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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-18T17:24:28 | 2022-07-22T06:30:56 | 2022-07-22T06:18:11 | MEMBER | null | null | null | null | Currently there is a bug in `require_torchaudio` (indeed it is requiring `sox` instead):
```python
def require_torchaudio(test_case):
if find_spec("sox") is None:
...
```
The bug was introduced by:
- #3685
- Commit: https://github.com/huggingface/datasets/pull/3685/commits/b5a3e7122d49c4dcc9333b1d8d18a833fc04b940
which moved
```python
require_sndfile = pytest.mark.skipif(
# In Windows and OS X, soundfile installs sndfile
(sys.platform != "linux" and find_spec("soundfile") is None)
# In Linux, soundfile throws RuntimeError if sndfile not installed with distribution package manager
or (sys.platform == "linux" and find_library("sndfile") is None),
reason="Test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)
require_sox = pytest.mark.skipif(
find_library("sox") is None,
reason="Test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`",
)
require_torchaudio = pytest.mark.skipif(find_spec("torchaudio") is None, reason="Test requires 'torchaudio'")
```
to
```python
def require_sndfile(test_case):
"""
Decorator marking a test that requires soundfile.
These tests are skipped when soundfile isn't installed.
"""
if (sys.platform != "linux" and find_spec("soundfile") is None) or (
sys.platform == "linux" and find_library("sndfile") is None
):
test_case = unittest.skip(
"test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)(test_case)
return test_case
def require_sox(test_case):
"""
Decorator marking a test that requires sox.
These tests are skipped when sox isn't installed.
"""
if find_library("sox") is None:
return unittest.skip("test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`")(
test_case
)
return test_case
def require_torchaudio(test_case):
"""
Decorator marking a test that requires torchaudio.
These tests are skipped when torchaudio isn't installed.
"""
if find_spec("sox") is None:
return unittest.skip("test requires 'torchaudio'")(test_case)
return test_case
```
This PR;
- fixes the bug in `require_torchaudio`
- refactors the test requirements back to using `pytest` instead of `unittest`
- the text in `pytest.skipif` `reason` can be used if needed in a test body: `require_torchaudio.kwargs["reason"]` | {
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https://api.github.com/repos/huggingface/datasets/issues/4707 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4707/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4707/comments | https://api.github.com/repos/huggingface/datasets/issues/4707/events | https://github.com/huggingface/datasets/issues/4707 | 1,308,251,405 | I_kwDODunzps5N-lUN | 4,707 | Dataset Viewer issue for TheNoob3131/mosquito-data | {
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"Thanks for reporting. I refreshed the dataset viewer and it now works as expected.\r\n\r\nhttps://huggingface.co/datasets/TheNoob3131/mosquito-data\r\n\r\n<img width=\"1135\" alt=\"Capture d’écran 2022-07-18 à 13 15 22\" src=\"https://user-images.githubusercontent.com/1676121/179566497-e47f1a27-fd84-4a8d-9d7f-2e... | 2022-07-18T17:07:19 | 2022-07-18T19:44:46 | 2022-07-18T17:15:50 | NONE | null | null | {
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} | {
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} | ### Link
_No response_
### Description
Getting this error when trying to view dataset preview:
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/TheNoob3131/mosquito-data/resolve/8aceebd6c4a359d216d10ef020868bd9e8c986dd/0_Africa_train.csv')
### Owner
_No response_ | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I guess the report link is this instead: https://huggingface.co/datasets/xtreme/discussions/1"
] | 2022-07-18T16:22:46 | 2022-07-19T06:41:14 | 2022-07-19T06:29:17 | MEMBER | null | null | null | null | As reported in https://huggingface.co/muibk, there are empty examples in xtreme/bucc18.de
I applied your fix @mustaszewski
I also used a dict to make the dataset generation much faster | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-18T15:53:44 | 2022-07-21T17:18:44 | 2022-07-21T17:06:30 | MEMBER | null | null | null | null | As reported in https://huggingface.co/datasets/crd3/discussions/1#62cc377073b2512b81662794, each split of the dataset was containing the same data. This issues comes from a bug in the dataset script
I fixed it and also uploaded the data to hf.co to make the dataset work in streaming mode | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-18T15:41:40 | 2022-07-19T13:02:31 | 2022-07-19T12:49:18 | MEMBER | null | null | null | null | Currently, if `zstandard` or `lz4` are not installed, `test_compression_filesystems` and `test_streaming_dl_manager_extract_all_supported_single_file_compression_types` are skipped for all compression format parameters.
