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https://api.github.com/repos/huggingface/datasets/issues/7296 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7296/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7296/comments | https://api.github.com/repos/huggingface/datasets/issues/7296/events | https://github.com/huggingface/datasets/pull/7296 | 2,675,573,974 | PR_kwDODunzps6ChJIJ | 7,296 | Remove upper version limit of fsspec[http] | {
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https://api.github.com/repos/huggingface/datasets/issues/7295 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7295/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7295/comments | https://api.github.com/repos/huggingface/datasets/issues/7295/events | https://github.com/huggingface/datasets/issues/7295 | 2,672,003,384 | I_kwDODunzps6fQ4k4 | 7,295 | [BUG]: Streaming from S3 triggers `unexpected keyword argument 'requote_redirect_url'` | {
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} | [] | open | false | null | [] | null | [] | 2024-11-19T12:23:36 | 2024-11-19T13:01:53 | null | NONE | null | null | null | ### Describe the bug
Note that this bug is only triggered when `streaming=True`. #5459 introduced always calling fsspec with `client_kwargs={"requote_redirect_url": False}`, which seems to have incompatibility issues even in the newest versions.
Analysis of what's happening:
1. `datasets` passes the `client_kwargs` through `fsspec`
2. `fsspec` passes the `client_kwargs` through `s3fs`
3. `s3fs` passes the `client_kwargs` to `aiobotocore` which uses `aiohttp`
```
s3creator = self.session.create_client(
"s3", config=conf, **init_kwargs, **client_kwargs
)
```
4. The `session` tries to create an `aiohttp` session but the `**kwargs` are not just kept as unfolded `**kwargs` but passed in as individual variables (`requote_redirect_url` and `trust_env`).
Error:
```
Traceback (most recent call last):
File "/Users/cxrh/Documents/GitHub/nlp_foundation/nlp_train/test.py", line 14, in <module>
batch = next(iter(ds))
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__
for key, example in ex_iterable:
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 255, in __iter__
for key, pa_table in self.generate_tables_fn(**self.kwargs):
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/packaged_modules/json/json.py", line 78, in _generate_tables
for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 840, in __iter__
yield from self.generator(*self.args, **self.kwargs)
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 921, in _iter_from_urlpaths
elif xisdir(urlpath, download_config=download_config):
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 305, in xisdir
return fs.isdir(inner_path)
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/fsspec/spec.py", line 721, in isdir
return self.info(path)["type"] == "directory"
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/fsspec/archive.py", line 38, in info
self._get_dirs()
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/filesystems/compression.py", line 64, in _get_dirs
f = {**self.file.fs.info(self.file.path), "name": self.uncompressed_name}
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/fsspec/asyn.py", line 118, in wrapper
return sync(self.loop, func, *args, **kwargs)
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/fsspec/asyn.py", line 103, in sync
raise return_result
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/fsspec/asyn.py", line 56, in _runner
result[0] = await coro
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/s3fs/core.py", line 1302, in _info
out = await self._call_s3(
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/s3fs/core.py", line 341, in _call_s3
await self.set_session()
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/s3fs/core.py", line 524, in set_session
s3creator = self.session.create_client(
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/aiobotocore/session.py", line 114, in create_client
return ClientCreatorContext(self._create_client(*args, **kwargs))
TypeError: AioSession._create_client() got an unexpected keyword argument 'requote_redirect_url'
```
### Steps to reproduce the bug
1. Install the necessary libraries, datasets having a requirement for being at least 2.19.0:
```
pip install s3fs fsspec aiohttp aiobotocore botocore 'datasets>=2.19.0'
```
2. Run this code:
```
from datasets import load_dataset
ds = load_dataset(
"json",
data_files="s3://your_path/*.jsonl.gz",
streaming=True,
split="train",
)
batch = next(iter(ds))
print(batch)
```
3. You get the `unexpected keyword argument 'requote_redirect_url'` error.
### Expected behavior
The datasets is able to load a batch from the dataset stored on S3, without triggering this `requote_redirect_url` error.
Fix: I could fix this by directly removing the `requote_redirect_url` and `trust_env` - then it loads properly.
<img width="1127" alt="image" src="https://github.com/user-attachments/assets/4c40efa9-8787-4919-b613-e4908c3d1ab2">
### Environment info
- `datasets` version: 3.1.0
- Platform: macOS-15.1-arm64-arm-64bit
- Python version: 3.10.15
- `huggingface_hub` version: 0.26.2
- PyArrow version: 18.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0 | null | {
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} | The dependency is only used for catching an exception from other code. That can be done with an import guard.
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"Updated! 😄 ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7293). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@lhoestq, can you help with this failing test please? 🙏 "
] | 2024-11-16T16:20:57 | 2024-11-18T18:34:37 | null | NONE | null | false | {
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} | fix #7129
@stevhliu | null | {
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"Hi ! If the dataset owner uses `push_to_hub()` instead of `save_to_disk()` and upload the local files it will fix the issue.\r\nRight now `datasets` sees the train/test/valid pickle files but they are not supported file formats.",
"Alternatively you can load the arrow file instead:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('OpenMol/PubChemSFT', data_files='stage1/*.arrow')\r\n```",
"Thanks! I'll have a try."
