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I_kwDODunzps6EzdUj

Image cast_storage very slow for arrays (e.g. numpy, tensors)

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Update: see comments below ### Describe the bug Operations that save an image from a path are very slow. I believe the reason for this is that the image data (`numpy`) is converted into `pyarrow` format but then back to python using `.pylist()` before being converted to a numpy array again. `pylist` is already slow but used on a multi-dimensional numpy array such as an image it takes a very long time. From the trace below we can see that `__arrow_array__` takes a long time. It is currently also called in `get_inferred_type`, this should be removable #6781 but doesn't change the underyling issue. The conversion to `pyarrow` and back also leads to the `numpy` array having type `int64` which causes a warning message because the image type excepts `uint8`. However, originally the `numpy` image array was in `uint8`. ### Steps to reproduce the bug ```python from PIL import Image import numpy as np import datasets import cProfile image = Image.fromarray(np.random.randint(0, 255, (2048, 2048, 3), dtype=np.uint8)) image.save("test_image.jpg") ds = datasets.Dataset.from_dict( {"image": ["test_image.jpg"]}, features=datasets.Features({"image": datasets.Image(decode=True)}), ) # load as numpy array, e.g. for further processing with map # same result as map returning numpy arrays ds.set_format("numpy") cProfile.run("ds.map(writer_batch_size=1, load_from_cache_file=False)", "restats") ``` ```bash Fri Apr 5 14:56:17 2024 restats 66817 function calls (64992 primitive calls) in 33.382 seconds Ordered by: cumulative time List reduced from 1073 to 20 due to restriction <20> ncalls tottime percall cumtime percall filename:lineno(function) 46/1 0.000 0.000 33.382 33.382 {built-in method builtins.exec} 1 0.000 0.000 33.382 33.382 <string>:1(<module>) 1 0.000 0.000 33.382 33.382 arrow_dataset.py:594(wrapper) 1 0.000 0.000 33.382 33.382 arrow_dataset.py:551(wrapper) 1 0.000 0.000 33.379 33.379 arrow_dataset.py:2916(map) 4 0.000 0.000 33.327 8.332 arrow_dataset.py:3277(_map_single) 1 0.000 0.000 33.311 33.311 arrow_writer.py:465(write) 2 0.000 0.000 33.311 16.656 arrow_writer.py:423(write_examples_on_file) 1 0.000 0.000 33.311 33.311 arrow_writer.py:527(write_batch) 2 14.484 7.242 33.260 16.630 arrow_writer.py:161(__arrow_array__) 1 0.001 0.001 16.438 16.438 arrow_writer.py:121(get_inferred_type) 1 0.000 0.000 14.398 14.398 threading.py:637(wait) 1 0.000 0.000 14.398 14.398 threading.py:323(wait) 8 14.398 1.800 14.398 1.800 {method 'acquire' of '_thread.lock' objects} 4/2 0.000 0.000 4.337 2.169 table.py:1800(wrapper) 2 0.000 0.000 4.337 2.169 table.py:1950(cast_array_to_feature) 2 0.475 0.238 4.337 2.169 image.py:209(cast_storage) 9 2.583 0.287 2.583 0.287 {built-in method numpy.array} 2 0.000 0.000 1.284 0.642 image.py:319(encode_np_array) 2 0.000 0.000 1.246 0.623 image.py:301(image_to_bytes) ``` ### Expected behavior The `numpy` image data should be passed through as it will be directly consumed by `pillow` to convert it to bytes. As an example one can replace `list_of_np_array_to_pyarrow_listarray(data)` in `__arrow_array__` with just `out = data` as a test. We have to change `cast_storage` of the `Image` feature so it handles the passed through data (& if to handle type before) ```python bytes_array = pa.array( [encode_np_array(arr)["bytes"] if arr is not None else None for arr in storage], type=pa.binary(), ) ``` Leading to the following: ```bash Fri Apr 5 15:44:27 2024 restats 66419 function calls (64595 primitive calls) in 0.937 seconds Ordered by: cumulative time List reduced from 1023 to 20 due to restriction <20> ncalls tottime percall cumtime percall filename:lineno(function) 47/1 0.000 0.000 0.935 0.935 {built-in method builtins.exec} 2/1 0.000 0.000 0.935 0.935 <string>:1(<module>) 2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:594(wrapper) 2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:551(wrapper) 2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:2916(map) 4 0.000 0.000 0.933 0.233 arrow_dataset.py:3277(_map_single) 1 0.000 0.000 0.883 0.883 arrow_writer.py:466(write) 2 0.000 0.000 0.883 0.441 arrow_writer.py:424(write_examples_on_file) 1 0.000 0.000 0.882 0.882 arrow_writer.py:528(write_batch) 2 0.000 0.000 0.877 0.439 arrow_writer.py:161(__arrow_array__) 4/2 0.000 0.000 0.877 0.439 table.py:1800(wrapper) 2 0.000 0.000 0.877 0.439 table.py:1950(cast_array_to_feature) 2 0.009 0.005 0.877 0.439 image.py:209(cast_storage) 2 0.000 0.000 0.868 0.434 image.py:335(encode_np_array) 2 0.000 0.000 0.856 0.428 image.py:317(image_to_bytes) 2 0.000 0.000 0.822 0.411 Image.py:2376(save) 2 0.000 0.000 0.822 0.411 PngImagePlugin.py:1233(_save) 2 0.000 0.000 0.822 0.411 ImageFile.py:517(_save) 2 0.000 0.000 0.821 0.411 ImageFile.py:545(_encode_tile) 589 0.803 0.001 0.803 0.001 {method 'encode' of 'ImagingEncoder' objects} ``` This is of course only a test as it passes through all `numpy` arrays irrespective of if they should be an image. Also I guess `cast_storage` is meant for casting `pyarrow` storage exclusively. Converting to `pyarrow` array seems like a good solution as it also handles `pytorch` tensors etc., maybe there is a more efficient way to create a PIL image from a `pyarrow` array? Not sure how this should be handled but I would be happy to help if there is a good solution. ### Environment info - `datasets` version: 2.18.1.dev0 - Platform: Linux-6.7.11-200.fc39.x86_64-x86_64-with-glibc2.38 - Python version: 3.12.2 - `huggingface_hub` version: 0.22.2 - PyArrow version: 15.0.2 - Pandas version: 2.2.1 - `fsspec` version: 2024.3.1

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https://github.com/huggingface/datasets/pull/6781

PR_kwDODunzps5r2DMe

Remove get_inferred_type from ArrowWriter write_batch

closed

CONTRIBUTOR

Inferring the type seems to be unnecessary given that the pyarrow array has already been created. Because pyarrow array creation is sometimes extremely slow this doubles the time write_batch takes.

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https://github.com/huggingface/datasets/pull/6780

PR_kwDODunzps5rvkyj

Fix CI

closed

COLLABORATOR

Updates the `wmt_t2t` test to pin the `revision` to the version with a loading script (cc @albertvillanova). Additionally, it replaces the occurrences of the `lhoestq/test` repo id with `hf-internal-testing/dataset_with_script` and re-enables logging checks in the `Dataset.from_sql` tests.

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PR_kwDODunzps5rvSA8

Install dependencies with `uv` in CI

closed

COLLABORATOR

`diffusers` (https://github.com/huggingface/diffusers/pull/7116) and `huggingface_hub` (https://github.com/huggingface/huggingface_hub/pull/2072) also use `uv` to install their dependencies, so we can do the same here. It seems to make the "Install dependencies" step in the `ubuntu` jobs 5-8x faster and 1.5-2x in the `windows` one. Besides introducing `uv` in CI, this PR bumps the `tensorflow` minimal version requirement to align with Transformers and simplifies the SpaCy hashing tests (use blank language models instead of the pre-trained ones)

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I_kwDODunzps6Erq88

Dataset.to_csv() missing commas in columns with lists

open

NONE

### Describe the bug The `to_csv()` method does not output commas in lists. So when the Dataset is loaded back in the data structure of the column with a list is not correct. Here's an example: Obviously, it's not as trivial as inserting commas in the list, since its a comma-separated file. But hopefully there's a way to export the list in a way that it'll be imported by `load_dataset()` correctly. ### Steps to reproduce the bug Here's some code to reproduce the bug: ```python from datasets import Dataset ds = Dataset.from_dict( { "pokemon": ["bulbasaur", "squirtle"], "type": ["grass", "water"] } ) def ascii_to_hex(text): return [ord(c) for c in text] ds = ds.map(lambda x: {"int": ascii_to_hex(x['pokemon'])}) ds.to_csv('../output/temp.csv') ``` temp.csv then contains: ``` ### Expected behavior ACTUAL OUTPUT: ``` pokemon,type,int bulbasaur,grass,[ 98 117 108 98 97 115 97 117 114] squirtle,water,[115 113 117 105 114 116 108 101] ``` EXPECTED OUTPUT: ``` pokemon,type,int bulbasaur,grass,[98, 117, 108, 98, 97, 115, 97, 117, 114] squirtle,water,[115, 113, 117, 105, 114, 116, 108, 101] ``` or probably something more like this since it's a CSV file: ``` pokemon,type,int bulbasaur,grass,"[98, 117, 108, 98, 97, 115, 97, 117, 114]" squirtle,water,"[115, 113, 117, 105, 114, 116, 108, 101]" ``` ### Environment info ### Package Version Name: datasets Version: 2.16.1 ### Python version: 3.10.12 ### OS Info PRETTY_NAME="Ubuntu 22.04.4 LTS" NAME="Ubuntu" VERSION_ID="22.04" VERSION="22.04.4 LTS (Jammy Jellyfish)" VERSION_CODENAME=jammy ID=ubuntu ID_LIKE=debian ... UBUNTU_CODENAME=jammy

