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1,463,669,201 | https://api.github.com/repos/huggingface/datasets/issues/5293/labels{/name} | Extend support for streaming datasets that use `pathlib.Path.with_suffix`.
This feature will be useful e.g. for datasets containing text files and annotated files with the same name but different extension. | 2022-11-29T07:06:33Z | 5,293 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,463,053,832 | https://api.github.com/repos/huggingface/datasets/issues/5292/labels{/name} | After the patch releases [2.7.1](https://github.com/huggingface/datasets/releases/tag/2.7.1) and [2.6.2](https://github.com/huggingface/datasets/releases/tag/2.6.2), the online docs were not properly built (the build_documentation workflow was not triggered).
There was a fix by:
- #5291
However, both documentations were built from main branch, instead of their corresponding version branch.
We are rebuilding them. | 2022-11-24T10:10:02Z | 5,292 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"- Build docs for 2.6.2:\r\n - Commit: a6a5a1cf4cdf1e0be65168aed5a327f543001fe8\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539470622/jobs/5941404044\r\n- Build docs for 2.7.1:\r\n - Commit: 5ef1ab1cc06c2b7a574bf2df454cd9fcb071ccb2\r\n - Build docs GH Action: https://github... | {
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1,462,983,472 | https://api.github.com/repos/huggingface/datasets/issues/5291/labels{/name} | Do NOT merge. Using this PR to build docs for [v2.7.1](https://github.com/huggingface/datasets/pull/5291/commits/f4914af20700f611b9331a9e3ba34743bbeff934) & [v2.6.2](https://github.com/huggingface/datasets/pull/5291/commits/025f85300a0874eeb90a20393c62f25ac0accaa0) | 2022-11-24T09:14:10Z | 5,291 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-24T08:54:47Z | https://api.github.com/repos/huggingface/datasets/issues/5291/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5291/timeline | [build doc] for v2.7.1 & v2.6.2 | https://api.github.com/repos/huggingface/datasets/issues/5291/events | null | {
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1,462,716,766 | https://api.github.com/repos/huggingface/datasets/issues/5290/labels{/name} | null | 2022-11-25T03:21:54Z | 5,290 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-24T03:53:46Z | https://api.github.com/repos/huggingface/datasets/issues/5290/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5290/timeline | fix error where reading breaks when batch missing an assigned column feature | https://api.github.com/repos/huggingface/datasets/issues/5290/events | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5290). All of your documentation changes will be reflected on that endpoint."
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1,462,543,139 | https://api.github.com/repos/huggingface/datasets/issues/5289/labels{/name} | JPEG-XL is the most advanced of the next-generation of image codecs, supporting both lossless and lossy files β with better compression and quality than PNG and JPG respectively. It has reduced the disk sizes and bandwidth required for many of the datasets I use.
Pillow does not yet support JXL, but there's a plugin as a separate Python library that does (`pip install jxlpy`), and I've tested that this change works as expected when the plugin is imported.
Dataset used for testing, you must `git pull` as loading it from Python won't work until `datasets-server` is also changed to support JXL files:
https://huggingface.co/datasets/texturedesign/td01_natural-ground-textures
The case where the plugin is not imported first raises an error:
```
PIL.UnidentifiedImageError: cannot identify image file 'td01/train/set01/01_145523.jxl'
```
In order to enable support for JXL even before pillow supports this, should this exception be handled with a better error message? I'd expect/hope JXL support to follow in one of the pillow quarterly releases in the next 6-9 months. | 2022-11-29T18:49:46Z | 5,289 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-23T23:16:33Z | https://api.github.com/repos/huggingface/datasets/issues/5289/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5289/timeline | Added support for JXL images. | https://api.github.com/repos/huggingface/datasets/issues/5289/events | null | {
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"I'm fine with the addition of jxl in the list of known image extensions, this way users that have the plugin can work with their JXL datasets. WDYT @mariosasko ?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5289). All of your documentation changes will be reflected on ... | {
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1,462,134,067 | https://api.github.com/repos/huggingface/datasets/issues/5288/labels{/name} | ### Describe the bug
Saving a dataset to disk as json (using `to_json`) and then loading it again (using `load_dataset`) results in features whose labels are not type-cast correctly. In the code snippet below, `features.label` should have a label of type `ClassLabel` but has type `Value` instead.
### Steps to reproduce the bug
```
from datasets import load_dataset
def test_serdes_from_json(d):
dataset = load_dataset(d, split="train")
dataset.to_json('_test')
dataset_loaded = load_dataset("json", data_files='_test', split='train')
try:
assert dataset_loaded.info.features == dataset.info.features, "features unequal!"
except Exception as ex:
print(f'{ex}')
print(f'expected {dataset.info.features}, \nactual { dataset_loaded.info.features }')
test_serdes_from_json('rotten_tomatoes')
```
Output
```
features unequal!
expected {'text': Value(dtype='string', id=None), 'label': ClassLabel(names=['neg', 'pos'], id=None)},
actual {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}
```
### Expected behavior
The deserialized `features.label` should have type `ClassLabel`.
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.10.144-127.601.amzn2.x86_64-x86_64-with-glibc2.17
- Python version: 3.7.13
- PyArrow version: 7.0.0
- Pandas version: 1.2.3 | 2022-11-25T12:53:51Z | 5,288 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-23T17:20:15Z | https://api.github.com/repos/huggingface/datasets/issues/5288/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5288/timeline | Lossy json serialization - deserialization of dataset info | https://api.github.com/repos/huggingface/datasets/issues/5288/events | null | {
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"Hi ! JSON is a lossy format indeed. If you want to keep the feature types or other metadata I'd encourage you to store them as well. For example you can use `dataset.info.write_to_directory` and `DatasetInfo.from_directory` to store the feature types, split info, description, license etc."
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1,461,971,889 | https://api.github.com/repos/huggingface/datasets/issues/5287/labels{/name} | As currently `IterableDataset.map` is setting the `info.features` to `None` every time as we don't know the output of the dataset in advance, `IterableDataset` methods such as `rename_column`, `rename_columns`, and `remove_columns`. that internally use `map` lead to the features being `None`.
This PR is related to #3888, #5245, and #5284
## β
Current solution
The code in this PR is basically making sure that if the features were there since the beginning and a `rename_column`/`rename_columns` happens, those are kept and the rename is applied to the `Features` too. Also, if the features were not there before applying `rename_column`, `rename_columns` or `remove_columns`, a batch is prefetched and the features are being inferred (that could potentially be part of `IterableDataset.__init__` in case the `info.features` value is `None`).
## π‘ Ideas
Some ideas were proposed in https://github.com/huggingface/datasets/issues/3888, but probably the most consistent solution even though it may take some time is to actually do the type inferencing during the `IterableDataset.__init__` in case the provided `info.features` is `None`, otherwise, we can just use the provided features.
Additionally, as mentioned at https://github.com/huggingface/datasets/issues/3888, we could also include a `features` parameter to the `map` function, but that's probably more tedious.
Also thanks to @lhoestq for sharing some ideas in both https://github.com/huggingface/datasets/issues/3888 and https://github.com/huggingface/datasets/issues/5245 :hugs: | 2022-11-28T15:43:14Z | 5,287 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-23T15:33:25Z | https://api.github.com/repos/huggingface/datasets/issues/5287/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5287/timeline | Fix methods using `IterableDataset.map` that lead to `features=None` | https://api.github.com/repos/huggingface/datasets/issues/5287/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Maybe other options are:\r\n* Keep the `info.features` to `None` if those were initially `None`\r\n* Infer the features with pre-fetching just if the `... | {
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1,461,908,087 | https://api.github.com/repos/huggingface/datasets/issues/5286/labels{/name} | ### Describe the bug
I follow the steps provided on the website [https://huggingface.co/datasets/wikipedia](https://huggingface.co/datasets/wikipedia)
$ pip install apache_beam mwparserfromhell
>>> from datasets import load_dataset
>>> load_dataset("wikipedia", "20220301.en")
however this results in the following error:
raise MissingBeamOptions(
datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
If I then prompt the system with:
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
the following error occurs:
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
Here is the exact code:
Python 3.10.6 (main, Nov 2 2022, 18:53:38) [GCC 11.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> load_dataset('wikipedia', '20220301.en')
Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 15.3k/15.3k [00:00<00:00, 22.2MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1879, in _download_and_prepare
raise MissingBeamOptions(
datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 15.3k/15.3k [00:00<00:00, 18.8MB/s]
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1909, in _download_and_prepare
super()._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 891, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/rorytol/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py", line 945, in _split_generators
downloaded_files = dl_manager.download_and_extract({"info": info_url})
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 447, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 311, in download
downloaded_path_or_paths = map_nested(
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 444, in map_nested
mapped = [
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 445, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 346, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 338, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 183, in cached_path
output_path = get_from_cache(
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 530, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
### Steps to reproduce the bug
$ pip install apache_beam mwparserfromhell
>>> from datasets import load_dataset
>>> load_dataset("wikipedia", "20220301.en")
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
### Expected behavior
Download the dataset
### Environment info
Running linux on a remote workstation operated through a macbook terminal
Python 3.10.6
| 2023-10-03T19:46:44Z | 5,286 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-23T14:54:15Z | https://api.github.com/repos/huggingface/datasets/issues/5286/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5286/timeline | FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json | https://api.github.com/repos/huggingface/datasets/issues/5286/events | null | {
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} | [] | null | completed | NONE | 2022-11-25T11:33:14Z | null | I_kwDODunzps5XIvJ3 | [
"I found a solution \r\n\r\nIf you specifically install datasets==1.18 and then run\r\n\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.en')\r\nthen this should work (it worked for me.)",
"I have the same problem here but installing datasets==1.18 wont work for me\r\n"
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1,461,521,215 | https://api.github.com/repos/huggingface/datasets/issues/5285/labels{/name} | Having the file name is useful in case we need to check the extension of the file (e.g. mp3), or in general in case it includes some metadata information (track id, image id etc.)
Related to https://github.com/huggingface/datasets/issues/5276 | 2022-11-24T14:11:41Z | 5,285 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-23T10:55:54Z | https://api.github.com/repos/huggingface/datasets/issues/5285/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5285/timeline | Save file name in embed_storage | https://api.github.com/repos/huggingface/datasets/issues/5285/events | null | {
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1,461,519,733 | https://api.github.com/repos/huggingface/datasets/issues/5284/labels{/name} | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# check original features
print("Original features: ", dataset.features.keys())
# define features to remove: we KEEP audio and text
COLUMNS_TO_REMOVE = ['chapter_id', 'speaker_id', 'file', 'id']
dataset = dataset.remove_columns(COLUMNS_TO_REMOVE)
# check processed features, uh-oh!
print("Processed features: ", dataset.features)
# streaming the first audio sample still works
print("First sample:", next(iter(ds)))
```
**Print Output:**
```
Original features: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])
Processed features: None
First sample: {'audio': {'path': '2277-149896-0000.flac', 'array': array([ 0.00186157, 0.0005188 , 0.00024414, ..., -0.00097656,
-0.00109863, -0.00146484]), 'sampling_rate': 16000}, 'text': "HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE"}
```
### Expected behavior
The features should be those **not** removed by the `remove_column` method, i.e. audio and text.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
(Running on Google Colab for a blog post: https://colab.research.google.com/drive/1ySCQREPZEl4msLfxb79pYYOWjUZhkr9y#scrollTo=8pRDGiVmH2ml)
cc @polinaeterna @lhoestq | 2023-02-02T09:05:51Z | 5,284 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"#self-assign",
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1,459,930,271 | https://api.github.com/repos/huggingface/datasets/issues/5281/labels{/name} | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been requested several times already. Some users want to use their data from private cloud storage to train models
related:
https://github.com/huggingface/datasets/issues/3490
https://github.com/huggingface/datasets/issues/5244
[forum](https://discuss.huggingface.co/t/how-to-use-s3-path-with-load-dataset-with-streaming-true/25739/2) | 2024-03-11T15:38:14Z | 5,281 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Or for example an archive on GitHub releases! Before I added support for JXL (locally only, PR still pending) I was considering hosting my files on GitHub instead...",
"+1 to this. I would like to use 'audiofolder' with a data_dir that's on S3, for example. I don't want to upload my dataset to the Hub, but I wo... | {
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1,459,823,179 | https://api.github.com/repos/huggingface/datasets/issues/5280/labels{/name} | https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28
Hy,
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong) | 2022-12-15T19:57:40Z | 5,280 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-22T12:56:43Z | https://api.github.com/repos/huggingface/datasets/issues/5280/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5280/timeline | Import error | https://api.github.com/repos/huggingface/datasets/issues/5280/events | null | {
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"Hi ! Can you \r\n```python\r\nimport platform\r\nprint(platform.python_version())\r\n```\r\nto see that it returns ?",
"Hi,\n\n3.8.13\n\nGet Outlook for Android<https://aka.ms/AAb9ysg>\n________________________________\nFrom: Quentin Lhoest ***@***.***>\nSent: Tuesday, November 22, 2022 2:37:02 PM\nTo: huggingfa... | {
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1,459,635,002 | https://api.github.com/repos/huggingface/datasets/issues/5279/labels{/name} | It takes a lot of time on big datasets to compute the checksums, we should at least add a warning to notify the user about this step. I also mentioned how to disable it, and added a tqdm bar (delay=5 seconds)
cc @ola13 | 2022-11-23T11:43:50Z | 5,279 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-22T10:58:48Z | https://api.github.com/repos/huggingface/datasets/issues/5279/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5279/timeline | Warn about checksums | https://api.github.com/repos/huggingface/datasets/issues/5279/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I'm also in favor of disabling this by default - it's kinda impractical",
"Great, thanks for the quick turnaround on this!"
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1,459,574,490 | https://api.github.com/repos/huggingface/datasets/issues/5278/labels{/name} | ### Describe the bug
Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features.
Below is code to reproduce my exact example/problem.
