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https://github.com/huggingface/datasets/issues/7079 | HfHubHTTPError: 500 Server Error: Internal Server Error for url: | [
"same issue here. @albertvillanova @lhoestq ",
"Also impacted by this issue in many of my datasets (though not all) - in my case, this also seems to affect datasets that have been updated recently. Git cloning and the web interface still work:\r\n- https://huggingface.co/api/datasets/acmc/cheat_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_reuter_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_wp_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_essay_reduced\r\n\r\nOddly enough, the system status looks good: https://status.huggingface.co/",
"Hey how to download these datasets using git cloning?",
"Also reported here\r\nhttps://github.com/huggingface/huggingface_hub/issues/2425",
"I have been getting the same error for the past 8 hours as well",
"Same error since yesterday, fails on any new dataset created",
"Same here. I cannot download the HelpSteer2 dataset: https://huggingface.co/datasets/nvidia/HelpSteer2 which has been uploaded about a month ago",
"> Hey how to download these datasets using git cloning?\n\nYou'll find a guide [here](https://huggingface.co/docs/hub/en/datasets-downloading) 👍🏻",
"Same here for imdb dataset",
"It also happens with this dataset: https://huggingface.co/datasets/ylacombe/jenny-tts-6h-tagged",
"same here for all datsets in the sentence-tramsformers repo and related collections.\r\n\r\nsame issue with dataset that i recently uploaded on my repo.\r\nseems that the upload date of the datset is not relevat (getting this issue with both old datasets and newer ones)\r\n\r\nfor some reason, i was able to get the dataset by turning it private and accessing it with the id token (accessing it as public while providing the token doesn not work)..... but i can say if that is just a random coincidence.\r\n\r\nseems not much deterministic, for a specific dataset (sentence-transformer nq ) , that was \"down\" since some hours , worked for like 5-10 minutes, then stopped again\r\n\r\nnow even this dataset (that worked since some min ago, and that i'm in the middle of processing steps) stopped working: https://huggingface.co/datasets/bobox/msmarco-bm25-EduScore/"
] | ### Describe the bug
newly uploaded datasets, since yesterday, yields an error.
old datasets, works fine.
Seems like the datasets api server returns a 500
I'm getting the same error, when I invoke `load_dataset` with my dataset.
Long discussion about it here, but I'm not sure anyone from huggingface have seen it.
https://discuss.huggingface.co/t/hfhubhttperror-500-server-error-internal-server-error-for-url/99580/1
### Steps to reproduce the bug
this api url:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
respond with:
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Expected behavior
return no error with newer datasets.
With older datasets I can load the datasets fine.
### Environment info
# Browser
When I access the api in the browser:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Request headers
```
Accept text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8
Accept-Encoding gzip, deflate, br, zstd
Accept-Language en-US,en;q=0.5
Connection keep-alive
Host huggingface.co
Priority u=1
Sec-Fetch-Dest document
Sec-Fetch-Mode navigate
Sec-Fetch-Site cross-site
Upgrade-Insecure-Requests 1
User-Agent Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:127.0) Gecko/20100101 Firefox/127.0
```
### Response headers
```
X-Firefox-Spdy h2
access-control-allow-origin https://huggingface.co
access-control-expose-headers X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,X-Total-Count,ETag,Link,Accept-Ranges,Content-Range
content-length 80
content-type application/json; charset=utf-8
cross-origin-opener-policy same-origin
date Fri, 26 Jul 2024 19:09:45 GMT
etag W/"50-9qrwU+BNI4SD0Fe32p/nofkmv0c"
referrer-policy strict-origin-when-cross-origin
vary Origin
via 1.1 1624c79cd07e6098196697a6a7907e4a.cloudfront.net (CloudFront)
x-amz-cf-id SP8E7n5qRaP6i9c9G83dNAiOzJBU4GXSrDRAcVNTomY895K35H0nJQ==
x-amz-cf-pop CPH50-C1
x-cache Error from cloudfront
x-error-message Internal Error - We're working hard to fix this as soon as possible!
x-powered-by huggingface-moon
x-request-id Root=1-66a3f479-026417465ef42f49349fdca1
``` | 7,079 |
https://github.com/huggingface/datasets/issues/7077 | column_names ignored by load_dataset() when loading CSV file | [] | ### Describe the bug
load_dataset() ignores the column_names kwarg when loading a CSV file. Instead, it uses whatever values are on the first line of the file.
### Steps to reproduce the bug
Call `load_dataset` to load data from a CSV file and specify `column_names` kwarg.
### Expected behavior
The resulting dataset should have the specified column names **and** the first line of the file should be considered as data values.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.10.0-30-cloud-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- `huggingface_hub` version: 0.24.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | 7,077 |
https://github.com/huggingface/datasets/issues/7073 | CI is broken for convert_to_parquet: Invalid rev id: refs/pr/1 404 error causes RevisionNotFoundError | [
"Any recent change in the API backend rejecting parameter `revision=\"refs/pr/1\"` to `HfApi.preupload_lfs_files`?\r\n```\r\nf\"{endpoint}/api/{repo_type}s/{repo_id}/preupload/{revision}\"\r\n\r\nhttps://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5188a8-17219154347516/preupload/refs%2Fpr%2F1.\r\nInvalid rev id: refs/pr/1\r\n```\r\n@Wauplin @huggingface/datasets @huggingface/moon-landing @huggingface/moon-landing-back ",
"I have temporarily fixed the CI with:\r\n- #7074\r\n\r\nHowever, the underlying issue must be fixed and #7074 must be reverted.",
"Hmm does it do the preupload call before creating the ref cc @Wauplin ?\r\n\r\n(in that case it should do a preupload call on the base branch with `?create_pr=1`)",
"@coyotte508, the CI test was implemented 2 months ago and it was working OK until yesterday. See the CI status of the commits in the main branch of `datasets`: https://github.com/huggingface/datasets/commits/main/",
"Yes i get that\r\n\r\nWe changed the preupload response to return the commit id in https://github.com/huggingface-internal/moon-landing/pull/10756\r\n\r\nThis line is probably causing the error: https://github.com/huggingface-internal/moon-landing/pull/10756/files#diff-558f6f9865e30bfa091b94d6a4a900138103ddb4eb0bec96b6deec5bf5626fa0R2322\r\n\r\nIt's weird the error is returned, it means that maybe a ref with 0 history (not even the first commit) was created\r\n\r\nDoes this change have any impact in production, or just the CI test? If it's just the CI test it should be fixed on your side, if it impacts production we can look at a solution",
"@coyotte508 it impacts production: `convert_to_parquet` raises the above error when the dataset has more that one configs/subsets:\r\n- First subset calls `push_to_hub` with `create_pr=True`\r\n- Second subset uses the `refs/pr/#` returned by the call above, and calls `push_to_hub` with `revision=\"refs/pr/#\"`",
"I tried removing the `mock_commit` call: https://github.com/huggingface/datasets/pull/7076\r\n\r\nAnd the tests seem to work.\r\n\r\nSo it's probably because the commit is not actually called, it doesn't actually create the pull request on the remote (and the associated `refs/pr/1`). But the `preupload` call is not mocked.\r\n\r\nAnyway it shouldn't impact production, since production isn't mocked",
"@coyotte508 thanks a lot for the investigation and sorry for the noise. \r\nI promise not trying to fix things when I have a slight fever: my head does not work well.\r\n\r\nWe need indeed to mock `preupload_lfs_files`: before it was not necessary, but now it is.",
"I fixed the test in:\r\n- #7078\r\n\r\nThanks again, @coyotte508."
] | See: https://github.com/huggingface/datasets/actions/runs/10095313567/job/27915185756
```
FAILED tests/test_hub.py::test_convert_to_parquet - huggingface_hub.utils._errors.RevisionNotFoundError: 404 Client Error. (Request ID: Root=1-66a25839-31ce7b475e70e7db1e4d44c2;b0c8870f-d5ef-4bf2-a6ff-0191f3df0f64)
Revision Not Found for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5188a8-17219154347516/preupload/refs%2Fpr%2F1.
Invalid rev id: refs/pr/1
```
```
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/hub.py:86: in convert_to_parquet
dataset.push_to_hub(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/dataset_dict.py:1722: in push_to_hub
split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/arrow_dataset.py:5511: in _push_parquet_shards_to_hub
api.preupload_lfs_files(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/hf_api.py:4231: in preupload_lfs_files
_fetch_upload_modes(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:118: in _inner_fn
return fn(*args, **kwargs)
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/_commit_api.py:507: in _fetch_upload_modes
hf_raise_for_status(resp)
``` | 7,073 |
https://github.com/huggingface/datasets/issues/7072 | nm | [] | null | 7,072 |
https://github.com/huggingface/datasets/issues/7071 | Filter hangs | [] | ### Describe the bug
When trying to filter my custom dataset, the process hangs, regardless of the lambda function used. It appears to be an issue with the way the Images are being handled. The dataset in question is a preprocessed version of https://huggingface.co/datasets/danaaubakirova/patfig where notably, I have converted the data to the Parquet format.
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('lcolonn/patfig', split='test')
ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y')
```
Eventually I ctrl+C and I obtain this stack trace:
```
>>> ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y')
Filter: 0%| | 0/998 [00:00<?, ? examples/s]Filter: 0%| | 0/998 [00:35<?, ? examples/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/fingerprint.py", line 482, in wrapper
out = func(dataset, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3714, in filter
indices = self.map(
^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 602, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3161, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3552, in _map_single
batch = apply_function_on_filtered_inputs(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3421, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 6478, in get_indices_from_mask_function
num_examples = len(batch[next(iter(batch.keys()))])
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 273, in __getitem__
value = self.format(key)
^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 376, in format
return self.formatter.format_column(self.pa_table.select([key]))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 443, in format_column
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 219, in decode_column
return self.features.decode_column(column, column_name) if self.features else column
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in decode_column
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in <listcomp>
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 1351, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/image.py", line 188, in decode_example
image.load() # to avoid "Too many open files" errors
^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/PIL/ImageFile.py", line 293, in load
n, err_code = decoder.decode(b)
^^^^^^^^^^^^^^^^^
KeyboardInterrupt
```
Warning! This can even seem to cause some computers to crash.
### Expected behavior
Should return the filtered dataset
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.24.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | 7,071 |
https://github.com/huggingface/datasets/issues/7070 | how set_transform affects batch size? | [] | ### Describe the bug
I am trying to fine-tune w2v-bert for ASR task. Since my dataset is so big, I preferred to use the on-the-fly method with set_transform. So i change the preprocessing function to this:
```
def prepare_dataset(batch):
input_features = processor(batch["audio"], sampling_rate=16000).input_features[0]
input_length = len(input_features)
labels = processor.tokenizer(batch["text"], padding=False).input_ids
batch = {
"input_features": [input_features],
"input_length": [input_length],
"labels": [labels]
}
return batch
train_ds.set_transform(prepare_dataset)
val_ds.set_transform(prepare_dataset)
```
After this, I also had to change the DataCollatorCTCWithPadding class like this:
```
@dataclass
class DataCollatorCTCWithPadding:
processor: Wav2Vec2BertProcessor
padding: Union[bool, str] = True
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# Separate input_features and labels
input_features = [{"input_features": feature["input_features"][0]} for feature in features]
labels = [feature["labels"][0] for feature in features]
# Pad input features
batch = self.processor.pad(
input_features,
padding=self.padding,
return_tensors="pt",
)
# Pad and process labels
label_features = self.processor.tokenizer.pad(
{"input_ids": labels},
padding=self.padding,
return_tensors="pt",
)
labels = label_features["input_ids"]
attention_mask = label_features["attention_mask"]
# Replace padding with -100 to ignore these tokens during loss calculation
labels = labels.masked_fill(attention_mask.ne(1), -100)
batch["labels"] = labels
return batch
```
But now a strange thing is happening, no matter how much I increase the batch size, the amount of V-RAM GPU usage does not change, while the number of total steps in the progress-bar (logging) changes. Is this normal or have I made a mistake?
### Steps to reproduce the bug
i can share my code if needed
### Expected behavior
Equal to the batch size value, the set_transform function is applied to the dataset and given to the model as a batch.
### Environment info
all updated versions | 7,070 |
https://github.com/huggingface/datasets/issues/7067 | Convert_to_parquet fails for datasets with multiple configs | [
"Many users have encountered the same issue, which has caused inconvenience.\r\n\r\nhttps://discuss.huggingface.co/t/convert-to-parquet-fails-for-datasets-with-multiple-configs/86733\r\n",
"Thanks for reporting.\r\n\r\nI will make the code more robust.",
"I have opened an issue in the huggingface-hub repo:\r\n- https://github.com/huggingface/huggingface_hub/issues/2419\r\n\r\nI am opening a PR to avoid calling `create_branch` if the branch already exists."
] | If the dataset has multiple configs, when using the `datasets-cli convert_to_parquet` command to avoid issues with the data viewer caused by loading scripts, the conversion process only successfully converts the data corresponding to the first config. When it starts converting the second config, it throws an error:
```
Traceback (most recent call last):
File "/opt/anaconda3/envs/dl/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/datasets_cli.py", line 41, in main
service.run()
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/convert_to_parquet.py", line 83, in run
dataset.push_to_hub(
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/dataset_dict.py", line 1713, in push_to_hub
api.create_branch(repo_id, branch=revision, token=token, repo_type="dataset", exist_ok=True)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 5503, in create_branch
hf_raise_for_status(response)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 358, in hf_raise_for_status
raise BadRequestError(message, response=response) from e
huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-669fc665-7c2e80d75f4337496ee95402;731fcdc7-0950-4eec-99cf-ce047b8d003f)
Bad request:
Invalid reference for a branch: refs/pr/1
``` | 7,067 |
https://github.com/huggingface/datasets/issues/7066 | One subset per file in repo ? | [] | Right now we consider all the files of a dataset to be the same data, e.g.
```
single_subset_dataset/
├── train0.jsonl
├── train1.jsonl
└── train2.jsonl
```
but in cases like this, each file is actually a different subset of the dataset and should be loaded separately
```
many_subsets_dataset/
├── animals.jsonl
├── trees.jsonl
└── metadata.jsonl
```
It would be nice to detect those subsets automatically using a simple heuristic. For example we can group files together if their paths names are the same except some digits ? | 7,066 |
https://github.com/huggingface/datasets/issues/7065 | Cannot get item after loading from disk and then converting to iterable. | [] | ### Describe the bug
The dataset generated from local file works fine.
```py
root = "/home/data/train"
file_list1 = glob(os.path.join(root, "*part1.flac"))
file_list2 = glob(os.path.join(root, "*part2.flac"))
ds = (
Dataset.from_dict({"part1": file_list1, "part2": file_list2})
.cast_column("part1", Audio(sampling_rate=None, mono=False))
.cast_column("part2", Audio(sampling_rate=None, mono=False))
)
ids = ds.to_iterable_dataset(128)
ids = ids.shuffle(buffer_size=10000, seed=42)
dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True)
for batch in dataloader:
break
```
But after saving it to disk and then loading it from disk, I cannot get data as expected.
```py
root = "/home/data/train"
file_list1 = glob(os.path.join(root, "*part1.flac"))
file_list2 = glob(os.path.join(root, "*part2.flac"))
ds = (
Dataset.from_dict({"part1": file_list1, "part2": file_list2})
.cast_column("part1", Audio(sampling_rate=None, mono=False))
.cast_column("part2", Audio(sampling_rate=None, mono=False))
)
ds.save_to_disk("./train")
ds = datasets.load_from_disk("./train")
ids = ds.to_iterable_dataset(128)
ids = ids.shuffle(buffer_size=10000, seed=42)
dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True)
for batch in dataloader:
break
```
After a long time waiting, an error occurs:
```
Loading dataset from disk: 100%|█████████████████████████████████████████████████████████████████████████| 165/165 [00:00<00:00, 6422.18it/s]
Traceback (most recent call last):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1133, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/queues.py", line 113, in get
if not self._poll(timeout):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 257, in poll
return self._poll(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 424, in _poll
r = wait([self], timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 931, in wait
ready = selector.select(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/selectors.py", line 416, in select
fd_event_list = self._selector.poll(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 3490529) is killed by signal: Killed.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module>
cli.main()
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
run()
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
runpy.run_path(target, run_name="__main__")
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
exec(code, run_globals)
File "/home/hanzerui/workspace/NetEase/test/test_datasets.py", line 60, in <module>
for batch in dataloader:
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
data = self._next_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1329, in _next_data
idx, data = self._get_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1295, in _get_data
success, data = self._try_get_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1146, in _try_get_data
raise RuntimeError(f'DataLoader worker (pid(s) {pids_str}) exited unexpectedly') from e
RuntimeError: DataLoader worker (pid(s) 3490529) exited unexpectedly
```
It seems that streaming is not supported by `laod_from_disk`, so does that mean I cannot convert it to iterable?