] | 2024-11-16T11:54:31 | 2024-11-19T00:53:00 | 2024-11-19T00:52:59 | NONE | null | null | null | ### Describe the bug
Cannot load the dataset https://huggingface.co/datasets/OpenMol/PubChemSFT
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset('OpenMol/PubChemSFT')
```
### Expected behavior
```
---------------------------------------------------------------------------
DataFilesNotFoundError Traceback (most recent call last)
Cell In[7], [line 2](vscode-notebook-cell:?execution_count=7&line=2)
[1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset
----> [2](vscode-notebook-cell:?execution_count=7&line=2) dataset = load_dataset('OpenMol/PubChemSFT')
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/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)
[2582](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2582) verification_mode = VerificationMode(
[2583](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2583) (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
[2584](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2584) )
[2586](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2586) # Create a dataset builder
-> [2587](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2587) builder_instance = load_dataset_builder(
[2588](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2588) path=path,
[2589](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2589) name=name,
[2590](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2590) data_dir=data_dir,
[2591](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2591) data_files=data_files,
[2592](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2592) cache_dir=cache_dir,
[2593](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2593) features=features,
[2594](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2594) download_config=download_config,
[2595](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2595) download_mode=download_mode,
[2596](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2596) revision=revision,
[2597](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2597) token=token,
[2598](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2598) storage_options=storage_options,
[2599](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2599) trust_remote_code=trust_remote_code,
[2600](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2600) _require_default_config_name=name is None,
[2601](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2601) **config_kwargs,
[2602](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2602) )
[2604](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2604) # Return iterable dataset in case of streaming
[2605](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2605) if streaming:
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2259, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
[2257](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2257) download_config = download_config.copy() if download_config else DownloadConfig()
[2258](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2258) download_config.storage_options.update(storage_options)
-> [2259](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2259) dataset_module = dataset_module_factory(
[2260](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2260) path,
[2261](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2261) revision=revision,
[2262](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2262) download_config=download_config,
[2263](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2263) download_mode=download_mode,
[2264](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2264) data_dir=data_dir,
[2265](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2265) data_files=data_files,
[2266](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2266) cache_dir=cache_dir,
[2267](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2267) trust_remote_code=trust_remote_code,
[2268](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2268) _require_default_config_name=_require_default_config_name,
[2269](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2269) _require_custom_configs=bool(config_kwargs),
[2270](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2270) )
[2271](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2271) # Get dataset builder class from the processing script
[2272](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2272) builder_kwargs = dataset_module.builder_kwargs
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1904, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
[1902](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1902) raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
[1903](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1903) if isinstance(e1, (DataFilesNotFoundError, DatasetNotFoundError, EmptyDatasetError)):
-> [1904](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1904) raise e1 from None
[1905](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1905) if isinstance(e1, FileNotFoundError):
[1906](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1906) raise FileNotFoundError(
[1907](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1907) f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
[1908](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1908) f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
[1909](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1909) ) from None
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1885, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
[1876](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1876) return HubDatasetModuleFactoryWithScript(
[1877](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1877) path,
[1878](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1878) revision=revision,
(...)
[1882](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1882) trust_remote_code=trust_remote_code,
[1883](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1883) ).get_module()
[1884](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1884) else:
-> [1885](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1885) return HubDatasetModuleFactoryWithoutScript(
[1886](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1886) path,
[1887](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1887) revision=revision,
[1888](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1888) data_dir=data_dir,
[1889](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1889) data_files=data_files,
[1890](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1890) download_config=download_config,
[1891](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1891) download_mode=download_mode,
[1892](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1892) ).get_module()
[1893](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1893) except Exception as e1:
[1894](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1894) # All the attempts failed, before raising the error we should check if the module is already cached
[1895](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1895) try:
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1270, in HubDatasetModuleFactoryWithoutScript.get_module(self)
[1263](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1263) patterns = get_data_patterns(base_path, download_config=self.download_config)
[1264](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1264) data_files = DataFilesDict.from_patterns(
[1265](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1265) patterns,
[1266](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1266) base_path=base_path,
[1267](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1267) allowed_extensions=ALL_ALLOWED_EXTENSIONS,
[1268](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1268) download_config=self.download_config,
[1269](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1269) )
-> [1270](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1270) module_name, default_builder_kwargs = infer_module_for_data_files(
[1271](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1271) data_files=data_files,
[1272](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1272) path=self.name,
[1273](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1273) download_config=self.download_config,
[1274](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1274) )
[1275](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1275) data_files = data_files.filter_extensions(_MODULE_TO_EXTENSIONS[module_name])
[1276](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1276) # Collect metadata files if the module supports them
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:597, in infer_module_for_data_files(data_files, path, download_config)
[595](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:595) raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}")
[596](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:596) if not module_name:
--> [597](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:597) raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
[598](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:598) return module_name, default_builder_kwargs
DataFilesNotFoundError: No (supported) data files found in OpenMol/PubChemSFT
```
### Environment info
```
- `datasets` version: 3.1.0
- Platform: Linux-5.15.0-125-generic-x86_64-with-glibc2.31
- Python version: 3.9.18
- `huggingface_hub` version: 0.25.2
- PyArrow version: 18.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.9.2
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7291 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7291/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7291/comments | https://api.github.com/repos/huggingface/datasets/issues/7291/events | https://github.com/huggingface/datasets/issues/7291 | 2,662,244,643 | I_kwDODunzps6erqEj | 7,291 | Why return_tensors='pt' doesn't work？ | {
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"Hi ! `datasets` uses Arrow as storage backend which is agnostic to deep learning frameworks like torch. If you want to get torch tensors back, you need to do `dataset = dataset.with_format(\"torch\")`",
"> Hi ! `datasets` uses Arrow as storage backend which is agnostic to deep learning frameworks like torch. If you want to get torch tensors back, you need to do `dataset = dataset.with_format(\"torch\")`\r\n\r\nIt does work! Thanks for your suggestion!"