### Steps to reproduce the bug
```ruby
dataset_link="19Unu89Ih_kP6zsE7f9Mkw8dy3NwHopRF"
id = dataset_link
output = 'Godardv01.zip'
gdown.download(id=id, output=output, quiet=False)
ds = load_dataset("imagefolder", data_dir="/kaggle/working/Volumes/TOSHIBA/Godard_imgs/Volumes/TOSHIBA/Godard_imgs/Full/train", split="train", drop_labels=False)
print(ds)
```
### Expected behavior
I would expect that it returned "image" and "text" columns from the code above.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5 | 2023-02-14T14:48:16Z | 5,278 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-22T10:24:46Z | https://api.github.com/repos/huggingface/datasets/issues/5278/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5278/timeline | load_dataset does not read jsonl metadata file properly | https://api.github.com/repos/huggingface/datasets/issues/5278/events | null | {
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"Can you try to remove \"drop_labels=false\" ? It may force the loader to infer the labels instead of reading the metadata",
"Hi, thanks for responding. I tried that, but it does not change anything.",
"Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4",
"Probably the issue, will ... | {
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1,459,388,551 | https://api.github.com/repos/huggingface/datasets/issues/5277/labels{/name} | Fix partially #5275. | 2022-11-22T13:58:26Z | 5,277 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-22T08:34:07Z | https://api.github.com/repos/huggingface/datasets/issues/5277/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5277/timeline | Remove YAML integer keys from class_label metadata | https://api.github.com/repos/huggingface/datasets/issues/5277/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Also note that this approach is valid when metadata keys are str, but also if they are int.\r\n- This will be helpful for any community dataset using old integer keys in their metadata",
"perfect !"
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1,459,363,442 | https://api.github.com/repos/huggingface/datasets/issues/5276/labels{/name} | ### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11 | 2023-07-21T14:33:10Z | 5,276 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-22T08:17:53Z | https://api.github.com/repos/huggingface/datasets/issues/5276/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5276/timeline | Bug in downloading common_voice data and snall chunk of it to one's own hub | https://api.github.com/repos/huggingface/datasets/issues/5276/events | null | {
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"Sounds like one of the file is not a valid one, can you make sure you uploaded valid mp3 files ?",
"Well I just sharded the original commonVoice dataset and pushed a small chunk of it in a private rep\n\nWhat did go wrong?\n\nHolen Sie sich Outlook fΓΌr iOS<https://aka.ms/o0ukef>\n________________________________... | {
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1,459,358,919 | https://api.github.com/repos/huggingface/datasets/issues/5275/labels{/name} | After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets | 2023-01-26T10:52:35Z | 5,275 | null | https://api.github.com/repos/huggingface/datasets | null | [
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} | https://api.github.com/repos/huggingface/datasets/issues/5275/timeline | YAML integer keys are not preserved Hub server-side | https://api.github.com/repos/huggingface/datasets/issues/5275/events | null | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | completed | MEMBER | 2023-01-26T10:40:21Z | null | I_kwDODunzps5W_AzH | [
"@huggingface/datasets if you agree, I can make the bulk edit on the Hub to fix integer keys into strings.",
"Ok for me, and we can merge (internal) https://github.com/huggingface/moon-landing/pull/4609",
"FYI there are still 2k+ weekly users on `datasets` 2.6.1 which doesn't support the string label format for... | {
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1,458,646,455 | https://api.github.com/repos/huggingface/datasets/issues/5274/labels{/name} | ### Describe the bug
When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:
```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
165 if repo_id.count("/") > 1:
166 raise HFValidationError(
--> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
168 f" '{repo_id}'. Use `repo_type` argument if needed."
169 )
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```
### Steps to reproduce the bug
Install requirements:
```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```
Then:
```
from datasets import load_dataset
auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```
### Expected behavior
Downloading of the datset
### Environment info
Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing | 2023-05-27T00:06:14Z | 5,274 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-21T21:59:53Z | https://api.github.com/repos/huggingface/datasets/issues/5274/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5274/timeline | load_dataset possibly broken for gated datasets? | https://api.github.com/repos/huggingface/datasets/issues/5274/events | null | {
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} | [] | null | completed | CONTRIBUTOR | 2022-11-28T02:50:42Z | null | I_kwDODunzps5W8S23 | [
"@BradleyHsu",
"Btw, thanks very much for finding the hub rollback temporary fix and bringing the issue to our attention @KhoomeiK!",
"I see the same issue when calling `load_dataset('poloclub/diffusiondb', 'large_random_1k')` with `datasets==2.7.1` and `huggingface-hub=0.11.0`. No issue with `datasets=2.6.1` a... | {
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1,458,018,050 | https://api.github.com/repos/huggingface/datasets/issues/5273/labels{/name} | ### Describe the bug
`load_datasets` does not refresh dataset when features are imported from external file, even with `download_mode="force_redownload"`. The bug is not limited to nested fields, however it is more likely to occur with nested fields.
### Steps to reproduce the bug
To reproduce the bug 3 files are needed: `dataset.py` (contains dataset loading script), `schema.py` (contains features of dataset) and `main.py` (to run `load_datasets`)
`dataset.py`
```python
import datasets
from schema import features
class NewDataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
features=features
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN
)
]
def _generate_examples(self):
data = [
{"id": 0, "nested": []},
{"id": 1, "nested": []}
]
for key, example in enumerate(data):
yield key, example
```
`schema.py`
```python
import datasets
features = datasets.Features(
{
"id": datasets.Value("int32"),
"nested": [
{"text": datasets.Value("string")}
]
}
)
```
`main.py`
```python
import datasets
a = datasets.load_dataset("dataset.py")
print(a["train"].info.features)
```
Now if `main.py` is run it prints the following correct output: `{'id': Value(dtype='int32', id=None), 'nested': [{'text': Value(dtype='string', id=None)}]}`. However, if f.e. the label of the feature "text" is changed to something else, f.e. to
`schema.py`
```python
import datasets
features = datasets.Features(
{
"id": datasets.Value("int32"),
"nested": [
{"textfoo": datasets.Value("string")}
]
}
)
```
`main.py` still prints `{'id': Value(dtype='int32', id=None), 'nested': [{'text': Value(dtype='string', id=None)}]}`, even if run with `download_mode="force_redownload"`. The only fix is to delete the folder in the cache.
### Expected behavior
The cached dataset is deleted and refreshed when using `load_datasets` with `download_mode="force_redownload"`.
### Environment info
- `datasets` version: 2.7.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.9
- PyArrow version: 10.0.0
- Pandas version: 1.3.5 | 2022-11-21T14:13:03Z | 5,273 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-21T14:12:43Z | https://api.github.com/repos/huggingface/datasets/issues/5273/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5273/timeline | download_mode="force_redownload" does not refresh cached dataset | https://api.github.com/repos/huggingface/datasets/issues/5273/events | null | {
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1,456,940,021 | https://api.github.com/repos/huggingface/datasets/issues/5272/labels{/name} | ### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary | 2023-08-17T21:09:11Z | 5,272 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi ! We're using the Arrow format for the datasets, and PyArrow tensors are not part of the Arrow format AFAIK:\r\n\r\n> There is no direct support in the arrow columnar format to store Tensors as column values.\r\n\r\nsource: https://github.com/apache/arrow/issues/4802#issuecomment-508494694",
"@wesm @rok its b... | {
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1,456,807,738 | https://api.github.com/repos/huggingface/datasets/issues/5271/labels{/name} | ```
$ datasets-cli convert --datasets_directory <TAB>
datasets_directory
benchmarks/ docs/ metrics/ notebooks/ src/ templates/ tests/ utils/
```
| 2022-11-21T15:07:19Z | 5,271 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-20T07:50:49Z | https://api.github.com/repos/huggingface/datasets/issues/5271/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5271/timeline | Fix #5269 | https://api.github.com/repos/huggingface/datasets/issues/5271/events | null | {
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1,456,508,990 | https://api.github.com/repos/huggingface/datasets/issues/5270/labels{/name} | ### Describe the bug
```python
In [9]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2.53k/2.53k [00:00<00:00, 1.88MB/s]
[11/19/22 22:16:21] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/bd1cc3434212e3e654f7e16ad618f8a1470b5982b086c91b1d6bc7187183c6e9...
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^CProcess ForkPoolWorker-47:
Process ForkPoolWorker-46:
Process ForkPoolWorker-36:
Process ForkPoolWorker-38:ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:05<00:00, 5.04s/obj]
Process ForkPoolWorker-37:
Process ForkPoolWorker-45:
Process ForkPoolWorker-39:
Process ForkPoolWorker-43:
Process ForkPoolWorker-33:
Process ForkPoolWorker-18:
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 365, in get
res = self._reader.recv_bytes()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/connection.py", line 221, in recv_bytes
buf = self._recv_bytes(maxlength)
KeyboardInterrupt
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/connection.py", line 419, in _recv_bytes
buf = self._recv(4)
File "/usr/lib/python3.10/multiprocessing/connection.py", line 384, in _recv
chunk = read(handle, remaining)
KeyboardInterrupt
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
Process ForkPoolWorker-20:
Process ForkPoolWorker-44:
Process ForkPoolWorker-22:
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#1: 0%| | 0/2 [03:00<?, ?obj/s]
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 659, in get_from_cache
http_get(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 442, in http_get
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
KeyboardInterrupt
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#3: 0%| | 0/2 [03:00<?, ?obj/s]
#11: 0%| | 0/1 [00:49<?, ?obj/s]
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in send
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in <listcomp>
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 266, in resolve_redirects
resp = self.send(
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#5: 0%| | 0/1 [03:00<?, ?obj/s]
KeyboardInterrupt
Process ForkPoolWorker-42:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#9: 0%| | 0/1 [00:51<?, ?obj/s]
```
### Steps to reproduce the bug
```python
"""Kodak.
Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import datasets
NUMBER = 17
_DESCRIPTION = """\
The pictures below link to lossless, true color (24 bits per pixel, aka "full
color") images. It is my understanding they have been released by the Eastman
Kodak Company for unrestricted usage. Many sites use them as a standard test
suite for compression testing, etc. Prior to this site, they were only
available in the Sun Raster format via ftp. This meant that the images could
not be previewed before downloading. Since their release, however, the lossless
PNG format has been incorporated into all the major browsers. Since PNG
supports 24-bit lossless color (which GIF and JPEG do not), it is now possible
to offer this browser-friendly access to the images.
"""
_HOMEPAGE = "https://r0k.us/graphics/kodak/"
_LICENSE = "GPLv3"
_URLS = [
f"https://github.com/MohamedBakrAli/Kodak-Lossless-True-Color-Image-Suite/raw/master/PhotoCD_PCD0992/{i}.png"
for i in range(1, 1 + NUMBER)
]
class Kodak(datasets.GeneratorBasedBuilder):
"""Kodak datasets."""
VERSION = datasets.Version("0.0.1")
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Return SplitGenerators."""
file_paths = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"file_paths": file_paths,
},
),
]
def _generate_examples(self, file_paths):
"""Yield examples."""
for file_path in file_paths:
yield file_path, {"image": file_path}
```
### Expected behavior
When `len(_URLS) < 16`, it works.
```python
In [3]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2.53k/2.53k [00:00<00:00, 3.02MB/s]
[11/19/22 22:04:28] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475...
Downloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 593k/593k [00:00<00:00, 2.88MB/s]
Downloading: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 621k/621k [00:03<00:00, 166kB/s]
Downloading: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 531k/531k [00:01<00:00, 366kB/s]
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 16/16 [00:13<00:00, 1.18it/s]
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 16/16 [00:00<00:00, 3832.38it/s]
Dataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475. Subsequent calls will reuse this data.
```
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-6.0.8-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.8
- PyArrow version: 9.0.0
- Pandas version: 1.4.4 | 2022-11-21T15:27:16Z | 5,270 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-19T14:27:41Z | https://api.github.com/repos/huggingface/datasets/issues/5270/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5270/timeline | When len(_URLS) > 16, download will hang | https://api.github.com/repos/huggingface/datasets/issues/5270/events | null | {
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"following_url": "https://api.github.com/users/Freed-Wu/following{/other_user}",
"gists_url": "https://api.github.com/users/Freed-Wu/gists{/gist_id}",
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"repos_url": "https://api.github.com/users/Freed-Wu/repos",
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"starred_url": "https://api.github.com/users/Freed-Wu/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/Freed-Wu/subscriptions",
"type": "User",
"url": "https://api.github.com/users/Freed-Wu"
} | [] | null | null | NONE | null | null | I_kwDODunzps5W0JA- | [
"It can fix the bug temporarily.\r\n```python\r\nfrom datasets import DownloadConfig\r\nconfig = DownloadConfig(num_proc=8)\r\nIn [5]: dataset = load_dataset('Freed-Wu/kodak', split='test', download_config=config)\r\nDownloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu__... | {
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} | open | false | https://api.github.com/repos/huggingface/datasets/issues/5270 | https://github.com/huggingface/datasets/issues/5270 | false |
1,456,485,799 | https://api.github.com/repos/huggingface/datasets/issues/5269/labels{/name} | ### Feature request
Like <https://github.com/huggingface/huggingface_hub/issues/1197>, datasets-cli maybe need it, too.
### Motivation
See above.
### Your contribution
Maybe. | 2022-11-21T15:06:15Z | 5,269 | null | https://api.github.com/repos/huggingface/datasets | null | [
{
"color": "a2eeef",
"default": true,
"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement"
}
] | 2022-11-19T13:48:59Z | https://api.github.com/repos/huggingface/datasets/issues/5269/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5269/timeline | Shell completions | https://api.github.com/repos/huggingface/datasets/issues/5269/events | null | {
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"login": "Freed-Wu",
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"repos_url": "https://api.github.com/users/Freed-Wu/repos",
"site_admin": false,
"starred_url": "https://api.github.com/users/Freed-Wu/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/Freed-Wu/subscriptions",
"type": "User",
"url": "https://api.github.com/users/Freed-Wu"
} | [] | null | completed | NONE | 2022-11-21T15:06:14Z | null | I_kwDODunzps5W0DWn | [
"I don't think we need completion on the datasets-cli, since we're mainly developing huggingface-cli",
"I see."
