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Issue Downloading Certain Datasets After Setting Custom `HF_ENDPOINT`

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2024-03-11T09:06:38Z

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### Describe the bug This bug is triggered under the following conditions: - datasets repo ids without organization names trigger errors, such as `bookcorpus`, `gsm8k`, `wikipedia`. - If `HF_ENDPOINT` is set and the hostname is not in the form of `(hub-ci.)?huggingface.co`. - specific versions of `datasets` and `huggingface-hub`. When installing their latest versions, `datasets==2.18.0` and `huggingface-hub==0.21.4`, The issue disappears when `datasets==2.14.6` or `huggingface-hub==0.19.4`.(I only tested this version; whether the behavior persists in other versions is unknown. ### Steps to reproduce the bug the issue can be reproduced with the following code: 1. install specific datasets and huggingface_hub ```bash pip install datasets==2.18.0 pip install huggingface_hub==0.21.4 ``` 2. execute python code. ```Python import os os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' from datasets import load_dataset bookcorpus = load_dataset('bookcorpus', split='train') ``` console output: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset builder_instance = load_dataset_builder( File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 2228, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 1879, in dataset_module_factory raise e1 from None File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 1830, in dataset_module_factory with fs.open(f"datasets/{path}/{filename}", "r", encoding="utf-8") as f: File "/home/padeoe/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1295, in open self.open( File "/home/padeoe/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1307, in open f = self._open( File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 228, in _open return HfFileSystemFile(self, path, mode=mode, revision=revision, block_size=block_size, **kwargs) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 615, in __init__ self.resolved_path = fs.resolve_path(path, revision=revision) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 180, in resolve_path repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 117, in _repo_and_revision_exist self._api.repo_info(repo_id, revision=revision, repo_type=repo_type) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2413, in repo_info return method( File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2286, in dataset_info hf_raise_for_status(r) File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 362, in hf_raise_for_status raise HfHubHTTPError(str(e), response=response) from e huggingface_hub.utils._errors.HfHubHTTPError: 401 Client Error: Unauthorized for url: https://hf-mirror.com/api/datasets/bookcorpus/bookcorpus.py (Request ID: Root=1-65ee8659-5ab10eec5960c63e71f2bb58;b00bdbea-fd6e-4a74-8fe0-bc4682ae090e) ``` ### Expected behavior The dataset was downloaded correctly without any errors. ### Environment info datasets==2.18.0 huggingface-hub==0.21.4

2024-03-11T09:09:07Z

https://github.com/huggingface/datasets/issues/6728

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PR_kwDODunzps5pLJyE

open

Using a registry instead of calling globals for fetching feature types

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2024-03-10T17:47:51Z

https://api.github.com/repos/huggingface/datasets/issues/6727

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

2024-03-10T18:25:01Z

https://github.com/huggingface/datasets/pull/6727

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I_kwDODunzps6Bw94Q

open

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

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2024-03-09T07:08:45Z

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

2024-03-09T07:11:08Z

https://github.com/huggingface/datasets/issues/6726

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I_kwDODunzps6Bq-pq

open

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

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2024-03-08T08:23:01Z

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

2024-03-08T08:23:01Z

https://github.com/huggingface/datasets/issues/6725

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I_kwDODunzps6Bmq8T

open

Dataset with loading script does not work in renamed repos

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2024-03-07T17:38:38Z

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

2024-03-07T20:06:25Z

https://github.com/huggingface/datasets/issues/6724

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2024-03-07T17:21:20Z

PR_kwDODunzps5o_fPU

closed

get_dataset_default_config_name docstring

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2024-03-07T17:09:29Z

https://api.github.com/repos/huggingface/datasets/issues/6723

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MEMBER

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

2024-03-07T17:27:29Z

https://github.com/huggingface/datasets/pull/6723

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2024-03-07T17:21:08Z

PR_kwDODunzps5o_ch0

closed

Add details in docstring

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2024-03-07T17:02:07Z

https://api.github.com/repos/huggingface/datasets/issues/6722

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

2024-03-07T17:21:10Z

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

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I_kwDODunzps6Bk5DC

open

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

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2024-03-07T13:58:40Z

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

2024-03-07T13:58:40Z

https://github.com/huggingface/datasets/issues/6721

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2024-03-07T15:13:58Z

completed

I_kwDODunzps6Bjo6D

closed

TypeError: 'str' object is not callable

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2024-03-07T11:07:09Z

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CONTRIBUTOR

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

2024-03-08T07:34:53Z

https://github.com/huggingface/datasets/issues/6720

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I_kwDODunzps6BUUA_

open

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

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2024-03-05T15:55:13Z

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

2024-03-05T15:55:13Z

https://github.com/huggingface/datasets/issues/6719

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2024-03-07T13:58:04Z

PR_kwDODunzps5ouwwE

closed

Fix concurrent script loading with force_redownload

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2024-03-05T15:04:20Z

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

2024-03-07T14:05:53Z

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

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I_kwDODunzps6BRCOg

open

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

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2024-03-05T09:33:26Z

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

2024-03-05T10:32:19Z

https://github.com/huggingface/datasets/issues/6717

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I_kwDODunzps6BQ9X-

open

Non-deterministic `Dataset.builder_name` value

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2024-03-05T09:23:21Z

https://api.github.com/repos/huggingface/datasets/issues/6716

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NONE

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

2024-03-05T09:27:24Z

https://github.com/huggingface/datasets/issues/6716

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2024-03-05T11:17:04Z

PR_kwDODunzps5oo36i

closed

Fix sliced ConcatenationTable pickling with mixed schemas vertically

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2024-03-04T21:02:07Z

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

2024-03-05T11:23:05Z

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

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2024-03-04T20:22:15Z

PR_kwDODunzps5ooQd2

closed

Expand no-code dataset info with datasets-server info

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2024-03-04T19:18:10Z

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CONTRIBUTOR

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

2024-03-04T20:28:30Z

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

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2024-03-04T18:06:05Z

PR_kwDODunzps5olmqh

closed

Bump huggingface-hub lower version to 0.21.2

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2024-03-04T13:00:52Z

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This should fix the version compatibility issue when using `huggingface_hub` < 0.21.2 and latest fsspec (>=2023.12.0). See my comment: https://github.com/huggingface/datasets/pull/6687#issuecomment-1976493336 >> EDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main` > >Please note that people using `huggingface_hub` < 0.21.2 and latest `fsspec` will have issues when using `datasets`: >- https://github.com/huggingface/lighteval/actions/runs/8139147047/job/22241658122?pr=86 >- https://github.com/huggingface/lighteval/pull/84 CC: @clefourrier

2024-03-04T18:14:03Z

https://github.com/huggingface/datasets/pull/6713

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2024-03-04T20:17:17Z

PR_kwDODunzps5ok4VF

closed

fix CastError pickling

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2024-03-04T11:14:18Z

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reported in https://discuss.huggingface.co/t/datasetdict-save-to-disk-with-num-proc-1-seems-to-hang-with-error/75595