] | 2024-11-15T15:01:23 | 2024-11-18T13:47:08 | null | NONE | null | null | null | ### Describe the bug
I tried to add input_ids to dataset with map(), and I used the return_tensors='pt', but why I got the callback with the type of List？
### Steps to reproduce the bug
### Expected behavior
Sorry for this silly question, I'm noob on using this tool. But I think it should return a tensor value as I have used the protocol？
When I tokenize only one sentence using tokenized_input=tokenizer(input, return_tensors='pt' )，it does return in tensor type. Why doesn't it work in map()？
### Environment info
transformers>=4.41.2,<=4.45.0
datasets>=2.16.0,<=2.21.0
accelerate>=0.30.1,<=0.34.2
peft>=0.11.1,<=0.12.0
trl>=0.8.6,<=0.9.6
gradio>=4.0.0
pandas>=2.0.0
scipy
einops
sentencepiece
tiktoken
protobuf
uvicorn
pydantic
fastapi
sse-starlette
matplotlib>=3.7.0
fire
packaging
pyyaml
numpy<2.0.0
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/7290 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7290/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7290/comments | https://api.github.com/repos/huggingface/datasets/issues/7290/events | https://github.com/huggingface/datasets/issues/7290 | 2,657,620,816 | I_kwDODunzps6eaBNQ | 7,290 | `Dataset.save_to_disk` hangs when using num_proc > 1 | {
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} | [] | open | false | null | [] | null | [] | 2024-11-14T05:25:13 | 2024-11-14T05:25:13 | null | NONE | null | null | null | ### Describe the bug
Hi, I'm encountered a small issue when saving datasets that led to the saving taking up to multiple hours.
Specifically, [`Dataset.save_to_disk`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.save_to_disk) is a lot slower when using `num_proc>1` than when using `num_proc=1`
The documentation mentions that "Multiprocessing is disabled by default.", but there is no explanation on how to enable it.
### Steps to reproduce the bug
```
import numpy as np
from datasets import Dataset
n_samples = int(4e6)
n_tokens_sample = 100
data_dict = {
'tokens' : np.random.randint(0, 100, (n_samples, n_tokens_sample)),
}
dataset = Dataset.from_dict(data_dict)
dataset.save_to_disk('test_dataset', num_proc=1)
dataset.save_to_disk('test_dataset', num_proc=4)
dataset.save_to_disk('test_dataset', num_proc=8)
```
This results in:
```
>>> dataset.save_to_disk('test_dataset', num_proc=1)
Saving the dataset (7/7 shards): 100%|██████████████| 4000000/4000000 [00:17<00:00, 228075.15 examples/s]
>>> dataset.save_to_disk('test_dataset', num_proc=4)
Saving the dataset (7/7 shards): 100%|██████████████| 4000000/4000000 [01:49<00:00, 36583.75 examples/s]
>>> dataset.save_to_disk('test_dataset', num_proc=8)
Saving the dataset (8/8 shards): 100%|██████████████| 4000000/4000000 [02:11<00:00, 30518.43 examples/s]
```
With larger datasets it can take hours, but I didn't benchmark that for this bug report.
### Expected behavior
I would expect using `num_proc>1` to be faster instead of slower than `num_proc=1`.
### Environment info
- `datasets` version: 3.1.0
- Platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.26.2
- PyArrow version: 18.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1 | null | {
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"i think this issue is more for https://github.com/huggingface/dataset-viewer"
] | 2024-11-11T03:29:27 | 2024-11-13T13:02:25 | 2024-11-13T13:02:25 | NONE | null | null | null | ### Describe the bug
In [my dataset](https://huggingface.co/datasets/speedcell4/opus-unigram2), there is a column called `lang2`, and there are 94 different classes in total, but the viewer says there are 83 values only. This issue only arises in the `train` split. The total number of values is also 94 in the `test` and `dev` columns, viewer tells the correct number of them.
<img width="177" alt="image" src="https://github.com/user-attachments/assets/78d76ef2-fe0e-4fa3-85e0-fb2552813d1c">
### Steps to reproduce the bug
```python3
from datasets import load_dataset
ds = load_dataset('speedcell4/opus-unigram2').unique('lang2')
for key, lang2 in ds.items():
print(key, len(lang2))
```
This script returns the following and tells that the `train` split has 94 values in the `lang2` column.
```
train 94
dev 94
test 94
zero 5
```
### Expected behavior
94 in the reviewer.
### Environment info
Collecting environment information...
PyTorch version: 2.4.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: CentOS Linux release 8.2.2004 (Core) (x86_64)
GCC version: (GCC) 8.3.1 20191121 (Red Hat 8.3.1-5)
Clang version: Could not collect
CMake version: version 3.11.4
Libc version: glibc-2.28
Python version: 3.9.20 (main, Oct 3 2024, 07:27:41) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.28
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-40GB
GPU 1: NVIDIA A100-SXM4-40GB
GPU 2: NVIDIA A100-SXM4-40GB
GPU 3: NVIDIA A100-SXM4-40GB
GPU 4: NVIDIA A100-SXM4-40GB
GPU 5: NVIDIA A100-SXM4-40GB
GPU 6: NVIDIA A100-SXM4-40GB
GPU 7: NVIDIA A100-SXM4-40GB
Nvidia driver version: 525.85.05
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 64
On-line CPU(s) list: 0-63
Thread(s) per core: 1
Core(s) per socket: 32
Socket(s): 2
NUMA node(s): 4
Vendor ID: AuthenticAMD
CPU family: 23
Model: 49
Model name: AMD EPYC 7542 32-Core Processor
Stepping: 0
CPU MHz: 3389.114
BogoMIPS: 5789.40
Virtualization: AMD-V
L1d cache: 32K
L1i cache: 32K
L2 cache: 512K
L3 cache: 16384K
NUMA node0 CPU(s): 0-15
NUMA node1 CPU(s): 16-31
NUMA node2 CPU(s): 32-47
NUMA node3 CPU(s): 48-63
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.4.1+cu121
[pip3] torchaudio==2.4.1+cu121