] | {
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1,455,633,978 | https://api.github.com/repos/huggingface/datasets/issues/5268/labels{/name} | Added `num_shards=` and `num_proc=` to `save_to_disk()`
EDIT: also added `max_shard_size=` to `save_to_disk()`, and also `num_shards=` to `push_to_hub`
I also:
- deprecated the fs parameter in favor of storage_options (for consistency with the rest of the lib) in save_to_disk and load_from_disk
- always embed the image/audio data in arrow when doing `save_to_disk`
- added a tqdm bar in `save_to_disk`
- Use the MockFileSystem in tests for `save_to_disk` and `load_from_disk`
- removed the unused integration tests with S3, since we can now test with `mockfs` instead of `s3fs`
TODO:
- [x] implem save_to_disk for dataset dict
- [x] save_to_disk for dataset dict tests
- [x] deprecate fs in dataset dict load_from_disk as well
- [x] update docs
Close #5263
Close https://github.com/huggingface/datasets/issues/4196
Close https://github.com/huggingface/datasets/issues/4351 | 2022-12-14T18:25:52Z | 5,268 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-18T18:50:01Z | https://api.github.com/repos/huggingface/datasets/issues/5268/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5268/timeline | Sharded save_to_disk + multiprocessing | https://api.github.com/repos/huggingface/datasets/issues/5268/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Added both num_shards and max_shard_size in push_to_hub/save_to_disk. Will take care of updating the tests later",
"It's ready for a final review @mariosasko and @albertvillanova, let me know what you think :)",
"Took your commen... | {
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1,455,466,464 | https://api.github.com/repos/huggingface/datasets/issues/5267/labels{/name} | null | 2022-11-18T17:28:58Z | 5,267 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-18T16:55:22Z | https://api.github.com/repos/huggingface/datasets/issues/5267/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5267/timeline | Fix `max_shard_size` docs | https://api.github.com/repos/huggingface/datasets/issues/5267/events | null | {
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1,455,281,310 | https://api.github.com/repos/huggingface/datasets/issues/5266/labels{/name} | Fixes a warning and future deprecation from `librosa.reshape`:
```
FutureWarning: Pass orig_sr=16000, target_sr=48000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
``` | 2022-11-21T15:45:02Z | 5,266 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-18T14:58:47Z | https://api.github.com/repos/huggingface/datasets/issues/5266/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5266/timeline | Specify arguments as keywords in librosa.reshape to avoid future errors | https://api.github.com/repos/huggingface/datasets/issues/5266/events | null | {
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1,455,274,864 | https://api.github.com/repos/huggingface/datasets/issues/5265/labels{/name} | This is useful to leverage iterable datasets specific features like:
- fast approximate shuffling
- lazy map, filter etc.
Iterating over the resulting iterable dataset should be at least as fast at iterating over the map-style dataset.
Here are some ideas regarding the API:
```python
# 1.
# - consistency with load_dataset(..., streaming=True)
# - gives intuition that map/filter/etc. are done on-the-fly
ids = ds.stream()
# 2.
# - more explicit on the output type
# - but maybe sounds like a conversion tool rather than a step in a processing pipeline
ids = ds.as_iterable_dataset()
``` | 2023-02-01T16:36:03Z | 5,265 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"gists... | null | completed | MEMBER | 2023-02-01T16:36:03Z | null | I_kwDODunzps5Wvbtw | [
"I think `stream` could be misleading since the data is not being streamed from remote endpoints (one could think that's the case when they see `load_dataset` followed by `stream`). Hence, I prefer the second option.\r\n\r\nPS: When we resolve https://github.com/huggingface/datasets/issues/4542, we could add `as_tf... | {
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1,455,252,906 | https://api.github.com/repos/huggingface/datasets/issues/5264/labels{/name} | ### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
``` | 2023-05-07T09:52:59Z | 5,264 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Could you share the full stack trace please ?\r\n\r\n\r\nCan you also try running this code ? It can be useful to determine if the issue comes from `datasets` or `fsspec` (streaming) or `pyarrow` (parquet reading):\r\n```python\r\nds = load_dataset(\"parquet\", data_files=a_parquet_file_url, use_auth_token=True)\r... | {
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1,455,252,626 | https://api.github.com/repos/huggingface/datasets/issues/5263/labels{/name} | This is useful to distribute the shards to training nodes.
This can be implemented in `save_to_disk` and can also leverage multiprocessing to speed up the process | 2022-12-14T18:22:59Z | 5,263 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,455,171,100 | https://api.github.com/repos/huggingface/datasets/issues/5262/labels{/name} | Hello
I'm trying to build a model for custom token classification
I already followed the token classification course on huggingface
while adapting the code to my work, this message occures :
'Value' object has no attribute 'names'
Here's my code:
`raw_datasets`
generates
DatasetDict({
train: Dataset({
features: ['isDisf', 'pos', 'tokens', 'id'],
num_rows: 14
})
})
`raw_datasets["train"][3]["isDisf"]`
generates
['B_RM', 'I_RM', 'I_RM', 'B_RP', 'I_RP', 'O', 'O']
`dis_feature = raw_datasets["train"].features["isDisf"]
dis_feature`
generates
Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)
and
`label_names = dis_feature.feature.names
label_names`
generates
AttributeError Traceback (most recent call last)
[<ipython-input-28-972fd54a869a>](https://localhost:8080/#) in <module>
----> 1 label_names = dis_feature.feature.names
2 label_names
AttributeError: 'Value' object has
AttributeError: 'Value' object has no attribute 'names'
Thank you for your help | 2022-11-22T10:09:24Z | 5,262 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-18T13:58:42Z | https://api.github.com/repos/huggingface/datasets/issues/5262/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5262/timeline | AttributeError: 'Value' object has no attribute 'names' | https://api.github.com/repos/huggingface/datasets/issues/5262/events | null | {
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} | [] | null | completed | NONE | 2022-11-22T10:09:23Z | null | I_kwDODunzps5WvCYc | [
"Hi ! It looks like your \"isDif\" column is a Sequence of Value(\"string\"), not a Sequence of ClassLabel.\r\n\r\nYou can convert your Value(\"string\") feature type to a ClassLabel feature type this way:\r\n```python\r\nfrom datasets import ClassLabel, Sequence\r\n\r\n# provide the label_names yourself\r\nlabel_n... | {
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1,454,647,861 | https://api.github.com/repos/huggingface/datasets/issues/5261/labels{/name} | ### Name
PubTables-1M
### Paper
https://openaccess.thecvf.com/content/CVPR2022/html/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.html
### Data
https://github.com/microsoft/table-transformer
### Motivation
Table Transformer is now available in π€ Transformer, and it was trained on PubTables-1M. It's a large dataset for table extraction and structure recognition in unstructured documents. | 2022-11-18T08:02:18Z | 5,261 | null | https://api.github.com/repos/huggingface/datasets | null | [
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} | [] | null | null | CONTRIBUTOR | null | null | I_kwDODunzps5WtCo1 | [
"cc @albertvillanova the author would like to add this dataset to the hub: https://github.com/microsoft/table-transformer/issues/68#issuecomment-1319114621. Could you help him out?"
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1,453,921,697 | https://api.github.com/repos/huggingface/datasets/issues/5260/labels{/name} | ### Describe the bug
Error during dataset loading
### Steps to reproduce the bug
```
>>> import datasets
>>> cf_raw = datasets.load_dataset("consumer-finance-complaints")
Downloading builder script: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 8.42k/8.42k [00:00<00:00, 3.33MB/s]
Downloading metadata: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 5.60k/5.60k [00:00<00:00, 2.90MB/s]
Downloading readme: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 16.6k/16.6k [00:00<00:00, 510kB/s]
Downloading and preparing dataset consumer-finance-complaints/default to /root/.cache/huggingface/datasets/consumer-finance-complaints/default/0.0.0/30e483d37fb4b25bb98cad1bfd2dc48f6ed6d1f3371eb4568c625a61d1a79b69...
Downloading data: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 511M/511M [00:04<00:00, 103MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare
super()._download_and_prepare(
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 931, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=1605177353, num_examples=2455765, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=2043641693, num_examples=3079747, shard_lengths=[721000, 656000, 788000, 846000, 68747], dataset_name='consumer-finance-complaints')}]
```
### Expected behavior
dataset should load
### Environment info
>>> datasets.__version__
'2.7.0'
Python 3.8.10
"Ubuntu 20.04.4 LTS" | 2022-11-18T10:16:53Z | 5,260 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-17T20:10:26Z | https://api.github.com/repos/huggingface/datasets/issues/5260/comments | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | null | NONE | null | null | I_kwDODunzps5WqRWh | [
"Thanks for reporting, @adiprasad.\r\n\r\nWe are having a look at it.",
"I have opened an issue in that dataset Community tab on the Hub: https://huggingface.co/datasets/consumer-finance-complaints/discussions/1\r\n\r\nPlease note that in the meantime, you can load the dataset by passing `ignore_verifications=Tru... | {
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1,453,555,923 | https://api.github.com/repos/huggingface/datasets/issues/5259/labels{/name} | ### Describe the bug
dataset fails to load with runtime error
`RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.`
### Steps to reproduce the bug
With datasets[audio] 2.7 loaded, and logged into hugging face,
`data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)`
creates the error.
Full stack trace:
```---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
[<ipython-input-7-8cb9ca0f79f0>](https://localhost:8080/#) in <module>
----> 1 data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)
5 frames
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1745 try_from_hf_gcs=try_from_hf_gcs,
1746 use_auth_token=use_auth_token,
-> 1747 num_proc=num_proc,
1748 )
1749
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
824 verify_infos=verify_infos,
825 **prepare_split_kwargs,
--> 826 **download_and_prepare_kwargs,
827 )
828 # Sync info
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
1554 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs):
1555 super()._download_and_prepare(
-> 1556 dl_manager, verify_infos, check_duplicate_keys=verify_infos, **prepare_splits_kwargs
1557 )
1558
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
911 try:
912 # Prepare split will record examples associated to the split
--> 913 self._prepare_split(split_generator, **prepare_split_kwargs)
914 except OSError as e:
915 raise OSError(
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1362 fpath = path_join(self._output_dir, fname)
1363
-> 1364 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
1365 if num_input_shards <= 1 and num_proc is not None:
1366 logger.warning(
[/usr/local/lib/python3.7/dist-packages/datasets/utils/sharding.py](https://localhost:8080/#) in _number_of_shards_in_gen_kwargs(gen_kwargs)
16 + "\n".join(f"\t- key {key} has length {length}" for key, length in lists_lengths.items())
17 + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
---> 18 + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
19 )
20 )
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.```
### Expected behavior
the dataset loads in datasets version 2.6.1 and should load with datasets 2.7
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 2022-12-24T01:44:02Z | 5,259 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-17T15:36:52Z | https://api.github.com/repos/huggingface/datasets/issues/5259/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5259/timeline | datasets 2.7 introduces sharding error | https://api.github.com/repos/huggingface/datasets/issues/5259/events | null | {
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} | [] | null | completed | NONE | 2022-11-18T12:52:05Z | null | I_kwDODunzps5Wo4DT | [
"I notice a comment in the code says:\r\n`Having lists of different sizes makes sharding ambigious, raise an error in this case until we decide how to define sharding without ambiguity for users` \r\n \r\n ... which suggests this update was pushed knowing that it might break some things. But, it didn't seem to h... | {
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1,453,516,636 | https://api.github.com/repos/huggingface/datasets/issues/5258/labels{/name} | After a bulk edit of canonical datasets to create the YAML `dataset_info` metadata, the split names were accidentally sorted alphabetically. See for example:
- https://huggingface.co/datasets/bc2gm_corpus/commit/2384629484401ecf4bb77cd808816719c424e57c
Note that this order is the one appearing in the preview of the datasets.
I'm making a bulk edit to align the order of the splits appearing in the metadata info with the order appearing in the loading script.
Related to:
- #5202 | 2023-02-16T09:49:05Z | 5,258 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"The bulk edit is running...\r\n\r\nSee for example: \r\n- A single config: https://huggingface.co/datasets/acronym_identification/discussions/2\r\n- Multiple configs: https://huggingface.co/datasets/babi_qa/discussions/1",
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1,452,631,517 | https://api.github.com/repos/huggingface/datasets/issues/5255/labels{/name} | ### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it. | 2022-12-17T12:20:38Z | 5,255 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Also cc @mariosasko and @lhoestq ",
"Cool ! Let us know if you have questions or if we can help :)\r\n\r\nI guess we'll also have to create the NYU CS Department on the Hub ?",
"> I guess we'll also have to create the NYU CS Department on the Hub ?\r\n\r\nYes, you're right! Let me add it to my profile first, a... | {
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1,451,765,838 | https://api.github.com/repos/huggingface/datasets/issues/5252/labels{/name} | Add support for decoding the `Image`/`Audio` types in `map` for the formats (Numpy, TF, Jax, PyTorch) other than the default one (Python).
Additional improvements:
* make `Dataset`'s "iter" API cleaner by removing `_iter` and replacing `_iter_batches` with `iter(batch_size)` (also implemented for `IterableDataset`)
* iterate over arrow tables in `map` to avoid `_getitem` calls, which are much slower than `__iter__`/`iter(batch_size)`, when the `format_type` is not Python
* fix `_iter_batches` (now named `iter`) when `drop_last_batch=True` and `pyarrow<=8.0.0` is installed
* lazily extract and decode arrow data in the default format
TODO:
* [x] update the `iter` benchmark in the docs (the `BeamBuilder` cannot load the preprocessed datasets from our bucket, so wait for this to be fixed (cc @lhoestq))
Fix https://github.com/huggingface/datasets/issues/3992, fix https://github.com/huggingface/datasets/issues/3756 | 2022-12-13T17:01:54Z | 5,252 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-16T15:02:13Z | https://api.github.com/repos/huggingface/datasets/issues/5252/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5252/timeline | Support for decoding Image/Audio types in map when format type is not default one | https://api.github.com/repos/huggingface/datasets/issues/5252/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5252). All of your documentation changes will be reflected on that endpoint.",
"Yes, if the image column is the first in the batch keys, it will ... | {
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1,451,761,321 | https://api.github.com/repos/huggingface/datasets/issues/5251/labels{/name} | After the latest `datasets` release version 0.7.0, the docs were not generated.