2024-03-04T20:23:47Z

https://github.com/huggingface/datasets/pull/6712

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PR_kwDODunzps5ohM1a

open

3x Faster Text Preprocessing

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2024-03-03T19:03:04Z

https://api.github.com/repos/huggingface/datasets/issues/6711

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I was preparing some datasets for AI training and noticed that `datasets` by HuggingFace uses the conventional `open` mechanism to read the file and split it into chunks. I thought it can be significantly accelerated, and [started with a benchmark](https://gist.github.com/ashvardanian/55c2052e9f78b05b8d614aa90cb12347): ```sh $ pip install --upgrade --force-reinstall datasets $ python benchmark_huggingface_datasets.py xlsum.csv Generating train split: 1004598 examples [00:47, 21116.16 examples/s] Time taken to load the dataset: 48.66838526725769 seconds Time taken to chunk the dataset into parts of size 10000: 0.11466407775878906 seconds Total time taken: 48.78304934501648 seconds ``` For benchmarks I've used a [large CSV file with mixed UTF-8 content](https://github.com/ashvardanian/StringZilla/blob/main/CONTRIBUTING.md#benchmarking-datasets), most common in modern large-scale pre-training pipelines. I've later patched the `datasets` library to use `stringzilla`, which resulted in significantly lower memory consumption and in 2.9x throughput improvement on the AWS `r7iz` instances. That's using slow SSDs mounted over the network. Performance on local SSDs on something like a DGX-H100 should be even higher: ```sh $ pip install -e . $ python benchmark_huggingface_datasets.py xlsum.csv Generating train split: 1004598 examples [00:15, 64529.90 examples/s] Time taken to load the dataset: 16.45028805732727 seconds Time taken to chunk the dataset into parts of size 10000: 0.1291060447692871 seconds Total time taken: 16.579394102096558 seconds ``` I've already [pushed the patches to my fork](https://github.com/ashvardanian/datasets/tree/faster-text-parsers), and would love to contribute them to the upstream repository. --- All the tests pass, but they leave a couple of important questions open. The default Python `open(..., newline=None)` uses universal newlines, where `\n`, `\r`, and `\r\n` are all converted to `\n` on the fly. I am not sure if its a good idea for a general purpose dataset preparation pipeline? I can simulate the same behavior (which I don't yet do) for `"line"` splitter. Adjusting it for `"paragraph"`-splitter would be harder. Should we stick exactly to the old Pythonic behavior or stay closer to how C and other programming languages do that?

2024-03-04T15:15:51Z

https://github.com/huggingface/datasets/pull/6711

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PR_kwDODunzps5oe4ov

open

Persist IterableDataset epoch in workers

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2024-03-02T12:08:50Z

https://api.github.com/repos/huggingface/datasets/issues/6710

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Use shared memory for the IterableDataset epoch. This way calling `ds.set_epoch()` in the main process will update the epoch in the DataLoader workers as well. This is useful especially because the epoch is used to compute the `effective_seed` used for shuffling. I used torch's shared memory in case users want to send dataset copies without shared memory using pickle. I also find it easier to use than using `multiprocessing.shared_memory` than requires unlinking only in the main process, or `mp.Value` that is not picklable. close https://github.com/huggingface/datasets/issues/6673 cc @rwightman

2024-03-06T14:41:54Z

https://github.com/huggingface/datasets/pull/6710

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2024-03-01T21:01:23Z

PR_kwDODunzps5oc2Fg

closed

set dev version

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2024-03-01T21:01:14Z

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2024-03-01T21:07:35Z

https://github.com/huggingface/datasets/pull/6709

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2024-03-01T20:56:50Z

PR_kwDODunzps5oczmi

closed

Release: 2.18.0

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2024-03-01T20:52:17Z

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2024-03-01T21:03:01Z

https://github.com/huggingface/datasets/pull/6708

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2024-03-01T17:25:46Z

PR_kwDODunzps5obkhA

closed

Silence ruff deprecation messages

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2024-03-01T16:52:29Z

https://api.github.com/repos/huggingface/datasets/issues/6707

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2024-03-01T17:32:14Z

https://github.com/huggingface/datasets/pull/6707

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2024-03-01T16:52:17Z

PR_kwDODunzps5obgt-

closed

Update ruff

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2024-03-01T16:44:58Z

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2024-03-01T17:02:13Z

https://github.com/huggingface/datasets/pull/6706

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2024-03-01T18:52:49Z

PR_kwDODunzps5obdbY

closed

Fix data_files when passing data_dir

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2024-03-01T16:38:53Z

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This code should not return empty data files ```python from datasets import load_dataset_builder revision = "3d406e70bc21c3ca92a9a229b4c6fc3ed88279fd" b = load_dataset_builder("bigcode/the-stack-v2-dedup", data_dir="data/Dockerfile", revision=revision) print(b.config.data_files) ``` Previously it would return no data files because it would apply the YAML `data_files: data/**/train-*` pattern to this directory cc @anton-l

2024-03-01T18:59:06Z

https://github.com/huggingface/datasets/pull/6705

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PR_kwDODunzps5obZyj

open

Improve default patterns resolution

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2024-03-01T16:31:25Z

https://api.github.com/repos/huggingface/datasets/issues/6704

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CONTRIBUTOR

Separate the default patterns that match directories from the ones matching files and ensure directories are checked first (reverts the change from https://github.com/huggingface/datasets/pull/6244, which merged these patterns). Also, ensure that the glob patterns do not overlap to avoid duplicates in the result. fix https://github.com/huggingface/datasets/issues/6259 fix https://github.com/huggingface/datasets/issues/6272 TODO: - [ ] optimize repeated `LocalFileSystem.ls` calls when resolving the default patterns

2024-03-07T17:23:44Z

https://github.com/huggingface/datasets/pull/6704

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2024-03-04T13:46:20Z

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I_kwDODunzps6A8JWe

closed

Unable to load dataset that was saved with `save_to_disk`

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2024-03-01T11:59:56Z

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### Describe the bug I get the following error message: You are trying to load a dataset that was saved using `save_to_disk`. Please use `load_from_disk` instead. ### Steps to reproduce the bug 1. Save a dataset with `save_to_disk` 2. Try to load it with `load_datasets` ### Expected behavior I am able to load the dataset again with `load_datasets` which most packages uses over `load_from_disk`. I want to have a workaround that allows me to create the same indexing that `push_to_hub` creates for you before using `save_to_disk` - how can that be achieved? ### Environment info datasets 2.17.1, python 3.10

2024-03-04T13:46:20Z

https://github.com/huggingface/datasets/issues/6703

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2024-03-08T21:08:38Z

completed

I_kwDODunzps6A3JA0

closed

Push samples to dataset on hub without having the dataset locally

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2024-02-29T19:17:12Z

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### Feature request Say I have the following code: ``` from datasets import Dataset import pandas as pd new_data = { "column_1": ["value1", "value2"], "column_2": ["value3", "value4"], } df_new = pd.DataFrame(new_data) dataset_new = Dataset.from_pandas(df_new) # add these samples to a remote dataset ``` It would be great to have a way to push dataset_new to a remote dataset that respects the same schema. This way one would not have to do the following: ``` from datasets import load_dataset dataset = load_dataset('username/dataset_name', use_auth_token='your_hf_token_here') updated_dataset = dataset['train'].concatenate(dataset_new) updated_dataset.push_to_hub('username/dataset_name', use_auth_token='your_hf_token_here') ``` ### Motivation No need to download the dataset. ### Your contribution Maybe this feature already exists, didnt see it though. I do not have the expertise to do this.

2024-03-08T21:08:38Z

https://github.com/huggingface/datasets/issues/6702

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2024-02-29T15:08:55Z

PR_kwDODunzps5oTfO_

closed

Base parquet batch_size on parquet row group size

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2024-02-29T14:53:01Z

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This allows to stream datasets like [Major-TOM/Core-S2L2A](https://huggingface.co/datasets/Major-TOM/Core-S2L2A) which have row groups with few rows (one row is ~10MB). Previously the cold start would take a lot of time and OOM because it would download many row groups before yielding the first example. I tried on OpenOrca and imagenet-hard and it does't affect overall throughput. Even if the overall throughput doesn't change for datasets like imagenet-hard with big rows, note that it does create shorter and more frequent pauses to download the next row group. Though I find it fine because previously the pauses were less frequent but very long (downloading multiple row groups at a time)