[pip3] torchdevice==0.1.1
[pip3] torchglyph==0.3.2
[pip3] torchmetrics==1.5.0
[pip3] torchrua==0.5.1
[pip3] torchvision==0.19.1+cu121
[pip3] triton==3.0.0
[pip3] datasets==3.0.1
[conda] numpy 1.26.4 pypi_0 pypi
[conda] torch 2.4.1+cu121 pypi_0 pypi
[conda] torchaudio 2.4.1+cu121 pypi_0 pypi
[conda] torchdevice 0.1.1 pypi_0 pypi
[conda] torchglyph 0.3.2 pypi_0 pypi
[conda] torchmetrics 1.5.0 pypi_0 pypi
[conda] torchrua 0.5.1 pypi_0 pypi
[conda] torchvision 0.19.1+cu121 pypi_0 pypi
[conda] triton 3.0.0 pypi_0 pypi | {
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"Hi ! You can already configure the README.md to have multiple sets of splits, e.g.\r\n\r\n```yaml\r\nconfigs:\r\n- config_name: my_first_set_of_split\r\n data_files:\r\n - split: train\r\n path: *.csv\r\n- config_name: my_second_set_of_split\r\n data_files:\r\n - split: train\r\n path: train-*.csv\r\n - split: test\r\n path: test-*.csv\r\n```",
"Hi - I had something slightly different in mind:\r\n\r\nCurrently the yaml splits specified like this only allow specifying which filenames to pass to each split.\r\nBut what if I have a situation where I know which individual *training examples* I want to put in each split.\r\n\r\nI could build split-specific files, however for large datasets with overlapping (e.g. multiple sets of) splits this could result in significant duplication of data.\r\n\r\nI can see that this could actually be very much intended (i.e. to discourage overlapping splits), but wondered whether some support for handling splits based on individual identifiers is something that could be considered. ",
"This is not supported right now :/ Though you can load the data in two steps like this\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nfull_dataset = load_dataset(\"username/dataset\", split=\"train\")\r\nmy_first_set_indices = load_dataset(\"username/dataset\", \"my_first_set_of_split\", split=\"train\")\r\n\r\nmy_first_set = full_dataset.select(my_first_set_indices[\"indices\"])\r\n```\r\n\r\nyou can create such a dataset by adapting this code for example\r\n```python\r\n# upload the full dataset\r\nfull_dataset.push_to_hub(\"username/dataset\")\r\n# then upload the indices for each set\r\nDatasetDict({\r\n \"train\": Dataset.from_dict({\"indices\": [0, 1, 2, 3]}),\r\n \"test\": Dataset.from_dict({\"indices\": [4, 5]}),\r\n}).push_to_hub(\"username/dataset\", \"my_first_set_of_split\")"
] | 2024-11-10T07:45:19 | 2024-11-19T14:37:02 | null | CONTRIBUTOR | null | null | null | ### Feature request
As far as I understand, automated construction of splits for hub datasets is currently based on either file names or directory structure ([as described here](https://huggingface.co/docs/datasets/en/repository_structure))
It would seem to be pretty useful to also allow splits to be based on identifiers of individual examples
This could be configured like
{"split_name": {"column_name": [column values in split]}}
(This in turn requires unique 'index' columns, which could be explicitly supported or just assumed to be defined appropriately by the user).
I guess a potential downside would be that shards would end up spanning different splits - is this something that can be handled somehow? Would this only affect streaming from hub?
### Motivation
The main motivation would be that all data files could be stored in a single directory, and multiple sets of splits could be generated from the same data. This is often useful for large datasets with multiple distinct sets of splits.
This could all be configured via the README.md yaml configs
### Your contribution
May be able to contribute if it seems like a good idea | null | {
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] | closed | false | null | [] | null | [] | 2024-11-08T23:21:40 | 2024-11-09T16:14:37 | 2024-11-09T16:14:37 | NONE | null | null | null | ### Feature request
https://github.com/huggingface/datasets/pull/6464 mentions a `num_proc` param while loading dataset from disk, but can't find that in the documentation and code anywhere
### Motivation
Make loading large datasets from disk faster
### Your contribution
Happy to contribute if given pointers | {
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"@lhoestq "
] | 2024-11-08T15:04:08 | 2024-11-20T22:53:41 | null | CONTRIBUTOR | null | false | {
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} | Fix for https://github.com/huggingface/datasets/issues/7220 as suggested in discussion, in preference to #7221
(only concern would be on effect on type checking with custom feature types that aren't covered by FeatureType?) | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7283 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7283/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7283/comments | https://api.github.com/repos/huggingface/datasets/issues/7283/events | https://github.com/huggingface/datasets/pull/7283 | 2,642,537,708 | PR_kwDODunzps6BQUgH | 7,283 | Allow for variation in metadata file names as per issue #7123 | {
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Resolves #7123. | null | {
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} | [] | open | false | null | [] | null | [] | 2024-11-07T20:15:01 | 2024-11-07T20:15:42 | null | CONTRIBUTOR | null | null | null | ### Describe the bug
Trying to download only the 'validation' split of my dataset; instead hit the error `datasets.exceptions.ExpectedMoreSplitsError`.
Appears to be the same undesired behavior as reported in [#6939](https://github.com/huggingface/datasets/issues/6939), but with `data_files`, not `data_dir`.