This PR fixes these tests, so that if `zstandard` or `lz4` are not installed, the tests are skipped only for the corresponding compression parameters (`zstd` or `lz4`), whereas the tests are not skipped for all the other compression parameters (`gzip`, `xz` and `bz2`).
Related to:
- #4688 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-18T11:55:49 | 2022-07-22T11:17:42 | 2022-07-22T11:05:24 | COLLABORATOR | null | null | null | null | Make the cast in `from_pandas` more robust (as it was done for the packaged modules in https://github.com/huggingface/datasets/pull/4364)
This should be useful in situations like [this one](https://discuss.huggingface.co/t/loading-custom-audio-dataset-and-fine-tuning-model/8836/4). | {
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"Hi! I added a link to this issue in our internal request for adding keywords/topics to the Hub, which is identical to the `topic tags` solution. The `collections` solution seems too complex (as you point out). Regarding the `domain tags` solution, we primarily focus on machine learning, so I'm not sure if it's a g... | 2022-07-18T11:14:03 | 2024-02-12T09:53:43 | null | MEMBER | null | null | {
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} | **Is your feature request related to a problem? Please describe.**
## The problem
The datasets hub currently has `8,239` datasets. These datasets span a wide range of different modalities and tasks (currently with a bias towards textual data).
There are various ways of identifying datasets that may be relevant for a particular use case:
- searching
- various filters
Currently, however, there isn't an easy way to identify datasets belonging to a specific domain. For example, I want to browse machine learning datasets related to 'social science' or 'climate change research'.
The ability to identify datasets relating to a specific domain has come up in discussions around the [BigLA](https://github.com/bigscience-workshop/lam/) datasets hackathon https://github.com/bigscience-workshop/lam/discussions/31#discussioncomment-3123610. As part of the hackathon, we're currently collecting datasets related to Libraries, Archives and Museums and making them available via the hub. We currently do this under a Hugging Face organization (https://huggingface.co/biglam). However, going forward, I can see some of these datasets being migrated to sit under an organization that is the custodian of the dataset (for example, a national library the data was originally from). At this point, it becomes more difficult to quickly identify datasets from this domain without relying on search.
This is also related to some existing issues on Github related to metadata on the hub:
- https://github.com/huggingface/datasets/issues/3625
- https://github.com/huggingface/datasets/issues/3877
**Describe the solution you'd like**
### Some possible solutions that may help with this:
#### Enable domain tags (from a controlled vocabulary)
- This would add metadata field to the YAML for the domain a dataset relates to
- Advantages:
- the list is controlled, allowing it to be more easily integrated into the datasets tag app (https://huggingface.co/space/huggingface/datasets-tagging)
- the controlled vocabulary could align with an existing controlled vocabulary
- this additional metadata can be used to perform filtering by domain
- disadvantages
- choosing the best controlled vocab may be difficult
- there are many datasets that are likely to fit into the 'machine learning' domain (i.e. there is a long tail of datasets that aren't in more 'generic' machine learning domain
#### Enable topic tags (user-generated)
Enable 'free form' topic tags for datasets and models. This would be closer to GitHub's repository topics which can be chosen from a controlled list (https://github.com/topics/) but can also be more user/org specific. This could potentially be useful for organizations to also manage their own models and datasets as the number they hold in their org grows. For example, they may create 'topic tags' for a specific project, so it's clearer which datasets /models are related to that project.
#### Collections
This solution would likely be the biggest shift and may require significant changes in the hub fronted. Collections could work in several different ways but would include:
Users can curate particular datasets, models, spaces, etc., into a collection. For example, they may create a collection of 'historic newspapers suitable for training language models'. These collections would not be mutually exclusive, i.e. a dataset can belong to zero, one or many collections. Collections can also potentially be nested under other collections.
This is fairly common on other data reposotiores for example the following collections:
<img width="293" alt="Screenshot 2022-07-18 at 11 50 44" src="https://user-images.githubusercontent.com/8995957/179496445-963ed122-5e26-4574-96e8-41081bce3e2b.png">
all belong under a higher level collection (https://bl.iro.bl.uk/collections/353c908d-b495-4413-b047-87236d2573e3?locale=en).