As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action:
https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8
Related to:
- #5250
CC: @mishig25 | 2022-11-22T16:27:50Z | 5,251 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,451,720,030 | https://api.github.com/repos/huggingface/datasets/issues/5250/labels{/name} | This PR changes the release procedure so that:
- it only make changes to main branch via pull requests
- it is no longer necessary to directly commit/push to main branch
Close #5251.
| 2022-11-22T16:30:58Z | 5,250 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-16T14:35:32Z | https://api.github.com/repos/huggingface/datasets/issues/5250/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5250/timeline | Change release procedure to use only pull requests | https://api.github.com/repos/huggingface/datasets/issues/5250/events | null | {
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1,451,692,247 | https://api.github.com/repos/huggingface/datasets/issues/5249/labels{/name} | We have decided to implement a protection mechanism in this repository, so that nobody (not even administrators) can inadvertently push accidentally directly to the main branch.
See context here:
- d7c942228b8dcf4de64b00a3053dce59b335f618
To do:
- [x] Protect main branch
- Settings > Branches > Branch protection rules > main > Edit
- [x] Check: Do not allow bypassing the above settings
- The above settings will apply to administrators and custom roles with the "bypass branch protections" permission.
- [x] Additionally, uncheck: Require approvals [under "Require a pull request before merging", which was already checked]
- Before, we could exceptionally merge a non-approved PR, using Administrator bypass
- Now that Administrator bypass is no longer possible, we would always need an approval to be able to merge; and pull request authors cannot approve their own pull requests. This could be an inconvenient in some exceptional circumstances when an urgent fix is needed
- Nevertheless, although it is no longer enforced, it is strongly recommended to merge PRs only if they have at least one approval
- [x] #5250
- So that direct pushes to main branch are no longer necessary | 2023-12-21T10:28:27Z | 5,249 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,451,338,676 | https://api.github.com/repos/huggingface/datasets/issues/5248/labels{/name} | Reverts huggingface/datasets#5214
Everything is handled on the doc-builder side now π | 2022-11-16T15:06:50Z | 5,248 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-16T10:41:04Z | https://api.github.com/repos/huggingface/datasets/issues/5248/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5248/timeline | Complete doc migration | https://api.github.com/repos/huggingface/datasets/issues/5248/events | null | {
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1,450,376,433 | https://api.github.com/repos/huggingface/datasets/issues/5245/labels{/name} | ### Describe the bug
Trying to rename column in a streaming datasets, destroys the features object.
### Steps to reproduce the bug
The following code illustrates the error:
```
from datasets import load_dataset
dataset = load_dataset('mc4', 'en', streaming=True, split='train')
dataset.info.features
# {'text': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
dataset = dataset.rename_column("text", "content")
dataset.info.features
# This returned object is now None!
```
### Expected behavior
This should just alter the renamed column.
### Environment info
datasets 2.6.1 | 2022-11-28T12:53:24Z | 5,245 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-15T21:04:41Z | https://api.github.com/repos/huggingface/datasets/issues/5245/comments | {
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"Hi @peregilk this bug is directly related to https://github.com/huggingface/datasets/issues/3888, and still not fixed... But I'll try to have a look!",
"Thanks @alvarobartt. It is great if you are able to fix it, but when reading the explanation it seems like it is possible to work around it.\r\n\r\nWe also trie... | {
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1,450,019,225 | https://api.github.com/repos/huggingface/datasets/issues/5244/labels{/name} | ### Feature request
Add arguments to the function _get_authentication_headers_for_url_ like custom_endpoint and custom_token in order to add flexibility when downloading files from a private source.
It should also be possible to provide these arguments from the dataset loading script, maybe giving them to the dl_manager
### Motivation
It is possible to share a dataset hosted on another platform by writing a dataset loading script. It works perfectly for publicly available resources.
For resources that require authentication, you can provide a [download_custom](https://huggingface.co/docs/datasets/package_reference/builder_classes#datasets.DownloadManager) method to the download_manager.
Unfortunately, this function doesn't work with **dataset streaming**.
A solution so as to allow dataset streaming from private sources would be a more flexible _get_authentication_headers_for_url_ function.
### Your contribution
Would you be interested in this improvement ?
If so I could provide a PR. I've got something working locally, but it's not very clean, I'd need some guidance regarding integration. | 2022-11-23T14:02:30Z | 5,244 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi ! What kind of private source ? We're exploring adding support for cloud storage and URIs like s3://, gs:// etc. with authentication in the download manager",
"Hello! It's a google cloud storage, so gs://, but I'm using it with https.\r\nBeing able to provide a file system like [here](https://huggingface.co/d... | {
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1,449,523,962 | https://api.github.com/repos/huggingface/datasets/issues/5243/labels{/name} | ### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test",
cache_dir="cache/path...",
use_auth_token=True,
download_config=DownloadConfig(delete_extracted='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')
)
### Motivation
efficiency improvement
### Your contribution
n/a | 2024-03-06T13:28:17Z | 5,243 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi @capsabogdan! Unfortunately, it's hard to implement because quite often datasets data is being hosted in a single archive for all splits :( So we have to download the whole archive to split it into splits. This is the case for CommonVoice too. \r\n\r\nHowever, for cases when data is distributed in separate arch... | {
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1,449,069,382 | https://api.github.com/repos/huggingface/datasets/issues/5242/labels{/name} | I went to autotrain and under image classification arrived where it was time to prepare my dataset. Screenshot below
I chose the method 2 option. I have a csv file with two columns. ~23,000 files.
I uploaded this and chose the image_relpath, and target columns.
The image uploader said that I could only upload 10,000 singular images at a time so the 2nd option was to zip the images up and upload a zip archive which I did.
That all uploaded.
Now I have the message below. It appears the zip archive does just uncompress on the Hugging Face end?
What am I missing here?
| 2022-11-15T17:59:23Z | 5,242 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-15T02:47:52Z | https://api.github.com/repos/huggingface/datasets/issues/5242/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5242/timeline | Failed Data Processing upon upload with zip file full of images | https://api.github.com/repos/huggingface/datasets/issues/5242/events | null | {
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"cc @abhishekkrthakur @SBrandeis "
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1,448,510,407 | https://api.github.com/repos/huggingface/datasets/issues/5241/labels{/name} | otherwise the code doesn't work for hfh 0.11.0rc0
following #5237 | 2022-11-15T16:11:30Z | 5,241 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-14T18:05:47Z | https://api.github.com/repos/huggingface/datasets/issues/5241/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5241/timeline | Support hfh rc version | https://api.github.com/repos/huggingface/datasets/issues/5241/events | null | {
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1,448,478,617 | https://api.github.com/repos/huggingface/datasets/issues/5240/labels{/name} | Make the traceback of the errors raised in `_generate_examples` cleaner for easier debugging. Additionally, initialize the `writer` in the for-loop to avoid the `ValueError` from `ArrowWriter.finalize` raised in the `finally` block when no examples are yielded before the `_generate_examples` error.
<details>
<summary>
The full traceback of the "SQLAlchemy ImportError" error that gets printed with these changes:
</summary>
```bash
ImportError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split_single(self, arg)
1759 _time = time.time()
-> 1760 for _, table in generator:
1761 # Only initialize the writer when we have the first record (to avoid having to do the clean-up if an error occurs before that)
9 frames
/usr/local/lib/python3.7/dist-packages/datasets/packaged_modules/sql/sql.py in _generate_tables(self)
112 sql_reader = pd.read_sql(
--> 113 self.config.sql, self.config.con, chunksize=chunksize, **self.config.pd_read_sql_kwargs
114 )
/usr/local/lib/python3.7/dist-packages/pandas/io/sql.py in read_sql(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)
598 """
--> 599 pandas_sql = pandasSQL_builder(con)
600
/usr/local/lib/python3.7/dist-packages/pandas/io/sql.py in pandasSQL_builder(con, schema, meta, is_cursor)
789 elif isinstance(con, str):
--> 790 raise ImportError("Using URI string without sqlalchemy installed.")
791 else:
ImportError: Using URI string without sqlalchemy installed.
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
<ipython-input-4-5af11af4737b> in <module>
----> 1 ds = Dataset.from_sql('''SELECT * from states WHERE state=="New York";''', "sqlite:///us_covid_data.db")
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in from_sql(sql, con, features, cache_dir, keep_in_memory, **kwargs)
1152 cache_dir=cache_dir,
1153 keep_in_memory=keep_in_memory,
-> 1154 **kwargs,
1155 ).read()
1156
/usr/local/lib/python3.7/dist-packages/datasets/io/sql.py in read(self)
47 # try_from_hf_gcs=try_from_hf_gcs,
48 base_path=base_path,
---> 49 use_auth_token=use_auth_token,
50 )
51
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
825 verify_infos=verify_infos,
826 **prepare_split_kwargs,
--> 827 **download_and_prepare_kwargs,
828 )
829 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
912 try:
913 # Prepare split will record examples associated to the split
--> 914 self._prepare_split(split_generator, **prepare_split_kwargs)
915 except OSError as e:
916 raise OSError(
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1652 job_id = 0
1653 for job_id, done, content in self._prepare_split_single(
-> 1654 {"gen_kwargs": gen_kwargs, "job_id": job_id, **_prepare_split_args}
1655 ):
1656 if done:
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split_single(self, arg)
1789 raise DatasetGenerationError(
1790 f"An error occured while generating the dataset"
-> 1791 ) from e
1792 finally:
1793 yield job_id, False, num_examples_progress_update
DatasetGenerationError: An error occurred while generating the dataset
```
</details>
PS: I've also considered raising the error as follows:
```python
tb = sys.exc_info()[2]
raise DatasetGenerationError(f"An error occurred while generating the dataset: {type(e).__name__}: {e}").with_traceback(tb) from None # this raises the DatasetGenerationError with "e"'s traceback
```
But it seems like "from e" is now the [preferred](https://docs.python.org/3/library/exceptions.html#BaseException.with_traceback) way to chain exceptions.
Fix https://github.com/huggingface/datasets/issues/5186
cc @nateraw
| 2022-11-15T18:26:48Z | 5,240 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-14T17:42:02Z | https://api.github.com/repos/huggingface/datasets/issues/5240/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5240/timeline | Cleaner error tracebacks for dataset script errors | https://api.github.com/repos/huggingface/datasets/issues/5240/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq Good catch! This currently leads to an AttributeError (due to `writer` being None) on this line:\r\nhttps://github.com/huggingface/datasets/blob/fed1628d49a91f9ae259ddf6edbb252c7972d9a3/src/datasets/builder.py#L1552\r\n"
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1,448,211,373 | https://api.github.com/repos/huggingface/datasets/issues/5239/labels{/name} | Allow multiprocessing to from_* methods | 2022-12-06T15:39:10Z | 5,239 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-14T14:53:00Z | https://api.github.com/repos/huggingface/datasets/issues/5239/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5239/timeline | Add num_proc to from_csv/generator/json/parquet/text | https://api.github.com/repos/huggingface/datasets/issues/5239/events | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5239). All of your documentation changes will be reflected on that endpoint.",
"I ended up moving `num_proc` to `AbstractDatasetReader.__init__` :)\r\n\r\nLet me know if it sounds good to you now"
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1,448,211,251 | https://api.github.com/repos/huggingface/datasets/issues/5238/labels{/name} | Add `__hash__` to the `Version` class to make it hashable (and remove the unneeded methods), as `Version("0.0.0")` is the default value of `BuilderConfig.version` and the default fields of a dataclass need to be hashable in Python 3.11.
Fix https://github.com/huggingface/datasets/issues/5230 | 2022-11-14T15:30:02Z | 5,238 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-14T14:52:55Z | https://api.github.com/repos/huggingface/datasets/issues/5238/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5238/timeline | Make `Version` hashable | https://api.github.com/repos/huggingface/datasets/issues/5238/events | null | {
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1,448,202,491 | https://api.github.com/repos/huggingface/datasets/issues/5237/labels{/name} | Next version of `huggingface-hub` 0.11 does encode the `path`, and we don't want to encode twice | 2022-11-14T17:38:18Z | 5,237 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-14T14:46:57Z | https://api.github.com/repos/huggingface/datasets/issues/5237/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5237/timeline | Encode path only for old versions of hfh | https://api.github.com/repos/huggingface/datasets/issues/5237/events | null | {
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1,448,190,801 | https://api.github.com/repos/huggingface/datasets/issues/5236/labels{/name} | Handle the `ArrowNotImplementedError` thrown when `try_type` is `Image` or `Audio` and the input array cannot be converted to their storage formats.
Reproducer:
```python
from datasets import Dataset
from PIL import Image
import requests
ds = Dataset.from_dict({"image": [Image.open(requests.get("https://upload.wikimedia.org/wikipedia/commons/e/e9/Felis_silvestris_silvestris_small_gradual_decrease_of_quality.png", stream=True).raw)]})
ds.map(lambda x: {"image": True}) # ArrowNotImplementedError
```
PS: This could also be fixed by raising `TypeError` in `{Image, Audio}.cast_storage` for unsupported types instead of passing the array to `array_cast.` | 2022-11-14T16:04:29Z | 5,236 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-14T14:38:59Z | https://api.github.com/repos/huggingface/datasets/issues/5236/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5236/timeline | Handle ArrowNotImplementedError caused by try_type being Image or Audio in cast | https://api.github.com/repos/huggingface/datasets/issues/5236/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Not sure how we can have a test that is relevant for this though - feel free to add one if you have ideas\r\n\r\nYes, this was my reasoning for not adding a test. This change is pretty simple, so I think it's OK not to have a test ... | {
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1,448,052,660 | https://api.github.com/repos/huggingface/datasets/issues/5235/labels{/name} | Otherwise `click` fails on Windows:
```
Traceback (most recent call last):
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\spacy\__main__.py", line 4, in <module>
setup_cli()
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\spacy\cli\_util.py", line 71, in setup_cli
command(prog_name=COMMAND)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\click\core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\typer\core.py", line 785, in main
**extra,
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\typer\core.py", line 190, in _main
args = click.utils._expand_args(args)
AttributeError: module 'click.utils' has no attribute '_expand_args'
```
See https://github.com/tiangolo/typer/issues/427 | 2022-11-14T15:43:01Z | 5,235 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-14T13:17:02Z | https://api.github.com/repos/huggingface/datasets/issues/5235/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5235/timeline | Pin `typer` version in tests to <0.5 to fix Windows CI | https://api.github.com/repos/huggingface/datasets/issues/5235/events | null | {
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1,447,999,062 | https://api.github.com/repos/huggingface/datasets/issues/5234/labels{/name} | cache_file_name depends on dataset's path.