### Steps to reproduce the bug
1. Create a `Dataset` from local files with `from_dict`
2. Save it to disk with `save_to_disk`
3. Load it from disk with `load_from_disk`
4. Convert to iterable with `to_iterable_dataset`
5. Loop the dataset
### Expected behavior
Get items faster than the original dataset generated from dict.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.23.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | 7,065 |
https://github.com/huggingface/datasets/issues/7063 | Add `batch` method to `Dataset` | [] | ### Feature request
Add a `batch` method to the Dataset class, similar to the one recently implemented for `IterableDataset` in PR #7054.
### Motivation
A batched iteration speeds up data loading significantly (see e.g. #6279)
### Your contribution
I plan to open a PR to implement this. | 7,063 |
https://github.com/huggingface/datasets/issues/7061 | Custom Dataset | Still Raise Error while handling errors in _generate_examples | [] | ### Describe the bug
I follow this [example](https://discuss.huggingface.co/t/error-handling-in-iterabledataset/72827/3) to handle errors in custom dataset. I am writing a dataset script which read jsonl files and i need to handle errors and continue reading files without raising exception and exit the execution.
```
def _generate_examples(self, filepaths):
errors=[]
id_ = 0
for filepath in filepaths:
try:
with open(filepath, 'r') as f:
for line in f:
json_obj = json.loads(line)
yield id_, json_obj
id_ += 1
except Exception as exc:
logger.error(f"error occur at filepath: {filepath}")
errors.append(error)
```
seems the logger.error is printed but still exception is raised the the run is exit.
```
Downloading and preparing dataset custom_dataset/default to /home/myuser/.cache/huggingface/datasets/custom_dataset/default-a14cdd566afee0a6/1.0.0/acfcc9fb9c57034b580c4252841
ERROR: datasets_modules.datasets.custom_dataset.acfcc9fb9c57034b580c4252841bb890a5617cbd28678dd4be5e52b81188ad02.custom_dataset: 2024-07-22 10:47:42,167: error occur at filepath: '/home/myuser/ds/corrupted-file.jsonl
Traceback (most recent call last):
File "/home/myuser/.cache/huggingface/modules/datasets_modules/datasets/custom_dataset/ac..2/custom_dataset.py", line 48, in _generate_examples
json_obj = json.loads(line)
File "myenv/lib/python3.8/json/__init__.py", line 357, in loads
return _default_decoder.decode(s)
File "myenv/lib/python3.8/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "myenv/lib/python3.8/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Invalid control character at: line 1 column 4 (char 3)
Generating train split: 0 examples [00:06, ? examples/s]>
RemoteTraceback:
"""
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1637, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "myenv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 594, in finalize
raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "myenv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 1353, in
_write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1646, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
"""
The above exception was the direct cause of the following exception:
│ │
│ myenv/lib/python3.8/site-packages/datasets/utils/py_utils. │
│ py:1377 in <listcomp> │
│ │
│ 1374 │ │ │ │ if all(async_result.ready() for async_result in async_results) and queue │
│ 1375 │ │ │ │ │ break │
│ 1376 │ │ # we get the result in case there's an error to raise │
│ ❱ 1377 │ │ [async_result.get() for async_result in async_results] │
│ 1378 │
│ │
│ ╭──────────────────────────────── locals ─────────────────────────────────╮ │
│ │ .0 = <list_iterator object at 0x7f2cc1f0ce20> │ │
│ │ async_result = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │
│ ╰─────────────────────────────────────────────────────────────────────────╯ │
│ │
│ myenv/lib/python3.8/site-packages/multiprocess/pool.py:771 │
│ in get │
│ │
│ 768 │ │ if self._success: │
│ 769 │ │ │ return self._value │
│ 770 │ │ else: │
│ ❱ 771 │ │ │ raise self._value │
│ 772 │ │
│ 773 │ def _set(self, i, obj): │
│ 774 │ │ self._success, self._value = obj │
│ │
│ ╭────────────────────────────── locals ──────────────────────────────╮ │
│ │ self = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │
│ │ timeout = None │ │
│ ╰────────────────────────────────────────────────────────────────────╯ │
DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
same as above
### Expected behavior
should handle error and continue reading remaining files
### Environment info
python 3.9 | 7,061 |
https://github.com/huggingface/datasets/issues/7059 | None values are skipped when reading jsonl in subobjects | [] | ### Describe the bug
I have been fighting against my machine since this morning only to find out this is some kind of a bug.
When loading a dataset composed of `metadata.jsonl`, if you have nullable values (Optional[str]), they can be ignored by the parser, shifting things around.
E.g., let's take this example
Here are two version of a same dataset:
[not-buggy.tar.gz](https://github.com/user-attachments/files/16333532/not-buggy.tar.gz)
[buggy.tar.gz](https://github.com/user-attachments/files/16333553/buggy.tar.gz)
### Steps to reproduce the bug
1. Load the `buggy.tar.gz` dataset
2. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
3. Load the `not-buggy.tar.gz` dataset
4. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
### Expected behavior
Both should have 4 baseline entries:
1. Buggy should have None followed by three lists
2. Non-Buggy should have four lists, and the first one should be an empty list.
One does not work, 2 works. Despite accepting None in another position than the first one.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
| 7,059 |
https://github.com/huggingface/datasets/issues/7058 | New feature type: Document | [] | It would be useful for PDF.
https://github.com/huggingface/dataset-viewer/issues/2991#issuecomment-2242656069 | 7,058 |
https://github.com/huggingface/datasets/issues/7055 | WebDataset with different prefixes are unsupported | [
"Since `datasets` uses is built on Arrow to store the data, it requires each sample to have the same columns.\r\n\r\nThis can be fixed by specifyign in advance the name of all the possible columns in the `dataset_info` in YAML, and missing values will be `None`",
"Thanks. This currently doesn't work for WebDataset because there's no `BuilderConfig` with `features` and in turn `_info` is missing `features=self.config.features`. I'll prepare a PR to fix this.\r\n\r\nNote it may be useful to add the [expected format of `features`](https://github.com/huggingface/datasets/blob/16fa4421f44b22bbbc607f379a93f45af468d1fc/src/datasets/features/features.py#L1757) to the documentation for [`Builder Parameters`](https://huggingface.co/docs/datasets/repository_structure#builder-parameters).\r\n",
"Oh good catch ! thanks\r\n\r\n> Note it may be useful to add the [expected format of features](https://github.com/huggingface/datasets/blob/16fa4421f44b22bbbc607f379a93f45af468d1fc/src/datasets/features/features.py#L1757) to the documentation for [Buil](https://huggingface.co/docs/datasets/repository_structure#builder-parameters)\r\n\r\nGood idea, let me open a PR",
"#7060 ",
"Actually I just tried with `datasets` on the `main` branch and having `features` defined in `dataset_info` worked for me\r\n\r\n```python\r\n>>> list(load_dataset(\"/Users/quentinlhoest/tmp\", streaming=True, split=\"train\"))\r\n[{'txt': 'hello there\\n', 'other': None}]\r\n```\r\nwhere `tmp` contains data.tar with \"hello there\\n\" in a text file and the README.md:\r\n```\r\n---\r\ndataset_info:\r\n features:\r\n - name: txt\r\n dtype: string\r\n - name: other\r\n dtype: string\r\n---\r\n\r\nThis is a dataset card\r\n```\r\n\r\nWhat error did you get when you tried to specify the columns in `dataset_info` ?",
"If you review the changes in #7060 you'll note that `features` are not passed to `DatasetInfo`.\r\n\r\nIn your case the features are being extracted by [this code](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/webdataset/webdataset.py#L72-L98).\r\n\r\nTry with the `Steps to reproduce the bug`. It's the same error mentioned in `Describe the bug` because `features` are not passed to `DatasetInfo`.\r\n\r\n`features` are [not used](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/builder.py#L365-L366) when the `BuilderConfig` has no `features` attribute. `WebDataset` uses the default [`BuilderConfig`](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/builder.py#L101-L124).\r\n\r\nThere is a [warning](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/load.py#L640-L648) that `features` are ignored.\r\n\r\nNote that as mentioned in `Describe the bug` this could also be resolved by removing the check [here](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/webdataset/webdataset.py#L76-L80) because Arrow actually handles this itself, Arrow sets any missing fields to `None`, at least in my case.",
"Note for anyone else who encounters this issue, every dataset type except folder-based types supported features in the [documented](https://huggingface.co/docs/datasets/repository_structure#builder-parameters) manner; [Arrow](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/arrow/arrow.py#L15-L21), [csv](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/csv/csv.py#L25-L68), [generator](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/generator/generator.py#L8-L19), [json](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/json/json.py#L42-L52), [pandas](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/pandas/pandas.py#L14-L20), [parquet](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/parquet/parquet.py#L16-L24), [spark](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/spark/spark.py#L31-L37), [sql](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/sql/sql.py#L24-L35) and [text](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/text/text.py#L18-L27). `WebDataset` is different and requires [`dataset_info` which is vaguely documented](https://huggingface.co/docs/datasets/dataset_script#optional-generate-dataset-metadata) under dataset loading scripts.",
"Thanks for explaining. I see the Dataset Viewer is still failing - I'll update `datasets` in the Viewer to fix this"
] | ### Describe the bug
Consider a WebDataset with multiple images for each item where the number of images may vary: [example](https://huggingface.co/datasets/bigdata-pw/fashion-150k)
Due to this [code](https://github.com/huggingface/datasets/blob/87f4c2088854ff33e817e724e75179e9975c1b02/src/datasets/packaged_modules/webdataset/webdataset.py#L76-L80) an error is given.
```
The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
```
The purpose of this check is unclear because PyArrow supports different keys.
Removing the check allows the dataset to be loaded and there's no issue when iterating through the dataset.
```
>>> from datasets import load_dataset
>>> path = "shards/*.tar"
>>> dataset = load_dataset("webdataset", data_files={"train": path}, split="train", streaming=True)
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 152/152 [00:00<00:00, 56458.93it/s]
>>> dataset
IterableDataset({
features: ['__key__', '__url__', '1.jpg', '2.jpg', '3.jpg', '4.jpg', 'json'],
n_shards: 152
})
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("bigdata-pw/fashion-150k")
```
### Expected behavior
Dataset loads without error
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.14.0-467.el9.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.19
- `huggingface_hub` version: 0.23.4
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | 7,055 |
https://github.com/huggingface/datasets/issues/7053 | Datasets.datafiles resolve_pattern `TypeError: can only concatenate tuple (not "str") to tuple` | [
"Hi,\r\n\r\nThis issue was fixed in `datasets` 2.15.0:\r\n- #6105\r\n\r\nYou will need to update your `datasets`:\r\n```\r\npip install -U datasets\r\n```",
"Duplicate of:\r\n- #6100"
] | ### Describe the bug
in data_files.py, line 332,
`fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options)`
If we run the code on AWS, as fs.protocol will be a tuple like: `('file', 'local')`
So, `isinstance(fs.protocol, str) == False` and
`protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else ""` will raise
`TypeError: can only concatenate tuple (not "str") to tuple`.
### Steps to reproduce the bug
Steps to reproduce:
1. Run on a cloud server like AWS,
2. `import datasets.data_files as datafile`
3. datafile.resolve_pattern('path/to/dataset', '.')
4. `TypeError: can only concatenate tuple (not "str") to tuple`
### Expected behavior
Should return path of the dataset, with fs.protocol at the beginning
### Environment info
- `datasets` version: 2.14.0
- Platform: Linux-3.10.0-1160.119.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.19
- Huggingface_hub version: 0.23.5
- PyArrow version: 16.1.0
- Pandas version: 1.1.5 | 7,053 |
https://github.com/huggingface/datasets/issues/7051 | How to set_epoch with interleave_datasets? | [
"This is not possible right now afaik :/\r\n\r\nMaybe we could have something like this ? wdyt ?\r\n\r\n```python\r\nds = interleave_datasets(\r\n [shuffled_dataset_a, dataset_b],\r\n probabilities=probabilities,\r\n stopping_strategy='all_exhausted',\r\n reshuffle_each_iteration=True,\r\n)",
"That would be helpful for this case! \r\n\r\nIf there was some way for from_generator to iterate over just a single shard of some dataset that would probably be more ideal. Maybe something like\r\n\r\n```\r\ndef from_dataset_generator(dataset, generator_fn, gen_kwargs):\r\n # calls generator_fn(dataset=dataset_shard, **gen_kwargs)\r\n```\r\n\r\nAnother transform I was trying to implement is an input bucketing transform. Essentially you need to iterate through a dataset and reorder the examples in them, which is not really possible with a `map()` call. But using `from_generator()` causes the final dataset to be a single shard and loses speed gains from multiple dataloader workers",
"I see, there are some internal functions to get a single shard already but the public `.shard()` method hasn't been implemented yet for `IterableDataset` :/\r\n\r\n(see the use of `ex_iterable.shard_data_sources` in `IterableDataset._prepare_ex_iterable_for_iteration` for example)",
"Would that be something planned on the roadmap for the near future, or do you suggest hacking through with internal APIs for now?",
"Ok this turned out to be not too difficult. Are there any obvious issues with my implementation?\r\n\r\n```\r\nclass ShuffleEveryEpochIterable(iterable_dataset._BaseExamplesIterable):\r\n \"\"\"ExamplesIterable that reshuffles the dataset every epoch.\"\"\"\r\n\r\n def __init__(\r\n self,\r\n ex_iterable: iterable_dataset._BaseExamplesIterable,\r\n generator: np.random.Generator,\r\n ):\r\n \"\"\"Constructor.\"\"\"\r\n super().__init__()\r\n self.ex_iterable = ex_iterable\r\n self.generator = generator\r\n\r\n def _init_state_dict(self) -> dict:\r\n self._state_dict = {\r\n 'ex_iterable': self.ex_iterable._init_state_dict(),\r\n 'epoch': 0,\r\n }\r\n return self._state_dict\r\n\r\n @typing.override\r\n def __iter__(self):\r\n epoch = self._state_dict['epoch'] if self._state_dict else 0\r\n for i in itertools.count(epoch):\r\n # Create effective seed using i (subtract in order to avoir overflow in long_scalars)\r\n effective_seed = copy.deepcopy(self.generator).integers(0, 1 << 63) - i\r\n effective_seed = (1 << 63) + effective_seed if effective_seed < 0 else effective_seed\r\n generator = np.random.default_rng(effective_seed)\r\n self.ex_iterable = self.ex_iterable.shuffle_data_sources(generator)\r\n if self._state_dict:\r\n self._state_dict['epoch'] = i\r\n self._state_dict['ex_iterable'] = self.ex_iterable._init_state_dict()\r\n it = iter(self.ex_iterable)\r\n yield from it\r\n\r\n @typing.override\r\n def shuffle_data_sources(self, generator):\r\n ex_iterable = self.ex_iterable.shuffle_data_sources(generator)\r\n return ShuffleEveryEpochIterable(ex_iterable, generator=generator)\r\n\r\n @typing.override\r\n def shard_data_sources(self, worker_id: int, num_workers: int):\r\n ex_iterable = self.ex_iterable.shard_data_sources(worker_id, num_workers)\r\n return ShuffleEveryEpochIterable(ex_iterable, generator=self.generator)\r\n\r\n @typing.override\r\n @property\r\n def n_shards(self) -> int:\r\n return self.ex_iterable.n_shards\r\n \r\ngenerator = np.random.default_rng(seed)\r\nshuffling = iterable_dataset.ShufflingConfig(generator=generator, _original_seed=seed)\r\nex_iterable = iterable_dataset.BufferShuffledExamplesIterable(\r\n dataset._ex_iterable, buffer_size=buffer_size, generator=generator\r\n)\r\nex_iterable = ShuffleEveryEpochIterable(ex_iterable, generator=generator)\r\ndataset = datasets.IterableDataset(\r\n ex_iterable=ex_iterable,\r\n info=dataset._info.copy(),\r\n split=dataset._split,\r\n formatting=dataset._formatting,\r\n shuffling=shuffling,\r\n distributed=copy.deepcopy(dataset._distributed),\r\n token_per_repo_id=dataset._token_per_repo_id,\r\n)\r\n```\r\n",
"Nice ! This iterable is infinite though no ? How would `interleave_dataset` know when to stop ?\r\n\r\nMaybe the re-shuffling can be implemented directly in `RandomlyCyclingMultiSourcesExamplesIterable` (which is the iterable used by `interleave_dataset`) ?",
"Infinite is fine for my usecases fortunately."