2024-02-29T15:15:18Z

https://github.com/huggingface/datasets/pull/6701

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I_kwDODunzps6ArcH-

open

remove_columns is not in-place but the doc shows it is in-place

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2024-02-28T12:36:22Z

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### Describe the bug The doc of `datasets` v2.17.0/v2.17.1 shows that `remove_columns` is in-place. [link](https://huggingface.co/docs/datasets/v2.17.1/en/package_reference/main_classes#datasets.DatasetDict.remove_columns) In the text classification example of transformers v4.38.1, the columns are not removed. https://github.com/huggingface/transformers/blob/a0857740c0e6127485c11476650314df3accc2b6/examples/pytorch/text-classification/run_classification.py#L421 ### Steps to reproduce the bug https://github.com/huggingface/transformers/blob/a0857740c0e6127485c11476650314df3accc2b6/examples/pytorch/text-classification/run_classification.py#L421 ### Expected behavior Actually remove the columns. ### Environment info 1. datasets v2.17.0 2. transformers v4.38.1

2024-02-29T03:02:54Z

https://github.com/huggingface/datasets/issues/6700

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I_kwDODunzps6AosqV

open

`Dataset` unexpected changed dict data and may cause error

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2024-02-28T05:30:10Z

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### Describe the bug Will unexpected get keys with `None` value in the parsed json dict. ### Steps to reproduce the bug ```jsonl test.jsonl {"id": 0, "indexs": {"-1": [0, 10]}} {"id": 1, "indexs": {"-1": [0, 10]}} ``` ```python dataset = Dataset.from_json('.test.jsonl') print(dataset[0]) ``` Result: ``` {'id': 0, 'indexs': {'-1': [...], '-2': None, '-3': None, '-4': None, '-5': None, '-6': None, '-7': None, '-8': None, '-9': None, ...}} ``` Those keys with `None` value will unexpected appear in the dict. ### Expected behavior Result should be ``` {'id': 0, 'indexs': {'-1': [0, 10]}} ``` ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-6.5.0-14-generic-x86_64-with-glibc2.35 - Python version: 3.11.6 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

2024-02-28T19:14:36Z

https://github.com/huggingface/datasets/issues/6699

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2024-02-27T23:38:14Z

PR_kwDODunzps5oG6Xt

closed

Faster `xlistdir`

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2024-02-27T22:55:08Z

https://api.github.com/repos/huggingface/datasets/issues/6698

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CONTRIBUTOR

Pass `detail=False` to the `fsspec` `listdir` to avoid unnecessarily fetching expensive metadata about the paths.

2024-02-27T23:44:49Z

https://github.com/huggingface/datasets/pull/6698

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2024-02-29T17:32:41Z

completed

I_kwDODunzps6Alh_w

closed

Unable to Load Dataset in Kaggle

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2024-02-27T18:19:34Z

https://api.github.com/repos/huggingface/datasets/issues/6697

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### Describe the bug Having installed the latest versions of transformers==4.38.1 and datasets==2.17.1 Unable to load the dataset in a kaggle notebook. Get this Error: ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[8], line 3 1 from datasets import load_dataset ----> 3 dataset = load_dataset("llm-blender/mix-instruct") File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1664, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1661 ignore_verifications = ignore_verifications or save_infos 1663 # Create a dataset builder -> 1664 builder_instance = load_dataset_builder( 1665 path=path, 1666 name=name, 1667 data_dir=data_dir, 1668 data_files=data_files, 1669 cache_dir=cache_dir, 1670 features=features, 1671 download_config=download_config, 1672 download_mode=download_mode, 1673 revision=revision, 1674 use_auth_token=use_auth_token, 1675 **config_kwargs, 1676 ) 1678 # Return iterable dataset in case of streaming 1679 if streaming: File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1490, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs) 1488 download_config = download_config.copy() if download_config else DownloadConfig() 1489 download_config.use_auth_token = use_auth_token -> 1490 dataset_module = dataset_module_factory( 1491 path, 1492 revision=revision, 1493 download_config=download_config, 1494 download_mode=download_mode, 1495 data_dir=data_dir, 1496 data_files=data_files, 1497 ) 1499 # Get dataset builder class from the processing script 1500 builder_cls = import_main_class(dataset_module.module_path) File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1242, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1237 if isinstance(e1, FileNotFoundError): 1238 raise FileNotFoundError( 1239 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1240 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" 1241 ) from None -> 1242 raise e1 from None 1243 else: 1244 raise FileNotFoundError( 1245 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory." 1246 ) File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1230, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1215 return HubDatasetModuleFactoryWithScript( 1216 path, 1217 revision=revision, (...) 1220 dynamic_modules_path=dynamic_modules_path, 1221 ).get_module() 1222 else: 1223 return HubDatasetModuleFactoryWithoutScript( 1224 path, 1225 revision=revision, 1226 data_dir=data_dir, 1227 data_files=data_files, 1228 download_config=download_config, 1229 download_mode=download_mode, -> 1230 ).get_module() 1231 except Exception as e1: # noqa: all the attempts failed, before raising the error we should check if the module is already cached. 1232 try: File /opt/conda/lib/python3.10/site-packages/datasets/load.py:846, in HubDatasetModuleFactoryWithoutScript.get_module(self) 836 token = self.download_config.use_auth_token 837 hfh_dataset_info = HfApi(config.HF_ENDPOINT).dataset_info( 838 self.name, 839 revision=self.revision, 840 token=token, 841 timeout=100.0, 842 ) 843 patterns = ( 844 sanitize_patterns(self.data_files) 845 if self.data_files is not None --> 846 else get_patterns_in_dataset_repository(hfh_dataset_info) 847 ) 848 data_files = DataFilesDict.from_hf_repo( 849 patterns, 850 dataset_info=hfh_dataset_info, 851 allowed_extensions=ALL_ALLOWED_EXTENSIONS, 852 ) 853 infered_module_names = { 854 key: infer_module_for_data_files(data_files_list, use_auth_token=self.download_config.use_auth_token) 855 for key, data_files_list in data_files.items() 856 } File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:471, in get_patterns_in_dataset_repository(dataset_info) 469 resolver = partial(_resolve_single_pattern_in_dataset_repository, dataset_info) 470 try: --> 471 return _get_data_files_patterns(resolver) 472 except FileNotFoundError: 473 raise FileNotFoundError( 474 f"The dataset repository at '{dataset_info.id}' doesn't contain any data file." 475 ) from None File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:99, in _get_data_files_patterns(pattern_resolver) 97 try: 98 for pattern in patterns: ---> 99 data_files = pattern_resolver(pattern) 100 if len(data_files) > 0: 101 non_empty_splits.append(split) File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:303, in _resolve_single_pattern_in_dataset_repository(dataset_info, pattern, allowed_extensions) 301 data_files_ignore = FILES_TO_IGNORE 302 fs = HfFileSystem(repo_info=dataset_info) --> 303 glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)] 304 matched_paths = [ 305 filepath 306 for filepath in glob_iter 307 if filepath.name not in data_files_ignore and not filepath.name.startswith(".") 308 ] 309 if allowed_extensions is not None: File /opt/conda/lib/python3.10/site-packages/fsspec/spec.py:606, in AbstractFileSystem.glob(self, path, maxdepth, **kwargs) 602 depth = None 604 allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs) --> 606 pattern = glob_translate(path + ("/" if ends_with_sep else "")) 607 pattern = re.compile(pattern) 609 out = { 610 p: info 611 for p, info in sorted(allpaths.items()) (...) 618 ) 619 } File /opt/conda/lib/python3.10/site-packages/fsspec/utils.py:734, in glob_translate(pat) 732 continue 733 elif "**" in part: --> 734 raise ValueError( 735 "Invalid pattern: '**' can only be an entire path component" 736 ) 737 if part: 738 results.extend(_translate(part, f"{not_sep}*", not_sep)) ValueError: Invalid pattern: '**' can only be an entire path component ``` ``` After loading this dataset ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset("llm-blender/mix-instruct") ``` ### Expected behavior The dataset should load with desired split. ### Environment info - `datasets` version: 2.17.1 - Platform: Linux-5.15.133+-x86_64-with-glibc2.31 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

2024-02-29T17:32:42Z

https://github.com/huggingface/datasets/issues/6697

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2024-02-28T06:39:12Z

PR_kwDODunzps5n6ipH

closed

Make JSON builder support an array of strings

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2024-02-26T13:18:31Z

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Support JSON file with an array of strings. Fix #6695.