A simple way where this could cause a problem:
```
import os
import datasets
def add_prefix(example):
example["text"] = "Review: " + example["text"]
return example
ds = datasets.load_from_disk("a/relative/path")
os.chdir("/tmp")
ds_1 = ds.map(add_prefix)
```
while it may feel that the `chdir` is quite constructed, there are many scenarios when the current working dir can/will change... | 2022-12-07T23:49:22Z | 5,234 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-14T12:47:40Z | https://api.github.com/repos/huggingface/datasets/issues/5234/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5234/timeline | fix: dataset path should be absolute | https://api.github.com/repos/huggingface/datasets/issues/5234/events | null | {
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} | PR_kwDODunzps5C1diq | [
"Good catch thanks ! Have you tried to use the absolue path in `MemoryMappedTable.__init__` in `table.py`?\r\n\r\nI think it can fix issues with relative paths at more levels than just fixing it `load_from_disk`. If it works I think it would be a more robust fix to this issue",
"@lhoestq right, that actually fixe... | {
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1,447,906,868 | https://api.github.com/repos/huggingface/datasets/issues/5233/labels{/name} | Allow to define a sharded iterable dataset | 2022-11-14T14:16:03Z | 5,233 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-14T11:42:09Z | https://api.github.com/repos/huggingface/datasets/issues/5233/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5233/timeline | Fix shards in IterableDataset.from_generator | https://api.github.com/repos/huggingface/datasets/issues/5233/events | null | {
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1,446,294,165 | https://api.github.com/repos/huggingface/datasets/issues/5232/labels{/name} | ### Describe the bug
datasets version 2.6.1 has a dependency on dill<0.3.6. This causes a conflict with dill>=0.3.6 used by multiprocess dependency in datasets 2.6.1
This issue is already fixed in https://github.com/huggingface/datasets/pull/5166/files, but not yet been released. Please release a new version of the datasets library to fix this.
### Steps to reproduce the bug
1. Create requirements.in with only dependency being datasets (or datasets[s3])
2. Run pip-compile
3. The output is as follows:
```
Could not find a version that matches dill<0.3.6,>=0.3.6 (from datasets[s3]==2.6.1->-r requirements.in (line 1))
Tried: 0.2, 0.2, 0.2.1, 0.2.1, 0.2.2, 0.2.2, 0.2.3, 0.2.3, 0.2.4, 0.2.4, 0.2.5, 0.2.5, 0.2.6, 0.2.7, 0.2.7.1, 0.2.8, 0.2.8.1, 0.2.8.2, 0.2.9, 0.3.0, 0.3.1, 0.3.1.1, 0.3.2, 0.3.3, 0.3.3, 0.3.4, 0.3.4, 0.3.5, 0.3.5, 0.3.5.1, 0.3.5.1, 0.3.6, 0.3.6
Skipped pre-versions: 0.1a1, 0.2a1, 0.2a1, 0.2b1, 0.2b1
There are incompatible versions in the resolved dependencies:
dill<0.3.6 (from datasets[s3]==2.6.1->-r requirements.in (line 1))
dill>=0.3.6 (from multiprocess==0.70.14->datasets[s3]==2.6.1->-r requirements.in (line 1))
```
### Expected behavior
pip-compile produces requirements.txt without any conflicts
### Environment info
datasets version 2.6.1 | 2022-11-14T08:24:43Z | 5,232 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-12T06:46:23Z | https://api.github.com/repos/huggingface/datasets/issues/5232/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5232/timeline | Incompatible dill versions in datasets 2.6.1 | https://api.github.com/repos/huggingface/datasets/issues/5232/events | null | {
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"Thanks for reporting, @vinaykakade.\r\n\r\nWe are discussing about making a release early this week.\r\n\r\nPlease note that in the meantime, in your specific case (as we also pointed out here: https://github.com/huggingface/datasets/issues/5162#issuecomment-1291720293), you can circumvent the issue by pinning `mu... | {
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1,445,883,267 | https://api.github.com/repos/huggingface/datasets/issues/5231/labels{/name} | I have a Dataset with two Features defined as follows:
```
'image': Array3D(dtype="int64", shape=(3, 224, 224)),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
```
On said dataset, if I `dataset.set_format(type='torch')` and then use the dataset in a dataloader, these columns are correctly cast to Tensors of (batch_size, 3, 224, 244) for example.
However, if I `dataset.set_format(type='torch', columns=['image', 'bbox'])` these columns are cast to Lists of tensors and miss the batch size completely (the 3 dimension is the list length).
I'm currently digging through datasets formatting code to try and find out why, but was curious if someone knew an immediate solution for this. | 2022-11-11T20:42:29Z | 5,231 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-11T18:54:36Z | https://api.github.com/repos/huggingface/datasets/issues/5231/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5231/timeline | Using `set_format(type='torch', columns=columns)` makes Array2D/3D columns stop formatting correctly | https://api.github.com/repos/huggingface/datasets/issues/5231/events | null | {
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"In case others find this, the problem was not with set_format, but my usages of `to_pandas()` and `from_pandas()` which I was using during dataset splitting; somewhere in the chain of converting to and from pandas the `Array2D/Array3D` types get converted to series of `Sequence()` types"
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1,445,507,580 | https://api.github.com/repos/huggingface/datasets/issues/5230/labels{/name} | ### Describe the bug
When I import datasets using python 3.11 the dataclasses standard library raises the following error:
`ValueError: mutable default <class 'datasets.utils.version.Version'> for field version is not allowed: use default_factory`
When I tried to import the library using the following jupyter notebook:
```
%%bash
# create python 3.11 conda env
conda create --yes --quiet -n myenv -c conda-forge python=3.11
# activate is
source activate myenv
# install pyarrow
/opt/conda/envs/myenv/bin/python -m pip install --quiet --extra-index-url https://pypi.fury.io/arrow-nightlies/ \
--prefer-binary --pre pyarrow
# install datasets
/opt/conda/envs/myenv/bin/python -m pip install --quiet datasets
```
```
# create a python file that only imports datasets
with open("import_datasets.py", 'w') as f:
f.write("import datasets")
# run it with the env
!/opt/conda/envs/myenv/bin/python import_datasets.py
```
I get the following error:
```
Traceback (most recent call last):
File "/kaggle/working/import_datasets.py", line 1, in <module>
import datasets
File "/opt/conda/envs/myenv/lib/python3.11/site-packages/datasets/__init__.py", line 45, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/opt/conda/envs/myenv/lib/python3.11/site-packages/datasets/builder.py", line 91, in <module>
@dataclass
^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 1221, in dataclass
return wrap(cls)
^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 1211, in wrap
return _process_class(cls, init, repr, eq, order, unsafe_hash,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 959, in _process_class
cls_fields.append(_get_field(cls, name, type, kw_only))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 816, in _get_field
raise ValueError(f'mutable default {type(f.default)} for field '
ValueError: mutable default <class 'datasets.utils.version.Version'> for field version is not allowed: use default_factory
```
This is probably due to one of the following changes in the [dataclasses standard library](https://docs.python.org/3/library/dataclasses.html) in version 3.11:
1. Changed in version 3.11: Instead of looking for and disallowing objects of type list, dict, or set, unhashable objects are now not allowed as default values. Unhashability is used to approximate mutability.
2. fields may optionally specify a default value, using normal Python syntax:
```
@dataclass
class C:
a: int # 'a' has no default value
b: int = 0 # assign a default value for 'b'
In this example, both a and b will be included in the added __init__() method, which will be defined as:
def __init__(self, a: int, b: int = 0):
```
3. Changed in version 3.11: If a field name is already included in the __slots__ of a base class, it will not be included in the generated __slots__ to prevent [overriding them](https://docs.python.org/3/reference/datamodel.html#datamodel-note-slots). Therefore, do not use __slots__ to retrieve the field names of a dataclass. Use [fields()](https://docs.python.org/3/library/dataclasses.html#dataclasses.fields) instead. To be able to determine inherited slots, base class __slots__ may be any iterable, but not an iterator.
4. weakref_slot: If true (the default is False), add a slot named β__weakref__β, which is required to make an instance weakref-able. It is an error to specify weakref_slot=True without also specifying slots=True.
[TypeError](https://docs.python.org/3/library/exceptions.html#TypeError) will be raised if a field without a default value follows a field with a default value. This is true whether this occurs in a single class, or as a result of class inheritance.
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. go to [the notebook in kaggle](https://www.kaggle.com/yonikremer/repreducing-issue)
2. rub both of the cells
### Expected behavior
I'm expecting no issues.
This error should not occur.
### Environment info
kaggle kernels, with default settings:
pin to original environment, no accelerator. | 2023-05-25T04:37:05Z | 5,230 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-11T13:53:49Z | https://api.github.com/repos/huggingface/datasets/issues/5230/comments | {
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... | null | completed | NONE | 2022-11-14T15:27:37Z | null | I_kwDODunzps5WKLH8 | [
"I opened [this issue](https://github.com/python/cpython/issues/99401).\r\nPython's maintainers say that the issue is caused by [this change](https://docs.python.org/3.11/whatsnew/3.11.html#dataclasses).\r\nI believe adding a `__hash__` method to `datasets.utils.version.Version` should solve (at least partially) th... | {
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1,445,121,028 | https://api.github.com/repos/huggingface/datasets/issues/5229/labels{/name} | ### Describe the bug
0-dimensional tensors in a dataset lead to `TypeError: iteration over a 0-d array` when calling `map`. It is easy to generate such tensors by using `.with_format("...")` on the whole dataset.
### Steps to reproduce the bug
```
ds = datasets.Dataset.from_list([{"a": 1}, {"a": 1}]).with_format("torch")
ds.map(None)
```
### Expected behavior
Getting back `ds` without errors.
### Environment info
Python 3.10.8
datasets 2.6.
torch 1.13.0 | 2023-01-13T16:00:53Z | 5,229 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-11T08:27:28Z | https://api.github.com/repos/huggingface/datasets/issues/5229/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5229/timeline | Type error when calling `map` over dataset containing 0-d tensors | https://api.github.com/repos/huggingface/datasets/issues/5229/events | null | {
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"Hi! \r\n\r\nWe could address this by calling `.item()` on such tensors to extract the value, but this would lose us the type, which could lead to storing the generated dataset in a suboptimal format. Considering this, I think the only proper fix would be implementing support for 0-D tensors on Apache Arrow's side ... | {
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1,444,763,105 | https://api.github.com/repos/huggingface/datasets/issues/5228/labels{/name} | ### Describe the bug
I'm not 100% sure this should be considered a bug, but it was certainly annoying to figure out the cause of. And perhaps I am just missing a specific argument needed to avoid this conflict. Basically I had a situation where multiple workers were downloading different parts of the glue dataset and then training on them. Additionally, they were writing their checkpoints to a folder called `glue`. This meant that once one worker had created the `glue` folder to write checkpoints to, the next worker to try to load a glue dataset would fail as shown in the minimal repro below. I'm not sure what the solution would be since I'm not super familiar with the `datasets` code, but I would expect `load_dataset` to not crash just because i have a local folder with the same name as a dataset from the hub.
### Steps to reproduce the bug
```
In [1]: import datasets
In [2]: rte = datasets.load_dataset('glue', 'rte')
Downloading and preparing dataset glue/rte to /Users/danielking/.cache/huggingface/datasets/glue/rte/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...
Downloading data: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 697k/697k [00:00<00:00, 6.08MB/s]
Dataset glue downloaded and prepared to /Users/danielking/.cache/huggingface/datasets/glue/rte/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 773.81it/s]
In [3]: import os
In [4]: os.mkdir('glue')
In [5]: rte = datasets.load_dataset('glue', 'rte')
---------------------------------------------------------------------------
EmptyDatasetError Traceback (most recent call last)
<ipython-input-5-0d6b9ad8bbd0> in <cell line: 1>()
----> 1 rte = datasets.load_dataset('glue', 'rte')
~/miniconda3/envs/composer/lib/python3.9/site-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)
1717
1718 # Create a dataset builder
-> 1719 builder_instance = load_dataset_builder(
1720 path=path,
1721 name=name,
~/miniconda3/envs/composer/lib/python3.9/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1495 download_config = download_config.copy() if download_config else DownloadConfig()
1496 download_config.use_auth_token = use_auth_token
-> 1497 dataset_module = dataset_module_factory(
1498 path,
1499 revision=revision,
~/miniconda3/envs/composer/lib/python3.9/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1152 ).get_module()
1153 elif os.path.isdir(path):
-> 1154 return LocalDatasetModuleFactoryWithoutScript(
1155 path, data_dir=data_dir, data_files=data_files, download_mode=download_mode
1156 ).get_module()
~/miniconda3/envs/composer/lib/python3.9/site-packages/datasets/load.py in get_module(self)
624 base_path = os.path.join(self.path, self.data_dir) if self.data_dir else self.path
625 patterns = (
--> 626 sanitize_patterns(self.data_files) if self.data_files is not None else get_data_patterns_locally(base_path)
627 )
628 data_files = DataFilesDict.from_local_or_remote(
~/miniconda3/envs/composer/lib/python3.9/site-packages/datasets/data_files.py in get_data_patterns_locally(base_path)
458 return _get_data_files_patterns(resolver)
459 except FileNotFoundError:
--> 460 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
461
462
EmptyDatasetError: The directory at glue doesn't contain any data files
```
### Expected behavior
Dataset is still able to be loaded from the hub even if I have a local folder with the same name.