] | Let's say I have dataset A which has 100k examples, and dataset B which has 100m examples.
I want to train on an interleaved dataset of A+B, with stopping_strategy='all_exhausted' so dataset B doesn't repeat any examples. But every time A is exhausted I want it to be reshuffled (eg. calling set_epoch)
Of course I want to interleave as IterableDatasets / streaming mode so B doesn't have to get tokenized completely at the start.
How could I achieve this? I was thinking something like, if I wrap dataset A in some new IterableDataset with from_generator() and manually call set_epoch before interleaving it? But I'm not sure how to keep the number of shards in that dataset...
Something like
```
dataset_a = load_dataset(...)
dataset_b = load_dataset(...)
def epoch_shuffled_dataset(ds):
# How to make this maintain the number of shards in ds??
for epoch in itertools.count():
ds.set_epoch(epoch)
yield from iter(ds)
shuffled_dataset_a = IterableDataset.from_generator(epoch_shuffled_dataset, gen_kwargs={'ds': dataset_a})
interleaved = interleave_datasets([shuffled_dataset_a, dataset_b], probs, stopping_strategy='all_exhausted')
``` | 7,051 |
https://github.com/huggingface/datasets/issues/7049 | Save nparray as list | [
"In addition, when I use `set_format ` and index the ds, the following error occurs:\r\nthe code\r\n```python\r\nds.set_format(type=\"np\", colums=\"pixel_values\")\r\n```\r\nerror\r\n<img width=\"918\" alt=\"image\" src=\"https://github.com/user-attachments/assets/b28bbff2-20ea-4d28-ab62-b4ed2d944996\">\r\n",
"> Some people use the set_format function to convert the column back, but doesn't this lose precision?\r\n\r\nUnder the hood the data is saved in Arrow format using the same precision as your numpy arrays?\r\nBy default the Arrow data is read as python lists, but you can indeed read them back as numpy arrays with the same precision",
"(you can fix your second issue by fixing the typo `colums` -> `columns`)",
"> (you can fix your second issue by fixing the typo `colums` -> `columns`)\r\n\r\nYou are right, I was careless. Thank you.",
"> > Some people use the set_format function to convert the column back, but doesn't this lose precision?\r\n> \r\n> Under the hood the data is saved in Arrow format using the same precision as your numpy arrays? By default the Arrow data is read as python lists, but you can indeed read them back as numpy arrays with the same precision\r\n\r\nYes, after testing I found that there was no loss of precision. Thanks again for your answer."
] | ### Describe the bug
When I use the `map` function to convert images into features, datasets saves nparray as a list. Some people use the `set_format` function to convert the column back, but doesn't this lose precision?
### Steps to reproduce the bug
the map function
```python
def convert_image_to_features(inst, processor, image_dir):
image_file = inst["image_url"]
file = image_file.split("/")[-1]
image_path = os.path.join(image_dir, file)
image = Image.open(image_path)
image = image.convert("RGBA")
inst["pixel_values"] = processor(images=image, return_tensors="np")["pixel_values"]
return inst
```
main function
```python
map_fun = partial(
convert_image_to_features, processor=processor, image_dir=image_dir
)
ds = ds.map(map_fun, batched=False, num_proc=20)
print(type(ds[0]["pixel_values"])
```
### Expected behavior
(type < list>)
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-4.19.91-009.ali4000.alios7.x86_64-x86_64-with-glibc2.35
- Python version: 3.11.5
- `huggingface_hub` version: 0.23.4
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0 | 7,049 |
https://github.com/huggingface/datasets/issues/7048 | ImportError: numpy.core.multiarray when using `filter` | [
"Could you please check your `numpy` version?",
"I got this issue while using numpy version 2.0. \r\n\r\nI solved it by switching back to numpy 1.26.0 :) ",
"We recently added support for numpy 2.0, but it is not released yet.",
"Ok I see, thanks! I think we can close this issue for now as switching back to version 1.26.0 solves the problem :) "
] | ### Describe the bug
I can't apply the filter method on my dataset.
### Steps to reproduce the bug
The following snippet generates a bug:
```python
from datasets import load_dataset
ami = load_dataset('kamilakesbi/ami', 'ihm')
ami['train'].filter(
lambda example: example["file_name"] == 'EN2001a'
)
```
I get the following error:
`ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).`
### Expected behavior
It should work properly!
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | 7,048 |
https://github.com/huggingface/datasets/issues/7047 | Save Dataset as Sharded Parquet | [
"To anyone else who finds themselves in this predicament, it's possible to read the parquet file in the same way that datasets writes it, and then manually break it into pieces. Although, you need a couple of magic options (`thrift_*`) to deal with the huge metadata, otherwise pyarrow immediately crashes.\r\n```python\r\nimport pyarrow.parquet as pq\r\nimport pyarrow as pa\r\n\r\nr = pq.ParquetReader()\r\n\r\nr.open(\"./outrageous-file.parquet\",thrift_string_size_limit=2**31-1, thrift_container_size_limit=2**31-1)\r\n\r\nfrom more_itertools import chunked\r\nimport tqdm\r\n\r\nfor i,chunk in tqdm.tqdm(enumerate(chunked(range(r.num_row_groups),10000))):\r\n w = pq.ParquetWriter(f\"./chunks.parquet/chunk{i}.parquet\",schema=r.schema_arrow)\r\n for idx in chunk:\r\n w.write_table(r.read_row_group(idx))\r\n w.close()\r\n```",
"You can also use `.shard()` and call `to_parquet()` on each shard in the meantime:\r\n\r\n```python\r\nnum_shards = 128\r\noutput_path_template = \"output_dir/{index:05d}.parquet\"\r\nfor index in range(num_shards):\r\n shard = ds.shard(index=index, num_shards=num_shards, contiguous=True)\r\n shard.to_parquet(output_path_template.format(index=index))\r\n```"
] | ### Feature request
`to_parquet` currently saves the dataset as one massive, monolithic parquet file, rather than as several small parquet files. It should shard large datasets automatically.
### Motivation
This default behavior makes me very sad because a program I ran for 6 hours saved its results using `to_parquet`, putting the entire billion+ row dataset into a 171 GB *single shard parquet file* which pyarrow, apache spark, etc. all cannot work with without completely exhausting the memory of my system. I was previously able to work with larger-than-memory parquet files, but not this one. I *assume* the reason why this is happening is because it is a single shard. Making sharding the default behavior puts datasets in parity with other frameworks, such as spark, which automatically shard when a large dataset is saved as parquet.
### Your contribution
I could change the logic here https://github.com/huggingface/datasets/blob/bf6f41e94d9b2f1c620cf937a2e85e5754a8b960/src/datasets/io/parquet.py#L109-L158
to use `pyarrow.dataset.write_dataset`, which seems to support sharding, or periodically open new files. We would only shard if the user passed in a path rather than file handle. | 7,047 |
https://github.com/huggingface/datasets/issues/7041 | `sort` after `filter` unreasonably slow | [
"`filter` add an indices mapping on top of the dataset, so `sort` has to gather all the rows that are kept to form a new Arrow table and sort the table. Gathering all the rows can take some time, but is a necessary step. You can try calling `ds = ds.flatten_indices()` before sorting to remove the indices mapping."
] | ### Describe the bug
as the tittle says ...
### Steps to reproduce the bug
`sort` seems to be normal.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
but `sort` after `filter` is extremely slow.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
ds = ds.filter(lambda x:x > 100, input_columns="k")
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
### Expected behavior
Is this a bug, or is it a misuse of the `sort` function?
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-3.10.0-1127.19.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2023.10.0 | 7,041 |
https://github.com/huggingface/datasets/issues/7040 | load `streaming=True` dataset with downloaded cache | [
"When you pass `streaming=True`, the cache is ignored. The remote data URL is used instead and the data is streamed from the remote server.",
"Thanks for your reply! So is there any solution to get my expected behavior besides clone the whole repo ? Or could I adjust my script to load the downloaded arrow files and generate the dataset streamingly?"
] | ### Describe the bug
We build a dataset which contains several hdf5 files and write a script using `h5py` to generate the dataset. The hdf5 files are large and the processed dataset cache takes more disk space. So we hope to try streaming iterable dataset. Unfortunately, `h5py` can't convert a remote URL into a hdf5 file descriptor. So we use `fsspec` as an interface like below:
```python
def _generate_examples(self, filepath, split):
for file in filepath:
with fsspec.open(file, "rb") as fs:
with h5py.File(fs, "r") as fp:
# for event_id in sorted(list(fp.keys())):
event_ids = list(fp.keys())
......
```
### Steps to reproduce the bug
The `fsspec` works, but it takes 10+ min to print the first 10 examples, which is even longer than the downloading time. I'm not sure if it just caches the whole hdf5 file and generates the examples.
### Expected behavior
So does the following make sense so far?
1. download the files
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True)
```
2. load the iterable dataset faster (using the raw file cache at path `.cache/huggingface/datasets/downloads`)
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True, streaming=true)
```
I made some tests, but the code above can't get the expected result. I'm not sure if this is supported. I also find the issue #6327 . It seemed similar to mine, but I couldn't find a solution.
### Environment info
- `datasets` = 2.18.0
- `h5py` = 3.10.0
- `fsspec` = 2023.10.0 | 7,040 |
https://github.com/huggingface/datasets/issues/7038 | Yes, can definitely elaborate: | [
"This is the `datasets` repository, and the issue should be opened in the `transformers` repo instead."
] | Yes, can definitely elaborate:
Say I want to use HF Trainer with an arbitrary PyTorch optimizer (`AdamW` here just as an example). Then I should intuitively extend `Trainer` like:
```python
class CustomOptimizerTrainer(Trainer):
@staticmethod
def get_optimizer_cls_and_kwargs(args: HfTrainingArguments, model=None) -> tuple[type[torch.optim.Optimizer], dict[str, Any]]:
optimizer = torch.optim.AdamW
optimizer_kwargs = {
"lr": 4e-3,
"betas": (0.9, 0.999),
"weight_decay": 0.05,
}
return optimizer, optimizer_kwargs
```
However, this won't take effect, because `Trainer.create_optimizer` hardcodes the `Trainer` class name when calling `get_optimizer_cls_and_kwargs`:
https://github.com/huggingface/transformers/blob/6c1d0b069de22d7ed8aa83f733c25045eea0585d/src/transformers/trainer.py#L1076
`CustomOptimizerTrainer.get_optimizer_cls_and_kwargs` will never be called.
So I could either:
- also override the entire `create_optimizer` and rewrite `Trainer.get_optimizer_cls_and_kwargs` to `self.get_optimizer_cls_and_kwargs` (overkill)
- or monkey-patch (not ideal):
```python
class CustomOptimizerTrainer(Trainer):
# def get_optimizer_cls_and_kwargs ...
def create_optimizer(self):
trainer_get_optimizer_fn = Trainer.get_optimizer_cls_and_kwargs
Trainer.get_optimizer_cls_and_kwargs = self.get_optimizer_cls_and_kwargs
optimizer = super().create_optimizer()
Trainer.get_optimizer_cls_and_kwargs = trainer_get_optimizer_fn
return optimizer
```
But I think the best fix is to change `Trainer.get_optimizer_cls_and_kwargs` to `self.get_optimizer_cls_and_kwargs` in the original source of `Trainer.create_optimizer`.
I also made `get_optimizer_cls_and_kwargs` an instance method instead of a static method, but that probably doesn't matter as much and can be reverted. It breaks the syntax of the tests.
Please let me know if that's clearer and if you agree! Thanks!
_Originally posted by @apoorvkh in https://github.com/huggingface/transformers/issues/31875#issuecomment-2221491647_
| 7,038 |
https://github.com/huggingface/datasets/issues/7037 | A bug of Dataset.to_json() function | [
"Thanks for reporting, @LinglingGreat.\r\n\r\nI confirm this is a bug."
] | ### Describe the bug
When using the Dataset.to_json() function, an unexpected error occurs if the parameter is set to lines=False. The stored data should be in the form of a list, but it actually turns into multiple lists, which causes an error when reading the data again.
The reason is that to_json() writes to the file in several segments based on the batch size. This is not a problem when lines=True, but it is incorrect when lines=False, because writing in several times will produce multiple lists(when len(dataset) > batch_size).
### Steps to reproduce the bug
try this code:
```python
from datasets import load_dataset
import json
train_dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")["train"]
output_path = "./harmless-base_hftojs.json"
print(len(train_dataset))
train_dataset.to_json(output_path, lines=False, force_ascii=False, indent=2)
with open(output_path, encoding="utf-8") as f:
data = json.loads(f.read())
```
it raise error: json.decoder.JSONDecodeError: Extra data: line 4003 column 1 (char 1373709)
Extra square brackets have appeared here:
<img width="265" alt="image" src="https://github.com/huggingface/datasets/assets/26499566/81492332-386d-42e8-88d1-b6d4ae3682cc">
### Expected behavior
The code runs normally.
### Environment info
datasets=2.20.0 | 7,037 |
https://github.com/huggingface/datasets/issues/7035 | Docs are not generated when a parameter defaults to a NamedSplit value | [] | While generating the docs, we get an error when some parameter defaults to a `NamedSplit` value, like:
```python
def call_function(split=Split.TRAIN):
...
```
The error is: ValueError: Equality not supported between split train and <class 'inspect._empty'>
See: https://github.com/huggingface/datasets/actions/runs/9869660902/job/27254359863?pr=7015
```
Building the MDX files: 97%|█████████▋| 58/60 [00:00<00:00, 91.94it/s]
Traceback (most recent call last):
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 197, in build_mdx_files
content, new_anchors, source_files, errors = resolve_autodoc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 123, in resolve_autodoc
doc = autodoc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 499, in autodoc
method_doc, check = document_object(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 395, in document_object
signature = format_signature(obj)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 126, in format_signature
if param.default != inspect._empty:
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 136, in __ne__
return not self.__eq__(other)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 379, in __eq__
raise ValueError(f"Equality not supported between split {self} and {other}")
ValueError: Equality not supported between split train and <class 'inspect._empty'>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/datasets/datasets/.venv/bin/doc-builder", line 8, in <module>
sys.exit(main())
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/doc_builder_cli.py", line 47, in main
args.func(args)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/build.py", line 102, in build_command
build_doc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 367, in build_doc
anchors_mapping, source_files_mapping = build_mdx_files(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 230, in build_mdx_files
raise type(e)(f"There was an error when converting {file} to the MDX format.\n" + e.args[0]) from e
ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format.
Equality not supported between split train and <class 'inspect._empty'>
``` | 7,035 |
https://github.com/huggingface/datasets/issues/7033 | `from_generator` does not allow to specify the split name | [
"Thanks for reporting, @pminervini.\r\n\r\nI agree we should give the option to define the split name.\r\n\r\nIndeed, there is a PR that addresses precisely this issue:\r\n- #7015\r\n\r\nI am reviewing it.",
"Booom! thank you guys :)"
] | ### Describe the bug
I'm building train, dev, and test using `from_generator`; however, in all three cases, the logger prints `Generating train split:`
It's not possible to change the split name since it seems to be hardcoded: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/generator/generator.py
### Steps to reproduce the bug
```
In [1]: from datasets import Dataset
In [2]: def gen():
...: yield {"pokemon": "bulbasaur", "type": "grass"}
...:
In [3]: ds = Dataset.from_generator(gen)
Generating train split: 1 examples [00:00, 133.89 examples/s]
```
### Expected behavior
It should be possible to specify any split name
### Environment info
- `datasets` version: 2.19.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- `huggingface_hub` version: 0.23.3
- PyArrow version: 15.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0 | 7,033 |
https://github.com/huggingface/datasets/issues/7031 | CI quality is broken: use ruff check instead | [] | CI quality is broken: https://github.com/huggingface/datasets/actions/runs/9838873879/job/27159697027
```
error: `ruff <path>` has been removed. Use `ruff check <path>` instead.
``` | 7,031 |
https://github.com/huggingface/datasets/issues/7030 | Add option to disable progress bar when reading a dataset ("Loading dataset from disk") | [
"You can disable progress bars for all of `datasets` with `disable_progress_bars`. [Link](https://huggingface.co/docs/datasets/en/package_reference/utilities#datasets.enable_progress_bars)\r\n\r\nSo you could do something like:\r\n\r\n```python\r\nfrom datasets import load_from_disk, enable_progress_bars, disable_progress_bars\r\n\r\ndisable_progress_bars()\r\n# Your code\r\nload_from_disk(....)\r\n\r\nenable_progress_bars()\r\n```\r\n",
"Thank you! Closing the issue."