2024-02-28T06:45:23Z

https://github.com/huggingface/datasets/pull/6696

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2024-02-28T06:39:13Z

completed

I_kwDODunzps6AZJV1

closed

Support JSON file with an array of strings

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2024-02-26T12:35:11Z

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Support loading a dataset from a JSON file with an array of strings. See: https://huggingface.co/datasets/CausalLM/Refined-Anime-Text/discussions/1

2024-03-08T14:16:25Z

https://github.com/huggingface/datasets/issues/6695

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PR_kwDODunzps5n23Jz

open

__add__ for Dataset, IterableDataset

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2024-02-26T01:46:55Z

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It's too cumbersome to write this command every time we perform a dataset merging operation. ```pythonfrom datasets import concatenate_datasets``` We have added a simple `__add__` magic method to each class using `concatenate_datasets.` ```python from datasets import load_dataset bookcorpus = load_dataset("bookcorpus", split="train") wiki = load_dataset("wikimedia/wikipedia", "20231101.ab", split="train") wiki = wiki.remove_columns([col for col in wiki.column_names if col != "text"]) # only keep the 'text' column bookcorpus + wiki #Dataset({ # features: ['text'], # num_rows: 74004228 #}) #Dataset({ # features: ['text'], # num_rows: 6152 #}) #Dataset({ # features: ['text'], # num_rows: 74010380 #}) ```

2024-02-29T16:52:58Z

https://github.com/huggingface/datasets/pull/6694

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2024-02-25T19:51:02Z

PR_kwDODunzps5n2ObO

closed

Update the print message for chunked_dataset in process.mdx

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2024-02-25T18:37:07Z

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Update documentation to align with `Dataset.__repr__` change after #423

2024-02-25T19:57:12Z

https://github.com/huggingface/datasets/pull/6693

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2024-02-26T07:14:03Z

PR_kwDODunzps5n0XN1

closed

Enhancement: Enable loading TSV files in load_dataset()

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2024-02-24T11:38:59Z

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Fix #6691

2024-02-26T15:33:50Z

https://github.com/huggingface/datasets/pull/6692

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2024-02-26T07:09:35Z

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I_kwDODunzps6ARvWZ

closed

load_dataset() does not support tsv

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2024-02-24T05:56:04Z

https://api.github.com/repos/huggingface/datasets/issues/6691

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### Feature request the load_dataset() for local functions support file types like csv, json etc but not of type tsv (tab separated values). ### Motivation cant easily load files of type tsv, have to convert them to another type like csv then load ### Your contribution Can try by raising a PR with a little help, currently went through the code but didn't fully understand

2024-02-26T07:15:07Z

https://github.com/huggingface/datasets/issues/6691

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open

Add function to convert a script-dataset to Parquet

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2024-02-23T10:28:20Z

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Add function to convert a script-dataset to Parquet and push it to the Hub, analogously to the Space: "Convert a Hugging Face dataset to Parquet"

2024-02-23T10:28:20Z

https://github.com/huggingface/datasets/issues/6690

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2024-03-07T14:54:15Z

completed

I_kwDODunzps6AIAFb

closed

.load_dataset() method defaults to zstandard

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2024-02-22T17:39:27Z

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### Describe the bug Regardless of what method I use, datasets defaults to zstandard for unpacking my datasets. This is poor behavior, because not only is zstandard not a dependency in the huggingface package (and therefore, your dataset loading will be interrupted while it asks you to install the package), but it happens on datasets that are uploaded in json format too, meaning the dataset loader will attempt to convert the data to a zstandard compatible format, and THEN try to unpackage it. My 4tb drive runs out of room when using zstandard on slimpajama. It loads fine on 1.5tb when using json, however I lack the understanding of the "magic numbers" system used to select the unpackaging algorithm, so I can't push a change myself. Commenting out this line, in "/datasets/utils/extract.py" fixes the issue, and causes SlimPajama to properly extract using rational amounts of storage, however it completely disables zstandard, which is probably undesirable behavior. Someone with an understanding of the "magic numbers" system should probably take a pass over this issue. ``` class Extractor: # Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip) extractors: Dict[str, Type[BaseExtractor]] = { "tar": TarExtractor, "gzip": GzipExtractor, "zip": ZipExtractor, "xz": XzExtractor, #"zstd": ZstdExtractor, # This line needs to go, in order for datasets to work w/o non-dependent packages "rar": RarExtractor, "bz2": Bzip2Extractor, "7z": SevenZipExtractor, # <Added version="2.4.0"/> "lz4": Lz4Extractor, # <Added version="2.4.0"/> } ``` ### Steps to reproduce the bug ''' from datasaets import load_dataset load_dataset(path="/cerebras/SlimPajama-627B") ''' This alone should trigger the error on any system that does not have zstandard pip installed. ### Expected behavior This repository (which is encoded in json format, not zstandard) should check whether zstandard is installed before defaulting to it. Additionally, using zstandard should not use more than 3x the required space that other extraction mechanisms use. ### Environment info - `datasets` version: 2.17.1 - Platform: Linux-6.5.0-18-generic-x86_64-with-glibc2.35 - Python version: 3.12.0 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

2024-03-07T14:54:16Z

https://github.com/huggingface/datasets/issues/6689

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I_kwDODunzps6AES9D

open

Tensor type (e.g. from `return_tensors`) ignored in map

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2024-02-22T09:27:57Z

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### Describe the bug I don't know if it is a bug or an expected behavior, but the tensor type seems to be ignored after applying map. For example, mapping over to tokenize text with a transformers' tokenizer always returns lists and it ignore the `return_tensors` argument. If this is an expected behaviour (e.g., for caching/Arrow compatibility/etc.) it should be clearly documented. For example, current documentation (see [here](https://huggingface.co/docs/datasets/v2.17.1/en/nlp_process#map)) clearly state to "set `return_tensors="np"` when you tokenize your text" to have Numpy arrays. ### Steps to reproduce the bug ```py # %%% import datasets import numpy as np import tensorflow as tf import torch from transformers import AutoTokenizer # %% ds = datasets.load_dataset("cnn_dailymail", "1.0.0", split="train[:1%]") tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") #%% for return_tensors in [None, "np", "pt", "tf", "jax"]: print(f"********** no map, return_tensors={return_tensors} **********") _ds = tokenizer(ds["article"], return_tensors=return_tensors, truncation=True, padding=True) print('Type <input_ids>:', type(_ds["input_ids"])) # %% for return_tensors in [None, "np", "pt", "tf", "jax"]: print(f"********** map, return_tensors={return_tensors} **********") _ds = ds.map( lambda examples: tokenizer(examples["article"], return_tensors=return_tensors, truncation=True, padding=True), batched=True, remove_columns=["article"], ) print('Type <input_ids>:', type(_ds[0]["input_ids"])) ``` ### Expected behavior The output from the script above. I would expect the second half to be the same. ``` ********** no map, return_tensors=None ********** Type <input_ids>: <class 'list'> ********** no map, return_tensors=np ********** Type <input_ids>: <class 'numpy.ndarray'> ********** no map, return_tensors=pt ********** Type <input_ids>: <class 'torch.Tensor'> ********** no map, return_tensors=tf ********** Type <input_ids>: <class 'tensorflow.python.framework.ops.EagerTensor'> ********** no map, return_tensors=jax ********** Type <input_ids>: <class 'jaxlib.xla_extension.ArrayImpl'> ********** map, return_tensors=None ********** Type <input_ids>: <class 'list'> ********** map, return_tensors=np ********** Type <input_ids>: <class 'list'> ********** map, return_tensors=pt ********** Type <input_ids>: <class 'list'> ********** map, return_tensors=tf ********** Type <input_ids>: <class 'list'> ********** map, return_tensors=jax ********** Type <input_ids>: <class 'list'> ``` ### Environment info - `datasets` version: 2.17.1 - Platform: Redacted (linux) - Python version: 3.10.12 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.1.3 - `fsspec` version: 2023.10.0

2024-02-22T15:56:21Z

https://github.com/huggingface/datasets/issues/6688

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2024-02-29T15:12:17Z

PR_kwDODunzps5nnqBB

closed

fsspec: support fsspec>=2023.12.0 glob changes

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2024-02-22T08:59:32Z

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- adds support for the `fs.glob` changes introduced in `fsspec==2023.12.0` and unpins the current upper bound Should close #6644 Should close #6645 The `test_data_files` glob/pattern tests pass for me in: - `fsspec==2023.10.0` (the pinned max version in datasets `main`) - `fsspec==2023.12.0` (#6644) - `fsspec==2024.2.0` (#6645)

2024-03-04T12:59:42Z

https://github.com/huggingface/datasets/pull/6687

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I_kwDODunzps6ABMCf

open

Question: Is there any way for uploading a large image dataset?

