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PR_kwDODunzps5mXRo8

Minor multi gpu doc improvement

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just added torch.no_grad and eval()

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PR_kwDODunzps5mW1MA

Document usage of hfh cli instead of git

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PR_kwDODunzps5mSB2B

Update loading.mdx to include "jsonl" file loading.

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* A small update to the documentation, noting the ability to load jsonl files.

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PR_kwDODunzps5mRIma

Better multi-gpu example

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Use Qwen1.5-0.5B-Chat as an easy example for multi-GPU the previous example was using a model for translation and the way it was setup was not really the right way to use the model.

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I_kwDODunzps5-icAS

Support fsspec 2024.2

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Support fsspec 2024.2. First, we should address: - #6644

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I_kwDODunzps5-iboS

Support fsspec 2023.12

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Support fsspec 2023.12 by handling previous and new glob behavior.

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I_kwDODunzps5-b4n_

Faiss GPU index cannot be serialised when passed to trainer

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### Describe the bug I am working on a retrieval project and encountering I have encountered two issues in the hugging face faiss integration: 1. I am trying to pass in a dataset with a faiss index to the Huggingface trainer. The code works for a cpu faiss index, but doesn't for a gpu one, getting error: ``` File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 1543, in train return inner_training_loop( File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 1555, in _inner_training_loop train_dataloader = self.get_train_dataloader() File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 831, in get_train_dataloader train_dataset = self._remove_unused_columns(train_dataset, description="training") File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 725, in _remove_unused_columns return dataset.remove_columns(ignored_columns) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/fingerprint.py", line 481, in wrapper out = func(dataset, *args, **kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2146, in remove_columns dataset = copy.deepcopy(self) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, *rv) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 271, in _reconstruct state = deepcopy(state, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy y = copier(x, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy y = copier(x, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, *rv) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 271, in _reconstruct state = deepcopy(state, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy y = copier(x, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 161, in deepcopy rv = reductor(4) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/__init__.py", line 556, in index_getstate return {"this": serialize_index(self).tobytes()} File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/__init__.py", line 1607, in serialize_index write_index(index, writer) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/swigfaiss.py", line 9843, in write_index return _swigfaiss.write_index(*args) RuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /project/faiss/faiss/impl/index_write.cpp:590: don't know how to serialize this type of index ``` The index was created with the add_faiss_index method ``` train_dataset.add_faiss_index( column='embeddings', index_name='embeddings', string_factory=faiss_index_string, train_size=config.faiss_train_size, device=0, # Use -1 for CPU, or specify GPU device ID faiss_verbose=True ) ``` 2. Athough faiss is written to be compatible on the gpu for searching [https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU) I am getting error when trying to use the hugggingface code to do the search on gpu. This seems to be caused by this line https://github.com/huggingface/datasets/blob/f9975f636542df7f95c27065ea93147440d690b7/src/datasets/search.py#L376 producing error ``` total_scores, total_examples = self.dataset.get_nearest_examples_batch('embeddings', embeddings, k=self.k) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 773, in get_nearest_examples_batch total_scores, total_indices = self.search_batch(index_name, queries, k, **kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 727, in search_batch return self._indexes[index_name].search_batch(queries, k, **kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 376, in search_batch if not queries.flags.c_contiguous: AttributeError: 'Tensor' object has no attribute 'flags' ``` ### Steps to reproduce the bug ``` train_dataset.add_faiss_index( column='embeddings', index_name='embeddings', string_factory=faiss_index_string, train_size=config.faiss_train_size, device=0, # Use -1 for CPU, or specify GPU device ID faiss_verbose=True ) Trainer( model=model, args=args, train_dataset=train_dataset, eval_dataset=eval_dataset, data_collator=data_collator, tokenizer=tokenizer ) train_dataset.get_nearest_examples_batch('embeddings', embeddings, k=self.k) ``` ### Expected behavior I would expect the faiss database code to be gpu compatible ### Environment info huggingface Version: 2.16.1

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I_kwDODunzps5-Tq7G

Differently dataset object saved than it is loaded.

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### Describe the bug Differently sized object is saved than it is loaded. ### Steps to reproduce the bug Hi, I save dataset in a following way: ``` dataset = load_dataset("json", data_files={ "train": os.path.join(input_folder, f"{task_meta_type}_{task_type}_train.jsonl"), "test": os.path.join(input_folder, f"{task_meta_type}_{task_type}_test.jsonl")}) print(os.path.join(output_folder, f"{task_meta_type}_{task_type}")) print(f"Length of train dataset: {len(dataset['train'])}") print(f"Length of test dataset: {len(dataset['test'])}") dataset.save_to_disk(os.path.join(output_folder, f"{task_meta_type}_{task_type}")) ``` this yields output ``` .data/hf_dataset/propaganda_zanr Length of train dataset: 7642 Length of test dataset: 1000 ``` Everything looks fine. Then I load the dataset ```python from datasets import load_dataset dataset_path = ".data/hf_dataset/propaganda_zanr" dataset = load_dataset(dataset_path) print(f"Length of train dataset: {len(dataset['train'])}") print(f"Length of test dataset: {len(dataset['test'])}") ``` this prints ``` Generating train split: 1 examples [00:00, 72.10 examples/s] Generating test split: 1 examples [00:00, 100.69 examples/s] Length of train dataset: 1 Length of test dataset: 1 ``` I dont' understand :( ### Expected behavior same object is loaded ### Environment info datasets==2.16.1

