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### Describe the bug I wanted to use my own script to handle the processing, and followed the tutorial documentation by rewriting the MyDatasetConfig and MyDatasetBuilder (which contains the _info,_split_generators and _generate_examples methods) classes. Testing with simple data was able to output the results of the processing, but when I wished to do more complex processing, I found that I was unable to debug (even the simple samples were inaccessible). There are no errors reported, and I am able to print the _info,_split_generators and _generate_examples messages, but I am unable to access the breakpoints. ### Steps to reproduce the bug # my_dataset.py import json import datasets class MyDatasetConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(MyDatasetConfig, self).__init__(**kwargs) class MyDataset(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ MyDatasetConfig( name="default", version=VERSION, description="myDATASET" ), ] def _info(self): print("info") # breakpoints return datasets.DatasetInfo( description="myDATASET", features=datasets.Features( { "id": datasets.Value("int32"), "text": datasets.Value("string"), "label": datasets.ClassLabel(names=["negative", "positive"]), } ), supervised_keys=("text", "label"), ) def _split_generators(self, dl_manager): print("generate") # breakpoints data_file = "data.json" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file} ), ] def _generate_examples(self, filepath): print("example") # breakpoints with open(filepath, encoding="utf-8") as f: data = json.load(f) for idx, sample in enumerate(data): yield idx, { "id": sample["id"], "text": sample["text"], "label": sample["label"], } #main.py import os os.environ["TRANSFORMERS_NO_MULTIPROCESSING"] = "1" from datasets import load_dataset dataset = load_dataset("my_dataset.py", split="train", cache_dir=None) print(dataset[:5]) ### Expected behavior Pause at breakpoints while running debugging ### Environment info pycharm
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ModuleNotFoundError: No module named 'datasets.tasks'
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### Describe the bug --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) [<ipython-input-9-13b5f31bd391>](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in <cell line: 1>() ----> 1 dataset = load_dataset('knowledgator/events_classification_biotech') 11 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) 2130 2131 # Create a dataset builder -> 2132 builder_instance = load_dataset_builder( 2133 path=path, 2134 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs) 1886 raise ValueError(error_msg) 1887 -> 1888 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name) 1889 # Instantiate the dataset builder 1890 builder_instance: DatasetBuilder = builder_cls( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in get_dataset_builder_class(dataset_module, dataset_name) 246 dataset_module.importable_file_path 247 ) if dataset_module.importable_file_path else nullcontext(): --> 248 builder_cls = import_main_class(dataset_module.module_path) 249 if dataset_module.builder_configs_parameters.builder_configs: 250 dataset_name = dataset_name or dataset_module.builder_kwargs.get("dataset_name") [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in import_main_class(module_path) 167 def import_main_class(module_path) -> Optional[Type[DatasetBuilder]]: 168 """Import a module at module_path and return its main class: a DatasetBuilder""" --> 169 module = importlib.import_module(module_path) 170 # Find the main class in our imported module 171 module_main_cls = None [/usr/lib/python3.10/importlib/__init__.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in import_module(name, package) 124 break 125 level += 1 --> 126 return _bootstrap._gcd_import(name[level:], package, level) 127 128 /usr/lib/python3.10/importlib/_bootstrap.py in _gcd_import(name, package, level) /usr/lib/python3.10/importlib/_bootstrap.py in _find_and_load(name, import_) /usr/lib/python3.10/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_) /usr/lib/python3.10/importlib/_bootstrap.py in _load_unlocked(spec) /usr/lib/python3.10/importlib/_bootstrap_external.py in exec_module(self, module) /usr/lib/python3.10/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds) [~/.cache/huggingface/modules/datasets_modules/datasets/knowledgator--events_classification_biotech/9c8086d498c3104de3a3c5b6640837e18ccd829dcaca49f1cdffe3eb5c4a6361/events_classification_biotech.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in <module> 1 import datasets 2 from datasets import load_dataset ----> 3 from datasets.tasks import TextClassification 4 5 DESCRIPTION = """ ModuleNotFoundError: No module named 'datasets.tasks' --------------------------------------------------------------------------- NOTE: If your import is failing due to a missing package, you can manually install dependencies using either !pip or !apt. To view examples of installing some common dependencies, click the "Open Examples" button below. --------------------------------------------------------------------------- ### Steps to reproduce the bug !pip install datasets from datasets import load_dataset dataset = load_dataset('knowledgator/events_classification_biotech') ### Expected behavior no ModuleNotFoundError ### Environment info google colab
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Adding column with dict struction when mapping lead to wrong order
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### Describe the bug in `map()` function, I want to add a new column with a dict structure. ``` def map_fn(example): example['text'] = {'user': ..., 'assistant': ...} return example ``` However this leads to a wrong order `{'assistant':..., 'user':...}` in the dataset. Thus I can't concatenate two datasets due to the different feature structures. [Here](https://colab.research.google.com/drive/1zeaWq9Ith4DKWP_EiBNyLfc8S8I68LyY?usp=sharing) is a minimal reproducible example This seems an issue in low level pyarrow library instead of datasets, however, I think datasets should allow concatenate two datasets actually in the same structure. ### Steps to reproduce the bug [Here](https://colab.research.google.com/drive/1zeaWq9Ith4DKWP_EiBNyLfc8S8I68LyY?usp=sharing) is a minimal reproducible example ### Expected behavior two datasets could be concatenated. ### Environment info N/A
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2024-10-22T13:27:16Z
2024-10-22T14:10:45Z
2024-10-22T14:10:20Z
MEMBER
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and better handling of LocalEntryNotfoundError cc @Wauplin follow up to #7234
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7,243
ArrayXD with None as leading dim incompatible with DatasetCardData
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2024-10-21T15:08:13Z
2024-10-22T14:18:10Z
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### Describe the bug Creating a dataset with ArrayXD features leads to errors when downloading from hub due to DatasetCardData removing the Nones @lhoestq ### Steps to reproduce the bug ```python import numpy as np from datasets import Array2D, Dataset, Features, load_dataset def examples_generator(): for i in range(4): yield { "array_1d": np.zeros((10,1), dtype="uint16"), "array_2d": np.zeros((10, 1), dtype="uint16"), } features = Features(array_1d=Array2D((None,1), "uint16"), array_2d=Array2D((None, 1), "uint16")) dataset = Dataset.from_generator(examples_generator, features=features) dataset.push_to_hub("alex-hh/test_array_1d2d") ds = load_dataset("alex-hh/test_array_1d2d") ``` Source of error appears to be DatasetCardData.to_dict invoking DatasetCardData._remove_none ```python from huggingface_hub import DatasetCardData from datasets.info import DatasetInfosDict dataset_card_data = DatasetCardData() DatasetInfosDict({"default": dataset.info.copy()}).to_dataset_card_data(dataset_card_data) print(dataset_card_data.to_dict()) # removes Nones in shape ``` ### Expected behavior Should be possible to load datasets saved with shape None in leading dimension ### Environment info 3.0.2 and latest huggingface_hub
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7,241
`push_to_hub` overwrite argument
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2024-10-20T03:23:26Z
2024-10-24T17:39:08Z
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### Feature request Add an `overwrite` argument to the `push_to_hub` method. ### Motivation I want to overwrite a repo without deleting it on Hugging Face. Is this possible? I couldn't find anything in the documentation or tutorials. ### Your contribution I can create a PR.
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7,240
Feature Request: Add functionality to pass split types like train, test in DatasetDict.map
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2024-10-19T09:59:12Z
2025-01-06T08:04:08Z
2025-01-06T08:04:08Z
CONTRIBUTOR
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Hello datasets! We often encounter situations where we need to preprocess data differently depending on split types such as train, valid, and test. However, while DatasetDict.map has features to pass rank or index, there's no functionality to pass split types. Therefore, I propose adding a 'with_splits' parameter to DatasetDict, which would allow passing the split type through fn_kwargs.
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7,238
incompatibily issue when using load_dataset with datasets==3.0.1
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2024-12-09T09:49:32Z
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### Describe the bug There is a bug when using load_dataset with dataset version at 3.0.1 . Please see below in the "steps to reproduce the bug". To resolve the bug, I had to downgrade to version 2.21.0 OS: Ubuntu 24 (AWS instance) Python: same bug under 3.12 and 3.10 The error I had was: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/load.py", line 2096, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 924, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 1647, in _download_and_prepare super()._download_and_prepare( File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 977, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_6_0/cb17afd34f5799f97e8f48398748f83006335b702bd785f9880797838d541b81/common_voice_6_0.py", line 159, in _split_generators archive_path = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_manager.py", line 150, in download download_config = self.download_config.copy() File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_config.py", line 73, in copy return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) TypeError: DownloadConfig.__init__() got an unexpected keyword argument 'ignore_url_params' ### Steps to reproduce the bug 1. install dataset with ```pip install datasets --upgrade``` 2. launch python; from datasets import loaad_dataset 3. run load_dataset("mozilla-foundation/common_voice_6_0") 4. exit python 5. uninstall datasets; then ```pip install datasets==2.21.0``` 6. launch python; from datasets import loaad_dataset 7. run load_dataset("mozilla-foundation/common_voice_6_0") 8. Everything runs great now ### Expected behavior Be able to download a dataset without error ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 3.0.1 - Platform: Linux-6.8.0-1017-aws-x86_64-with-glibc2.39 - Python version: 3.12.4 - `huggingface_hub` version: 0.26.0 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1
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[MINOR:TYPO] Update arrow_dataset.py
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2024-10-18T12:10:03Z
2024-10-24T15:06:43Z
2024-10-24T15:06:43Z
CONTRIBUTOR
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Fix wrong link. csv kwargs docstring link was pointing to pandas json docs.