] | ### Feature request
Add an option in load_from_disk to disable the progress bar even if the number of files is larger than 16.
### Motivation
I am reading a lot of datasets that it creates lots of logs.
<img width="1432" alt="image" src="https://github.com/huggingface/datasets/assets/57996478/8d4bbf03-6b89-44b6-937c-932f01b4eb2a">
### Your contribution
Seems like an easy fix to make. I can create a PR if necessary. | 7,030 |
https://github.com/huggingface/datasets/issues/7029 | load_dataset on AWS lambda throws OSError(30, 'Read-only file system') error | [
"hi ! can you share the full stack trace ? this should help locate what files is not written in the cache_dir"
] | ### Describe the bug
I'm using AWS lambda to run a python application. I run the `load_dataset` function with cache_dir="/tmp" and is still throws the OSError(30, 'Read-only file system') error. Is even updated all the HF envs to point to /tmp dir but the issue still persists. I can confirm that the I can write to /tmp directory.
### Steps to reproduce the bug
```python
d = load_dataset(
path=hugging_face_link,
split=split,
token=token,
cache_dir="/tmp/hugging_face_cache",
)
```
### Expected behavior
Everything written to the file system as part of the load_datasets function should be in the /tmp directory.
### Environment info
datasets version: 2.16.1
Platform: Linux-5.10.216-225.855.amzn2.x86_64-x86_64-with-glibc2.26
Python version: 3.11.9
huggingface_hub version: 0.19.4
PyArrow version: 16.1.0
Pandas version: 2.2.2
fsspec version: 2023.10.0 | 7,029 |
https://github.com/huggingface/datasets/issues/7024 | Streaming dataset not returning data | [] | ### Describe the bug
I'm deciding to post here because I'm still not sure what the issue is, or if I am using IterableDatasets wrongly.
I'm following the guide on here https://huggingface.co/learn/cookbook/en/fine_tuning_code_llm_on_single_gpu pretty much to a tee and have verified that it works when I'm fine-tuning on the provided dataset.
However, I'm doing some data preprocessing steps (filtering out entries), when I try to swap out the dataset for mine, it fails to train. However, I eventually fixed this by simply setting `stream=False` in `load_dataset`.
Coud this be some sort of network / firewall issue I'm facing?
### Steps to reproduce the bug
I made a post with greater description about how I reproduced this problem before I found my workaround: https://discuss.huggingface.co/t/problem-with-custom-iterator-of-streaming-dataset-not-returning-anything/94551
Here is the problematic dataset snippet, which works when streaming=False (and with buffer keyword removed from shuffle)
```
commitpackft = load_dataset(
"chargoddard/commitpack-ft-instruct", split="train", streaming=True
).filter(lambda example: example["language"] == "Python")
def form_template(example):
"""Forms a template for each example following the alpaca format for CommitPack"""
example["content"] = (
"### Human: " + example["instruction"] + " " + example["input"] + " ### Assistant: " + example["output"]
)
return example
dataset = commitpackft.map(
form_template,
remove_columns=["id", "language", "license", "instruction", "input", "output"],
).shuffle(
seed=42, buffer_size=10000
) # remove everything since its all inside "content" now
validation_data = dataset.take(4000)
train_data = dataset.skip(4000)
```
The annoying part about this is that it only fails during training and I don't know when it will fail, except that it always fails during evaluation.
### Expected behavior
The expected behavior is that I should be able to get something from the iterator when called instead of getting nothing / stuck in a loop somewhere.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.31
- Python version: 3.11.7
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
| 7,024 |
https://github.com/huggingface/datasets/issues/7022 | There is dead code after we require pyarrow >= 15.0.0 | [] | There are code lines specific for pyarrow versions < 15.0.0.
However, we require pyarrow >= 15.0.0 since the merge of PR:
- #6892
Those code lines are now dead code and should be removed. | 7,022 |
https://github.com/huggingface/datasets/issues/7020 | Casting list array to fixed size list raises error | [] | When trying to cast a list array to fixed size list, an AttributeError is raised:
> AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'
Steps to reproduce the bug:
```python
import pyarrow as pa
from datasets.table import array_cast
arr = pa.array([[0, 1]])
array_cast(arr, pa.list_(pa.int64(), 2))
```
Stack trace:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-6cb90a1d8216> in <module>
3
4 arr = pa.array([[0, 1]])
----> 5 array_cast(arr, pa.list_(pa.int64(), 2))
~/huggingface/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
1802 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1803 else:
-> 1804 return func(array, *args, **kwargs)
1805
1806 return wrapper
~/huggingface/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str)
1920 else:
1921 array_values = array.values[
-> 1922 array.offset * pa_type.length : (array.offset + len(array)) * pa_type.length
1923 ]
1924 return pa.FixedSizeListArray.from_arrays(_c(array_values, pa_type.value_type), pa_type.list_size)
AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'
``` | 7,020 |
https://github.com/huggingface/datasets/issues/7018 | `load_dataset` fails to load dataset saved by `save_to_disk` | [
"In my case the error was:\r\n```\r\nValueError: You are trying to load a dataset that was saved using `save_to_disk`. Please use `load_from_disk` instead.\r\n```\r\nDid you try `load_from_disk`?"
] | ### Describe the bug
This code fails to load the dataset it just saved:
```python
from datasets import load_dataset
from transformers import AutoTokenizer
MODEL = "google-bert/bert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
dataset = load_dataset("yelp_review_full")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
tokenized_datasets.save_to_disk("dataset")
tokenized_datasets = load_dataset("dataset/") # raises
```
It raises `ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})}`.
I believe this bug is caused by the [logic that tries to infer dataset format](https://github.com/huggingface/datasets/blob/9af8dd3de7626183a9a9ec8973cebc672d690400/src/datasets/load.py#L556). It counts the most common file extension. However, a small dataset can fit in a single `.arrow` file and have two JSON metadata files, causing the format to be inferred as JSON:
```shell
$ ls -l dataset/test
-rw-r--r-- 1 sliedes sliedes 191498784 Jul 1 13:55 data-00000-of-00001.arrow
-rw-r--r-- 1 sliedes sliedes 1730 Jul 1 13:55 dataset_info.json
-rw-r--r-- 1 sliedes sliedes 249 Jul 1 13:55 state.json
```
### Steps to reproduce the bug
Execute the code above.
### Expected behavior
The dataset is loaded successfully.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.9.3-arch1-1-x86_64-with-glibc2.39
- Python version: 3.12.4
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
| 7,018 |
https://github.com/huggingface/datasets/issues/7016 | `drop_duplicates` method | [
"There is an open issue #2514 about this which also proposes solutions."
] | ### Feature request
`drop_duplicates` method for huggingface datasets (similiar in simplicity to the `pandas` one)
### Motivation
Ease of use
### Your contribution
I don't think i am good enough to help | 7,016 |
https://github.com/huggingface/datasets/issues/7013 | CI is broken for faiss tests on Windows: node down: Not properly terminated | [] | Faiss tests on Windows make the CI run indefinitely until maximum execution time (360 minutes) is reached.
See: https://github.com/huggingface/datasets/actions/runs/9712659783
```
test (integration, windows-latest, deps-minimum)
The job running on runner GitHub Actions 60 has exceeded the maximum execution time of 360 minutes.
test (integration, windows-latest, deps-latest)
The job running on runner GitHub Actions 238 has exceeded the maximum execution time of 360 minutes.
```
```
____________________________ tests/test_search.py _____________________________
[gw1] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw1' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
____________________________ tests/test_search.py _____________________________
[gw2] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw2' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
```
```
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw0] node down: Not properly terminated
[gw0] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw0
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw1] node down: Not properly terminated
[gw1] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw1
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw2] node down: Not properly terminated
[gw2] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw2
``` | 7,013 |
https://github.com/huggingface/datasets/issues/7010 | Re-enable raising error from huggingface-hub FutureWarning in CI | [] | Re-enable raising error from huggingface-hub FutureWarning in CI, which was disabled by PR:
- #6876
Note that this can only be done once transformers releases the fix:
- https://github.com/huggingface/transformers/pull/31007 | 7,010 |
https://github.com/huggingface/datasets/issues/7008 | Support ruff 0.5.0 in CI | [] | Support ruff 0.5.0 in CI.
Also revert:
- #7007 | 7,008 |
https://github.com/huggingface/datasets/issues/7006 | CI is broken after ruff-0.5.0: E721 | [] | After ruff-0.5.0 release (https://github.com/astral-sh/ruff/releases/tag/0.5.0), our CI is broken due to E721 rule.
See: https://github.com/huggingface/datasets/actions/runs/9707641618/job/26793170961?pr=6983
> src/datasets/features/features.py:844:12: E721 Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks | 7,006 |
https://github.com/huggingface/datasets/issues/7005 | EmptyDatasetError: The directory at /metadata.jsonl doesn't contain any data files | [
"Hi ! `data_dir=` is for directories, can you try using `data_files=` instead ?",
"If you are trying to load your image dataset from a local folder, you should replace \"data_dir=path/to/jsonl/metadata.jsonl\" with the real folder path in your computer.\r\n\r\nhttps://huggingface.co/docs/datasets/en/image_load#imagefolder",
"Ah yes. My bad. I was giving file name. I should have given the folder directory as the path. That solved my issue. Thank you @albertvillanova and @lhoestq. "
] | ### Describe the bug
while trying to load custom dataset from jsonl file, I get the error: "metadata.jsonl doesn't contain any data files"
### Steps to reproduce the bug
This is my [metadata_v2.jsonl](https://github.com/user-attachments/files/16016011/metadata_v2.json) file. I have this file in the folder with all images mentioned in that json(l) file.
Through below mentioned command I am trying to load_dataset so that I can upload it as mentioned here on the [official website](https://huggingface.co/docs/datasets/en/image_dataset#upload-dataset-to-the-hub).
````
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="path/to/jsonl/metadata.jsonl")
````
error:
````
EmptyDatasetError Traceback (most recent call last)
Cell In[18], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("imagefolder",
4 data_dir="path/to/jsonl/file/metadata.jsonl")
5 dataset[0]["objects"]
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2594, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2589 verification_mode = VerificationMode(
2590 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2591 )
2593 # Create a dataset builder
-> 2594 builder_instance = load_dataset_builder(
2595 path=path,
2596 name=name,
2597 data_dir=data_dir,
2598 data_files=data_files,
2599 cache_dir=cache_dir,
2600 features=features,
2601 download_config=download_config,
2602 download_mode=download_mode,
2603 revision=revision,
2604 token=token,
2605 storage_options=storage_options,
2606 trust_remote_code=trust_remote_code,
2607 _require_default_config_name=name is None,
2608 **config_kwargs,
2609 )
2611 # Return iterable dataset in case of streaming
2612 if streaming:
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2266, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
2264 download_config = download_config.copy() if download_config else DownloadConfig()
2265 download_config.storage_options.update(storage_options)
-> 2266 dataset_module = dataset_module_factory(
2267 path,
2268 revision=revision,
2269 download_config=download_config,
2270 download_mode=download_mode,
2271 data_dir=data_dir,
2272 data_files=data_files,
2273 cache_dir=cache_dir,
2274 trust_remote_code=trust_remote_code,
2275 _require_default_config_name=_require_default_config_name,
2276 _require_custom_configs=bool(config_kwargs),
2277 )
2278 # Get dataset builder class from the processing script
2279 builder_kwargs = dataset_module.builder_kwargs
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1805, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
1782 # We have several ways to get a dataset builder:
1783 #
1784 # - if path is the name of a packaged dataset module
(...)
1796
1797 # Try packaged
1798 if path in _PACKAGED_DATASETS_MODULES:
1799 return PackagedDatasetModuleFactory(
1800 path,
1801 data_dir=data_dir,
1802 data_files=data_files,
1803 download_config=download_config,
1804 download_mode=download_mode,
-> 1805 ).get_module()
1806 # Try locally
1807 elif path.endswith(filename):
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1140, in PackagedDatasetModuleFactory.get_module(self)
1135 def get_module(self) -> DatasetModule:
1136 base_path = Path(self.data_dir or "").expanduser().resolve().as_posix()
1137 patterns = (
1138 sanitize_patterns(self.data_files)
1139 if self.data_files is not None
-> 1140 else get_data_patterns(base_path, download_config=self.download_config)
1141 )
1142 data_files = DataFilesDict.from_patterns(
1143 patterns,
1144 download_config=self.download_config,
1145 base_path=base_path,
1146 )
1147 supports_metadata = self.name in _MODULE_SUPPORTS_METADATA
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/data_files.py:503, in get_data_patterns(base_path, download_config)
501 return _get_data_files_patterns(resolver)
502 except FileNotFoundError:
--> 503 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
EmptyDatasetError: The directory at path/to/jsonl/file/metadata.jsonl doesn't contain any data files`
```
### Expected behavior
It should be able load the whole file in a format of "dataset" inside the dataset variable. But it gives error "The directory at "path/to/jsonl/metadata.jsonl" doesn't contain any data files."
### Environment info
I am using conda environment. | 7,005 |
https://github.com/huggingface/datasets/issues/7001 | Datasetbuilder Local Download FileNotFoundError | [
"Ok it seems the solution is to use the directory string without the trailing \"/\" which in my case as: \r\n\r\n`parquet_dir = \"~/data/Parquet\" `\r\n\r\nStill i think this is a weird behavior... "
] | ### Describe the bug
So I was trying to download a dataset and save it as parquet and I follow the [tutorial](https://huggingface.co/docs/datasets/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage) of Huggingface. However, during the excution I face a FileNotFoundError.