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I_kwDODunzps6EmN-v

.Jsonl metadata not detected

open

NONE

### Describe the bug Hi I have the following directory structure: |--dataset | |-- images | |-- metadata1000.csv | |-- metadata1000.jsonl | |-- padded_images Example of metadata1000.jsonl file {"caption": "a drawing depicts a full shot of a black t-shirt with a triangular pattern on the front there is a white label on the left side of the triangle", "image": "images/212734.png", "gaussian_padded_image": "padded_images/p_212734.png"} {"caption": "an eye-level full shot of a large elephant and a baby elephant standing in a watering hole on the left side is a small elephant with its head turned to the right of dry land, trees, and bushes", "image": "images/212735.png", "gaussian_padded_image": "padded_images/p_212735.png"} . . . I'm trying to use dataset = load_dataset("imagefolder", data_dir='/dataset/', split='train') to load the the dataset, however it is not able to load according to the fields in the metadata1000.jsonl . please assist to load the data properly also getting ``` File "/workspace/train_trans_vae.py", line 1089, in <module> print(get_metadata_patterns('/dataset/')) File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 499, in get_metadata_patterns raise FileNotFoundError(f"The directory at {base_path} doesn't contain any metadata file") from None FileNotFoundError: The directory at /dataset/ doesn't contain any metadata file ``` when trying ``` from datasets.data_files import get_metadata_patterns print(get_metadata_patterns('/dataset/')) ``` ### Steps to reproduce the bug dataset Version: 2.18.0 make a similar jsonl and similar directory format ### Expected behavior creates a dataset object with the column names, caption,image,gaussian_padded_image ### Environment info dataset Version: 2.18.0

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I_kwDODunzps6Eh0YA

IndexError: Invalid key: 0 is out of bounds for size 0

open

NONE

### Describe the bug I am trying to fine-tune llama2-7b model in GCP. The notebook I am using for this can be found [here](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_llama2_peft_finetuning.ipynb). When I use the dataset given in the example, the training gets successfully completed (example dataset can be found [here](https://huggingface.co/datasets/timdettmers/openassistant-guanaco)). However when I use my own dataset which is in the same format as the example dataset, I get the below error (my dataset can be found [here](https://huggingface.co/datasets/kk2491/finetune_dataset_002)).  I see the files are being read correctly from the logs:  ### Steps to reproduce the bug 1. Clone the [vertex-ai-samples](https://github.com/GoogleCloudPlatform/vertex-ai-samples) repository. 2. Run the [llama2-7b peft fine-tuning](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_llama2_peft_finetuning.ipynb). 3. Change the dataset `kk2491/finetune_dataset_002` ### Expected behavior The training should complete successfully, and model gets deployed to an endpoint. ### Environment info Python version : Python 3.10.12 Dataset : https://huggingface.co/datasets/kk2491/finetune_dataset_002

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I_kwDODunzps6Ec4lc

Generating split is very slow when Image format is PNG

open

NONE

### Describe the bug When I create a dataset, it gets stuck while generating cached data. The image format is PNG, and it will not get stuck when the image format is jpeg.  After debugging, I know that it is because of the `pa.array` operation in [arrow_writer](https://github.com/huggingface/datasets/blob/2.13.0/src/datasets/arrow_writer.py#L553), but i don't why. ### Steps to reproduce the bug ``` from datasets import Dataset def generator(lines): for line in lines: img = Image.open(open(line["url"], "rb")) # print(img.format) # "PNG" yield { "image": img, } lines = open(dataset_path, "r") dataset = Dataset.from_generator( generator, gen_kwargs={"lines": lines} ) ``` ### Expected behavior Generating split done. ### Environment info datasets 2.13.0

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I_kwDODunzps6EYoUh

Dataset on Hub re-downloads every time?

closed

NONE

### Describe the bug Hi, I have a dataset on the hub [here](https://huggingface.co/datasets/manestay/borderlines). It has 1k+ downloads, which I sure is mostly just me and my colleagues working with it. It should have far fewer, since I'm using the same machine with a properly set up HF_HOME variable. However, whenever I run the below function `load_borderlines_hf`, it downloads the entire dataset from the hub and then does the other logic: https://github.com/manestay/borderlines/blob/4e161f444661e2ebfe643f3fe149d9258d63a57d/run_gpt/lib.py#L80 Let me know what I'm doing wrong here, or if it's a bug with the `datasets` library itself. On the hub I have my data stored in CSVs, but several columns are lists, so that's why I have the code to map splitting on `;`. I looked into dataset loading scripts, but it seemed difficult to set up. I have verified that other `datasets` and `models` on my system are using the cache properly (e.g. I have a 13B parameter model and large datasets, but those are cached and don't redownload). __EDIT: __ as pointed out in the discussion below, it may be the `map()` calls that aren't being cached properly. Supposing the `load_dataset()` retrieve from the cache, then it should be the case that the `map()` calls also retrieve from the cached output. But the `map()` commands re-execute sometimes. ### Steps to reproduce the bug 1. Copy and paste the function from [here](https://github.com/manestay/borderlines/blob/4e161f444661e2ebfe643f3fe149d9258d63a57d/run_gpt/lib.py#L80) (lines 80-100) 2. Run it in Python `load_borderlines_hf(None)` 3. It completes successfully, downloading from HF hub, then doing the mapping logic etc. 4. If you run it again after some time, it will re-download, ignoring the cache ### Expected behavior Re-running the code, which calls `datasets.load_dataset('manestay/borderlines', 'territories')`, should use the cached version ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-5.14.21-150500.55.7-default-x86_64-with-glibc2.31 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.10.0

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PR_kwDODunzps5rdKZ2

`remove_columns`/`rename_columns` doc fixes

closed

COLLABORATOR

Use more consistent wording in `remove_columns` to explain why it's faster than `map` and update `remove_columns`/`rename_columns` docstrings to fix in-place calls. Reported in https://github.com/huggingface/datasets/issues/6700

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I_kwDODunzps6EVISB

Datasets FileNotFoundError when trying to generate examples.

closed

NONE

### Discussed in https://github.com/huggingface/datasets/discussions/6768 <div type='discussions-op-text'> ^{Originally posted by **RitchieP** April 1, 2024} Currently, I have a dataset hosted on Huggingface with a custom script [here](https://huggingface.co/datasets/RitchieP/VerbaLex_voice). I'm loading my dataset as below. ```py from datasets import load_dataset, IterableDatasetDict dataset = IterableDatasetDict() dataset["train"] = load_dataset("RitchieP/VerbaLex_voice", "ar", split="train", use_auth_token=True, streaming=True) dataset["test"] = load_dataset("RitchieP/VerbaLex_voice", "ar", split="test", use_auth_token=True, streaming=True) ``` And when I try to see the data I have loaded with ```py list(dataset["train"].take(1)) ``` And it gives me this stack trace ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[2], line 1 ----> 1 list(dataset["train"].take(1)) File /opt/conda/lib/python3.10/site-packages/datasets/iterable_dataset.py:1388, in IterableDataset.__iter__(self) 1385 yield formatter.format_row(pa_table) 1386 return -> 1388 for key, example in ex_iterable: 1389 if self.features: 1390 # `IterableDataset` automatically fills missing columns with None. 1391 # This is done with `_apply_feature_types_on_example`. 1392 example = _apply_feature_types_on_example( 1393 example, self.features, token_per_repo_id=self._token_per_repo_id 1394 ) File /opt/conda/lib/python3.10/site-packages/datasets/iterable_dataset.py:1044, in TakeExamplesIterable.__iter__(self) 1043 def __iter__(self): -> 1044 yield from islice(self.ex_iterable, self.n) File /opt/conda/lib/python3.10/site-packages/datasets/iterable_dataset.py:234, in ExamplesIterable.__iter__(self) 233 def __iter__(self): --> 234 yield from self.generate_examples_fn(**self.kwargs) File ~/.cache/huggingface/modules/datasets_modules/datasets/RitchieP--VerbaLex_voice/9465eaee58383cf9d7c3e14111d7abaea56398185a641b646897d6df4e4732f7/VerbaLex_voice.py:127, in VerbaLexVoiceDataset._generate_examples(self, local_extracted_archive_paths, archives, meta_path) 125 for i, audio_archive in enumerate(archives): 126 print(audio_archive) --> 127 for path, file in audio_archive: 128 _, filename = os.path.split(path) 129 if filename in metadata: File /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:869, in _IterableFromGenerator.__iter__(self) 868 def __iter__(self): --> 869 yield from self.generator(*self.args, **self.kwargs) File /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:919, in ArchiveIterable._iter_from_urlpath(cls, urlpath, download_config) 915 @classmethod 916 def _iter_from_urlpath( 917 cls, urlpath: str, download_config: Optional[DownloadConfig] = None 918 ) -> Generator[Tuple, None, None]: --> 919 compression = _get_extraction_protocol(urlpath, download_config=download_config) 920 # Set block_size=0 to get faster streaming 921 # (e.g. for hf:// and https:// it uses streaming Requests file-like instances) 922 with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f: File /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:400, in _get_extraction_protocol(urlpath, download_config) 398 urlpath, storage_options = _prepare_path_and_storage_options(urlpath, download_config=download_config) 399 try: --> 400 with fsspec.open(urlpath, **(storage_options or {})) as f: 401 return _get_extraction_protocol_with_magic_number(f) 402 except FileNotFoundError: File /opt/conda/lib/python3.10/site-packages/fsspec/core.py:100, in OpenFile.__enter__(self) 97 def __enter__(self): 98 mode = self.mode.replace("t", "").replace("b", "") + "b" --> 100 f = self.fs.open(self.path, mode=mode) 102 self.fobjects = [f] 104 if self.compression is not None: File /opt/conda/lib/python3.10/site-packages/fsspec/spec.py:1307, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs) 1305 else: 1306 ac = kwargs.pop("autocommit", not self._intrans) -> 1307 f = self._open( 1308 path, 1309 mode=mode, 1310 block_size=block_size, 1311 autocommit=ac, 1312 cache_options=cache_options, 1313 **kwargs, 1314 ) 1315 if compression is not None: 1316 from fsspec.compression import compr File /opt/conda/lib/python3.10/site-packages/fsspec/implementations/local.py:180, in LocalFileSystem._open(self, path, mode, block_size, **kwargs) 178 if self.auto_mkdir and "w" in mode: 179 self.makedirs(self._parent(path), exist_ok=True) --> 180 return LocalFileOpener(path, mode, fs=self, **kwargs) File /opt/conda/lib/python3.10/site-packages/fsspec/implementations/local.py:302, in LocalFileOpener.__init__(self, path, mode, autocommit, fs, compression, **kwargs) 300 self.compression = get_compression(path, compression) 301 self.blocksize = io.DEFAULT_BUFFER_SIZE --> 302 self._open() File /opt/conda/lib/python3.10/site-packages/fsspec/implementations/local.py:307, in LocalFileOpener._open(self) 305 if self.f is None or self.f.closed: 306 if self.autocommit or "w" not in self.mode: --> 307 self.f = open(self.path, mode=self.mode) 308 if self.compression: 309 compress = compr[self.compression] FileNotFoundError: [Errno 2] No such file or directory: '/kaggle/working/h' ``` After looking into the stack trace, and referring to the source codes, it looks like its trying to access a directory in the notebook's environment and I don't understand why. Not sure if its a bug in Datasets library, so I'm opening a discussions first. Feel free to ask for more information if needed. Appreciate any help in advance!</div> Hi, referring to the discussion title above, after further digging, I think it's an issue within the datasets library. But not quite sure where it is. If you require any more info or actions from me, please let me know. Appreciate any help in advance!