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No need for dataset_info
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MEMBER
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save a useless call to /api/datasets/repo_id
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数据集数量问题
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### Describe the bug 这里我进行大模型微调,当数据集数量为718时,模型可以正常微调,但是当我添加一个在前718个数据集中的数据或者新增一个数据就会报错 ### Steps to reproduce the bug 1. 这里我的数据集可以微调的最后两个数据集是: { "messages": [ { "role": "user", "content": "完成校正装置设计后需要进行哪些工作?" }, { "role": "assistant", "content": "一旦完成校正装置设计后,需要进行系统实际调校工作,或者进行MATLAB仿真以检查系统的时间响应特性。这时,需将系统建模时省略的部分尽可能加入系统,以保证仿真结果的逼真度。如果由于系统各种固有非线性因素影响,或者由于系统噪声和负载效应等因素的影响,使已校正系统不能满足全部性能指标要求,则需要适当调整校正装置的形式或参数,直到已校正系统满足全部性能指标为止。" } ] } { "messages": [ { "role": "user", "content": "为什么闭环带宽要求会影响串联超前校正的效果?" }, { "role": "assistant", "content": "如果待校正系统不稳定,为了满足相角裕度的要求,超前网络需要提供很大的相角超前量。这会导致超前网络的a值必须选得很大,从而使已校正系统的带宽过大。带宽过大会使通过系统的高频噪声电平变高,可能导致系统失控。因此,闭环带宽的要求会影响串联超前校正的效果。" } ] } 2. 当我把倒数第二个数据集再次添加到数据集中时,此时我的最后三个数据集是 { "messages": [ { "role": "user", "content": "完成校正装置设计后需要进行哪些工作?" }, { "role": "assistant", "content": "一旦完成校正装置设计后,需要进行系统实际调校工作,或者进行MATLAB仿真以检查系统的时间响应特性。这时,需将系统建模时省略的部分尽可能加入系统,以保证仿真结果的逼真度。如果由于系统各种固有非线性因素影响,或者由于系统噪声和负载效应等因素的影响,使已校正系统不能满足全部性能指标要求,则需要适当调整校正装置的形式或参数,直到已校正系统满足全部性能指标为止。" } ] } { "messages": [ { "role": "user", "content": "为什么闭环带宽要求会影响串联超前校正的效果?" }, { "role": "assistant", "content": "如果待校正系统不稳定,为了满足相角裕度的要求,超前网络需要提供很大的相角超前量。这会导致超前网络的a值必须选得很大,从而使已校正系统的带宽过大。带宽过大会使通过系统的高频噪声电平变高,可能导致系统失控。因此,闭环带宽的要求会影响串联超前校正的效果。" } ] } { "messages": [ { "role": "user", "content": "完成校正装置设计后需要进行哪些工作?" }, { "role": "assistant", "content": "一旦完成校正装置设计后,需要进行系统实际调校工作,或者进行MATLAB仿真以检查系统的时间响应特性。这时,需将系统建模时省略的部分尽可能加入系统,以保证仿真结果的逼真度。如果由于系统各种固有非线性因素影响,或者由于系统噪声和负载效应等因素的影响,使已校正系统不能满足全部性能指标要求,则需要适当调整校正装置的形式或参数,直到已校正系统满足全部性能指标为止。" } ] } 这时系统会显示bug: root@autodl-container-027f4cad3d-6baf4e64:~/autodl-tmp# python GLM-4/finetune_demo/finetune.py datasets/ ZhipuAI/glm-4-9b-chat GLM-4/finetune_demo/configs/lora.yaml Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:02<00:00, 4.04it/s] The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable. trainable params: 2,785,280 || all params: 9,402,736,640 || trainable%: 0.0296 Generating train split: 0 examples [00:00, ? examples/s]Failed to load JSON from file '/root/autodl-tmp/datasets/train.jsonl' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 718 Generating train split: 0 examples [00:00, ? examples/s] ╭──────────────────────────────────────────────────────────────────────────────────────────────────────── Traceback (most recent call last) ─────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ /root/miniconda3/lib/python3.10/site-packages/datasets/packaged_modules/json/json.py:153 in _generate_tables │ │ │ │ 150 │ │ │ │ │ │ │ │ with open( │ │ 151 │ │ │ │ │ │ │ │ │ file, encoding=self.config.encoding, errors=self.con │ │ 152 │ │ │ │ │ │ │ │ ) as f: │ │ ❱ 153 │ │ │ │ │ │ │ │ │ df = pd.read_json(f, dtype_backend="pyarrow") │ │ 154 │ │ │ │ │ │ │ except ValueError: │ │ 155 │ │ │ │ │ │ │ │ logger.error(f"Failed to load JSON from file '{file}' wi │ │ 156 │ │ │ │ │ │ │ │ raise e │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/pandas/io/json/_json.py:815 in read_json │ │ │ │ 812 │ if chunksize: │ │ 813 │ │ return json_reader │ │ 814 │ else: │ │ ❱ 815 │ │ return json_reader.read() │ │ 816 │ │ 817 │ │ 818 class JsonReader(abc.Iterator, Generic[FrameSeriesStrT]): │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/pandas/io/json/_json.py:1025 in read │ │ │ │ 1022 │ │ │ │ │ │ data_lines = data.split("\n") │ │ 1023 │ │ │ │ │ │ obj = self._get_object_parser(self._combine_lines(data_lines)) │ │ 1024 │ │ │ │ else: │ │ ❱ 1025 │ │ │ │ │ obj = self._get_object_parser(self.data) │ │ 1026 │ │ │ │ if self.dtype_backend is not lib.no_default: │ │ 1027 │ │ │ │ │ return obj.convert_dtypes( │ │ 1028 │ │ │ │ │ │ infer_objects=False, dtype_backend=self.dtype_backend │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/pandas/io/json/_json.py:1051 in _get_object_parser │ │ │ │ 1048 │ │ } │ │ 1049 │ │ obj = None │ │ 1050 │ │ if typ == "frame": │ │ ❱ 1051 │ │ │ obj = FrameParser(json, **kwargs).parse() │ │ 1052 │ │ │ │ 1053 │ │ if typ == "series" or obj is None: │ │ 1054 │ │ │ if not isinstance(dtype, bool): │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/pandas/io/json/_json.py:1187 in parse │ │ │ │ 1184 │ │ │ 1185 │ @final │ │ 1186 │ def parse(self): │ │ ❱ 1187 │ │ self._parse() │ │ 1188 │ │ │ │ 1189 │ │ if self.obj is None: │ │ 1190 │ │ │ return None │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/pandas/io/json/_json.py:1403 in _parse │ │ │ │ 1400 │ │ │ │ 1401 │ │ if orient == "columns": │ │ 1402 │ │ │ self.obj = DataFrame( │ │ ❱ 1403 │ │ │ │ ujson_loads(json, precise_float=self.precise_float), dtype=None │ │ 1404 │ │ │ ) │ │ 1405 │ │ elif orient == "split": │ │ 1406 │ │ │ decoded = { │ ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ValueError: Trailing data During handling of the above exception, another exception occurred: ╭──────────────────────────────────────────────────────────────────────────────────────────────────────── Traceback (most recent call last) ─────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ /root/miniconda3/lib/python3.10/site-packages/datasets/builder.py:1997 in _prepare_split_single │ │ │ │ 1994 │ │ │ ) │ │ 1995 │ │ │ try: │ │ 1996 │ │ │ │ _time = time.time() │ │ ❱ 1997 │ │ │ │ for _, table in generator: │ │ 1998 │ │ │ │ │ if max_shard_size is not None and writer._num_bytes > max_shard_size │ │ 1999 │ │ │ │ │ │ num_examples, num_bytes = writer.finalize() │ │ 2000 │ │ │ │ │ │ writer.close() │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/packaged_modules/json/json.py:156 in _generate_tables │ │ │ │ 153 │ │ │ │ │ │ │ │ │ df = pd.read_json(f, dtype_backend="pyarrow") │ │ 154 │ │ │ │ │ │ │ except ValueError: │ │ 155 │ │ │ │ │ │ │ │ logger.error(f"Failed to load JSON from file '{file}' wi │ │ ❱ 156 │ │ │ │ │ │ │ │ raise e │ │ 157 │ │ │ │ │ │ │ if df.columns.tolist() == [0]: │ │ 158 │ │ │ │ │ │ │ │ df.columns = list(self.config.features) if self.config.f │ │ 159 │ │ │ │ │ │ │ try: │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/packaged_modules/json/json.py:130 in _generate_tables │ │ │ │ 127 │ │ │ │ │ │ try: │ │ 128 │ │ │ │ │ │ │ while True: │ │ 129 │ │ │ │ │ │ │ │ try: │ │ ❱ 130 │ │ │ │ │ │ │ │ │ pa_table = paj.read_json( │ │ 131 │ │ │ │ │ │ │ │ │ │ io.BytesIO(batch), read_options=paj.ReadOptions( │ │ 132 │ │ │ │ │ │ │ │ │ ) │ │ 133 │ │ │ │ │ │ │ │ │ break │ │ │ │ in pyarrow._json.read_json:308 │ │ │ │ in pyarrow.lib.pyarrow_internal_check_status:154 │ │ │ │ in pyarrow.lib.check_status:91 │ ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ArrowInvalid: JSON parse error: Missing a name for object member. in row 718 The above exception was the direct cause of the following exception: ╭──────────────────────────────────────────────────────────────────────────────────────────────────────── Traceback (most recent call last) ─────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ /root/autodl-tmp/GLM-4/finetune_demo/finetune.py:406 in main │ │ │ │ 403 ): │ │ 404 │ ft_config = FinetuningConfig.from_file(config_file) │ │ 405 │ tokenizer, model = load_tokenizer_and_model(model_dir, peft_config=ft_config.peft_co │ │ ❱ 406 │ data_manager = DataManager(data_dir, ft_config.data_config) │ │ 407 │ │ │ 408 │ train_dataset = data_manager.get_dataset( │ │ 409 │ │ Split.TRAIN, │ │ │ │ /root/autodl-tmp/GLM-4/finetune_demo/finetune.py:204 in __init__ │ │ │ │ 201 │ def __init__(self, data_dir: str, data_config: DataConfig): │ │ 202 │ │ self._num_proc = data_config.num_proc │ │ 203 │ │ │ │ ❱ 204 │ │ self._dataset_dct = _load_datasets( │ │ 205 │ │ │ data_dir, │ │ 206 │ │ │ data_config.data_format, │ │ 207 │ │ │ data_config.data_files, │ │ │ │ /root/autodl-tmp/GLM-4/finetune_demo/finetune.py:189 in _load_datasets │ │ │ │ 186 │ │ num_proc: Optional[int], │ │ 187 ) -> DatasetDict: │ │ 188 │ if data_format == '.jsonl': │ │ ❱ 189 │ │ dataset_dct = load_dataset( │ │ 190 │ │ │ data_dir, │ │ 191 │ │ │ data_files=data_files, │ │ 192 │ │ │ split=None, │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/load.py:2616 in load_dataset │ │ │ │ 2613 │ │ return builder_instance.as_streaming_dataset(split=split) │ │ 2614 │ │ │ 2615 │ # Download and prepare data │ │ ❱ 2616 │ builder_instance.download_and_prepare( │ │ 2617 │ │ download_config=download_config, │ │ 2618 │ │ download_mode=download_mode, │ │ 2619 │ │ verification_mode=verification_mode, │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/builder.py:1029 in download_and_prepare │ │ │ │ 1026 │ │ │ │ │ │ │ prepare_split_kwargs["max_shard_size"] = max_shard_size │ │ 1027 │ │ │ │ │ │ if num_proc is not None: │ │ 1028 │ │ │ │ │ │ │ prepare_split_kwargs["num_proc"] = num_proc │ │ ❱ 1029 │ │ │ │ │ │ self._download_and_prepare( │ │ 1030 │ │ │ │ │ │ │ dl_manager=dl_manager, │ │ 1031 │ │ │ │ │ │ │ verification_mode=verification_mode, │ │ 1032 │ │ │ │ │ │ │ **prepare_split_kwargs, │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/builder.py:1124 in _download_and_prepare │ │ │ │ 1121 │ │ │ │ │ 1122 │ │ │ try: │ │ 1123 │ │ │ │ # Prepare split will record examples associated to the split │ │ ❱ 1124 │ │ │ │ self._prepare_split(split_generator, **prepare_split_kwargs) │ │ 1125 │ │ │ except OSError as e: │ │ 1126 │ │ │ │ raise OSError( │ │ 1127 │ │ │ │ │ "Cannot find data file. " │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/builder.py:1884 in _prepare_split │ │ │ │ 1881 │ │ │ gen_kwargs = split_generator.gen_kwargs │ │ 1882 │ │ │ job_id = 0 │ │ 1883 │ │ │ with pbar: │ │ ❱ 1884 │ │ │ │ for job_id, done, content in self._prepare_split_single( │ │ 1885 │ │ │ │ │ gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args │ │ 1886 │ │ │ │ ): │ │ 1887 │ │ │ │ │ if done: │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/builder.py:2040 in _prepare_split_single │ │ │ │ 2037 │ │ │ │ e = e.__context__ │ │ 2038 │ │ │ if isinstance(e, DatasetGenerationError): │ │ 2039 │ │ │ │ raise │ │ ❱ 2040 │ │ │ raise DatasetGenerationError("An error occurred while generating the dataset │ │ 2041 │ │ │ │ 2042 │ │ yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_ │ │ 2043 │ ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ DatasetGenerationError: An error occurred while generating the dataset 3.请问是否可以帮我解决 ### Expected behavior 希望问题可以得到解决 ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.20.0 - Platform: Linux-4.19.90-2107.6.0.0192.8.oe1.bclinux.x86_64-x86_64-with-glibc2.35 - Python version: 3.10.8 - `huggingface_hub` version: 0.24.6 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2023.12.2
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7,232
(Super tiny doc update) Mention to_polars
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2024-10-17T06:08:53Z
2024-10-24T23:11:05Z
2024-10-24T15:06:16Z
CONTRIBUTOR
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polars is also quite popular now, thus this tiny update can tell users polars is supported
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7,231
Fix typo in image dataset docs
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2024-10-16T14:05:46Z
2024-10-16T17:06:21Z
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Fix typo in image dataset docs. Typo reported by @datavistics.