Here is the Traceback:
```
Traceback (most recent call last):
File "/home/user/app/app.py", line 12, in <module>
ds = load_dataset('datacomp/imagenet-1k-random0.0', token=GATED_IMAGENET, data_files={'validation': 'data/val*'}, split='validation', trust_remote_code=True)
File "/usr/local/lib/python3.10/site-packages/datasets/load.py", line 2154, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/site-packages/datasets/builder.py", line 924, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/site-packages/datasets/builder.py", line 1018, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/usr/local/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 68, in verify_splits
raise ExpectedMoreSplitsError(str(set(expected_splits) - set(recorded_splits)))
datasets.exceptions.ExpectedMoreSplitsError: {'train', 'test'}
```
Note: I am using the `data_files` argument only because I am trying to specify that I only want the 'validation' split, and the whole dataset will be downloaded even when the `split='validation'` argument is specified, unless you also specify `data_files`, as described here: https://discuss.huggingface.co/t/how-can-i-download-a-specific-split-of-a-dataset/79027
### Steps to reproduce the bug
1. Create a Space with the default blank 'gradio' SDK https://huggingface.co/new-space
2. Create a file 'app.py' that loads a dataset to only extract a 'validation' split:
`ds = load_dataset('datacomp/imagenet-1k-random0.0', token=GATED_IMAGENET, data_files={'validation': 'data/val*'}, split='validation', trust_remote_code=True)`
### Expected behavior
Downloading validation split.
### Environment info
Default environment for creating a new Space. Relevant to this bug, that is:
```
FROM docker.io/library/python:3.10@sha256:fd0fa50d997eb56ce560c6e5ca6a1f5cf8fdff87572a16ac07fb1f5ca01eb608
--> RUN pip install --no-cache-dir pip==22.3.1 && pip install --no-cache-dir datasets "huggingface-hub>=0.19" "hf-transfer>=0.1.4" "protobuf<4" "click<8.1"
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7281 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7281/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7281/comments | https://api.github.com/repos/huggingface/datasets/issues/7281/events | https://github.com/huggingface/datasets/issues/7281 | 2,640,346,339 | I_kwDODunzps6dYHzj | 7,281 | File not found error | {
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"Link to the dataset: https://huggingface.co/datasets/MichielBontenbal/UrbanSounds "
] | 2024-11-07T09:04:49 | 2024-11-07T09:22:43 | null | NONE | null | null | null | ### Describe the bug
I get a FileNotFoundError:
<img width="944" alt="image" src="https://github.com/user-attachments/assets/1336bc08-06f6-4682-a3c0-071ff65efa87">
### Steps to reproduce the bug
See screenshot.
### Expected behavior
I want to load one audiofile from the dataset.
### Environment info
MacOs Intel 14.6.1 (23G93)
Python 3.10.9
Numpy 1.23
Datasets latest version | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7280 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7280/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7280/comments | https://api.github.com/repos/huggingface/datasets/issues/7280/events | https://github.com/huggingface/datasets/issues/7280 | 2,639,977,077 | I_kwDODunzps6dWtp1 | 7,280 | Add filename in error message when ReadError or similar occur | {
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"Hi Elisa, please share the error traceback here, and if you manage to find the location in the `datasets` code where the error occurs, feel free to open a PR to add the necessary logging / improve the error message.",
"> please share the error traceback\n\nI don't have access to it but it should be during [this exception](https://github.com/huggingface/datasets/blob/2049c00921c59cdeb835137a1c49639cf175af07/src/datasets/builder.py#L1643) which happens during the loading of a dataset. If one of the downloaded files is corrupted, the for loop will not yield correctly, and the error will come from, say, in the case of tar files, [this iterable](https://github.com/huggingface/datasets/blob/2049c00921c59cdeb835137a1c49639cf175af07/src/datasets/utils/file_utils.py#L1293) which has no explicit error handling that leaves clues as to which file has failed.\n\nI only know the case for tar files, but I consider this issue could be happening across different file types too.",
"I think having a better error handling for this tar iterable would be useful already, maybe a simple try/except in `_iter_from_urlpath` that checks for `tarfile.ReadError` and raises an error with the `urlpath` mentioned in the error ?",
"I think not just from higher calls like the `_iter_from_urlpath` but directly wherever a file is attempted to be opened would be the best case, as the traceback would simply lead to that.",
"so maybe there should be better error messages in each dataset builder definition ? e.g. in https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/webdataset/webdataset.py for webdataset TAR archives"
] | 2024-11-07T06:00:53 | 2024-11-20T13:23:12 | null | NONE | null | null | null | Please update error messages to include relevant information for debugging when loading datasets with `load_dataset()` that may have a few corrupted files.
Whenever downloading a full dataset, some files might be corrupted (either at the source or from downloading corruption).
However the errors often only let me know it was a tar file if `tarfile.ReadError` appears on the traceback, and I imagine similarly for other file types.
This makes it really hard to debug which file is corrupted, and when dealing with very large datasets, it shouldn't be necessary to force download everything again. | null | {
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} | ### Introduction
Hello there,
I noticed that there are two ways to combine multiple datasets: Either through `datasets.concatenate_datasets` or `datasets.interleave_datasets`. However, to my knowledge (please correct me if I am wrong) both approaches require the datasets that are combined to have the same features.
I think it would be a great idea to add support for combining multiple datasets that might not follow the same schema (i.e. have different features), for example an image and text dataset. That is why I propose a third function of the `datasets.combine` module called `stack_datasets`, which can be used to combine a list of datasets with (potentially) different features. This would look as follows:
```python
>>> from datasets import stack_datasets
>>> image_dataset = ...
>>> next(iter(image_dataset))
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=555x416 at 0x313E79CD0> }
>>> text_dataset = ...
>>> next(iter(text_dataset))
{'text': "This is a test."}
>>> stacked = stack_datasets(datasets={'i_ds': image_dataset, 't_ds': text_dataset}, stopping_strategy='all_exhausted')
>>> next(iter(stacked))
{
'i_ds': {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=555x416 at 0x313E79CD0> }
't_ds': {'text': "This is a test."}
}
```
<br />
### Motivation
I motivate this by:
**A**: The fact that Pytorch offers a similar functionality under `torch.utils.data.StackDataset` ([link](https://pytorch.org/docs/stable/data.html#torch.utils.data.StackDataset)).
**B**: In settings where one would like to e.g. train a Vision-Language model using an image-text dataset, an image dataset, and a text dataset, this functionality would offer a clean and intuitive solution to create multimodal datasets. I am aware that the aforementioned is also feasible without my proposed function, but I believe this offers a nice approach that aligns with existing functionality and is directly provided within the `datasets` package.