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I_kwDODunzps6EQyEL

[Bug Report] `datasets==2.18.0` is not compatible with `fsspec==2023.12.2`

closed

NONE

### Describe the bug `Datasets==2.18.0` is not compatible with `fsspec==2023.12.2`. I have to downgrade fsspec to `fsspec==2023.10.0` to make `Datasets==2.18.0` work properly. ### Steps to reproduce the bug To reproduce the bug: 1. Make sure that `Datasets==2.18.0` and `fsspec==2023.12.2`. 2. Run the following code: ``` from datasets import load_dataset dataset = load_dataset("trec") ``` 3. Then one will get the following error message: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset builder_instance = load_dataset_builder( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2265, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 371, in __init__ self.config, self.config_id = self._create_builder_config( File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 620, in _create_builder_config builder_config._resolve_data_files( File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 211, in _resolve_data_files self.data_files = self.data_files.resolve(base_path, download_config) File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 799, in resolve out[key] = data_files_patterns_list.resolve(base_path, download_config) File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 752, in resolve resolve_pattern( File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 393, in resolve_pattern raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find 'hf://datasets/trec@65752bf53af25bc935a0dce92fb5b6c930728450/default/train/0000.parquet' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.geoparquet', '.gpq', '.arrow', '.txt', '.tar', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] ``` 4. Similar issue also found for the following code: ``` dataset = load_dataset("sst", "default") ``` ### Expected behavior If the dataset is loaded correctly, one will have: ``` >>> print(dataset) DatasetDict({ train: Dataset({ features: ['text', 'coarse_label', 'fine_label'], num_rows: 5452 }) test: Dataset({ features: ['text', 'coarse_label', 'fine_label'], num_rows: 500 }) }) >>> ``` ### Environment info - `datasets` version: 2.18.0 - Platform: Linux-6.2.0-35-generic-x86_64-with-glibc2.31 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.1 - Pandas version: 2.2.1 - `fsspec` version: 2023.12.2

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I_kwDODunzps6EN6_f

(Willing to PR) Datasets with custom python objects

open

CONTRIBUTOR

### Feature request Hi thanks for the library! I would like to have a huggingface Dataset, and one of its column is custom (non-serializable) Python objects. For example, a minimal code: ``` class MyClass: pass dataset = datasets.Dataset.from_list([ dict(a=MyClass(), b='hello'), ]) ``` It gives error: ``` ArrowInvalid: Could not convert <__main__.MyClass object at 0x7a852830d050> with type MyClass: did not recognize Python value type when inferring an Arrow data type ``` I guess it is because Dataset forces to convert everything into arrow format. However, is there any ways to make the scenario work? Thanks! ### Motivation (see above) ### Your contribution Yes, I am happy to PR! Cross-posted: https://discuss.huggingface.co/t/datasets-with-custom-python-objects/79050?u=fzyzcjy EDIT: possibly related https://github.com/huggingface/datasets/issues/5766

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PR_kwDODunzps5rQO9J

fixing the issue 6755(small typo)

closed

CONTRIBUTOR

Fixed the issue #6755 on the typo mistake

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I_kwDODunzps6EFHZL

Compatibility issue between s3fs, fsspec, and datasets

closed

NONE

### Describe the bug Here is the full error stack when installing: ``` ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. datasets 2.18.0 requires fsspec[http]<=2024.2.0,>=2023.1.0, but you have fsspec 2024.3.1 which is incompatible. Successfully installed aiobotocore-2.12.1 aioitertools-0.11.0 botocore-1.34.51 fsspec-2024.3.1 jmespath-1.0.1 s3fs-2024.3.1 urllib3-2.0.7 wrapt-1.16.0 ``` When I install with pip, pip allows this error to exist while still installing s3fs, but this error breaks poetry, since poetry will refuse to install s3fs because of the dependency conflict. Maybe I'm missing something so maybe it's not a bug but some mistake on my end? Any input would be helpful. Thanks! ### Steps to reproduce the bug 1. conda create -n tmp python=3.10 -y 2. conda activate tmp 3. pip install datasets 4. pip install s3fs ### Expected behavior I would expect there to be no error. ### Environment info MacOS (ARM), Python3.10, conda 23.11.0.

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I_kwDODunzps6EEexP

load_dataset can't work with symbolic links

open

NONE

### Feature request Enable the `load_dataset` function to load local datasets with symbolic links. E.g, this dataset can be loaded: ├── example_dataset/ │ ├── data/ │ │ ├── train/ │ │ │ ├── file0 │ │ │ ├── file1 │ │ ├── dev/ │ │ │ ├── file2 │ │ │ ├── file3 │ ├── metadata.csv while this dataset can't: ├── example_dataset_symlink/ │ ├── data/ │ │ ├── train/ │ │ │ ├── sym0 -> file0 │ │ │ ├── sym1 -> file1 │ │ ├── dev/ │ │ │ ├── sym2 -> file2 │ │ │ ├── sym3 -> file3 │ ├── metadata.csv I have created an example dataset in order to reproduce the problem: 1. Unzip `example_dataset.zip`. 2. Run `no_symlink.sh`. Training should start without issues. 3. Run `symlink.sh`. You will see that all four examples will be in train split, instead of having two examples in train and two examples in dev. The script won't load the correct audio files. [example_dataset.zip](https://github.com/huggingface/datasets/files/14807053/example_dataset.zip) ### Motivation I have a very large dataset locally. Instead of initiating training on the entire dataset, I need to start training on smaller subsets of the data. Due to the purpose of the experiments I am running, I will need to create many smaller datasets with overlapping data. Instead of copying the all the files for each subset, I would prefer copying symbolic links of the data. This way, the memory usage would not significantly increase beyond the initial dataset size. Advantages of this approach: - It would leave a smaller memory footprint on the hard drive - Creating smaller datasets would be much faster ### Your contribution I would gladly contribute, if this is something useful to the community. It seems like a simple change of code, something like `file_path = os.path.realpath(file_path)` should be added before loading the files. If anyone has insights on how to incorporate this functionality, I would greatly appreciate your knowledge and input.

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PR_kwDODunzps5rENat

Fix issue with case sensitivity when loading dataset from local cache

open

NONE

When a dataset with upper-cases in its name is first loaded using `load_dataset()`, the local cache directory is created with all lowercase letters. However, upon subsequent loads, the current version attempts to locate the cache directory using the dataset's original name, which includes uppercase letters. This discrepancy can lead to confusion and, particularly in offline mode, results in errors. ### Reproduce ```bash ~$ python Python 3.9.19 (main, Mar 21 2024, 17:11:28) [GCC 11.2.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> dataset = load_dataset("locuslab/TOFU", "full") >>> quit() ~$ export HF_DATASETS_OFFLINE=1 ~$ python Python 3.9.19 (main, Mar 21 2024, 17:11:28) [GCC 11.2.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> dataset = load_dataset("locuslab/TOFU", "full") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "xxxxxx/anaconda3/envs/llm/lib/python3.9/site-packages/datasets/load.py", line 2556, in load_dataset builder_instance = load_dataset_builder( File "xxxxxx/anaconda3/envs/llm/lib/python3.9/site-packages/datasets/load.py", line 2228, in load_dataset_builder dataset_module = dataset_module_factory( File "xxxxxx/anaconda3/envs/llm/lib/python3.9/site-packages/datasets/load.py", line 1871, in dataset_module_factory raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None ConnectionError: Couldn't reach the Hugging Face Hub for dataset 'locuslab/TOFU': Offline mode is enabled. >>> ``` I fix this issue by lowering the dataset name (`.lower()`) when generating cache_dir.

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PR_kwDODunzps5rDpBe

Allow polars as valid output type

closed

CONTRIBUTOR

I was trying out polars as an output for a map function and found that it wasn't a valid return type in `validate_function_output`. Thought that we should accommodate this by creating and adding it to the `allowed_processed_input_types` variable.

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PR_kwDODunzps5rCAu8

Remove deprecated code

closed

CONTRIBUTOR

What does this PR do? 1. remove `list_files_info` in favor of `list_repo_tree`. As of `0.23`, `list_files_info` will be removed for good. `datasets` had a utility to support both pre-0.20 and post-0.20 versions. Since `hfh` version is already pinned to `>=0.21.2`, I removed the legacy part. 2. `preupload_lfs_files` had also a different behavior between `<0.20` and `>=0.20`. I remove it since huggingface_hub is now pinned to `>=0.21.2` 3. `hf_hub_url` is overwritten to default to the dataset repo_type. I do think it is misleading to keep the same method naming for it. I renamed it to `get_dataset_url` for clarity. Let me know if you prefer to see this change reverted.