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I_kwDODunzps5-Lks8

unicodedecodeerror: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte

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### Describe the bug unicodedecodeerror: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte ### Steps to reproduce the bug ``` import sys sys.getdefaultencoding() 'utf-8' from datasets import load_dataset print(f"Train dataset size: {len(dataset['train'])}") print(f"Test dataset size: {len(dataset['test'])}") Resolving data files: 100% 159/159 [00:00<00:00, 9909.28it/s] Using custom data configuration samsum-0b1209637541c9e6 Downloading and preparing dataset json/samsum to C:/Users/Administrator/.cache/huggingface/datasets/json/samsum-0b1209637541c9e6/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51... Downloading data files: 100% 3/3 [00:00<00:00, 119.99it/s] Extracting data files: 100% 3/3 [00:00<00:00, 9.54it/s] Generating train split: 88392/0 [00:15<00:00, 86848.17 examples/s] Generating test split: 0/0 [00:00<?, ? examples/s] --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:132, in Json._generate_tables(self, files) 131 try: --> 132 pa_table = paj.read_json( 133 io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) 134 ) 135 break File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\_json.pyx:290, in pyarrow._json.read_json() File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status() File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:100, in pyarrow.lib.check_status() ArrowInvalid: JSON parse error: Invalid value. in row 0 During handling of the above exception, another exception occurred: UnicodeDecodeError Traceback (most recent call last) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1819, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1818 _time = time.time() -> 1819 for _, table in generator: 1820 if max_shard_size is not None and writer._num_bytes > max_shard_size: File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:153, in Json._generate_tables(self, files) 152 with open(file, encoding="utf-8") as f: --> 153 dataset = json.load(f) 154 except json.JSONDecodeError: File ~\AppData\Local\Programs\Python\Python310\lib\json\__init__.py:293, in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 276 """Deserialize ``fp`` (a ``.read()``-supporting file-like object containing 277 a JSON document) to a Python object. 278 (...) 291 kwarg; otherwise ``JSONDecoder`` is used. 292 """ --> 293 return loads(fp.read(), 294 cls=cls, object_hook=object_hook, 295 parse_float=parse_float, parse_int=parse_int, 296 parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) File ~\AppData\Local\Programs\Python\Python310\lib\codecs.py:322, in BufferedIncrementalDecoder.decode(self, input, final) 321 data = self.buffer + input --> 322 (result, consumed) = self._buffer_decode(data, self.errors, final) 323 # keep undecoded input until the next call UnicodeDecodeError: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[81], line 5 1 from datasets import load_dataset 3 # Load dataset from the hub 4 #dataset = load_dataset("json",data_files="C:/Users/Administrator/Desktop/samsum/samsum/data/corpus/train.json",field="data") ----> 5 dataset = load_dataset('json',"samsum") 6 #dataset = load_dataset("samsum") 7 print(f"Train dataset size: {len(dataset['train'])}") File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py:1758, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs) 1755 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1757 # Download and prepare data -> 1758 builder_instance.download_and_prepare( 1759 download_config=download_config, 1760 download_mode=download_mode, 1761 ignore_verifications=ignore_verifications, 1762 try_from_hf_gcs=try_from_hf_gcs, 1763 num_proc=num_proc, 1764 ) 1766 # Build dataset for splits 1767 keep_in_memory = ( 1768 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1769 ) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:860, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 858 if num_proc is not None: 859 prepare_split_kwargs["num_proc"] = num_proc --> 860 self._download_and_prepare( 861 dl_manager=dl_manager, 862 verify_infos=verify_infos, 863 **prepare_split_kwargs, 864 **download_and_prepare_kwargs, 865 ) 866 # Sync info 867 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:953, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 949 split_dict.add(split_generator.split_info) 951 try: 952 # Prepare split will record examples associated to the split --> 953 self._prepare_split(split_generator, **prepare_split_kwargs) 954 except OSError as e: 955 raise OSError( 956 "Cannot find data file. " 957 + (self.manual_download_instructions or "") 958 + "\nOriginal error:\n" 959 + str(e) 960 ) from None File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1708, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1706 gen_kwargs = split_generator.gen_kwargs 1707 job_id = 0 -> 1708 for job_id, done, content in self._prepare_split_single( 1709 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1710 ): 1711 if done: 1712 result = content File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1851, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1849 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1850 e = e.__context__ -> 1851 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1853 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior can't load dataset ### Environment info dataset:samsum system :win10 gpu:m40 24G

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I_kwDODunzps5-HYfT

Sign Language Support

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### Feature request Currently, there are only several Sign Language labels, I would like to propose adding all the Signed Languages as new labels which are described in this ISO standard: https://www.evertype.com/standards/iso639/sign-language.html ### Motivation Datasets currently only have labels for several signed languages. There are more signed languages in the world. Furthermore, some signed languages that have a lot of online data cannot be found because of this reason (for instance, German Sign Language, and there is no German Sign Language label on huggingface datasets even though there are a lot of readily available sign language datasets exist for German Sign Language, which are used very frequently in Sign Language Processing papers, and models.) ### Your contribution I can submit a PR for this as well, adding the ISO codes and languages to the labels in datasets.

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PR_kwDODunzps5l0KPG

Run download_and_prepare if missing splits

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A first step towards https://github.com/huggingface/datasets/issues/6529

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I_kwDODunzps599thp

Cannot download wmt16 dataset

closed

NONE

### Describe the bug As of this morning (PST) 2/1/2024, seeing the wmt16 dataset is missing from opus , could you suggest an alternative? ``` Downloading data files: 0%| | 0/4 [00:00<?, ?it/s]Traceback (most recent call last): File "test.py", line 2, in <module> raw_datasets = load_dataset("wmt16","ro-en",split="train") File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1717, in _download_and_prepare super()._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1027, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/746749a11d25c02058042da7502d973ff410e73457f3d305fc1177dc0e8c4227/wmt_utils.py", line 754, in _split_generators downloaded_files = dl_manager.download_and_extract(urls_to_download) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 565, in download_and_extract return self.extract(self.download(url_or_urls)) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 428, in download downloaded_path_or_paths = map_nested( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 464, in map_nested mapped = [ File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 465, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 384, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 384, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 367, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 454, in _download return cached_path(url_or_filename, download_config=download_config) File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 182, in cached_path output_path = get_from_cache( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 596, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz ``` ### Steps to reproduce the bug ``` from datasets import load_dataset raw_datasets = load_dataset("wmt16","ro-en",split="train") ``` ### Expected behavior Expect the dataset to be downloaded/ at least a clean exit with error explaining dataset is missing and a suggestion for next steps ### Environment info - `datasets` version: 2.14.7 - Platform: Linux-5.15.0-92-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.17.3 - PyArrow version: 15.0.0 - Pandas version: 2.0.1

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I_kwDODunzps598je3

'with_format' is extremely slow when used together with 'interleave_datasets' or 'shuffle' on IterableDatasets

open

NONE

### Describe the bug If you: 1. Interleave two iterable datasets together with the interleave_datasets function, or shuffle an iterable dataset 2. Set the output format to torch tensors with .with_format('torch') Then iterating through the dataset becomes over 100x slower than it is if you don't apply the torch formatting. ### Steps to reproduce the bug ```python import datasets import torch from tqdm import tqdm rand_a = torch.randn(3,224,224) rand_b = torch.randn(3,224,224) a = torch.stack([rand_a] * 1000) b = torch.stack([rand_b] * 1000) features = datasets.Features({"tensor": datasets.Array3D(shape=(3,224,224), dtype="float32")}) ds_a = datasets.Dataset.from_dict({"tensor": a}, features=features).to_iterable_dataset() ds_b = datasets.Dataset.from_dict({"tensor": b}, features=features).to_iterable_dataset() # Iterating through either dataset with torch formatting is really fast (2000it/s on my machine) for example in tqdm(ds_a.with_format('torch')): pass # Iterating through either dataset shuffled is also pretty fast (100it/s on my machine) for example in tqdm(ds_a.shuffle()): pass # Iterating through this interleaved dataset is pretty fast (200it/s on my machine) ds_fast = datasets.interleave_datasets([ds_a, ds_b]) for example in tqdm(ds_fast): pass # Iterating through either dataset with torch formatting *after shuffling* is really slow... (<2it/s on my machine) for example in tqdm(ds_a.shuffle().with_format('torch')): pass # Iterating through this torch formatted interleaved dataset is also really slow (<2it/s on my machine)... ds_slow = datasets.interleave_datasets([ds_a, ds_b]).with_format('torch') for example in tqdm(ds_slow): pass # Even doing this is way faster!! (70it/s on my machine) for example in tqdm(ds_fast): test = torch.tensor(example['tensor']) ``` ### Expected behavior Applying torch formatting to the interleaved dataset shouldn't increase the time taken to iterate through the dataset by very much, since even explicitly converting every example is over 70x faster than calling .with_format('torch'). ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.38 - Python version: 3.11.6 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

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PR_kwDODunzps5lm4zI

Faster column validation and reordering

closed

CONTRIBUTOR

I work with bioinformatics data and often these tables have thousands and even tens of thousands of features. These tables are also accompanied by metadata that I do not want to pass in the model. When I perform `set_format('pt', columns=large_column_list)` , it can take several minutes before it finishes. The culprit is when the following check is performed: `any(col not in self._data.column_names for col in columns)`. Replacing this by `set(columns) - (self._data.column_names)` is more efficient.