### API
`stack_datasets` has two arguments: `datasets` and `stopping_strategy `.
<br />
`datasets` is a dictionary of either type `Dict[str, Dataset]` or `Dict[str, IterableDatasets]`, a mixture is not allowed. It contains the names of the datasets (the keys) and the datasets themselves (the values) that should be stacked. Each item returned is a dictionary with one key-value pair for each dataset. The keys are the names of the datasets as provided in the argument `datasets`, and the values are the respective examples from the datasets.
<br />
`stopping_strategy` is the same as for `interleave_datasets`. If it is `first_exhausted` we stop if the smallest dataset runs out of examples, if it is `all_exhausted` we stop if all datasets ran out of examples at least once. For `all_exhausted` that means that we may visit examples from datasets multiple times.
### Docs
I saw that there are multiple documentations and guides on the HuggingFace website that introduce `concatenate_datasets` and `interleave_datasets`, for example [here](https://huggingface.co/docs/datasets/process#concatenate). If this request is merged I would be willing to add the new functionality at the appropriate points in the documentation (if desired).
### Tests
I also added some tests to ensure correctness. Some tests I wrote in [tests/test_iterable_dataset.py](https://github.com/TimCares/datasets/blob/fadc1159debf2a65d44e40cbf7758f2bd2cc8b08/tests/test_iterable_dataset.py#L2169)
run for both `Dataset` and `IterableDataset` even though tests for `Dataset` technically do not belong in this script, but I found that this was a nice way to cover more cases with mostly the same code.
### Additional information
I tried to write the code in a way so that it is similar to that of `concatenate_datasets` and `interleave_datasets`.
I’m open to feedback and willing to make adjustments based on your suggestions, so feel free to give me your take. :)
| null | {
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} | - [x] Fixes #7276 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7277 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7277/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7277/comments | https://api.github.com/repos/huggingface/datasets/issues/7277/events | https://github.com/huggingface/datasets/pull/7277 | 2,632,459,184 | PR_kwDODunzps6AyB7O | 7,277 | Add link to video dataset | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7277). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-11-04T10:45:12 | 2024-11-04T17:05:06 | 2024-11-04T17:05:06 | CONTRIBUTOR | null | false | {
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} | This PR updates https://huggingface.co/docs/datasets/loading to also link to the new video loading docs.
cc @mfarre | {
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https://api.github.com/repos/huggingface/datasets/issues/7276 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7276/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7276/comments | https://api.github.com/repos/huggingface/datasets/issues/7276/events | https://github.com/huggingface/datasets/issues/7276 | 2,631,917,431 | I_kwDODunzps6c3993 | 7,276 | Accessing audio dataset value throws Format not recognised error | {
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"Hi ! can you try if this works ?\r\n\r\n```python\r\nimport soundfile as sf\r\n\r\nwith open('C:\\\\Users\\\\Nawaz-Server\\\\.cache\\\\huggingface\\\\hub\\\\datasets--fawazahmed0--bug-audio\\\\snapshots\\\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\\\data\\\\Ghamadi\\\\037136.mp3', 'rb') as f:\r\n print(sf.read(f))\r\n```",
"@lhoestq Same error, here is the output:\r\n\r\n```bash\r\n(mypy) C:\\Users\\Nawaz-Server\\Documents\\ml>python myest.py\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\Nawaz-Server\\Documents\\ml\\myest.py\", line 5, in <module>\r\n print(sf.read(f))\r\n ^^^^^^^^^^\r\n File \"C:\\Users\\Nawaz-Server\\.conda\\envs\\mypy\\Lib\\site-packages\\soundfile.py\", line 285, in read\r\n with SoundFile(file, 'r', samplerate, channels,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\Nawaz-Server\\.conda\\envs\\mypy\\Lib\\site-packages\\soundfile.py\", line 658, in __init__\r\n self._file = self._open(file, mode_int, closefd)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\Nawaz-Server\\.conda\\envs\\mypy\\Lib\\site-packages\\soundfile.py\", line 1216, in _open\r\n raise LibsndfileError(err, prefix=\"Error opening {0!r}: \".format(self.name))\r\nsoundfile.LibsndfileError: Error opening <_io.BufferedReader name='C:\\\\Users\\\\Nawaz-Server\\\\.cache\\\\huggingface\\\\hub\\\\datasets--fawazahmed0--bug-audio\\\\snapshots\\\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\\\data\\\\Ghamadi\\\\037136.mp3'>: Format not recognised.\r\n\r\n```",
"upstream bug: https://github.com/bastibe/python-soundfile/issues/439"
] | 2024-11-04T05:59:13 | 2024-11-09T18:51:52 | null | NONE | null | null | null | ### Describe the bug
Accessing audio dataset value throws `Format not recognised error`
### Steps to reproduce the bug
**code:**
```py
from datasets import load_dataset
dataset = load_dataset("fawazahmed0/bug-audio")
for data in dataset["train"]:
print(data)
```
**output:**
```bash
(mypy) C:\Users\Nawaz-Server\Documents\ml>python myest.py
[C:\vcpkg\buildtrees\mpg123\src\0d8db63f9b-3db975bc05.clean\src\libmpg123\layer3.c:INT123_do_layer3():1801] error: dequantization failed!
