint16", id=None)) ds = ds.add_column("new_col", lst, feature=val) print(ds.features) ```

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I_kwDODunzps6VvdDK

Specifying datatype when adding a column to a dataset.

closed

CONTRIBUTOR

### Feature request There should be a way to specify the datatype of a column in `datasets.add_column()`. ### Motivation To specify a custom datatype, we have to use `datasets.add_column()` followed by `datasets.cast_column()` which is slow for large datasets. Another workaround is to pass a `numpy.array()` of desired type to the `datasets.add_column()` function. IMO this functionality should be natively supported. https://discuss.huggingface.co/t/add-column-with-a-particular-type-in-datasets/95674 ### Your contribution I can submit a PR for this.

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I_kwDODunzps6Vp7tV

Older datasets throwing safety errors with 2.21.0

closed

NONE

### Describe the bug The dataset loading was throwing some safety errors for this popular dataset `wmt14`. [in]: ``` import datasets # train_data = datasets.load_dataset("wmt14", "de-en", split="train") train_data = datasets.load_dataset("wmt14", "de-en", split="train") val_data = datasets.load_dataset("wmt14", "de-en", split="validation[:10%]") ``` [out]: ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-9-445f0ecc4817>](https://localhost:8080/#) in <cell line: 4>() 2 3 # train_data = datasets.load_dataset("wmt14", "de-en", split="train") ----> 4 train_data = datasets.load_dataset("wmt14", "de-en", split="train") 5 val_data = datasets.load_dataset("wmt14", "de-en", split="validation[:10%]") 12 frames [/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py](https://localhost:8080/#) in __init__(self, **kwargs) 636 if security is not None: 637 security = BlobSecurityInfo( --> 638 safe=security["safe"], av_scan=security["avScan"], pickle_import_scan=security["pickleImportScan"] 639 ) 640 self.security = security KeyError: 'safe' ``` ### Steps to reproduce the bug See above. ### Expected behavior Dataset properly loaded. ### Environment info version: 2.21.0

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I_kwDODunzps6Vfj8K

Use load_dataset to load imagenet-1K But find a empty dataset

open

NONE

### Describe the bug ```python def get_dataset(data_path, train_folder="train", val_folder="val"): traindir = os.path.join(data_path, train_folder) valdir = os.path.join(data_path, val_folder) def transform_val_examples(examples): transform = Compose([ Resize(256), CenterCrop(224), ToTensor(), ]) examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]] return examples def transform_train_examples(examples): transform = Compose([ RandomResizedCrop(224), RandomHorizontalFlip(), ToTensor(), ]) examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]] return examples # @fengsicheng: This way is very slow for big dataset like ImageNet-1K (but can pass the network problem using local dataset) # train_set = load_dataset("imagefolder", data_dir=traindir, num_proc=4) # test_set = load_dataset("imagefolder", data_dir=valdir, num_proc=4) train_set = load_dataset("imagenet-1K", split="train", trust_remote_code=True) test_set = load_dataset("imagenet-1K", split="test", trust_remote_code=True) print(train_set["label"]) train_set.set_transform(transform_train_examples) test_set.set_transform(transform_val_examples) return train_set, test_set ``` above the code, but output of the print is a list of None: <img width="952" alt="image" src="https://github.com/user-attachments/assets/c4e2fdd8-3b8f-481e-8f86-9bbeb49d79fb"> ### Steps to reproduce the bug 1. just ran the code 2. see the print ### Expected behavior I do not know how to fix this, can anyone provide help or something? It is hurry for me ### Environment info - `datasets` version: 2.21.0 - Platform: Linux-5.4.0-190-generic-x86_64-with-glibc2.31 - Python version: 3.10.14 - `huggingface_hub` version: 0.24.6 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1

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I_kwDODunzps6VeQzE

Cache only changed columns?

open

CONTRIBUTOR

### Feature request Cache only the actual changes to the dataset i.e. changed columns. ### Motivation I realized that caching actually saves the complete dataset again. This is especially problematic for image datasets if one wants to only change another column e.g. some metadata and then has to save 5 TB again. ### Your contribution Is this even viable in the current architecture of the package? I quickly looked into it and it seems it would require significant changes. I would spend some time looking into this but maybe somebody could help with the feasibility and some plan to implement before spending too much time on it?

