number
int64 2
7.91k
| title
stringlengths 1
290
| body
stringlengths 0
228k
| state
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values | created_at
timestamp[s]date 2020-04-14 18:18:51
2025-12-16 10:45:02
| updated_at
timestamp[s]date 2020-04-29 09:23:05
2025-12-16 19:34:46
| closed_at
timestamp[s]date 2020-04-29 09:23:05
2025-12-16 14:20:48
β | url
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|---|---|---|---|---|---|---|---|---|---|---|
6,287
|
map() not recognizing "text"
|
### Describe the bug
The [map() documentation](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/main_classes#datasets.Dataset.map) reads:
`
ds = ds.map(lambda x: tokenizer(x['text'], truncation=True, padding=True), batched=True)`
I have been trying to reproduce it in my code as:
`tokenizedDataset = dataset.map(lambda x: tokenizer(x['text']), batched=True)`
But it doesn't work as it throws the error:
> KeyError: 'text'
Can you please guide me on how to fix it?
### Steps to reproduce the bug
1. `from datasets import load_dataset
dataset = load_dataset("amazon_reviews_multi")`
2. Then this code: `from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")`
3. The line I quoted above (which I have been trying)
### Expected behavior
As mentioned in the documentation, it should run without any error and map the tokenization on the whole dataset.
### Environment info
Python 3.10.2
|
CLOSED
| 2023-10-09T10:27:30
| 2023-10-11T20:28:45
| 2023-10-11T20:28:45
|
https://github.com/huggingface/datasets/issues/6287
|
EngineerKhan
| 1
|
[] |
6,285
|
TypeError: expected str, bytes or os.PathLike object, not dict
|
### Describe the bug
my dataset is in form : train- image /n -labels
and tried the code:
```
from datasets import load_dataset
data_files = {
"train": "/content/datasets/PotholeDetectionYOLOv8-1/train/",
"validation": "/content/datasets/PotholeDetectionYOLOv8-1/valid/",
"test": "/content/datasets/PotholeDetectionYOLOv8-1/test/"
}
dataset = load_dataset("imagefolder", data_dir=data_files)
dataset
```
got error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-29-2ef1926f73d9>](https://localhost:8080/#) in <cell line: 8>()
6 "test": "/content/datasets/PotholeDetectionYOLOv8-1/test/"
7 }
----> 8 dataset = load_dataset("imagefolder", data_dir=data_files)
9 dataset
6 frames
[/usr/lib/python3.10/pathlib.py](https://localhost:8080/#) in _parse_args(cls, args)
576 parts += a._parts
577 else:
--> 578 a = os.fspath(a)
579 if isinstance(a, str):
580 # Force-cast str subclasses to str (issue #21127)
TypeError: expected str, bytes or os.PathLike object, not dict
```
### Steps to reproduce the bug
as share above
### Expected behavior
load images and labels , but my dataset only uploads images
- https://huggingface.co/datasets/Andyrasika/potholes-dataset
### Environment info
colab pro
|
OPEN
| 2023-10-09T04:56:26
| 2023-10-10T13:17:33
| null |
https://github.com/huggingface/datasets/issues/6285
|
andysingal
| 4
|
[] |
6,284
|
Add Belebele multiple-choice machine reading comprehension (MRC) dataset
|
### Feature request
Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the [FLORES-200](https://github.com/facebookresearch/flores/tree/main/flores200) dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems.
Please refer to paper for more details, [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://arxiv.org/abs/2308.16884).
## Composition
- 900 questions per language variant
- 488 distinct passages, there are 1-2 associated questions for each.
- For each question, there is 4 multiple-choice answers, exactly 1 of which is correct.
- 122 language/language variants (including English).
- 900 x 122 = 109,800 total questions.
### Motivation
official repo https://github.com/facebookresearch/belebele
### Your contribution
-
|
CLOSED
| 2023-10-06T06:58:03
| 2023-10-06T13:26:51
| 2023-10-06T13:26:51
|
https://github.com/huggingface/datasets/issues/6284
|
rajveer43
| 1
|
[
"enhancement"
] |
6,280
|
Couldn't cast array of type fixed_size_list to Sequence(Value(float64))
|
### Describe the bug
I have a dataset with an embedding column, when I try to map that dataset I get the following exception:
```
Traceback (most recent call last):
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3189, in map
for rank, done, content in iflatmap_unordered(
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered
[async_result.get(timeout=0.05) for async_result in async_results]
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1387, in <listcomp>
[async_result.get(timeout=0.05) for async_result in async_results]
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/multiprocess/pool.py", line 774, in get
raise self._value
TypeError: Couldn't cast array of type
fixed_size_list<item: float>[2]
to
Sequence(feature=Value(dtype='float32', id=None), length=2, id=None)
```
### Steps to reproduce the bug
Here's a simple repro script:
```
from datasets import Features, Value, Sequence, ClassLabel, Dataset
dataset_features = Features({
'text': Value('string'),
'embedding': Sequence(Value('double'), length=2),
'categories': Sequence(ClassLabel(names=sorted([
'one',
'two',
'three'
]))),
})
dataset = Dataset.from_dict(
{
'text': ['A'] * 10000,
'embedding': [[0.0, 0.1]] * 10000,
'categories': [[0]] * 10000,
},
features=dataset_features
)
def test_mapper(r):
r['text'] = list(map(lambda t: t + ' b', r['text']))
return r
dataset = dataset.map(test_mapper, batched=True, batch_size=10, features=dataset_features, num_proc=2)
```
Removing the embedding column fixes the issue!