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2024-02-21T22:07:21Z

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I am uploading an image dataset like this: ``` dataset = load_dataset( "json", data_files={"train": "data/custom_dataset/train.json", "validation": "data/custom_dataset/val.json"}, ) dataset = dataset.cast_column("images", Sequence(Image())) dataset.push_to_hub("StanfordAIMI/custom_dataset", max_shard_size="1GB") ``` where it takes a long time in the `Map` process. Do you think I can use multi-processing to map all the image data to the memory first? For the `Map()` function, I can set `num_proc`. But for `push_to_hub` and `cast_column`, I can not find it. Thanks in advance! Best,

2024-02-21T22:07:21Z

https://github.com/huggingface/datasets/issues/6686

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2024-02-25T18:48:08Z

PR_kwDODunzps5ndZQa

closed

Updated Quickstart Notebook link

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2024-02-21T01:04:18Z

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Fixed Quickstart Notebook Link in the [Overview notebook](https://github.com/huggingface/datasets/blob/main/notebooks/Overview.ipynb)

2024-02-25T18:54:26Z

https://github.com/huggingface/datasets/pull/6685

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2024-02-20T15:33:56Z

PR_kwDODunzps5nYUIf

closed

Improve error message for gated datasets on load

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2024-02-20T10:51:27Z

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Internal Slack discussion: https://huggingface.slack.com/archives/C02V51Q3800/p1708424971135029

2024-02-20T15:40:52Z

https://github.com/huggingface/datasets/pull/6684

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2024-02-19T17:18:10Z

PR_kwDODunzps5nTxGu

closed

Fix imagefolder dataset url

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2024-02-19T16:26:51Z

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2024-02-19T17:24:25Z

https://github.com/huggingface/datasets/pull/6683

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2024-02-28T06:56:34Z

PR_kwDODunzps5nRME6

closed

Update GitHub Actions to Node 20

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2024-02-19T10:10:50Z

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Update GitHub Actions to Node 20. Fix #6679.

2024-02-28T07:02:40Z

https://github.com/huggingface/datasets/pull/6682

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2024-02-28T07:17:22Z

PR_kwDODunzps5nRItQ

closed

Update release instructions

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2024-02-19T10:03:08Z

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Update release instructions.

2024-02-28T07:23:49Z

https://github.com/huggingface/datasets/pull/6681

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2024-02-19T10:00:40Z

PR_kwDODunzps5nRHcz

closed

Set dev version

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2024-02-19T10:00:31Z

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2024-02-19T10:06:43Z

https://github.com/huggingface/datasets/pull/6680

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2024-02-28T06:56:35Z

completed

I_kwDODunzps5_q5-9

closed

Node.js 16 GitHub Actions are deprecated

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2024-02-19T09:47:37Z

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`Node.js` 16 GitHub Actions are deprecated. See: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/ We should update them to Node 20. See warnings in our CI, e.g.: https://github.com/huggingface/datasets/actions/runs/7957295009?pr=6678 > Node.js 16 actions are deprecated. Please update the following actions to use Node.js 20: actions/checkout@v3, actions/setup-python@v4. For more information see: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/.

2024-02-28T06:56:35Z

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2024-02-19T09:56:52Z

PR_kwDODunzps5nQ2ZO

closed

Release: 2.17.1

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2024-02-19T09:24:29Z

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2024-02-19T10:03:00Z

https://github.com/huggingface/datasets/pull/6678

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2024-02-28T18:51:15Z

PR_kwDODunzps5nOmo_

closed

Pass through information about location of cache directory.

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2024-02-18T23:48:57Z

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If cache directory is set, information is not passed through. Pass download config in as an arg too.

2024-02-28T18:57:39Z

https://github.com/huggingface/datasets/pull/6677

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I_kwDODunzps5_l7Sr

open

Can't Read List of JSON Files Properly

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2024-02-17T22:58:15Z

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### Describe the bug Trying to read a bunch of JSON files into Dataset class but default approach doesn't work. I don't get why it works when I read it one by one but not when I pass as a list :man_shrugging: The code fails with ``` ArrowInvalid: JSON parse error: Invalid value. in row 0 UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug This doesn't work ``` from datasets import Dataset # dir contains 100 json files. Dataset.from_json("/PUT SOME PATH HERE/*") ``` This works: ``` from datasets import concatenate_datasets ls_ds = [] for file in list_of_json_files: ls_ds.append(Dataset.from_json(file)) ds = concatenate_datasets(ls_ds) ``` ### Expected behavior I expect this to read json files properly as error is not clear ### Environment info - `datasets` version: 2.17.0 - Platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.35 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

2024-03-02T20:47:22Z

https://github.com/huggingface/datasets/issues/6676

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I_kwDODunzps5_iFI9

open

Allow image model (color conversion) to be specified as part of datasets Image() decode

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2024-02-16T23:43:20Z

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### Feature request Typical torchvision / torch Datasets in image applications apply color conversion in the Dataset portion of the code as part of image decode, separately from the image transform stack. This is true for PIL.Image where convert is usually called in dataset, for native torchvision https://pytorch.org/vision/main/generated/torchvision.io.decode_jpeg.html, and similarly in tensorflow.data pipelines decode_jpeg or https://www.tensorflow.org/api_docs/python/tf/io/decode_and_crop_jpeg have a channels arg that allows controlling the image mode in the decode step. datasets currently requires this pattern (from [examples](https://huggingface.co/docs/datasets/main/en/image_process)): ``` from torchvision.transforms import Compose, ColorJitter, ToTensor jitter = Compose( [ ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.7), ToTensor(), ] ) def transforms(examples): examples["pixel_values"] = [jitter(image.convert("RGB")) for image in examples["image"]] return examples ``` ### Motivation It would be nice to be able to handle `image.convert("RGB")` (or other modes) in the decode step, before applying torchvision transforms, this would reduce differences in code when handling pipelines that can handle torchvision, webdatset, or hf datasets with fewer code differences and without needing to handle image mode argument passing in two different stages of the pipelines... ### Your contribution Can do a PR with guidance on how mode should be passed / set on the dataset.