I debug the code and it seems there is a bug there:
So first it creates a .incomplete folder and before moving its contents the following code deletes the directory
[Code](https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/builder.py#L984)
hence as a result I face with:
``` FileNotFoundError: [Errno 2] No such file or directory: '~/data/Parquet/.incomplete '```
### Steps to reproduce the bug
```
from datasets import load_dataset_builder
from pathlib import Path
parquet_dir = "~/data/Parquet/"
Path(parquet_dir).mkdir(parents=True, exist_ok=True)
builder = load_dataset_builder(
"rotten_tomatoes",
)
builder.download_and_prepare(parquet_dir, file_format="parquet")
```
### Expected behavior
Downloads the files and saves as parquet
### Environment info
Ubuntu,
Python 3.10
```
datasets 2.19.1
``` | 7,001 |
https://github.com/huggingface/datasets/issues/7000 | IterableDataset: Unsupported ScalarType BFloat16 | [
"@lhoestq Thank you for merging #6607, but unfortunately the issue persists for `IterableDataset` :pensive: ",
"Hi ! I opened https://github.com/huggingface/datasets/pull/7002 to fix this bug",
"Amazing, thank you so much @lhoestq! :pray:"
] | ### Describe the bug
`IterableDataset.from_generator` crashes when using BFloat16:
```
File "/usr/local/lib/python3.11/site-packages/datasets/utils/_dill.py", line 169, in _save_torchTensor
args = (obj.detach().cpu().numpy(),)
^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Got unsupported ScalarType BFloat16
```
### Steps to reproduce the bug
```python
import torch
from datasets import IterableDataset
def demo(x):
yield {"x": x}
x = torch.tensor([1.], dtype=torch.bfloat16)
dataset = IterableDataset.from_generator(
demo,
gen_kwargs=dict(x=x),
)
example = next(iter(dataset))
print(example)
```
### Expected behavior
Code sample should print:
```python
{'x': tensor([1.], dtype=torch.bfloat16)}
```
### Environment info
```
datasets==2.20.0
torch==2.2.2
``` | 7,000 |
https://github.com/huggingface/datasets/issues/6997 | CI is broken for tests using hf-internal-testing/librispeech_asr_dummy | [] | CI is broken: https://github.com/huggingface/datasets/actions/runs/9657882317/job/26637998686?pr=6996
```
FAILED tests/test_inspect.py::test_get_dataset_config_names[hf-internal-testing/librispeech_asr_dummy-expected4] - AssertionError: assert ['clean'] == ['clean', 'other']
Right contains one more item: 'other'
Full diff:
[
'clean',
- 'other',
]
FAILED tests/test_inspect.py::test_get_dataset_default_config_name[hf-internal-testing/librispeech_asr_dummy-None] - AssertionError: assert 'clean' is None
```
Note that repository was recently converted to Parquet: https://huggingface.co/datasets/hf-internal-testing/librispeech_asr_dummy/commit/5be91486e11a2d616f4ec5db8d3fd248585ac07a | 6,997 |
https://github.com/huggingface/datasets/issues/6995 | ImportError when importing datasets.load_dataset | [
"What is the version of your installed `huggingface-hub`:\r\n```python\r\nimport huggingface_hub\r\nprint(huggingface_hub.__version__)\r\n```\r\n\r\nIt seems you have a very old version of `huggingface-hub`, where `CommitInfo` was not still implemented. You need to update it:\r\n```\r\npip install -U huggingface-hub\r\n```\r\n\r\nNote that `CommitInfo` was implemented in huggingface-hub 0.10.0 and datasets requires \"huggingface-hub>=0.21.2\"",
"The version of my huggingface-hub is 0.23.4.",
"The error message says there is no CommitInfo in your installed huggingface-hub library:\r\n```\r\nImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\\Anaconda3\\envs\\CS224S\\Lib\\site-packages\\huggingface_hub_init_.py)\r\n```\r\n\r\nAnd this is implemented since version 0.10.0:\r\n- https://github.com/huggingface/huggingface_hub/pull/1066",
"I am getting the exact same issue when I `import datasets`. The version of my huggingface-hub is also 0.23.4. I dont see a solution in the comments. Not sure why is this issue closed?",
"I closed the issue because the problem is not related to the `datasets` library.\r\n\r\nThe problem is with your local Python environment: it seems corrupted. You could try to remove it and regenerate it again.",
"I have recreated my conda environment but still run into the same issue. Here is my environment:\r\n```\r\nconda create --name esm python=3.10\r\n conda activate esm\r\n conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia\r\n pip3 install -r requirements.txt\r\n```\r\nRequirements.txt\r\n```\r\naccelerate\r\ndatasets==2.20.0\r\npyfastx\r\ntransformers\r\nboto3\r\nhuggingface_hub==0.23.4\r\n```\r\n\r\nAnd then I get:\r\n```\r\n>>> import datasets\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/fsx/ubuntu/miniconda3/envs/esm2/lib/python3.10/site-packages/datasets/__init__.py\", line 17, in <module>\r\n from .arrow_dataset import Dataset\r\n File \"/fsx/ubuntu/miniconda3/envs/esm2/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 63, in <module>\r\n from huggingface_hub import (\r\nImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (/fsx/ubuntu/miniconda3/envs/esm2/lib/python3.10/site-packages/huggingface_hub/__init__.py)\r\n>>>\r\n```\r\n\r\n",
"You can check:\r\n```\r\n>>> import huggingface_hub\r\n>>> print(huggingface_hub.__version__)\r\n```",
"This is what I see:\r\n```\r\n>>> import huggingface_hub\r\n>>> print(huggingface_hub.__version__)\r\n0.23.4\r\n```",
"Installing `chardet` makes it work for some reason"
] | ### Describe the bug
I encountered an ImportError while trying to import `load_dataset` from the `datasets` module in Hugging Face. The error message indicates a problem with importing 'CommitInfo' from 'huggingface_hub'.
### Steps to reproduce the bug
1. pip install git+https://github.com/huggingface/datasets
2. from datasets import load_dataset
### Expected behavior
ImportError Traceback (most recent call last)
Cell In[7], [line 1](vscode-notebook-cell:?execution_count=7&line=1)
----> [1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset
[3](vscode-notebook-cell:?execution_count=7&line=3) train_set = load_dataset("mispeech/speechocean762", split="train")
[4](vscode-notebook-cell:?execution_count=7&line=4) test_set = load_dataset("mispeech/speechocean762", split="test")
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py:[1](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:1)7
1 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
[2](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:2) #
[3](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:3) # Licensed under the Apache License, Version 2.0 (the "License");
(...)
[12](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:12) # See the License for the specific language governing permissions and
[13](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:13) # limitations under the License.
[15](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:15) __version__ = "2.20.1.dev0"
---> [17](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:17) from .arrow_dataset import Dataset
[18](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:18) from .arrow_reader import ReadInstruction
[19](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:19) from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py:63
[61](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:61) import pyarrow.compute as pc
[62](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:62) from fsspec.core import url_to_fs
---> [63](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:63) from huggingface_hub import (
[64](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:64) CommitInfo,
[65](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:65) CommitOperationAdd,
...
[70](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:70) )
[71](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:71) from huggingface_hub.hf_api import RepoFile
[72](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:72) from multiprocess import Pool
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (d:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?580889ab-0f61-4f37-9214-eaa2b3807f85) or open in a [text editor](command:workbench.action.openLargeOutput?580889ab-0f61-4f37-9214-eaa2b3807f85). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Environment info
Leo@DESKTOP-9NHUAMI MSYS /d/Anaconda3/envs/CS224S/Lib/site-packages/huggingface_hub
$ datasets-cli env
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "D:\Anaconda3\envs\CS224S\Scripts\datasets-cli.exe\__main__.py", line 4, in <module>
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py", line 17, in <module>
from .arrow_dataset import Dataset
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py", line 63, in <module>
from huggingface_hub import (
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
(CS224S) | 6,995 |
https://github.com/huggingface/datasets/issues/6992 | Dataset with streaming doesn't work with proxy | [
"Hi ! can you try updating `datasets` and `huggingface_hub` ?\r\n\r\n```\r\npip install -U datasets huggingface_hub\r\n```"
] | ### Describe the bug
I'm currently trying to stream data using dataset since the dataset is too big but it hangs indefinitely without loading the first batch. I use AIMOS which is a supercomputer that uses proxy to connect to the internet. I assume it has to do with the network configurations. I've already set up both HTTP_PROXY and HTTPS_PROXY. streaming = False works fine.
### Steps to reproduce the bug
use load_dataset with streaming = True in AIMOS
### Expected behavior
does not hang indefinitely and loads batches to start training run
### Environment info
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
_pytorch_select 2.0 cuda_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
abseil-cpp 20220623.0 h9888cd1_6 conda-forge
absl-py 1.0.0 py311h399429b_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
aiofiles 23.2.1 pyhd8ed1ab_0 conda-forge
aiohttp 3.8.6 py311hf118e41_0
aiosignal 1.2.0 pyhd3eb1b0_0
archspec 0.2.3 pyhd8ed1ab_0 conda-forge
arrow-cpp 11.0.0 ha3edaa6_5_cpu conda-forge
async-timeout 4.0.2 py311h6ffa863_0
attrs 23.1.0 py311h6ffa863_0
av 10.0.0 py311he6153ed_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
aws-c-auth 0.6.24 hb81f6d7_5 conda-forge
aws-c-cal 0.5.20 h3c2b4d9_6 conda-forge
aws-c-common 0.8.11 h4194056_0 conda-forge
aws-c-compression 0.2.16 ha19333d_3 conda-forge
aws-c-event-stream 0.2.18 h12a9399_6 conda-forge
aws-c-http 0.7.4 ha2cde00_2 conda-forge
aws-c-io 0.13.17 h9189062_2 conda-forge
aws-c-mqtt 0.8.6 h40d1a04_6 conda-forge
aws-c-s3 0.2.4 hbdbe4f0_3 conda-forge
aws-c-sdkutils 0.1.7 ha19333d_3 conda-forge
aws-checksums 0.1.14 ha19333d_3 conda-forge
aws-crt-cpp 0.19.7 hd018011_7 conda-forge
aws-sdk-cpp 1.10.57 hb9575ba_4 conda-forge
blas 1.0 openblas
blinker 1.8.2 pyhd8ed1ab_0 conda-forge
boltons 23.0.0 py311h6ffa863_0
boost-cpp 1.82.0 h25e6d66_2
bottleneck 1.3.5 py311h34f6284_0
brotli 1.0.9 hf118e41_7
brotli-bin 1.0.9 hf118e41_7
brotli-python 1.0.9 py311h4a02239_7
bzip2 1.0.8 h7b6447c_0
c-ares 1.19.1 hf118e41_0
ca-certificates 2024.6.2 h0f6029e_0 conda-forge
cachetools 5.3.3 pyhd8ed1ab_0 conda-forge
certifi 2024.6.2 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 py311hf118e41_3
charset-normalizer 2.0.4 pyhd3eb1b0_0
click 8.1.7 unix_pyh707e725_0 conda-forge
conda 24.5.0 py311h1af927a_0 conda-forge
conda-content-trust 0.2.0 py311h6ffa863_0
conda-libmamba-solver 23.11.1 py311h6ffa863_0
conda-package-handling 2.2.0 py311h6ffa863_0
conda-package-streaming 0.9.0 py311h6ffa863_0
contourpy 1.0.5 py311h25e6d66_0
cryptography 41.0.3 py311hb0e80e7_0
cudatoolkit 11.8.0 hedcfb66_13 conda-forge
cudnn 8.9.2_11.8 h9ceb136_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
cycler 0.11.0 pyhd3eb1b0_0
datasets 2.12.0 py311h6ffa863_0
dill 0.3.6 py311h6ffa863_0
distro 1.9.0 pyhd8ed1ab_0 conda-forge
ffmpeg 4.2.2 opence_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
filelock 3.9.0 py311h6ffa863_0
fmt 9.1.0 h25e6d66_0
fonttools 4.25.0 pyhd3eb1b0_0
freetype 2.12.1 hd23a775_0
frozendict 2.4.4 py311hb02d432_0 conda-forge
frozenlist 1.4.0 py311hf118e41_0
fsspec 2023.9.2 py311h6ffa863_0
gflags 2.2.2 he6710b0_0
giflib 5.2.1 hf118e41_3
glog 0.6.0 hbe088e0_0 conda-forge
gmp 6.3.0 h46f38da_0 conda-forge
gmpy2 2.1.5 py311h2758da7_1 conda-forge
google-auth 2.30.0 pyhff2d567_0 conda-forge
google-auth-oauthlib 0.5.3 pyhd8ed1ab_0 conda-forge
grpc-cpp 1.51.1 h8ba971d_1 conda-forge
grpcio 1.54.3 py311h414e0d3_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
huggingface_hub 0.17.3 py311h6ffa863_0
icu 73.1 h4a02239_0
idna 3.4 py311h6ffa863_0
importlib-metadata 6.0.0 py311h6ffa863_0
jinja2 3.1.4 pyhd8ed1ab_0 conda-forge
jpeg 9e hf118e41_1
jsonpatch 1.32 pyhd3eb1b0_0
jsonpointer 2.1 pyhd3eb1b0_0
kiwisolver 1.4.4 py311h4a02239_0
krb5 1.20.1 hc019ccd_1
lame 3.100 hb283c62_1003 conda-forge
lcms2 2.12 h2045e0b_0
ld_impl_linux-ppc64le 2.38 hec883e6_1
lerc 3.0 h29c3540_0
leveldb 1.23 h24532b4_1 conda-forge
libabseil 20220623.0 cxx17_h9235812_6 conda-forge
libarchive 3.6.2 hd8ab008_2
libarrow 11.0.0 h837770b_5_cpu conda-forge
libboost 1.82.0 haf51a6a_2
libbrotlicommon 1.0.9 hf118e41_7
libbrotlidec 1.0.9 hf118e41_7
libbrotlienc 1.0.9 hf118e41_7
libcrc32c 1.1.2 h3b9df90_0 conda-forge
libcurl 8.4.0 h4d62439_0
libdeflate 1.17 hf118e41_1
libedit 3.1.20221030 hf118e41_0
libev 4.33 h140841e_1
libevent 2.1.10 h19c23f1_4 conda-forge
libexpat 2.6.2 h46f38da_0 conda-forge
libffi 3.4.4 h4a02239_0
libgcc-ng 13.2.0 h31e42bb_10 conda-forge
libgfortran-ng 11.2.0 hb3889a9_1
libgfortran5 11.2.0 h1234567_1
libgomp 13.2.0 h31e42bb_10 conda-forge
libgoogle-cloud 2.7.0 h11140b6_1 conda-forge
libgrpc 1.51.1 h4d29a31_1 conda-forge
libmamba 1.5.3 h7c6fafd_0
libmambapy 1.5.3 py311h828bf7b_0
libnghttp2 1.57.0 h44e5816_0
libnsl 2.0.1 ha17a0cc_0 conda-forge
libopenblas 0.3.23 hc5a31fb_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
libopus 1.3.1 h4e0d66e_1 conda-forge
libpng 1.6.39 hf118e41_0
libprotobuf 3.21.12 h1776448_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
libsolv 0.7.24 h0f529ac_0
libsqlite 3.45.3 hd4bbf49_0 conda-forge
libssh2 1.10.0 h50fa78f_2
libstdcxx-ng 13.2.0 h262982c_10 conda-forge
libthrift 0.18.0 h82f1162_0 conda-forge
libtiff 4.5.1 h4a02239_0
libutf8proc 2.8.0 hb283c62_0 conda-forge
libuuid 2.38.1 h4194056_0 conda-forge
libvpx 1.13.1 h46f38da_0 conda-forge
libwebp 1.3.2 h0f96ee2_0
libwebp-base 1.3.2 hf118e41_0
libxcrypt 4.4.36 ha17a0cc_1 conda-forge
libxml2 2.10.4 h18e3229_1
libzlib 1.2.13 h1f2b957_6 conda-forge
llvm-openmp 14.0.6 hc028133_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
lmdb 0.9.31 ha17a0cc_1 conda-forge
lz4-c 1.9.4 h4a02239_0
markdown 3.4.4 pyhd8ed1ab_0 conda-forge
markupsafe 2.1.5 py311h32d8acf_0 conda-forge
matplotlib 3.8.0 py311h6ffa863_0
matplotlib-base 3.8.0 py311h52e1fcc_0
menuinst 2.1.1 py311h1af927a_0 conda-forge
mpc 1.3.1 heaf1863_0 conda-forge
mpfr 4.2.1 haad2271_1 conda-forge
mpmath 1.3.0 pyhd8ed1ab_0 conda-forge
multidict 6.0.2 py311hf118e41_0
multiprocess 0.70.14 py311h6ffa863_0
munkres 1.1.4 py_0
mypy_extensions 1.0.0 pyha770c72_0 conda-forge
nccl 2.18.3 cuda11.8_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
ncurses 6.4 h4a02239_0
nest-asyncio 1.6.0 pyhd8ed1ab_0 conda-forge
networkx 2.8.8 pyhd8ed1ab_0 conda-forge
nomkl 3.0 0 https://ftp.osuosl.org/pub/open-ce/1.10.0
numactl 2.0.16 hba61f60_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
numexpr 2.8.7 py311hc46fc55_0
numpy 1.24.3 py311h148a09e_0
numpy-base 1.24.3 py311h06b82f6_0
oauthlib 3.2.2 pyhd8ed1ab_0 conda-forge
openjpeg 2.4.0 hfe35807_0
openssl 3.3.1 h1f2b957_0 conda-forge
orc 1.8.2 h341c9a4_2 conda-forge
packaging 23.1 py311h6ffa863_0
pandas 2.1.1 py311h52e1fcc_0
pcre2 10.42 h280155c_0
pillow 10.0.1 py311he33076b_0
pip 23.3 py311h6ffa863_0
platformdirs 4.2.2 pyhd8ed1ab_0 conda-forge
pluggy 1.0.0 py311h6ffa863_1
pooch 1.8.2 pyhd8ed1ab_0 conda-forge
protobuf 4.21.12 py311ha7baec7_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
psutil 5.9.8 py311hd26027c_0 conda-forge
pyarrow 11.0.0 py311h04a18d5_1
pyasn1 0.6.0 pyhd8ed1ab_0 conda-forge
pyasn1-modules 0.4.0 pyhd8ed1ab_0 conda-forge
pybind11-abi 4 hd3eb1b0_1
pycosat 0.6.6 py311hf118e41_0
pycparser 2.21 pyhd3eb1b0_0
pyjwt 2.8.0 pyhd8ed1ab_1 conda-forge
pyopenssl 23.2.0 py311h6ffa863_0
pyparsing 3.0.9 py311h6ffa863_0
pyre-extensions 0.0.30 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 py311h6ffa863_0
python 3.11.8 h3332dee_0_cpython conda-forge
python-dateutil 2.8.2 pyhd3eb1b0_0
python-tzdata 2023.3 pyhd3eb1b0_0
python-xxhash 2.0.2 py311hf118e41_1
python_abi 3.11 4_cp311 conda-forge
pytorch 2.0.1 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
pytorch-base 2.0.1 cuda11.8_py311_pb4.21.12_4 https://ftp.osuosl.org/pub/open-ce/1.10.0
pytz 2023.3.post1 py311h6ffa863_0
pyu2f 0.1.5 pyhd8ed1ab_0 conda-forge
pyyaml 6.0.1 py311hf118e41_0
re2 2023.02.01 h883269e_0 conda-forge
readline 8.2 hf118e41_0
regex 2023.10.3 py311hf118e41_0
reproc 14.2.4 h29c3540_1
reproc-cpp 14.2.4 h29c3540_1
requests 2.31.0 py311h6ffa863_0
requests-oauthlib 2.0.0 pyhd8ed1ab_0 conda-forge
responses 0.13.3 pyhd3eb1b0_0
rsa 4.9 pyhd8ed1ab_0 conda-forge
ruamel.yaml 0.17.21 py311hf118e41_0
s2n 1.3.37 h5e47323_0 conda-forge
safetensors 0.4.0 py311hda16d9e_0
scipy 1.11.1 py311hd69e9bb_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
sentencepiece 0.1.97 h1e74c73_py311_pb4.21.12_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
setuptools 68.0.0 py311h6ffa863_0
six 1.16.0 pyhd3eb1b0_1
snappy 1.1.9 h29c3540_0
sqlite 3.41.2 hf118e41_0
sympy 1.12.1 pypyh2585a3b_103 conda-forge
tabulate 0.8.10 pyhd8ed1ab_0 conda-forge
tensorboard 2.13.0 pyhab0730d_pb4.21.12_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
tensorboard-data-server 0.7.0 pyh6f84499_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
tensorboard-plugin-wit 1.6.0 pyh9f0ad1d_0 conda-forge
tk 8.6.13 hd4bbf49_0 conda-forge
tokenizers 0.13.3 py311h3d4f45a_0
torchdata 0.6.0 py311_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
torchsnapshot 0.1.0 pyhd8ed1ab_0 conda-forge
torchtext-base 0.15.2 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
torchtnt 0.2.4 pyhd8ed1ab_0 conda-forge
torchvision-base 0.15.2 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
tornado 6.3.3 py311hf118e41_0
tqdm 4.65.0 py311h7837921_0
transformers 4.32.1 py311h6ffa863_0
truststore 0.8.0 py311h6ffa863_0
typing-extensions 4.7.1 py311h6ffa863_0
typing_extensions 4.7.1 py311h6ffa863_0
typing_inspect 0.9.0 pyhd8ed1ab_0 conda-forge
tzdata 2023c h04d1e81_0
urllib3 1.26.18 py311h6ffa863_0
utf8proc 2.6.1 h140841e_0
werkzeug 2.3.8 pyhd8ed1ab_0 conda-forge
wheel 0.41.2 py311h6ffa863_0
xxhash 0.8.0 h140841e_3
xz 5.4.2 hf118e41_0
yaml 0.2.5 h7b6447c_0
yaml-cpp 0.8.0 h4a02239_0
yarl 1.8.1 py311hf118e41_0
zipp 3.11.0 py311h6ffa863_0
zlib 1.2.13 h1f2b957_6 conda-forge
zstandard 0.19.0 py311hf118e41_0
zstd 1.5.5 h57e4825_0 | 6,992 |
https://github.com/huggingface/datasets/issues/6990 | Problematic rank after calling `split_dataset_by_node` twice | [
"ah yes good catch ! feel free to open a PR with your suggested fix"
] | ### Describe the bug
I'm trying to split `IterableDataset` by `split_dataset_by_node`.