### Expected behavior
The mapping completes successfully.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-14.0-arm64-arm-64bit
- Python version: 3.10.12
- Huggingface_hub version: 0.17.1
- PyArrow version: 13.0.0
- Pandas version: 2.0.3
|
CLOSED
| 2023-10-05T12:48:31
| 2024-02-06T19:24:20
| 2024-02-06T19:24:20
|
https://github.com/huggingface/datasets/issues/6280
|
jmif
| 4
|
[] |
6,279
|
Batched IterableDataset
|
### Feature request
Hi,
could you add an implementation of a batched `IterableDataset`. It already support an option to do batch iteration via `.iter(batch_size=...)` but this cannot be used in combination with a torch `DataLoader` since it just returns an iterator.
### Motivation
The current implementation loads each element of a batch individually which can be very slow in cases of a big batch_size. I did some experiments [here](https://discuss.huggingface.co/t/slow-dataloader-with-big-batch-size/57224) and using a batched iteration would speed up data loading significantly.
### Your contribution
N/A
|
OPEN
| 2023-10-05T11:12:49
| 2024-11-07T10:01:22
| null |
https://github.com/huggingface/datasets/issues/6279
|
lneukom
| 9
|
[
"enhancement"
] |
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.
|
### 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
|
CLOSED
| 2023-10-04T22:01:25
| 2023-10-08T17:05:46
| 2023-10-08T17:05:46
|
https://github.com/huggingface/datasets/issues/6277
|
diegogonzalezc
| 1
|
[] |
6,276
|
I'm trying to fine tune the openai/whisper model from huggingface using jupyter notebook and i keep getting this error
|
### 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
|
OPEN
| 2023-10-04T11:03:41
| 2023-11-27T10:39:16
| null |
https://github.com/huggingface/datasets/issues/6276
|
valaofficial
| 3
|
[] |
6,275
|
Would like to Contribute a dataset
|
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
|
CLOSED
| 2023-10-02T07:00:21
| 2023-10-10T16:27:54
| 2023-10-10T16:27:54
|
https://github.com/huggingface/datasets/issues/6275
|
vikas70607
| 1
|
[] |
6,274
|
FileNotFoundError for dataset with multiple builder config
|
### 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
|
CLOSED
| 2023-10-01T23:45:56
| 2024-08-14T04:42:02
| 2023-10-02T20:09:38
|
https://github.com/huggingface/datasets/issues/6274
|
LouisChen15
| 2
|
[] |
6,273
|
Broken Link to PubMed Abstracts dataset .
|
### 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
.
|
OPEN
| 2023-10-01T19:08:48
| 2024-04-28T02:30:42
| null |
https://github.com/huggingface/datasets/issues/6273
|
sameemqureshi
| 5
|
[] |
6,272
|
Duplicate `data_files` when named `<split>/<split>.parquet`
|
e.g. with `u23429/stock_1_minute_ticker`
```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
|
CLOSED
| 2023-10-01T15:43:56
| 2024-03-15T15:22:05
| 2024-03-15T15:22:05
|
https://github.com/huggingface/datasets/issues/6272
|
lhoestq
| 7
|
[
"bug"