2024-03-01T19:43:55Z

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

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2024-02-25T18:48:09Z

completed

I_kwDODunzps5_h6M4

closed

Depprcated Overview.ipynb Link to new Quickstart Notebook invalid

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2024-02-16T22:51:35Z

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CONTRIBUTOR

### Describe the bug For the dreprecated notebook found [here](https://github.com/huggingface/datasets/blob/main/notebooks/Overview.ipynb). The link to the new notebook is broken. ### Steps to reproduce the bug Click the [Quickstart notebook](https://github.com/huggingface/notebooks/blob/main/datasets_doc/quickstart.ipynb) link in the notebook. ### Expected behavior I believe is it suposed to link [here](https://github.com/huggingface/notebooks/blob/main/datasets_doc/en/quickstart.ipynb). That is mentioned in the readme. ### Environment info Colab

2024-02-25T18:48:09Z

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I_kwDODunzps5_hocL

open

IterableDataset `set_epoch` is ignored when DataLoader `persistent_workers=True`

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### Describe the bug When persistent workers are enabled, the epoch that's set via the IterableDataset instance held by the training process is ignored by the workers as they are disconnected across processes. PyTorch samplers for non-iterable datasets have a mechanism to sync this, datasets.IterableDataset does not. In my own use of IterableDatasets I usually track the epoch count which crosses process boundaries in a multiprocessing.Value ### Steps to reproduce the bug Use a streaming dataset (Iterable) w/ the recommended pattern below and `persistent_workers=True` in the torch DataLoader. ``` for epoch in range(epochs): shuffled_dataset.set_epoch(epoch) for example in shuffled_dataset: ... ``` ### Expected behavior When the canonical bit of code above is used with `num_workers > 0` and `persistent_workers=True`, the epoch set via `set_epoch()` is propagated to the IterableDataset instances in the worker processes ### Environment info N/A

2024-02-22T13:17:14Z

https://github.com/huggingface/datasets/issues/6673

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2024-02-19T09:20:22Z

PR_kwDODunzps5nGAlw

closed

Remove deprecated verbose parameter from CSV builder

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2024-02-16T14:26:21Z

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Remove deprecated `verbose` parameter from CSV builder. Note that the `verbose` parameter is deprecated since pandas 2.2.0. See: - https://github.com/pandas-dev/pandas/pull/56556 - https://github.com/pandas-dev/pandas/pull/57450 Fix #6671.

2024-02-19T09:26:34Z

https://github.com/huggingface/datasets/pull/6672

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2024-02-19T09:20:23Z

completed

I_kwDODunzps5_emW-

closed

CSV builder raises deprecation warning on verbose parameter

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2024-02-16T14:23:46Z

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CSV builder raises a deprecation warning on `verbose` parameter: ``` FutureWarning: The 'verbose' keyword in pd.read_csv is deprecated and will be removed in a future version. ``` See: - https://github.com/pandas-dev/pandas/pull/56556 - https://github.com/pandas-dev/pandas/pull/57450

2024-02-19T09:20:23Z

https://github.com/huggingface/datasets/issues/6671

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### Describe the bug ValueError Traceback (most recent call last) [<ipython-input-11-9b99bc80ec23>](https://localhost:8080/#) in <cell line: 11>() 9 import numpy as np 10 import matplotlib.pyplot as plt ---> 11 from datasets import DatasetDict, Dataset 12 from transformers import AutoTokenizer, AutoModelForSequenceClassification 13 from transformers import Trainer, TrainingArguments 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 16 __version__ = "2.17.0" 17 ---> 18 from .arrow_dataset import Dataset 19 from .arrow_reader import ReadInstruction 20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 65 66 from . import config ---> 67 from .arrow_reader import ArrowReader 68 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 69 from .data_files import sanitize_patterns [/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module> 27 28 import pyarrow as pa ---> 29 import pyarrow.parquet as pq 30 from tqdm.contrib.concurrent import thread_map 31 [/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/__init__.py](https://localhost:8080/#) in <module> 18 # flake8: noqa 19 ---> 20 from .core import * [/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/core.py](https://localhost:8080/#) in <module> 34 import pyarrow as pa 35 import pyarrow.lib as lib ---> 36 import pyarrow._parquet as _parquet 37 38 from pyarrow._parquet import (ParquetReader, Statistics, # noqa /usr/local/lib/python3.10/dist-packages/pyarrow/_parquet.pyx in init pyarrow._parquet() ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject ### Steps to reproduce the bug ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject ### Expected behavior Resolve the binary incompatibility ### Environment info Google Colab Note book

2024-02-17T04:26:34Z

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attribute error when writing trainer.train()

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### Describe the bug AttributeError Traceback (most recent call last) Cell In[39], line 2 1 # Start the training process ----> 2 trainer.train() File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs) 1537 hf_hub_utils.enable_progress_bars() 1538 else: -> 1539 return inner_training_loop( 1540 args=args, 1541 resume_from_checkpoint=resume_from_checkpoint, 1542 trial=trial, 1543 ignore_keys_for_eval=ignore_keys_for_eval, 1544 ) File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1836, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval) 1833 rng_to_sync = True 1835 step = -1 -> 1836 for step, inputs in enumerate(epoch_iterator): 1837 total_batched_samples += 1 1839 if self.args.include_num_input_tokens_seen: File /opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py:451, in DataLoaderShard.__iter__(self) 449 # We iterate one batch ahead to check when we are at the end 450 try: --> 451 current_batch = next(dataloader_iter) 452 except StopIteration: 453 yield File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630, in _BaseDataLoaderIter.__next__(self) 627 if self._sampler_iter is None: 628 # TODO([https://github.com/pytorch/pytorch/issues/76750)](https://github.com/pytorch/pytorch/issues/76750)%3C/span%3E) 629 self._reset() # type: ignore[call-arg] --> 630 data = self._next_data() 631 self._num_yielded += 1 632 if self._dataset_kind == _DatasetKind.Iterable and \ 633 self._IterableDataset_len_called is not None and \ 634 self._num_yielded > self._IterableDataset_len_called: File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674, in _SingleProcessDataLoaderIter._next_data(self) 672 def _next_data(self): 673 index = self._next_index() # may raise StopIteration --> 674 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 675 if self._pin_memory: 676 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key) 1762 def __getitem__(self, key): # noqa: F811 1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 1764 return self._getitem( 1765 key, 1766 ) File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs) 1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs) 1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 1749 formatted_output = format_table( 1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1751 ) 1752 return formatted_output File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:540, in format_table(table, key, formatter, format_columns, output_all_columns) 538 else: 539 pa_table_to_format = pa_table.drop(col for col in pa_table.column_names if col not in format_columns) --> 540 formatted_output = formatter(pa_table_to_format, query_type=query_type) 541 if output_all_columns: 542 if isinstance(formatted_output, MutableMapping): File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:57, in TorchFormatter.format_row(self, pa_table) 56 def format_row(self, pa_table: pa.Table) -> dict: ---> 57 row = self.numpy_arrow_extractor().extract_row(pa_table) 58 return self.recursive_tensorize(row) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:154, in NumpyArrowExtractor.extract_row(self, pa_table) 153 def extract_row(self, pa_table: pa.Table) -> dict: --> 154 return _unnest(self.extract_batch(pa_table)) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in NumpyArrowExtractor.extract_batch(self, pa_table) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in <dictcomp>(.0) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:196, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: --> 196 if any( 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, **self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:197, in <genexpr>(.0) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: 196 if any( --> 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, **self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr) 319 warnings.warn( 320 f"In the future `np.{attr}` will be defined as the " 321 "corresponding NumPy scalar.", FutureWarning, stacklevel=2) 323 if attr in __former_attrs__: --> 324 raise AttributeError(__former_attrs__[attr]) 326 if attr == 'testing': 327 import numpy.testing as testing AttributeError: module 'numpy' has no attribute 'object'. `np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecationsAttributeError Traceback (most recent call last) Cell In[39], line 2 1 # Start the training process ----> 2 trainer.train() File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs) 1537 hf_hub_utils.enable_progress_bars() 1538 else: -> 1539 return inner_training_loop( 1540 args=args, 1541 resume_from_checkpoint=resume_from_checkpoint, 1542 trial=trial, 1543 ignore_keys_for_eval=ignore_keys_for_eval, 1544 ) File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1836, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval) 1833 rng_to_sync = True 1835 step = -1 -> 1836 for step, inputs in enumerate(epoch_iterator): 1837 total_batched_samples += 1 1839 if self.args.include_num_input_tokens_seen: File /opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py:451, in DataLoaderShard.__iter__(self) 449 # We iterate one batch ahead to check when we are at the end 450 try: --> 451 current_batch = next(dataloader_iter) 452 except StopIteration: 453 yield File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630, in _BaseDataLoaderIter.__next__(self) 627 if self._sampler_iter is None: 628 # TODO([https://github.com/pytorch/pytorch/issues/76750)](https://github.com/pytorch/pytorch/issues/76750)%3C/span%3E) 629 self._reset() # type: ignore[call-arg] --> 630 data = self._next_data() 631 self._num_yielded += 1 632 if self._dataset_kind == _DatasetKind.Iterable and \ 633 self._IterableDataset_len_called is not None and \ 634 self._num_yielded > self._IterableDataset_len_called: File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674, in _SingleProcessDataLoaderIter._next_data(self) 672 def _next_data(self): 673 index = self._next_index() # may raise StopIteration --> 674 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 675 if self._pin_memory: 676 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key) 1762 def __getitem__(self, key): # noqa: F811 1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 1764 return self._getitem( 1765 key, 1766 ) File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs) 1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs) 1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 1749 formatted_output = format_table( 1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1751 ) 1752 return formatted_output File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:540, in format_table(table, key, formatter, format_columns, output_all_columns) 538 else: 539 pa_table_to_format = pa_table.drop(col for col in pa_table.column_names if col not in format_columns) --> 540 formatted_output = formatter(pa_table_to_format, query_type=query_type) 541 if output_all_columns: 542 if isinstance(formatted_output, MutableMapping): File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:57, in TorchFormatter.format_row(self, pa_table) 56 def format_row(self, pa_table: pa.Table) -> dict: ---> 57 row = self.numpy_arrow_extractor().extract_row(pa_table) 58 return self.recursive_tensorize(row) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:154, in NumpyArrowExtractor.extract_row(self, pa_table) 153 def extract_row(self, pa_table: pa.Table) -> dict: --> 154 return _unnest(self.extract_batch(pa_table)) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in NumpyArrowExtractor.extract_batch(self, pa_table) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in <dictcomp>(.0) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:196, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: --> 196 if any( 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, **self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:197, in <genexpr>(.0) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: 196 if any( --> 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, **self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr) 319 warnings.warn( 320 f"In the future `np.{attr}` will be defined as the " 321 "corresponding NumPy scalar.", FutureWarning, stacklevel=2) 323 if attr in __former_attrs__: --> 324 raise AttributeError(__former_attrs__[attr]) 326 if attr == 'testing': 327 import numpy.testing as testing AttributeError: module 'numpy' has no attribute 'object'. `np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations Please help me to resolve the above error ### Steps to reproduce the bug Please resolve the issue of deprecated function np.object to object in the numpy ### Expected behavior np.object should be written as object only ### Environment info kaggle notebook