There are different models one could use for how these collections could be created:
- only within an org
- for any dataset/model
- the owner or a dataset/model has to agree to be added to a collection
- a collection owner can have people suggest additions to their collection
- other models....
These collections could be thematic, related to particular training approaches, curate models with particular inference properties etc. Whilst some of these features may duplicate current/or future tag filters on the hub, they offer the advantage of being flexible and not having to predict what users will want to do upfront.
There is also potential for automating the creation of these collections based on existing metadata. For example, one could collect models trained on a collection of datasets so for example, if we had a collection of 'historic newspapers suitable for training language models' that contained 30 datasets, we could create another collection 'historic newspaper language models' that takes any model on the hub whose metadata says it used one or more of those 30 datasets.
There is also the option of exploring ML approaches to suggest models/datasets may be relevant to a particular collection.
This approach is likely to be quite difficult to implement well and would require significant thought. There is also likely to be a benefit in doing quite a bit of upfront work in curating useful collections to demonstrate the benefits of collections.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
It is possible to collate this information externally, i.e. one could link back to the relevant models/datasets from an external platform.
**Additional context**
Add any other context about the feature request here.
I'm cc'ing others involved in the BigLAM hackathon who may also have thoughts @cakiki @clancyoftheoverflow @albertvillanova | null | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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"Hi, thanks for working on this, but the fix for this has already been merged in https://github.com/huggingface/datasets/pull/4608."
] | 2022-07-18T08:03:41 | 2022-07-20T13:10:44 | 2022-07-20T13:10:43 | NONE | null | null | null | null | I fixed a few errors when it occurs while streaming the private dataset on the Huggingface Hub.
```
from datasets import load_dataset
dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True)
for d in dataset['train']:
print(d)
break # this is for checking
```
This code is an example for streaming private datasets.
when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this,
```
/usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self)
1007 status=self.status,
1008 message=self.reason,
→ 1009 headers=self.headers,
1010 )
1011
ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/.../train-00000-of-00001-168b451062c67c34.parquet')
```
(this is an example on the dataset has `parquet` extenstion)
It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co/~".
so I add three lines.
With this change, there is no error anymore(but this code is ad-hoc). | {
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https://api.github.com/repos/huggingface/datasets/issues/4698 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4698/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4698/comments | https://api.github.com/repos/huggingface/datasets/issues/4698/events | https://github.com/huggingface/datasets/pull/4698 | 1,307,539,585 | PR_kwDODunzps47i9gN | 4,698 | Enable streaming dataset to use the "all" split | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4698). All of your documentation changes will be reflected on that endpoint.",
"@albertvillanova \r\nAdding the validation split causes these two `assert_called_once` assertions to fail with `AssertionError: Expected 'ArrowWrit... | 2022-07-18T07:47:39 | 2025-05-21T13:17:19 | 2025-05-21T13:17:19 | CONTRIBUTOR | null | null | null | null | Fixes #4637 | {
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https://api.github.com/repos/huggingface/datasets/issues/4697 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4697/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4697/comments | https://api.github.com/repos/huggingface/datasets/issues/4697/events | https://github.com/huggingface/datasets/issues/4697 | 1,307,332,253 | I_kwDODunzps5N7E6d | 4,697 | Trouble with streaming frgfm/imagenette vision dataset with TAR archive | {
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"Hi @frgfm, thanks for reporting.\r\n\r\nAs the error message says, streaming mode is not supported out of the box when the dataset contains TAR archive files.\r\n\r\nTo make the dataset streamable, you have to use `dl_manager.iter_archive`.\r\n\r\nThere are several examples in other datasets, e.g. food101: https:/... | 2022-07-18T02:51:09 | 2022-08-01T15:10:57 | 2022-08-01T15:10:57 | NONE | null | null | {
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} | ### Link
https://huggingface.co/datasets/frgfm/imagenette
### Description
Hello there :wave:
Thanks for the amazing work you've done with HF Datasets! I've just started playing with it, and managed to upload my first dataset. But for the second one, I'm having trouble with the preview since there is some archive extraction involved :sweat_smile:
Basically, I get a:
```
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
I've tried several things and checked this issue https://github.com/huggingface/datasets/issues/4181 as well, but no luck so far!