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PR_kwDODunzps5lmeNO

Fix missing info when loading some datasets from Parquet export

closed

MEMBER

Fix getting the info for script-based datasets with Parquet export with a single config not named "default". E.g. ```python from datasets import load_dataset_builder b = load_dataset_builder("bookcorpus") print(b.info.features) # should print {'text': Value(dtype='string', id=None)} ``` I fixed this by setting the default config name when there is only one config.

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PR_kwDODunzps5llB9a

Support data_dir parameter in push_to_hub

closed

MEMBER

Support `data_dir` parameter in `push_to_hub`. This allows users to organize the data files according to their specific needs. For example, "wikimedia/wikipedia" files could be organized by year and/or date, e.g. "2024/20240101/20240101.en".

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PR_kwDODunzps5lknz9

dataset viewer requires no-script

closed

CONTRIBUTOR

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PR_kwDODunzps5lfPuk

Fix reload cache with data dir

closed

MEMBER

The cache used to only check for the latest cache directory with a given config_name, but it was wrong (e.g. `default-data_dir=data%2Ffortran-data_dir=data%2Ffortran` instead of `default-data_dir=data%2Ffortran`) I fixed this by not passing the `config_kwargs` to the parent Builder `__init__`, and passing the config_id forged from the `config_kwargs` directly close https://github.com/huggingface/datasets/issues/6609

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PR_kwDODunzps5lcu9A

Fix filelock: use current umask for filelock >= 3.10

closed

MEMBER

reported in https://github.com/huggingface/evaluate/issues/542 cc @stas00 @williamberrios close https://github.com/huggingface/datasets/issues/6589

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PR_kwDODunzps5lYPi3

Bump max range of dill to 0.3.8

closed

CONTRIBUTOR

Release on Jan 27, 2024: https://pypi.org/project/dill/0.3.8/#history

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PR_kwDODunzps5lV0aF

Support push_to_hub without org/user to default to logged-in user

closed

MEMBER

This behavior is aligned with: - the behavior of `datasets` before merging #6519 - the behavior described in the corresponding docstring - the behavior of `huggingface_hub.create_repo` Revert "Support push_to_hub canonical datasets (#6519)" - This reverts commit a887ee78835573f5d80f9e414e8443b4caff3541. Fix #6597.

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PR_kwDODunzps5lVxXU

Make CLI test support multi-processing

closed

MEMBER

Support passing `--num_proc` to CLI test. This was really useful recently to run the command on `pubmed`: https://huggingface.co/datasets/pubmed/discussions/11

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PR_kwDODunzps5lVr-t

Disable `tqdm` bars in non-interactive environments

closed

CONTRIBUTOR

Replace `disable=False` with `disable=None` in the `tqdm` bars to disable them in non-interactive environments (by default). For more info, see a [similar PR](https://github.com/huggingface/huggingface_hub/pull/2000) in `huggingface_hub`.

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PR_kwDODunzps5lU0I2

Raise error on bad split name

closed

MEMBER

e.g. dashes '-' are not allowed in split names This should add an error message on datasets with unsupported split names like https://huggingface.co/datasets/open-source-metrics/test cc @AndreaFrancis

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I_kwDODunzps59Z71-

How to download the laion-coco dataset

closed

NONE

The laion coco dataset is not available now. How to download it https://huggingface.co/datasets/laion/laion-coco

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I_kwDODunzps59ZoKr

streaming datasets doesn't work properly with multi-node

open

NONE

### Feature request Let’s say I have a dataset with 5 samples with values [1, 2, 3, 4, 5], with 2 GPUs (for DDP) and batch size of 2. This dataset is an `IterableDataset` since I am streaming it. Now I split the dataset using `split_dataset_by_node` to ensure it doesn’t get repeated. And since it’s already splitted, I don’t have to use `DistributedSampler` (also they don't work with iterable datasets anyway)? But in this case I noticed that the: First iteraton: first GPU will get → [1, 2] first GPU will get → [3, 4] Second iteraton: first GPU will get → [5] first GPU will get → Nothing which actually creates an issue since in case of `DistributedSampler`, the samples are repeated internally to ensure non of the GPUs at any iteration is missing any data for gradient sync. So my questions are: 1. Here since splitting is happening before hand, how to make sure each GPU get’s a batch at each iteration to avoid gradient sync issues? 2. Do we need to use `DistributedSampler`? If yes, how? 3. in the docstrings of `split_dataset_by_node`, this is mentioned: *"If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples."* Can you explain the last part here? 4. If `dataset.n_shards % world_size != 0`, is it possible to shard the streaming dataset on the fly to avoid the case where data is missing? ### Motivation Somehow streaming datasets should work with DDP since for big LLMs a lot of data is required and DDP/multi-node is mostly used to train such models and streaming can actually help solve the data part of it. ### Your contribution Yes, I can help in submitting the PR once we get mutual understanding on how it should behave.

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I_kwDODunzps59ZSVZ

multi-GPU map does not work

closed

NONE

### Describe the bug Here is the code for single-GPU processing: https://pastebin.com/bfmEeK2y Here is the code for multi-GPU processing: https://pastebin.com/gQ7i5AQy Here is the video showing that the multi-GPU mapping does not work as expected (there are so many things wrong here, it's better to watch the 3-minute video than explain here): https://youtu.be/RNbdPkSppc4 ### Steps to reproduce the bug - ### Expected behavior - ### Environment info x2 RTX A4000

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I_kwDODunzps59Y4me

deleted

closed

NONE

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I_kwDODunzps59WuuI

wiki_dpr.py error (ID mismatch between lines {id} and vector {vec_id}

closed

NONE

### Describe the bug I'm trying to run a rag example, and the dataset is wiki_dpr. wiki_dpr download and extracting have been completed successfully. However, at the generating train split stage, an error from wiki_dpr.py keeps popping up. Especially in "_generate_examples" : 1. The following error occurs in the line **id, text, title = line.strip().split("\t")** ValueError: not enough values ​​to unpack (expected 3, got 2) -> This part handles exceptions so that even if an error occurs, it passes. 2. **ID mismatch between lines {id} and vector {vec_id}** This error seems to occur at the line " assert int(id) == int(vec_id),". After I handled the exception in the split error, generating train split progressed to 80%, but an id mismatch error occurred at about the 16200000th vector id. Debugging is even more difficult because it takes a long time to download and split wiki_dpr. I need help. thank you in advance!! ### Steps to reproduce the bug Occurs in the generating train split step when running the rag example in the transformers repository. Specifically, it is an error in wiki_dpr.py. ### Expected behavior . ### Environment info python 3.8

