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https://api.github.com/repos/huggingface/datasets/issues/6277 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6277/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6277/comments | https://api.github.com/repos/huggingface/datasets/issues/6277/events | https://github.com/huggingface/datasets/issues/6277 | 1,927,044,546 | I_kwDODunzps5y3F3C | 6,277 | FileNotFoundError: Couldn't find a module script at /content/paws-x/paws-x.py. Module 'paws-x' doesn't exist on the Hugging Face Hub either. | {
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} | [] | closed | false | null | [] | null | 1 | 2023-10-04T22:01:25 | 2023-10-08T17:05:46 | 2023-10-08T17:05:46 | NONE | null | {
"total": 0,
"completed": 0,
"percent_completed": 0
} | null | null | null | ### Describe the bug
I'm encountering a "FileNotFoundError" while attempting to use the "paws-x" dataset to retrain the DistilRoBERTa-base model. The error message is as follows:
FileNotFoundError: Couldn't find a module script at /content/paws-x/paws-x.py. Module 'paws-x' doesn't exist on the Hugging Face Hub either.
### Steps to reproduce the bug
https://colab.research.google.com/drive/11xUUFxloClpmqLvDy_Xxfmo3oUzjY5nx#scrollTo=kUn74FigzhHm
### Expected behavior
The the trained model
### Environment info
colab, "paws-x" dataset , DistilRoBERTa-base model | {
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https://api.github.com/repos/huggingface/datasets/issues/6276 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6276/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6276/comments | https://api.github.com/repos/huggingface/datasets/issues/6276/events | https://github.com/huggingface/datasets/issues/6276 | 1,925,961,878 | I_kwDODunzps5yy9iW | 6,276 | I'm trying to fine tune the openai/whisper model from huggingface using jupyter notebook and i keep getting this error | {
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} | [] | open | false | null | [] | null | 3 | 2023-10-04T11:03:41 | 2023-11-27T10:39:16 | null | NONE | null | {
"total": 0,
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} | null | null | null | ### Describe the bug
I'm trying to fine tune the openai/whisper model from huggingface using jupyter notebook and i keep getting this error, i'm following the steps in this blog post
https://huggingface.co/blog/fine-tune-whisper
I tried google collab and it works but because I'm on the free version the training doesn't complete
the error comes in jupyter notebook when i run this line
`common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)`
here is the error message
```
Map (num_proc=4): 0% 0/2506 [00:52<?, ? examples/s]
The above exception was the direct cause of the following exception:
NameError Traceback (most recent call last) Cell In[19], line 1 ----> 1 common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)
File ~\anaconda\Lib\site-packages\datasets\dataset_dict.py:853, in DatasetDict.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc) 850 if cache_file_names is None: 851 cache_file_names = {k: None for k in self} 852 return DatasetDict( --> 853 { 854 k: dataset.map( 855 function=function, 856 with_indices=with_indices, 857 with_rank=with_rank, 858 input_columns=input_columns, 859 batched=batched, 860 batch_size=batch_size, 861 drop_last_batch=drop_last_batch, 862 remove_columns=remove_columns, 863 keep_in_memory=keep_in_memory, 864 load_from_cache_file=load_from_cache_file, 865 cache_file_name=cache_file_names[k], 866 writer_batch_size=writer_batch_size, 867 features=features, 868 disable_nullable=disable_nullable, 869 fn_kwargs=fn_kwargs, 870 num_proc=num_proc, 871 desc=desc, 872 ) 873 for k, dataset in self.items() 874 } 875 )
File ~\anaconda\Lib\site-packages\datasets\dataset_dict.py:854, in <dictcomp>(.0) 850 if cache_file_names is None: 851 cache_file_names = {k: None for k in self} 852 return DatasetDict( 853 { --> 854 k: dataset.map( 855 function=function, 856 with_indices=with_indices, 857 with_rank=with_rank, 858 input_columns=input_columns, 859 batched=batched, 860 batch_size=batch_size, 861 drop_last_batch=drop_last_batch, 862 remove_columns=remove_columns, 863 keep_in_memory=keep_in_memory, 864 load_from_cache_file=load_from_cache_file, 865 cache_file_name=cache_file_names[k], 866 writer_batch_size=writer_batch_size, 867 features=features, 868 disable_nullable=disable_nullable, 869 fn_kwargs=fn_kwargs, 870 num_proc=num_proc, 871 desc=desc, 872 ) 873 for k, dataset in self.items() 874 } 875 )
File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:592, in transmit_tasks.<locals>.wrapper(*args, **kwargs) 590 self: "Dataset" = kwargs.pop("self") 591 # apply actual function --> 592 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 593 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 594 for dataset in datasets: 595 # Remove task templates if a column mapping of the template is no longer valid
File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:557, in transmit_format.<locals>.wrapper(*args, **kwargs) 550 self_format = { 551 "type": self._format_type, 552 "format_kwargs": self._format_kwargs, 553 "columns": self._format_columns, 554 "output_all_columns": self._output_all_columns, 555 } 556 # apply actual function --> 557 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 558 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 559 # re-apply format to the output
File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:3189, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 3182 logger.info(f"Spawning {num_proc} processes") 3183 with logging.tqdm( 3184 disable=not logging.is_progress_bar_enabled(), 3185 unit=" examples", 3186 total=pbar_total, 3187 desc=(desc or "Map") + f" (num_proc={num_proc})", 3188 ) as pbar: -> 3189 for rank, done, content in iflatmap_unordered( 3190 pool, Dataset._map_single, kwargs_iterable=kwargs_per_job 3191 ): 3192 if done: 3193 shards_done += 1
File ~\anaconda\Lib\site-packages\datasets\utils\py_utils.py:1394, in iflatmap_unordered(pool, func, kwargs_iterable) 1391 finally: 1392 if not pool_changed: 1393 # we get the result in case there's an error to raise -> 1394 [async_result.get(timeout=0.05) for async_result in async_results]
File ~\anaconda\Lib\site-packages\datasets\utils\py_utils.py:1394, in <listcomp>(.0) 1391 finally: 1392 if not pool_changed: 1393 # we get the result in case there's an error to raise -> 1394 [async_result.get(timeout=0.05) for async_result in async_results]
File ~\anaconda\Lib\site-packages\multiprocess\pool.py:774, in ApplyResult.get(self, timeout) 772 return self._value 773 else: --> 774 raise self._value
NameError: name 'feature_extractor' is not defined
```
### Steps to reproduce the bug
1. follow the steps in this blog post
https://huggingface.co/blog/fine-tune-whisper
2. run this line of code
`common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)`
3. I'm using jupyter notebook from anaconda
### Expected behavior
No error message
### Environment info
datasets version: 2.8.0
Python version: 3.11
Windows 10 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6275 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6275/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6275/comments | https://api.github.com/repos/huggingface/datasets/issues/6275/events | https://github.com/huggingface/datasets/issues/6275 | 1,921,354,680 | I_kwDODunzps5yhYu4 | 6,275 | Would like to Contribute a dataset | {
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} | [] | closed | false | null | [] | null | 1 | 2023-10-02T07:00:21 | 2023-10-10T16:27:54 | 2023-10-10T16:27:54 | NONE | null | {
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} | null | null | null | I have a dataset of 2500 images that can be used for color-blind machine-learning algorithms. Since , there was no dataset available online , I made this dataset myself and would like to contribute this now to community | {
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https://api.github.com/repos/huggingface/datasets/issues/6274 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6274/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6274/comments | https://api.github.com/repos/huggingface/datasets/issues/6274/events | https://github.com/huggingface/datasets/issues/6274 | 1,921,036,328 | I_kwDODunzps5ygLAo | 6,274 | FileNotFoundError for dataset with multiple builder config | {
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} | [] | closed | false | null | [] | null | 2 | 2023-10-01T23:45:56 | 2024-08-14T04:42:02 | 2023-10-02T20:09:38 | NONE | null | {
"total": 0,
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} | null | null | null | ### Describe the bug
When there is only one config and only the dataset name is entered when using datasets.load_dataset(), it works fine. But if I create a second builder_config for my dataset and enter the config name when using datasets.load_dataset(), the following error will happen.
FileNotFoundError: [Errno 2] No such file or directory: 'C:/Users/chenx/.cache/huggingface/datasets/my_dataset/0_shot_multiple_choice/1.0.0/97c3854a012cfd6b045e3be4c864739902af2d818bb9235b047baa94c302e9a2.incomplete/my_dataset-test-00000-00000-of-NNNNN.arrow'
The "XXX.incomplete folder" in the cache folder of my dataset will disappear before "generating test split", which does not happen when config name is not entered and the config name is "default"
C:\Users\chenx\.cache\huggingface\datasets\my_dataset\0_shot_multiple_choice\1.0.0
The folder that is supposed to remain under the above directory will disappear, and the data generator will not have a place to generate data into.
### Steps to reproduce the bug
test = load_dataset('my_dataset', '0_shot_multiple_choice')
### Expected behavior
FileNotFoundError: [Errno 2] No such file or directory: 'C:/Users/chenx/.cache/huggingface/datasets/my_dataset/0_shot_multiple_choice/1.0.0/97c3854a012cfd6b045e3be4c864739902af2d818bb9235b047baa94c302e9a2.incomplete/my_dataset-test-00000-00000-of-NNNNN.arrow'
### Environment info
datasets 2.14.5
python 3.8.18 | {
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https://api.github.com/repos/huggingface/datasets/issues/6273 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6273/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6273/comments | https://api.github.com/repos/huggingface/datasets/issues/6273/events | https://github.com/huggingface/datasets/issues/6273 | 1,920,922,260 | I_kwDODunzps5yfvKU | 6,273 | Broken Link to PubMed Abstracts dataset . | {
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} | null | null | null | ### Describe the bug
The link provided for the dataset is broken,
data_files =
[https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst](url)
The
### Steps to reproduce the bug
Steps to reproduce:
1) Head over to [https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt#big-data-datasets-to-the-rescue](url)
2) In the Section "What is the Pile?", you can see a code snippet that contains the broken link.
### Expected behavior
The link should Redirect to the "PubMed Abstracts dataset" as expected .
### Environment info
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```ipython
In [1]: from datasets import *
In [2]: b = load_dataset_builder("u23429/stock_1_minute_ticker")
Downloading readme: 100%|ββββββββββββββββββββββββββ| 627/627 [00:00<00:00, 246kB/s]
In [3]: b.config.data_files
Out[3]:
{NamedSplit('train'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet'],
NamedSplit('validation'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet'],
NamedSplit('test'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet']}
```
This bug issue is present in the current `datasets` 2.14.5 and also on `main` even after https://github.com/huggingface/datasets/pull/6244 cc @mariosasko | {
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https://api.github.com/repos/huggingface/datasets/issues/6271 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6271/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6271/comments | https://api.github.com/repos/huggingface/datasets/issues/6271/events | https://github.com/huggingface/datasets/issues/6271 | 1,920,420,295 | I_kwDODunzps5yd0nH | 6,271 | Overwriting Split overwrites data but not metadata, corrupting dataset | {
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} | [] | closed | false | null | [] | null | 0 | 2023-09-30T22:37:31 | 2023-10-16T13:30:50 | 2023-10-16T13:30:50 | NONE | null | {
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} | null | null | null | ### Describe the bug
I want to be able to overwrite/update/delete splits in my dataset. Currently the only way to do is to manually go into the dataset and delete the split. If I try to overwrite programmatically I end up in an error state and (somewhat) corrupting the dataset. Read below.
**Current Behavior**
When I push to an existing split I get this error:
`ValueError: Split complexRoofLocation_01Apr2023_to_31May2023test already present`
This seems to suggest that the library doesn't support overwriting splits.
**Potential Bug**
Whatβs strange is that datasets, despite the operation erroring out with the ValueError above, does, in fact, overwrite the split:
`Pushing dataset shards to the dataset hub: 100% [.....................] 1/1 [00:00<00:00, 55.04it/s]`
Even though you got an error message and your code fails, your dataset is now changed. That seems like a bug. Either don't change the dataset, or don't throw the error and allow the script to proceed.
Additional Bug
While it overwrites the split, it doesnβt overwrite the splitβs information. Because of this when you pull down the dataset you may end up getting a `NonMatchingSplitsSizesError` if the size of the dataset during the overwrite is different. For example, my original split had 5 rows, but on my overwrite, I only had 4. Then when I try to download the dataset, I get a `NonMatchingSplitsSizesError` because the dataset's data.json states thereβs 5 but only 4 exist in the split.
Expected Behavior
This corrupts the dataset rendering it unusable (until you take manual intervention). Either the library should let the overwrite happen (which it does but should also update the metadata) or it shouldnβt do anything.
### Steps to reproduce the bug
[Colab Notebook](https://colab.research.google.com/drive/1bqVkD06Ngs9MQNdSk_ygCG6y1UqXA4pC?usp=sharing)
### Expected behavior
The split should be overwritten and I should be able to use the new version of the dataset without issue.
### Environment info
- `datasets` version: 2.14.5
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.17.3
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/6270 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6270/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6270/comments | https://api.github.com/repos/huggingface/datasets/issues/6270/events | https://github.com/huggingface/datasets/issues/6270 | 1,920,329,373 | I_kwDODunzps5ydead | 6,270 | Dataset.from_generator raises with sharded gen_args | {
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} | [] | closed | false | null | [] | null | 6 | 2023-09-30T16:50:06 | 2023-10-11T20:29:12 | 2023-10-11T20:29:11 | CONTRIBUTOR | null | {
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} | null | null | null | ### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards in `gen_kwargs`.
```
So I'd expect that if gen_kwargs was a list, then my generator would be called once for each element in the list with the dict in the list for that element.
It doesn't work that way though.