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Video support
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2024-10-15T18:17:29Z
2024-10-24T16:39:51Z
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(wip and experimental) adding the `Video` type based on `VideoReader` from `decord` ```python >>>from datasets import load_dataset >>> ds = load_dataset("path/to/videos", split="train").with_format("torch") >>> print(ds[0]["video"]) <decord.video_reader.VideoReader object at 0x337a47910> >>> print(ds[0]["video"][0]) tensor([[[73, 73, 73], [73, 73, 73], [73, 73, 73], ..., [23, 23, 23], [23, 23, 23], [23, 23, 23]]], dtype=torch.uint8) ``` the storage is the same as for audio and images: `{"path": pa.string(), "bytes": pa.binary()}` and I did a small to keep the hf:// URL in the "path" field if possible, this way the viewer can link to fiels on the hub if possible
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handle config_name=None in push_to_hub
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This caught me out - thought it might be better to explicitly handle None?
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Composite (multi-column) features
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### Feature request Structured data types (graphs etc.) might often be most efficiently stored as multiple columns, which then need to be combined during feature decoding Although it is currently possible to nest features as structs, my impression is that in particular when dealing with e.g. a feature composed of multiple numpy array / ArrayXD's, it would be more efficient to store each ArrayXD as a separate column (though I'm not sure by how much) Perhaps specification / implementation could be supported by something like: ``` features=Features(**{("feature0", "feature1")=Features(feature0=Array2D((None,10), dtype="float32"), feature1=Array2D((None,10), dtype="float32")) ``` ### Motivation Defining efficient composite feature types based on numpy arrays for representing data such as graphs with multiple node and edge attributes is currently challenging. ### Your contribution Possibly able to contribute
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fast array extraction
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Implements #7210 using method suggested in https://github.com/huggingface/datasets/pull/7207#issuecomment-2411789307 ```python import numpy as np from datasets import Dataset, Features, Array3D features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")}) dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,10,10), dtype=np.float32) for x in [2000,1000]*25] for i in range(2)}, features=features) ``` ~0.02 s vs 0.9s on main ```python ds = dataset.to_iterable_dataset() t0 = time.time() for ex in ds: pass t1 = time.time() ``` < 0.01 s vs 1.3 s on main @lhoestq I can see this breaks a bunch of array-related tests but can update the test cases if you would support making this change? I also added an Array1D feature which will always be decoded into a numpy array and likewise improves extraction performance: ```python from datasets import Dataset, Features, Array1D, Sequence, Value array_features=Features(**{"array0": Array1D((None,), dtype="float32"), "array1": Array1D((None,), dtype="float32")}) sequence_features=Features(**{"array0": Sequence(feature=Value("float32"), length=-1), "array1": Sequence(feature=Value("float32"), length=-1)}) array_dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,), dtype=np.float32) for x in [20000,10000]*25] for i in range(2)}, features=array_features) sequence_dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,), dtype=np.float32) for x in [20000,10000]*25] for i in range(2)}, features=sequence_features) ```python t0 = time.time() for ex in array_dataset.to_iterable_dataset(): pass t1 = time.time() ``` < 0.01 s ```python t0 = time.time() for ex in sequence_dataset.to_iterable_dataset(): pass t1 = time.time() ``` ~1.1s And also added support for extracting structs of arrays as dicts of numpy arrays: ```python import numpy as np from datasets import Dataset, Features, Array3D, Sequence features=Features(struct={"array0": Array3D((None,10,10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")}, _list=Sequence(feature=Array3D((None,10,10), dtype="float32"))) dataset = Dataset.from_dict({"struct": [{f"array{i}": np.zeros((x,10,10), dtype=np.float32) for i in range(2)} for x in [2000,1000]*25], "_list": [[np.zeros((x,10,10), dtype=np.float32) for i in range(2)] for x in [2000,1000]*25]}, features=features) ``` ```python t0 = time.time() for ex in dataset.to_iterable_dataset(): pass t1 = time.time() assert isinstance(ex["struct"]["array0"], np.ndarray) and ex["struct"]["array0"].ndim == 3 ``` ~0.02 s and no exception vs ~7s with an exception on main
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Add R as a How to use from the Polars (R) Library as an option
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### Feature request The boiler plate code to access a dataset via the hugging face file system is very useful. Please addd ## Add Polars (R) option The equivailent code works, because the [Polars-R](https://github.com/pola-rs/r-polars) wrapper has hugging faces funcitonaliy as well. ```r library(polars) df <- pl$read_parquet("hf://datasets/SALURBAL/core__admin_cube_public/core__admin_cube_public.parquet") ``` ## Polars (python) option ![image](https://github.com/user-attachments/assets/8f1bcd19-e578-4b18-b324-7cc00b80ac0a) ## Libraries Currently ![image](https://github.com/user-attachments/assets/0cf50063-f9db-443c-97b4-3ef0664b6e6e) ### Motivation There are many data/analysis/research/statistics teams (particularly in academia and pharma) that use R as the default language. R has great integration with most of the newer data techs (arrow, parquet, polars) and having this included could really help in bringing this community into the hugging faces ecosystem. **This is a small/low-hanging-fruit front end change but would make a big impact expanding the community** ### Your contribution I am not sure which repositroy this should be in, but I have experience in R, Python and JS and happy to submit a PR in the appropriate repository.
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### Describe the bug We push changes to our datasets programmatically. Our git client jGit reports that the hf git server returns null as Content-Type after a push. ### Steps to reproduce the bug A basic kotlin application: ``` val person = PersonIdent( "padmalcom", "[email protected]" ) val cp = UsernamePasswordCredentialsProvider( "padmalcom", "mysecrettoken" ) val git = KGit.cloneRepository { setURI("https://huggingface.co/datasets/sth/images") setTimeout(60) setProgressMonitor(TextProgressMonitor()) setCredentialsProvider(cp) } FileOutputStream("./images/images.csv").apply { writeCsv(images) } git.add { addFilepattern("images.csv") } for (i in images) { FileUtils.copyFile( File("./files/${i.id}"), File("./images/${i.id + File(i.fileName).extension }") ) git.add { addFilepattern("${i.id + File(i.fileName).extension }") } } val revCommit = git.commit { author = person message = "Uploading images at " + LocalDateTime.now() .format(DateTimeFormatter.ISO_DATE_TIME) setCredentialsProvider(cp) } val push = git.push { setCredentialsProvider(cp) } ``` ### Expected behavior The git server is expected to return the Content-Type _application/x-git-receive-pack-result_. ### Environment info It is independent from the datasets library.
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fallback to default feature casting in case custom features not available during dataset loading
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2024-10-12T16:13:56Z
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a fix for #7223 in case datasets is happy to support this kind of extensibility! seems cool / powerful for allowing sharing of datasets with potentially different feature types
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Fallback to arrow defaults when loading dataset with custom features that aren't registered locally
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CONTRIBUTOR
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### Describe the bug Datasets allows users to create and register custom features. However if datasets are then pushed to the hub, this means that anyone calling load_dataset without registering the custom Features in the same way as the dataset creator will get an error message. It would be nice to offer a fallback in this case. ### Steps to reproduce the bug ```python load_dataset("alex-hh/custom-features-example") ``` (Dataset creation process - must be run in separate session so that NewFeature isn't registered in session in which download is attempted:) ```python from dataclasses import dataclass, field import pyarrow as pa from datasets.features.features import register_feature from datasets import Dataset, Features, Value, load_dataset from datasets import Feature @dataclass class NewFeature(Feature): _type: str = field(default="NewFeature", init=False, repr=False) def __call__(self): return pa.int32() def examples_generator(): for i in range(5): yield {"feature": i} ds = Dataset.from_generator(examples_generator, features=Features(feature=NewFeature())) ds.push_to_hub("alex-hh/custom-features-example") register_feature(NewFeature, "NewFeature") ``` ### Expected behavior It would be nice, and offer greater extensibility, if there was some kind of graceful fallback mechanism in place for cases where user-defined features are stored in the dataset but not available locally. ### Environment info 3.0.2
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TypeError: Couldn't cast array of type string to null in long json
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2025-02-23T13:01:47Z
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### Describe the bug In general, changing the type from string to null is allowed within a dataset — there are even examples of this in the documentation. However, if the dataset is large and unevenly distributed, this allowance stops working. The schema gets locked in after reading a chunk. Consequently, if all values in the first chunk of a field are, for example, null, the field will be locked as type null, and if a string appears in that field in the second chunk, it will trigger this error: <details> <summary>Traceback </summary> ``` TypeError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1868 try: -> 1869 writer.write_table(table) 1870 except CastError as cast_error: 14 frames [/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_table(self, pa_table, writer_batch_size) 579 pa_table = pa_table.combine_chunks() --> 580 pa_table = table_cast(pa_table, self._schema) 581 if self.embed_local_files: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in table_cast(table, schema) 2291 if table.schema != schema: -> 2292 return cast_table_to_schema(table, schema) 2293 elif table.schema.metadata != schema.metadata: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_table_to_schema(table, schema) 2244 ) -> 2245 arrays = [ 2246 cast_array_to_feature( [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in <listcomp>(.0) 2245 arrays = [ -> 2246 cast_array_to_feature( 2247 table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type), [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in wrapper(array, *args, **kwargs) 1794 if isinstance(array, pa.ChunkedArray): -> 1795 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1796 else: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in <listcomp>(.0) 1794 if isinstance(array, pa.ChunkedArray): -> 1795 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1796 else: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_array_to_feature(array, feature, allow_primitive_to_str, allow_decimal_to_str) 2101 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 2102 return array_cast( 2103 array, [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in wrapper(array, *args, **kwargs) 1796 else: -> 1797 return func(array, *args, **kwargs) 1798 [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str) 1947 if pa.types.is_null(pa_type) and not pa.types.is_null(array.type): -> 1948 raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}") 1949 return array.cast(pa_type) TypeError: Couldn't cast array of type string to null The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [<ipython-input-353-e02f83980611>](https://localhost:8080/#) in <cell line: 1>() ----> 1 dd = load_dataset("json", data_files=["TEST.json"]) [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) 2094 2095 # Download and prepare data -> 2096 builder_instance.download_and_prepare( 2097 download_config=download_config, 2098 download_mode=download_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, dl_manager, base_path, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 922 if num_proc is not None: 923 prepare_split_kwargs["num_proc"] = num_proc --> 924 self._download_and_prepare( 925 dl_manager=dl_manager, 926 verification_mode=verification_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 997 try: 998 # Prepare split will record examples associated to the split --> 999 self._prepare_split(split_generator, **prepare_split_kwargs) 1000 except OSError as e: 1001 raise OSError( [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1738 job_id = 0 1739 with pbar: -> 1740 for job_id, done, content in self._prepare_split_single( 1741 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1742 ): [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1894 if isinstance(e, DatasetGenerationError): 1895 raise -> 1896 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1897 1898 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` </details> ### Steps to reproduce the bug ```python import json from datasets import load_dataset with open("TEST.json", "w") as f: row = {"ballast": "qwerty" * 1000, "b": None} row_str = json.dumps(row) + "\n" line_size = len(row_str) chunk_size = 10 << 20 lines_in_chunk = chunk_size // line_size + 1 print(f"Writing {lines_in_chunk} lines") for i in range(lines_in_chunk): f.write(row_str) null_row = {"ballast": "Gotcha", "b": "Not Null"} f.write(json.dumps(null_row) + "\n") load_dataset("json", data_files=["TEST.json"]) ``` ### Expected behavior Concatenation of the chunks without errors ### Environment info - `datasets` version: 3.0.1 - Platform: Linux-6.1.85+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.24.7 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1