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PR_kwDODunzps5lK2VY

Migrate from `setup.cfg` to `pyproject.toml`

closed

CONTRIBUTOR

Based on https://github.com/huggingface/huggingface_hub/pull/1971 in `hfh`

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I_kwDODunzps59R_am

While importing load_dataset from datasets

closed

NONE

### Describe the bug cannot import name 'DEFAULT_CIPHERS' from 'urllib3.util.ssl_' this is the error i received ### Steps to reproduce the bug from datasets import load_dataset ### Expected behavior No errors ### Environment info python 3.11.5

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PR_kwDODunzps5lEagV

Fix CI: pyarrow 15, pandas 2.2 and sqlachemy

closed

MEMBER

this should fix the CI failures on `main` close https://github.com/huggingface/datasets/issues/5477

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PR_kwDODunzps5lDSEL

Use schema metadata only if it matches features

closed

MEMBER

e.g. if we use `map` in arrow format and transform the table, the returned table might have new columns but the metadata might be wrong

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I_kwDODunzps59G3Tx

...

closed

NONE

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I_kwDODunzps59Gm-o

`datasets/downloads` cleanup tool

open

CONTRIBUTOR

### Feature request Splitting off https://github.com/huggingface/huggingface_hub/issues/1997 - currently `huggingface-cli delete-cache` doesn't take care of cleaning `datasets` temp files e.g. I discovered having millions of files under `datasets/downloads` cache, I had to do: ``` sudo find /data/huggingface/datasets/downloads -type f -mtime +3 -exec rm {} \+ sudo find /data/huggingface/datasets/downloads -type d -empty -delete ``` could the cleanup be integrated into `huggingface-cli` or a different tool provided to keep the folders tidy and not consume inodes and space e.g. there were tens of thousands of `.lock` files - I don't know why they never get removed - lock files should be temporary for the duration of the operation requiring the lock and not remain after the operation finished, IMHO. Also I think one should be able to nuke `datasets/downloads` w/o hurting the cache, but I think there are some datasets that rely on files extracted under this dir - or at least they did in the past - which is very difficult to manage since one has no idea what is safe to delete and what not. Thank you @Wauplin (requested to be tagged)

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I_kwDODunzps59DiIC

cnn_dailymail repeats itself

closed

NONE

### Describe the bug When I try to load `cnn_dailymail` dataset, it takes longer than usual and when I checked the dataset it's 3x bigger than it's supposed to be. Check https://huggingface.co/datasets/cnn_dailymail: it says 287k rows for train. But when I check length of train split it says 861339. Also I checked data: ``` >>> ds['train']['highlights'][0] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday . Young actor says he has no plans to fritter his cash away . Radcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][0] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday . Young actor says he has no plans to fritter his cash away . Radcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][287113] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\nYoung actor says he has no plans to fritter his cash away .\nRadcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][574226] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\nYoung actor says he has no plans to fritter his cash away .\nRadcliffe's earnings from first five Potter films have been held in trust fund ." ``` The datasets seems to be updated 6 days ago to convert it to Parquet. Probably, there is some issue with backward compatability. ### Steps to reproduce the bug 1. ``` from datasets import load_dataset ds = load_dataset('cnn_dailymail', '3.0.0') len(ds['train']) ``` ### Expected behavior It should not repeat itself. ### Environment info datasets==2.13.2 Python==3.7.13

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I_kwDODunzps587n76

`load_from_disk` with large dataset from S3 runs into `botocore.exceptions.ClientError`

open

NONE

### Describe the bug When loading a large dataset (>1000GB) from S3 I run into the following error: ``` Traceback (most recent call last): File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 113, in _error_wrapper return await func(*args, **kwargs) File "/home/alp/.local/lib/python3.10/site-packages/aiobotocore/client.py", line 383, in _make_api_call raise error_class(parsed_response, operation_name) botocore.exceptions.ClientError: An error occurred (RequestTimeTooSkewed) when calling the GetObject operation: The difference between the request time and the current time is too large. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/alp/phoneme-classification.monorepo/aws_sagemaker/data_processing/inspect_final_dataset.py", line 13, in <module> dataset = load_from_disk("s3://speech-recognition-processed-data/whisper/de/train_data/", storage_options=storage_options) File "/home/alp/.local/lib/python3.10/site-packages/datasets/load.py", line 1902, in load_from_disk return Dataset.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) File "/home/alp/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1686, in load_from_disk fs.download(src_dataset_path, [dest_dataset_path.as](http://dest_dataset_path.as/)_posix(), recursive=True) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1480, in download return self.get(rpath, lpath, recursive=recursive, **kwargs) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper return sync(self.loop, func, *args, **kwargs) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync raise return_result File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner result[0] = await coro File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 604, in _get return await _run_coros_in_chunks( File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 257, in _run_coros_in_chunks await asyncio.gather(*chunk, return_exceptions=return_exceptions), File "/usr/lib/python3.10/asyncio/tasks.py", line 408, in wait_for return await fut File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1193, in _get_file body, content_length = await _open_file(range=0) File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1184, in _open_file resp = await self._call_s3( File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 348, in _call_s3 return await _error_wrapper( File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 140, in _error_wrapper raise err PermissionError: The difference between the request time and the current time is too large. ``` The usual problem for this error is that the time on my local machine is out of sync with the current time. However, this is not the case here. I checked the time and even reset it with no success. See resources here: - https://stackoverflow.com/questions/4770635/s3-error-the-difference-between-the-request-time-and-the-current-time-is-too-la - https://stackoverflow.com/questions/25964491/aws-s3-upload-fails-requesttimetooskewed The error does not appear when loading a smaller dataset (e.g. our test set) from the same s3 path. ### Steps to reproduce the bug 1. Create large dataset 2. Try loading it from s3 using: ``` dataset = load_from_disk("s3://...", storage_options=storage_options) ``` ### Expected behavior Load dataset without running into this error. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.19.3 - PyArrow version: 12.0.1 - Pandas version: 2.0.3

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I_kwDODunzps586Pw_

cast_column to Sequence(subfeatures_dict) has err

closed

NONE

### Describe the bug I am working with the following demo code: ``` from datasets import load_dataset from datasets.features import Sequence, Value, ClassLabel, Features ais_dataset = load_dataset("/data/ryan.gao/ais_dataset_cache/raw/1978/") ais_dataset = ais_dataset["train"] def add_class(example): example["my_labeled_bbox"] = {"bbox": [100,100,200,200], "label": "cat"} return example ais_dataset = ais_dataset.map(add_class, batched=False, num_proc=32) ais_dataset = ais_dataset.cast_column("my_labeled_bbox", Sequence( { "bbox": Sequence(Value(dtype="int64")), "label": ClassLabel(names=["cat", "dog"]) })) print(ais_dataset[0]) ``` However, executing this code results in an error: ``` File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type int64 to Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None) ``` Upon examining the source code in datasets/table.py at line 2035: ``` if isinstance(feature, Sequence) and isinstance(feature.feature, dict): feature = { name: Sequence(subfeature, length=feature.length) for name, subfeature in feature.feature.items() } ``` I noticed that if subfeature is of type Sequence, the code results in Sequence(Sequence(...), ...) and Sequence(ClassLabel(...), ...), which appears to be the source of the error. ### Steps to reproduce the bug run my demo code ### Expected behavior no exception ### Environment info python 3.9 datasets: 2.16.1