### Steps to reproduce the bug
```python
#!/usr/bin/python
from pathlib import Path
import datasets
def process_yaml(file):
yield dict(example=42)
if __name__ == '__main__':
import sys
dir = Path(sys.argv[0]).parent
ds = datasets.Dataset.from_generator(process_yaml, gen_kwargs=[{'file':f} for f in dir.glob('*.yml')],
)
ds.to_json('training.jsonl')
```
```
Generating train split: 0 examples [00:00, ? examples/s]
Traceback (most recent call last):
File "/tmp/dataset_bug.py", line 13, in <module>
ds = datasets.Dataset.from_generator(process_yaml, gen_kwargs=[{'file':f} for f in dir.glob('*.yml')],
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 1072, in from_generator
).read()
^^^^^^
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/io/generator.py", line 47, in read
self.builder.download_and_prepare(
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare
self._download_and_prepare(
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _download_and_prepare
super()._download_and_prepare(
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1656, in _prepare_split_single
generator = self._generate_examples(**gen_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: datasets.packaged_modules.generator.generator.Generator._generate_examples() argument after ** must be a ```
mapping, not list
### Expected behavior
I would expect that process_yaml would be called once for each yaml file in the directory where the script is run.
I also tried with the list being in gen_kwargs, but in that case process_yaml gets called with a list.
### Environment info
- `datasets` version: 2.14.6.dev0 (git commit 0cc77d7f45c7369; also tested with 2.14.0)
- Platform: Linux-6.1.0-10-amd64-x86_64-with-glibc2.36
- Python version: 3.11.2
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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https://api.github.com/repos/huggingface/datasets/issues/6269 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6269/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6269/comments | https://api.github.com/repos/huggingface/datasets/issues/6269/events | https://github.com/huggingface/datasets/pull/6269 | 1,919,572,790 | PR_kwDODunzps5bjbDc | 6,269 | Reduce the number of commits in `push_to_hub` | {
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} | Reduces the number of commits in `push_to_hub` by using the `preupload` API from https://github.com/huggingface/huggingface_hub/pull/1699. Each commit contains a maximum of 50 uploaded files.
A shard's fingerprint no longer needs to be added as a suffix to support resuming an upload, meaning the shards' naming scheme is the same as the initial one.
Also, it adds support for the following params: `create_pr`, `commit_message` and `revision` (`branch` deprecated; unlike the previous implementation, this one creates a branch if the branch does not exist to be consistent with `transformers`).
(Nit) This implementation keeps the markdown section of the generated README.md empty to enable importing the card template (when the card is accessed on the Hub).
Fixes https://github.com/huggingface/datasets/issues/5492, fixes https://github.com/huggingface/datasets/issues/6257, fixes https://github.com/huggingface/datasets/issues/5045, fixes https://github.com/huggingface/datasets/issues/6271
TODO:
- [x] set the minimal version to the next `hfh` release (once it's published) | {
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https://api.github.com/repos/huggingface/datasets/issues/6268 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6268/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6268/comments | https://api.github.com/repos/huggingface/datasets/issues/6268/events | https://github.com/huggingface/datasets/pull/6268 | 1,919,010,645 | PR_kwDODunzps5bhgs7 | 6,268 | Add repo_id to DatasetInfo | {
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} | ```python
from datasets import load_dataset
ds = load_dataset("lhoestq/demo1", split="train")
ds = ds.map(lambda x: {}, num_proc=2).filter(lambda x: True).remove_columns(["id"])
print(ds.repo_id)
# lhoestq/demo1
```
- repo_id is None when the dataset doesn't come from the Hub, e.g. from Dataset.from_dict
- repo_id is set to None when concatenating datasets with different repo ids
related to https://github.com/huggingface/datasets/issues/4129
TODO:
- [ ] discuss if it's ok for now
- [ ] tests | null | {
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] | open | false | null | [] | null | 7 | 2023-09-27T22:48:08 | 2023-10-26T18:46:08 | null | NONE | null | {
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} | null | null | null | ### Feature request
I have a multi label dataset and I'd like to be able to class encode the column and store the mapping directly in the features just as I can with a single label column. `class_encode_column` currently does not support multi labels.
Here's an example of what I'd like to encode:
```
data = {
'text': ['one', 'two', 'three', 'four'],
'labels': [['a', 'b'], ['b'], ['b', 'c'], ['a', 'd']]
}
dataset = Dataset.from_dict(data)
dataset = dataset.class_encode_column('labels')
```
I did some digging into the code base to evaluate the feasibility of this (note I'm very new to this code base) and from what I noticed the `ClassLabel` feature is still stored as an underlying raw data type of int so I thought a `MultiLabel` feature could similarly be stored as a Sequence of ints, thus not requiring significant serialization / conversion work to / from arrow.
I did a POC of this [here](https://github.com/huggingface/datasets/commit/15443098e9ce053943172f7ec6fce3769d7dff6e) and included a simple test case (please excuse all the commented out tests, going for speed of POC here and didn't want to fight IDE to debug a single test). In the test I just assert that `num_classes` is the same to show that things are properly serializing, but if you break after loading from disk you'll see the dataset correct and the dataset feature is as expected.
After digging more I did notice a few issues
- After loading from disk I noticed type of the `labels` class is `Sequence` not `MultiLabel` (though the added `feature` attribute came through). This doesn't happen for `ClassLabel` but I couldn't find the encode / decode code paths that handle this.
- I subclass `Sequence` in `MultiLabel` to leverage existing serialization, but this does miss the custom encode logic that `ClassLabel` has. I'm not sure of the best way to approach this as I haven't fully understood the encode / decode flow for datasets. I suspect my simple implementation will need some improvement as it'll require a significant amount of repeated logic to mimic `ClassLabel` behavior.
### Motivation
See above - would like to support multi label class encodings.
### Your contribution
This would be a big help for us and we're open to contributing but I'll likely need some guidance on how to implement to fit the encode / decode flow. Some suggestions on tests / would be great too, I'm guessing in addition to the class encode tests (that I'll need to expand) we'll need encode / decode tests. | null | {
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} | PyYAML, the YAML framework used in this library, allows the use of LibYAML to accelerate the methods `load` and `dump`. To use it, a user would need to first install a PyYAML version that uses LibYAML (not available in PyPI; needs to be manually installed). Then, to actually use them, PyYAML suggests importing the LibYAML version of the `Loader` and `Dumper` and falling back to the default ones. This PR implements this change. See [PyYAML docs](https://pyyaml.org/wiki/PyYAMLDocumentation) for more info.
This change was motivated after trying to use any of [the SugarCREPE datasets in the Hub](https://huggingface.co/datasets?search=sugarcrepe) provided by [the org HuggingFaceM4](https://huggingface.co/datasets/HuggingFaceM4). Such datasets save a lot of information (~1MB) in the YAML metadata from the `README.md` file and I noticed this slowed down the data loading process. BTW, I also noticed cache files for it is also slow because it tries to hash an instance of `DatasetInfo`, which in turn has all this metadata.
Also, I changed two list comprehensions into generator expressions to avoid allocating extra memory unnecessarily.
And BTW, there's [an issue in PyYAML suggesting to make this automatic](https://github.com/yaml/pyyaml/issues/437). | null | {
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} | ... to avoid an `ImportError` raised in `BeamBasedBuilder._save_info` when `apache_beam` is not installed (e.g., when downloading the processed version of a dataset from the HF GCS)
Fix https://github.com/huggingface/datasets/issues/6260 | {
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} | Temporarily pin tensorflow < 2.14.0 until permanent solution is found.
Hot fix #6263. | {
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] | null | 0 | 2023-09-27T08:12:05 | 2023-09-27T08:36:40 | 2023-09-27T08:36:40 | MEMBER | null | {
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} | null | null | null | Python 3.10 CI is broken for `test_py310`.
See: https://github.com/huggingface/datasets/actions/runs/6322990957/job/17169678812?pr=6262
```
FAILED tests/test_py_utils.py::TempSeedTest::test_tensorflow - ImportError: cannot import name 'context' from 'tensorflow.python' (/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/tensorflow/python/__init__.py)
```
```
_________________________ TempSeedTest.test_tensorflow _________________________
[gw1] linux -- Python 3.10.13 /opt/hostedtoolcache/Python/3.10.13/x64/bin/python
self = <tests.test_py_utils.TempSeedTest testMethod=test_tensorflow>
@require_tf
def test_tensorflow(self):
import tensorflow as tf
from tensorflow.keras import layers
model = layers.Dense(2)
def gen_random_output():
x = tf.random.uniform((1, 3))
return model(x).numpy()
> with temp_seed(42, set_tensorflow=True):
tests/test_py_utils.py:155:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/contextlib.py:135: in __enter__
return next(self.gen)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
seed = 42, set_pytorch = False, set_tensorflow = True
@contextmanager
def temp_seed(seed: int, set_pytorch=False, set_tensorflow=False):
"""Temporarily set the random seed. This works for python numpy, pytorch and tensorflow."""
np_state = np.random.get_state()
np.random.seed(seed)
if set_pytorch and config.TORCH_AVAILABLE:
import torch
torch_state = torch.random.get_rng_state()
torch.random.manual_seed(seed)
if torch.cuda.is_available():
torch_cuda_states = torch.cuda.get_rng_state_all()
torch.cuda.manual_seed_all(seed)
if set_tensorflow and config.TF_AVAILABLE:
import tensorflow as tf
> from tensorflow.python import context as tfpycontext
E ImportError: cannot import name 'context' from 'tensorflow.python' (/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/tensorflow/python/__init__.py)
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/datasets/utils/py_utils.py:257: ImportError
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6262/comments | https://api.github.com/repos/huggingface/datasets/issues/6262/events | https://github.com/huggingface/datasets/pull/6262 | 1,914,895,459 | PR_kwDODunzps5bTh6H | 6,262 | Fix CI 404 errors | {
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"type": "User",
"user_view_type": "public",
"site_admin": false
} | [] | closed | false | null | [] | null | 9 | 2023-09-27T07:40:18 | 2023-09-28T15:39:16 | 2023-09-28T15:30:40 | MEMBER | null | {
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"merged_at": "2023-09-28T15:30:40"
} | Currently our CI usually raises 404 errors when trying to delete temporary repositories. See, e.g.: https://github.com/huggingface/datasets/actions/runs/6314980985/job/17146507884
```
FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files_with_max_shard_size - huggingface_hub.utils._errors.RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-6512fb99-4a52c561752ece3d77eb6d57;2b61cae4-613d-4a73-bbb1-2faf9e32b02d)
Repository Not Found for url: https://hub-ci.huggingface.co/api/repos/delete.
Please make sure you specified the correct `repo_id` and `repo_type`.
If you are trying to access a private or gated repo, make sure you are authenticated.
FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features_audio - huggingface_hub.utils._errors.RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-6512fbb2-0333dd666d42f0e173c2bb68;dfdc4271-b49b-4008-8c49-f05cf7c1d53d)
Repository Not Found for url: https://hub-ci.huggingface.co/api/repos/delete.
Please make sure you specified the correct `repo_id` and `repo_type`.
If you are trying to access a private or gated repo, make sure you are authenticated.
FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_custom_splits - huggingface_hub.utils._errors.RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-6512fbca-167690694f39770a5b3a444e;baeaa905-0a57-4585-ac97-9aaae12dd47d)
Repository Not Found for url: https://hub-ci.huggingface.co/api/repos/delete.
Please make sure you specified the correct `repo_id` and `repo_type`.
If you are trying to access a private or gated repo, make sure you are authenticated.
```
I think this can be caused by collisions in temporary repository IDs because we create them in multiprocessing:
```python
with temporary_repo(f"{CI_HUB_USER}/test-{int(time.time() * 10e3)}") as ds_name:
```
This can also be caused when there is another issue that does not allow the creation of the repository, thus making it impossible to delete it.
This PR tries to fix this issue by increasing the precision of the number on the repository ID: `10e6` instead of `10e3`.
Additionally, this PR catches RepositoryNotFoundError.
| {
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https://api.github.com/repos/huggingface/datasets/issues/6261 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6261/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6261/comments | https://api.github.com/repos/huggingface/datasets/issues/6261/events | https://github.com/huggingface/datasets/issues/6261 | 1,913,813,178 | I_kwDODunzps5yEni6 | 6,261 | Can't load a dataset | {
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"user_view_type": "public",
"site_admin": false
} | [] | closed | false | null | [] | null | 5 | 2023-09-26T15:46:25 | 2023-10-05T10:23:23 | 2023-10-05T10:23:22 | NONE | null | {
"total": 0,
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"percent_completed": 0
} | null | null | null | ### Describe the bug
Can't seem to load the JourneyDB dataset.
It throws the following error:
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[15], line 2
1 # If the dataset is gated/private, make sure you have run huggingface-cli login
----> 2 dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True)
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1664, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1661 ignore_verifications = ignore_verifications or save_infos
1663 # Create a dataset builder
-> 1664 builder_instance = load_dataset_builder(
1665 path=path,
1666 name=name,
1667 data_dir=data_dir,
1668 data_files=data_files,
1669 cache_dir=cache_dir,
1670 features=features,
1671 download_config=download_config,
1672 download_mode=download_mode,
1673 revision=revision,
1674 use_auth_token=use_auth_token,
1675 **config_kwargs,
1676 )
1678 # Return iterable dataset in case of streaming
1679 if streaming:
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1490, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1488 download_config = download_config.copy() if download_config else DownloadConfig()
1489 download_config.use_auth_token = use_auth_token
-> 1490 dataset_module = dataset_module_factory(
1491 path,
1492 revision=revision,
1493 download_config=download_config,
1494 download_mode=download_mode,
1495 data_dir=data_dir,
1496 data_files=data_files,
1497 )
1499 # Get dataset builder class from the processing script
1500 builder_cls = import_main_class(dataset_module.module_path)
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1238, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
1237 if isinstance(e1, FileNotFoundError):
-> 1238 raise FileNotFoundError(
1239 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
1240 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1241 ) from None
1242 raise e1 from None
1243 else:
FileNotFoundError: Couldn't find a dataset script at /kaggle/working/JourneyDB/JourneyDB/JourneyDB.py or any data file in the same directory. Couldn't find 'JourneyDB/JourneyDB' on the Hugging Face Hub either: FileNotFoundError: Unable to find data in dataset repository JourneyDB/JourneyDB with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
### Steps to reproduce the bug
1)
```
from huggingface_hub import notebook_login
notebook_login()
```
2)
```
!pip install -q datasets
from datasets import load_dataset
```
3)
`dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True)`
### Expected behavior
Load the dataset
### Environment info
Notebook | {
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https://api.github.com/repos/huggingface/datasets/issues/6260 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6260/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6260/comments | https://api.github.com/repos/huggingface/datasets/issues/6260/events | https://github.com/huggingface/datasets/issues/6260 | 1,912,593,466 | I_kwDODunzps5x_9w6 | 6,260 | REUSE_DATASET_IF_EXISTS don't work | {
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"type": "User",
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"site_admin": false
} | [] | closed | false | null | [] | null | 3 | 2023-09-26T03:02:16 | 2023-09-28T18:23:36 | 2023-09-28T18:23:36 | NONE | null | {
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} | null | null | null | ### Describe the bug
I use the following code to download natural_question dataset. Even though I have completely download it, the next time I run this code, the new download procedure will start and cover the original /data/lxy/NQ
config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ')
data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
---
Since I don't have apache_beam installed, it throw a exception. After I pip install apache_beam ,the download restart..