2024-03-01T10:58:00Z

https://github.com/huggingface/datasets/issues/6669

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Chapter 6 - Issue Loading `cnn_dailymail` dataset

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### Describe the bug So I am getting this bug when I try to run cell 4 of the Chapter 6 notebook code: `dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0")` Error Message: ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[4], line 4 1 #hide_output 2 from datasets import load_dataset ----> 4 dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0") 7 # dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0", trust_remote_code=True) 8 print(f"Features: {dataset['train'].column_names}") File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\load.py:2587, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) 2583 # Build dataset for splits 2584 keep_in_memory = ( 2585 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2586 ) -> 2587 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) 2588 # Rename and cast features to match task schema 2589 if task is not None: 2590 # To avoid issuing the same warning twice File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1244, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory) 1241 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS) 1243 # Create a dataset for each of the given splits -> 1244 datasets = map_nested( 1245 partial( 1246 self._build_single_dataset, 1247 run_post_process=run_post_process, 1248 verification_mode=verification_mode, 1249 in_memory=in_memory, 1250 ), 1251 split, 1252 map_tuple=True, 1253 disable_tqdm=True, 1254 ) 1255 if isinstance(datasets, dict): 1256 datasets = DatasetDict(datasets) File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:477, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc) 466 mapped = [ 467 map_nested( 468 function=function, (...) 474 for obj in iterable 475 ] 476 elif num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length: --> 477 mapped = [ 478 _single_map_nested((function, obj, types, None, True, None)) 479 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc) 480 ] 481 else: 482 with warnings.catch_warnings(): File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:478, in <listcomp>(.0) 466 mapped = [ 467 map_nested( 468 function=function, (...) 474 for obj in iterable 475 ] 476 elif num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length: 477 mapped = [ --> 478 _single_map_nested((function, obj, types, None, True, None)) 479 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc) 480 ] 481 else: 482 with warnings.catch_warnings(): File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:370, in _single_map_nested(args) 368 # Singleton first to spare some computation 369 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 370 return function(data_struct) 372 # Reduce logging to keep things readable in multiprocessing with tqdm 373 if rank is not None and logging.get_verbosity() < logging.WARNING: File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1274, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory) 1271 split = Split(split) 1273 # Build base dataset -> 1274 ds = self._as_dataset( 1275 split=split, 1276 in_memory=in_memory, 1277 ) 1278 if run_post_process: 1279 for resource_file_name in self._post_processing_resources(split).values(): File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1348, in DatasetBuilder._as_dataset(self, split, in_memory) 1346 if self._check_legacy_cache(): 1347 dataset_name = self.name -> 1348 dataset_kwargs = ArrowReader(cache_dir, self.info).read( 1349 name=dataset_name, 1350 instructions=split, 1351 split_infos=self.info.splits.values(), 1352 in_memory=in_memory, 1353 ) 1354 fingerprint = self._get_dataset_fingerprint(split) 1355 return Dataset(fingerprint=fingerprint, **dataset_kwargs) File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\arrow_reader.py:254, in BaseReader.read(self, name, instructions, split_infos, in_memory) 252 if not files: 253 msg = f'Instruction "{instructions}" corresponds to no data!' --> 254 raise ValueError(msg) 255 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) **ValueError: Instruction "validation" corresponds to no data!** ```` Looks like the data is not being loaded. Any advice would be appreciated. Thanks! ### Steps to reproduce the bug Run all cells of Chapter 6 notebook. ### Expected behavior Data should load correctly without any errors. ### Environment info - `datasets` version: 2.17.0 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.9.18 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

2024-02-16T04:40:56Z

https://github.com/huggingface/datasets/issues/6668

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Default config for squad is incorrect

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2024-02-16T02:36:55Z

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### Describe the bug If you download Squad, it will download the plain_text version, but the config still specifies "default", so if you set the offline mode the cache will try to look it up according to the config_id which is "default" and this will say; ValueError: Couldn't find cache for squad for config 'default' Available configs in the cache: ['plain_text'] ### Steps to reproduce the bug 1. export HF_DATASETS_OFFLINE=0 2. load_dataset("squad") 3. export HF_DATASETS_OFFLINE=1 4. load_dataset("squad") ### Expected behavior We should change the config_name I guess? ### Environment info linux, latest version of datasets

2024-02-23T09:10:00Z

https://github.com/huggingface/datasets/issues/6667

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2024-03-01T15:56:38Z

PR_kwDODunzps5m9JgW

closed

Allow SplitDict setitem to replace existing SplitInfo

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2024-02-15T10:17:08Z

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Fix this code provided by @clefourrier ```python import datasets import os token = os.getenv("TOKEN") results = datasets.load_dataset("gaia-benchmark/results_public", "2023", token=token, download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD) results["test"] = datasets.Dataset.from_list([row for row in results["test"] if row["model"] != "StateFlow"]) results["test"].push_to_hub("gaia-benchmark/results_public", "2023", token=token, split="test") ``` ``` ValueError Traceback (most recent call last) Cell In[43], line 1 ----> 1 results["test"].push_to_hub("gaia-benchmark/results_public", "2023", token=token, split="test") File ~/miniconda3/envs/default310/lib/python3.10/site-packages/datasets/arrow_dataset.py:5498, in Dataset.push_to_hub(self, repo_id, config_name, split, private, token, branch, max_shard_size, num_shards, embed_external_files) 5496 repo_info.dataset_size = (repo_info.dataset_size or 0) + dataset_nbytes 5497 repo_info.size_in_bytes = repo_info.download_size + repo_info.dataset_size -> 5498 repo_info.splits[split] = SplitInfo( 5499 split, num_bytes=dataset_nbytes, num_examples=len(self), dataset_name=dataset_name 5500 ) 5501 info_to_dump = repo_info 5502 # create the metadata configs if it was uploaded with push_to_hub before metadata configs existed File ~/miniconda3/envs/default310/lib/python3.10/site-packages/datasets/splits.py:541, in SplitDict.__setitem__(self, key, value) 539 raise ValueError(f"Cannot add elem. (key mismatch: '{key}' != '{value.name}')") 540 if key in self: --> 541 raise ValueError(f"Split {key} already present") 542 super().__setitem__(key, value) ValueError: Split test already present ```