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I_kwDODunzps6D3NZ6

Load codeparrot/apps raising UnicodeDecodeError in datasets-2.18.0

open

NONE

### Describe the bug This happens with datasets-2.18.0; I downgraded the version to 2.14.6 fixing this temporarily. ``` Traceback (most recent call last): File "/home/xxx/miniconda3/envs/py310/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset builder_instance = load_dataset_builder( File "/home/xxx/miniconda3/envs/py310/lib/python3.10/site-packages/datasets/load.py", line 2228, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/xxx/miniconda3/envs/py310/lib/python3.10/site-packages/datasets/load.py", line 1879, in dataset_module_factory raise e1 from None File "/home/xxx/miniconda3/envs/py310/lib/python3.10/site-packages/datasets/load.py", line 1831, in dataset_module_factory can_load_config_from_parquet_export = "DEFAULT_CONFIG_NAME" not in f.read() File "/home/xxx/miniconda3/envs/py310/lib/python3.10/codecs.py", line 322, in decode (result, consumed) = self._buffer_decode(data, self.errors, final) UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte ``` ### Steps to reproduce the bug 1. Using Python3.10/3.11 2. Install datasets-2.18.0 3. test with ``` from datasets import load_dataset dataset = load_dataset("codeparrot/apps") ``` ### Expected behavior Normally it should manage to download and load the dataset without such error. ### Environment info Ubuntu, Python3.10/3.11

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I_kwDODunzps6DqQfb

Persistent multi-process Pool

open

NONE

### Feature request Running .map and filter functions with `num_procs` consecutively instantiates several multiprocessing pools iteratively. As instantiating a Pool is very resource intensive it can be a bottleneck to performing iteratively filtering. My ideas: 1. There should be an option to declare `persistent_workers` similar to pytorch DataLoader. Downside would be that would be complex to determine the correct resource allocation and deallocation of the pool. i.e. the dataset can outlive the utility of the pool. 2. Provide a pool as an argument. Downside would be the expertise required by the user. Upside, is that there is better resource management. ### Motivation Is really slow to iteratively perform map and filter operations on a dataset. ### Your contribution If approved I could integrate it. I would need to know what method would be most suitable to implement from the two options above.

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I_kwDODunzps6DovLe

Passing `sample_by` to `load_dataset` when loading text data does not work

closed

NONE

### Describe the bug I have a dataset that consists of a bunch of text files, each representing an example. There is an undocumented `sample_by` argument for the `TextConfig` class that is used by `Text` to decide whether to split files into lines, paragraphs or take them whole. Passing `sample_by=“document”` to `load_dataset` results in files getting split into lines regardless. I have edited `src/datasets/packaged_modules/text/text.py` for myself to switch the default and it works fine. As a side note, the `if-else` for `sample_by` will silently load an empty dataset if someone makes a typo in the argument, which is not ideal. ### Steps to reproduce the bug 1. Prepare data as a bunch of files in a directory. 2. Load that data via `load_dataset(“text”, data_files=<data_dir>/<files_glob>, …, sample_by=“document”)`. 3. Inspect the resultant dataset — every item should have the form of `{“text”: <a line from a file>}`. ### Expected behavior `load_dataset(“text”, data_files=<data_dir>/<files_glob>, …, sample_by=“document”)` should result in a dataset with items of the form `{“text”: <one document>}`. ### Environment info - `datasets` version: 2.18.0 - Platform: Linux-5.15.0-1046-nvidia-x86_64-with-glibc2.35 - Python version: 3.11.8 - `huggingface_hub` version: 0.21.4 - PyArrow version: 15.0.2 - Pandas version: 2.2.1 - `fsspec` version: 2024.2.0

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PR_kwDODunzps5qr7Li

Test disabling transformers containers in docs CI

open

CONTRIBUTOR

Related to https://github.com/huggingface/doc-builder/pull/487 and [internal slack thread](https://huggingface.slack.com/archives/C04F8N7FQNL/p1711384899462349?thread_ts=1711041424.720769&cid=C04F8N7FQNL). There is now a `custom_container` option when building docs in CI. When set to `""` (instead of `"huggingface/transformers-doc-builder"` by default), we don't run the CI inside a container, therefore saving ~2min of download time. The plan is to test disabling the transformers container on a few "big" repo and if everything works correctly, we will stop making it the default container. More details on https://github.com/huggingface/doc-builder/pull/487. cc @mishig25

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I_kwDODunzps6DdiPd

Support SQLite files?

closed

COLLABORATOR

### Feature request Support loading a dataset from a SQLite file https://huggingface.co/datasets/severo/test_iris_sqlite/tree/main ### Motivation SQLite is a popular file format. ### Your contribution See discussion on slack: https://huggingface.slack.com/archives/C04L6P8KNQ5/p1702481859117909 (internal) In particular: a SQLite file can contain multiple tables, which might be matched to multiple configs. Maybe the detail of splits and configs should be defined in the README YAML, or use the same format as for ZIP files: `Iris.sqlite::Iris`. See dataset here: https://huggingface.co/datasets/severo/test_iris_sqlite Note: should we also support DuckDB files?

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I_kwDODunzps6DZx5p

Small typo on the documentation

closed

NONE

### Describe the bug There is a small typo on https://github.com/huggingface/datasets/blob/d5468836fe94e8be1ae093397dd43d4a2503b926/src/datasets/dataset_dict.py#L938 It should be `caching is enabled`. ### Steps to reproduce the bug Please visit https://github.com/huggingface/datasets/blob/d5468836fe94e8be1ae093397dd43d4a2503b926/src/datasets/dataset_dict.py#L938 ### Expected behavior `caching is enabled` ### Environment info - `datasets` version: 2.17.1 - Platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35 - Python version: 3.11.7 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.1 - `fsspec` version: 2023.10.0

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PR_kwDODunzps5qk-nr

Fix cache path to snakecase for `CachedDatasetModuleFactory` and `Cache`

closed

CONTRIBUTOR

Fix https://github.com/huggingface/datasets/issues/6750#issuecomment-2016678729 I didn't find a guideline on how to run the tests, so i just run the following steps to make sure that this bug is fixed. 1. `python test.py`, 2. then `HF_DATASETS_OFFLINE=1 python test.py` The `test.py` is ``` import datasets datasets.utils.logging.set_verbosity_info() ds = datasets.load_dataset('izhx/STS17-debug') print(ds) ds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4') print(ds) ```

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I_kwDODunzps6DYLzT

Type error when importing datasets on Kaggle

closed

NONE

### Describe the bug When trying to run ``` import datasets print(datasets.__version__) ``` It generates the following error ``` TypeError: expected string or bytes-like object ``` It looks like It cannot find the valid versions of `fsspec` though fsspec version is fine when I checked Via command ``` import fsspec print(fsspec.__version__) ​ # output: 2024.3.1 ``` Detailed crash report ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[1], line 1 ----> 1 import datasets 2 print(datasets.__version__) File /opt/conda/lib/python3.10/site-packages/datasets/__init__.py:18 1 # ruff: noqa 2 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors. 3 # (...) 13 # See the License for the specific language governing permissions and 14 # limitations under the License. 16 __version__ = "2.18.0" ---> 18 from .arrow_dataset import Dataset 19 from .arrow_reader import ReadInstruction 20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:66 63 from multiprocess import Pool 64 from tqdm.contrib.concurrent import thread_map ---> 66 from . import config 67 from .arrow_reader import ArrowReader 68 from .arrow_writer import ArrowWriter, OptimizedTypedSequence File /opt/conda/lib/python3.10/site-packages/datasets/config.py:41 39 # Imports 40 DILL_VERSION = version.parse(importlib.metadata.version("dill")) ---> 41 FSSPEC_VERSION = version.parse(importlib.metadata.version("fsspec")) 42 PANDAS_VERSION = version.parse(importlib.metadata.version("pandas")) 43 PYARROW_VERSION = version.parse(importlib.metadata.version("pyarrow")) File /opt/conda/lib/python3.10/site-packages/packaging/version.py:49, in parse(version) 43 """ 44 Parse the given version string and return either a :class:`Version` object 45 or a :class:`LegacyVersion` object depending on if the given version is 46 a valid PEP 440 version or a legacy version. 47 """ 48 try: ---> 49 return Version(version) 50 except InvalidVersion: 51 return LegacyVersion(version) File /opt/conda/lib/python3.10/site-packages/packaging/version.py:264, in Version.__init__(self, version) 261 def __init__(self, version: str) -> None: 262 263 # Validate the version and parse it into pieces --> 264 match = self._regex.search(version) 265 if not match: 266 raise InvalidVersion(f"Invalid version: '{version}'") TypeError: expected string or bytes-like object ``` ### Steps to reproduce the bug 1. run `!pip install -U datasets` on kaggle 2. check datasets is installed via ``` import datasets print(datasets.__version__) ``` ### Expected behavior Expected to print datasets version, like `2.18.0` ### Environment info Running on Kaggle, latest enviornment , here is the notebook https://www.kaggle.com/code/jtv199/mistrial-7b-part2

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I_kwDODunzps6DXwo_

Precision being changed from float16 to float32 unexpectedly

open

NONE

### Describe the bug I'm loading a HuggingFace Dataset for images. I'm running a preprocessing (map operation) step that runs a few operations, one of them being conversion to float16. The Dataset features also say that the 'img' is of type float16. Whenever I take an image from that HuggingFace Dataset instance, the type turns out to be float32. ### Steps to reproduce the bug ```python import torchvision.transforms.v2 as transforms from datasets import load_dataset dataset = load_dataset('cifar10', split='test') dataset = dataset.with_format("torch") data_transform = transforms.Compose([transforms.Resize((32, 32)), transforms.ToDtype(torch.float16, scale=True), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), ]) def _preprocess(examples): # Permutes from (BS x H x W x C) to (BS x C x H x W) images = torch.permute(examples['img'], (0, 3, 2, 1)) examples['img'] = data_transform(images) return examples dataset = dataset.map(_preprocess, batched=True, batch_size=8) ``` Now at this point the dataset.features are showing float16 which is great because that's what I want. ```python print(data_loader.features['img']) Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float16', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None) ``` But when I try to sample an image from this dataloader; I'm getting a float32 image, when I'm expecting float16: ```python print(next(iter(data_loader))['img'].dtype) torch.float32 ``` ### Expected behavior I'm expecting the images loaded after the transformation to stay in float16. ### Environment info - `datasets` version: 2.18.0 - Platform: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.9 - `huggingface_hub` version: 0.21.4 - PyArrow version: 14.0.2 - Pandas version: 2.0.3 - `fsspec` version: 2023.10.0

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PR_kwDODunzps5qkKLH

Use 'with' operator for some download functions

closed

NONE

Some functions in `streaming_download_manager.py` are not closing the file they open which lead to `Unclosed file` warnings in our code. This fixes a few of them.