### Steps to reproduce the bug
run this two line code
config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ')
data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
### Expected behavior
Download behavior can be correctly follow DownloadMode
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-3.10.0-1160.88.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.17
- Huggingface_hub version: 0.16.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/6259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6259/comments | https://api.github.com/repos/huggingface/datasets/issues/6259/events | https://github.com/huggingface/datasets/issues/6259 | 1,911,965,758 | I_kwDODunzps5x9kg- | 6,259 | Duplicated Rows When Loading Parquet Files from Root Directory with Subdirectories | {
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} | null | null | null | ### Describe the bug
When parquet files are saved in "train" and "val" subdirectories under a root directory, and datasets are then loaded using `load_dataset("parquet", data_dir="root_directory")`, the resulting dataset has duplicated rows for both the training and validation sets.
### Steps to reproduce the bug
1. Create a root directory, e.g., "testing123".
2. Under "testing123", create two subdirectories: "train" and "val".
3. Create and save a parquet file with 3 unique rows in the "train" subdirectory.
4. Create and save a parquet file with 4 unique rows in the "val" subdirectory.
5. Load the datasets from the root directory using `load_dataset("parquet", data_dir="testing123")`
6. Iterate through the datasets and print the rows
Here's a collab reproducing these steps:
https://colab.research.google.com/drive/11NEdImnQ3OqJlwKSHRMhr7jCBesNdLY4?usp=sharing
### Expected behavior
- Training set should contain 3 unique rows.
- Validation set should contain 4 unique rows.
### Environment info
- `datasets` version: 2.14.5
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.17.2
- PyArrow version: 9.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/6257 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6257/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6257/comments | https://api.github.com/repos/huggingface/datasets/issues/6257/events | https://github.com/huggingface/datasets/issues/6257 | 1,910,741,044 | I_kwDODunzps5x45g0 | 6,257 | HfHubHTTPError - exceeded our hourly quotas for action: commit | {
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} | null | null | null | ### Describe the bug
I try to upload a very large dataset of images, and get the following error:
```
File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/hf_api.py:2712, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit, run_as_future)
2710 try:
2711 commit_resp = get_session().post(url=commit_url, headers=headers, data=data, params=params)
-> 2712 hf_raise_for_status(commit_resp, endpoint_name="commit")
2713 except RepositoryNotFoundError as e:
2714 e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)
File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:301, in hf_raise_for_status(response, endpoint_name)
297 raise BadRequestError(message, response=response) from e
299 # Convert `HTTPError` into a `HfHubHTTPError` to display request information
300 # as well (request id and/or server error message)
--> 301 raise HfHubHTTPError(str(e), response=response) from e
HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/yuvalkirstain/pickapic_v2/commit/main (Request ID: Root=1-65112399-12d63f7d7f28bfa40a36a0fd)
You have exceeded our hourly quotas for action: commit. We invite you to retry later.
```
this makes it much less convenient to host large datasets on HF hub.
### Steps to reproduce the bug
Upload a very large dataset of images
### Expected behavior
the upload to work well
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.15.0-1033-aws-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/6256 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6256/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6256/comments | https://api.github.com/repos/huggingface/datasets/issues/6256/events | https://github.com/huggingface/datasets/issues/6256 | 1,910,275,199 | I_kwDODunzps5x3Hx_ | 6,256 | load_dataset() function's cache_dir does not seems to work | {
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} | null | null | null | ### Describe the bug
datasets version: 2.14.5
when trying to run the following command
trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir')
I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine.
It seems the cache_dir parameter cannot change the dataset saving directory from the default
what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work
### Steps to reproduce the bug
datasets version: 2.14.5
when trying to run the following command
trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir')
I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine.
It seems the cache_dir parameter cannot change the dataset saving directory from the default
what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work
### Expected behavior
the dataset should be saved to the cache_dir points to
### Environment info
datasets version: 2.14.5
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} | For datasets with lots of configs defined in YAML
E.g. `load_dataset("uonlp/CulturaX", "fr", revision="refs/pr/6")` from >1min to 15sec | {
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} | null | null | null | ### Describe the bug
Hey there,
Iβm using Dataset.from_generator() to convert a torch_dataset to the Huggingface Dataset.
However, when I debug my code on vscode, I find that it runs really slow on Dataset.from_generator() which may even 20 times longer then run the script on terminal.
### Steps to reproduce the bug
I write a simple test code :
```python
import os
from functools import partial
from typing import Callable
import torch
import time
from torch.utils.data import Dataset as TorchDataset
from datasets import load_from_disk, Dataset as HFDataset
import torch
from torch.utils.data import Dataset
class SimpleDataset(Dataset):
def __init__(self, data):
self.data = data
self.keys = list(data[0].keys())
def __len__(self):
return len(self.data)
def __getitem__(self, index):
sample = self.data[index]
return {key: sample[key] for key in self.keys}
def TorchDataset2HuggingfaceDataset(torch_dataset: TorchDataset, cache_dir: str = None
) -> HFDataset:
"""
convert torch dataset to huggingface dataset
"""
generator : Callable[[], TorchDataset] = lambda: (sample for sample in torch_dataset)
return HFDataset.from_generator(generator, cache_dir=cache_dir)
if __name__ == '__main__':
data = [
{'id': 1, 'name': 'Alice'},
{'id': 2, 'name': 'Bob'},
{'id': 3, 'name': 'Charlie'}
]
torch_dataset = SimpleDataset(data)
start_time = time.time()
huggingface_dataset = TorchDataset2HuggingfaceDataset(torch_dataset)
end_time = time.time()
print("time: ", end_time - start_time)
print(huggingface_dataset)
```
### Expected behavior
this test on my machine report that the running time on terminal is 0.086,
however the running time in debugging mode on vscode is 0.25, which I think is much longer than expected.
Iβd like to know is the anything wrong in the code or just because of debugging?
I have traced the code and I find is this func which I get stuck.
```python
def create_config_id(
self,
config_kwargs: dict,
custom_features: Optional[Features] = None,
) -> str:
...
# stuck in this line
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
```
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31
- Python version: 3.11.3
- Huggingface_hub version: 0.17.2
- PyArrow version: 11.0.0
- Pandas version: 2.0.1 | {
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} | Fix https://github.com/huggingface/datasets-server/issues/1812
this was causing this issue:
```ipython
In [1]: from datasets import *
In [2]: inspect.get_dataset_config_names("aakanksha/udpos")
Out[2]: ['default']
In [3]: load_dataset_builder("aakanksha/udpos").config.name
Out[3]: 'en'
``` | {
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} | null | null | null | ### Feature request
I noticed that some of my images loaded using PIL have some metadata related to exif that can rotate them when loading.
Since the dataset.features.Image uses PIL for loading, the loaded image may be rotated (width and height will be inverted) thus for tasks as object detection and layoutLM this can create some inconsistencies (between input bboxes and input images).
For now there is no option in datasets.features.Image to specify that. We need to do the following when preparing examples (when preparing images for training, test or inference):
```
from PIL import Image, ImageOps
pil = ImageOps.exif_transpose(pil)
```
reference: https://stackoverflow.com/a/63950647/5720150
Is it possible to add this by default to the datasets.feature.Image ? or to add the option to do the ImageOps.exif_transpose?
Thank you
### Motivation
Prevent having inverted data related to exif metadata that may affect object detection tasks
### Your contribution
Changing in datasets.featrues.Image I can help with that. | {
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https://api.github.com/repos/huggingface/datasets/issues/6251 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6251/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6251/comments | https://api.github.com/repos/huggingface/datasets/issues/6251/events | https://github.com/huggingface/datasets/pull/6251 | 1,904,418,426 | PR_kwDODunzps5awQsy | 6,251 | Support streaming datasets with pyarrow.parquet.read_table | {
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} | Support streaming datasets with `pyarrow.parquet.read_table`.
See: https://huggingface.co/datasets/uonlp/CulturaX/discussions/2
CC: @AndreaFrancis | {
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https://api.github.com/repos/huggingface/datasets/issues/6247 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6247/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6247/comments | https://api.github.com/repos/huggingface/datasets/issues/6247/events | https://github.com/huggingface/datasets/pull/6247 | 1,901,390,945 | PR_kwDODunzps5amAQ1 | 6,247 | Update create_dataset.mdx | {
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} | modified , as AudioFolder and ImageFolder not in Dataset Library.
``` from datasets import AudioFolder ``` and ```from datasets import ImageFolder``` to ```from datasets import load_dataset```
```
cannot import name 'AudioFolder' from 'datasets' (/home/eswardivi/miniconda3/envs/Hugformers/lib/python3.10/site-packages/datasets/__init__.py)
``` | {
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```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-9-bd197b36b6a0>](https://localhost:8080/#) in <cell line: 1>()
----> 1 dataset['train']['/workspace/data']
3 frames
[/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_column_key(key, columns)
518 def _check_valid_column_key(key: str, columns: List[str]) -> None:
519 if key not in columns:
--> 520 raise KeyError(f"Column {key} not in the dataset. Current columns in the dataset: {columns}")
521
522
KeyError: "Column train not in the dataset. Current columns in the dataset: ['image', '/workspace/data']"
```
### Steps to reproduce the bug
please find the notebook for reference: https://colab.research.google.com/drive/10lZ_zLtU4itYVmIVTvIEVbjfOtCZaAZy?usp=sharing
### Expected behavior
add column to the dataset
### Environment info
colab pro | {
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When a dataset saved with a specified inner sequence length is loaded without specifying that length, the original data is altered and becomes inconsistent.
### Steps to reproduce the bug
```python
from datasets import Dataset, Features, Value, Sequence, load_dataset
# Repository ID
repo_id = "my_repo_id"
# Define features with a specific length of 3 for each inner sequence
specified_features = Features({"key": Sequence(Sequence(Value("float32"), length=3))})
# Create a dataset with the specified features
data = [
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
[[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]],
]
dataset = Dataset.from_dict({"key": data}, features=specified_features)
# Push the dataset to the hub
dataset.push_to_hub(repo_id)
# Define features without specifying the length
unspecified_features = Features({"key": Sequence(Sequence(Value("float32")))})
# Load the dataset from the hub with this new feature definition
dataset = load_dataset(f"qgallouedec/{repo_id}", split="train", features=unspecified_features)
# The obtained data is altered
print(dataset.to_dict()) # {'key': [[[1.0], [2.0]], [[3.0], [4.0]]]}
```
### Expected behavior
```python
print(dataset.to_dict()) # {'key': [[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]]}
```
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-6.2.0-32-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/6240 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6240/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6240/comments | https://api.github.com/repos/huggingface/datasets/issues/6240/events | https://github.com/huggingface/datasets/issues/6240 | 1,895,723,888 | I_kwDODunzps5w_nNw | 6,240 | Dataloader stuck on multiple GPUs | {
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} | [] | closed | false | null | [] | null | 2 | 2023-09-14T05:30:30 | 2023-09-14T23:54:42 | 2023-09-14T23:54:42 | NONE | null | {
"total": 0,
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"percent_completed": 0
} | null | null | null | ### Describe the bug
I am trying to get CLIP to fine-tuning with my code.
When I tried to run it on multiple GPUs using accelerate, I encountered the following phenomenon.
- Validation dataloader stuck in 2nd epoch only on multi-GPU
Specifically, when the "for inputs in valid_loader:" process is finished, it does not proceed to the next step. train_loader process is completed. Also, both train and valid are working correctly in the first epoch.
The accelerate command at that time is as follows.
`accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...`
- This will not happen when single GPU is used.
`CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...`
- Setting num_workers=0 in dataloader did not change the result.
### Steps to reproduce the bug
1. The codes for fine-tuning the regular CLIP were updated for accelerate.
2. Run the code with the accelerate command as `accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...` and the above problem will occur.
3. CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...` , it works fine.
### Expected behavior
It Should end normally as if it was run on a single GPU.
### Environment info
Since `datasets-cli env` did not work, the environment is described below.
- OS: Ubuntu 22.04 with Docker
- Docker: 24.0.5, build ced0996
- Python: 3.10.12
- torch==2.0.1
- accelerate==0.21.0
- transformers==4.33.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/6239 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6239/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6239/comments | https://api.github.com/repos/huggingface/datasets/issues/6239/events | https://github.com/huggingface/datasets/issues/6239 | 1,895,349,382 | I_kwDODunzps5w-LyG | 6,239 | Load local audio data doesn't work | {
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} | [] | closed | false | null | [] | null | 2 | 2023-09-13T22:30:01 | 2023-09-15T14:32:10 | 2023-09-15T14:32:10 | NONE | null | {
"total": 0,
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} | null | null | null | ### Describe the bug
I get a RuntimeError from the following code:
```python
audio_dataset = Dataset.from_dict({"audio": ["/kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3"]}).cast_column("audio", Audio())
audio_dataset[0]
```
### Traceback
<details>
```python
RuntimeError Traceback (most recent call last)
Cell In[33], line 1
----> 1 train_dataset[0]
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key)
1762 def __getitem__(self, key): # noqa: F811
1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 1764 return self._getitem(
1765 key,
1766 )
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs)
1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 1749 formatted_output = format_table(
1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
1751 )
1752 return formatted_output
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
--> 532 return formatter(pa_table, query_type=query_type)
533 elif query_type == "column":
534 if key in format_columns:
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:312, in PythonFormatter.format_row(self, pa_table)
310 row = self.python_arrow_extractor().extract_row(pa_table)
311 if self.decoded:
--> 312 row = self.python_features_decoder.decode_row(row)
313 return row
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:221, in PythonFeaturesDecoder.decode_row(self, row)
220 def decode_row(self, row: dict) -> dict:
--> 221 return self.features.decode_example(row) if self.features else row
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1386, in Features.decode_example(self, example)
1376 def decode_example(self, example: dict):
1377 """Decode example with custom feature decoding.