### Environment info
datasets version: 2.6.1 | 2023-05-03T23:23:04Z | 5,228 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-11T00:51:54Z | https://api.github.com/repos/huggingface/datasets/issues/5228/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5228/timeline | Loading a dataset from the hub fails if you happen to have a folder of the same name | https://api.github.com/repos/huggingface/datasets/issues/5228/events | null | {
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"`load_dataset` first checks for a local directory before checking for the Hub.\r\n\r\nTo make it explicit that it has to fetch the Hub, we could support the `hffs` syntax:\r\n```python\r\nload_dataset(\"hf://datasets/glue\")\r\n```\r\n\r\nwould that work for you ? Also cc @mariosasko who's leading the `hffs` proje... | {
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1,444,620,094 | https://api.github.com/repos/huggingface/datasets/issues/5227/labels{/name} | ### Describe the bug
From these lines:
from datasets import list_datasets, load_dataset
dataset = load_dataset("wikisql","binary")
I get error message:
datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files
And yet the 'wikisql' is reported to exist via the list_datasets().
Any help appreciated.
### Steps to reproduce the bug
From these lines:
from datasets import list_datasets, load_dataset
dataset = load_dataset("wikisql","binary")
I get error message:
datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files
And yet the 'wikisql' is reported to exist via the list_datasets().
Any help appreciated.
### Expected behavior
Dataset should load. This same code used to work.
### Environment info
Mac OS | 2023-10-07T05:04:41Z | 5,227 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-10T21:57:06Z | https://api.github.com/repos/huggingface/datasets/issues/5227/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5227/timeline | datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files | https://api.github.com/repos/huggingface/datasets/issues/5227/events | null | {
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} | [] | null | completed | NONE | 2022-11-10T22:05:43Z | null | I_kwDODunzps5WGyc- | [
"Fixed. Please close.",
"how to fix?i need your help"
] | {
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1,444,385,148 | https://api.github.com/repos/huggingface/datasets/issues/5226/labels{/name} | ### Describe the bug
How do I release memory when I use methods like `.remove_columns()` or `clear()` in notebooks?
```python
from datasets import load_dataset
common_voice = load_dataset("mozilla-foundation/common_voice_11_0", "ja", use_auth_token=True)
# check memory -> RAM Used (GB): 0.704 / Total (GB) 33.670
common_voice = common_voice.remove_columns(column_names=common_voice.column_names['train'])
common_voice.clear()
# check memory -> RAM Used (GB): 0.705 / Total (GB) 33.670
```
I tried `gc.collect()` but did not help
### Steps to reproduce the bug
1. load dataset
2. remove all the columns
3. check memory is reduced or not
[link to reproduce](https://www.kaggle.com/code/bayartsogtya/huggingface-dataset-memory-issue/notebook?scriptVersionId=110630567)
### Expected behavior
Memory released when I remove the column
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5 | 2022-11-29T15:10:10Z | 5,226 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-10T18:35:27Z | https://api.github.com/repos/huggingface/datasets/issues/5226/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5226/timeline | Q: Memory release when removing the column? | https://api.github.com/repos/huggingface/datasets/issues/5226/events | null | {
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"Hi ! Datasets are memory mapped from your disk, i.e. they're not loaded in RAM. This is possible thanks to the Arrow data format.\r\n\r\nTherefore the column you remove is not in RAM, so removing it doesn't cause the RAM to decrease.",
"Thanks for the explanation! @lhoestq \r\nI wonder since it is memory mapped,... | {
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1,444,305,183 | https://api.github.com/repos/huggingface/datasets/issues/5225/labels{/name} | ### Feature request
Add a `Video` feature to the library so folks can include videos in their datasets.
### Motivation
Being able to load Video data would be quite helpful. However, there are some challenges when it comes to videos:
1. Videos, unlike images, can end up being extremely large files
2. Often times when training video models, you need to do some very specific sampling. Videos might end up needing to be broken down into X number of clips used for training/inference
3. Videos have an additional audio stream, which must be accounted for
4. The feature needs to be able to encode/decode videos (with right video settings) from bytes.
### Your contribution
I did work on this a while back in [this (now closed) PR](https://github.com/huggingface/datasets/pull/4532). It used a library I made called [encoded_video](https://github.com/nateraw/encoded-video), which is basically the utils from [pytorchvideo](https://github.com/facebookresearch/pytorchvideo), but without the `torch` dep. It included the ability to read/write from bytes, as we need to do here. We don't want to be using a sketchy library that I made as a dependency in this repo, though.
Would love to use this issue as a place to:
- brainstorm ideas on how to do this right
- list ways/examples to work around it for now
CC @sayakpaul @mariosasko @fcakyon | 2022-12-02T15:13:15Z | 5,225 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"default": true... | 2022-11-10T17:36:11Z | https://api.github.com/repos/huggingface/datasets/issues/5225/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5225/timeline | Add video feature | https://api.github.com/repos/huggingface/datasets/issues/5225/events | null | {
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"@NielsRogge @rwightman may have additional requirements regarding this feature.\r\n\r\nWhen adding a new (decodable) type, the hardest part is choosing the right decoding library. What I mean by \"right\" here is that it has all the features we need and is easy to install (with GPU support?).\r\n\r\nSome candidate... | {
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1,443,640,867 | https://api.github.com/repos/huggingface/datasets/issues/5224/labels{/name} | ### Describe the bug
I'm following the instructions in [https://huggingface.co/docs/datasets/audio_load#audiofolder-with-metadata](url) to be able to load a dataset from a local folder.
I have everything into a folder, into a train folder and then the audios and csv. When I try to load the dataset and run from terminal, seems to work but then freezes with no apparent reason.
The metadata.csv file contains a few columns but the important ones, `file_name` with the filename and `transcription` with the transcription are okay.
The audios are `.wav` files, I don't know if that might be the problem (I will proceed to try to change them all to `.mp3` and try again).
### Steps to reproduce the bug
The code I'm using:
```python
from datasets import load_dataset
dataset = load_dataset("audiofolder", data_dir="../archive/Dataset")
dataset[0]["audio"]
```
The output I obtain:
```
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 311135.43it/s]
Using custom data configuration default-38d4546ffd010f3e
Downloading and preparing dataset audiofolder/default to /Users/mine/.cache/huggingface/datasets/audiofolder/default-38d4546ffd010f3e/0.0.0/6cbdd16f8688354c63b4e2a36e1585d05de285023ee6443ffd71c4182055c0fc...
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 166467.72it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 187772.74it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 59623.71it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 138090.55it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 106065.64it/s]
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 56036.38it/s]
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 74004.24it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 162343.45it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 101881.23it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 60145.67it/s]
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 80890.02it/s]
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 54036.67it/s]
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 95851.09it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 155897.00it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 137656.96it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 131230.81it/s]
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
```
And then here it just freezes and nothing more happens.
### Expected behavior
Load the dataset.
### Environment info
Datasets version:
datasets 2.6.1 pypi_0 pypi
| 2023-04-25T09:54:05Z | 5,224 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-10T10:29:31Z | https://api.github.com/repos/huggingface/datasets/issues/5224/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5224/timeline | Seems to freeze when loading audio dataset with wav files from local folder | https://api.github.com/repos/huggingface/datasets/issues/5224/events | null | {
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"I just tried to do the same but changing the `.wav` files to `.mp3` files and that doesn't fix it.",
"I don't know if anyone will ever read this but I've tried to upload the same dataset with google colab and the output seems more clarifying. I didn't specify the train/test split so the dataset wasn't fully uplo... | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5223). All of your documentation changes will be reflected on that endpoint.",
"I think we may want more content on this page that's not SQL related. Some of that content probably already lives in the main `load` docs page, but... | {
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1,442,412,507 | https://api.github.com/repos/huggingface/datasets/issues/5222/labels{/name} | ### Describe the bug
HuggingFace is incorrectly reporting that my datasets are pickled. They are not picked, they are simple ZIP files containing PNG images.
Hopefully this is the right location to report this bug.
### Steps to reproduce the bug
Inspect my dataset respository here: https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images
### Expected behavior
They should not be reported as being pickled.
### Environment info
N/A | 2022-11-09T18:10:46Z | 5,222 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-09T16:41:16Z | https://api.github.com/repos/huggingface/datasets/issues/5222/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5222/timeline | HuggingFace website is incorrectly reporting that my datasets are pickled | https://api.github.com/repos/huggingface/datasets/issues/5222/events | null | {
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"cc @McPatate maybe you know what's happening ?",
"Yes I think I know what is happening. We check in zips for pickles, and the UI must display the pickle jar when a scan has an associated list of imports, even when empty.\r\n~I'll fix ASAP !~",
"> I'll fix ASAP !\r\n\r\nActually I'd rather leave it like that f... | {
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1,442,309,094 | https://api.github.com/repos/huggingface/datasets/issues/5221/labels{/name} | ### Describe the bug
I am facing the issue when I try to push the tar.gz file around 11G to HUB.
```
(venv) ββlaptop@laptop ~/PersonalProjects/data/ulaanbal_v0 βΉmainββΊ
β°β$ du -sh *
4.0K README.md
13G data
516K test.jsonl
18M train.jsonl
4.0K ulaanbal_v0.py
11G ulaanbal_v0.tar.gz
452K validation.jsonl
(venv) ββlaptop@laptop~/PersonalProjects/data/ulaanbal_v0 βΉmainββΊ
β°β$ git add ulaanbal_v0.tar.gz && git commit -m 'large version'
(venv) ββlaptop@laptop ~/PersonalProjects/data/ulaanbal_v0 βΉmainββΊ
β°β$ git push
EOFoading LFS objects: 0% (0/1), 0 B | 0 B/s
Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done.
error: failed to push some refs to 'https://huggingface.co/datasets/bayartsogt/ulaanbal_v0'
```
I have already tried pushing a small version of this and it was working fine. So my guess it is probably because of the big file.
Following I run before the commit:
```
β°β$ git lfs install
β°β$ huggingface-cli lfs-enable-largefiles .
```
### Steps to reproduce the bug
Create a private dataset on huggingface and push 12G tar.gz file
### Expected behavior
To be pushed with no issue
### Environment info
- `datasets` version: 2.6.1
- Platform: Darwin-21.6.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 10.0.0
- Pandas version: 1.3.5
| 2022-11-10T18:11:21Z | 5,221 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-09T15:32:05Z | https://api.github.com/repos/huggingface/datasets/issues/5221/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5221/timeline | Cannot push | https://api.github.com/repos/huggingface/datasets/issues/5221/events | null | {
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"Did you run `huggingface-cli lfs-enable-largefiles` before committing or before adding ? Maybe you can try before adding\r\n\r\nAnyway I'd encourage you to split your data into several TAR archives if possible, this way the dataset can loaded faster using multiprocessing (by giving each process a subset of shards ... | {
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1,441,664,377 | https://api.github.com/repos/huggingface/datasets/issues/5220/labels{/name} | ### Describe the bug
```
ds = Dataset.from_list([{'a':[1,2,3]}])
ds.to_pandas().a.values[0]
```
Results in `array([1, 2, 3])` -- a rather unexpected conversion of types which made downstream tools expecting lists not happy.
### Steps to reproduce the bug
See snippet
### Expected behavior
Keep the original type
### Environment info
datasets 2.6.1
python 3.8.10 | 2022-11-10T16:12:26Z | 5,220 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-09T08:40:18Z | https://api.github.com/repos/huggingface/datasets/issues/5220/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5220/timeline | Implicit type conversion of lists in to_pandas | https://api.github.com/repos/huggingface/datasets/issues/5220/events | null | {
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} | [] | null | completed | CONTRIBUTOR | 2022-11-10T16:12:26Z | null | I_kwDODunzps5V7g15 | [
"I think this behavior comes from PyArrow:\r\n```python\r\nimport pyarrow as pa\r\nt = pa.table({\"a\": [[0]]})\r\nt.to_pandas().a.values[0]\r\n# array([0])\r\n```\r\n\r\nI believe this has to do with zero-copy: you can get a pandas DataFrame without copying the buffers from arrow, and therefore end up with numpy a... | {
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1,441,255,910 | https://api.github.com/repos/huggingface/datasets/issues/5219/labels{/name} | ### Feature request
Adding compatibility of Datasets library with Delta Format. Elevating the utilities of Datasets library from Machine Learning Scope to Data Engineering Scope as well.
### Motivation
We know datasets library can absorb csv, json, parquet, etc. file formats but it would be great if Datasets library could work with Delta Tables (with delta format) as it has different features such as time travelling, layout optimization, query performance, aids in Data Engineering.
This will help and enhance Datasets library from Machine Learning utility to Data Engineering utilities and expand horizons thereafter. I am totally using Datasets library in all my usecases and as my role expands so does the work, compatibility with Datasets library is something I don't want to lose.
### Your contribution
Would love to work on this feature, even if this has to picked up from scratch, including design paradigms and patterns.
I have basic idea about Delta Live Tables, would brush it easily for this feature. | 2023-03-02T19:29:12Z | 5,219 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi ! Interesting :) Can you provide concrete examples of cases where it can be useful ?",
"Few example blogs and posts that might help on this - \r\n\r\n1. https://hevodata.com/learn/databricks-delta-tables/\r\n2. https://docs.databricks.com/delta/index.html\r\n\r\nBasically, we are looking at utility of Dataset... | {
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1,441,254,194 | https://api.github.com/repos/huggingface/datasets/issues/5218/labels{/name} | ### Feature request
Adding compatibility of Datasets library with Delta Format. Elevating the utilities of Datasets library from Machine Learning Scope to Data Engineering Scope as well.
### Motivation
We know datasets library can absorb csv, json, parquet, etc. file formats but it would be great if Datasets library could work with Delta Tables (with delta format) as it has different features such as time travelling, layout optimization, query performance, aids in Data Engineering.
This will help and enhance Datasets library from Machine Learning utility to Data Engineering utilities and expand horizons thereafter. I am totally using Datasets library in all my usecases and as my role expands so does the work, compatibility with Datasets library is something I don't want to lose.