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add CustomFeature base class to support user-defined features with encoding/decoding logic
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intended as fix for #7220 if this kind of extensibility is something that datasets is willing to support! ```python from datasets.features.features import CustomFeature class ListOfStrs(CustomFeature): requires_encoding = True def _encode_example(self, value): if isinstance(value, str): return [str] else: return value feats = Features(strlist=ListOfStrs()) feats.encode_example({"strlist": "a"})["strlist"] == feats["strlist"].encode_example("a") ```
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Custom features not compatible with special encoding/decoding logic
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2024-11-08T15:10:58Z
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### Describe the bug It is possible to register custom features using datasets.features.features.register_feature (https://github.com/huggingface/datasets/pull/6727) However such features are not compatible with Features.encode_example/decode_example if they require special encoding / decoding logic because encode_nested_example / decode_nested_example checks whether the feature is in a fixed list of encodable types: https://github.com/huggingface/datasets/blob/16a121d7821a7691815a966270f577e2c503473f/src/datasets/features/features.py#L1349 This prevents the extensibility of features to complex cases ### Steps to reproduce the bug ```python class ListOfStrs: def encode_example(self, value): if isinstance(value, str): return [str] else: return value feats = Features(strlist=ListOfStrs()) assert feats.encode_example({"strlist": "a"})["strlist"] = feats["strlist"].encode_example("a")} ``` ### Expected behavior Registered feature types should be encoded based on some property of the feature (e.g. requires_encoding)? ### Environment info 3.0.2
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bump fsspec
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ds.map(f, num_proc=10) is slower than df.apply
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### Describe the bug pandas columns: song_id, song_name ds = Dataset.from_pandas(df) def has_cover(song_name): if song_name is None or pd.isna(song_name): return False return 'cover' in song_name.lower() df['has_cover'] = df.song_name.progress_apply(has_cover) ds = ds.map(lambda x: {'has_cover': has_cover(x['song_name'])}, num_proc=10) time cost: 1. df.apply: 100%|██████████| 12500592/12500592 [00:13<00:00, 959825.47it/s] 2. ds.map: Map (num_proc=10):  31%  3899028/12500592 [00:28<00:38, 222532.89 examples/s] ### Steps to reproduce the bug pandas columns: song_id, song_name ds = Dataset.from_pandas(df) def has_cover(song_name): if song_name is None or pd.isna(song_name): return False return 'cover' in song_name.lower() df['has_cover'] = df.song_name.progress_apply(has_cover) ds = ds.map(lambda x: {'has_cover': has_cover(x['song_name'])}, num_proc=10) ### Expected behavior ds.map is ~num_proc faster than df.apply ### Environment info pandas: 2.2.2 datasets: 2.19.1
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Iterable dataset map with explicit features causes slowdown for Sequence features
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### Describe the bug When performing map, it's nice to be able to pass the new feature type, and indeed required by interleave and concatenate datasets. However, this can cause a major slowdown for certain types of array features due to the features being re-encoded. This is separate to the slowdown reported in #7206 ### Steps to reproduce the bug ``` from datasets import Dataset, Features, Array3D, Sequence, Value import numpy as np import time features=Features(**{"array0": Sequence(feature=Value("float32"), length=-1), "array1": Sequence(feature=Value("float32"), length=-1)}) dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,), dtype=np.float32) for x in [5000,10000]*25] for i in range(2)}, features=features) ``` ``` ds = dataset.to_iterable_dataset() ds = ds.with_format("numpy").map(lambda x: x) t0 = time.time() for ex in ds: pass t1 = time.time() ``` ~1.5 s on main ``` ds = dataset.to_iterable_dataset() ds = ds.with_format("numpy").map(lambda x: x, features=features) t0 = time.time() for ex in ds: pass t1 = time.time() ``` ~ 3 s on main ### Expected behavior I'm not 100% sure whether passing new feature types to formatted outputs of map should be supported or not, but assuming it should, then there should be a cost-free way to specify the new feature type - knowing feature type is required by interleave_datasets and concatenate_datasets for example ### Environment info 3.0.2
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Formatted map + with_format(None) changes array dtype for iterable datasets
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### Describe the bug When applying with_format -> map -> with_format(None), array dtypes seem to change, even if features are passed ### Steps to reproduce the bug ```python features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32")}) dataset = Dataset.from_dict({f"array0": [np.zeros((100,10,10), dtype=np.float32)]*25}, features=features) ds = dataset.to_iterable_dataset().with_format("numpy").map(lambda x: x, features=features) ex_0 = next(iter(ds)) ds = dataset.to_iterable_dataset().with_format("numpy").map(lambda x: x, features=features).with_format(None) ex_1 = next(iter(ds)) assert ex_1["array0"].dtype == ex_0["array0"].dtype, f"{ex_1['array0'].dtype} {ex_0['array0'].dtype}" ``` ### Expected behavior Dtypes should be preserved. ### Environment info 3.0.2
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Add with_rank to Dataset.from_generator
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### Feature request Add `with_rank` to `Dataset.from_generator` similar to `Dataset.map` and `Dataset.filter`. ### Motivation As for `Dataset.map` and `Dataset.filter`, this is useful when creating cache files using multi-GPU, where the rank can be used to select GPU IDs. For now, rank can be added in the `gen_kwars` argument; however, this, in turn, includes the rank when computing the fingerprint. ### Your contribution Added #7199 which passes rank based on the `job_id` set by `num_proc`.
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Windows do not supprot signal.alarm and singal.signal
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### Describe the bug signal.alarm and signal.signal are used in the load.py module, but these are not supported by Windows. ### Steps to reproduce the bug lighteval accelerate --model_args "pretrained=gpt2,trust_remote_code=True" --tasks "community|kinit_sts" --custom_tasks "community_tasks/kinit_evals.py" --output_dir "./evals" ### Expected behavior proceed with input(..) method ### Environment info Windows 11
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Describe only selected fields in README
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### Feature request Hi Datasets team! Is it possible to add the ability to describe only selected fields of the dataset files in `README.md`? For example, I have this open dataset ([open-llm-leaderboard/results](https://huggingface.co/datasets/open-llm-leaderboard/results?row=0)) and I want to describe only some fields in order not to overcomplicate the Dataset Preview and filter out some fields ### Motivation The `Results` dataset for the Open LLM Leaderboard contains json files with a complex nested structure. I would like to add `README.md` there to use the SQL console, for example. But if I describe the structure of this dataset completely, it will overcomplicate the use of Dataset Preview and the total number of columns will exceed 50 ### Your contribution I'm afraid I'm not familiar with the project structure, so I won't be able to open a PR, but I'll try to help with something else if possible
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Convert Array features to numpy arrays rather than lists by default
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2024-10-09T13:05:21Z
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CONTRIBUTOR
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### Feature request It is currently quite easy to cause massive slowdowns when using datasets and not familiar with the underlying data conversions by e.g. making bad choices of formatting. Would it be more user-friendly to set defaults that avoid this as much as possible? e.g. format Array features as numpy arrays rather than python lists ### Motivation Default array formatting leads to slow performance: e.g. ```python import numpy as np from datasets import Dataset, Features, Array3D features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")}) dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,10,10), dtype=np.float32) for x in [2000,1000]*25] for i in range(2)}, features=features) ``` ```python t0 = time.time() for ex in ds: pass t1 = time.time() ``` ~1.4 s ```python ds = dataset.to_iterable_dataset() t0 = time.time() for ex in ds: pass t1 = time.time() ``` ~10s ```python ds = dataset.with_format("numpy") t0 = time.time() for ex in ds: pass t1 = time.time() ``` ~0.04s ```python ds = dataset.to_iterable_dataset().with_format("numpy") t0 = time.time() for ex in ds: pass t1 = time.time() ``` ~0.04s ### Your contribution May be able to contribute
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7,209
Preserve features in iterable dataset.filter
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2024-10-09T10:42:05Z
2024-10-16T11:27:22Z
2024-10-09T16:04:07Z
CONTRIBUTOR
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Fixes example in #7208 - I'm not sure what other checks I should do? @lhoestq I also haven't thought hard about the concatenate / interleaving example iterables but think this might work assuming that features are either all identical or None?
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7,208
Iterable dataset.filter should not override features
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2024-10-09T10:23:45Z
2024-10-09T16:08:46Z
2024-10-09T16:08:45Z
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### Describe the bug When calling filter on an iterable dataset, the features get set to None ### Steps to reproduce the bug import numpy as np import time from datasets import Dataset, Features, Array3D ```python features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")}) dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,10,10), dtype=np.float32) for x in [2000,1000]*25] for i in range(2)}, features=features) ds = dataset.to_iterable_dataset() orig_column_names = ds.column_names ds = ds.filter(lambda x: True) assert ds.column_names == orig_column_names ``` ### Expected behavior Filter should preserve features information ### Environment info 3.0.2
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7,207
apply formatting after iter_arrow to speed up format -> map, filter for iterable datasets
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2024-10-08T15:44:53Z
2025-01-14T18:36:03Z
2025-01-14T16:59:30Z
CONTRIBUTOR
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I got to this by hacking around a bit but it seems to solve #7206 I have no idea if this approach makes sense or would break something else? Could maybe work on a full pr if this looks reasonable @lhoestq ? I imagine the same issue might affect other iterable dataset methods?