{'audio': {'path': 'C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037135.mp3', 'array': array([ 0.00000000e+00, -2.86519935e-22, -2.56504911e-21, ...,
-1.94239747e-02, -2.42924765e-02, -2.99104657e-02]), 'sampling_rate': 22050}, 'reciter': 'Ghamadi', 'transcription': 'الا عجوز ا في الغبرين', 'line': 3923, 'chapter': 37, 'verse': 135, 'text': 'إِلَّا عَجُوزࣰ ا فِي ٱلۡغَٰبِرِينَ'}
Traceback (most recent call last):
File "C:\Users\Nawaz-Server\Documents\ml\myest.py", line 5, in <module>
for data in dataset["train"]:
~~~~~~~^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\arrow_dataset.py", line 2372, in __iter__
formatted_output = format_table(
^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 639, in format_table
return formatter(pa_table, query_type=query_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 403, in __call__
return self.format_row(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 444, in format_row
row = self.python_features_decoder.decode_row(row)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 222, in decode_row
return self.features.decode_example(row) if self.features else row
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\features.py", line 2042, in decode_example
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\features.py", line 1403, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\audio.py", line 184, in decode_example
array, sampling_rate = sf.read(f)
^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 285, in read
with SoundFile(file, 'r', samplerate, channels,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 658, in __init__
self._file = self._open(file, mode_int, closefd)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 1216, in _open
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BufferedReader name='C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037136.mp3'>: Format not recognised.
```
### Expected behavior
Everything should work fine, as loading the problematic audio file directly with soundfile package works fine
**code:**
```
import soundfile as sf
print(sf.read('C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037136.mp3'))
```
**output:**
```bash
(mypy) C:\Users\Nawaz-Server\Documents\ml>python myest.py
[C:\vcpkg\buildtrees\mpg123\src\0d8db63f9b-3db975bc05.clean\src\libmpg123\layer3.c:INT123_do_layer3():1801] error: dequantization failed!
(array([ 0.00000000e+00, -8.43723821e-22, -2.45370628e-22, ...,
-7.71464454e-03, -6.90496899e-03, -8.63333419e-03]), 22050)
```
### Environment info
- `datasets` version: 3.0.2
- Platform: Windows-11-10.0.22621-SP0
- Python version: 3.12.7
- `huggingface_hub` version: 0.26.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.10.0
- soundfile: 0.12.1 | null | {
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} | [] | open | false | null | [] | null | [] | 2024-11-04T03:01:44 | 2024-11-04T03:01:44 | null | NONE | null | null | null | ### Describe the bug
I am performing two operations I see on a hugging face tutorial (Fine-tune a language model), and I am defining every aspect inside the mapped functions, also some imports of the library because it doesnt identify anything not defined outside that function where the dataset elements are being mapped:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb#scrollTo=iaAJy5Hu3l_B
`- lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
batch_size=batch_size,
num_proc=4,
)
- tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"])
def tokenize_function(examples):
model_checkpoint = 'gpt2'
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)
return tokenizer(examples["text"])`
### Steps to reproduce the bug
Currently handle all the imports inside the function
### Expected behavior
The code must work es expected in the notebook, but currently this is not happening.
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb#scrollTo=iaAJy5Hu3l_B
### Environment info
print(transformers.__version__)
4.46.1 | null | {
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"PTAL @lhoestq @albertvillanova ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7273). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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} | Raise error when `lines = False` and `batch_size < Dataset.num_rows` in `Dataset.to_json()`.
Issue: #7037
Related PRs:
#7039 #7181 | {
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"I seem to have encountered the same problem when loading non streaming datasets. load_from_disk. Causing hundreds of GB of memory, but the dataset actually only has 50GB",
"FYI when streaming parquet data, only one row group per worker is loaded in memory at a time.\r\n\r\nBtw for datasets of embeddings you can surely optimize your RAM by reading the data as torch tensors directly instead of the default python lists\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\ndataset = load_dataset(\"WaveGenAI/dataset\", streaming=True).with_format(\"torch\")\r\n\r\ndataloader = DataLoader(dataset[\"train\"], num_workers=3)\r\n```"
] | 2024-10-31T13:33:52 | 2024-11-18T11:46:07 | null | NONE | null | null | null | ### Describe the bug
I try to use a dataset with streaming=True, the issue I have is that the RAM usage becomes higher and higher until it is no longer sustainable.
I understand that huggingface store data in ram during the streaming, and more worker in dataloader there are, more a lot of shard will be stored in ram, but the issue I have is that the ram usage is not constant. So after each new shard loaded, the ram usage will be higher and higher.
### Steps to reproduce the bug
You can run this code and see you ram usage, after each shard of 255 examples, your ram usage will be extended.
```py
from datasets import load_dataset
from torch.utils.data import DataLoader
dataset = load_dataset("WaveGenAI/dataset", streaming=True)
dataloader = DataLoader(dataset["train"], num_workers=3)
for i, data in enumerate(dataloader):
print(i, end="\r")
```
### Expected behavior
The Ram usage should be always the same (just 3 shards loaded in the ram).
### Environment info
- `datasets` version: 3.0.1
- Platform: Linux-6.10.5-arch1-1-x86_64-with-glibc2.40
- Python version: 3.12.4
- `huggingface_hub` version: 0.26.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1 | null | {
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"Hello, It's an interesting issue here. I have the same problem, I have a local dataset and I want to push the dataset to the hub but huggingface does a copy of it.\r\n\r\n```py\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"webdataset\", data_files=\"/media/works/data/*.tar\") # copy here\r\ndataset.push_to_hub(\"WaveGenAI/audios2\")\r\n```\r\n\r\nEdit: I can use HfApi for my use case\r\n"
] | 2024-10-31T11:51:56 | 2024-10-31T14:43:47 | null | NONE | null | null | null | ### Describe the bug
I have data saved with save_to_disk. The data is big (700Gb). When I try loading it, the only option is load_from_disk, and this function copies the data to a tmp directory, causing me to run out of disk space. Is there an alternative solution to that?