### Your contribution
Would love to work on this feature, even if this has to picked up from scratch, including design paradigms and patterns.
I have basic idea about Delta Live Tables, would brush it easily for this feature. | 2022-11-09T02:42:36Z | 5,218 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,441,252,740 | https://api.github.com/repos/huggingface/datasets/issues/5217/labels{/name} | Reference: https://github.com/huggingface/datasets/pull/5188#discussion_r1012148730 | 2022-11-10T01:38:09Z | 5,217 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-09T02:40:01Z | https://api.github.com/repos/huggingface/datasets/issues/5217/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5217/timeline | Reword E2E training and inference tips in the vision guides | https://api.github.com/repos/huggingface/datasets/issues/5217/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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1,441,041,947 | https://api.github.com/repos/huggingface/datasets/issues/5216/labels{/name} | Hi,
I am new to Dataset and elasticsearch. I was wondering is there any equivalent approach to save elasticsearch index as of save_faiss_index locally for later use, to remove the need to re-index a dataset? | 2022-11-09T13:16:45Z | 5,216 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-08T23:06:52Z | https://api.github.com/repos/huggingface/datasets/issues/5216/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5216/timeline | save_elasticsearch_index | https://api.github.com/repos/huggingface/datasets/issues/5216/events | null | {
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"Hi ! I think there exist tools to dump and reload an index in your elastic search but I'm not super familiar with it.\r\n\r\nAnyway after reloading an index in elastic search you can call `ds.load_elasticsearch_index` which will connect the index to the dataset without re-indexing"
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1,440,037,534 | https://api.github.com/repos/huggingface/datasets/issues/5213/labels{/name} | will solve #5151
@lhoestq @albertvillanova @mariosasko
This is still a super draft so please ignore code issues but I want to discuss some conceptually important things.
I suggest a way to do `.push_to_hub("repo_id", "config_name")` with pushing parquet files to directories named as `config_name` (inside `data/` dir as it is now), for example:
```
data
|__config-v1
train-00000-00002-...-.parquet
train-00001-00002-...-.parquet
...
|__config-v2
....
```
When loading a dataset, I parse these configs from repository data files (only for `"data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*"` pattern that is used for parquet datasets pushed with `.push_to_hub`).
Therefore,
- when user tries to load a dataset that has configs parsed from data files dir names without providing a config (like `load_dataset("repo")` instead of `load_dataset("repo", "config-v1")`) - raise error and asks for config - to be aligned with how it works in datasets with scripts.
- for backward compatibility: if user tries to `.push_to_hub(""repo", "config_name")` to an existing parquet repo with no configurations (all parquet files are directly in `data/` dir) - raise error. My initial idea was to raise a warning and move these files to another dir with name (config) like "default" or smth but in a PR and suggest user to merge it on the Hub. But there is no support for renaming (moving) files via `HfApi` yet so it would require deleting and pushing again if I understand it right.
This parsing approach can be extended to other Hub packaged modules, and to local packaged modules and other data files patterns
(except for cases when splits are in dir names `KEYWORDS_IN_DIR_NAME_BASE_PATTERNS` because we allow for arbitrary depth of directory hierarchy).
Do you think it's reasonable? Not sure how to provide flexibility (and backward compatibility) to not parsing configs and load all the data in a single config as it is now.
I also thought about getting information about configs from Readme.md `dataset_info` ([example](https://huggingface.co/datasets/polinaeterna/test_push_two_configs/blob/main/README.md)). But that way we
are dependent on if it exists. It is created automatically with `.push_to_hub` but what if it is
accidentally deleted or smth).
Also, what I don't like is that this parsing is a part of Module/DataFiles logic, not Builder's one, which is not aligned with datasets with custom scripts. But I don't know to implement the second approach in current library's logic.
What do you think about this all? Am I missing smth?
TODO:
- [ ] save cache in the same dir for configs of the same datasets
- [ ] fix verification errors
- [ ] correctly update `dataset_infos.json` too
- [ ] ...
| 2022-12-02T16:48:23Z | 5,213 | null | https://api.github.com/repos/huggingface/datasets | true | [
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1,439,642,483 | https://api.github.com/repos/huggingface/datasets/issues/5212/labels{/name} | A previous commit to main branch introduced an additional requirement on maximum compatible `dill` version with `apache-beam` in our CI `require_beam`:
- d7c942228b8dcf4de64b00a3053dce59b335f618
- ec222b220b79f10c8d7b015769f0999b15959feb
This PR fixes the maximum compatible `dill` version with `apache-beam`, which is <0.3.2 (and not 0.3.6): https://github.com/apache/beam/blob/v2.42.0/sdks/python/setup.py#L219 | 2022-11-15T06:32:27Z | 5,212 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-08T07:30:01Z | https://api.github.com/repos/huggingface/datasets/issues/5212/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5212/timeline | Fix CI require_beam maximum compatible dill version | https://api.github.com/repos/huggingface/datasets/issues/5212/events | null | {
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1,438,544,617 | https://api.github.com/repos/huggingface/datasets/issues/5211/labels{/name} | - removed metrics stuff
- added image example
- added audio example (with ffmpeg instructions)
- updated the "add a new dataset" section | 2022-11-29T15:59:48Z | 5,211 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-07T15:23:52Z | https://api.github.com/repos/huggingface/datasets/issues/5211/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5211/timeline | Update Overview.ipynb google colab | https://api.github.com/repos/huggingface/datasets/issues/5211/events | null | {
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1,438,367,678 | https://api.github.com/repos/huggingface/datasets/issues/5209/labels{/name} | ### Feature request
If you go here: https://huggingface.co/datasets/inria-soda/tabular-benchmark/tree/main you will see bunch of folders that has various CSV files. Iβd like dataset viewer to show these files instead of only one dataset like it currently does. (and also people to be able to load them as splits instead of loading through `data_files`)
e.g GLUE has various splits on viewer but itβs too overkill to ask people to implement loading script, so it would be better to let them define these in the README file instead.
Also pinging @polinaeterna @lhoestq @adrinjalali
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"@merveenoyan Do you want different files to be splits or configurations?\r\n\r\nFrom [what you specified in `Readme.md`](https://huggingface.co/datasets/inria-soda/tabular-benchmark/commit/fb4575853772c62a20203bdd6cc0202f5db4ce4e) I hypothesize that you want to have 4 **configs** corresponding to directories: `\"c... | {
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1,437,858,506 | https://api.github.com/repos/huggingface/datasets/issues/5207/labels{/name} | ### Describe the bug
It's weird. I could not normally connect the dataset Hub of HuggingFace due to a SSLError in my office.
Even when I try to connect using my company's proxy address (e.g., http_proxy and https_proxy),
I'm getting the SSLError issue. What should I do to download the datanet stored in HuggingFace normally?
I welcome any comments. I think those comments will be helpful to me.
* Dataset address - https://huggingface.co/datasets/moyix/debian_csrc/viewer/moyix--debian_csrc
* Log message
```
............ OMISSION ..............
Traceback (most recent call last):
File "/data/home/geunsik-lim/qtlab/./transformers/examples/pytorch/language-modeling/run_clm.py", line 587, in <module>
main()
File "/data/home/geunsik-lim/qtlab/./transformers/examples/pytorch/language-modeling/run_clm.py", line 278, in main
raw_datasets = load_dataset(
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1719, in load_dataset
builder_instance = load_dataset_builder(
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1497, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1222, in dataset_module_factory
raise e1 from None
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1179, in dataset_module_factory
raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})")
ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError)
[2022-11-07 15:23:38,476] [INFO] [launch.py:318:sigkill_handler] Killing subprocess 6760
[2022-11-07 15:23:38,476] [ERROR] [launch.py:324:sigkill_handler] ['/home/geunsik-lim/anaconda3/envs/deepspeed/bin/python', '-u', './transformers/examples/pytorch/language-modeling/run_clm.py', '--local_rank=0', '--model_name_or_path=Salesforce/codegen-350M-multi', '--per_device_train_batch_size=1', '--learning_rate', '2e-5', '--num_train_epochs', '1', '--output_dir=./codegen-350M-finetuned', '--overwrite_output_dir', '--dataset_name', 'moyix/debian_csrc', '--cache_dir', '/data/home/geunsik-lim/.cache', '--tokenizer_name', 'Salesforce/codegen-350M-multi', '--block_size', '2048', '--gradient_accumulation_steps', '32', '--do_train', '--fp16', '--deepspeed', 'ds_config_zero2.json'] exits with return code = 1
real 0m7.742s
user 0m4.930s
```
### Steps to reproduce the bug
Steps to reproduce this behavior.
```
(deepspeed) geunsik-lim@ai02:~/qtlab$ ./test_debian_csrc_dataset.py
Traceback (most recent call last):
File "/data/home/geunsik-lim/qtlab/./test_debian_csrc_dataset.py", line 6, in <module>
dataset = load_dataset("moyix/debian_csrc")
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1719, in load_dataset
builder_instance = load_dataset_builder(
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1497, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1222, in dataset_module_factory
raise e1 from None
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1179, in dataset_module_factory
raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})")
ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError)
(deepspeed) geunsik-lim@ai02:~/qtlab$
(deepspeed) geunsik-lim@ai02:~/qtlab$
(deepspeed) geunsik-lim@ai02:~/qtlab$
(deepspeed) geunsik-lim@ai02:~/qtlab$ cat ./test_debian_csrc_dataset.py
#!/usr/bin/env python
from datasets import load_dataset
dataset = load_dataset("moyix/debian_csrc")
```
1. Adde proxy address of a company in /etc/profile
2. Download dataset with load_dataset() function of datasets package that is provided by HuggingFace.
3. In this case, the address would be "moyix--debian_csrc".
4. I get the "`ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError`)" error message.
### Expected behavior
* error message:
ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError)
### Environment info
* software version information:
```
(deepspeed) geunsik-lim@ai02:~$
(deepspeed) geunsik-lim@ai02:~$ conda list -f pytorch
# packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed:
#
# Name Version Build Channel
pytorch 1.13.0 py3.10_cuda11.7_cudnn8.5.0_0 pytorch
(deepspeed) geunsik-lim@ai02:~$ conda list -f python
# packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed:
#
# Name Version Build Channel
python 3.10.6 haa1d7c7_1
(deepspeed) geunsik-lim@ai02:~$ conda list -f datasets
# packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed:
#
# Name Version Build Channel
datasets 2.6.1 py_0 huggingface
(deepspeed) geunsik-lim@ai02:~$ uname -a
Linux ai02 5.4.0-131-generic #147-Ubuntu SMP Fri Oct 14 17:07:22 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux
(deepspeed) geunsik-lim@ai02:~$ cat /etc/lsb-release
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=20.04
DISTRIB_CODENAME=focal
DISTRIB_DESCRIPTION="Ubuntu 20.04.5 LTS"
``` | 2024-03-20T21:52:22Z | 5,207 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-07T06:56:23Z | https://api.github.com/repos/huggingface/datasets/issues/5207/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5207/timeline | Connection error of the HuggingFace's dataset Hub due to SSLError with proxy | https://api.github.com/repos/huggingface/datasets/issues/5207/events | null | {
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"Hi ! It looks like an issue with your python environment, can you make sure you're able to run GET requests to https://huggingface.co using `requests` in python ?",
"Thanks for your reply. Does this mean that I have to use the `do_dataset `function and the `requests `function to download the dataset from the com... | {
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1,437,223,894 | https://api.github.com/repos/huggingface/datasets/issues/5206/labels{/name} | ### Describe the bug
Some logs ([here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L778), [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L786), and [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L830)) generated by the `DatasetBuilder` are printed to the console instead of passed to `datasets` logger.
### Steps to reproduce the bug
```python
>> import datasets
>> datasets.load_dataset("some-dataset")
Downloading and preparing dataset csv/data to <path>...
Downloading data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 7729.06it/s]
Extracting data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 527.23it/s]
Dataset csv downloaded and prepared to <path>. Subsequent calls will reuse this data.
```
### Expected behavior
The logs should not be printed to the console directly but passed to the logger so that the user can redirect them wherever he wants.
### Environment info
- `datasets` version: 2.6.1
- Platform: macOS-13.0-x86_64-i386-64bit
- Python version: 3.9.15
- PyArrow version: 10.0.0
- Pandas version: 1.5.1 | 2022-11-06T00:06:00Z | 5,206 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-05T23:48:02Z | https://api.github.com/repos/huggingface/datasets/issues/5206/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5206/timeline | Use logging instead of printing to console | https://api.github.com/repos/huggingface/datasets/issues/5206/events | null | {
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"Actually upon closer inspection, it is documented in the code that this behavior is intentional, so I'll close this."
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1,437,221,987 | https://api.github.com/repos/huggingface/datasets/issues/5205/labels{/name} | This PR solves https://github.com/huggingface/datasets/issues/5204
Now the `token` is propagated so that `DownloadConfig.use_auth_token` value is set before trying to download private files from existing datasets in the Hub. | 2022-11-08T08:13:00Z | 5,205 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-05T23:36:36Z | https://api.github.com/repos/huggingface/datasets/issues/5205/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5205/timeline | Add missing `DownloadConfig.use_auth_token` value | https://api.github.com/repos/huggingface/datasets/issues/5205/events | null | {
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1,437,221,259 | https://api.github.com/repos/huggingface/datasets/issues/5204/labels{/name} | ### Describe the bug
When trying to upload a new π€ Dataset to the Hub via Python, and providing the `token` as a parameter to the `Dataset.push_to_hub` function, it just works for the first time, assuming that the dataset didn't exist before.
But when trying to run `Dataset.push_to_hub` again over the same dataset, instead of updating it, it throws a `ConnectionError` when trying to retrieve the `README.md` that may contain some metadata about the dataset, so as to also update it, but since the `token` is not propagated, the `DownloadConfig` provided to the `datasets.utils.file_utils.get_from_cache` function doesn't contain the `use_auth_token` value set to `token`, it's just using the default one which is None/False.