But when doing split on a already split dataset, the resulting `rank` is greater than `world_size`.
### Steps to reproduce the bug
Here is the minimal code for reproduction:
```py
>>> from datasets import load_dataset
>>> from datasets.distributed import split_dataset_by_node
>>> dataset = load_dataset('fla-hub/slimpajama-test', split='train', streaming=True)
>>> dataset = split_dataset_by_node(dataset, 1, 32)
>>> dataset._distributed
DistributedConfig(rank=1, world_size=32)
>>> dataset = split_dataset_by_node(dataset, 1, 15)
>>> dataset._distributed
DistributedConfig(rank=481, world_size=480)
```
As you can see, the second rank 481 > 480, which is problematic.
### Expected behavior
I think this error comes from this line @lhoestq
https://github.com/huggingface/datasets/blob/a6ccf944e42c1a84de81bf326accab9999b86c90/src/datasets/iterable_dataset.py#L2943-L2944
We may need to obtain the rank first. Then the above code gives
```py
>>> dataset._distributed
DistributedConfig(rank=16, world_size=480)
```
### Environment info
datasets==2.20.0 | 6,990 |
https://github.com/huggingface/datasets/issues/6989 | cache in nfs error | [] | ### Describe the bug
- When reading dataset, a cache will be generated to the ~/. cache/huggingface/datasets directory
- When using .map and .filter operations, runtime cache will be generated to the /tmp/hf_datasets-* directory
- The default is to use the path of tempfile.tempdir
- If I modify this path to the NFS disk, an error will be reported, but the program will continue to run
- https://github.com/huggingface/datasets/blob/main/src/datasets/config.py#L257
```
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs000000038330a012000030b4'
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs0000000400064d4a000030e5'
```
### Steps to reproduce the bug
```
import os
import time
import tempfile
from datasets import load_dataset
def add_column(sample):
# print(type(sample))
# time.sleep(0.1)
sample['__ds__stats__'] = {'data': 123}
return sample
def filt_column(sample):
# print(type(sample))
if len(sample['content']) > 10:
return True
else:
return False
if __name__ == '__main__':
input_dir = '/mnt/temp/CN/small' # some json dataset
dataset = load_dataset('json', data_dir=input_dir)
temp_dir = '/media/release/release/temp/temp' # a nfs folder
os.makedirs(temp_dir, exist_ok=True)
# change huggingface-datasets runtime cache in nfs（default in /tmp）
tempfile.tempdir = temp_dir
aa = dataset.map(add_column, num_proc=64)
aa = aa.filter(filt_column, num_proc=64)
print(aa)
```
### Expected behavior
no error occur
### Environment info
datasets==2.18.0
ubuntu 20.04 | 6,989 |
https://github.com/huggingface/datasets/issues/6985 | AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType' | [
"Please note that the error is raised just at import:\r\n```python\r\nimport pyarrow.parquet as pq\r\n```\r\n\r\nTherefore it must be caused by some problem with your pyarrow installation. I would recommend you uninstall and install pyarrow again.\r\n\r\nI also see that it seems you use conda to install pyarrow. Please note that pyarrow offers 3 different packages in conda-forge: https://arrow.apache.org/docs/python/install.html#using-conda\r\n```\r\nconda install -c conda-forge pyarrow\r\n```\r\n> While the pyarrow [conda-forge](https://conda-forge.org/) package is the right choice for most users, both a minimal and maximal variant of the package exist, either of which may be better for your use case. See [Differences between conda-forge packages](https://arrow.apache.org/docs/python/install.html#python-conda-differences).\r\n\r\nPlease, make sure you install the right one: I guess it is either `pyarrow` (or `pyarrow-all`).",
"I have same issue, please downgrade pyarrow==15.0.2, it seem datasets library need to be fix",
"It is not a problem with the `datasets` library: we support latest version of `pyarrow` and our Continuous Integration tests are using pyarrow 16.1.0 without any problem.\r\n\r\nThe error reported here is raised when importing pyarrow.parquet:\r\n```\r\n---> 29 import pyarrow.parquet as pq\r\n```\r\n```\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20\r\n 1 # Licensed to the Apache Software Foundation (ASF) under one\r\n 2 # or more contributor license agreements. See the NOTICE file\r\n 3 # distributed with this work for additional information\r\n (...)\r\n 17 \r\n 18 # flake8: noqa\r\n---> 20 from .core import *\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33\r\n 30 import pyarrow as pa\r\n 32 try:\r\n---> 33 import pyarrow._parquet as _parquet\r\n 34 except ImportError as exc:\r\n 35 raise ImportError(\r\n 36 \"The pyarrow installation is not built with support \"\r\n 37 f\"for the Parquet file format ({str(exc)})\"\r\n 38 ) from None\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet()\r\n\r\nAttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'\r\n```\r\n\r\nThis can only be explained if pyarrow was not properly installed. \r\n\r\nIf the user just installed `pyarrow-core` from conda-forge, then its parquet subpackage is not installed and cannot be imported. You can check pyarrow docs:\r\n- Differences between conda-forge packages: https://arrow.apache.org/docs/python/install.html#python-conda-differences\r\n> The `pyarrow-core` package includes the following functionality:\r\n> ...\r\n> The `pyarrow` package adds the following:\r\n> ...\r\n> Parquet (i.e., `pyarrow.parquet`)",
"I'm still seeing the same issue on datasets version 2.20.0. I installed pyarrow version 17.0.0 with `pip install`. Downgrading to pyarrow==15.0.2 also did not resolve the issue."
] | ### Describe the bug
I have been struggling with this for two days, any help would be appreciated. Python 3.10
```
from setfit import SetFitModel
from huggingface_hub import login
access_token_read = "cccxxxccc"
# Authenticate with the Hugging Face Hub
login(token=access_token_read)
# Load the models from the Hugging Face Hub
trainer_relv = SetFitModel.from_pretrained("snowdere/trainer_relevance")
trainer_trust = SetFitModel.from_pretrained("snowdere/trainer_trust")
trainer_sent = SetFitModel.from_pretrained("snowdere/trainer_sent")
trainer_topic = SetFitModel.from_pretrained("snowdere/trainer_topic")
```
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 from setfit import SetFitModel
2 from huggingface_hub import login
4 access_token_read = "ccsddsds"
File /opt/conda/lib/python3.10/site-packages/setfit/__init__.py:7
4 import os
5 import warnings
----> 7 from .data import get_templated_dataset, sample_dataset
8 from .model_card import SetFitModelCardData
9 from .modeling import SetFitHead, SetFitModel
File /opt/conda/lib/python3.10/site-packages/setfit/data.py:5
3 import pandas as pd
4 import torch
----> 5 from datasets import Dataset, DatasetDict, load_dataset
6 from torch.utils.data import Dataset as TorchDataset
8 from . import logging
File /opt/conda/lib/python3.10/site-packages/datasets/__init__.py:18
1 # ruff: noqa
2 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
3 #
(...)
13 # See the License for the specific language governing permissions and
14 # limitations under the License.
16 __version__ = "2.19.0"
---> 18 from .arrow_dataset import Dataset
19 from .arrow_reader import ReadInstruction
20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:76
73 from tqdm.contrib.concurrent import thread_map
75 from . import config
---> 76 from .arrow_reader import ArrowReader
77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
78 from .data_files import sanitize_patterns
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_reader.py:29
26 from typing import TYPE_CHECKING, List, Optional, Union
28 import pyarrow as pa
---> 29 import pyarrow.parquet as pq
30 from tqdm.contrib.concurrent import thread_map
32 from .download.download_config import DownloadConfig
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20
1 # Licensed to the Apache Software Foundation (ASF) under one
2 # or more contributor license agreements. See the NOTICE file
3 # distributed with this work for additional information
(...)
17
18 # flake8: noqa
---> 20 from .core import *
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33
30 import pyarrow as pa
32 try:
---> 33 import pyarrow._parquet as _parquet
34 except ImportError as exc:
35 raise ImportError(
36 "The pyarrow installation is not built with support "
37 f"for the Parquet file format ({str(exc)})"
38 ) from None
File /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet()
AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'
```
setfit: 1.0.3
transformers: 4.41.2
lingua-language-detector: 2.0.2
polars: 0.20.31
lightning: None
google-cloud-bigquery: 3.24.0
shapely: 2.0.4
pyarrow: 16.0.0
### Steps to reproduce the bug
I have tried all version combinations for Dataset and Pyarrow, the all have the same error since a few days ago. This is accross multiple scripts I have.
### Expected behavior
Just ron normally.
### Environment info
3.10 | 6,985 |
https://github.com/huggingface/datasets/issues/6984 | Convert polars DataFrame back to datasets | [
"Hi ! Thanks for reporting :)\r\n\r\nWe don't support `large_list` yet, though it should be added to `Sequence` IMO (maybe with a parameter `large=True` ?)"
] | ### Feature request
This returns error.
```python
from datasets import Dataset
dsdf = Dataset.from_dict({"x": [[1, 2], [3, 4, 5]], "y": ["a", "b"]})
Dataset.from_polars(dsdf.to_polars())
```
ValueError: Arrow type large_list<item: int64> does not have a datasets dtype equivalent.
### Motivation
When datasets contain Sequence data type, it will be converted to Arrow type large_list. However, the reverse (from large_list to Sequence) does not work.
### Your contribution
No | 6,984 |
https://github.com/huggingface/datasets/issues/6982 | cannot split dataset when using load_dataset | [
"it seems the bug will happened in all windows system, I tried it in windows8.1, 10, 11 and all of them failed. But it won't happened in the Linux(Ubuntu and Centos7) and Mac (both my virtual and physical machine). I still don't know what the problem is. May be related to the path? I cannot run the split file in my windows server which created in Linux (even I replace the path in the arrow document)....work for it for a week but still cannot fix it .....upset",
"Have you properly logged in? Are you using the a valid token?\r\n\r\nNote that this dataset is gated and you must follow the right procedure to be able to access it. You can find more info in the docs: https://huggingface.co/docs/hub/datasets-gated#access-gated-datasets-as-a-user",
"> Have you properly logged in? Are you using the a valid token?\r\n> \r\n> Note that this dataset is gated and you must follow the right procedure to be able to access it. You can find more info in the docs: https://huggingface.co/docs/hub/datasets-gated#access-gated-datasets-as-a-user\r\n\r\nI finally found it what happened. It is not about the logging. When I copy the dataset from its original path (C:/Users/cybes/.cache/huggingface/datasets/downloads/extracted/XXX/cv-corpus-7.0-2021-07-21) to the desktop and load each tsv in it one by one , when I load the test spilt, the following warning occurs:\r\n\"ArrowInvalid: Failed to parse string: 'Benchmark' as a scalar of type double\"\r\n\r\nThen I manually deleted them in the \"segment\", the error won't happen anymore, even I replace the original path with these revised tsv and use the previous loading method (common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\", trust_remote_code=True)). It can work properly."
] | ### Describe the bug
when I use load_dataset methods to load mozilla-foundation/common_voice_7_0, it can successfully download and extracted the dataset but It cannot generating the arrow document,
This bug happened in my server, my laptop, so as #6906 , but it won't happen in the google colab. I work for it for days, even I load the datasets from local path, it can Generating train split and validation split but bug happen again in test split.
### Steps to reproduce the bug
from datasets import load_dataset, load_metric, Audio
common_voice_train = load_dataset("mozilla-foundation/common_voice_7_0", "ja", split="train", token=selftoken, trust_remote_code=True)
### Expected behavior
```
{
"name": "ValueError",
"message": "Instruction \"train\" corresponds to no data!",
"stack": "---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[2], line 3
1 from datasets import load_dataset, load_metric, Audio
----> 3 common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\",token='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')#,trust_remote_code=True)#,streaming=True)
4 common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"test\",token='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')#,trust_remote_code=True)#,streaming=True)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\load.py:2626, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2622 # Build dataset for splits
2623 keep_in_memory = (
2624 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2625 )
-> 2626 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
2627 # Rename and cast features to match task schema
2628 if task is not None:
2629 # To avoid issuing the same warning twice
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1266, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)
1263 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS)
1265 # Create a dataset for each of the given splits
-> 1266 datasets = map_nested(
1267 partial(
1268 self._build_single_dataset,
1269 run_post_process=run_post_process,
1270 verification_mode=verification_mode,
1271 in_memory=in_memory,
1272 ),
1273 split,
1274 map_tuple=True,
1275 disable_tqdm=True,
1276 )
1277 if isinstance(datasets, dict):
1278 datasets = DatasetDict(datasets)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\utils\\py_utils.py:484, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc)
482 if batched:
483 data_struct = [data_struct]
--> 484 mapped = function(data_struct)
485 if batched:
486 mapped = mapped[0]
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1296, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory)
1293 split = Split(split)
1295 # Build base dataset
-> 1296 ds = self._as_dataset(
1297 split=split,
1298 in_memory=in_memory,
1299 )
1300 if run_post_process:
1301 for resource_file_name in self._post_processing_resources(split).values():
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1370, in DatasetBuilder._as_dataset(self, split, in_memory)
1368 if self._check_legacy_cache():
1369 dataset_name = self.name
-> 1370 dataset_kwargs = ArrowReader(cache_dir, self.info).read(
1371 name=dataset_name,
1372 instructions=split,
1373 split_infos=self.info.splits.values(),
1374 in_memory=in_memory,
1375 )
1376 fingerprint = self._get_dataset_fingerprint(split)
1377 return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\arrow_reader.py:256, in BaseReader.read(self, name, instructions, split_infos, in_memory)
254 msg = f'Instruction \"{instructions}\" corresponds to no data!'