1378
1379 Args:
(...)
1383 :obj:`dict[str, Any]`
1384 """
-> 1386 return {
1387 column_name: decode_nested_example(feature, value)
1388 if self._column_requires_decoding[column_name]
1389 else value
1390 for column_name, (feature, value) in zip_dict(
1391 {key: value for key, value in self.items() if key in example}, example
1392 )
1393 }
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1387, in <dictcomp>(.0)
1376 def decode_example(self, example: dict):
1377 """Decode example with custom feature decoding.
1378
1379 Args:
(...)
1383 :obj:`dict[str, Any]`
1384 """
1386 return {
-> 1387 column_name: decode_nested_example(feature, value)
1388 if self._column_requires_decoding[column_name]
1389 else value
1390 for column_name, (feature, value) in zip_dict(
1391 {key: value for key, value in self.items() if key in example}, example
1392 )
1393 }
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1087, in decode_nested_example(schema, obj)
1085 # Object with special decoding:
1086 elif isinstance(schema, (Audio, Image)):
-> 1087 return schema.decode_example(obj) if obj is not None else None
1088 return obj
File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:103, in Audio.decode_example(self, value)
101 raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.")
102 elif path is not None and path.endswith("mp3"):
--> 103 array, sampling_rate = self._decode_mp3(file if file else path)
104 elif path is not None and path.endswith("opus"):
105 if file:
File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:241, in Audio._decode_mp3(self, path_or_file)
238 except RuntimeError as err:
239 raise ImportError("To support decoding 'mp3' audio files, please install 'sox'.") from err
--> 241 array, sampling_rate = torchaudio.load(path_or_file, format="mp3")
242 if self.sampling_rate and self.sampling_rate != sampling_rate:
243 if not hasattr(self, "_resampler") or self._resampler.orig_freq != sampling_rate:
File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:256, in load(filepath, frame_offset, num_frames, normalize, channels_first, format)
254 if ret is not None:
255 return ret
--> 256 return _fallback_load(filepath, frame_offset, num_frames, normalize, channels_first, format)
File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:30, in _fail_load(filepath, frame_offset, num_frames, normalize, channels_first, format)
22 def _fail_load(
23 filepath: str,
24 frame_offset: int = 0,
(...)
28 format: Optional[str] = None,
29 ) -> Tuple[torch.Tensor, int]:
---> 30 raise RuntimeError("Failed to load audio from {}".format(filepath))
RuntimeError: Failed to load audio from /kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3
```
</details>
### Steps to reproduce the bug
1. - Create a custom dataset using Local files of type mp3.
3. - Try to read the first audio item.
### Expected behavior
Expected output
```python
audio_dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': 'path/to/audio_1',
'sampling_rate': 16000}
```
### Environment info
N/A | {
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https://api.github.com/repos/huggingface/datasets/issues/6238 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6238/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6238/comments | https://api.github.com/repos/huggingface/datasets/issues/6238/events | https://github.com/huggingface/datasets/issues/6238 | 1,895,207,828 | I_kwDODunzps5w9pOU | 6,238 | `dataset.filter` ALWAYS removes the first item from the dataset when using batched=True | {
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} | [] | closed | false | null | [] | null | 2 | 2023-09-13T20:20:37 | 2023-09-17T07:05:07 | 2023-09-17T07:05:07 | NONE | null | {
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} | null | null | null | ### Describe the bug
If you call batched=True when calling `filter`, the first item is _always_ filtered out, regardless of the filter condition.
### Steps to reproduce the bug
Here's a minimal example:
```python
def filter_batch_always_true(batch, indices):
print("First index being passed into this filter function: ", indices[0])
return indices # Keep all indices
data = {"value": list(range(10))}
dataset = Dataset.from_dict(data)
filtered_dataset = dataset.filter(filter_batch_always_true, with_indices=True, batched=True)
print("Length of original dataset: ", len(dataset))
print("Length of filtered_dataset: ", len(filtered_dataset))
print("Is equal to original? ", len(filtered_dataset) == len(dataset))
print("First item of filtered dataset: ", filtered_dataset[0])
print("Last item of filtered dataset: ", filtered_dataset[-1])
```
prints:
```
First index being passed into this filter function: 0
Length of original dataset: 10
Length of filtered_dataset: 9
Is equal to original? False
First item of filtered dataset: {'value': 1}
Last item of filtered dataset: {'value': 9}
```
### Expected behavior
Filter should respect the filter condition.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-13.5-arm64-arm-64bit
- Python version: 3.9.18
- Huggingface_hub version: 0.17.1
- PyArrow version: 10.0.1
- Pandas version: 2.0.2
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https://api.github.com/repos/huggingface/datasets/issues/6237 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6237/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6237/comments | https://api.github.com/repos/huggingface/datasets/issues/6237/events | https://github.com/huggingface/datasets/issues/6237 | 1,893,822,321 | I_kwDODunzps5w4W9x | 6,237 | Tokenization with multiple workers is too slow | {
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} | [] | closed | false | null | [] | null | 1 | 2023-09-13T06:18:34 | 2023-09-19T21:54:58 | 2023-09-19T21:54:58 | NONE | null | {
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} | null | null | null | I am trying to tokenize a few million documents with multiple workers but the tokenization process is taking forever.
Code snippet:
```
raw_datasets.map(
encode_function,
batched=False,
num_proc=args.preprocessing_num_workers,
load_from_cache_file=not args.overwrite_cache,
remove_columns=[name for name in raw_datasets["train"].column_names if name not in ["input_ids", "labels", "attention_mask"]],
desc="Tokenizing data",
)
```
Details:
```
transformers==4.28.0.dev0
datasets==4.28.0.dev0
preprocessing_num_workers==48
```
tokenizer == decapoda-research/llama-7b-hf
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https://api.github.com/repos/huggingface/datasets/issues/6236 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6236/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6236/comments | https://api.github.com/repos/huggingface/datasets/issues/6236/events | https://github.com/huggingface/datasets/issues/6236 | 1,893,648,480 | I_kwDODunzps5w3shg | 6,236 | Support buffer shuffle for to_tf_dataset | {
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} | null | null | null | ### Feature request
I'm using to_tf_dataset to convert a large dataset to tf.data.Dataset and use Keras fit to train model.
Currently, to_tf_dataset only supports full size shuffle, which can be very slow on large dataset.
tf.data.Dataset support buffer shuffle by default.
shuffle(
buffer_size, seed=None, reshuffle_each_iteration=None, name=None
)
### Motivation
I'm very frustrated to find the loading with shuffling large dataset is very slow. It seems impossible to shuffle before training Keras with big dataset.
### Your contribution
NA | null | {
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] | open | false | null | [] | null | 0 | 2023-09-12T21:51:08 | 2023-09-12T21:51:08 | null | NONE | null | {
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} | null | null | null | ### Feature request
Current multiprocessing for download/extract is not done nestedly. For example, when processing SlimPajama, there is only 3 processes (for train/test/val), while there are many files inside these 3 folders
```
Downloading data files #0: 0%| | 0/1 [00:00<?, ?obj/s]
Downloading data files #1: 0%| | 0/1 [00:00<?, ?obj/s]
Downloading data files #2: 0%| | 0/1 [00:00<?, ?obj/s]
Extracting data files #0: 0%| | 0/1 [00:00<?, ?obj/s]
Extracting data files #1: 0%| | 0/1 [00:00<?, ?obj/s][A
Extracting data files #2: 0%| | 0/1 [00:00<?, ?obj/s][A[A
```
### Motivation
speedup dataset loading
### Your contribution
I can help test the feature | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6233 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6233/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6233/comments | https://api.github.com/repos/huggingface/datasets/issues/6233/events | https://github.com/huggingface/datasets/pull/6233 | 1,891,804,286 | PR_kwDODunzps5aF3kd | 6,233 | Update README.md | {
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https://api.github.com/repos/huggingface/datasets/issues/6232 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6232/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6232/comments | https://api.github.com/repos/huggingface/datasets/issues/6232/events | https://github.com/huggingface/datasets/pull/6232 | 1,891,109,762 | PR_kwDODunzps5aDhhK | 6,232 | Improve error message for missing function parameters | {
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} | The error message in the fingerprint module was missing the f-string 'f' symbol, so the error message returned by fingerprint.py, line 469 was literally "function {func} is missing parameters {fingerprint_names} in signature."
This has been fixed. | {
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https://api.github.com/repos/huggingface/datasets/issues/6231 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6231/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6231/comments | https://api.github.com/repos/huggingface/datasets/issues/6231/events | https://github.com/huggingface/datasets/pull/6231 | 1,890,863,249 | PR_kwDODunzps5aCr8_ | 6,231 | Overwrite legacy default config name in `dataset_infos.json` in packaged datasets | {
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} | Currently if we push data as default config with `.push_to_hub` to a repo that has a legacy `dataset_infos.json` file containing a legacy default config name like `{username}--{dataset_name}`, new key `"default"` is added to `dataset_infos.json` along with the legacy one. I think the legacy one should be dropped in this case.
Also, in `load.py` I suggest to check if a legacy config name is indeed a legacy config name because after this fix it might not be the case (this check was first introduced in https://github.com/huggingface/datasets/pull/6218) | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6230 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6230/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6230/comments | https://api.github.com/repos/huggingface/datasets/issues/6230/events | https://github.com/huggingface/datasets/pull/6230 | 1,890,521,006 | PR_kwDODunzps5aBh6L | 6,230 | Don't skip hidden files in `dl_manager.iter_files` when they are given as input | {
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https://api.github.com/repos/huggingface/datasets/issues/6229 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6229/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6229/comments | https://api.github.com/repos/huggingface/datasets/issues/6229/events | https://github.com/huggingface/datasets/issues/6229 | 1,889,050,954 | I_kwDODunzps5wmKFK | 6,229 | Apply inference on all images in the dataset | {
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} | null | null | null | ### Describe the bug
```
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
Cell In[14], line 11
9 for idx, example in enumerate(dataset['train']):
10 image_path = example['image']
---> 11 mask_image = process_image(image_path)
12 mask_image.save(f"mask_{idx}.png")
Cell In[14], line 4, in process_image(image_path)
2 def process_image(image_path):
3 print("Processing image:", image_path)
----> 4 result = inferencer(image_path)['predictions']
5 mask = np.where(result == 12, 255, 0).astype('uint8')
6 return Image.fromarray(mask)
File /usr/local/lib/python3.10/dist-packages/mmseg/apis/mmseg_inferencer.py:183, in MMSegInferencer.__call__(self, inputs, return_datasamples, batch_size, show, wait_time, out_dir, img_out_dir, pred_out_dir, **kwargs)
180 pred_out_dir = ''
181 img_out_dir = ''
--> 183 return super().__call__(
184 inputs=inputs,
185 return_datasamples=return_datasamples,
186 batch_size=batch_size,
187 show=show,
188 wait_time=wait_time,
189 img_out_dir=img_out_dir,
190 pred_out_dir=pred_out_dir,
191 **kwargs)
File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:221, in BaseInferencer.__call__(self, inputs, return_datasamples, batch_size, **kwargs)
218 inputs = self.preprocess(
219 ori_inputs, batch_size=batch_size, **preprocess_kwargs)
220 preds = []
--> 221 for data in (track(inputs, description='Inference')
222 if self.show_progress else inputs):
223 preds.extend(self.forward(data, **forward_kwargs))
224 visualization = self.visualize(
225 ori_inputs, preds,
226 **visualize_kwargs) # type: ignore # noqa: E501
File /usr/local/lib/python3.10/dist-packages/rich/progress.py:168, in track(sequence, description, total, auto_refresh, console, transient, get_time, refresh_per_second, style, complete_style, finished_style, pulse_style, update_period, disable, show_speed)
157 progress = Progress(
158 *columns,
159 auto_refresh=auto_refresh,
(...)
164 disable=disable,
165 )
167 with progress:
--> 168 yield from progress.track(
169 sequence, total=total, description=description, update_period=update_period
170 )
File /usr/local/lib/python3.10/dist-packages/rich/progress.py:1210, in Progress.track(self, sequence, total, task_id, description, update_period)
1208 if self.live.auto_refresh:
1209 with _TrackThread(self, task_id, update_period) as track_thread:
-> 1210 for value in sequence:
1211 yield value
1212 track_thread.completed += 1
File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:291, in BaseInferencer.preprocess(self, inputs, batch_size, **kwargs)
266 """Process the inputs into a model-feedable format.
267
268 Customize your preprocess by overriding this method. Preprocess should
(...)
287 Any: Data processed by the ``pipeline`` and ``collate_fn``.
288 """
289 chunked_data = self._get_chunk_data(
290 map(self.pipeline, inputs), batch_size)
--> 291 yield from map(self.collate_fn, chunked_data)
File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:588, in BaseInferencer._get_chunk_data(self, inputs, chunk_size)
586 chunk_data = []
587 for _ in range(chunk_size):
--> 588 processed_data = next(inputs_iter)
589 chunk_data.append(processed_data)
590 yield chunk_data
File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results)
9 def __call__(self,
10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]:
---> 12 return self.transform(results)
File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/wrappers.py:88, in Compose.transform(self, results)
79 """Call function to apply transforms sequentially.
80
81 Args:
(...)