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I_kwDODunzps584HhS

Wrong path for cache directory in offline mode

closed

NONE

### Describe the bug Dear huggingfacers, I'm trying to use a subset of the-stack dataset. When I run the command the first time ``` dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' ) ``` It downloads the files and caches them normally. Nevertheless, since my compute nodes are not online (`HF_DATASETS_OFFLINE=1`) . Whenever I try to run the command again, the library is passing the wrong cache path: `Cache directory for the-stack doesn't exist at /Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data%2Ffortran-data_dir=data%2Ffortran` when the right path is: `'/Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data\%2Ffortran` Not sure why those redundancies are included in the path. If I try adding the correct path through the the cache_dir argument it throws an error: ConnectionError: Couldn't reach the Hugging Face Hub for dataset 'bigcode/the-stack': Offline mode is enabled. Your help with this issue is greatly appreciated. Thanks a lot for the great work. ### Steps to reproduce the bug 1: `dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' )` 2: `HF_DATASETS_OFFLINE=1` 3: `dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' )` ### Expected behavior being able to use the cached data ### Environment info several different systems

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PR_kwDODunzps5ku_lN

Add `with_rank` param to `Dataset.filter`

closed

CONTRIBUTOR

Fix #6564

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PR_kwDODunzps5knGse

Update features.py to avoid bfloat16 unsupported error

open

NONE

Fixes https://github.com/huggingface/datasets/issues/6566 Let me know if there's any tests I need to clear.

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PR_kwDODunzps5kk3KB

Dedicated RNG object for fingerprinting

closed

CONTRIBUTOR

Closes https://github.com/huggingface/datasets/issues/6604, closes https://github.com/huggingface/datasets/issues/2775

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I_kwDODunzps58lb5Y

ELI5 no longer available, but referenced in example code

closed

NONE

Here, an example code is given: https://huggingface.co/docs/transformers/tasks/language_modeling This code + article references the ELI5 dataset. ELI5 is no longer available, as the ELI5 dataset page states: https://huggingface.co/datasets/eli5 "Defunct: Dataset "eli5" is defunct and no longer accessible due to unavailability of the source data. Reddit recently [changed the terms of access](https://www.reddit.com/r/reddit/comments/12qwagm/an_update_regarding_reddits_api/) to its API, making the source data for this dataset unavailable. " Please change the example code to use a different dataset.

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I_kwDODunzps58joEZ

Transform fingerprint collisions due to setting fixed random seed

closed

NONE

### Describe the bug The transform fingerprinting logic relies on the `random` library for random bits when the function is not hashable (e.g. bound methods as used in `trl`: https://github.com/huggingface/trl/blob/main/trl/trainer/dpo_trainer.py#L356). This causes collisions when the training code sets a fixed random seed, which is common practice: https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/sft/config_full.yaml#L45. This results in fingerprint collisions which leads to silently loading incorrect cache files corresponding to completely different datasets. ### Steps to reproduce the bug n/a ### Expected behavior Use `uuid` v4 instead of `random.getrandbits()` ### Environment info `datasets` main branch

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I_kwDODunzps58hyGu

datasets map `cache_file_name` does not work

open

NONE

### Describe the bug In the documentation `datasets.Dataset.map` arg `cache_file_name` is said to be a string, but it doesn't work. ### Steps to reproduce the bug 1. pick a dataset 2. write a map function 3. do `ds.map(..., cache_file_name='some_filename')` 4. it crashes ### Expected behavior It will tell you the filename you specified does not exist or it will generate a new file and tell you the filename does not exist. ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-5.10.201-168.748.amzn2int.x86_64-x86_64-with-glibc2.26 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.12.2

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I_kwDODunzps58hu3L

Index error when data is large

open

NONE

### Describe the bug At `save_to_disk` step, the `max_shard_size` by default is `500MB`. However, one row of the dataset might be larger than `500MB` then the saving will throw an index error. Without looking at the source code, the bug is due to wrong calculation of number of shards which i think is `total_size / min(max_shard_size, row_size)` which should be `total_size / max(max_shard_size, row_size)` The fix is setting a larger `max_shard_size` ### Steps to reproduce the bug 1. create a dataset with large dense tensors per row 2. set a small `max_shard_size` say 1MB 3. `save_to_disk` ### Expected behavior ``` raise IndexError(f"Index {index} out of range for dataset of size {size}.") IndexError: Index 10 out of range for dataset of size 10. ``` ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-5.10.201-168.748.amzn2int.x86_64-x86_64-with-glibc2.26 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.12.2

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PR_kwDODunzps5kcWN0

add safety checks when using only part of dataset

open

NONE

Added some checks to prevent errors that arrise when using evaluate.py on only a portion of the squad 2.0 dataset.

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I_kwDODunzps58eymx

Loading CSV exported dataset has unexpected format

open

NONE

### Describe the bug I wanted to be able to save a HF dataset for translations and load it again in another script, but I'm a bit confused with the documentation and the result I've got so I'm opening this issue to ask if this behavior is as expected. ### Steps to reproduce the bug The documentation I've mainly consulted is https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/loading_methods#datasets.load_dataset and https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset (where I've found `.to_csv()`) ```python # Load a dataset of translations test_dataset = load_dataset("opus100", name="en-fr", split="test") # Save with .to_csv() test_csv_path = "try_testset_save.csv" test_dataset.to_csv(test_csv_path) # Load dataset from the CSV loaded_dataset = load_dataset("csv", data_files=test_csv_path) print(test_dataset_fromfile[0]["translation"]) print(test_dataset_fromfile[0]["translation"]["en"]) ``` ``` Creating CSV from Arrow format: 100% 2/2 [00:00<00:00, 47.99ba/s] Downloading data files: 100% 1/1 [00:00<00:00, 65.33it/s] Extracting data files: 100% 1/1 [00:00<00:00, 42.10it/s] Generating train split: 2000/0 [00:00<00:00, 47486.09 examples/s] {'en': "She wasn't going to vaccinate her kid against polio, no way.", 'fr': 'Elle ne vaccinerait pas son enfant contre la polio. Pas question.'} --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[29], line 11 9 loaded_dataset = load_dataset("csv", data_files=test_csv_path) 10 print(test_dataset_fromfile[0]["translation"]) ---> 11 print(test_dataset_fromfile[0]["translation"]["en"]) TypeError: string indices must be integers, not 'str' ``` ### Expected behavior Each translation was saved as a stringified dict like `"{'en': ""She wasn't going to vaccinate her kid against polio, no way."", 'fr': 'Elle ne vaccinerait pas son enfant contre la polio. Pas question.'}"` where I would have expected 2 columns (1st with English segments, and 2nd with French segments), and I was expecting `load_dataset` to infer the type of feature automatically as I haven't seen anything about it in the documentation. Do you have an example of how to effectively save and load datasets of translations ? ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-3.10.0-1160.36.2.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.11.5 - `huggingface_hub` version: 0.16.4 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

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I_kwDODunzps58YEf4

Easy way to segment into 30s snippets given an m4a file and a vtt file

closed

NONE

### Feature request Uploading datasets is straightforward thanks to the ability to push Audio to hub. However, it would be nice if the data (text and audio) could be segmented when being pushed (if not possible already). ### Motivation It's easy to create a vtt file from an audio file. If there could be auto-segmenting, this would make the creation of datasets much faster. ### Your contribution I have made a custom script to do this but it's not all that clean - uses librosa and pydub.