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I_kwDODunzps6DWCAC

`load_dataset` requires a network connection for local download?

closed

NONE

### Describe the bug Hi all - I see that in the past a network dependency has been mistakenly introduced into `load_dataset` even for local loads. Is it possible this has happened again? ### Steps to reproduce the bug ``` >>> import datasets >>> datasets.load_dataset("hh-rlhf") Repo card metadata block was not found. Setting CardData to empty. *hangs bc i'm firewalled* ```` stack trace from ctrl-c: ``` ^CTraceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/load.py", line 2582, in load_dataset builder_instance.download_and_prepare( output_path = get_from_cache( [0/122] File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 532, in get_from_cache response = http_head( File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 419, in http_head response = _request_with_retry( File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 304, in _request_with_retry response = requests.request(method=method.upper(), url=url, timeout=timeout, **params) File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/api.py", line 59, in request return session.request(method=method, url=url, **kwargs) File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 587, in request resp = self.send(prep, **send_kwargs) File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 701, in send r = adapter.send(request, **kwargs) File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/adapters.py", line 487, in send resp = conn.urlopen( File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen httplib_response = self._make_request( File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request self._validate_conn(conn) File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn conn.connect() File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 363, in connect self.sock = conn = self._new_conn() File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn conn = connection.create_connection( File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection sock.connect(sa) KeyboardInterrupt ``` ### Expected behavior loads the dataset ### Environment info ``` > pip show datasets Name: datasets Version: 2.18.0 ``` Python 3.10.2

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PR_kwDODunzps5qeoSk

Fix fsspec tqdm callback

closed

MEMBER

Following changes at https://github.com/fsspec/filesystem_spec/pull/1497 for `fsspec>=2024.2.0`

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I_kwDODunzps6DOH0k

Strange slicing behavior

open

NONE

### Describe the bug I have loaded a dataset, and then slice first 300 samples using `:` ops, however, the resulting dataset is not expected, as the output below: ```bash len(dataset)=1050324 len(dataset[:300])=2 len(dataset[0:300])=2 len(dataset.select(range(300)))=300 ``` ### Steps to reproduce the bug load a dataset then: ```bash dataset = load_from_disk(args.train_data_dir) print(f"{len(dataset)=}", flush=True) print(f"{len(dataset[:300])=}", flush=True) print(f"{len(dataset[0:300])=}", flush=True) print(f"{len(dataset.select(range(300)))=}", flush=True) ``` ### Expected behavior ```bash len(dataset)=1050324 len(dataset[:300])=300 len(dataset[0:300])=300 len(dataset.select(range(300)))=300 ``` ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.35 - Python version: 3.10.11 - `huggingface_hub` version: 0.20.2 - PyArrow version: 10.0.1 - Pandas version: 1.5.3 - `fsspec` version: 2023.10.0

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PR_kwDODunzps5qa5L-

chore(deps): bump fsspec

closed

CONTRIBUTOR

There were a few fixes released recently, some DVC ecosystem packages require newer version of `fsspec`.

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I_kwDODunzps6DEfwd

ExpectedMoreSplits error when loading C4 dataset

closed

NONE

### Describe the bug I encounter bug when running the example command line ```python python main.py \ --model decapoda-research/llama-7b-hf \ --prune_method wanda \ --sparsity_ratio 0.5 \ --sparsity_type unstructured \ --save out/llama_7b/unstructured/wanda/ ``` The bug occurred at these lines of code (when loading c4 dataset) ```python traindata = load_dataset('allenai/c4', 'allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train') valdata = load_dataset('allenai/c4', 'allenai--c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation') ``` The error message states: ``` raise ExpectedMoreSplits(str(set(expected_splits) - set(recorded_splits))) datasets.utils.info_utils.ExpectedMoreSplits: {'validation'} ``` ### Steps to reproduce the bug 1. I encounter bug when running the example command line ### Expected behavior The error message states: ``` raise ExpectedMoreSplits(str(set(expected_splits) - set(recorded_splits))) datasets.utils.info_utils.ExpectedMoreSplits: {'validation'} ``` ### Environment info I'm using cuda 12.4, so I use ```pip install pytorch``` instead of conda provided in install.md Also, I've tried another environment using the same commands in install.md, but the same bug occured

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I_kwDODunzps6DCxWk

Scraping the whole of github including private repos is bad; kindly stop

closed

NONE

### Feature request https://github.com/bigcode-project/opt-out-v2 - opt out is not consent. kindly quit this ridiculous nonsense. ### Motivation [EDITED: insults not tolerated] ### Your contribution [EDITED: insults not tolerated]

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I_kwDODunzps6DAXKY

Option to disable file locking

open

NONE

### Feature request Commands such as `load_dataset` creates file locks with `filelock.FileLock`. It would be good if there was a way to disable this. ### Motivation File locking doesn't work on all file-systems (in my case NFS mounted Weka). If the `cache_dir` only had small files then it would be possible to point to local disk and the problem would be solved. However, as cache_dir is both where the small info files are written and the processed datasets are put this isn't a feasible solution. Considering https://github.com/huggingface/datasets/issues/6395 I still do think this is something that belongs in HuggingFace. The possibility to control packages separately is valuable. It might be that a user has their dataset on a file-system that doesn't support file-locking while they are using file locking on local disk to control some other type of access. ### Your contribution My suggested solution: ``` diff --git a/src/datasets/utils/_filelock.py b/src/datasets/utils/_filelock.py index 19620e6e..58f41a02 100644 --- a/src/datasets/utils/_filelock.py +++ b/src/datasets/utils/_filelock.py @@ -18,11 +18,15 @@ import os from filelock import FileLock as FileLock_ -from filelock import UnixFileLock +from filelock import SoftFileLock, UnixFileLock from filelock import __version__ as _filelock_version from packaging import version +if os.getenv('HF_USE_SOFTFILELOCK', 'false').lower() in ('true', '1'): + FileLock_ = SoftFileLock + + class FileLock(FileLock_): """ A `filelock.FileLock` initializer that handles long paths. ```

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PR_kwDODunzps5qHeMZ

Allow null values in dict columns

closed

COLLABORATOR

Fix #6738

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PR_kwDODunzps5qGSfG

Fix missing download_config in get_data_patterns

closed

MEMBER

Reported in https://github.com/huggingface/datasets-server/issues/2607

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PR_kwDODunzps5qEiu3

Fix offline mode with single config

closed

MEMBER

Reported in https://github.com/huggingface/datasets/issues/4760 The cache was not able to reload a dataset with a single config form the cache if the config name is not specificed For example ```python from datasets import load_dataset, config config.HF_DATASETS_OFFLINE = True load_dataset("openai_humaneval") ``` This was due to a regression in https://github.com/huggingface/datasets/pull/6632

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I_kwDODunzps6CuSZq

Support for loading geotiff files as a part of the ImageFolder

closed

NONE

### Feature request Request for adding rasterio support to load geotiff as a part of ImageFolder, instead of using PIL ### Motivation As of now, there are many datasets in HuggingFace Hub which are predominantly focussed towards RemoteSensing or are from RemoteSensing. The current ImageFolder (if I have understood correctly) uses PIL. This is not really optimized because mostly these datasets have images with many channels and additional metadata. Using PIL makes one loose it unless we provide a custom script. Hence, maybe an API could be added to have this in common? ### Your contribution If the issue is accepted - i can contribute the code, because I would like to have it automated and generalised.

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PR_kwDODunzps5p-Bwe

Transpose images with EXIF Orientation tag

closed

COLLABORATOR

Closes https://github.com/huggingface/datasets/issues/6252

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I_kwDODunzps6CrSno

Dict feature is non-nullable while nested dict feature is

closed

CONTRIBUTOR

When i try to create a `Dataset` object with None values inside a dict column, like this: ```python from datasets import Dataset, Features, Value Dataset.from_dict( { "dict": [{"a": 0, "b": 0}, None], }, features=Features( {"dict": {"a": Value("int16"), "b": Value("int16")}} ) ) ``` i get `ValueError: Got None but expected a dictionary instead`. At the same time, having None in _nested_ dict feature works, for example, this doesn't throw any errors: ```python from datasets import Dataset, Features, Value, Sequence dataset = Dataset.from_dict( { "list_dict": [[{"a": 0, "b": 0}], None], "sequence_dict": [[{"a": 0, "b": 0}], None], }, features=Features({ "list_dict": [{"a": Value("int16"), "b": Value("int16")}], "sequence_dict": Sequence({"a": Value("int16"), "b": Value("int16")}), }) ) ``` Other types of features also seem to be nullable (but I haven't checked all of them). Version of `datasets` is the latest atm (2.18.0) Is this an expected behavior or a bug?

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I_kwDODunzps6Ci8aZ

Invalid pattern: '**' can only be an entire path component

closed

NONE

### Describe the bug ValueError: Invalid pattern: '**' can only be an entire path component when loading any dataset ### Steps to reproduce the bug import datasets ds = datasets.load_dataset("TokenBender/code_instructions_122k_alpaca_style") ### Expected behavior loading the dataset successfully ### Environment info - `datasets` version: 2.18.0 - Platform: Windows-10-10.0.22631-SP0 - Python version: 3.11.7 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.1 - `fsspec` version: 2023.12.2

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I_kwDODunzps6Ci4Qu

Mosaic Streaming (MDS) Support

open

NONE

### Feature request I'm a huge fan of the current HF Datasets `webdataset` integration (especially the built-in streaming support). However, I'd love to upload some robotics and multimodal datasets I've processed for use with [Mosaic Streaming](https://docs.mosaicml.com/projects/streaming/en/stable/), specifically their [MDS Format](https://docs.mosaicml.com/projects/streaming/en/stable/fundamentals/dataset_format.html#mds). Because the shard files have similar semantics to WebDataset, I'm hoping that adding such support won't be too much trouble? ### Motivation One of the downsides with WebDataset is a lack of out-of-the-box determinism (especially for large-scale training and reproducibility), easy job resumption, and the ability to quickly debug / visualize individual examples. Mosaic Streaming provides a [great interface for this out of the box](https://docs.mosaicml.com/projects/streaming/en/stable/#key-features), so I'd love to see it supported in HF Datasets. ### Your contribution Happy to help test things / provide example data. Can potentially submit a PR if maintainers could point me to the necessary WebDataset logic / steps for adding a new streaming format!