85 dict or None: Transformed results.
86 """
87 for t in self.transforms:
---> 88 results = t(results) # type: ignore
89 if results is None:
90 return None
File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results)
9 def __call__(self,
10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]:
---> 12 return self.transform(results)
File /usr/local/lib/python3.10/dist-packages/mmseg/datasets/transforms/loading.py:496, in InferencerLoader.transform(self, single_input)
494 inputs = single_input
495 else:
--> 496 raise NotImplementedError
498 if 'img' in inputs:
499 return self.from_ndarray(inputs)
NotImplementedError:
````
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset('Andyrasika/cat_kingdom')
dataset
from mmseg.apis import MMSegInferencer
checkpoint_name = 'segformer_mit-b5_8xb2-160k_ade20k-640x640'
inferencer = MMSegInferencer(model=checkpoint_name)
# Define a function to apply the code to each image in the dataset
def process_image(image_path):
print("Processing image:", image_path)
result = inferencer(image_path)['predictions']
mask = np.where(result == 12, 255, 0).astype('uint8')
return Image.fromarray(mask)
# Process and save masks for each image in the dataset
for idx, example in enumerate(dataset['train']):
image_path = example['image']
mask_image = process_image(image_path)
mask_image.save(f"mask_{idx}.png")
```
### Expected behavior
create a separate column with masks in the dataset and further shows as a separate column in hub
### Environment info
jupyter notebook RTX 3090 | {
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https://api.github.com/repos/huggingface/datasets/issues/6228 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6228/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6228/comments | https://api.github.com/repos/huggingface/datasets/issues/6228/events | https://github.com/huggingface/datasets/pull/6228 | 1,887,959,311 | PR_kwDODunzps5Z5HZi | 6,228 | Remove RGB -> BGR image conversion in Object Detection tutorial | {
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https://api.github.com/repos/huggingface/datasets/issues/6226 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6226/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6226/comments | https://api.github.com/repos/huggingface/datasets/issues/6226/events | https://github.com/huggingface/datasets/pull/6226 | 1,887,462,591 | PR_kwDODunzps5Z3arq | 6,226 | Add push_to_hub with multiple configs docs | {
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https://api.github.com/repos/huggingface/datasets/issues/6225 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6225/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6225/comments | https://api.github.com/repos/huggingface/datasets/issues/6225/events | https://github.com/huggingface/datasets/issues/6225 | 1,887,054,320 | I_kwDODunzps5weinw | 6,225 | Conversion from RGB to BGR in Object Detection tutorial | {
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} | null | null | null | The [tutorial](https://huggingface.co/docs/datasets/main/en/object_detection) mentions the necessity of conversion the input image from BGR to RGB
> albumentations expects the image to be in BGR format, not RGB, so youβll have to convert the image before applying the transform.
[Link to tutorial](https://github.com/huggingface/datasets/blob/0a068dbf3b446417ffd89d32857608394ec699e6/docs/source/object_detection.mdx#L77)
However, relevant albumentations' tutorials [on channels conversion](https://albumentations.ai/docs/examples/example/#read-the-image-from-the-disk-and-convert-it-from-the-bgr-color-space-to-the-rgb-color-space) and [on boxes](https://albumentations.ai/docs/examples/example_bboxes/) imply that it's not really true no more.
I suggest removing this outdated conversion from the tutorial. | {
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https://api.github.com/repos/huggingface/datasets/issues/6224 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6224/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6224/comments | https://api.github.com/repos/huggingface/datasets/issues/6224/events | https://github.com/huggingface/datasets/pull/6224 | 1,886,043,692 | PR_kwDODunzps5Zym3j | 6,224 | Ignore `dataset_info.json` in data files resolution | {
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} | `save_to_disk` creates this file, but also [`HugginFaceDatasetSever`](https://github.com/gradio-app/gradio/blob/26fef8c7f85a006c7e25cdbed1792df19c512d02/gradio/flagging.py#L214), so this is needed to avoid issues such as [this one](https://discord.com/channels/879548962464493619/1149295819938349107/1149295819938349107). | {
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https://api.github.com/repos/huggingface/datasets/issues/6223 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6223/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6223/comments | https://api.github.com/repos/huggingface/datasets/issues/6223/events | https://github.com/huggingface/datasets/pull/6223 | 1,885,710,696 | PR_kwDODunzps5Zxd5c | 6,223 | Update README.md | {
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} | fixed a few typos | {
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https://api.github.com/repos/huggingface/datasets/issues/6222 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6222/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6222/comments | https://api.github.com/repos/huggingface/datasets/issues/6222/events | https://github.com/huggingface/datasets/pull/6222 | 1,884,875,510 | PR_kwDODunzps5Zup2f | 6,222 | fix typo in Audio dataset documentation | {
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} | There is a typo in the section of the documentation dedicated to creating an audio dataset. The Dataset is incorrectly suffixed with a `Config`
https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia/blob/main/librivox-indonesia.py#L59 | {
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} | [] | open | false | null | [] | null | 1 | 2023-09-06T16:03:32 | 2023-09-06T18:32:07 | null | COLLABORATOR | null | {
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} | null | null | null | Requested in https://discuss.huggingface.co/t/using-set-transform-on-a-dataset-leads-to-an-exception/53036.
I am not sure if supporting this is the best idea for the following reasons:
>For this to work, we would have to pickle a custom transform, which means the transform and the objects it references need to be serializable. Also, deserializing these bytes would make `load_from_disk` unsafe, so I'm not sure this is a good idea.
@lhoestq WDYT?
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} | Fix
```python
from datasets import load_dataset_builder
b = load_dataset_builder("lambdalabs/pokemon-blip-captions", "default")
print(b.info)
```
which should return
```
DatasetInfo(
features={'image': Image(decode=True, id=None), 'text': Value(dtype='string', id=None)},
dataset_name='pokemon-blip-captions',
config_name='default',
version=0.0.0,
splits={'train': SplitInfo(name='train', num_bytes=119417410.0, num_examples=833, shard_lengths=None, dataset_name='pokemon-blip-captions')},
download_checksums=None,
download_size=99672355,
dataset_size=119417410.0,
size_in_bytes=219089765.0,
...
)
```
instead of and empty dataset info.
The dataset has a dataset_infos.json file with a deprecated config name "lambdalabs--pokemon-blip-captions". We switched those config names to "default" in 2.14, so the builder.info should take this into account. | {
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https://api.github.com/repos/huggingface/datasets/issues/6217 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6217/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6217/comments | https://api.github.com/repos/huggingface/datasets/issues/6217/events | https://github.com/huggingface/datasets/issues/6217 | 1,883,614,607 | I_kwDODunzps5wRa2P | 6,217 | `Dataset.to_dict()` ignore `decode=True` with Image feature | {
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} | null | null | null | ### Describe the bug
`Dataset.to_dict` seems to ignore the decoding instruction passed in features.
### Steps to reproduce the bug
```python
import datasets
import numpy as np
from PIL import Image
img = np.random.randint(0, 256, (5, 5, 3), dtype=np.uint8)
img = Image.fromarray(img)
features = datasets.Features({"image": datasets.Image(decode=True)})
dataset = datasets.Dataset.from_dict({"image": [img]}, features=features)
print({key: dataset[key] for key in dataset.column_names})
# {'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=5x5 at 0x7EFBC80E15B0>]}
print(dataset.to_dict())
# {'image': [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x05\x00\x00\x00\x05\x08\x02\x00\x00\x00\x02\r\xb1\xb2\x00\x00\x00[IDATx\x9c\x01P\x00\xaf\xff\x01\x13\x1b<7\xe7\xe0\xdc^6\xed\x04\xc7M\xd2\x9f\x00X\x1b\xb0?\x1ba\x15\xc5 o\xd0\x80\xbe\x19/\x01\xec\x95\x1f\x9f\xffj\xfa1\xa7\xc4X\xea\xbe\xa4g\x00\xc4\x15\xdeC\xc7 \xbbaqe\xc8\xb9\xa9q\xe7\x00,?M\xc0)\xdaD`}\xb1\xdci\x1e\xafC\xa9]%.@\xa6\xf0\xb3\x00\x00\x00\x00IEND\xaeB`\x82', 'path': None}]}
```
### Expected behavior
I would expect `{key: dataset[key] for key in dataset.column_names}` and `dataset.to_dict()` to be equivalent. If the previous behavior is expected, then it should be stated [in the doc](https://huggingface.co/docs/datasets/v2.14.4/en/package_reference/main_classes#datasets.Dataset.to_dict).
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- Pillow 9.5.0
- numpy 1.25.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6215 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6215/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6215/comments | https://api.github.com/repos/huggingface/datasets/issues/6215/events | https://github.com/huggingface/datasets/pull/6215 | 1,882,176,970 | PR_kwDODunzps5ZlcqC | 6,215 | Fix checking patterns to infer packaged builder | {
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https://api.github.com/repos/huggingface/datasets/issues/6214 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6214/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6214/comments | https://api.github.com/repos/huggingface/datasets/issues/6214/events | https://github.com/huggingface/datasets/issues/6214 | 1,881,736,469 | I_kwDODunzps5wKQUV | 6,214 | Unpin fsspec < 2023.9.0 | {
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} | null | null | null | Once root issue is fixed, remove temporary pin of fsspec < 2023.9.0 introduced by:
- #6210
Related to issue:
- #6209
After investigation, I think the root issue is related to the new glob behavior with double asterisk `**` they have introduced in:
- https://github.com/fsspec/filesystem_spec/pull/1329 | {
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https://api.github.com/repos/huggingface/datasets/issues/6213 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6213/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6213/comments | https://api.github.com/repos/huggingface/datasets/issues/6213/events | https://github.com/huggingface/datasets/pull/6213 | 1,880,592,987 | PR_kwDODunzps5ZgHLO | 6,213 | Better list array values handling in cast/embed storage | {
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https://api.github.com/repos/huggingface/datasets/issues/6212 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6212/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6212/comments | https://api.github.com/repos/huggingface/datasets/issues/6212/events | https://github.com/huggingface/datasets/issues/6212 | 1,880,399,516 | I_kwDODunzps5wFJ6c | 6,212 | Tilde (~) is not supported for data_files | {
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} | [] | open | false | null | [] | null | 2 | 2023-09-04T14:23:49 | 2023-09-05T08:28:39 | null | NONE | null | {
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} | null | null | null | ### Describe the bug
Attempting to `load_dataset` from a path starting with `~` (as a shorthand for the user's home directory) seems not to be fully working - at least as far as the `parquet` dataset builder is concerned.
(the same file can be loaded correctly if providing its absolute path instead)
I think that this is very similar to https://github.com/huggingface/datasets/issues/5757, but for `data_files` rather than `data_dir`
### Steps to reproduce the bug
```python
import datasets
# save a parquet file at ~/path/to/data.parquet
data_files = "~/path/to/data.parquet"
dataset = datasets.load_dataset("parquet", data_files=data_files)
```
```
Downloading data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 12671.61it/s]
Extracting data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 22671.91it/s]
Generating train split: 0 examples [00:00, ? examples/s]
Traceback (most recent call last):
File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1949, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/datasets/arrow_writer.py", line 598, 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 ".venv/lib/python3.11/site-packages/datasets/load.py", line 2133, in load_dataset
builder_instance.download_and_prepare(
File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare
self._download_and_prepare(
File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1813, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1958, 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
```
### Expected behavior
Can use `~` shorthand in paths when loading local (parquet) datasets.
### Environment info
`datasets 2.14.3`
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https://api.github.com/repos/huggingface/datasets/issues/6211 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6211/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6211/comments | https://api.github.com/repos/huggingface/datasets/issues/6211/events | https://github.com/huggingface/datasets/pull/6211 | 1,880,265,906 | PR_kwDODunzps5Ze-pv | 6,211 | Fix empty splitinfo json | {
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} | If a split is empty, then the JSON split info should mention num_bytes = 0 and num_examples = 0.
Until now they were omited because the JSON dumps ignore the fields that are equal to the default values.
This is needed in datasets-server since we parse this information to the viewer | {
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https://api.github.com/repos/huggingface/datasets/issues/6210 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6210/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6210/comments | https://api.github.com/repos/huggingface/datasets/issues/6210/events | https://github.com/huggingface/datasets/pull/6210 | 1,879,649,731 | PR_kwDODunzps5Zc4JF | 6,210 | Temporarily pin fsspec < 2023.9.0 | {
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Hot fix #6209. | {
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https://api.github.com/repos/huggingface/datasets/issues/6209 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6209/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6209/comments | https://api.github.com/repos/huggingface/datasets/issues/6209/events | https://github.com/huggingface/datasets/issues/6209 | 1,879,622,000 | I_kwDODunzps5wCMFw | 6,209 | CI is broken with AssertionError: 3 failed, 12 errors | {
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See: https://github.com/huggingface/datasets/actions/runs/6069947111/job/16465138041
```
=========================== short test summary info ============================
FAILED tests/test_load.py::ModuleFactoryTest::test_LocalDatasetModuleFactoryWithoutScript_with_data_dir - AssertionError: assert ({NamedSplit('train'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt'], NamedSplit('test'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt']} is not None and 2 == 1)
+ where 2 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt'])
FAILED tests/test_load.py::test_load_dataset_arrow[False] - AssertionError: assert 20 == 10
+ where 20 = Dataset({\n features: ['col_1'],\n num_rows: 20\n}).num_rows
FAILED tests/test_load.py::test_load_dataset_arrow[True] - assert 20 == 10
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv'])
= 3 failed, 2383 passed, 26 skipped, 9 warnings, 12 errors in 280.79s (0:04:40) =
``` | {
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} | This PR is a hotfix of:
- #6207
That PR introduced the filtering out of `.zip` extensions. This PR reverts that.
Hot fix #6207.
Maybe we should do patch releases: the bug was introduced in 2.13.1.
CC: @lhoestq | {
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] | null | 0 | 2023-09-04T05:50:27 | 2023-09-04T09:13:33 | 2023-09-04T09:13:33 | MEMBER | null | {
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} | null | null | null | While investigating an issue on a Hub dataset, I have discovered the no-script datasets containing ZIP files do not load.