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I_kwDODunzps58Ou09

Unexpected keyword argument 'hf' when downloading CSV dataset from S3

open

NONE

### Describe the bug I receive this error message when using `load_dataset` with "csv" path and `dataset_files=s3://...`: ``` TypeError: Session.__init__() got an unexpected keyword argument 'hf' ``` I found a similar issue here: https://stackoverflow.com/questions/77596258/aws-issue-load-dataset-from-s3-fails-with-unexpected-keyword-argument-error-in Full stacktrace: ``` .../site-packages/datasets/load.py:2549: in load_dataset builder_instance.download_and_prepare( .../site-packages/datasets/builder.py:1005: in download_and_prepare self._download_and_prepare( .../site-packages/datasets/builder.py:1078: in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) .../site-packages/datasets/packaged_modules/csv/csv.py:147: in _split_generators data_files = dl_manager.download_and_extract(self.config.data_files) .../site-packages/datasets/download/download_manager.py:562: in download_and_extract return self.extract(self.download(url_or_urls)) .../site-packages/datasets/download/download_manager.py:426: in download downloaded_path_or_paths = map_nested( .../site-packages/datasets/utils/py_utils.py:466: in map_nested mapped = [ .../site-packages/datasets/utils/py_utils.py:467: in <listcomp> _single_map_nested((function, obj, types, None, True, None)) .../site-packages/datasets/utils/py_utils.py:387: in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] .../site-packages/datasets/utils/py_utils.py:387: in <listcomp> mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] .../site-packages/datasets/utils/py_utils.py:370: in _single_map_nested return function(data_struct) .../site-packages/datasets/download/download_manager.py:451: in _download out = cached_path(url_or_filename, download_config=download_config) .../site-packages/datasets/utils/file_utils.py:188: in cached_path output_path = get_from_cache( ...1/site-packages/datasets/utils/file_utils.py:511: in get_from_cache response = fsspec_head(url, storage_options=storage_options) .../site-packages/datasets/utils/file_utils.py:316: in fsspec_head fs, _, paths = fsspec.get_fs_token_paths(url, storage_options=storage_options) .../site-packages/fsspec/core.py:622: in get_fs_token_paths fs = filesystem(protocol, **inkwargs) .../site-packages/fsspec/registry.py:290: in filesystem return cls(**storage_options) .../site-packages/fsspec/spec.py:79: in __call__ obj = super().__call__(*args, **kwargs) .../site-packages/s3fs/core.py:187: in __init__ self.s3 = self.connect() _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <s3fs.core.S3FileSystem object at 0x1500a1310>, refresh = True def connect(self, refresh=True): """ Establish S3 connection object. Parameters ---------- refresh : bool Whether to create new session/client, even if a previous one with the same parameters already exists. If False (default), an existing one will be used if possible """ if refresh is False: # back compat: we store whole FS instance now return self.s3 anon, key, secret, kwargs, ckwargs, token, ssl = ( self.anon, self.key, self.secret, self.kwargs, self.client_kwargs, self.token, self.use_ssl) if not self.passed_in_session: > self.session = botocore.session.Session(**self.kwargs) E TypeError: Session.__init__() got an unexpected keyword argument 'hf' ``` ### Steps to reproduce the bug 1. Assuming a valid CSV file located at `s3://bucket/data.csv` 2. Run the below code: ``` storage_options = { "key": "...", "secret": "...", "client_kwargs": { "endpoint_url": "...", } } load_dataset("csv", data_files="s3://bucket/data.csv", storage_options=storage_options) ``` Encountered in version `2.16.1` but also reproduced in `2.16.0` and `2.15.0`. Note: I encountered this in a unit test using a `moto` mock for S3, however since the error occurs before the session is instantiated, it should not be the issue. ### Expected behavior No exception is raised, the boto3 session is created successfully, and the CSV file is downloaded successfully and returned as a dataset. === After some research I found that `DownloadConfig` has a `__post_init__` method that always forces this value to be set in its `storage_options`, even though in case of an S3 location the storage options get passed on to the S3 Session which does not expect this parameter. I assume this parameter is needed when reading from the huggingface hub and should not be set in this context. Unfortunately there is nothing the user can do to work around it. Even if you manually do something like: ``` download_config = DownloadConfig() del download_config.storage_options["hf"] load_dataset("csv", data_files="s3://bucket/data.csv", download_config=download_config) ``` the library will still reinsert this parameter when `download_config = self.download_config.copy()` in line 418 of `download_manager.py` (`DownloadManager.download`). Therefore `load_dataset` currently cannot be used to read a dataset in CSV format from an S3 location. ### Environment info - `datasets` version: 2.16.1 - Platform: macOS-14.2.1-arm64-arm-64bit - Python version: 3.11.7 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

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I_kwDODunzps58Mt5p

Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace

closed

MEMBER

While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace. ## Steps to reproduce the bug The command: ```python commit_info = ds.push_to_hub( "caner", config_name="default", commit_message="Convert dataset to Parquet", commit_description="Convert dataset to Parquet.", create_pr=True, token=token, ) ``` creates the additional dataset `albertvillanova/caner`.

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PR_kwDODunzps5kJceH

Drop redundant None guard.

closed

CONTRIBUTOR

`xxx if xxx is not None else None` is no-op.