255 #msg = f'Instruction \"{self._path}\",\"{name}\",\"{instructions}\",\"{split_infos}\" corresponds to no data!'
--> 256 raise ValueError(msg)
257 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
ValueError: Instruction \"train\" corresponds to no data!"
}
```
### Environment info
Environment:
python 3.9
windows 11 pro
VScode+jupyter | 6,982 |
https://github.com/huggingface/datasets/issues/6980 | Support NumPy 2.0 | [] | ### Feature request
Support NumPy 2.0.
### Motivation
NumPy introduces the Array API, which bridges the gap between machine learning libraries. Many clients of HuggingFace are eager to start using the Array API.
Besides that, NumPy 2 provides a cleaner interface than NumPy 1.
### Tasks
NumPy 2.0 was released for testing so that libraries could ensure compatibility [since mid-March](https://github.com/numpy/numpy/issues/24300#issuecomment-1986815755). What needs to be done for HuggingFace to support Numpy 2?
- [x] Fix use of `array`: https://github.com/huggingface/datasets/pull/6976
- [ ] Remove [NumPy version limit](https://github.com/huggingface/datasets/pull/6975): https://github.com/huggingface/datasets/pull/6991 | 6,980 |
https://github.com/huggingface/datasets/issues/7079 | HfHubHTTPError: 500 Server Error: Internal Server Error for url: | [
"same issue here. @albertvillanova @lhoestq ",
"Also impacted by this issue in many of my datasets (though not all) - in my case, this also seems to affect datasets that have been updated recently. Git cloning and the web interface still work:\r\n- https://huggingface.co/api/datasets/acmc/cheat_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_reuter_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_wp_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_essay_reduced\r\n\r\nOddly enough, the system status looks good: https://status.huggingface.co/",
"Hey how to download these datasets using git cloning?",
"Also reported here\r\nhttps://github.com/huggingface/huggingface_hub/issues/2425",
"I have been getting the same error for the past 8 hours as well",
"Same error since yesterday, fails on any new dataset created",
"Same here. I cannot download the HelpSteer2 dataset: https://huggingface.co/datasets/nvidia/HelpSteer2 which has been uploaded about a month ago",
"> Hey how to download these datasets using git cloning?\n\nYou'll find a guide [here](https://huggingface.co/docs/hub/en/datasets-downloading) 👍🏻",
"Same here for imdb dataset",
"It also happens with this dataset: https://huggingface.co/datasets/ylacombe/jenny-tts-6h-tagged",
"same here for all datsets in the sentence-tramsformers repo and related collections.\r\n\r\nsame issue with dataset that i recently uploaded on my repo.\r\nseems that the upload date of the datset is not relevat (getting this issue with both old datasets and newer ones)\r\n\r\nfor some reason, i was able to get the dataset by turning it private and accessing it with the id token (accessing it as public while providing the token doesn not work)..... but i can say if that is just a random coincidence.\r\n\r\nseems not much deterministic, for a specific dataset (sentence-transformer nq ) , that was \"down\" since some hours , worked for like 5-10 minutes, then stopped again\r\n\r\nnow even this dataset (that worked since some min ago, and that i'm in the middle of processing steps) stopped working: https://huggingface.co/datasets/bobox/msmarco-bm25-EduScore/"
] | ### Describe the bug
newly uploaded datasets, since yesterday, yields an error.
old datasets, works fine.
Seems like the datasets api server returns a 500
I'm getting the same error, when I invoke `load_dataset` with my dataset.
Long discussion about it here, but I'm not sure anyone from huggingface have seen it.
https://discuss.huggingface.co/t/hfhubhttperror-500-server-error-internal-server-error-for-url/99580/1
### Steps to reproduce the bug
this api url:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
respond with:
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Expected behavior
return no error with newer datasets.
With older datasets I can load the datasets fine.
### Environment info
# Browser
When I access the api in the browser:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Request headers
```
Accept text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8
Accept-Encoding gzip, deflate, br, zstd
Accept-Language en-US,en;q=0.5
Connection keep-alive
Host huggingface.co
Priority u=1
Sec-Fetch-Dest document
Sec-Fetch-Mode navigate
Sec-Fetch-Site cross-site
Upgrade-Insecure-Requests 1
User-Agent Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:127.0) Gecko/20100101 Firefox/127.0
```
### Response headers
```
X-Firefox-Spdy h2
access-control-allow-origin https://huggingface.co
access-control-expose-headers X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,X-Total-Count,ETag,Link,Accept-Ranges,Content-Range
content-length 80
content-type application/json; charset=utf-8
cross-origin-opener-policy same-origin
date Fri, 26 Jul 2024 19:09:45 GMT
etag W/"50-9qrwU+BNI4SD0Fe32p/nofkmv0c"
referrer-policy strict-origin-when-cross-origin
vary Origin
via 1.1 1624c79cd07e6098196697a6a7907e4a.cloudfront.net (CloudFront)
x-amz-cf-id SP8E7n5qRaP6i9c9G83dNAiOzJBU4GXSrDRAcVNTomY895K35H0nJQ==
x-amz-cf-pop CPH50-C1
x-cache Error from cloudfront
x-error-message Internal Error - We're working hard to fix this as soon as possible!
x-powered-by huggingface-moon
x-request-id Root=1-66a3f479-026417465ef42f49349fdca1
``` | 7,079 |
https://github.com/huggingface/datasets/issues/7077 | column_names ignored by load_dataset() when loading CSV file | [] | ### Describe the bug
load_dataset() ignores the column_names kwarg when loading a CSV file. Instead, it uses whatever values are on the first line of the file.
### Steps to reproduce the bug
Call `load_dataset` to load data from a CSV file and specify `column_names` kwarg.
### Expected behavior
The resulting dataset should have the specified column names **and** the first line of the file should be considered as data values.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.10.0-30-cloud-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- `huggingface_hub` version: 0.24.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | 7,077 |
https://github.com/huggingface/datasets/issues/7073 | CI is broken for convert_to_parquet: Invalid rev id: refs/pr/1 404 error causes RevisionNotFoundError | [
"Any recent change in the API backend rejecting parameter `revision=\"refs/pr/1\"` to `HfApi.preupload_lfs_files`?\r\n```\r\nf\"{endpoint}/api/{repo_type}s/{repo_id}/preupload/{revision}\"\r\n\r\nhttps://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5188a8-17219154347516/preupload/refs%2Fpr%2F1.\r\nInvalid rev id: refs/pr/1\r\n```\r\n@Wauplin @huggingface/datasets @huggingface/moon-landing @huggingface/moon-landing-back ",
"I have temporarily fixed the CI with:\r\n- #7074\r\n\r\nHowever, the underlying issue must be fixed and #7074 must be reverted.",
"Hmm does it do the preupload call before creating the ref cc @Wauplin ?\r\n\r\n(in that case it should do a preupload call on the base branch with `?create_pr=1`)",
"@coyotte508, the CI test was implemented 2 months ago and it was working OK until yesterday. See the CI status of the commits in the main branch of `datasets`: https://github.com/huggingface/datasets/commits/main/",
"Yes i get that\r\n\r\nWe changed the preupload response to return the commit id in https://github.com/huggingface-internal/moon-landing/pull/10756\r\n\r\nThis line is probably causing the error: https://github.com/huggingface-internal/moon-landing/pull/10756/files#diff-558f6f9865e30bfa091b94d6a4a900138103ddb4eb0bec96b6deec5bf5626fa0R2322\r\n\r\nIt's weird the error is returned, it means that maybe a ref with 0 history (not even the first commit) was created\r\n\r\nDoes this change have any impact in production, or just the CI test? If it's just the CI test it should be fixed on your side, if it impacts production we can look at a solution",
"@coyotte508 it impacts production: `convert_to_parquet` raises the above error when the dataset has more that one configs/subsets:\r\n- First subset calls `push_to_hub` with `create_pr=True`\r\n- Second subset uses the `refs/pr/#` returned by the call above, and calls `push_to_hub` with `revision=\"refs/pr/#\"`",
"I tried removing the `mock_commit` call: https://github.com/huggingface/datasets/pull/7076\r\n\r\nAnd the tests seem to work.\r\n\r\nSo it's probably because the commit is not actually called, it doesn't actually create the pull request on the remote (and the associated `refs/pr/1`). But the `preupload` call is not mocked.\r\n\r\nAnyway it shouldn't impact production, since production isn't mocked",
"@coyotte508 thanks a lot for the investigation and sorry for the noise. \r\nI promise not trying to fix things when I have a slight fever: my head does not work well.\r\n\r\nWe need indeed to mock `preupload_lfs_files`: before it was not necessary, but now it is.",
"I fixed the test in:\r\n- #7078\r\n\r\nThanks again, @coyotte508."
] | See: https://github.com/huggingface/datasets/actions/runs/10095313567/job/27915185756
```
FAILED tests/test_hub.py::test_convert_to_parquet - huggingface_hub.utils._errors.RevisionNotFoundError: 404 Client Error. (Request ID: Root=1-66a25839-31ce7b475e70e7db1e4d44c2;b0c8870f-d5ef-4bf2-a6ff-0191f3df0f64)
Revision Not Found for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5188a8-17219154347516/preupload/refs%2Fpr%2F1.
Invalid rev id: refs/pr/1
```
```
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/hub.py:86: in convert_to_parquet
dataset.push_to_hub(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/dataset_dict.py:1722: in push_to_hub
split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/arrow_dataset.py:5511: in _push_parquet_shards_to_hub
api.preupload_lfs_files(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/hf_api.py:4231: in preupload_lfs_files
_fetch_upload_modes(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:118: in _inner_fn
return fn(*args, **kwargs)
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/_commit_api.py:507: in _fetch_upload_modes
hf_raise_for_status(resp)
``` | 7,073 |
https://github.com/huggingface/datasets/issues/7072 | nm | [] | null | 7,072 |
https://github.com/huggingface/datasets/issues/7071 | Filter hangs | [] | ### Describe the bug
When trying to filter my custom dataset, the process hangs, regardless of the lambda function used. It appears to be an issue with the way the Images are being handled. The dataset in question is a preprocessed version of https://huggingface.co/datasets/danaaubakirova/patfig where notably, I have converted the data to the Parquet format.
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('lcolonn/patfig', split='test')
ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y')
```
Eventually I ctrl+C and I obtain this stack trace:
```
>>> ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y')
Filter: 0%| | 0/998 [00:00<?, ? examples/s]Filter: 0%| | 0/998 [00:35<?, ? examples/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/fingerprint.py", line 482, in wrapper
out = func(dataset, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3714, in filter
indices = self.map(
^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 602, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3161, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3552, in _map_single
batch = apply_function_on_filtered_inputs(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3421, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 6478, in get_indices_from_mask_function
num_examples = len(batch[next(iter(batch.keys()))])
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 273, in __getitem__
value = self.format(key)
^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 376, in format
return self.formatter.format_column(self.pa_table.select([key]))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 443, in format_column
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 219, in decode_column
return self.features.decode_column(column, column_name) if self.features else column
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in decode_column
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in <listcomp>
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 1351, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/image.py", line 188, in decode_example
image.load() # to avoid "Too many open files" errors
^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/PIL/ImageFile.py", line 293, in load
n, err_code = decoder.decode(b)
^^^^^^^^^^^^^^^^^
KeyboardInterrupt
```
Warning! This can even seem to cause some computers to crash.
### Expected behavior
Should return the filtered dataset
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.24.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | 7,071 |
https://github.com/huggingface/datasets/issues/7070 | how set_transform affects batch size? | [] | ### Describe the bug
I am trying to fine-tune w2v-bert for ASR task. Since my dataset is so big, I preferred to use the on-the-fly method with set_transform. So i change the preprocessing function to this:
```
def prepare_dataset(batch):
input_features = processor(batch["audio"], sampling_rate=16000).input_features[0]
input_length = len(input_features)
labels = processor.tokenizer(batch["text"], padding=False).input_ids
batch = {
"input_features": [input_features],
"input_length": [input_length],
"labels": [labels]
}
return batch
train_ds.set_transform(prepare_dataset)
val_ds.set_transform(prepare_dataset)
```
After this, I also had to change the DataCollatorCTCWithPadding class like this:
```
@dataclass
class DataCollatorCTCWithPadding:
processor: Wav2Vec2BertProcessor
padding: Union[bool, str] = True
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# Separate input_features and labels
input_features = [{"input_features": feature["input_features"][0]} for feature in features]
labels = [feature["labels"][0] for feature in features]
# Pad input features
batch = self.processor.pad(
input_features,
padding=self.padding,
return_tensors="pt",
)
# Pad and process labels
label_features = self.processor.tokenizer.pad(
{"input_ids": labels},
padding=self.padding,
return_tensors="pt",
)
labels = label_features["input_ids"]
attention_mask = label_features["attention_mask"]
# Replace padding with -100 to ignore these tokens during loss calculation
labels = labels.masked_fill(attention_mask.ne(1), -100)
batch["labels"] = labels
return batch
```
But now a strange thing is happening, no matter how much I increase the batch size, the amount of V-RAM GPU usage does not change, while the number of total steps in the progress-bar (logging) changes. Is this normal or have I made a mistake?
### Steps to reproduce the bug
i can share my code if needed
### Expected behavior
Equal to the batch size value, the set_transform function is applied to the dataset and given to the model as a batch.
### Environment info
all updated versions | 7,070 |
https://github.com/huggingface/datasets/issues/7067 | Convert_to_parquet fails for datasets with multiple configs | [
"Many users have encountered the same issue, which has caused inconvenience.\r\n\r\nhttps://discuss.huggingface.co/t/convert-to-parquet-fails-for-datasets-with-multiple-configs/86733\r\n",
"Thanks for reporting.\r\n\r\nI will make the code more robust.",
"I have opened an issue in the huggingface-hub repo:\r\n- https://github.com/huggingface/huggingface_hub/issues/2419\r\n\r\nI am opening a PR to avoid calling `create_branch` if the branch already exists."
] | If the dataset has multiple configs, when using the `datasets-cli convert_to_parquet` command to avoid issues with the data viewer caused by loading scripts, the conversion process only successfully converts the data corresponding to the first config. When it starts converting the second config, it throws an error:
```
Traceback (most recent call last):
File "/opt/anaconda3/envs/dl/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/datasets_cli.py", line 41, in main
service.run()
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/convert_to_parquet.py", line 83, in run
dataset.push_to_hub(
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/dataset_dict.py", line 1713, in push_to_hub
api.create_branch(repo_id, branch=revision, token=token, repo_type="dataset", exist_ok=True)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 5503, in create_branch
hf_raise_for_status(response)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 358, in hf_raise_for_status
raise BadRequestError(message, response=response) from e
huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-669fc665-7c2e80d75f4337496ee95402;731fcdc7-0950-4eec-99cf-ce047b8d003f)
Bad request:
Invalid reference for a branch: refs/pr/1
``` | 7,067 |
https://github.com/huggingface/datasets/issues/7066 | One subset per file in repo ? | [] | Right now we consider all the files of a dataset to be the same data, e.g.
```
single_subset_dataset/
├── train0.jsonl
├── train1.jsonl
└── train2.jsonl
```
but in cases like this, each file is actually a different subset of the dataset and should be loaded separately
```
many_subsets_dataset/
├── animals.jsonl
├── trees.jsonl
└── metadata.jsonl
```
It would be nice to detect those subsets automatically using a simple heuristic. For example we can group files together if their paths names are the same except some digits ? | 7,066 |
https://github.com/huggingface/datasets/issues/7065 | Cannot get item after loading from disk and then converting to iterable. | [] | ### Describe the bug
The dataset generated from local file works fine.
```py
root = "/home/data/train"
file_list1 = glob(os.path.join(root, "*part1.flac"))
file_list2 = glob(os.path.join(root, "*part2.flac"))
ds = (
Dataset.from_dict({"part1": file_list1, "part2": file_list2})
.cast_column("part1", Audio(sampling_rate=None, mono=False))
.cast_column("part2", Audio(sampling_rate=None, mono=False))
)
ids = ds.to_iterable_dataset(128)
ids = ids.shuffle(buffer_size=10000, seed=42)
dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True)
for batch in dataloader:
break
```
But after saving it to disk and then loading it from disk, I cannot get data as expected.