Could you point me in the right direction please? :pray:
### Owner
Yes | {
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"Hi @finiteautomata, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"lince\", \"ner_spaeng\")\r\nDownloading builder script: 20.8kB [00:00, 9.09MB/s] ... | 2022-07-17T19:01:54 | 2022-07-18T09:20:40 | 2022-07-18T07:24:22 | NONE | null | null | {
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} | {
"blocked_by": 0,
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"blocking": 0,
"total_blocking": 0
} | ## Describe the bug
Cannot load LinCE dataset due to a connection error
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("lince", "ner_spaeng")
```
A notebook with this code and corresponding error can be found at https://colab.research.google.com/drive/1pgX3bNB9amuUwAVfPFm-XuMV5fEg-cD2
## Expected results
It should load the dataset
## Actual results
```python
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-2-fc551ddcebef> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("lince", "ner_spaeng")
10 frames
/usr/local/lib/python3.7/dist-packages/datasets/load.py 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)
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
-> 1684 use_auth_token=use_auth_token,
1685 )
1686
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
703 if not downloaded_from_gcs:
704 self._download_and_prepare(
--> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1219
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1222
1223 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
772
773 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/lince/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589/lince.py in _split_generators(self, dl_manager)
481 def _split_generators(self, dl_manager):
482 """Returns SplitGenerators."""
--> 483 lince_dir = dl_manager.download_and_extract(f"{_LINCE_URL}/{self.config.name}.zip")
484 data_dir = os.path.join(lince_dir, self.config.data_dir)
485 return [
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download_and_extract(self, url_or_urls)
429 extracted_path(s): `str`, extracted paths of given URL(s).
430 """
--> 431 return self.extract(self.download(url_or_urls))
432
433 def get_recorded_sizes_checksums(self):
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download(self, url_or_urls)
313 num_proc=download_config.num_proc,
314 disable_tqdm=not is_progress_bar_enabled(),
--> 315 desc="Downloading data files",
316 )
317 duration = datetime.now() - start_time
/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
346 # Singleton
347 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 348 return function(data_struct)
349
350 disable_tqdm = disable_tqdm or not logging.is_progress_bar_enabled()
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in _download(self, url_or_filename, download_config)
333 # append the relative path to the base_path
334 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 335 return cached_path(url_or_filename, download_config=download_config)
336
337 def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
195 use_auth_token=download_config.use_auth_token,
196 ignore_url_params=download_config.ignore_url_params,
--> 197 download_desc=download_config.download_desc,
198 )
199 elif os.path.exists(url_or_filename):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
532 if head_error is not None:
--> 533 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
534 elif response is not None:
535 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))")))
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for your contribution, @bhavitvyamalik. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets... | 2022-07-17T15:53:05 | 2022-09-30T14:39:30 | 2022-09-30T14:37:16 | CONTRIBUTOR | null | null | null | null | This PR adds MANtIS dataset.
Arxiv: [https://arxiv.org/abs/1912.04639](https://arxiv.org/abs/1912.04639)
Github: [https://github.com/Guzpenha/MANtIS](https://github.com/Guzpenha/MANtIS)
README and dataset tags are WIP. | {
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https://api.github.com/repos/huggingface/datasets/issues/4694 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4694/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4694/comments | https://api.github.com/repos/huggingface/datasets/issues/4694/events | https://github.com/huggingface/datasets/issues/4694 | 1,306,958,380 | I_kwDODunzps5N5pos | 4,694 | Distributed data parallel training for streaming datasets | {
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"Hi ! According to https://huggingface.co/docs/datasets/use_with_pytorch#stream-data you can use the pytorch DataLoader with `num_workers>0` to distribute the shards across your workers (it uses `torch.utils.data.get_worker_info()` to get the worker ID and select the right subsets of shards to use)\r\n\r\n<s> EDIT:... | 2022-07-17T01:29:43 | 2023-04-26T18:21:09 | null | NONE | null | null | {
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Any documentations for the the `load_dataset(streaming=True)` for (multi-node multi-GPU) DDP training?
### Motivation
Given a bunch of data files, it is expected to split them onto different GPUs. Is there a guide or documentation?
### Your contribution
Does it requires manually split on data files for each worker in `DatasetBuilder._split_generator()`? What is`IterableDatasetShard` expected to do? | null | {
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] | 2022-07-16T11:53:05 | 2022-09-23T11:40:11 | 2022-09-23T11:37:57 | CONTRIBUTOR | null | null | null | null | update `samsum` script after #4672 was merged (citation is also updated) | {
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"Hi, thanks for reporting! A PR (https://github.com/huggingface/datasets/pull/4614) has already been opened to address this issue."