For example, that no-script dataset containing ZIP files, raises NonMatchingSplitsSizesError:
```python
In [2]: ds = load_dataset("sidovic/LearningQ-qg")
NonMatchingSplitsSizesError: [
{
'expected': SplitInfo(name='train', num_bytes=0, num_examples=188660, shard_lengths=None, dataset_name=None),
'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg')
}, {
'expected': SplitInfo(name='validation', num_bytes=0, num_examples=20630, shard_lengths=None, dataset_name=None),
'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg')
}, {
'expected': SplitInfo(name='test', num_bytes=0, num_examples=18227, shard_lengths=None, dataset_name=None),
'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg')
}
]
```
As another example, a no-script dataset containing just a (CSV)-ZIP file, raises a DatasetGenerationError:
```
> num_examples, num_bytes = writer.finalize()
src/datasets/builder.py:1949:
> raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
E datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
src/datasets/arrow_writer.py:598: SchemaInferenceError
The above exception was the direct cause of the following exception:
src/datasets/load.py:2143: in load_dataset
builder_instance.download_and_prepare(
src/datasets/builder.py:954: in download_and_prepare
self._download_and_prepare(
src/datasets/builder.py:1049: in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
src/datasets/builder.py:1813: in _prepare_split
for job_id, done, content in self._prepare_split_single(
> raise DatasetGenerationError("An error occurred while generating the dataset") from e
E datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
src/datasets/builder.py:1958: DatasetGenerationError
```
After investigating, I think this bug was introduced in this PR:
- #5972
Related to:
- https://huggingface.co/datasets/sidovic/LearningQ-qg/discussions/1
CC: @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/6206 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6206/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6206/comments | https://api.github.com/repos/huggingface/datasets/issues/6206/events | https://github.com/huggingface/datasets/issues/6206 | 1,879,473,745 | I_kwDODunzps5wBn5R | 6,206 | When calling load_dataset, raise error: pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays | {
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} | null | null | null | ### Describe the bug
When calling load_dataset, raise error
```
Traceback (most recent call last):
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1694, in _pre
pare_split_single
writer.write(example, key)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 490, in
write
self.write_examples_on_file()
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 448, in
write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 559, in
write_batch
self.write_table(pa_table, writer_batch_size)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 571, in
write_table
pa_table = pa_table.combine_chunks()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 3439, in pyarrow.lib.Table.combine_chunks
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
dataset = load_dataset(
^^^^^^^^^^^^^
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py", line 2133, in load_da
taset
builder_instance.download_and_prepare(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 954, in downl
oad_and_prepare
self._download_and_prepare(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _dow
nload_and_prepare
super()._download_and_prepare(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _dow
nload_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _pre
pare_split
for job_id, done, content in self._prepare_split_single(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1712, in _pre
pare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
Setting num_proc from 8 back to 1 for the train split to disable multiprocessing as it only contains one shard.
09/04/2023 12:02:04 - WARNING - datasets.builder - Setting num_proc from 8 back to 1 for the train split to dis
able multiprocessing as it only contains one shard.
```
### Steps to reproduce the bug
Call load_dataset with the large image as feature
### Expected behavior
no error
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/6203 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6203/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6203/comments | https://api.github.com/repos/huggingface/datasets/issues/6203/events | https://github.com/huggingface/datasets/issues/6203 | 1,877,491,602 | I_kwDODunzps5v6D-S | 6,203 | Support loading from a DVC remote repository | {
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] | closed | false | null | [] | null | 4 | 2023-09-01T14:04:52 | 2023-09-15T15:11:27 | 2023-09-15T15:11:27 | NONE | null | {
"total": 0,
"completed": 0,
"percent_completed": 0
} | null | null | null | ### Feature request
Adding support for loading a file from a DVC repository, tracked remotely on a SCM.
### Motivation
DVC is a popular version control system to version and manage datasets. The files are stored on a remote object storage platform, but they are tracked using Git. Integration with DVC is possible through the `DVCFileSystem`.
I have a Gitlab repository where multiple files are tracked using DVC and stored in a GCP bucket. I would like to be able to load these files using `datasets` directly using an URL. My goal is to write a generic code that abstracts the storage layer, such that my users will only have to pass in an `fsspec`-compliant URL and the corresponding files will be loaded.
### Your contribution
I managed to instantiate a `DVCFileSystem` pointing to a Gitlab repo from a `fsspec` chained URL in [this pull request](https://github.com/iterative/dvc/pull/9903) to DVC.
```python
from fsspec.core import url_to_fs
fs, _ = url_to_fs("dvc::https://gitlab.com/repository/group/my-repo")
```
From now I'm not sure how to continue, it seems that `datasets` expects the URL to be fully qualified like so: `dvc::https://gitlab.com/repository/group/my-repo/my-folder/my-file.json` but this fails because `DVCFileSystem` expects the URL to point to the root of an SCM repo. Is there a way to make this work with `datasets`? | {
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https://api.github.com/repos/huggingface/datasets/issues/6202 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6202/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6202/comments | https://api.github.com/repos/huggingface/datasets/issues/6202/events | https://github.com/huggingface/datasets/issues/6202 | 1,876,630,351 | I_kwDODunzps5v2xtP | 6,202 | avoid downgrading jax version | {
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} | null | null | null | ### Feature request
Whenever I `pip install datasets[jax]` it downgrades jax to version 0.3.25. I seem to be able to install this library first then upgrade jax back to version 0.4.13.
### Motivation
It would be nice to not overwrite currently installed version of jax if possible.
### Your contribution
I would be willing to beta test. Or maybe write some code if I could get pointed in the right direction, I'm not super familiar with this codebase. | {
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} | This PR fixes the root cause of the issue:
- #6197
This PR also removes the temporary pin of `pandas` introduced by:
- #6200
Note that for orient in ['records', 'values'], index value is ignored but
- in `pandas` < 2.1.0, a ValueError is raised if not index and orient not in ['split', 'table']
- for orient = 'records', we need index = True
- default index value is True
- in `pandas` = 2.1.0, a ValueError is raised if index is True and orient in ['records', 'values']
- for orient = 'records', we need index = False or None
- default index value is None
This PR fixes the issue by not passing index and thus using default index value (valid for all pandas versions), unless orient is 'split' or 'table' (where we pass index = False, as it was done before this fix). | {
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https://api.github.com/repos/huggingface/datasets/issues/6200 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6200/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6200/comments | https://api.github.com/repos/huggingface/datasets/issues/6200/events | https://github.com/huggingface/datasets/pull/6200 | 1,875,169,551 | PR_kwDODunzps5ZOCee | 6,200 | Temporarily pin pandas < 2.1.0 | {
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} | Temporarily pin `pandas` < 2.1.0 until permanent solution is found.
Hot fix #6197. | {
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https://api.github.com/repos/huggingface/datasets/issues/6199 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6199/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6199/comments | https://api.github.com/repos/huggingface/datasets/issues/6199/events | https://github.com/huggingface/datasets/issues/6199 | 1,875,165,185 | I_kwDODunzps5vxMAB | 6,199 | Use load_dataset for local json files, but it not works | {
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} | null | null | null | ### Describe the bug
when I use load_dataset to load my local datasetsοΌit always goes to Hugging Face to download the data instead of loading the local dataset.
### Steps to reproduce the bug
`raw_datasets = load_dataset(
βjsonβ,
data_files=data_files)`
### Expected behavior
### Environment info
python version 3.8.5
datasets version 2.12
os version unbuntu 18.04 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6198 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6198/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6198/comments | https://api.github.com/repos/huggingface/datasets/issues/6198/events | https://github.com/huggingface/datasets/pull/6198 | 1,875,092,027 | PR_kwDODunzps5ZNyBq | 6,198 | Preserve split order in DataFilesDict | {
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} | After investigation, I have found that this copy forces the splits to be sorted alphabetically: https://github.com/huggingface/datasets/blob/029227a116c14720afca71b9b22e78eb2a1c09a6/src/datasets/builder.py#L556
This PR removes the alphabetically sort of `DataFilesDict` keys.
- Note that for a `dict`, the order of keys is relevant when hashing:
```python
hash1 = Hasher.hash({'train': 'train.csv', 'test': 'test.csv'})
hash2 = Hasher.hash({'test': 'test.csv', 'train': 'train.csv'})
assert hash1 != hash2
```
- The `DataFilesDict` is a subclass of `dict`, thus the order should be relevant as well
```python
hash1 = Hasher.hash(DataFilesDict({'train': 'train.csv', 'test': 'test.csv'}))
hash2 = Hasher.hash(DataFilesDict({'test': 'test.csv', 'train': 'train.csv'}))
assert hash1 != hash2
```
Fix #6196. | {
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https://api.github.com/repos/huggingface/datasets/issues/6197 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6197/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6197/comments | https://api.github.com/repos/huggingface/datasets/issues/6197/events | https://github.com/huggingface/datasets/issues/6197 | 1,875,078,155 | I_kwDODunzps5vw2wL | 6,197 | ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns' | {
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} | null | null | null | ### Describe the bug
Saving a dataset `.to_json()` fails with a `ValueError` since the latest `pandas` [release](https://pandas.pydata.org/docs/dev/whatsnew/v2.1.0.html) (`2.1.0`)
In their latest release we have:
> Improved error handling when using [DataFrame.to_json()](https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json) with incompatible index and orient arguments ([GH 52143](https://github.com/pandas-dev/pandas/issues/52143))
i.e. an error is now raised for invalid combinations of `index` and `orient`.
This means that unfortunately the custom logic at this line might sometimes lead to contradictions:
https://github.com/huggingface/datasets/blob/029227a116c14720afca71b9b22e78eb2a1c09a6/src/datasets/io/json.py#L96
e.g. for the default case `orient=records` leads to `index=True`, which now raises a `ValueError`
### Steps to reproduce the bug
```python
import datasets
if __name__ == '__main__':
dataset = datasets.Dataset.from_dict({"A": [1, 2, 3], "B": [4, 5, 6]})
dataset.to_json("dataset.json")
```
```shell
>>>
ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns'.
```
### Expected behavior
The dataset is successfully saved as `.json`
### Environment info
`python >= 3.9`
`pandas >= 2.1.0` | {
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For example, consider a no-script dataset with configs:
```yaml
configs:
- config_name: default
data_files:
- split: train
path: train.csv
- split: test
path: test.csv
```
- Note the defined split order is [train, test]
Once the dataset is loaded, the split order is not preserved:
```python
In [16]: ds
Out[16]:
DatasetDict({
test: Dataset({
features: ['text', 'label'],
num_rows: 1
})
train: Dataset({
features: ['text', 'label'],
num_rows: 2
})
})
```
- Note the obtained split order is [test, train] | {
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https://api.github.com/repos/huggingface/datasets/issues/6195 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6195/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6195/comments | https://api.github.com/repos/huggingface/datasets/issues/6195/events | https://github.com/huggingface/datasets/issues/6195 | 1,874,195,585 | I_kwDODunzps5vtfSB | 6,195 | Force to reuse cache at given path | {
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I have run the official example of MLM like:
```bash
python run_mlm.py \
--model_name_or_path roberta-base \
--dataset_name togethercomputer/RedPajama-Data-1T \
--dataset_config_name arxiv \
--per_device_train_batch_size 10 \
--preprocessing_num_workers 20 \
--validation_split_percentage 0 \
--cache_dir /project/huggingface_cache/datasets \
--line_by_line \
--do_train \
--pad_to_max_length \
--output_dir /project/huggingface_cache/test-mlm
```
it successfully runs and at my cache folder has `cache-1982fea76aa54a13_00001_of_00020.arrow`..... `cache-1982fea76aa54a13_00020_of_00020.arrow ` as tokenization cache of `map` method. And the cache works fine every time I run the command above.
However, when I switched to jupyter notebook (since I do not want to load datasets every time when I changed other parameters not related to the dataloading). It is not recognizing the cache files and starts to re-run the entire tokenization process.
I changed my code to
```python
tokenized_datasets = raw_datasets["train"].map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=True,
desc="Running tokenizer on dataset line_by_line",
# cache_file_names= {"train": "cache-1982fea76aa54a13.arrow"}
cache_file_name="cache-1982fea76aa54a13.arrow",
new_fingerprint="1982fea76aa54a13"
)
```
it still does not recognize the previously cached files and trying to re-run the tokenization process.
### Steps to reproduce the bug
use jupyter notebook for dataset map function.
### Expected behavior
the map function accepts the given cache_file_name and new_fingerprint then load the previously cached files.
### Environment info
- `datasets` version: 2.14.4.dev0
- Platform: Linux-3.10.0-1160.59.1.el7.x86_64-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/6194 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6194/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6194/comments | https://api.github.com/repos/huggingface/datasets/issues/6194/events | https://github.com/huggingface/datasets/issues/6194 | 1,872,598,223 | I_kwDODunzps5vnZTP | 6,194 | Support custom fingerprinting with `Dataset.from_generator` | {
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} | null | null | null | ### Feature request
When using `Dataset.from_generator`, the generator is hashed when building the fingerprint. Similar to `.map`, it would be interesting to let the user bypass this hashing by accepting a `fingerprint` argument to `.from_generator`.
### Motivation
Using the `.from_generator` constructor with a non-picklable generator fails. By accepting a `fingerprint` argument to `.from_generator`, the user would have the opportunity to manually fingerprint the dataset and thus bypass the crash.
### Your contribution
If validated, I can try to submit a PR for this. | null | {
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} | null | null | null | ### Describe the bug
The huggingface dataset library specifically looks for β.pyβ file while loading the dataset using loading script approach and it does not work with β.pycβ file.
While deploying in production, it becomes an issue when we are restricted to use only .pyc files. Is there any work around for this ?
### Steps to reproduce the bug
1. Create a dataset loading script to read the custom data.
2. compile the code to make sure that .pyc file is created
3. Delete the loading script and re-run the code. Usually, python should make use of complied .pyc files. However, in this case, the dataset library errors out with the message that it's unable to find the data loader loading script.