### Steps to reproduce the bug
when trying to load data using load_From_disk after being saved using save_to_disk
### Expected behavior
run out of disk space
### Environment info
lateest version | null | {
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"I encountered the same problem on M1, a workaround I did was to simply comment out the dependency:\r\n\r\n```python\r\n...\r\n \"zstandard\",\r\n \"polars[timezone]>=0.20.0\",\r\n # \"decord==0.6.0\",\r\n]\r\n```\r\n\r\nThis worked for me as the adjustments I did to the code do not use the dependency, but I do not know if the same holds for you.\r\n\r\nI also do not think it is a good idea to rely on a dependency (I mean decord) that has not been maintained for 2 years, but I saw that even eva-decord hasn't been maintained since last year.\r\n\r\nDid you get it to work with eva-decord?"
] | 2024-10-31T10:18:45 | 2024-11-04T22:18:06 | null | NONE | null | null | null | ### Describe the bug
Hi,
Decord is a dev dependency not maintained since couple years.
It does not have an ARM package available rendering it uninstallable on non-intel based macs
Suggestion is to move to eva-decord (https://github.com/georgia-tech-db/eva-decord) which doesnt have this problem.
Happy to raise a PR
### Steps to reproduce the bug
Source installation as mentioned in contributinog.md
### Expected behavior
Installation without decord failing to be installed.
### Environment info
python=3.10, M3 Mac | null | {
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"Waiting is all you need. 10 hours later, it works."
] | 2024-10-30T16:32:00 | 2024-10-31T03:48:11 | 2024-10-31T03:48:10 | NONE | null | null | null | ### Describe the bug
After waiting for 2 hours, it still presents ``The dataset viewer should be available soon. Please retry later.''
### Steps to reproduce the bug
dataset link: https://huggingface.co/datasets/BryanW/HI_EDIT
### Expected behavior
Present the dataset viewer.
### Environment info
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7265). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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https://api.github.com/repos/huggingface/datasets/issues/7262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7262/comments | https://api.github.com/repos/huggingface/datasets/issues/7262/events | https://github.com/huggingface/datasets/pull/7262 | 2,620,879,059 | PR_kwDODunzps6AOWI8 | 7,262 | Allow video with disabeld decoding without decord | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7262). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-29T10:54:04 | 2024-10-29T10:56:19 | 2024-10-29T10:55:37 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/7261 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7261/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7261/comments | https://api.github.com/repos/huggingface/datasets/issues/7261/events | https://github.com/huggingface/datasets/issues/7261 | 2,620,510,840 | I_kwDODunzps6cMdJ4 | 7,261 | Cannot load the cache when mapping the dataset | {
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} | [] | open | false | null | [] | null | [] | 2024-10-29T08:29:40 | 2024-10-29T08:29:40 | null | NONE | null | null | null | ### Describe the bug
I'm training the flux controlnet. The train_dataset.map() takes long time to finish. However, when I killed one training process and want to restart a new training with the same dataset. I can't reuse the mapped result even I defined the cache dir for the dataset.
with accelerator.main_process_first():
from datasets.fingerprint import Hasher
# fingerprint used by the cache for the other processes to load the result
# details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
new_fingerprint = Hasher.hash(args)
train_dataset = train_dataset.map(
compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint, batch_size=10,
)
### Steps to reproduce the bug
train flux controlnet and start again
### Expected behavior
will not map again
### Environment info
latest diffusers
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https://api.github.com/repos/huggingface/datasets/issues/7260 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7260/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7260/comments | https://api.github.com/repos/huggingface/datasets/issues/7260/events | https://github.com/huggingface/datasets/issues/7260 | 2,620,014,285 | I_kwDODunzps6cKj7N | 7,260 | cache can't cleaned or disabled | {
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} | [] | open | false | null | [] | null | [] | 2024-10-29T03:15:28 | 2024-10-29T03:18:22 | null | NONE | null | null | null | ### Describe the bug
I tried following ways, the cache can't be disabled.
I got 2T data, but I also got more than 2T cache file. I got pressure on storage. I need to diable cache or cleaned immediately after processed. Following ways are all not working, please give some help!
```python
from datasets import disable_caching
from transformers import AutoTokenizer
disable_caching()
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
def tokenization_fn(examples):
column_name = 'text' if 'text' in examples else 'data'
tokenized_inputs = tokenizer(
examples[column_name], return_special_tokens_mask=True, truncation=False,
max_length=tokenizer.model_max_length
)
return tokenized_inputs
data = load_dataset('json', data_files=save_local_path, split='train', cache_dir=None)
data.cleanup_cache_files()
updated_dataset = data.map(tokenization_fn, load_from_cache_file=False)
updated_dataset .cleanup_cache_files()
```
### Expected behavior
no cache file generated
### Environment info
Ubuntu 20.04.6 LTS
datasets 3.0.2 | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7259). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-28T16:25:10 | 2024-10-28T16:27:34 | 2024-10-28T16:26:01 | MEMBER | null | false | {
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} | don't include video bytes when running download_and_prepare(format="parquet")
this also affects push_to_hub which will just upload the local paths of the videos though | {
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https://api.github.com/repos/huggingface/datasets/issues/7258 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7258/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7258/comments | https://api.github.com/repos/huggingface/datasets/issues/7258/events | https://github.com/huggingface/datasets/pull/7258 | 2,618,758,399 | PR_kwDODunzps6AHlK1 | 7,258 | Always set non-null writer batch size | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7258). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-28T15:26:14 | 2024-10-28T15:28:41 | 2024-10-28T15:26:29 | MEMBER | null | false | {
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} | bug introduced in #7230, it was preventing the Viewer limit writes to work | {
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https://api.github.com/repos/huggingface/datasets/issues/7257 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7257/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7257/comments | https://api.github.com/repos/huggingface/datasets/issues/7257/events | https://github.com/huggingface/datasets/pull/7257 | 2,618,602,173 | PR_kwDODunzps6AHEfy | 7,257 | fix ci for pyarrow 18 | {
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