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7,206
Slow iteration for iterable dataset with numpy formatting for array data
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2024-10-08T15:38:11Z
2024-10-17T17:14:52Z
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### Describe the bug When working with large arrays, setting with_format to e.g. numpy then applying map causes a significant slowdown for iterable datasets. ### Steps to reproduce the bug ```python import numpy as np import time from datasets import Dataset, Features, Array3D features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")}) dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,10,10), dtype=np.float32) for x in [2000,1000]*25] for i in range(2)}, features=features) ``` Then ```python ds = dataset.to_iterable_dataset() ds = ds.with_format("numpy").map(lambda x: x) t0 = time.time() for ex in ds: pass t1 = time.time() print(t1-t0) ``` takes 27 s, whereas ```python ds = dataset.to_iterable_dataset() ds = ds.with_format("numpy") ds = dataset.to_iterable_dataset() t0 = time.time() for ex in ds: pass t1 = time.time() print(t1 - t0) ``` takes ~1s ### Expected behavior Map should not introduce a slowdown when formatting is enabled. ### Environment info 3.0.2
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fix ci benchmark
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2024-10-08T15:06:18Z
2024-10-08T15:25:28Z
2024-10-08T15:25:25Z
MEMBER
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we're not using the benchmarks anymore + they were not working anyway due to token permissions I keep the code in case we ever want to re-run the benchmark manually
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fix unbatched arrow map for iterable datasets
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2024-10-08T13:54:09Z
2024-10-08T14:19:47Z
2024-10-08T14:19:47Z
CONTRIBUTOR
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Fixes the bug when applying map to an arrow-formatted iterable dataset described here: https://github.com/huggingface/datasets/issues/6833#issuecomment-2399903885 ```python from datasets import load_dataset ds = load_dataset("rotten_tomatoes", split="train", streaming=True) ds = ds.with_format("arrow").map(lambda x: x) for ex in ds: pass ``` @lhoestq
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with_format docstring
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reported at https://github.com/huggingface/datasets/issues/3444
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`from_parquet` return type annotation
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### Describe the bug As already posted in https://github.com/microsoft/pylance-release/issues/6534, the correct type hinting fails when building a dataset using the `from_parquet` constructor. Their suggestion is to comprehensively annotate the method's return type to better align with the docstring information. ### Steps to reproduce the bug ```python from datasets import Dataset dataset = Dataset.from_parquet(path_or_paths="file") dataset.map(lambda x: {"new": x["old"]}, batched=True) ``` ### Expected behavior map is a [valid](https://huggingface.co/docs/datasets/v3.0.1/en/package_reference/main_classes#datasets.Dataset.map), no error should be thrown. ### Environment info - `datasets` version: 3.0.1 - Platform: macOS-15.0.1-arm64-arm-64bit - Python version: 3.12.6 - `huggingface_hub` version: 0.25.1 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1
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`load_dataset()` of images from a single directory where `train.png` image exists
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### Describe the bug Hey! Firstly, thanks for maintaining such framework! I had a small issue, where I wanted to load a custom dataset of image+text captioning. I had all of my images in a single directory, and one of the images had the name `train.png`. Then, the loaded dataset had only this image. I guess it's related to "train" as a split name, but it's definitely an unexpected behavior :) Unfortunately I don't have time to submit a proper PR. I'm attaching a toy example to reproduce the issue. Thanks, Sagi ### Steps to reproduce the bug All of the steps I'm attaching are in a fresh env :) ``` (base) sagipolaczek@Sagis-MacBook-Pro ~ % conda activate hf_issue_env (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % python --version Python 3.10.15 (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % pip list | grep datasets datasets 3.0.1 (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % ls -la Documents/hf_datasets_issue total 352 drwxr-xr-x 6 sagipolaczek staff 192 Oct 7 11:59 . drwx------@ 23 sagipolaczek staff 736 Oct 7 11:46 .. -rw-r--r--@ 1 sagipolaczek staff 72 Oct 7 11:59 metadata.csv -rw-r--r--@ 1 sagipolaczek staff 160154 Oct 6 18:00 pika.png -rw-r--r--@ 1 sagipolaczek staff 5495 Oct 6 12:02 pika_pika.png -rw-r--r--@ 1 sagipolaczek staff 1753 Oct 6 11:50 train.png (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % cat Documents/hf_datasets_issue/metadata.csv file_name,text train.png,A train pika.png,Pika pika_pika.png,Pika Pika! (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % python Python 3.10.15 (main, Oct 3 2024, 02:33:33) [Clang 14.0.6 ] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> dataset = load_dataset("imagefolder", data_dir="Documents/hf_datasets_issue/") >>> dataset DatasetDict({ train: Dataset({ features: ['image', 'text'], num_rows: 1 }) }) >>> dataset["train"][0] {'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=354x84 at 0x10B50FD90>, 'text': 'A train'} ### DELETING `train.png` sample ### (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % vim Documents/hf_datasets_issue/metadata.csv (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % rm Documents/hf_datasets_issue/train.png (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % python Python 3.10.15 (main, Oct 3 2024, 02:33:33) [Clang 14.0.6 ] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> dataset = load_dataset("imagefolder", data_dir="Documents/hf_datasets_issue/") Generating train split: 2 examples [00:00, 65.99 examples/s] >>> dataset DatasetDict({ train: Dataset({ features: ['image', 'text'], num_rows: 2 }) }) >>> dataset["train"] Dataset({ features: ['image', 'text'], num_rows: 2 }) >>> dataset["train"][0],dataset["train"][1] ({'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=2356x1054 at 0x10DD11E70>, 'text': 'Pika'}, {'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=343x154 at 0x10E258C70>, 'text': 'Pika Pika!'}) ``` ### Expected behavior My expected behavior would be to get a dataset with the sample `train.png` in it (along with the others data points). ### Environment info I've attached it in the example: Python 3.10.15 datasets 3.0.1
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Fix the environment variable for huggingface cache
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2024-10-05T11:54:35Z
2024-10-30T23:10:27Z
2024-10-08T15:45:18Z
CONTRIBUTOR
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Resolve #6256. As far as I tested, `HF_DATASETS_CACHE` was ignored and I could not specify the cache directory at all except for the default one by this environment variable. `HF_HOME` has worked. Perhaps the recent change on file downloading by `huggingface_hub` could affect this bug. In my testing, I could not specify the cache directory even by `load_dataset("dataset_name" cache_dir="...")`. It might be another issue. I also welcome any advice to solve this issue.
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Add with_rank to Dataset.from_generator
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2024-10-04T16:51:53Z
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Adds `with_rank` to `Dataset.from_generator`. As for `Dataset.map` and `Dataset.filter`, this is useful when creating cache files using multi-GPU.
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Add repeat method to datasets
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Following up on discussion in #6623 and #7198 I thought this would be pretty useful for my case so had a go at implementing. My main motivation is to be able to call iterable_dataset.repeat(None).take(samples_per_epoch) to safely avoid timeout issues in a distributed training setting. This would provide a straightforward workaround for several open issues related to this situation: https://github.com/huggingface/datasets/issues/6437, https://github.com/huggingface/datasets/issues/6594, https://github.com/huggingface/datasets/issues/6623, https://github.com/huggingface/datasets/issues/6719. @lhoestq let me know if this looks on the right track!
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ConnectionError: Couldn't reach 'allenai/c4' on the Hub (ConnectionError)数据集下不下来,怎么回事
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### Describe the bug from datasets import load_dataset print("11") traindata = load_dataset('ptb_text_only', 'penn_treebank', split='train') print("22") valdata = load_dataset('ptb_text_only', 'penn_treebank', split='validation') ### Steps to reproduce the bug 1 ### Expected behavior 1 ### Environment info 1
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concatenate_datasets does not preserve shuffling state
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2024-10-03T14:30:38Z
2025-03-18T10:56:47Z
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CONTRIBUTOR
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### Describe the bug After concatenate datasets on an iterable dataset, the shuffling state is destroyed, similar to #7156 This means concatenation cant be used for resolving uneven numbers of samples across devices when using iterable datasets in a distributed setting as discussed in #6623 I also noticed that the number of shards is the same after concatenation, which I found surprising, but I don't understand the internals well enough to know whether this is actually surprising or not ### Steps to reproduce the bug ```python import datasets import torch.utils.data def gen(shards): yield {"shards": shards} def main(): dataset1 = datasets.IterableDataset.from_generator( gen, gen_kwargs={"shards": list(range(25))} # TODO: how to understand this? ) dataset2 = datasets.IterableDataset.from_generator( gen, gen_kwargs={"shards": list(range(25, 50))} # TODO: how to understand this? ) dataset1 = dataset1.shuffle(buffer_size=1) dataset2 = dataset2.shuffle(buffer_size=1) print(dataset1.n_shards) print(dataset2.n_shards) dataset = datasets.concatenate_datasets( [dataset1, dataset2] ) print(dataset.n_shards) # dataset = dataset1 dataloader = torch.utils.data.DataLoader( dataset, batch_size=8, num_workers=0, ) for i, batch in enumerate(dataloader): print(batch) print("\nNew epoch") dataset = dataset.set_epoch(1) for i, batch in enumerate(dataloader): print(batch) if __name__ == "__main__": main() ``` ### Expected behavior Shuffling state should be preserved ### Environment info Latest datasets
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Add support for 3D datasets
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See https://huggingface.co/datasets/allenai/objaverse for example
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datasets.exceptions.DatasetNotFoundError for private dataset
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### Describe the bug The following Python code tries to download a private dataset and fails with the error `datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed.`. Downloading a public dataset doesn't work. ``` py from datasets import load_dataset _ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly") ``` This seems to be just an issue with my machine config as the code above works with a colleague's machine. So far I have tried: - logging back out and in from the Huggingface CLI using `huggingface-cli logout` - manually removing the token cache at `/Users/kalyan/.cache/huggingface/token` (found using `huggingface-cli env`) - manually passing a token in `load_dataset` My output of `huggingface-cli whoami`: ``` kdutia orgs: ClimatePolicyRadar ``` ### Steps to reproduce the bug ``` python Python 3.12.2 (main, Feb 6 2024, 20:19:44) [Clang 15.0.0 (clang-1500.1.0.2.5)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> _ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 2074, in load_dataset builder_instance = load_dataset_builder( ^^^^^^^^^^^^^^^^^^^^^ File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1795, in load_dataset_builder dataset_module = dataset_module_factory( ^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1659, in dataset_module_factory raise e1 from None File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1597, in dataset_module_factory raise DatasetNotFoundError(f"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.") from e datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed. >>> ``` ### Expected behavior The dataset downloads successfully. ### Environment info From `huggingface-cli env`: ``` - huggingface_hub version: 0.25.1 - Platform: macOS-14.2.1-arm64-arm-64bit - Python version: 3.12.2 - Running in iPython ?: No - Running in notebook ?: No - Running in Google Colab ?: No - Running in Google Colab Enterprise ?: No - Token path ?: /Users/kalyan/.cache/huggingface/token - Has saved token ?: True - Who am I ?: kdutia - Configured git credential helpers: osxkeychain - FastAI: N/A - Tensorflow: N/A - Torch: N/A - Jinja2: 3.1.4 - Graphviz: N/A - keras: N/A - Pydot: N/A - Pillow: N/A - hf_transfer: N/A - gradio: N/A - tensorboard: N/A - numpy: 2.1.1 - pydantic: N/A - aiohttp: 3.10.8 - ENDPOINT: https://huggingface.co - HF_HUB_CACHE: /Users/kalyan/.cache/huggingface/hub - HF_ASSETS_CACHE: /Users/kalyan/.cache/huggingface/assets - HF_TOKEN_PATH: /Users/kalyan/.cache/huggingface/token - HF_HUB_OFFLINE: False - HF_HUB_DISABLE_TELEMETRY: False - HF_HUB_DISABLE_PROGRESS_BARS: None - HF_HUB_DISABLE_SYMLINKS_WARNING: False - HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False - HF_HUB_DISABLE_IMPLICIT_TOKEN: False - HF_HUB_ENABLE_HF_TRANSFER: False - HF_HUB_ETAG_TIMEOUT: 10 - HF_HUB_DOWNLOAD_TIMEOUT: 10 ``` from `datasets-cli env`: ``` - `datasets` version: 3.0.1 - Platform: macOS-14.2.1-arm64-arm-64bit - Python version: 3.12.2 - `huggingface_hub` version: 0.25.1 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1 ```
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7,193
Support of num_workers (multiprocessing) in map for IterableDataset
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### Feature request Currently, IterableDataset doesn't support setting num_worker in .map(), which results in slow processing here. Could we add support for it? As .map() can be run in the batch fashion (e.g., batch_size is default to 1000 in datasets), it seems to be doable for IterableDataset as the regular Dataset. ### Motivation Improving data processing efficiency ### Your contribution Testing
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7,192
Add repeat() for iterable datasets
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2025-03-18T10:48:33Z
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### Feature request It would be useful to be able to straightforwardly repeat iterable datasets indefinitely, to provide complete control over starting and ending of iteration to the user. An IterableDataset.repeat(n) function could do this automatically ### Motivation This feature was discussed in this issue https://github.com/huggingface/datasets/issues/7147, and would resolve the need to use the hack of interleave datasets with probability 0 as a simple way to achieve this functionality. An additional benefit might be the simplification of the use of iterable datasets in a distributed setting: If the user can assume that datasets will repeat indefinitely, then issues around different numbers of samples appearing on different devices (e.g. https://github.com/huggingface/datasets/issues/6437, https://github.com/huggingface/datasets/issues/6594, https://github.com/huggingface/datasets/issues/6623, https://github.com/huggingface/datasets/issues/6719) can potentially be straightforwardly resolved by simply doing: ids.repeat(None).take(n_samples_per_epoch) ### Your contribution I'm not familiar enough with the codebase to assess how straightforward this would be to implement. If it might be very straightforward, I could possibly have a go.