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I_kwDODunzps6Va4Lo

[BUG] dataset_info sequence unexpected behavior in README.md YAML

open

NONE

### Describe the bug When working on `dataset_info` yaml, I find my data column with format `list[dict[str, str]]` cannot be coded correctly. My data looks like ``` {"answers":[{"text": "ADDRESS", "label": "abc"}]} ``` My `dataset_info` in README.md is: ``` dataset_info: - config_name: default features: - name: answers sequence: - name: text dtype: string - name: label dtype: string ``` **Error log**: ``` pyarrow.lib.ArrowNotImplementedError: Unsupported cast from list<item: struct<text: string, label: string>> to struct using function cast_struct ``` ## Potential Reason After some analysis, it turns out that my yaml config is requiring `dict[str, list[str]]` instead of `list[dict[str, str]]`. It would work if I change my data to ``` {"answers":{"text": ["ADDRESS"], "label": ["abc", "def"]}} ``` These following 2 different `dataset_info` are actually equivalent. ``` dataset_info: - config_name: default features: - name: answers dtype: - name: text sequence: string - name: label sequence: string dataset_info: - config_name: default features: - name: answers sequence: - name: text dtype: string - name: label dtype: string ``` ### Steps to reproduce the bug ``` # README.md --- dataset_info: - config_name: default features: - name: answers sequence: - name: text dtype: string - name: label dtype: string configs: - config_name: default default: true data_files: - split: train path: - "test.jsonl" --- # test.jsonl # expected but not working {"answers":[{"text": "ADDRESS", "label": "abc"}]} # unexpected but working {"answers":{"text": ["ADDRESS"], "label": ["abc", "def"]}} ``` ### Expected behavior ``` dataset_info: - config_name: default features: - name: answers sequence: - name: text dtype: string - name: label dtype: string ``` Should work on following data format: ``` {"answers":[{"text":"ADDRESS", "label": "abc"}]} ``` ### Environment info - `datasets` version: 2.21.0 - Platform: macOS-14.6.1-arm64-arm-64bit - Python version: 3.12.4 - `huggingface_hub` version: 0.24.5 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1

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PR_kwDODunzps56b9R-

Do not consume unnecessary memory during sharding

open

NONE

When sharding `IterableDataset`s, a temporary list is created that is then indexed. There is no need to create a temporary list of a potentially very large step/world size, with standard `islice` functionality, so we avoid it. ```shell pytest tests/test_distributed.py -k iterable ``` Runs successfully.

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I_kwDODunzps6VNZs4

Bug: Type Mismatch in Dataset Mapping

open

NONE

# Issue: Type Mismatch in Dataset Mapping ## Description There is an issue with the `map` function in the `datasets` library where the mapped output does not reflect the expected type change. After applying a mapping function to convert an integer label to a string, the resulting type remains an integer instead of a string. ## Reproduction Code Below is a Python script that demonstrates the problem: ```python from datasets import Dataset # Original data data = { 'text': ['Hello', 'world', 'this', 'is', 'a', 'test'], 'label': [0, 1, 0, 1, 1, 0] } # Creating a Dataset object dataset = Dataset.from_dict(data) # Mapping function to convert label to string def add_one(example): example['label'] = str(example['label']) return example # Applying the mapping function dataset = dataset.map(add_one) # Iterating over the dataset to show results for item in dataset: print(item) print(type(item['label'])) ``` ## Expected Output After applying the mapping function, the expected output should have the `label` field as strings: ```plaintext {'text': 'Hello', 'label': '0'} <class 'str'> {'text': 'world', 'label': '1'} <class 'str'> {'text': 'this', 'label': '0'} <class 'str'> {'text': 'is', 'label': '1'} <class 'str'> {'text': 'a', 'label': '1'} <class 'str'> {'text': 'test', 'label': '0'} <class 'str'> ``` ## Actual Output The actual output still shows the `label` field values as integers: ```plaintext {'text': 'Hello', 'label': 0} <class 'int'> {'text': 'world', 'label': 1} <class 'int'> {'text': 'this', 'label': 0} <class 'int'> {'text': 'is', 'label': 1} <class 'int'> {'text': 'a', 'label': 1} <class 'int'> {'text': 'test', 'label': 0} <class 'int'> ``` ## Why necessary In the case of Image process we often need to convert PIL to tensor with same column name. Thank for every dev who review this issue. 🤗