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PR_kwDODunzps5px84g

Add `mode` parameter to `Image` feature

closed

COLLABORATOR

Fix https://github.com/huggingface/datasets/issues/6675

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I_kwDODunzps6CZNbm

Tokenization slows towards end of dataset

open

NONE

### Describe the bug Mapped tokenization slows down substantially towards end of dataset. train set started off very slow, caught up to 20k then tapered off til the end. what's particularly strange is that the tokenization crashed a few times before due to errors with invalid tokens somewhere or corrupted downloads, and the speed ups/downs consistently happened the same times ```bash Running tokenizer on dataset (num_proc=48): 0%| | 847000/881416735 [12:18<252:45:45, 967.72 examples/s] Running tokenizer on dataset (num_proc=48): 0%| | 848000/881416735 [12:19<224:16:10, 1090.66 examples/s] Running tokenizer on dataset (num_proc=48): 10%|▉ | 84964000/881416735 [3:48:00<11:21:34, 19476.01 examples/s] Running tokenizer on dataset (num_proc=48): 10%|▉ | 84967000/881416735 [3:48:00<12:04:01, 18333.79 examples/s] Running tokenizer on dataset (num_proc=48): 61%|██████ | 538631977/881416735 [13:46:40<27:50:04, 3420.84 examples/s] Running tokenizer on dataset (num_proc=48): 61%|██████ | 538632977/881416735 [13:46:40<23:48:20, 3999.77 examples/s] Running tokenizer on dataset (num_proc=48): 100%|█████████▉| 881365886/881416735 [38:30:19<04:34, 185.10 examples/s] Running tokenizer on dataset (num_proc=48): 100%|█████████▉| 881366886/881416735 [38:30:25<04:36, 180.57 examples/s] ``` and validation set as well ```bash Running tokenizer on dataset (num_proc=48): 90%|████████▉ | 41544000/46390354 [28:44<02:37, 30798.76 examples/s] Running tokenizer on dataset (num_proc=48): 90%|████████▉ | 41550000/46390354 [28:44<02:08, 37698.08 examples/s] Running tokenizer on dataset (num_proc=48): 96%|█████████▋| 44747422/46390354 [2:15:48<12:22:44, 36.87 examples/s] Running tokenizer on dataset (num_proc=48): 96%|█████████▋| 44747422/46390354 [2:16:00<12:22:44, 36.87 examples/s] ``` ### Steps to reproduce the bug using the following kwargs ```python with accelerator.main_process_first(): lm_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=48 load_from_cache_file=True, desc=f"Grouping texts in chunks of {block_size}", ) ``` running through slurm script ```bash #SBATCH --partition=gpu-nvidia-a100 #SBATCH --nodes=1 #SBATCH --ntasks=1 #SBATCH --gpus-per-task=8 #SBATCH --cpus-per-task=96 ``` using this dataset https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T ### Expected behavior Constant speed throughout ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.0-1049-aws-x86_64-with-glibc2.10 - Python version: 3.8.18 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.0.3 - `fsspec` version: 2023.10.0

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I_kwDODunzps6CWBlM

EmptyDatasetError when loading dataset downloaded with HuggingFace cli

open

NONE

### Describe the bug I am using a cluster that does not have access to the internet when given a job. I tried downloading the dataset using the huggingface-cli command and then loading it with load_dataset but I get an error: ```raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None``` The dataset I'm using is "lmsys/chatbot_arena_conversations". The folder structure is - README.md - data - train-00000-of-00001-cced8514c7ed782a.parquet ### Steps to reproduce the bug 1. Download dataset using HuggingFace CLI: ```huggingface-cli download lmsys/chatbot_arena_conversations --local-dir ./lmsys/chatbot_arena_conversations``` 2. In Python ``` from datasets import load_dataset load_dataset("lmsys/chatbot_arena_conversations") ``` ### Expected behavior Should return a Dataset Dict in the form of ``` DatasetDict({ train: Dataset({ features: [...], num_rows: 33,000 }) }) ``` ### Environment info Python 3.11.5 Datasets 2.18.0 Transformers 4.38.2 Pytorch 2.2.0 Pyarrow 15.0.1 Rocky Linux release 8.9 (Green Obsidian)

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I_kwDODunzps6CG5EB

Unexpected behavior when using load_dataset with streaming=True in a for loop

closed

NONE

### Describe the bug ### My Code ``` from datasets import load_dataset res=[] for i in [0,1]: di=load_dataset( "json", data_files='path_to.json', split='train', streaming=True, ).map(lambda x: {"source": i}) res.append(di) for e in res[0]: print(e) ``` ### Unexpected Behavior Data in `res[0]` has `source=1`. However the expected value is 0. ### FYI I further switch `streaming` to `False`. And the output value is as expected (0). So there may exist bugs in setting `streaming=True` in a for loop. ### Environment Python 3.8.0 datasets==2.18.0 transformers==4.28.1 ### Steps to reproduce the bug 1. Create a Json file with any content. 2. Run the provided code. 3. Switch `streaming` to `False` and run again to see the expected behavior. ### Expected behavior The expected behavior is the data are mapped with its corresponding value in the for loop. ### Environment info Python 3.8.0 datasets==2.18.0 transformers==4.28.1 Ubuntu 20.04

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PR_kwDODunzps5pZDsB

Deprecate Pandas builder

closed

COLLABORATOR

The Pandas packaged builder is undocumented and relies on `pickle` to read the data, making it **unsafe**. Moreover, I haven't seen a single instance of this builder being used (not even using the GH/Hub search), so we should deprecate it.

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I_kwDODunzps6B88dn

Support zipfiles that span multiple disks?

closed

COLLABORATOR

See https://huggingface.co/datasets/PhilEO-community/PhilEO-downstream The dataset viewer gives the following error: ``` Error code: ConfigNamesError Exception: BadZipFile Message: zipfiles that span multiple disks are not supported Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response get_dataset_config_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1871, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1846, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1240, in get_module module_name, default_builder_kwargs = infer_module_for_data_files( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 584, in infer_module_for_data_files split_modules = { File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 585, in <dictcomp> split: infer_module_for_data_files_list(data_files_list, download_config=download_config) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 526, in infer_module_for_data_files_list return infer_module_for_data_files_list_in_archives(data_files_list, download_config=download_config) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 554, in infer_module_for_data_files_list_in_archives for f in xglob(extracted, recursive=True, download_config=download_config)[ File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 576, in xglob fs, *_ = fsspec.get_fs_token_paths(urlpath, storage_options=storage_options) File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 622, in get_fs_token_paths fs = filesystem(protocol, **inkwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 290, in filesystem return cls(**storage_options) File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 79, in __call__ obj = super().__call__(*args, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 57, in __init__ self.zip = zipfile.ZipFile( File "/usr/local/lib/python3.9/zipfile.py", line 1266, in __init__ self._RealGetContents() File "/usr/local/lib/python3.9/zipfile.py", line 1329, in _RealGetContents endrec = _EndRecData(fp) File "/usr/local/lib/python3.9/zipfile.py", line 286, in _EndRecData return _EndRecData64(fpin, -sizeEndCentDir, endrec) File "/usr/local/lib/python3.9/zipfile.py", line 232, in _EndRecData64 raise BadZipFile("zipfiles that span multiple disks are not supported") zipfile.BadZipFile: zipfiles that span multiple disks are not supported ``` The files (https://huggingface.co/datasets/PhilEO-community/PhilEO-downstream/tree/main/data) are: <img width="629" alt="Capture d’écran 2024-03-11 à 22 07 30" src="https://github.com/huggingface/datasets/assets/1676121/0bb15a51-d54f-4d73-8572-e427ea644b36">

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I_kwDODunzps6B2uek

Issue Downloading Certain Datasets After Setting Custom `HF_ENDPOINT`

closed

NONE

### Describe the bug This bug is triggered under the following conditions: - datasets repo ids without organization names trigger errors, such as `bookcorpus`, `gsm8k`, `wikipedia`, rather than in the form of `A/B`. - If `HF_ENDPOINT` is set and the hostname is not in the form of `(hub-ci.)?huggingface.co`. - This issue occurs with `datasets>2.15.0` or `huggingface-hub>0.19.4`. For example, using the latest versions: `datasets==2.18.0` and `huggingface-hub==0.21.4`, ### Steps to reproduce the bug the issue can be reproduced with the following code: 1. install specific datasets and huggingface_hub. ```bash pip install datasets==2.18.0 pip install huggingface_hub==0.21.4 ``` 2. execute python code. ```Python import os os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' from datasets import load_dataset bookcorpus = load_dataset('bookcorpus', split='train') ``` console output: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset builder_instance = load_dataset_builder( File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 2228, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 1879, in dataset_module_factory raise e1 from None File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 1830, in dataset_module_factory with fs.open(f"datasets/{path}/{filename}", "r", encoding="utf-8") as f: File "/home/padeoe/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1295, in open self.open( File "/home/padeoe/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1307, in open f = self._open( File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 228, in _open return HfFileSystemFile(self, path, mode=mode, revision=revision, block_size=block_size, **kwargs) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 615, in __init__ self.resolved_path = fs.resolve_path(path, revision=revision) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 180, in resolve_path repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 117, in _repo_and_revision_exist self._api.repo_info(repo_id, revision=revision, repo_type=repo_type) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2413, in repo_info return method( File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2286, in dataset_info hf_raise_for_status(r) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 362, in hf_raise_for_status raise HfHubHTTPError(str(e), response=response) from e huggingface_hub.utils._errors.HfHubHTTPError: 401 Client Error: Unauthorized for url: https://hf-mirror.com/api/datasets/bookcorpus/bookcorpus.py (Request ID: Root=1-65ee8659-5ab10eec5960c63e71f2bb58;b00bdbea-fd6e-4a74-8fe0-bc4682ae090e) ``` ### Expected behavior The dataset was downloaded correctly without any errors. ### Environment info datasets==2.18.0 huggingface-hub==0.21.4