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} | ## Describe the bug
A clear and concise description of what the bug is.
When I create a dataset, then add a column to the created dataset through the `dataset.add_column` feature and then try to cast a column of the dataset (this column contains image paths) with `Image()` by using the `cast_column()` feature, I get the following error - ``` TypeError: Couldn't cast array of type
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ```
When I try and cast the same column, but without doing the `add_column` in the previous step, it works as expected.
## Steps to reproduce the bug
```python
from datasets import Dataset, Image
data_dict = {
"img_path": ["https://picsum.photos/200/300"]
}
dataset = Dataset.from_dict(data_dict)
#NOTE Comment out this line and use cast_column and it works properly
dataset = dataset.add_column("yeet", [1])
#NOTE This line fails to execute properly if `add_column` is called before
dataset = dataset.cast_column("img_path", Image())
# #NOTE This is my current workaround. This seems to work fine with/without `add_column`. While
# # running this, make sure to comment out the `cast_column` line
# new_features = dataset.features.copy()
# new_features["img_path"] = Image()
# dataset = dataset.cast(new_features)
print(dataset)
print(dataset.features)
print(dataset[0])
```
## Expected results
A clear and concise description of the expected results.
Able to successfully use `cast_column` to cast a column containing img_paths to now be Image() features after modifying the dataset using `add_column` in a previous step
## Actual results
Specify the actual results or traceback.
```
Traceback (most recent call last):
File "/home/surya/Desktop/hf_bug_test.py", line 14, in <module>
dataset = dataset.cast_column("img_path", Image())
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1580, in cast_column
dataset._data = dataset._data.cast(dataset.features.arrow_schema)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1487, in cast
new_tables.append(subtable.cast(subschema, *args, **kwargs))
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 834, in cast
return InMemoryTable(table_cast(self.table, *args, **kwargs))
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1897, in table_cast
return cast_table_to_schema(table, schema)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1846, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Ubuntu 20.04.3 LTS
- Python version: 3.9.7
- PyArrow version: 7.0.0
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"Hi, thanks for reporting. I triggered a refresh of the preview for this dataset, and it works now. I'm not sure what occurred.\r\n<img width=\"1019\" alt=\"Capture d’écran 2022-07-18 à 11 01 52\" src=\"https://user-images.githubusercontent.com/1676121/179541327-f62ecd5e-a18a-4d91-b316-9e2ebde77a28.png\">\r\n\r\n... | 2022-07-15T19:03:15 | 2022-07-18T15:02:03 | 2022-07-18T15:02:03 | NONE | null | null | {
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} | ### Link
https://huggingface.co/datasets/rajistics/indian_food_images/viewer/rajistics--indian_food_images/train
### Description
I have a train/test split in my dataset
<img width="410" alt="Screen Shot 2022-07-15 at 11 44 42 AM" src="https://user-images.githubusercontent.com/6808012/179293215-7b419ec3-3527-46f2-8dad-adbc5568cfa0.png">
t
The dataset viewer works for the test split (images of indian food), but does not show my train split. My guess is maybe there is some corrupt image file that is guessing this. But I have no idea.
The original dataset was pulled from here: https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification?resource=download-directory
### Owner
Yes | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-15T17:47:48 | 2022-07-18T08:46:56 | 2022-07-18T08:34:49 | MEMBER | null | null | null | null | This PR:
- Refactors base extractors as subclasses of `BaseExtractor`:
- this is an abstract class defining the interface with:
- `is_extractable`: abstract class method
- `extract`: abstract static method
- Implements abstract `MagicNumberBaseExtractor` (as subclass of `BaseExtractor`):
- this has a default implementation of `is_extractable`
- this improves performance (reducing the number of file reads) by allowing passing already read `magic_number`
- Refactors `Extractor`:
- reads magic number from file only once
This PR deprecates:
```python
is_extractable, extractor = self.extractor.is_extractable(input_path, return_extractor=True)
self.extractor.extract(input_path, output_path, extractor=extractor)
```
and uses more Pythonic instead:
```python
extractor_format = self.extractor.infer_extractor_format(input_path)
self.extractor.extract(input_path, output_path, extractor_format)
``` | {
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