```py
root = "/home/data/train"
file_list1 = glob(os.path.join(root, "*part1.flac"))
file_list2 = glob(os.path.join(root, "*part2.flac"))
ds = (
Dataset.from_dict({"part1": file_list1, "part2": file_list2})
.cast_column("part1", Audio(sampling_rate=None, mono=False))
.cast_column("part2", Audio(sampling_rate=None, mono=False))
)
ds.save_to_disk("./train")
ds = datasets.load_from_disk("./train")
ids = ds.to_iterable_dataset(128)
ids = ids.shuffle(buffer_size=10000, seed=42)
dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True)
for batch in dataloader:
break
```
After a long time waiting, an error occurs:
```
Loading dataset from disk: 100%|█████████████████████████████████████████████████████████████████████████| 165/165 [00:00<00:00, 6422.18it/s]
Traceback (most recent call last):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1133, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/queues.py", line 113, in get
if not self._poll(timeout):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 257, in poll
return self._poll(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 424, in _poll
r = wait([self], timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 931, in wait
ready = selector.select(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/selectors.py", line 416, in select
fd_event_list = self._selector.poll(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 3490529) is killed by signal: Killed.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module>
cli.main()
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
run()
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
runpy.run_path(target, run_name="__main__")
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
exec(code, run_globals)
File "/home/hanzerui/workspace/NetEase/test/test_datasets.py", line 60, in <module>
for batch in dataloader:
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
data = self._next_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1329, in _next_data
idx, data = self._get_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1295, in _get_data
success, data = self._try_get_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1146, in _try_get_data
raise RuntimeError(f'DataLoader worker (pid(s) {pids_str}) exited unexpectedly') from e
RuntimeError: DataLoader worker (pid(s) 3490529) exited unexpectedly
```
It seems that streaming is not supported by `laod_from_disk`, so does that mean I cannot convert it to iterable?
### Steps to reproduce the bug
1. Create a `Dataset` from local files with `from_dict`
2. Save it to disk with `save_to_disk`
3. Load it from disk with `load_from_disk`
4. Convert to iterable with `to_iterable_dataset`
5. Loop the dataset
### Expected behavior
Get items faster than the original dataset generated from dict.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.23.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | 7,065 |
https://github.com/huggingface/datasets/issues/7063 | Add `batch` method to `Dataset` | [] | ### Feature request
Add a `batch` method to the Dataset class, similar to the one recently implemented for `IterableDataset` in PR #7054.
### Motivation
A batched iteration speeds up data loading significantly (see e.g. #6279)
### Your contribution
I plan to open a PR to implement this. | 7,063 |
https://github.com/huggingface/datasets/issues/7061 | Custom Dataset | Still Raise Error while handling errors in _generate_examples | [] | ### Describe the bug
I follow this [example](https://discuss.huggingface.co/t/error-handling-in-iterabledataset/72827/3) to handle errors in custom dataset. I am writing a dataset script which read jsonl files and i need to handle errors and continue reading files without raising exception and exit the execution.
```
def _generate_examples(self, filepaths):
errors=[]
id_ = 0
for filepath in filepaths:
try:
with open(filepath, 'r') as f:
for line in f:
json_obj = json.loads(line)
yield id_, json_obj
id_ += 1
except Exception as exc:
logger.error(f"error occur at filepath: {filepath}")
errors.append(error)
```
seems the logger.error is printed but still exception is raised the the run is exit.
```
Downloading and preparing dataset custom_dataset/default to /home/myuser/.cache/huggingface/datasets/custom_dataset/default-a14cdd566afee0a6/1.0.0/acfcc9fb9c57034b580c4252841
ERROR: datasets_modules.datasets.custom_dataset.acfcc9fb9c57034b580c4252841bb890a5617cbd28678dd4be5e52b81188ad02.custom_dataset: 2024-07-22 10:47:42,167: error occur at filepath: '/home/myuser/ds/corrupted-file.jsonl
Traceback (most recent call last):
File "/home/myuser/.cache/huggingface/modules/datasets_modules/datasets/custom_dataset/ac..2/custom_dataset.py", line 48, in _generate_examples
json_obj = json.loads(line)
File "myenv/lib/python3.8/json/__init__.py", line 357, in loads
return _default_decoder.decode(s)
File "myenv/lib/python3.8/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "myenv/lib/python3.8/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Invalid control character at: line 1 column 4 (char 3)
Generating train split: 0 examples [00:06, ? examples/s]>
RemoteTraceback:
"""
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1637, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "myenv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 594, in finalize
raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "myenv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 1353, in
_write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1646, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
"""
The above exception was the direct cause of the following exception:
│ │
│ myenv/lib/python3.8/site-packages/datasets/utils/py_utils. │
│ py:1377 in <listcomp> │
│ │
│ 1374 │ │ │ │ if all(async_result.ready() for async_result in async_results) and queue │
│ 1375 │ │ │ │ │ break │
│ 1376 │ │ # we get the result in case there's an error to raise │
│ ❱ 1377 │ │ [async_result.get() for async_result in async_results] │
│ 1378 │
│ │
│ ╭──────────────────────────────── locals ─────────────────────────────────╮ │
│ │ .0 = <list_iterator object at 0x7f2cc1f0ce20> │ │
│ │ async_result = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │
│ ╰─────────────────────────────────────────────────────────────────────────╯ │
│ │
│ myenv/lib/python3.8/site-packages/multiprocess/pool.py:771 │
│ in get │
│ │
│ 768 │ │ if self._success: │
│ 769 │ │ │ return self._value │
│ 770 │ │ else: │
│ ❱ 771 │ │ │ raise self._value │
│ 772 │ │
│ 773 │ def _set(self, i, obj): │
│ 774 │ │ self._success, self._value = obj │
│ │
│ ╭────────────────────────────── locals ──────────────────────────────╮ │
│ │ self = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │
│ │ timeout = None │ │
│ ╰────────────────────────────────────────────────────────────────────╯ │
DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
same as above
### Expected behavior
should handle error and continue reading remaining files
### Environment info
python 3.9 | 7,061 |
https://github.com/huggingface/datasets/issues/7059 | None values are skipped when reading jsonl in subobjects | [] | ### Describe the bug
I have been fighting against my machine since this morning only to find out this is some kind of a bug.
When loading a dataset composed of `metadata.jsonl`, if you have nullable values (Optional[str]), they can be ignored by the parser, shifting things around.
E.g., let's take this example
Here are two version of a same dataset:
[not-buggy.tar.gz](https://github.com/user-attachments/files/16333532/not-buggy.tar.gz)
[buggy.tar.gz](https://github.com/user-attachments/files/16333553/buggy.tar.gz)
### Steps to reproduce the bug
1. Load the `buggy.tar.gz` dataset
2. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
3. Load the `not-buggy.tar.gz` dataset
4. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
### Expected behavior
Both should have 4 baseline entries:
1. Buggy should have None followed by three lists
2. Non-Buggy should have four lists, and the first one should be an empty list.
One does not work, 2 works. Despite accepting None in another position than the first one.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
| 7,059 |
https://github.com/huggingface/datasets/issues/7058 | New feature type: Document | [] | It would be useful for PDF.
https://github.com/huggingface/dataset-viewer/issues/2991#issuecomment-2242656069 | 7,058 |
https://github.com/huggingface/datasets/issues/7055 | WebDataset with different prefixes are unsupported | [
"Since `datasets` uses is built on Arrow to store the data, it requires each sample to have the same columns.\r\n\r\nThis can be fixed by specifyign in advance the name of all the possible columns in the `dataset_info` in YAML, and missing values will be `None`",
"Thanks. This currently doesn't work for WebDataset because there's no `BuilderConfig` with `features` and in turn `_info` is missing `features=self.config.features`. I'll prepare a PR to fix this.\r\n\r\nNote it may be useful to add the [expected format of `features`](https://github.com/huggingface/datasets/blob/16fa4421f44b22bbbc607f379a93f45af468d1fc/src/datasets/features/features.py#L1757) to the documentation for [`Builder Parameters`](https://huggingface.co/docs/datasets/repository_structure#builder-parameters).\r\n",
"Oh good catch ! thanks\r\n\r\n> Note it may be useful to add the [expected format of features](https://github.com/huggingface/datasets/blob/16fa4421f44b22bbbc607f379a93f45af468d1fc/src/datasets/features/features.py#L1757) to the documentation for [Buil](https://huggingface.co/docs/datasets/repository_structure#builder-parameters)\r\n\r\nGood idea, let me open a PR",
"#7060 ",
"Actually I just tried with `datasets` on the `main` branch and having `features` defined in `dataset_info` worked for me\r\n\r\n```python\r\n>>> list(load_dataset(\"/Users/quentinlhoest/tmp\", streaming=True, split=\"train\"))\r\n[{'txt': 'hello there\\n', 'other': None}]\r\n```\r\nwhere `tmp` contains data.tar with \"hello there\\n\" in a text file and the README.md:\r\n```\r\n---\r\ndataset_info:\r\n features:\r\n - name: txt\r\n dtype: string\r\n - name: other\r\n dtype: string\r\n---\r\n\r\nThis is a dataset card\r\n```\r\n\r\nWhat error did you get when you tried to specify the columns in `dataset_info` ?",
"If you review the changes in #7060 you'll note that `features` are not passed to `DatasetInfo`.\r\n\r\nIn your case the features are being extracted by [this code](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/webdataset/webdataset.py#L72-L98).\r\n\r\nTry with the `Steps to reproduce the bug`. It's the same error mentioned in `Describe the bug` because `features` are not passed to `DatasetInfo`.\r\n\r\n`features` are [not used](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/builder.py#L365-L366) when the `BuilderConfig` has no `features` attribute. `WebDataset` uses the default [`BuilderConfig`](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/builder.py#L101-L124).\r\n\r\nThere is a [warning](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/load.py#L640-L648) that `features` are ignored.\r\n\r\nNote that as mentioned in `Describe the bug` this could also be resolved by removing the check [here](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/webdataset/webdataset.py#L76-L80) because Arrow actually handles this itself, Arrow sets any missing fields to `None`, at least in my case.",
"Note for anyone else who encounters this issue, every dataset type except folder-based types supported features in the [documented](https://huggingface.co/docs/datasets/repository_structure#builder-parameters) manner; [Arrow](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/arrow/arrow.py#L15-L21), [csv](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/csv/csv.py#L25-L68), [generator](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/generator/generator.py#L8-L19), [json](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/json/json.py#L42-L52), [pandas](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/pandas/pandas.py#L14-L20), [parquet](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/parquet/parquet.py#L16-L24), [spark](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/spark/spark.py#L31-L37), [sql](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/sql/sql.py#L24-L35) and [text](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/text/text.py#L18-L27). `WebDataset` is different and requires [`dataset_info` which is vaguely documented](https://huggingface.co/docs/datasets/dataset_script#optional-generate-dataset-metadata) under dataset loading scripts.",
"Thanks for explaining. I see the Dataset Viewer is still failing - I'll update `datasets` in the Viewer to fix this"
] | ### Describe the bug
Consider a WebDataset with multiple images for each item where the number of images may vary: [example](https://huggingface.co/datasets/bigdata-pw/fashion-150k)
Due to this [code](https://github.com/huggingface/datasets/blob/87f4c2088854ff33e817e724e75179e9975c1b02/src/datasets/packaged_modules/webdataset/webdataset.py#L76-L80) an error is given.
```
The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
```
The purpose of this check is unclear because PyArrow supports different keys.
Removing the check allows the dataset to be loaded and there's no issue when iterating through the dataset.
```
>>> from datasets import load_dataset
>>> path = "shards/*.tar"
>>> dataset = load_dataset("webdataset", data_files={"train": path}, split="train", streaming=True)
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 152/152 [00:00<00:00, 56458.93it/s]
>>> dataset
IterableDataset({
features: ['__key__', '__url__', '1.jpg', '2.jpg', '3.jpg', '4.jpg', 'json'],
n_shards: 152
})
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("bigdata-pw/fashion-150k")
```
### Expected behavior
Dataset loads without error
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.14.0-467.el9.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.19
- `huggingface_hub` version: 0.23.4
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | 7,055 |
https://github.com/huggingface/datasets/issues/7053 | Datasets.datafiles resolve_pattern `TypeError: can only concatenate tuple (not "str") to tuple` | [
"Hi,\r\n\r\nThis issue was fixed in `datasets` 2.15.0:\r\n- #6105\r\n\r\nYou will need to update your `datasets`:\r\n```\r\npip install -U datasets\r\n```",
"Duplicate of:\r\n- #6100"
] | ### Describe the bug
in data_files.py, line 332,
`fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options)`
If we run the code on AWS, as fs.protocol will be a tuple like: `('file', 'local')`
So, `isinstance(fs.protocol, str) == False` and
`protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else ""` will raise
`TypeError: can only concatenate tuple (not "str") to tuple`.
### Steps to reproduce the bug
Steps to reproduce:
1. Run on a cloud server like AWS,
2. `import datasets.data_files as datafile`
3. datafile.resolve_pattern('path/to/dataset', '.')
4. `TypeError: can only concatenate tuple (not "str") to tuple`
### Expected behavior
Should return path of the dataset, with fs.protocol at the beginning
### Environment info
- `datasets` version: 2.14.0
- Platform: Linux-3.10.0-1160.119.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.19
- Huggingface_hub version: 0.23.5
- PyArrow version: 16.1.0
- Pandas version: 1.1.5 | 7,053 |
End of preview. Expand
in Dataset Viewer.
Title: GitHub Issues Dataset for Semantic Search
Dataset Summary
The differentiator is that this dataset is filtered and have selected fields that is it is a direct dataset which can be used to create sementic serach engine to resolve user quries on hf datasets repo, it doesn't have those issues which are PRs and also relevant fields only required to create engine. This dataset was created following the last two sections of Chapter 5 from the Hugging Face NLP course. The course outlined steps to create a dataset for building a semantic search system using GitHub issues from the Hugging Face datasets repository. However, during the creation process, several challenges were encountered, including handling null values and timestamp-related errors. This dataset was refined by focusing on fields relevant to semantic search, such as html_url, title, body, comments, and issue number.
Purpose
The dataset supports the development of an asymmetric semantic search application, which involves short queries and longer paragraphs that address these queries, specifically for issues related to Hugging Face datasets.
Dataset Info
- Configuration Name: default
- Splits:
- Train Split
- Number of Bytes: 10108947
- Number of Examples: 2893
- Train Split
- Download Size: 4360781 bytes
- Total Dataset Size: 10108947 bytes
Languages
This dataset is entirely in English, encompassing all titles, bodies, and comments from the issues of the Hugging Face datasets.
Dataset Structure
This is till now and they are the issues which are not pull requests.
Dataset({ features: ['html_url', 'title', 'comments', 'body', 'number'], num_rows: 2893 })
Data Instances
An example data instance looks like this:
{ "html_url": "https://github.com/huggingface/datasets/issues/7079", "title": "HfHubHTTPError: 500 Server Error: Internal Server Error for url:", "comments": ["same issue here. ... list of all comments csv"], "body": "### Describe the bug\n\nnewly uploaded datasets, since yesterday, yields an error.\r\n\r\n...body describing the issue", "number": 7079 }
Data Fields
The dataset includes the following fields:
html_url: URL of the GitHub issue (string). title: Title of the issue (string). comments: Sequence of comments made on the issue (list of strings). body: Detailed description of the issue (string). number: GitHub issue number (int64).
To use this data in an environment where transformers are installed , the data can be imported with 2 lines of code -
from datasets import load_dataset
ds = load_dataset("amannagrawall002/github-issues")
Source Data
The dataset is crafted from scratch using the GitHub REST API, focusing on open issues from the Hugging Face datasets repository.
Initial Data Collection and Normalization
The creation of this dataset involved handling over 5000 issues, which exceeds the GitHub REST API's rate limit of 5000 requests per hour. Additionally, extracting comments required significant computational resources, highlighting the involvement of both automated processes and manual oversight.
Considerations
This dataset is tailored for creating a semantic search application centered around GitHub issues. It does not contain data on pull requests, which may limit its applicability for tasks requiring such information.
Additional Notes
Anyone deeply engaged with the Hugging Face NLP course might attempt to create this dataset. While it's accessible remotely as described in the course, this specific version focuses solely on supporting semantic search applications. Other uses may require a dataset with broader field coverage. This README is designed to be clear and informative, providing all necessary details about the dataset in a structured manner.
Author: Aman Agrawal
Contact: [email protected]
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