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PR_kwDODunzps5pLJyE

Using a registry instead of calling globals for fetching feature types

closed

CONTRIBUTOR

Hello, When working with bio-data, each feature often has metadata associated with it (e.g. species, lineage, snp position, etc). To store this, I like to use the feature classes with the added `metadata` attribute. However, when saving or loading with custom features, you get an error since that class doesn't exist in the global namespace in `datasets.features.features`. Take for example, ```python from dataclasses import dataclass, field from datasets import Dataset from datasets.features.features import Value, Features @dataclass class FeatureA(Value): metadata: dict = field(default=dict) _type: str = field(default="FeatureA", init=False, repr=False) @dataclass class FeatureB(Value): metadata: dict = field(default_factory=dict) _type: str = field(default="FeatureB", init=False, repr=False) test_data = { "a": [1, 2, 3], "b": [4, 5, 6], } test_data = Dataset.from_dict( test_data, features=Features({ "a": FeatureA("int32", metadata={"species": "lactobacillus acetotolerans"}), "b": FeatureB("int32", metadata={"species": "lactobacillus iners"}), }) ) # returns an error since FeatureA and FeatureB are not in the global namespace test_data.save_to_disk('./test_data') ``` Saving the dataset (0/1 shards): 0%| | 0/3 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[2], line 28 19 test_data = Dataset.from_dict( 20 test_data, 21 features=Features({ (...) 24 }) 25 ) 27 # returns an error since FeatureA and FeatureB are not in the global namespace ---> 28 test_data.save_to_disk('./test_data') ... File ~\Documents\datasets\src\datasets\features\features.py:1361, in generate_from_dict(obj) 1359 return {key: generate_from_dict(value) for key, value in obj.items()} 1360 obj = dict(obj) -> 1361 class_type = globals()[obj.pop("_type")] 1363 if class_type == Sequence: 1364 return Sequence(feature=generate_from_dict(obj["feature"]), length=obj.get("length", -1)) KeyError: 'FeatureA' We can avoid this by having a registry (like formatters) and doing ```python from datasets.features.features import register_feature register_feature(FeatureA, "FeatureA") register_feature(FeatureB, "FeatureB") test_data.save_to_disk('./test_data') ``` Saving the dataset (1/1 shards): 100%|------| 3/3 [00:00<00:00, 211.13 examples/s] and loading from disk returns with all metadata information ```python from datasets import load_from_disk test_data = load_from_disk('./test_data') test_data.features ``` {'a': FeatureA(dtype='int32', id=None, metadata={'species': 'lactobacillus acetotolerans'}), 'b': FeatureB(dtype='int32', id=None, metadata={'species': 'lactobacillus iners'})}

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I_kwDODunzps6Bw94Q

Profiling for HF Filesystem shows there are easy performance gains to be made

open

NONE

### Describe the bug # Let's make it faster First, an evidence...  Figure 1: CProfile for loading 3 files from cerebras/SlimPajama-627B train split, and 3 files from test split using streaming=True. X axis is 1106 seconds long. See? It's pretty slow. What is resolve pattern doing? ``` resolve_pattern called with **/train/** and hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543 resolve_pattern took 20.815081119537354 seconds ``` Makes sense. How to improve it? ## Bigger project, biggest payoff Databricks (and consequently, spark) store a compressed manifest file of the files contained in the remote filesystem. Then, you download one tiny file, decompress it, and all the operations are local instead of this shenanigans. It seems pretty straightforward to make dataset uploads compute a manifest and upload it alongside their data. This would make resolution time so fast that nobody would ever think about it again. It also means you either need to have the uploader compute it _every time_, or have a hook that computes it. ## Smaller project, immediate payoff: Be diligent in avoiding deepcopy Revise the _ls_tree method to avoid deepcopy: ``` def _ls_tree( self, path: str, recursive: bool = False, refresh: bool = False, revision: Optional[str] = None, expand_info: bool = True, ): ..... omitted ..... for path_info in tree: if isinstance(path_info, RepoFile): cache_path_info = { "name": root_path + "/" + path_info.path, "size": path_info.size, "type": "file", "blob_id": path_info.blob_id, "lfs": path_info.lfs, "last_commit": path_info.last_commit, "security": path_info.security, } else: cache_path_info = { "name": root_path + "/" + path_info.path, "size": 0, "type": "directory", "tree_id": path_info.tree_id, "last_commit": path_info.last_commit, } parent_path = self._parent(cache_path_info["name"]) self.dircache.setdefault(parent_path, []).append(cache_path_info) out.append(cache_path_info) return copy.deepcopy(out) # copy to not let users modify the dircache ``` Observe this deepcopy at the end. It is making a copy of a very simple data structure. We do not need to copy. We can simply generate the data structure twice instead. It will be much faster. ``` def _ls_tree( self, path: str, recursive: bool = False, refresh: bool = False, revision: Optional[str] = None, expand_info: bool = True, ): ..... omitted ..... def make_cache_path_info(path_info): if isinstance(path_info, RepoFile): return { "name": root_path + "/" + path_info.path, "size": path_info.size, "type": "file", "blob_id": path_info.blob_id, "lfs": path_info.lfs, "last_commit": path_info.last_commit, "security": path_info.security, } else: return { "name": root_path + "/" + path_info.path, "size": 0, "type": "directory", "tree_id": path_info.tree_id, "last_commit": path_info.last_commit, } for path_info in tree: cache_path_info = make_cache_path_info(path_info) out_cache_path_info = make_cache_path_info(path_info) # copy to not let users modify the dircache parent_path = self._parent(cache_path_info["name"]) self.dircache.setdefault(parent_path, []).append(cache_path_info) out.append(out_cache_path_info) return out ``` Note there is no longer a deepcopy in this method. We have replaced it with generating the output twice. This is substantially faster. For me, the entire resolution went from 1100s to 360s. ## Medium project, medium payoff After the above change, we have this profile:  Figure 2: x-axis is 355 seconds. Note that globbing and _ls_tree deep copy is gone. No surprise there. It's much faster now, but we still spend ~187seconds in get_fs_token_paths. Well get_fs_token_paths is part of fsspec. We don't need to fix that because we can trust their developers to write high performance code. Probably the caller has misconfigured something. Let's take a look at the storage_options being provided to the filesystem that is constructed during this call. Ah yes, streaming_download_manager::_prepare_single_hop_path_and_storage_options. We know streaming download manager is not compatible with async right now, but we really need this specific part of the code to be async. We're spending so much time checking isDir on the remote filesystem, it's a huge waste. We can make the call easily 20-30x faster by using async, removing this performance bottleneck almost entirely (and reducing the total time of this part of the code to <30s. There is no reason to block async isDir calls for streaming. I'm not going to mess w/ this one myself; I didn't write the streaming impl, and I don't know how it works, but I know the isDir check can be async. ### Steps to reproduce the bug ``` with cProfile.Profile() as pr: pr.enable() # Begin Data if not os.path.exists(data_cache_dir): os.makedirs(data_cache_dir, exist_ok=True) training_dataset = load_dataset(training_dataset_name, split=training_split, cache_dir=data_cache_dir, streaming=True).take(training_slice) eval_dataset = load_dataset(eval_dataset_name, split=eval_split, cache_dir=data_cache_dir, streaming=True).take(eval_slice) # End Data pr.disable() pr.create_stats() if not os.path.exists(profiling_path): os.makedirs(profiling_path, exist_ok=True) pr.dump_stats(os.path.join(profiling_path, "cprofile.prof")) ``` run this code for "cerebras/SlimPajama-627B" and whatever other params ### Expected behavior Something better. ### Environment info - `datasets` version: 2.18.0 - Platform: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 - Python version: 3.10.13 - `huggingface_hub` version: 0.21.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.1 - `fsspec` version: 2024.2.0

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I_kwDODunzps6Bq-pq

Request for a comparison of huggingface datasets compared with other data format especially webdataset

open

NONE

### Feature request Request for a comparison of huggingface datasets compared with other data format especially webdataset ### Motivation I see huggingface datasets uses Apache Arrow as its backend, it seems to be great, but I'm curious about how it is good compared with other dataset format, like webdataset, what's the pros/cons of them. ### Your contribution More information

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I_kwDODunzps6Bmq8T

Dataset with loading script does not work in renamed repos

open

CONTRIBUTOR

### Describe the bug My data repository was first called `BramVanroy/hplt-mono-v1-2` but I then renamed to use underscores instead of dashes. However, it seems that `datasets` retrieves the old repo name when it checks whether the repo contains data loading scripts in this line. https://github.com/huggingface/datasets/blob/6fb6c834f008996c994b0a86c3808d0a33d44525/src/datasets/load.py#L1845 When I print `filename` it returns `hplt-mono-v1-2.py` but the files in the repo are of course `['.gitattributes', 'README.md', 'hplt_mono_v1_2.py']`. So the `filename` is the original reponame instead of the renamed one. I am not sure if this is a caching issue or not or how I can resolve it. ### Steps to reproduce the bug ``` from datasets import load_dataset ds = load_dataset( "BramVanroy/hplt-mono-v1-2", "ky", trust_remote_code=True ) ``` ### Expected behavior That the most recent repo name is used when `filename` is generated. ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-5.14.0-284.25.1.el9_2.x86_64-x86_64-with-glibc2.34 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.10.0

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PR_kwDODunzps5o_fPU

get_dataset_default_config_name docstring

closed

MEMBER

fix https://github.com/huggingface/datasets/pull/6722

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PR_kwDODunzps5o_ch0

Add details in docstring

closed

COLLABORATOR

see https://github.com/huggingface/datasets-server/pull/2554#discussion_r1516516867

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I_kwDODunzps6Bk5DC

Hi,do you know how to load the dataset from local file now?

open

NONE

Hi, if I want to load the dataset from local file, then how to specify the configuration name? _Originally posted by @WHU-gentle in https://github.com/huggingface/datasets/issues/2976#issuecomment-1333455222_