So on, when uploading a dataset via Python with `push_to_hub` with the `token` as a parameter with the HuggingFace API Token as value, it can just be uploaded when the dataset is new, otherwise it fails with to `ConnectionError` due to the `token` not being propagated as `use_auth_token`.
### Steps to reproduce the bug
Let's create a new dataset in our HF account via Python as:
```python
from datasets import Dataset
data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
```
When we create the `Dataset` for the first time it works and there are no issues, but when trying to actually upload a new version of the same dataset (same name under the same username), we encounter the following issue:
```python
from datasets import Dataset
data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
>>> ConnectionError: Couldn't reach https://huggingface.co/datasets/alvarobartt/demo/resolve/main/README.md (ConnectionError('Unauthorized for URL https://huggingface.co/datasets/<HF_USERNAME>/<HF_DATASET>/resolve/main/README.md. Please use the parameter `use_auth_token=True` after logging in with `huggingface-cli login`'))
```
### Expected behavior
Ideally, the `token` parameter provided to `push_to_hub` should be propagated and used to download the `README.md` when trying to update a `Dataset`, instead of throwing that exception, so that the authentication can be done directly through code without running `huggingface-cli login`as mentioned at https://huggingface.co/docs/datasets/upload_dataset#upload-with-python.
### Environment info
- `datasets` version: 2.6.1
- Platform: macOS-13.0-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.0
- Pandas version: 1.5.1 | 2022-11-08T10:12:09Z | 5,204 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-05T23:32:20Z | https://api.github.com/repos/huggingface/datasets/issues/5204/comments | {
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"following_url": "https://api.github.com/users/alvarobartt/following{/other_user}"... | null | completed | CONTRIBUTOR | 2022-11-08T10:12:08Z | null | I_kwDODunzps5VqkGL | [
"#self-assign",
"@lhoestq can you close this issue as part of the recent #5205 merge? Thanks π€ ",
"Thank you :)"
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1,436,710,518 | https://api.github.com/repos/huggingface/datasets/issues/5203/labels{/name} | This PR updates some of the canonical dataset links to their corresponding links on the Hub; closes #5200. | 2022-11-07T18:43:05Z | 5,203 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-04T22:50:50Z | https://api.github.com/repos/huggingface/datasets/issues/5203/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5203/timeline | Update canonical links to Hub links | https://api.github.com/repos/huggingface/datasets/issues/5203/events | null | {
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1,435,886,090 | https://api.github.com/repos/huggingface/datasets/issues/5202/labels{/name} | ```
______ test_get_dataset_config_info[paws-labeled_final-expected_splits2] _______
[gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
path = 'paws', config_name = 'labeled_final'
expected_splits = ['train', 'test', 'validation']
@pytest.mark.parametrize(
"path, config_name, expected_splits",
[
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
],
)
def test_get_dataset_config_info(path, config_name, expected_splits):
info = get_dataset_config_info(path, config_name=config_name)
assert info.config_name == config_name
> assert list(info.splits.keys()) == expected_splits
E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation']
E At index 0 diff: 'test' != 'train'
E Full diff:
E - ['train', 'test', 'validation']
E + ['test', 'train', 'validation']
tests/test_inspect.py:45: AssertionError
_ test_get_dataset_info[paws-expected_configs2-expected_splits_in_first_config2] _
[gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
path = 'paws'
expected_configs = ['labeled_final', 'labeled_swap', 'unlabeled_final']
expected_splits_in_first_config = ['train', 'test', 'validation']
@pytest.mark.parametrize(
"path, expected_configs, expected_splits_in_first_config",
[
("squad", ["plain_text"], ["train", "validation"]),
("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
],
)
def test_get_dataset_info(path, expected_configs, expected_splits_in_first_config):
infos = get_dataset_infos(path)
assert list(infos.keys()) == expected_configs
expected_config = expected_configs[0]
assert expected_config in infos
info = infos[expected_config]
assert info.config_name == expected_config
> assert list(info.splits.keys()) == expected_splits_in_first_config
E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation']
E At index 0 diff: 'test' != 'train'
E Full diff:
E - ['train', 'test', 'validation']
E + ['test', 'train', 'validation']
tests/test_inspect.py:90: AssertionError
______ test_get_dataset_split_names[paws-labeled_final-expected_splits2] _______
[gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
path = 'paws', expected_config = 'labeled_final'
expected_splits = ['train', 'test', 'validation']
@pytest.mark.parametrize(
"path, expected_config, expected_splits",
[
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
],
)
def test_get_dataset_split_names(path, expected_config, expected_splits):
infos = get_dataset_infos(path)
assert expected_config in infos
info = infos[expected_config]
assert info.config_name == expected_config
> assert list(info.splits.keys()) == expected_splits
E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation']
E At index 0 diff: 'test' != 'train'
E Full diff:
E - ['train', 'test', 'validation']
E + ['test', 'train', 'validation']
``` | 2023-02-16T09:11:10Z | 5,202 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Fixed by: https://huggingface.co/datasets/paws/discussions/1"
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1,435,881,554 | https://api.github.com/repos/huggingface/datasets/issues/5201/labels{/name} | I suggest not to sort splits by their names in dataset_info in README so that they are displayed in the order specified in the loading script. Otherwise `test` split is displayed first, see this repo: https://huggingface.co/datasets/paws
What do you think?
But I added sorting in tests to fix CI (for the same dataset). | 2022-11-04T14:47:37Z | 5,201 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-04T10:47:21Z | https://api.github.com/repos/huggingface/datasets/issues/5201/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5201/timeline | Do not sort splits in dataset info | https://api.github.com/repos/huggingface/datasets/issues/5201/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"It would be coherent with https://github.com/huggingface/datasets-server/issues/614#issuecomment-1290534153",
"I think we started working on this issue nearly at the same time... :sweat_smile: \r\n- CI was fixed with this: https://... | {
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1,435,831,559 | https://api.github.com/repos/huggingface/datasets/issues/5200/labels{/name} | As we don't have canonical datasets in the github repo anymore, some old links to them doesn't work. I don't know how many of them are there, I found link to SuperGlue here: https://huggingface.co/docs/datasets/dataset_script#multiple-configurations, probably there are more of them. These links should be replaced by links to the corresponding datasets on the Hub. | 2022-11-07T18:40:20Z | 5,200 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Thanks for catching this, I can go through the docs and replace the links to their corresponding datasets on the Hub!"
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1,434,818,836 | https://api.github.com/repos/huggingface/datasets/issues/5199/labels{/name} | Deprecate the `dummy_data` CLI command. | 2022-11-04T14:01:50Z | 5,199 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-03T15:05:54Z | https://api.github.com/repos/huggingface/datasets/issues/5199/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5199/timeline | Deprecate dummy data generation command | https://api.github.com/repos/huggingface/datasets/issues/5199/events | null | {
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1,434,699,165 | https://api.github.com/repos/huggingface/datasets/issues/5198/labels{/name} | Add note that a dataset script should has the same name as a repo/dir, a bit related to this issue https://github.com/huggingface/datasets/issues/5193
also fixed two minor issues in audio docs (broken links) | 2022-11-04T12:47:59Z | 5,198 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-03T13:51:32Z | https://api.github.com/repos/huggingface/datasets/issues/5198/comments | {
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1,434,676,150 | https://api.github.com/repos/huggingface/datasets/issues/5197/labels{/name} | ZstdDecompressor has a parameter `max_window_size` to limit max memory usage when decompressing zstd files. The default `max_window_size` is not enough when files are compressed by `zstd --ultra` flags.
Change `max_window_size` to the zstd's max window size. NOTE, the `zstd.WINDOWLOG_MAX` is the log_2 value of the max window size. | 2022-11-03T13:45:19Z | 5,197 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-03T13:35:58Z | https://api.github.com/repos/huggingface/datasets/issues/5197/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5197/timeline | [zstd] Use max window log size | https://api.github.com/repos/huggingface/datasets/issues/5197/events | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5197). All of your documentation changes will be reflected on that endpoint."
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1,434,401,646 | https://api.github.com/repos/huggingface/datasets/issues/5196/labels{/name} | Small refactoring to use `hf_hub_url` function from `huggingface_hub`.
This PR also creates the `hub` module that will contain all `huggingface_hub` functionalities relevant to `datasets`.
This is a necessary stage before implementing the use of the `hfh` caching system (which uses its `hf_hub_url` under the hood).
EDIT:
~~Finally, we use our `config.HUB_DATASETS_URL` when using `hfh.hf_hub_url`~~
There is a breaking change: the `hfh` `hf_hub_url` function uses
- `hfh` `HUGGINGFACE_CO_URL_TEMPLATE` URL template, different from the `datasets` `config.HUB_DATASETS_URL`
- also, `hfh` `DEFAULT_REVISION`, instead of `datasets` `config.HUB_DEFAULT_VERSION` | 2022-12-06T11:38:17Z | 5,196 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-03T10:08:09Z | https://api.github.com/repos/huggingface/datasets/issues/5196/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5196/timeline | Use hfh hf_hub_url function | https://api.github.com/repos/huggingface/datasets/issues/5196/events | null | {
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"@lhoestq I think we should first agree if `datasets` can introduce the breaking change of ignoring `config.HUB_DATASETS_URL`: some users may have o... | {
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1,434,290,689 | https://api.github.com/repos/huggingface/datasets/issues/5195/labels{/name} | null | 2023-04-04T15:10:37Z | 5,195 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-03T08:37:34Z | https://api.github.com/repos/huggingface/datasets/issues/5195/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5195/timeline | [wip testing docs] | https://api.github.com/repos/huggingface/datasets/issues/5195/events | null | {
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1,434,206,951 | https://api.github.com/repos/huggingface/datasets/issues/5194/labels{/name} | This PR fixes some misalignment in the docs after we transferred the dataset_info from `dataset_infos.json` to YAML in the dataset card:
- #4926
Related to:
- #5193 | 2022-11-03T13:31:27Z | 5,194 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-11-03T07:10:23Z | https://api.github.com/repos/huggingface/datasets/issues/5194/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5194/timeline | Fix docs about dataset_info in YAML | https://api.github.com/repos/huggingface/datasets/issues/5194/events | null | {
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1,433,883,780 | https://api.github.com/repos/huggingface/datasets/issues/5193/labels{/name} | ### Describe the bug
When loading my own dataset, on loading it I get an error.
Here is my dataset link: https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data
And the error after loading with:
```python
from datasets import load_dataset
load_dataset("corentinm7/MyoQuant-SDH-Data")
```
```python
Downloading readme: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3.34k/3.34k [00:00<00:00, 4.45MB/s]
Using custom data configuration SDH_16k-53e7301a92ab0025
Downloading and preparing dataset None/SDH_16k to /home/corentin/.cache/huggingface/datasets/corentinm7___imagefolder/SDH_16k-53e7301a92ab0025/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f...
Downloading data: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3.28M/3.28M [00:00<00:00, 4.31MB/s]
Downloading data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:01<00:00, 1.75s/it]
Downloading data: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1.13G/1.13G [00:15<00:00, 74.3MB/s]
Downloading data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:16<00:00, 16.09s/it]
Extracting data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:13<00:00, 13.16s/it]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/load.py", line 1742, in load_dataset
builder_instance.download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 814, in download_and_prepare
self._download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1423, in _download_and_prepare
super()._download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 905, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1374, in _prepare_split
for key, record in logging.tqdm(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 394, in _generate_examples
raise ValueError(
ValueError: One or several metadata. were found, but not in the same directory or in a parent directory of /home/corentin/.cache/huggingface/datasets/downloads/extracted/60c4aa8d4da3065bb3d310de4373dffd73bd4dc331aedcb4ee867febe4fdb7cd/validation/sick/2_CG_SDH_TAM_Bin1cKO_ko_pla_4_1640.tif.
```
However the test command is working fine. ```datasets-cli test hugging_face_play/ds_test/SDH_16k.py --save_info --all_configs --force_redownload```
```
Using custom data configuration SDH_16k
Testing builder 'SDH_16k' (1/1)
Downloading and preparing dataset sdh_16k/SDH_16k to /home/corentin/.cache/huggingface/datasets/sdh_16k/SDH_16k/1.0.0/21b584239a638aeeda33cba1ac2ca4869d48e4b4f20fb22274d5a5ddc487659d...
Downloading data: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1.13G/1.13G [00:14<00:00, 76.5MB/s]
Downloading data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:15<00:00, 15.66s/it]
Downloading data: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3.28M/3.28M [00:02<00:00, 1.44MB/s]
Downloading data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:03<00:00, 3.21s/it]
Downloading data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 11586.48it/s]
Extracting data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:13<00:00, 13.42s/it]
Dataset sdh_16k downloaded and prepared to /home/corentin/.cache/huggingface/datasets/sdh_16k/SDH_16k/1.0.0/21b584239a638aeeda33cba1ac2ca4869d48e4b4f20fb22274d5a5ddc487659d. Subsequent calls will reuse this data.
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 605.27it/s]
Dataset card saved at hugging_face_play/ds_test/README.md
Test successful.
```
### Steps to reproduce the bug
Simply run on python
```python
from datasets import load_dataset
load_dataset("corentinm7/MyoQuant-SDH-Data")
```
### Expected behavior
As the test command worked, this error should not appear
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.6
- PyArrow version: 10.0.0
- Pandas version: 1.5.1
| 2022-11-03T13:39:16Z | 5,193 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-11-02T22:46:25Z | https://api.github.com/repos/huggingface/datasets/issues/5193/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5193/timeline | "One or several metadata. were found, but not in the same directory or in a parent directory" | https://api.github.com/repos/huggingface/datasets/issues/5193/events | null | {
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} | [] | null | completed | NONE | 2022-11-03T13:35:44Z | null | I_kwDODunzps5Vd1SE | [
"Also unrelated but still: https://huggingface.co/docs/datasets/image_dataset#generate-the-dataset\r\n```If your loading script passed the test, you should now have a dataset_infos.json file in your dataset folder.```\r\nIt's not the case anymore as it's now in the readme.md, it was confusing to me",
"And here is... | {
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