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I_kwDODunzps58JnkU

Loading big dataset raises pyarrow.lib.ArrowNotImplementedError 2

closed

NONE

### Describe the bug I'm aware of the issue #5695 . I'm using a modified SDXL trainer: https://github.com/kopyl/diffusers/blob/5e70f604155aeecee254a5c63c5e4236ad4a0d3d/examples/text_to_image/train_text_to_image_sdxl.py#L1027C16-L1027C16 So i 1. Map dataset 2. Save to disk 3. Try to upload: ``` import datasets from datasets import load_from_disk dataset = load_from_disk("ds") datasets.config.DEFAULT_MAX_BATCH_SIZE = 1 dataset.push_to_hub("kopyl/ds", private=True, max_shard_size="500MB") ``` And i get this error: `pyarrow.lib.ArrowNotImplementedError: Unhandled type for Arrow to Parquet schema conversion: halffloat` Full traceback: ``` >>> dataset.push_to_hub("kopyl/3M_icons_monochrome_only_no_captioning_mapped-for-SDXL-2", private=True, max_shard_size="500MB") Map: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1451/1451 [00:00<00:00, 6827.40 examples/s] Uploading the dataset shards: 0%| | 0/2099 [00:00<?, ?it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py", line 1705, in push_to_hub split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py", line 5208, in _push_parquet_shards_to_hub shard.to_parquet(buffer) File "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py", line 4931, in to_parquet return ParquetDatasetWriter(self, path_or_buf, batch_size=batch_size, **parquet_writer_kwargs).write() File "/usr/local/lib/python3.10/dist-packages/datasets/io/parquet.py", line 129, in write written = self._write(file_obj=self.path_or_buf, batch_size=batch_size, **self.parquet_writer_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/io/parquet.py", line 141, in _write writer = pq.ParquetWriter(file_obj, schema=schema, **parquet_writer_kwargs) File "/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/core.py", line 1016, in __init__ self.writer = _parquet.ParquetWriter( File "pyarrow/_parquet.pyx", line 1869, in pyarrow._parquet.ParquetWriter.__cinit__ File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Unhandled type for Arrow to Parquet schema conversion: halffloat ``` Smaller datasets with the same way of saving and pushing work wonders. Big ones are not. I'm currently trying to upload dataset like this: `HfApi().upload_folder...` But i'm not sure that in this case "load_dataset" would work well. This setting num_shards does not help too: ``` dataset.push_to_hub("kopyl/3M_icons_monochrome_only_no_captioning_mapped-for-SDXL-2", private=True, num_shards={'train': 500}) ``` Tried 3000, 500, 478, 100 Also do you know if it's possible to push a dataset with multiple processes? It would take an eternity pushing 1TB... ### Steps to reproduce the bug Described above ### Expected behavior Should be able to upload... ### Environment info Total dataset size: 978G Amount of `.arrow` files: 2101 Each `.arrow` file size: 477M (i know 477 megabytes * 2101 does not equal 978G, but i just checked the size of a couple `.arrow` files, i don't know if some might have different size) Some files: - "ds/train/state.json": https://pastebin.com/tJ3ZLGAg - "ds/train/dataset_info.json": https://pastebin.com/JdXMQ5ih

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I_kwDODunzps58JDdz

IterableDataset sharding logic needs improvement

open

NONE

### Describe the bug The sharding of IterableDatasets with respect to distributed and dataloader worker processes appears problematic with significant performance traps and inconsistencies wrt to distributed train processes vs worker processes. Splitting across num_workers (per train process loader processes) and world_size (distributed training processes) appears inconsistent. * worker split: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/iterable_dataset.py#L1266-L1283 * distributed split: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/iterable_dataset.py#L1335-L1356 In the case of the distributed split, there is a modulus check that flips between two very different behaviours, why is this different than splitting across the data loader workers? For IterableDatasets the DataLoaders worker processes are independent, so whether it's workers within one train process or across a distributed world the shards should be distributed the same, across `world_size * num_worker` independent workers in either case... Further, the fallback case when the `n_shards % world_size == 0` check fails is a rather extreme change. I argue it is not desirable to do that implicitly, it should be an explicit case for specific scenarios (ie reliable validation). A train scenario would likely be much better handled with improved wrapping / stopping behaviour to eg also fix #6437. Changing from stepping shards to stepping samples means that every single process reads ALL of the shards. This was never an intended default for sharded training, shards gain their performance advantage in large scale distributed training by explicitly avoiding the need to have every process overlapping in the data they read, by default, only the data allocated to each process via their assigned shards should be read in each pass of the dataset. Using a large scale CLIP example, some of the larger datasets have 10-20k shards across 100+TB of data. Training with 1000 GPUs we are switching between reading 100 terabytes per epoch to 100 petabytes if say change 20k % 1000 and drop one gpu-node to 20k % 992. The 'step over samples' case might be worth the overhead in specific validation scenarios where gaurantees of at least/most once samples seen are more important and do not make up a significant portion of train time or are done in smaller world sizes outside of train. ### Steps to reproduce the bug N/A ### Expected behavior We have an iterable dataset with N shards, to split across workers * shuffle shards (same seed across all train processes) * step shard iterator across distributed processes * step shard iterator across dataloader worker processes * shuffle samples in every worker via shuffle buffer (different seed in each worker, but ideally controllable (based on base seed + worker id + epoch). * end up with (possibly uneven) number of shards per worker but each shard only ever accessed by 1 worker per pass (epoch) ### Environment info N/A

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I_kwDODunzps58Hw8R

Logs are delayed when doing .map when `docker logs`

open

NONE

### Describe the bug When I run my SD training in a Docker image and then listen to logs like `docker logs train -f`, the progress bar is delayed. It's updating every few percent. When you have a large dataset that has to be mapped (like 1+ million samples), it's crucial to see the updates in real-time, not every couple hours to make sure nothing got frozen or broken ### Steps to reproduce the bug 1. Run any huge dataset processing as a Docker image 2. `docker logs image_name` to it ### Expected behavior ... ### Environment info ...

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I_kwDODunzps58HpTN

The datasets models housed in Dropbox can't support a lot of users downloading them

closed

NONE

### Describe the bug I'm using the datasets ``` from datasets import load_dataset, Audio dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` And it seems that sometimes when I imagine a lot of users are accessing the same resources, the Dropbox host fails: `raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach https://www.dropbox.com/s/e2us0hcs3ilr20e/MInDS-14.zip?dl=1 (error 429)` My question is if we can somehow host these files elsewhere or can you change the limit of simultaneous users accessing those resources or any other solution? Also, has anyone had this issue before? Thanks ### Steps to reproduce the bug 1: Create a python script like so: ``` from datasets import load_dataset, Audio dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` 2: Execute this by a certain number of users at the same time ### Expected behavior I woudl expect that this shouldnt happen unless its a huge amount of users, which it is not the case ### Environment info This was done in an Ubuntu 22 environment.

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I_kwDODunzps58GMzU

Feature request: Multi-GPU dataset mapping for SDXL training

open

NONE

### Feature request We need to speed up SDXL dataset pre-process. Please make it possible to use multiple GPUs for the [official SDXL trainer](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py) :) ### Motivation Pre-computing 3 million of images takes around 2 days. Would be nice to be able to be able to do multi-GPU (or even better – multi-GPU + multi-node) vae and embedding precompute... ### Your contribution I'm not sure i can wrap my head around the multi-GPU mapping... Plus it's too expensive for me to take x2 A100 and spend a day just figuring out the staff since I don't have a job right now.