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I_kwDODunzps6U-xmJ

Attempting to return a rank 3 grayscale image from dataset.map results in extreme slowdown

open

NONE

### Describe the bug Background: Digital images are often represented as a (Height, Width, Channel) tensor. This is the same for huggingface datasets that contain images. These images are loaded in Pillow containers which offer, for example, the `.convert` method. I can convert an image from a (H,W,3) shape to a grayscale (H,W) image and I have no problems with this. But when attempting to return a (H,W,1) shaped matrix from a map function, it never completes and sometimes even results in an OOM from the OS. I've used various methods to expand a (H,W) shaped array to a (H,W,1) array. But they all resulted in extremely long map operations consuming a lot of CPU and RAM. ### Steps to reproduce the bug Below is a minimal example using two methods to get the desired output. Both of which don't work ```py import tensorflow as tf import datasets import numpy as np ds = datasets.load_dataset("project-sloth/captcha-images") to_gray_pillow = lambda sample: {'image': np.expand_dims(sample['image'].convert("L"), axis=-1)} ds_gray = ds.map(to_gray_pillow) # Alternatively ds = datasets.load_dataset("project-sloth/captcha-images").with_format("tensorflow") to_gray_tf = lambda sample: {'image': tf.expand_dims(tf.image.rgb_to_grayscale(sample['image']), axis=-1)} ds_gray = ds.map(to_gray_tf) ``` ### Expected behavior I expect the map operation to complete and return a new dataset containing grayscale images in a (H,W,1) shape. ### Environment info datasets 2.21.0 python tested with both 3.11 and 3.12 host os : linux

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PR_kwDODunzps557zng

remove filecheck to enable symlinks

open

CONTRIBUTOR

Enables streaming from local symlinks #7083 @lhoestq

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PR_kwDODunzps551k1C

Fix data file module inference

open

NONE

I saved a dataset with two splits to disk with `DatasetDict.save_to_disk`. The train is bigger and ended up in 10 shards, whereas the test split only resulted in 1 split. Now when trying to load the dataset, an error is raised that not all splits have the same data format: > ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})} This is not expected because both splits are saved as arrow files. I did some debugging and found that this is the case because the list of data_files includes a `state.json` file. Now this means for train split I get 10 ".arrow" and 1 ".json" file. Since datasets picks based on the most common extension this is correctly inferred as "arrow". In the test split, there is 1 .arrow and 1 .json file. Given the function description: > It picks the module based on the most common file extension. In case of a draw ".parquet" is the favorite, and then alphabetical order. This is not quite true though, because in a tie the extensions are actually based on reverse-alphabetical order: ``` for (ext, _), _ in sorted(extensions_counter.items(), key=sort_key, *reverse=True*): ``` Which thus leads to the module wrongly inferred as "json", whereas it should be "arrow", matching the train split. I first thought about adding "state.json" in the list of excluded files for the inference: https://github.com/huggingface/datasets/blob/main/src/datasets/load.py#L513. However, I think from digging into the code it looks like the right thing to do is to exclude it in the list of `data_files` to start with, because it is more of a metadata than a data file.