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Solution to issue: #7080 Modified load_dataset function, so that it prompts the user to select a dataset when subdatasets or splits (train, test) are available
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# Feel free to give suggestions please.. ### This PR is raised because of issue: https://github.com/huggingface/datasets/issues/7080 ![image](https://github.com/user-attachments/assets/8fbc604f-f0a5-4a59-a63e-aa4c26442c83) ### This PR gives solution to https://github.com/huggingface/datasets/issues/7080 1. Checking whether the dataset has splits or subdatasets. 2. Printing the available splits/subdatasets. 3. Asking the user to choose which one to load. 4. Loading only the selected dataset based on the user's input. ### Key Changes: 1. Available Splits/Subdatasets: The code checks for available splits/subdatasets using builder_instance.info.splits.keys(). 2. User Prompt: If splits are found, it prints them out and prompts the user to select one. 3. Loading Based on User Input: The dataset is loaded based on the user's choice. This way, the dataset loading function will interactively prompt the user to select which subdataset or split they want to load instead of automatically loading all of them.
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Datasets conflicts with fsspec 2024.9
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### Describe the bug Installing both in latest versions are not possible `pip install "datasets==3.0.1" "fsspec==2024.9.0"` But using older version of datasets is ok `pip install "datasets==1.24.4" "fsspec==2024.9.0"` ### Steps to reproduce the bug `pip install "datasets==3.0.1" "fsspec==2024.9.0"` ### Expected behavior install both versions. ### Environment info debian 11. python 3.10.15
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Audio preview in dataset viewer for audio array data without a path/filename
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### Feature request Huggingface has quite a comprehensive set of guides for [audio datasets](https://huggingface.co/docs/datasets/en/audio_dataset). It seems, however, all these guides assume the audio array data to be decoded/inserted into a HF dataset always originates from individual files. The [Audio-dataclass](https://github.com/huggingface/datasets/blob/3.0.1/src/datasets/features/audio.py#L20) appears designed with this assumption in mind. Looking at its source code it returns a dictionary with the keys `path`, `array` and `sampling_rate`. However, sometimes users may have different pipelines where they themselves decode the audio array. This feature request has to do with wishing some clarification in guides on whether it is possible, and in such case how users can insert already decoded audio array data into datasets (pandas DataFrame, HF dataset or whatever) that are later saved as parquet, and still get a functioning audio preview in the dataset viewer. Do I perhaps need to write a tempfile of my audio array slice to wav and capture the bytes object with `io.BytesIO` and pass that to `Audio()`? ### Motivation I'm working with large audio datasets, and my pipeline reads (decodes) audio from larger files, and slices the relevant portions of audio from that larger file based on metadata I have available. The pipeline is designed this way to avoid having to store multiple copies of data, and to avoid having to store tens of millions of small files. I tried [test-uploading parquet files](https://huggingface.co/datasets/Lauler/riksdagen_test) where I store the audio array data of decoded slices of audio in an `audio` column with a dictionary with the keys `path`, `array` and `sampling_rate`. But I don't know the secret sauce of what the Huggingface Hub expects and requires to be able to display audio previews correctly. ### Your contribution I could contribute a tool agnostic guide of creating HF audio datasets directly as parquet to the HF documentation if there is an interest. Provided you help me figure out the secret sauce of what the dataset viewer expects to display the preview correctly.
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Pin multiprocess<0.70.1 to align with dill<0.3.9
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Pin multiprocess<0.70.1 to align with dill<0.3.9. Note that multiprocess-0.70.1 requires dill-0.3.9: https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17 Fix #7186.
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shard_data_sources() got an unexpected keyword argument 'worker_id'
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### Describe the bug ``` [rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 238, in __iter__ [rank0]: for key_example in islice(self.generate_examples_fn(**gen_kwags), shard_example_idx_start, None): [rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/packaged_modules/generator/generator.py", line 32, in _generate_examples [rank0]: for idx, ex in enumerate(self.config.generator(**gen_kwargs)): [rank0]: File "/home/qinghao/workdir/doremi/doremi/dataloader.py", line 337, in take_data_generator [rank0]: for ex in ds: [rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1791, in __iter__ [rank0]: yield from self._iter_pytorch() [rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1704, in _iter_pytorch [rank0]: ex_iterable = ex_iterable.shard_data_sources(worker_id=worker_info.id, num_workers=worker_info.num_workers) [rank0]: TypeError: UpdatableRandomlyCyclingMultiSourcesExamplesIterable.shard_data_sources() got an unexpected keyword argument 'worker_id' ``` ### Steps to reproduce the bug IterableDataset cannot use ### Expected behavior can work on datasets==2.10, but will raise error for later versions. ### Environment info datasets==3.0.1
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pinning `dill<0.3.9` without pinning `multiprocess`
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2024-10-01T22:29:32Z
2024-10-02T06:08:24Z
2024-10-02T06:08:24Z
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### Describe the bug The [latest `multiprocess` release](https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17) requires `dill>=0.3.9` which causes issues when installing `datasets` without backtracking during package version resolution. Is it possible to add a pin for multiprocess so something like `multiprocess<=0.70.16` so that the `dill` version is compatible? ### Steps to reproduce the bug NA ### Expected behavior NA ### Environment info NA
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CI benchmarks are broken
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2024-10-09T16:07:48Z
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Since Aug 30, 2024, CI benchmarks are broken: https://github.com/huggingface/datasets/actions/runs/11108421214/job/30861323975 ``` {"level":"error","message":"Resource not accessible by integration","name":"HttpError","request":{"body":"{\"body\":\"<details>\\n<summary>Show benchmarks</summary>\\n\\nPyArrow==8.0.0\\n\\n<details>\\n<summary>Show updated benchmarks!</summary>\\n\\n### Benchmark: benchmark_array_xd.json\\n\\n| metric | read_batch_formatted_as_numpy after write_array2d | ... "headers":{"accept":"application/vnd.github.v3+json","authorization":"token [REDACTED]","content-type":"application/json; charset=utf-8","user-agent":"octokit-rest.js/18.0.0 octokit-core.js/3.6.0 Node.js/16.20.2 (linux; x64)"},"method":"POST","request":{"agent":{"_events":{},"_eventsCount":2,"cache": ... "response":{"data":{"documentation_url":"https://docs.github.com/rest/issues/comments#create-an-issue-comment","message":"Resource not accessible by integration","status":"403"}, ... "stack":"HttpError: Resource not accessible by integration\n at /usr/lib/node_modules/@dvcorg/cml/node_modules/@octokit/request/dist-node/index.js:86:21\n at processTicksAndRejections (node:internal/process/task_queues:96:5)\n at async Job.doExecute (/usr/lib/node_modules/@dvcorg/cml/node_modules/bottleneck/light.js:405:18)","status":403} ```
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Pin dill<0.3.9 to fix CI
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2024-09-30T14:26:25Z
2024-09-30T14:38:59Z
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Pin dill<0.3.9 to fix CI for deps-latest. Note that dill-0.3.9 was released yesterday Sep 29, 2024: - https://pypi.org/project/dill/0.3.9/ - https://github.com/uqfoundation/dill/releases/tag/0.3.9 Fix #7183.