2024-03-01T16:02:46Z

https://github.com/huggingface/datasets/pull/6665

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2024-02-16T02:31:11Z

PR_kwDODunzps5m67g0

closed

Revert the changes in `arrow_writer.py` from #6636

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2024-02-15T01:47:33Z

https://api.github.com/repos/huggingface/datasets/issues/6664

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#6636 broke `write_examples_on_file` and `write_batch` from the class `ArrowWriter`. I'm undoing these changes. See #6663. Note the current implementation doesn't keep the order of the columns and the schema, thus setting a wrong schema for each column.

2024-02-16T14:02:39Z

https://github.com/huggingface/datasets/pull/6664

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2024-02-16T09:25:00Z

completed

I_kwDODunzps5_SNnr

closed

`write_examples_on_file` and `write_batch` are broken in `ArrowWriter`

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2024-02-15T01:43:27Z

https://api.github.com/repos/huggingface/datasets/issues/6663

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CONTRIBUTOR

### Describe the bug `write_examples_on_file` and `write_batch` are broken in `ArrowWriter` since #6636. The order between the columns and the schema is not preserved anymore. So these functions don't work anymore unless the order happens to align well. ### Steps to reproduce the bug Try to do `write_batch` with anything that has many columns, and it's likely to break. ### Expected behavior I expect these functions to work, instead of it trying to cast a column to its incorrect type. ### Environment info - `datasets` version: 2.17.0 - Platform: Linux-5.15.0-1040-aws-x86_64-with-glibc2.35 - Python version: 3.10.13 - `huggingface_hub` version: 0.19.4 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

2024-02-16T09:25:00Z

https://github.com/huggingface/datasets/issues/6663

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2024-03-01T17:43:39Z

PR_kwDODunzps5mwgKP

closed

fix: show correct package name to install biopython

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2024-02-13T14:15:04Z

https://api.github.com/repos/huggingface/datasets/issues/6662

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CONTRIBUTOR

When you try to download a dataset that uses [biopython](https://github.com/biopython/biopython), like `load_dataset("InstaDeepAI/multi_species_genomes")`, you get the error: ``` >>> from datasets import load_dataset >>> dataset = load_dataset("InstaDeepAI/multi_species_genomes") /home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py:1454: FutureWarning: The repository for InstaDeepAI/multi_species_genomes contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/InstaDeepAI/multi_species_genomes You can avoid this message in future by passing the argument `trust_remote_code=True`. Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`. warnings.warn( Downloading builder script: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.51k/7.51k [00:00<00:00, 7.67MB/s] Downloading readme: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17.2k/17.2k [00:00<00:00, 11.0MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 2548, in load_dataset builder_instance = load_dataset_builder( File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 2220, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1871, in dataset_module_factory raise e1 from None File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1844, in dataset_module_factory ).get_module() File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1466, in get_module local_imports = _download_additional_modules( File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 346, in _download_additional_modules raise ImportError( ImportError: To be able to use InstaDeepAI/multi_species_genomes, you need to install the following dependency: Bio. Please install it using 'pip install Bio' for instance. >>> ``` `Bio` comes from the `biopython` package that can be installed with `pip install biopython`, not with `pip install Bio` as suggested. This PR adds special logic to show the correct package name in the error message of ` _download_additional_modules`, similarly as is done for `sklearn` / `scikit-learn` already. There are more packages where importable module name differs from the PyPI package name, so this could be made more generic, like: ``` # Mapping of importable module names to their PyPI package names package_map = { "sklearn": "scikit-learn", "Bio": "biopython", "PIL": "Pillow", "bs4": "beautifulsoup4" } for module_name, pypi_name in package_map.items(): if module_name in needs_to_be_installed.keys(): needs_to_be_installed[module_name] = pypi_name ```

2024-03-01T17:49:48Z

https://github.com/huggingface/datasets/pull/6662

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2024-02-14T08:04:47Z

completed

I_kwDODunzps5_GEJL

closed

Import error on Google Colab

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2024-02-13T13:12:40Z

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### Describe the bug Cannot be imported on Google Colab, the import throws the following error: ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject ### Steps to reproduce the bug 1. `! pip install -U datasets` 2. `import datasets` ### Expected behavior Should be possible to use the library ### Environment info - `datasets` version: 2.17.0 - Platform: Linux-6.1.58+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0

2024-02-25T16:37:54Z

https://github.com/huggingface/datasets/issues/6661

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2024-03-01T18:52:37Z

PR_kwDODunzps5mu9wU

closed

Automatic Conversion for uint16/uint32 to Compatible PyTorch Dtypes

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2024-02-13T10:24:33Z

https://api.github.com/repos/huggingface/datasets/issues/6660

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CONTRIBUTOR

This PR addresses an issue encountered when utilizing uint16 or uint32 datatypes with datasets, followed by attempting to convert these datasets into PyTorch-compatible formats. Currently, doing so results in a TypeError due to incompatible datatype conversion, as illustrated by the following example: ```python from datasets import Dataset, Sequence, Value, Features def gen(): for i in range(100): yield {'seq': list(range(i, i + 20))} ds = Dataset.from_generator(gen, features=Features({'seq': Sequence(feature=Value(dtype='uint16'), length=-1)})) ds.set_format('torch') print(ds[0]) ``` This code snippet triggers the following error due to the inability to convert numpy.uint16 arrays to a PyTorch-supported format: ``` TypeError: can't convert np.ndarray of type numpy.uint16. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool. ``` This PR introduces an automatic mechanism to convert np.uint16 and np.uint32 datatypes to np.int64 for seamless compatibility with PyTorch formats, simplifying workflows and improving developer experience by eliminating the need for manual conversion handling.

2024-03-01T19:01:57Z

https://github.com/huggingface/datasets/pull/6660

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2024-03-01T17:44:55Z

PR_kwDODunzps5mlmmo

closed

Change default compression argument for JsonDatasetWriter

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2024-02-11T23:49:07Z

https://api.github.com/repos/huggingface/datasets/issues/6659

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CONTRIBUTOR

Change default compression type from `None` to "infer", to align with pandas' defaults. Documentation asks the user to supply `to_json_kwargs` with arguments suitable for pandas' `to_json` method. At the same time, while pandas' by default uses ["infer"](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) for compression, datasets enforce `None` as default. This, likely, confuses user, as they expect the same behaviour, i.e they expect that if they name their output file as "dataset.jsonl.zst" then the compression would be inferred as "zstd" and file will be compressed before writing. Moreover, while it is probably outside of the scope of this pull request, `compression` argument needs to be capable of taking `dict` as input (along with `str`), as it does in pandas, in order to allow user to specify compression parameters. Current implementation will likely fail with `NotImplementedError`, as it expects either `None` or `str` specifying compression algo.

2024-03-01T17:51:50Z

https://github.com/huggingface/datasets/pull/6659

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PR_kwDODunzps5mlZyb

open

[Resumable IterableDataset] Add IterableDataset state_dict

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2024-02-11T20:35:52Z

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