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I_kwDODunzps6UiAb6

Inconsistent output in documentation example: `num_classes` not displayed in `ClassLabel` output

open

NONE

In the documentation for [ClassLabel](https://huggingface.co/docs/datasets/v2.21.0/en/package_reference/main_classes#datasets.ClassLabel), there is an example of usage with the following code: ```` from datasets import Features features = Features({'label': ClassLabel(num_classes=3, names=['bad', 'ok', 'good'])}) features ```` which expects to output (as stated in the documentation): ```` {'label': ClassLabel(num_classes=3, names=['bad', 'ok', 'good'], id=None)} ```` but it generates the following ```` {'label': ClassLabel(names=['bad', 'ok', 'good'], id=None)} ```` If my understanding is correct, this happens because although num_classes is used during the init of the object, it is afterward ignored: https://github.com/huggingface/datasets/blob/be5cff059a2a5b89d7a97bc04739c4919ab8089f/src/datasets/features/features.py#L975 I would like to work on this issue if this is something needed 😄

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I_kwDODunzps6UbpPX

Filter Large Dataset Entry by Entry

open

NONE

### Feature request I am not sure if this is a new feature, but I wanted to post this problem here, and hear if others have ways of optimizing and speeding up this process. Let's say I have a really large dataset that I cannot load into memory. At this point, I am only aware of `streaming=True` to load the dataset. Now, the dataset consists of many tables. Ideally, I would want to have some simple filtering criterion, such that I only see the "good" tables. Here is an example of what the code might look like: ``` dataset = load_dataset( "really-large-dataset", streaming=True ) # And let's say we process the dataset bit by bit because we want intermediate results dataset = islice(dataset, 10000) # Define a function to filter the data def filter_function(table): if some_condition: return True else: return False # Use the filter function on your dataset filtered_dataset = (ex for ex in dataset if filter_function(ex)) ``` And then I work on the processed dataset, which would be magnitudes faster than working on the original. I would love to hear if the problem setup + solution makes sense to people, and if anyone has suggestions! ### Motivation See description above ### Your contribution Happy to make PR if this is a new feature

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I_kwDODunzps6UNVwm

Caching shuffles by np.random.Generator results in unintiutive behavior

open

NONE

### Describe the bug Create a dataset. Save it to disk. Load from disk. Shuffle, usning a `np.random.Generator`. Iterate. Shuffle again. Iterate. The iterates are different since the supplied np.random.Generator has progressed between the shuffles. Load dataset from disk again. Shuffle and Iterate. See same result as before. Shuffle and iterate, and this time it does not have the same shuffling as ion previous run. The motivation is I have a deep learning loop with ``` for epoch in range(10): for batch in dataset.shuffle(generator=generator).iter(batch_size=32): .... # do stuff ``` where I want a new shuffling at every epoch. Instead I get the same shuffling. ### Steps to reproduce the bug Run the code below two times. ```python import datasets import numpy as np generator = np.random.default_rng(0) ds = datasets.Dataset.from_dict(mapping={"X":range(1000)}) ds.save_to_disk("tmp") print("First loop: ", end="") for _ in range(10): print(next(ds.shuffle(generator=generator).iter(batch_size=1))['X'], end=", ") print("") print("Second loop: ", end="") ds = datasets.Dataset.load_from_disk("tmp") for _ in range(10): print(next(ds.shuffle(generator=generator).iter(batch_size=1))['X'], end=", ") print("") ``` The output is: ``` $ python main.py Saving the dataset (1/1 shards): 100%|███████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 495019.95 examples/s] First loop: 459, 739, 72, 943, 241, 181, 845, 830, 896, 334, Second loop: 741, 847, 944, 795, 483, 842, 717, 865, 231, 840, $ python main.py Saving the dataset (1/1 shards): 100%|████████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 22243.40 examples/s] First loop: 459, 739, 72, 943, 241, 181, 845, 830, 896, 334, Second loop: 741, 741, 741, 741, 741, 741, 741, 741, 741, 741, ``` The second loop, on the second run, only spits out "741, 741, 741...." which is *not* the desired output ### Expected behavior I want the dataset to shuffle at every epoch since I provide it with a generator for shuffling. ### Environment info Datasets version 2.21.0 Ubuntu linux.