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CI is broken for deps-latest
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2024-09-30T14:38:58Z
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See: https://github.com/huggingface/datasets/actions/runs/11106149906/job/30853879890 ``` =========================== short test summary info ============================ FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_caching_on_disk - AssertionError: Lists differ: [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}] != [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}] First differing element 1: {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'} {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'} [{'filename': '/tmp/tmp6xcyyjs4/dataset0.arrow'}, - {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}] ? ^^^^^ -------- + {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}] ? ++++++++++ ^^ + FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - AssertionError: Lists differ: [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}] != [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}] First differing element 0: {'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'} {'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'} - [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}] ? ^^ ----------- + [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}] ? +++++++++++ ^^ FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_regex - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ignores_line_definition_of_function - AssertionError: '52e56ee04ad92499' != '0a4f75cec280f634' - 52e56ee04ad92499 + 0a4f75cec280f634 FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ipython_function - AssertionError: 'a6bd2041ca63d6c0' != '517bf36b7eecdef5' - a6bd2041ca63d6c0 + 517bf36b7eecdef5 FAILED tests/test_fingerprint.py::HashingTest::test_hash_tiktoken_encoding - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_compiled_module - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_generator - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_tensor - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_set_doesnt_depend_on_order - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_set_stable - NameError: name 'log' is not defined ERROR tests/test_iterable_dataset.py::test_iterable_dataset_from_file - NameError: name 'log' is not defined = 11 failed, 2850 passed, 3 skipped, 23 warnings, 1 error in 191.06s (0:03:11) = ```
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Support features in metadata configs
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2024-09-30T11:14:53Z
2024-10-09T16:03:57Z
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Support features in metadata configs, like: ``` configs: - config_name: default features: - name: id dtype: int64 - name: name dtype: string - name: score dtype: float64 ``` This will allow to avoid inference of data types. Currently, we allow passing this information in the `dataset_info` (instead of `configs`) field, but this is not intuitive and it is not properly documented. TODO: - [ ] Document usage
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Fix datasets export to JSON
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2024-09-29T12:45:20Z
2024-11-01T11:55:36Z
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Memory leak when wrapping datasets into PyTorch Dataset without explicit deletion
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2024-09-30T12:07:56Z
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### Describe the bug I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use. ### Steps to reproduce the bug Steps to reproduce: Create a PyTorch Dataset wrapper for 'nebula/cc12m': ```` from torch.utils.data import Dataset from tqdm import tqdm from datasets import load_dataset from torchvision import transforms Image.MAX_IMAGE_PIXELS = None class CC12M(Dataset): def __init__(self, path_or_name='nebula/cc12m', split='train', transform=None, single_caption=True): self.raw_dataset = load_dataset(path_or_name)[split] if transform is None: self.transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize( mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711] ) ]) else: self.transform = transforms.Compose(transform) self.single_caption = single_caption self.length = len(self.raw_dataset) def __len__(self): return self.length def __getitem__(self, index): item = self.raw_dataset[index] caption = item['txt'] with io.BytesIO(item['webp']) as buffer: image = Image.open(buffer).convert('RGB') if self.transform: image = self.transform(image) # del item # Uncomment this line to prevent the memory leak return image, caption ```` Iterate through the dataset without the del item line in __getitem__. Observe RAM usage increasing constantly. Add del item at the end of __getitem__: ``` def __getitem__(self, index): item = self.raw_dataset[index] caption = item['txt'] with io.BytesIO(item['webp']) as buffer: image = Image.open(buffer).convert('RGB') if self.transform: image = self.transform(image) del item # This line prevents the memory leak return image, caption ``` Iterate through the dataset again and observe that RAM usage remains stable. ### Expected behavior Expected behavior: RAM usage should remain stable during iteration without needing to explicitly delete items. Actual behavior: RAM usage constantly increases unless items are explicitly deleted after use ### Environment info - `datasets` version: 2.21.0 - Platform: Linux-4.18.0-513.5.1.el8_9.x86_64-x86_64-with-glibc2.28 - Python version: 3.12.4 - `huggingface_hub` version: 0.24.6 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1
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Support Python 3.11
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Support Python 3.11. Fix #7178.
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Support Python 3.11
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Support Python 3.11: https://peps.python.org/pep-0664/
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Fix release instructions
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Fix release instructions. During last release, I had to make this additional update.
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fix grammar in fingerprint.py
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2024-09-26T16:13:42Z
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I see this error all the time and it was starting to get to me.
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[FSTimeoutError] load_dataset
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2025-02-01T09:09:35Z
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### Describe the bug When using `load_dataset`to load [HuggingFaceM4/VQAv2](https://huggingface.co/datasets/HuggingFaceM4/VQAv2), I am getting `FSTimeoutError`. ### Error ``` TimeoutError: The above exception was the direct cause of the following exception: FSTimeoutError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/fsspec/asyn.py](https://klh9mr78js-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240924-060116_RC00_678132060#) in sync(loop, func, timeout, *args, **kwargs) 99 if isinstance(return_result, asyncio.TimeoutError): 100 # suppress asyncio.TimeoutError, raise FSTimeoutError --> 101 raise FSTimeoutError from return_result 102 elif isinstance(return_result, BaseException): 103 raise return_result FSTimeoutError: ``` It usually fails around 5-6 GB. <img width="847" alt="Screenshot 2024-09-26 at 9 10 19 PM" src="https://github.com/user-attachments/assets/ff91995a-fb55-4de6-8214-94025d6c8470"> ### Steps to reproduce the bug To reproduce it, run this in colab notebook: ``` !pip install -q -U datasets from datasets import load_dataset ds = load_dataset('HuggingFaceM4/VQAv2', split="train[:10%]") ``` ### Expected behavior It should download properly. ### Environment info Using Colab Notebook.
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Set dev version
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Add torchdata as a regular test dependency
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Add `torchdata` as a regular test dependency. Note that previously, `torchdata` was installed from their repo and current main branch (0.10.0.dev) requires Python>=3.9. Also note they made a recent release: 0.8.0 on Jul 31, 2024. Fix #7171.
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CI is broken: No solution found when resolving dependencies
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See: https://github.com/huggingface/datasets/actions/runs/11046967444/job/30687294297 ``` Run uv pip install --system -r additional-tests-requirements.txt --no-deps × No solution found when resolving dependencies: ╰─▶ Because the current Python version (3.8.18) does not satisfy Python>=3.9 and torchdata==0.10.0a0+1a98f21 depends on Python>=3.9, we can conclude that torchdata==0.10.0a0+1a98f21 cannot be used. And because only torchdata==0.10.0a0+1a98f21 is available and you require torchdata, we can conclude that your requirements are unsatisfiable. Error: Process completed with exit code 1. ```
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Support JSON lines with missing columns
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Support JSON lines with missing columns. Fix #7169. The implemented test raised: ``` datasets.table.CastError: Couldn't cast age: int64 to {'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)} because column names don't match ``` Related to: - #7160 - #7162
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JSON lines with missing columns raise CastError
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JSON lines with missing columns raise CastError: > CastError: Couldn't cast ... to ... because column names don't match Related to: - #7159 - #7161
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sd1.5 diffusers controlnet training script gives new error
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### Describe the bug This will randomly pop up during training now ``` Traceback (most recent call last): File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1192, in <module> main(args) File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1041, in main for step, batch in enumerate(train_dataloader): File "/usr/local/lib/python3.11/dist-packages/accelerate/data_loader.py", line 561, in __iter__ next_batch = next(dataloader_iter) ^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 630, in __next__ data = self._next_data() ^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 673, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/_utils/fetch.py", line 50, in fetch data = self.dataset.__getitems__(possibly_batched_index) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2746, in __getitems__ batch = self.__getitem__(keys) ^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2742, in __getitem__ return self._getitem(key) ^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2727, in _getitem formatted_output = format_table( ^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 639, in format_table return formatter(pa_table, query_type=query_type) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 407, in __call__ return self.format_batch(pa_table) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 521, in format_batch batch = self.python_features_decoder.decode_batch(batch) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 228, in decode_batch return self.features.decode_batch(batch) if self.features else batch ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2084, in decode_batch [ File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2085, in <listcomp> decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id) File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 1403, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/features/image.py", line 188, in decode_example image.load() # to avoid "Too many open files" errors ``` ### Steps to reproduce the bug Train on diffusers sd1.5 controlnet example script This will pop up randomly, you can see in wandb below when i manually resume run everytime this error appears ![image](https://github.com/user-attachments/assets/87e9a6af-cb3c-4398-82e7-d6a90add8d31) ### Expected behavior Training to continue without above error ### Environment info - datasets version: 3.0.0 - Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35 - Python version: 3.11.9 - huggingface_hub version: 0.25.1 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - fsspec version: 2024.6.1 Training on 4090
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Error Mapping on sd3, sdxl and upcoming flux controlnet training scripts in diffusers
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### Describe the bug ``` Map: 6%|██████ | 8000/138120 [19:27<5:16:36, 6.85 examples/s] Traceback (most recent call last): File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1416, in <module> main(args) File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1132, in main train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 560, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3035, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3461, in _map_single writer.write_batch(batch) File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 567, in write_batch self.write_table(pa_table, writer_batch_size) File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 579, in write_table pa_table = pa_table.combine_chunks() ^^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow/table.pxi", line 4387, in pyarrow.lib.Table.combine_chunks File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays Traceback (most recent call last): File "/usr/local/bin/accelerate", line 8, in <module> sys.exit(main()) ^^^^^^ File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/accelerate_cli.py", line 48, in main args.func(args) File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 1174, in launch_command simple_launcher(args) File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 769, in simple_launcher ``` ### Steps to reproduce the bug The dataset has no problem training on sd1.5 controlnet train script ### Expected behavior Script not randomly erroing with error above ### Environment info - `datasets` version: 3.0.0 - Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35 - Python version: 3.11.9 - `huggingface_hub` version: 0.25.1 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1 training on A100
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fix docstring code example for distributed shuffle
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2024-09-24T14:39:54Z
2024-09-24T14:42:41Z
2024-09-24T14:40:14Z
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close https://github.com/huggingface/datasets/issues/7163
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fix increase_load_count
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2024-09-24T10:14:40Z
2024-09-24T17:31:07Z
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MEMBER
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it was failing since 3.0 and therefore not updating download counts on HF or in our dashboard
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7,164
fsspec.exceptions.FSTimeoutError when downloading dataset
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2024-09-24T08:45:05Z
2025-04-09T22:25:56Z
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### Describe the bug I am trying to download the `librispeech_asr` `clean` dataset, which results in a `FSTimeoutError` exception after downloading around 61% of the data. ### Steps to reproduce the bug ``` import datasets datasets.load_dataset("librispeech_asr", "clean") ``` The output is as follows: > Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:06, 13.2MB/s]Traceback (most recent call last): > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 56, in _runner > result[0] = await coro > ^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/implementations/http.py", line 262, in _get_file > chunk = await r.content.read(chunk_size) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 393, in read > await self._wait("read") > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 311, in _wait > with self._timer: > ^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/helpers.py", line 713, in __exit__ > raise asyncio.TimeoutError from None > TimeoutError > > The above exception was the direct cause of the following exception: > > Traceback (most recent call last): > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/load_dataset.py", line 3, in <module> > datasets.load_dataset("librispeech_asr", "clean") > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/load.py", line 2096, in load_dataset > builder_instance.download_and_prepare( > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 924, in download_and_prepare > self._download_and_prepare( > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1647, in _download_and_prepare > super()._download_and_prepare( > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 977, in _download_and_prepare > split_generators = self._split_generators(dl_manager, **split_generators_kwargs) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/Users/Timon/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/2712a8f82f0d20807a56faadcd08734f9bdd24c850bb118ba21ff33ebff0432f/librispeech_asr.py", line 115, in _split_generators > archive_path = dl_manager.download(_DL_URLS[self.config.name]) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 159, in download > downloaded_path_or_paths = map_nested( > ^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 512, in map_nested > _single_map_nested((function, obj, batched, batch_size, types, None, True, None)) > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 380, in _single_map_nested > return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)] > ^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 216, in _download_batched > self._download_single(url_or_filename, download_config=download_config) > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 225, in _download_single > out = cached_path(url_or_filename, download_config=download_config) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 205, in cached_path > output_path = get_from_cache( > ^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 415, in get_from_cache > fsspec_get(url, temp_file, storage_options=storage_options, desc=download_desc, disable_tqdm=disable_tqdm) > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 334, in fsspec_get > fs.get_file(path, temp_file.name, callback=callback) > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 118, in wrapper > return sync(self.loop, func, *args, **kwargs) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 101, in sync > raise FSTimeoutError from return_result > fsspec.exceptions.FSTimeoutError > Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:09, 13.0MB/s] ### Expected behavior Complete the download ### Environment info Python version 3.12.6 Dependencies: > dependencies = [ > "accelerate>=0.34.2", > "datasets[audio]>=3.0.0", > "ipython>=8.18.1", > "librosa>=0.10.2.post1", > "torch>=2.4.1", > "torchaudio>=2.4.1", > "transformers>=4.44.2", > ] MacOS 14.6.1 (23G93)
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7,163
Set explicit seed in iterable dataset ddp shuffling example
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2024-09-23T11:34:06Z
2024-09-24T14:40:15Z
2024-09-24T14:40:15Z
CONTRIBUTOR
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### Describe the bug In the examples section of the iterable dataset docs https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.IterableDataset the ddp example shuffles without seeding ```python from datasets.distributed import split_dataset_by_node ids = ds.to_iterable_dataset(num_shards=512) ids = ids.shuffle(buffer_size=10_000) # will shuffle the shards order and use a shuffle buffer when you start iterating ids = split_dataset_by_node(ds, world_size=8, rank=0) # will keep only 512 / 8 = 64 shards from the shuffled lists of shards when you start iterating dataloader = torch.utils.data.DataLoader(ids, num_workers=4) # will assign 64 / 4 = 16 shards from this node's list of shards to each worker when you start iterating for example in ids: pass ``` This code would - I think - raise an error due to the lack of an explicit seed: https://github.com/huggingface/datasets/blob/2eb4edb97e1a6af2ea62738ec58afbd3812fc66e/src/datasets/iterable_dataset.py#L1707-L1711 ### Steps to reproduce the bug Run example code ### Expected behavior Add explicit seeding to example code ### Environment info latest datasets
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Support JSON lines with empty struct
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Support JSON lines with empty struct. Fix #7161. Related to: - #7160
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JSON lines with empty struct raise ArrowTypeError: struct fields don't match or are in the wrong order See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5 > ArrowTypeError: struct fields don't match or are in the wrong order: Input fields: struct<> output fields: struct<pov_count: int64, update_count: int64, citation_needed_count: int64> Related to: - #7159
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Support JSON lines with missing struct fields. Fix #7159. The implemented test raised: ``` TypeError: Couldn't cast array of type struct<age: int64> to {'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)} ```
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JSON lines with missing struct fields raise TypeError: Couldn't cast array of type. See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5 One would expect that the struct missing fields are added with null values.