### Expected behavior
The code should make use of .pyc file and run without any error.
### Environment info
NA | null | {
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} | Fix https://github.com/huggingface/datasets/issues/6141
Colab installs 2023.6.0, so we should be good π
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https://api.github.com/repos/huggingface/datasets/issues/6190 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6190/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6190/comments | https://api.github.com/repos/huggingface/datasets/issues/6190/events | https://github.com/huggingface/datasets/issues/6190 | 1,871,582,175 | I_kwDODunzps5vjhPf | 6,190 | `Invalid user token` even when correct user token is passed! | {
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} | [] | closed | false | null | [] | null | 2 | 2023-08-29T12:37:03 | 2023-08-29T13:01:10 | 2023-08-29T13:01:09 | MEMBER | null | {
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} | null | null | null | ### Describe the bug
I'm working on a dataset which comprises other datasets on the hub.
URL: https://huggingface.co/datasets/open-asr-leaderboard/datasets-test-only
Note: Some of the sub-datasets in this metadataset require explicit access.
All the other datasets work fine, except, `common_voice`.
### Steps to reproduce the bug
https://github.com/Vaibhavs10/scratchpad/blob/main/cv_datasets_bug_repro.ipynb
### Expected behavior
It should work if the provided access token is valid (as it does for all the other datasets)
### Environment info
datasets version -> 2.14.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/6189 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6189/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6189/comments | https://api.github.com/repos/huggingface/datasets/issues/6189/events | https://github.com/huggingface/datasets/pull/6189 | 1,871,569,855 | PR_kwDODunzps5ZB8Z9 | 6,189 | Don't alter input in Features.from_dict | {
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https://api.github.com/repos/huggingface/datasets/issues/6188 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6188/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6188/comments | https://api.github.com/repos/huggingface/datasets/issues/6188/events | https://github.com/huggingface/datasets/issues/6188 | 1,870,987,640 | I_kwDODunzps5vhQF4 | 6,188 | [Feature Request] Check the length of batch before writing so that empty batch is allowed | {
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} | [] | closed | false | null | [] | null | 1 | 2023-08-29T06:37:34 | 2023-09-19T21:55:38 | 2023-09-19T21:55:37 | NONE | null | {
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} | null | null | null | ### Use Case
I use `dataset.map(process_fn, batched=True)` to process the dataset, with data **augmentations or filtering**. However, when all examples within a batch is filtered out, i.e. **an empty batch is returned**, the following error will be thrown:
```
ValueError: Schema and number of arrays unequal
```
This is because the empty batch does not comply with the schema of other batches. I think an empty batch should be allowed to facilitate coding (one does not need to assign an empty list manually for all keys.)
A simple fix is to check the length of `batch` before writing:
```
if len(batch):
writer.write_batch(batch)
```
instead of
https://github.com/huggingface/datasets/blob/74d60213dcbd7c99484c62ce1d3dfd90a1df0770/src/datasets/arrow_dataset.py#L3493
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https://api.github.com/repos/huggingface/datasets/issues/6187 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6187/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6187/comments | https://api.github.com/repos/huggingface/datasets/issues/6187/events | https://github.com/huggingface/datasets/issues/6187 | 1,870,936,143 | I_kwDODunzps5vhDhP | 6,187 | Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory | {
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} | [] | open | false | null | [] | null | 1 | 2023-08-29T05:49:56 | 2023-08-29T16:21:45 | null | NONE | null | {
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} | null | null | null | ### Describe the bug
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
[<ipython-input-48-6a7b3e847019>](https://localhost:8080/#) in <cell line: 7>()
5 }
6
----> 7 csv_datasets_reloaded = load_dataset("tsv", data_files=data_files)
8 csv_datasets_reloaded
2 frames
[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1489 raise e1 from None
1490 if isinstance(e1, FileNotFoundError):
-> 1491 raise FileNotFoundError(
1492 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
1493 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Dataset 'tsv' doesn't exist on the Hub
```
### Steps to reproduce the bug
```
data_files = {
"train": "/content/PUBHEALTH/train.tsv",
"validation": "/content/PUBHEALTH/dev.tsv",
"test": "/content/PUBHEALTH/test.tsv",
}
tsv_datasets_reloaded = load_dataset("tsv", data_files=data_files)
tsv_datasets_reloaded
```
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-48-6a7b3e847019> in <cell line: 7>()
5 }
6
----> 7 csv_datasets_reloaded = load_dataset("tsv", data_files=data_files)
8 csv_datasets_reloaded
2 frames
/usr/local/lib/python3.10/dist-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1489 raise e1 from None
1490 if isinstance(e1, FileNotFoundError):
-> 1491 raise FileNotFoundError(
1492 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
1493 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Dataset 'tsv' doesn't exist on the Hub
```
### Expected behavior
load the data, push to hub
### Environment info
jupyter notebook RTX 3090 | null | {
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] | closed | false | null | [] | null | 18 | 2023-08-28T10:00:59 | 2024-10-07T09:39:51 | 2023-11-22T15:42:20 | CONTRIBUTOR | null | {
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} | null | null | null | ### Feature request
Would be great to add a code example of how to do multi-GPU processing with π€ Datasets in the documentation. cc @stevhliu
Currently the docs has a small [section](https://huggingface.co/docs/datasets/v2.3.2/en/process#map) on this saying "your big GPU call goes here", however it didn't work for me out-of-the-box.
Let's say you have a PyTorch model that can do translation, and you have multiple GPUs. In that case, you'd like to duplicate the model on each GPU, each processing (translating) a chunk of the data in parallel.
Here's how I tried to do that:
```
from datasets import load_dataset
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from multiprocess import set_start_method
import torch
import os
dataset = load_dataset("mlfoundations/datacomp_small")
tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
# put model on each available GPU
# also, should I do it like this or use nn.DataParallel?
model.to("cuda:0")
model.to("cuda:1")
set_start_method("spawn")
def translate_captions(batch, rank):
os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count())
texts = batch["text"]
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(model.device)
translated_tokens = model.generate(
**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["eng_Latn"], max_length=30
)
translated_texts = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
batch["translated_text"] = translated_texts
return batch
updated_dataset = dataset.map(translate_captions, with_rank=True, num_proc=2, batched=True, batch_size=256)
```
I've personally tried running this script on a machine with 2 A100 GPUs.
## Error 1
Running the code snippet above from the terminal (python script.py) resulted in the following error:
```
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 125, in _main
prepare(preparation_data)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 289, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 96, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/niels/python_projects/datacomp/datasets_multi_gpu.py", line 16, in <module>
set_start_method("spawn")
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 247, in set_start_method
raise RuntimeError('context has already been set')
RuntimeError: context has already been set
```
## Error 2
Then, based on [this Stackoverflow answer](https://stackoverflow.com/a/71616344/7762882), I put the `set_start_method("spawn")` section in a try: catch block. This resulted in the following error:
```
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/dataset_dict.py", line 817, in <dictcomp>
k: dataset.map(
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2926, in map
with Pool(nb_of_missing_shards, initargs=initargs, initializer=initializer) as pool:
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 119, in Pool
return Pool(processes, initializer, initargs, maxtasksperchild,
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 215, in __init__
self._repopulate_pool()
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 306, in _repopulate_pool
return self._repopulate_pool_static(self._ctx, self.Process,
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 329, in _repopulate_pool_static
w.start()
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/process.py", line 121, in start
self._popen = self._Popen(self)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 288, in _Popen
return Popen(process_obj)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 42, in _launch
prep_data = spawn.get_preparation_data(process_obj._name)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
```
So then I put the last line under a `if __name__ == '__main__':` block. Then the code snippet seemed to work, but it seemed that it's only leveraging a single GPU (based on monitoring `nvidia-smi`):
```
Mon Aug 28 12:19:24 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA A100-SXM... On | 00000000:01:00.0 Off | 0 |
| N/A 55C P0 76W / 275W | 8747MiB / 81920MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
| 1 NVIDIA A100-SXM... On | 00000000:47:00.0 Off | 0 |
| N/A 67C P0 274W / 275W | 59835MiB / 81920MiB | 100% Default |
| | | Disabled |
```
Both GPUs should have equal GPU usage, but I've always noticed that the last GPU has way more usage than the other ones. This made me think that `os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count())` might not work inside a Python script, especially if done after importing PyTorch?
### Motivation
Would be great to clarify how to do multi-GPU data processing.
### Your contribution
If my code snippet can be fixed, I can contribute it to the docs :) | {
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} | [] | open | false | null | [] | null | 1 | 2023-08-26T12:15:57 | 2023-08-29T14:49:58 | null | NONE | null | {
"total": 0,
"completed": 0,
"percent_completed": 0
} | null | null | null | ### Describe the bug
I am using the ArrowWriter from datasets.arrow_writer to save a json-style file as arrow files. Within the dictionary, it contains a feature called "image" which is a list of PIL.Image objects.
I am saving the json using the following script:
```
def save_to_arrow(path,temp):
with ArrowWriter(path=path,writer_batch_size=20) as writer:
writer.write_batch(temp)
writer.finalize()
```
However, when I attempt to restore the dataset and use the ```Dataset.from_file(path)``` function to load the arrow file, there seems to be an issue with the PIL.Image object in the dataset. The list of PIL.Images appears as follows rather than a normal PIL.Image object:
### Steps to reproduce the bug
1. Storing the data json into arrow files:
```
def save_to_arrow(path,temp):
with ArrowWriter(path=path,writer_batch_size=20) as writer:
writer.write_batch(temp)
writer.finalize()
save_to_arrow( path, json_file )
```
2. try to load the arrow file into the Dataset object using the ```Dataset.from_file(path)```
### Expected behavior
Except to saving the contained "image" feature as a list PIL.Image objects as the arrow file. And I can restore the dataset from the file.
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.4.0-150-generic-x86_64-with-glibc2.17
- Python version: 3.8.17
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.4.4 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6184 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6184/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6184/comments | https://api.github.com/repos/huggingface/datasets/issues/6184/events | https://github.com/huggingface/datasets/issues/6184 | 1,867,766,143 | I_kwDODunzps5vU9l_ | 6,184 | Map cache does not detect function changes in another module | {
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] | closed | false | null | [] | null | 2 | 2023-08-25T22:59:14 | 2023-08-29T20:57:07 | 2023-08-29T20:56:49 | NONE | null | {
"total": 0,
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} | null | null | null | ```python
# dataset.py
import os
import datasets
if not os.path.exists('/tmp/test.json'):
with open('/tmp/test.json', 'w') as file:
file.write('[{"text": "hello"}]')
def transform(example):
text = example['text']
# text += ' world'
return {'text': text}
data = datasets.load_dataset('json', data_files=['/tmp/test.json'], split='train')
data = data.map(transform)
```
```python
# test.py
import dataset
print(next(iter(dataset.data)))
```
Initialize cache
```
python3 test.py
# {'text': 'hello'}
```
Edit dataset.py and uncomment the commented line, run again
```
python3 test.py
# {'text': 'hello'}
# expected: {'text': 'hello world'}
```
Clear cache and run again
```
rm -rf ~/.cache/huggingface/datasets/*
python3 test.py
# {'text': 'hello world'}
```
If instead the two files are combined, then changes to the function are detected correctly. But it's expected when working on any realistic codebase that things will be modularized into separate files. | {
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} | [] | closed | false | null | [] | null | 2 | 2023-08-25T22:21:22 | 2023-08-29T13:26:22 | 2023-08-29T13:26:22 | NONE | null | {
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} | null | null | null | ### Describe the bug
When load a dataset from datasets and pass a wrong path to json with the data, error message does not contain something abount "wrong path" or "file do not exist" -
```SchemaInferenceError: Please pass `features` or at least one example when writing data```
### Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset('json', data_files='/home/alexey/unreal_file.json')
```
### Expected behavior
Raise os FileNotFound error or custom error with informative message
### Environment info
```
# packages in environment at /home/alexey/.conda/envs/alex_LoRA:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main
_openmp_mutex 5.1 1_gnu
accelerate 0.21.0 pypi_0 pypi
aiohttp 3.8.5 pypi_0 pypi
aiosignal 1.3.1 pypi_0 pypi
antlr4-python3-runtime 4.9.3 pypi_0 pypi
appdirs 1.4.4 pypi_0 pypi
asttokens 2.0.5 pyhd3eb1b0_0
async-timeout 4.0.3 pypi_0 pypi
attrs 23.1.0 pypi_0 pypi
backcall 0.2.0 pyhd3eb1b0_0
bitsandbytes 0.41.1 pypi_0 pypi
bzip2 1.0.8 h7b6447c_0
ca-certificates 2023.05.30 h06a4308_0
certifi 2023.7.22 pypi_0 pypi
charset-normalizer 3.2.0 pypi_0 pypi
click 8.1.6 pypi_0 pypi
cmake 3.27.2 pypi_0 pypi
comm 0.1.2 py310h06a4308_0
contourpy 1.1.0 pypi_0 pypi
cycler 0.11.0 pypi_0 pypi
datasets 2.14.4 pypi_0 pypi
debugpy 1.6.7 py310h6a678d5_0
decorator 5.1.1 pyhd3eb1b0_0
dill 0.3.7 pypi_0 pypi
docker-pycreds 0.4.0 pypi_0 pypi
executing 0.8.3 pyhd3eb1b0_0
filelock 3.12.2 pypi_0 pypi
fire 0.5.0 pypi_0 pypi
fonttools 4.42.0 pypi_0 pypi
frozenlist 1.4.0 pypi_0 pypi
fsspec 2023.6.0 pypi_0 pypi
gitdb 4.0.10 pypi_0 pypi