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PR_kwDODunzps55Y-Ws

Disable implicit token in CI

closed

MEMBER

Disable implicit token in CI. This PR allows running CI tests locally without implicitly using the local user HF token. For example, run locally the tests in: - #7124

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PR_kwDODunzps55Y4TM

Fix wrong SHA in CI tests of HubDatasetModuleFactoryWithParquetExport

closed

MEMBER

Fix wrong SHA in CI tests of HubDatasetModuleFactoryWithParquetExport.

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PR_kwDODunzps55YzWr

Test get_dataset_config_info with non-existing/gated/private dataset

closed

MEMBER

Test get_dataset_config_info with non-existing/gated/private dataset. Related to: - #7109 See also: - https://github.com/huggingface/dataset-viewer/pull/3037: https://github.com/huggingface/dataset-viewer/pull/3037/commits/bb1a7e00c53c242088597cab6572e4fd57797ecb

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I_kwDODunzps6UDuRh

Make dataset viewer more flexible in displaying metadata alongside images

open

NONE

### Feature request To display images with their associated metadata in the dataset viewer, a `metadata.csv` file is required. In the case of a dataset with multiple subsets, this would require the CSVs to be contained in the same folder as the images since they all need to be named `metadata.csv`. The request is that this be made more flexible for datasets with multiple subsets to avoid the need to put a `metadata.csv` into each image directory where they are not as easily accessed. ### Motivation When creating datasets with multiple subsets I can't get the images to display alongside their associated metadata (it's usually one or the other that will show up). Since this requires a file specifically named `metadata.csv`, I then have to place that file within the image directory, which makes it much more difficult to access. Additionally, it still doesn't necessarily display the images alongside their metadata correctly (see, for instance, [this discussion](https://huggingface.co/datasets/imageomics/2018-NEON-beetles/discussions/8)). It was suggested I bring this discussion to GitHub on another dataset struggling with a similar issue ([discussion](https://huggingface.co/datasets/imageomics/fish-vista/discussions/4)). In that case, it's a mix of data subsets, where some just reference the image URLs, while others actually have the images uploaded. The ones with images uploaded are not displaying images, but renaming that file to just `metadata.csv` would diminish the clarity of the construction of the dataset itself (and I'm not entirely convinced it would solve the issue). ### Your contribution I can make a suggestion for one approach to address the issue: For instance, even if it could just end in `_metadata.csv` or `-metadata.csv`, that would be very helpful to allow for more flexibility of dataset structure without impacting clarity. I would think that the functionality on the backend looking for `metadata.csv` could reasonably be adapted to look for such an ending on a filename (maybe also check that it has a `file_name` column?). Presumably, requiring the `configs` in a setup like on [this dataset](https://huggingface.co/datasets/imageomics/rare-species/blob/main/README.md) could also help in figuring out how it should work? ``` configs: - config_name: <image subset> data_files: - <image-metadata>.csv - <path/to/images>/*.jpg ``` I'd also be happy to look at whatever solution is decided upon and contribute to the ideation. Thanks for your time and consideration! The dataset viewer really is fabulous when it works :)

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I_kwDODunzps6T9896

[interleave_dataset] sample batches from a single source at a time

open

NONE

### Feature request interleave_dataset and [RandomlyCyclingMultiSourcesExamplesIterable](https://github.com/huggingface/datasets/blob/3813ce846e52824b38e53895810682f0a496a2e3/src/datasets/iterable_dataset.py#L816) enable us to sample data examples from different sources. But can we also sample batches in a similar manner (each batch only contains data from a single source)? ### Motivation Some recent research [[1](https://blog.salesforceairesearch.com/sfr-embedded-mistral/), [2](https://arxiv.org/pdf/2310.07554)] shows that source homogenous batching can be helpful for contrastive learning. Can we add a function called `RandomlyCyclingMultiSourcesBatchesIterable` to support this functionality? ### Your contribution I can contribute a PR. But I wonder what the best way is to test its correctness and robustness.

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PR_kwDODunzps55Iukl

Fix typed examples iterable state dict

closed