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7,157
Fix zero proba interleave datasets
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2024-09-21T15:19:14Z
2024-09-24T14:33:54Z
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fix https://github.com/huggingface/datasets/issues/7147
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7,156
interleave_datasets resets shuffle state
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2024-09-20T17:57:54Z
2025-03-18T10:56:25Z
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### Describe the bug ``` import datasets import torch.utils.data def gen(shards): yield {"shards": shards} def main(): dataset = datasets.IterableDataset.from_generator( gen, gen_kwargs={'shards': list(range(25))} ) dataset = dataset.shuffle(buffer_size=1) dataset = datasets.interleave_datasets( [dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted" ) dataloader = torch.utils.data.DataLoader( dataset, batch_size=8, num_workers=8, ) for i, batch in enumerate(dataloader): print(batch) if i >= 10: break if __name__ == "__main__": main() ``` ### Steps to reproduce the bug Run the script, it will output ``` {'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]} {'shards': [tensor([ 1, 9, 17, 1, 9, 17, 1, 9])]} {'shards': [tensor([ 2, 10, 18, 2, 10, 18, 2, 10])]} {'shards': [tensor([ 3, 11, 19, 3, 11, 19, 3, 11])]} {'shards': [tensor([ 4, 12, 20, 4, 12, 20, 4, 12])]} {'shards': [tensor([ 5, 13, 21, 5, 13, 21, 5, 13])]} {'shards': [tensor([ 6, 14, 22, 6, 14, 22, 6, 14])]} {'shards': [tensor([ 7, 15, 23, 7, 15, 23, 7, 15])]} {'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]} {'shards': [tensor([17, 1, 9, 17, 1, 9, 17, 1])]} {'shards': [tensor([18, 2, 10, 18, 2, 10, 18, 2])]} ``` ### Expected behavior The shards should be shuffled. ### Environment info - `datasets` version: 3.0.0 - Platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.25.0 - PyArrow version: 17.0.0 - Pandas version: 2.0.3 - `fsspec` version: 2023.6.0
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Dataset viewer not working! Failure due to more than 32 splits.
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Hello guys, I have a dataset and I didn't know I couldn't upload more than 32 splits. Now, my dataset viewer is not working. I don't have the dataset locally on my node anymore and recreating would take a week. And I have to publish the dataset coming Monday. I read about the practice, how I can resolve it and avoid this issue in the future. But, at the moment I need a hard fix for two of my datasets. And I don't want to mess or change anything and allow everyone in public to see the dataset and interact with it. Can you please help me? https://huggingface.co/datasets/laion/Wikipedia-X https://huggingface.co/datasets/laion/Wikipedia-X-Full
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Support ndjson data files
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2024-09-19T11:25:17Z
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Support `ndjson` (Newline Delimited JSON) data files. Fix #7153.
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Support data files with .ndjson extension
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2024-09-19T11:25:15Z
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### Feature request Support data files with `.ndjson` extension. ### Motivation We already support data files with `.jsonl` extension. ### Your contribution I am opening a PR.
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7,151
Align filename prefix splitting with WebDataset library
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Align filename prefix splitting with WebDataset library. This PR uses the same `base_plus_ext` function as the one used by the `webdataset` library. Fix #7150. Related to #7144.
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WebDataset loader splits keys differently than WebDataset library
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As reported by @ragavsachdeva (see discussion here: https://github.com/huggingface/datasets/pull/7144#issuecomment-2348307792), our webdataset loader is not aligned with the `webdataset` library when splitting keys from filenames. For example, we get a different key splitting for filename `/some/path/22.0/1.1.png`: - datasets library: `/some/path/22` and `0/1.1.png` - webdataset library: `/some/path/22.0/1`, `1.png` ```python import webdataset as wds wds.tariterators.base_plus_ext("/some/path/22.0/1.1.png") # ('/some/path/22.0/1', '1.png') ```
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Datasets Unknown Keyword Argument Error - task_templates
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### Describe the bug Issue ```python from datasets import load_dataset examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>) ``` Gives error ``` TypeError: DatasetInfo.__init__() got an unexpected keyword argument 'task_templates' ``` A simple downgrade to lower `datasets v 2.21.0` solves it. ### Steps to reproduce the bug 1. `pip install datsets` 2. ```python from datasets import load_dataset examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>) ``` ### Expected behavior Should load the dataset correctly. ### Environment info - Datasets version `3.0.0` - `transformers` version: 4.45.0.dev0 - Platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.35 - Python version: 3.12.4 - Huggingface_hub version: 0.24.6 - Safetensors version: 0.4.5 - Accelerate version: 0.35.0.dev0 - Accelerate config: not found - PyTorch version (GPU?): 2.4.1+cu121 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: Yes
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Bug: Error when downloading mteb/mtop_domain
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### Describe the bug When downloading the dataset "mteb/mtop_domain", ran into the following error: ``` Traceback (most recent call last): File "/share/project/xzy/test/test_download.py", line 3, in <module> data = load_dataset("mteb/mtop_domain", "en", trust_remote_code=True) File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2606, in load_dataset builder_instance = load_dataset_builder( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2277, in load_dataset_builder dataset_module = dataset_module_factory( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1923, in dataset_module_factory raise e1 from None File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1896, in dataset_module_factory ).get_module() File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1507, in get_module local_path = self.download_loading_script() File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1467, in download_loading_script return cached_path(file_path, download_config=download_config) File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 211, in cached_path output_path = get_from_cache( File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 689, in get_from_cache fsspec_get( File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 395, in fsspec_get fs.get_file(path, temp_file.name, callback=callback) File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 648, in get_file http_get( File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 578, in http_get raise EnvironmentError( OSError: Consistency check failed: file should be of size 2191 but has size 2190 ((…)ets/mteb/mtop_domain@main/mtop_domain.py). We are sorry for the inconvenience. Please retry with `force_download=True`. If the issue persists, please let us know by opening an issue on https://github.com/huggingface/huggingface_hub. ``` Try to download through HF datasets directly but got the same error as above. ```python from datasets import load_dataset data = load_dataset("mteb/mtop_domain", "en") ``` ### Steps to reproduce the bug ```python from datasets import load_dataset data = load_dataset("mteb/mtop_domain", "en", force_download=True) ``` With and without `force_download=True` both ran into the same error. ### Expected behavior Should download the dataset successfully. ### Environment info - datasets version: 2.21.0 - huggingface-hub version: 0.24.6
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IterableDataset strange deadlock
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2024-09-12T18:59:33Z
2024-09-23T09:32:27Z
2024-09-21T17:37:34Z
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### Describe the bug ``` import datasets import torch.utils.data num_shards = 1024 def gen(shards): for shard in shards: if shard < 25: yield {"shard": shard} def main(): dataset = datasets.IterableDataset.from_generator( gen, gen_kwargs={"shards": list(range(num_shards))}, ) dataset = dataset.shuffle(buffer_size=1) dataset = datasets.interleave_datasets( [dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted" ) dataset = dataset.shuffle(buffer_size=1) dataloader = torch.utils.data.DataLoader( dataset, batch_size=8, num_workers=8, ) for i, batch in enumerate(dataloader): print(batch) if i >= 10: break print() if __name__ == "__main__": for _ in range(100): main() ``` ### Steps to reproduce the bug Running the script above, at some point it will freeze. - Changing `num_shards` from 1024 to 25 avoids the issue - Commenting out the final shuffle avoids the issue - Commenting out the interleave_datasets call avoids the issue As an aside, if you comment out just the final shuffle, the output from interleave_datasets is not shuffled at all even though there's the shuffle before it. So something about that shuffle config is not being propagated to interleave_datasets. ### Expected behavior The script should not freeze. ### Environment info - `datasets` version: 3.0.0 - Platform: macOS-14.6.1-arm64-arm-64bit - Python version: 3.12.5 - `huggingface_hub` version: 0.24.7 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1 I observed this with 2.21.0 initially, then tried upgrading to 3.0.0 and could still repro.
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7,146
Set dev version
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Fix key error in webdataset
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2024-09-11T10:50:17Z
2025-01-15T10:32:43Z
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I was running into ``` example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]} KeyError: 'png' ``` The issue is that a filename may have multiple "." e.g. `22.05.png`. Changing `split` to `rsplit` fixes it. Related https://github.com/huggingface/datasets/issues/6880
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Modify add_column() to optionally accept a FeatureType as param
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2024-09-08T10:56:57Z
2024-09-17T06:01:23Z
2024-09-16T15:11:01Z
CONTRIBUTOR
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Fix #7142. **Before (Add + Cast)**: ``` from datasets import load_dataset, Value ds = load_dataset("rotten_tomatoes", split="test") lst = [i for i in range(len(ds))] ds = ds.add_column("new_col", lst) # Assigns int64 to new_col by default print(ds.features) ds = ds.cast_column("new_col", Value(dtype="uint16", id=None)) print(ds.features) ``` **Before (Numpy Workaround)**: ``` from datasets import load_dataset import numpy as np ds = load_dataset("rotten_tomatoes", split="test") lst = [i for i in range(len(ds))] ds = ds.add_column("new_col", np.array(lst, dtype=np.uint16)) print(ds.features) ``` **After**: ``` from datasets import load_dataset, Value ds = load_dataset("rotten_tomatoes", split="test") lst = [i for i in range(len(ds))] val = Value(dtype="uint16", id=None)) ds = ds.add_column("new_col", lst, feature=val) print(ds.features) ```
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