gitpython 3.1.32 pypi_0 pypi
huggingface-hub 0.16.4 pypi_0 pypi
idna 3.4 pypi_0 pypi
ipykernel 6.25.0 py310h2f386ee_0
ipython 8.12.2 py310h06a4308_0
ipython-genutils 0.2.0 pypi_0 pypi
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libffi 3.3 he6710b0_2
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libsodium 1.0.18 h7b6447c_0
libstdcxx-ng 11.2.0 h1234567_1
libuuid 1.41.5 h5eee18b_0
lightning-utilities 0.9.0 pypi_0 pypi
lit 16.0.6 pypi_0 pypi
markupsafe 2.1.3 pypi_0 pypi
matplotlib 3.7.2 pypi_0 pypi
matplotlib-inline 0.1.6 py310h06a4308_0
mpmath 1.3.0 pypi_0 pypi
multidict 6.0.4 pypi_0 pypi
multiprocess 0.70.15 pypi_0 pypi
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ncurses 6.4 h6a678d5_0
nest-asyncio 1.5.6 py310h06a4308_0
networkx 3.1 pypi_0 pypi
numpy 1.25.2 pypi_0 pypi
nvidia-cublas-cu11 11.10.3.66 pypi_0 pypi
nvidia-cuda-cupti-cu11 11.7.101 pypi_0 pypi
nvidia-cuda-nvrtc-cu11 11.7.99 pypi_0 pypi
nvidia-cuda-runtime-cu11 11.7.99 pypi_0 pypi
nvidia-cudnn-cu11 8.5.0.96 pypi_0 pypi
nvidia-cufft-cu11 10.9.0.58 pypi_0 pypi
nvidia-curand-cu11 10.2.10.91 pypi_0 pypi
nvidia-cusolver-cu11 11.4.0.1 pypi_0 pypi
nvidia-cusparse-cu11 11.7.4.91 pypi_0 pypi
nvidia-nccl-cu11 2.14.3 pypi_0 pypi
nvidia-nvtx-cu11 11.7.91 pypi_0 pypi
omegaconf 2.3.0 pypi_0 pypi
openssl 1.1.1v h7f8727e_0
packaging 23.0 py310h06a4308_0
pandas 2.0.3 pypi_0 pypi
parso 0.8.3 pyhd3eb1b0_0
pathtools 0.1.2 pypi_0 pypi
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pexpect 4.8.0 pyhd3eb1b0_3
pickleshare 0.7.5 pyhd3eb1b0_1003
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pyarrow 12.0.1 pypi_0 pypi
pygments 2.15.1 py310h06a4308_1
pyparsing 3.0.9 pypi_0 pypi
python 3.10.0 h12debd9_5
python-dateutil 2.8.2 pyhd3eb1b0_0
pytorch-lightning 2.0.6 pypi_0 pypi
pytz 2023.3 pypi_0 pypi
pyyaml 6.0.1 pypi_0 pypi
pyzmq 25.1.0 py310h6a678d5_0
readline 8.2 h5eee18b_0
referencing 0.30.2 pypi_0 pypi
regex 2023.8.8 pypi_0 pypi
requests 2.31.0 pypi_0 pypi
rpds-py 0.9.2 pypi_0 pypi
safetensors 0.3.2 pypi_0 pypi
scipy 1.11.1 pypi_0 pypi
sentencepiece 0.1.99 pypi_0 pypi
sentry-sdk 1.29.2 pypi_0 pypi
setproctitle 1.3.2 pypi_0 pypi
setuptools 68.0.0 py310h06a4308_0
six 1.16.0 pyhd3eb1b0_1
smmap 5.0.0 pypi_0 pypi
sqlite 3.41.2 h5eee18b_0
stack_data 0.2.0 pyhd3eb1b0_0
sympy 1.12 pypi_0 pypi
tenacity 8.2.3 pypi_0 pypi
termcolor 2.3.0 pypi_0 pypi
tk 8.6.12 h1ccaba5_0
tokenizers 0.13.3 pypi_0 pypi
torch 2.0.1 pypi_0 pypi
torchmetrics 1.0.3 pypi_0 pypi
tornado 6.3.2 py310h5eee18b_0
tqdm 4.66.1 pypi_0 pypi
traitlets 5.7.1 py310h06a4308_0
transformers 4.31.0 pypi_0 pypi
triton 2.0.0 pypi_0 pypi
typing-extensions 4.7.1 pypi_0 pypi
tzdata 2023.3 pypi_0 pypi
urllib3 2.0.4 pypi_0 pypi
wandb 0.15.8 pypi_0 pypi
wcwidth 0.2.5 pyhd3eb1b0_0
wheel 0.38.4 py310h06a4308_0
widgetsnbextension 4.0.5 py310h06a4308_0
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xz 5.4.2 h5eee18b_0
yarl 1.9.2 pypi_0 pypi
zeromq 4.3.4 h2531618_0
zlib 1.2.13 h5eee18b_0
active environment : None
user config file : /home/alexey/.condarc
populated config files :
conda version : 23.1.0
conda-build version : 3.22.0
python version : 3.9.13.final.0
virtual packages : __archspec=1=x86_64
__cuda=12.0=0
__glibc=2.35=0
__linux=5.19.0=0
__unix=0=0
base environment : /opt/anaconda/anaconda3 (read only)
conda av data dir : /opt/anaconda/anaconda3/etc/conda
conda av metadata url : None
channel URLs : https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /opt/anaconda/anaconda3/pkgs
/home/alexey/.conda/pkgs
envs directories : /home/alexey/.conda/envs
/opt/anaconda/anaconda3/envs
platform : linux-64
user-agent : conda/23.1.0 requests/2.31.0 CPython/3.9.13 Linux/5.19.0-46-generic ubuntu/22.04.2 glibc/2.35
UID:GID : 1009:1009
netrc file : /home/alexey/.netrc
offline mode : False
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6182 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6182/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6182/comments | https://api.github.com/repos/huggingface/datasets/issues/6182/events | https://github.com/huggingface/datasets/issues/6182 | 1,867,203,131 | I_kwDODunzps5vS0I7 | 6,182 | Loading Meteor metric in HF evaluate module crashes due to datasets import issue | {
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} | null | null | null | ### Describe the bug
When using python3.9 and ```evaluate``` module loading Meteor metric crashes at a non-existent import from ```datasets.config``` in ```datasets v2.14```
### Steps to reproduce the bug
```
from evaluate import load
meteor = load("meteor")
```
produces the following error:
```
from datasets.config import importlib_metadata, version
ImportError: cannot import name 'importlib_metadata' from 'datasets.config' (<path_to_project>/venv/lib/python3.9/site-packages/datasets/config.py)
```
### Expected behavior
```datasets``` of v2.10 has the following workaround in ```config.py```:
```
if PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
```
However, it's absent in v2.14 which might be the cause of the issue.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-13.5-arm64-arm-64bit
- Python version: 3.9.6
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- Evaluate version: 0.4.0 | {
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} | [] | closed | false | null | [] | null | 3 | 2023-08-25T13:12:19 | 2023-08-25T16:12:46 | 2023-08-25T16:02:24 | COLLABORATOR | null | {
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} | Reported on [Discord](https://discord.com/channels/879548962464493619/1144295822209581168/1144295822209581168) | {
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https://api.github.com/repos/huggingface/datasets/issues/6180 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6180/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6180/comments | https://api.github.com/repos/huggingface/datasets/issues/6180/events | https://github.com/huggingface/datasets/pull/6180 | 1,867,032,578 | PR_kwDODunzps5Yy1r- | 6,180 | Use `hf-internal-testing` repos for hosting test dataset repos | {
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} | Use `hf-internal-testing` for hosting instead of the maintainers' dataset repos. | {
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https://api.github.com/repos/huggingface/datasets/issues/6179 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6179/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6179/comments | https://api.github.com/repos/huggingface/datasets/issues/6179/events | https://github.com/huggingface/datasets/issues/6179 | 1,867,009,016 | I_kwDODunzps5vSEv4 | 6,179 | Map cache with tokenizer | {
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} | [] | open | false | null | [] | null | 4 | 2023-08-25T12:55:18 | 2023-08-31T15:17:24 | null | NONE | null | {
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} | null | null | null | Similar issue to https://github.com/huggingface/datasets/issues/5985, but across different sessions rather than two calls in the same session.
Unlike that issue, explicitly calling tokenizer(my_args) before the map() doesn't help, because the tokenizer was created with a different hash to begin with...
setup
```
from transformers import AutoTokenizer
AutoTokenizer.from_pretrained('bert-base-uncased').save_pretrained("tok")
```
this prints different value each time
```
from transformers import AutoTokenizer
from datasets.utils.py_utils import dumps # Huggingface datasets
print(hash(dumps(AutoTokenizer.from_pretrained("tok"))))
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6178 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6178/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6178/comments | https://api.github.com/repos/huggingface/datasets/issues/6178/events | https://github.com/huggingface/datasets/issues/6178 | 1,866,610,102 | I_kwDODunzps5vQjW2 | 6,178 | 'import datasets' throws "invalid syntax error" | {
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} | [] | closed | false | null | [] | null | 1 | 2023-08-25T08:35:14 | 2023-09-27T17:33:39 | 2023-09-27T17:33:39 | NONE | null | {
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} | null | null | null | ### Describe the bug
Hi,
I have been trying to import the datasets library but I keep gtting this error.
`Traceback (most recent call last):
File /opt/local/jupyterhub/lib64/python3.9/site-packages/IPython/core/interactiveshell.py:3508 in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
Cell In[2], line 1
import datasets
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/__init__.py:22
from .arrow_dataset import Dataset
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_dataset.py:67
from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_writer.py:27
from .features import Features, Image, Value
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/__init__.py:17
from .audio import Audio
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/audio.py:11
from ..download.streaming_download_manager import xopen, xsplitext
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/__init__.py:10
from .streaming_download_manager import StreamingDownloadManager
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/streaming_download_manager.py:18
from aiohttp.client_exceptions import ClientError
File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/__init__.py:7
from .connector import * # noqa
File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/connector.py:12
from .client import ClientRequest
File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/client.py:144
yield from asyncio.async(resp.release(), loop=loop)
^
SyntaxError: invalid syntax`
I have simply used these commands:
`import datasets`
and
`from datasets import load_dataset`
### Environment info
The library has been installed a virtual machine on JupyterHub. Although I have used this library multiple times (on the same VM) before, to train/test an ASR or other ML models, I had never encountered this error. | {
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https://api.github.com/repos/huggingface/datasets/issues/6176 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6176/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6176/comments | https://api.github.com/repos/huggingface/datasets/issues/6176/events | https://github.com/huggingface/datasets/issues/6176 | 1,864,436,408 | I_kwDODunzps5vIQq4 | 6,176 | how to limit the size of memory mapped file? | {
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Huggingface datasets use memory-mapped file to map large datasets in memory for fast access.
However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed.
So is there a way to explicitly limit the size of memory mapped file?
### Steps to reproduce the bug
python
>>> from datasets import load_dataset
>>> dataset = load_dataset("c4", "en", streaming=True)
### Expected behavior
In a normal environment, this will not have any problem.
However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed.
### Environment info
linux cluster with SGEοΌSun Grid EngineοΌ | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6175 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6175/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6175/comments | https://api.github.com/repos/huggingface/datasets/issues/6175/events | https://github.com/huggingface/datasets/pull/6175 | 1,863,592,678 | PR_kwDODunzps5YnKlx | 6,175 | PyArrow 13 CI fixes | {
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} | Fixes:
* bumps the PyArrow version check in the `cast_array_to_feature` to avoid the offset bug (still not fixed)
* aligns the Pandas formatting tests with the Numpy ones (the current test fails due to https://github.com/apache/arrow/pull/35656, which requires `.to_pandas(coerce_temporal_nanoseconds=True)` to always return `datetime [ns]` objects)
Fix #6173
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```
FAILED tests/test_formatting.py::ArrowExtractorTest::test_pandas_extractor - AssertionError: Attributes of Series are different
Attribute "dtype" are different
[left]: datetime64[us, UTC]
[right]: datetime64[ns, UTC]
```
```
FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type
fixed_size_list<item: int32>[3]
to
Sequence(feature=Value(dtype='int64', id=None), length=3, id=None)
```
e.g. in https://github.com/huggingface/datasets/actions/runs/5952253963/job/16143847230
first error may be related to https://github.com/apache/arrow/issues/33321
second one maybe because `feature.length * len(array) == len(array_values)` is not satisfied anymore somehow ? | {
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Streaming datasets, as intended, do not load the entire dataset in memory or disk. However, while querying the next data chunk from the remote, sometimes it is possible that the service is down or there might be other issues that may cause the query to fail. In such a scenario, it would be nice to make these queries retryable (perhaps with a backoff strategy).
### Motivation
I was working on a model and the model checkpoints after every 1000 steps. At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch `dataloader`. Given the size of my model and data, it took around 2 hours to reach 1800 steps and now it will take about an hour to recover the lost 800. It would be better to get a retryable querying strategy.
### Your contribution
It would be better if someone having experience in this area takes this up as this would require some testing. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6170 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6170/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6170/comments | https://api.github.com/repos/huggingface/datasets/issues/6170/events | https://github.com/huggingface/datasets/pull/6170 | 1,862,705,731 | PR_kwDODunzps5YkJOV | 6,170 | feat: Return the name of the currently loaded file | {
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} | Added an optional parameter return_file_name in the load_dataset function. When it is set to True, the function will include the name of the file corresponding to the current line as a feature in the returned output.
I added this here https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/json/json.py#L92.
fixes #5806 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6169 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6169/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6169/comments | https://api.github.com/repos/huggingface/datasets/issues/6169/events | https://github.com/huggingface/datasets/issues/6169 | 1,862,360,199 | I_kwDODunzps5vAVyH | 6,169 | Configurations in yaml not working | {
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} | null | null | null | ### Dataset configurations cannot be created in YAML/README
Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118
I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test):
```
configs:
- config_name: main_data
data_files: "main_data.csv"
- config_name: additional_data
data_files: "additional_data.csv"
```
Yet, I'm unable to load different configurations:
```
from datasets import get_dataset_config_names
get_dataset_config_names('tsor13/test', use_auth_token=True)
```
returns a single split, `['tsor13--test']`
Does anyone have any insights?
@polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas?
### Steps to reproduce the bug
from datasets import get_dataset_config_names
get_dataset_config_names('tsor13/test')
### Expected behavior
I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-13.4-arm64-arm-64bit
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.1 | null | {
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