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I_kwDODunzps6Bjo6D

TypeError: 'str' object is not callable

closed

CONTRIBUTOR

### Describe the bug I am trying to get the HPLT datasets on the hub. Downloading/re-uploading would be too time- and resource consuming so I wrote [a dataset loader script](https://huggingface.co/datasets/BramVanroy/hplt_mono_v1_2/blob/main/hplt_mono_v1_2.py). I think I am very close but for some reason I always get the error below. It happens during the clean-up phase where the directory cannot be removed because it is not empty. My only guess would be that this may have to do with zstandard ``` Traceback (most recent call last): File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1744, in _prepare_split_single writer.write(example, key) File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write self.write_examples_on_file() File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 434, in write_examples_on_file if self.schema File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 409, in schema else (pa.schema(self._features.type) if self._features is not None else None) File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1643, in type return get_nested_type(self) File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in get_nested_type {key: get_nested_type(schema[key]) for key in schema} File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in <dictcomp> {key: get_nested_type(schema[key]) for key in schema} File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1221, in get_nested_type value_type = get_nested_type(schema.feature) File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1228, in get_nested_type return schema() TypeError: 'str' object is not callable During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1753, in _prepare_split_single num_examples, num_bytes = writer.finalize() File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 588, in finalize self.write_examples_on_file() File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 434, in write_examples_on_file if self.schema File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 409, in schema else (pa.schema(self._features.type) if self._features is not None else None) File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1643, in type return get_nested_type(self) File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in get_nested_type {key: get_nested_type(schema[key]) for key in schema} File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in <dictcomp> {key: get_nested_type(schema[key]) for key in schema} File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1221, in get_nested_type value_type = get_nested_type(schema.feature) File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1228, in get_nested_type return schema() TypeError: 'str' object is not callable The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 959, in incomplete_dir yield tmp_dir File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1005, in download_and_prepare self._download_and_prepare( File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1767, in _download_and_prepare super()._download_and_prepare( File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1100, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1605, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1762, 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 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/pricie/vanroy/.config/JetBrains/PyCharm2023.3/scratches/scratch_5.py", line 4, in <module> ds = load_dataset( File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/load.py", line 2549, in load_dataset builder_instance.download_and_prepare( File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 985, in download_and_prepare with incomplete_dir(self._output_dir) as tmp_output_dir: File "/home/pricie/vanroy/.pyenv/versions/3.10.13/lib/python3.10/contextlib.py", line 153, in __exit__ self.gen.throw(typ, value, traceback) File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 966, in incomplete_dir shutil.rmtree(tmp_dir) File "/home/pricie/vanroy/.pyenv/versions/3.10.13/lib/python3.10/shutil.py", line 731, in rmtree onerror(os.rmdir, path, sys.exc_info()) File "/home/pricie/vanroy/.pyenv/versions/3.10.13/lib/python3.10/shutil.py", line 729, in rmtree os.rmdir(path) OSError: [Errno 39] Directory not empty: '/home/pricie/vanroy/.cache/huggingface/datasets/BramVanroy___hplt_mono_v1_2/ky/1.2.0/7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47.incomplete' ``` Interestingly, though, this directory _does_ appear to be empty: ```shell > cd /home/pricie/vanroy/.cache/huggingface/datasets/BramVanroy___hplt_mono_v1_2/ky/1.2.0/7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47.incomplete > ls -lah total 0 drwxr-xr-x. 1 vanroy vanroy 0 Mar 7 12:01 . drwxr-xr-x. 1 vanroy vanroy 304 Mar 7 11:52 .. > cd .. > ls 7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47_builder.lock 7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47.incomplete ``` ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset( "BramVanroy/hplt_mono_v1_2", "ky", trust_remote_code=True ) ``` ### Expected behavior No error. ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-5.14.0-284.25.1.el9_2.x86_64-x86_64-with-glibc2.34 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.10.0

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I_kwDODunzps6BUUA_

Is there any way to solve hanging of IterableDataset using split by node + filtering during inference

open

NONE

### Describe the bug I am using an iterable dataset in a multi-node setup, trying to do training/inference while filtering the data on the fly. I usually do not use `split_dataset_by_node` but it is very slow using the IterableDatasetShard in `accelerate` and `transformers`. When I filter after applying `split_dataset_by_node`, it results in shards that are not equal sizes due to unequal samples filtered from each one. The distributed process hangs when trying to accomplish this. Is there any way to resolve this or is it impossible to implement? ### Steps to reproduce the bug Here is a toy example of what I am trying to do that reproduces the behavior ``` # torchrun --nproc-per-node 2 file.py import os import pandas as pd import torch from accelerate import Accelerator from datasets import Features, Value, load_dataset from datasets.distributed import split_dataset_by_node from torch.utils.data import DataLoader accelerator = Accelerator(device_placement=True, dispatch_batches=False) if accelerator.is_main_process: if not os.path.exists("scratch_data"): os.mkdir("scratch_data") n_shards = 4 for i in range(n_shards): df = pd.DataFrame({"id": list(range(10 * i, 10 * (i + 1)))}) df.to_parquet(f"scratch_data/shard_{i}.parquet") world_size = accelerator.num_processes local_rank = accelerator.process_index def collate_fn(examples): input_ids = [] for example in examples: input_ids.append(example["id"]) return torch.LongTensor(input_ids) dataset = load_dataset( "parquet", data_dir="scratch_data", split="train", streaming=True ) dataset = ( split_dataset_by_node(dataset, rank=local_rank, world_size=world_size) .filter(lambda x: x["id"] < 35) .shuffle(seed=42, buffer_size=100) ) batch_size = 2 train_dataloader = DataLoader( dataset, batch_size=batch_size, collate_fn=collate_fn, num_workers=2 ) for x in train_dataloader: x = x.to(accelerator.device) print({"rank": local_rank, "id": x}) y = accelerator.gather_for_metrics(x) if accelerator.is_main_process: print("gathered", y) ``` ### Expected behavior Is there any way to continue training/inference on the GPUs that have remaining data left without waiting for the others? Is it impossible to filter when ### Environment info - `datasets` version: 2.18.0 - Platform: Linux-5.10.209-198.812.amzn2.x86_64-x86_64-with-glibc2.31 - Python version: 3.10.13 - `huggingface_hub` version: 0.21.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.1 - `fsspec` version: 2023.6.0

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https://github.com/huggingface/datasets/pull/6718

PR_kwDODunzps5ouwwE

Fix concurrent script loading with force_redownload

closed

MEMBER

I added `lock_importable_file` in `get_dataset_builder_class` and `extend_dataset_builder_for_streaming` to fix the issue, and I also added a test cc @clefourrier

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I_kwDODunzps6BRCOg

`remove_columns` method used with a streaming enable dataset mode produces a LibsndfileError on multichannel audio

open

NONE

### Describe the bug When loading a HF dataset in streaming mode and removing some columns, it is impossible to load a sample if the audio contains more than one channel. I have the impression that the time axis and channels are swapped or concatenated. ### Steps to reproduce the bug Minimal error code: ```python from datasets import load_dataset dataset_name = "zinc75/Vibravox_dummy" config_name = "BWE_Larynx_microphone" # if we use "ASR_Larynx_microphone" subset which is a monochannel audio, no error is thrown. dataset = load_dataset( path=dataset_name, name=config_name, split="train", streaming=True ) dataset = dataset.remove_columns(["sensor_id"]) # dataset = dataset.map(lambda x:x, remove_columns=["sensor_id"]) # The commented version does not produce an error, but loses the dataset features. sample = next(iter(dataset)) ``` Error: ``` Traceback (most recent call last): File "/home/julien/Bureau/github/vibravox/tmp.py", line 15, in <module> sample = next(iter(dataset)) ^^^^^^^^^^^^^^^^^^^ File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1392, in __iter__ example = _apply_feature_types_on_example( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1080, in _apply_feature_types_on_example encoded_example = features.encode_example(example) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1889, in encode_example return encode_nested_example(self, example) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1244, in encode_nested_example {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema} File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1244, in <dictcomp> {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema} ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1300, in encode_nested_example return schema.encode_example(obj) if obj is not None else None ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/audio.py", line 98, in encode_example sf.write(buffer, value["array"], value["sampling_rate"], format="wav") File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 343, in write with SoundFile(file, 'w', samplerate, channels, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 658, in __init__ self._file = self._open(file, mode_int, closefd) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 1216, in _open raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name)) soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7fd795d24680>: Format not recognised. Process finished with exit code 1 ``` ### Expected behavior I would expect this code to run without error. ### Environment info - `datasets` version: 2.18.0 - Platform: Linux-6.5.0-21-generic-x86_64-with-glibc2.35 - Python version: 3.11.0 - `huggingface_hub` version: 0.21.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.1 - `fsspec` version: 2023.10.0

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I_kwDODunzps6BQ9X-

Non-deterministic `Dataset.builder_name` value

closed

NONE

### Describe the bug I'm not sure if this is a bug, but `print(ds.builder_name)` in the following code sometimes prints out `rotten_tomatoes` instead of `parquet`: ```python import datasets for _ in range(100): ds = datasets.load_dataset("rotten_tomatoes", split="train") print(ds.builder_name) # prints out "rotten_tomatoes" sometimes instead of "parquet" ``` Output: ``` ... parquet parquet parquet rotten_tomatoes parquet parquet parquet ... ``` Here's a reproduction using GitHub Actions: https://github.com/mlflow/mlflow/actions/runs/8153247984/job/22284263613?pr=11329#step:12:241 One of our tests is flaky because `builder_name` is not deterministic. ### Steps to reproduce the bug 1. Run the code above. ### Expected behavior Always prints out `parquet`? ### Environment info ``` Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.18.0 - Platform: Linux-6.5.0-1015-azure-x86_64-with-glibc2.34 - Python version: 3.8.18 - `huggingface_hub` version: 0.21.3 - PyArrow version: 15.0.0 - Pandas version: 2.0.3 - `fsspec` version: 2024.2.0 ```

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https://github.com/huggingface/datasets/pull/6715

PR_kwDODunzps5oo36i

Fix sliced ConcatenationTable pickling with mixed schemas vertically

closed

MEMBER

A sliced + pickled ConcatenationTable could end up with a different schema than the original schema, if the slice only contains blocks with only a subset of the columns. This can lead to issues when saving datasets from a concatenation of datasets with mixed schemas Reported in https://discuss.huggingface.co/t/datasetdict-save-to-disk-with-num-proc-1-seems-to-hang-with-error/75595

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https://github.com/huggingface/datasets/pull/6714

PR_kwDODunzps5ooQd2

Expand no-code dataset info with datasets-server info

closed

COLLABORATOR

E.g., to have info about a dataset's number of examples for more informative TQDM bars.

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PR_kwDODunzps5olmqh