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I_kwDODunzps58DwMb

After `2.16.0` version, there are `PermissionError` when users use shared cache_dir

closed

NONE

### Describe the bug - We use shared `cache_dir` using `HF_HOME="{shared_directory}"` - After dataset version 2.16.0, datasets uses `filelock` package for file locking #6445 - But, `filelock` package make `.lock` file with `644` permission - Dataset is not available to other users except the user who created the lock file via `load_dataset`. ### Steps to reproduce the bug 1. `pip install datasets==2.16.0` 2. `export HF_HOME="{shared_directory}"` 3. download dataset with `load_dataset` 4. logout and login another user 5. `pip install datasets==2.16.0` 6. `export HF_HOME="{shared_directory}"` 7. download dataset with `load_dataset` 8. `PermissionError` occurs ### Expected behavior - Users can share `cache_dir` using environment variable `HF_HOME` ### Environment info - python == 3.9.10 - datasets == 2.16.0 - ubuntu 22.04 - shared_directory has ACL  - users are same group (developers)

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I_kwDODunzps58DeCd

fix os.listdir return name is empty string

closed

CONTRIBUTOR

### Describe the bug xlistdir return name is empty string Overloaded os.listdir ### Steps to reproduce the bug ```python from datasets.download.streaming_download_manager import xjoin from datasets.download.streaming_download_manager import xlistdir config = DownloadConfig(storage_options=options) manger = StreamingDownloadManager("ILSVRC2012",download_config=config) input_path = "lakefs://datalab/main/imagenet/ILSVRC2012.zip" download_files = manger.download_and_extract(input_path) current_dir = xjoin(download_files,"ILSVRC2012/Images/ILSVRC2012_img_train") folder_list = xlistdir(current_dir) ``` in xlistdir function Obj ["name"] ends with "/" last return "" ### Expected behavior Obj ["name"] ends with "/" return folder name ### Environment info no

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PR_kwDODunzps5kAT_5

Allow concatenation of datasets with mixed structs

open

CONTRIBUTOR

Fixes #6466 The idea is to do a recursive check for structs. PyArrow handles it well enough. For a demo you can do: ```python from datasets import Dataset, concatenate_datasets ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]}) ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': '[email protected]'}]}) print(concatenate_datasets([ds, ds2]).features) print(concatenate_datasets([ds, ds2]).to_dict()) ``` Now both the features and the rows are fixed. I note that Sequence suffers from the same problem, so I can fix that in a future PR once this one is merged.

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PR_kwDODunzps5j8aJn

keep more info in DatasetInfo.from_merge #6585

closed

CONTRIBUTOR

* try not to merge DatasetInfos if they're equal * fixes losing DatasetInfo during parallel Dataset.map

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I_kwDODunzps576RmV

losing DatasetInfo in Dataset.map when num_proc > 1

open

CONTRIBUTOR

### Describe the bug Hello and thanks for developing this package! When I process a Dataset with the map function using multiple processors some set attributes of the DatasetInfo get lost and are None in the resulting Dataset. ### Steps to reproduce the bug ```python from datasets import Dataset, DatasetInfo def run_map(num_proc): dataset = Dataset.from_dict( {"col1": [0, 1], "col2": [3, 4]}, info=DatasetInfo( dataset_name="my_dataset", ), ) ds = dataset.map(lambda x: x, num_proc=num_proc) print(ds.info.dataset_name) run_map(1) run_map(2) ``` This puts out: ```bash Map: 100%|██████████| 2/2 [00:00<00:00, 724.66 examples/s] my_dataset Map (num_proc=2): 100%|██████████| 2/2 [00:00<00:00, 18.25 examples/s] None ``` ### Expected behavior I expect the DatasetInfo to be kept as it was and there should be no difference in the output of running map with num_proc=1 and num_proc=2. Expected output: ```bash Map: 100%|██████████| 2/2 [00:00<00:00, 724.66 examples/s] my_dataset Map (num_proc=2): 100%|██████████| 2/2 [00:00<00:00, 18.25 examples/s] my_dataset ``` ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.17 - Python version: 3.8.18 - `huggingface_hub` version: 0.20.2 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - `fsspec` version: 2023.9.2

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I_kwDODunzps574rRe

np.fromfile not supported

open

CONTRIBUTOR

How to do np.fromfile to use it like np.load ```python def xnumpy_fromfile(filepath_or_buffer, *args, download_config: Optional[DownloadConfig] = None, **kwargs): import numpy as np if hasattr(filepath_or_buffer, "read"): return np.fromfile(filepath_or_buffer, *args, **kwargs) else: filepath_or_buffer = str(filepath_or_buffer) return np.fromfile(xopen(filepath_or_buffer, "rb", download_config=download_config).read(), *args, **kwargs) ``` this is not work

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https://github.com/huggingface/datasets/pull/6583

PR_kwDODunzps5j1DzY

remove eli5 test

closed

MEMBER

since the dataset is defunct

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https://github.com/huggingface/datasets/pull/6582

PR_kwDODunzps5jxpry

Fix for Incorrect ex_iterable used with multi num_worker

open

NONE

Corrects an issue where `self._ex_iterable` was erroneously used instead of `ex_iterable`, when both Distributed Data Parallel (DDP) and multi num_worker are used concurrently. This improper usage led to the generation of incorrect `shards_indices`, subsequently causing issues with the control flow responsible for worker creation. The fix ensures the appropriate iterable is used, thus providing a more accurate determination of whether a new worker should be instantiated or not.

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PR_kwDODunzps5jxIbt

fix os.listdir return name is empty string

closed

CONTRIBUTOR

fix #6588 xlistdir return name is empty string for example： ` from datasets.download.streaming_download_manager import xjoin from datasets.download.streaming_download_manager import xlistdir config = DownloadConfig(storage_options=options) manger = StreamingDownloadManager("ILSVRC2012",download_config=config) input_path = "lakefs://datalab/main/imagenet/ILSVRC2012.zip" download_files = manger.download_and_extract(input_path) current_dir = xjoin(download_files,"ILSVRC2012/Images/ILSVRC2012_img_train") folder_list = xlistdir(current_dir) `

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I_kwDODunzps57t9Ry

dataset cache only stores one config of the dataset in parquet dir, and uses that for all other configs resulting in showing same data in all configs.

closed

NONE

### Describe the bug ds = load_dataset("ai2_arc", "ARC-Easy"), i have tried to force redownload, delete cache and changing the cache dir. ### Steps to reproduce the bug dataset = [] dataset_name = "ai2_arc" possible_configs = [ 'ARC-Challenge', 'ARC-Easy' ] for config in possible_configs: dataset_slice = load_dataset(dataset_name, config,ignore_verifications=True,cache_dir='ai2_arc_files') dataset.append(dataset_slice) ### Expected behavior all configs should get saved in cache with their respective names. ### Environment info ai2_arc

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I_kwDODunzps57tDRx

Unable to load `eli5` dataset with streaming

closed

NONE

### Describe the bug Unable to load `eli5` dataset with streaming. ### Steps to reproduce the bug This fails with FileNotFoundError: https://files.pushshift.io/reddit/submissions ``` from datasets import load_dataset load_dataset("eli5", streaming=True) ``` This works correctly. ``` from datasets import load_dataset load_dataset("eli5") ``` ### Expected behavior - Loading `eli5` dataset should not raise an error under the streaming mode. - Or at the very least, show a warning that streaming mode is not supported with `eli5` dataset. ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-6.2.0-39-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - `fsspec` version: 2023.6.0

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PR_kwDODunzps5jtthB

Faster webdataset streaming

closed

MEMBER

requests.get(..., streaming=True) is faster than using HTTP range requests when streaming large TAR files it can be enabled using block_size=0 in fsspec cc @rwightman

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I_kwDODunzps57qsvA

502 Server Errors when streaming large dataset

closed

CONTRIBUTOR