] |
6,271
|
Overwriting Split overwrites data but not metadata, corrupting dataset
|
### 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
|
CLOSED
| 2023-09-30T22:37:31
| 2023-10-16T13:30:50
| 2023-10-16T13:30:50
|
https://github.com/huggingface/datasets/issues/6271
|
govindrai
| 0
|
[] |
6,270
|
Dataset.from_generator raises with sharded gen_args
|
### 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
|
CLOSED
| 2023-09-30T16:50:06
| 2023-10-11T20:29:12
| 2023-10-11T20:29:11
|
https://github.com/huggingface/datasets/issues/6270
|
hartmans
| 6
|
[] |
6,267
|
Multi label class encoding
|
### 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.
|
OPEN
| 2023-09-27T22:48:08
| 2023-10-26T18:46:08
| null |
https://github.com/huggingface/datasets/issues/6267
|
jmif
| 7
|
[
"enhancement"
] |
6,263
|
CI is broken: ImportError: cannot import name 'context' from 'tensorflow.python'
|
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
```
|
CLOSED
| 2023-09-27T08:12:05
| 2023-09-27T08:36:40
| 2023-09-27T08:36:40
|
https://github.com/huggingface/datasets/issues/6263
|
albertvillanova
| 0
|
[
"bug"
] |
6,261
|
Can't load a dataset
|
### 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
|
CLOSED
| 2023-09-26T15:46:25
| 2023-10-05T10:23:23
| 2023-10-05T10:23:22
|
https://github.com/huggingface/datasets/issues/6261
|
joaopedrosdmm
| 5
|
[] |
6,260
|
REUSE_DATASET_IF_EXISTS don't work
|
### 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
|
CLOSED
| 2023-09-26T03:02:16
| 2023-09-28T18:23:36
| 2023-09-28T18:23:36
|
https://github.com/huggingface/datasets/issues/6260
|
rangehow
| 3
|
[] |
6,259
|
Duplicated Rows When Loading Parquet Files from Root Directory with Subdirectories
|
### 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
|
CLOSED
| 2023-09-25T17:20:54
| 2024-03-15T15:22:04
| 2024-03-15T15:22:04
|
https://github.com/huggingface/datasets/issues/6259
|
MF-FOOM
| 1
|
[] |
6,257
|
HfHubHTTPError - exceeded our hourly quotas for action: commit
|
### 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
|
CLOSED
| 2023-09-25T06:11:43
| 2023-10-16T13:30:49
| 2023-10-16T13:30:48
|
https://github.com/huggingface/datasets/issues/6257
|
yuvalkirstain
| 4
|
[] |
6,256
|
load_dataset() function's cache_dir does not seems to work
|
### 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
macos X: Ventura 13.4.1 (c)
|
CLOSED
| 2023-09-24T15:34:06
| 2025-05-14T10:08:53
| 2024-10-08T15:45:18
|
https://github.com/huggingface/datasets/issues/6256
|
andyzhu
| 8
|
[] |
6,254
|
Dataset.from_generator() cost much more time in vscode debugging mode then running mode
|
### 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
|
CLOSED
| 2023-09-23T02:07:26
| 2023-10-03T14:42:53
| 2023-10-03T14:42:53
|
https://github.com/huggingface/datasets/issues/6254
|
dontnet-wuenze
| 1
|
[] |
6,252
|
exif_transpose not done to Image (PIL problem)
|
### 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.
|
CLOSED
| 2023-09-21T08:11:46
| 2024-03-19T15:29:43
| 2024-03-19T15:29:43
|
https://github.com/huggingface/datasets/issues/6252
|
rhajou
| 2
|
[
"enhancement"
] |
6,246
|
Add new column to dataset
|
### Describe the bug
```
---------------------------------------------------------------------------
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
|
CLOSED
| 2023-09-17T16:59:48
| 2023-09-18T16:20:09
| 2023-09-18T16:20:09
|
https://github.com/huggingface/datasets/issues/6246
|
andysingal
| 4
|
[] |
6,242
|
Data alteration when loading dataset with unspecified inner sequence length
|
### Describe the bug
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
|
CLOSED
| 2023-09-14T16:12:45
| 2023-09-19T17:53:18
| 2023-09-19T17:53:18
|
https://github.com/huggingface/datasets/issues/6242
|
qgallouedec
| 2
|
[] |
6,240
|
Dataloader stuck on multiple GPUs
|
### 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
|
CLOSED
| 2023-09-14T05:30:30
| 2023-09-14T23:54:42
| 2023-09-14T23:54:42
|
https://github.com/huggingface/datasets/issues/6240
|
kuri54
| 2
|
[] |
6,239
|
Load local audio data doesn't work
|
### 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
|
CLOSED
| 2023-09-13T22:30:01
| 2023-09-15T14:32:10
| 2023-09-15T14:32:10
|
https://github.com/huggingface/datasets/issues/6239
|
abodacs
| 2
|
[] |
6,238
|
`dataset.filter` ALWAYS removes the first item from the dataset when using batched=True
|
### 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
|
CLOSED
| 2023-09-13T20:20:37
| 2023-09-17T07:05:07
| 2023-09-17T07:05:07
|
https://github.com/huggingface/datasets/issues/6238
|
Taytay
| 2
|
[] |
6,237
|
Tokenization with multiple workers is too slow
|
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
|
CLOSED
| 2023-09-13T06:18:34
| 2023-09-19T21:54:58
| 2023-09-19T21:54:58
|
https://github.com/huggingface/datasets/issues/6237
|
macabdul9
| 1
|
[] |
6,236
|
Support buffer shuffle for to_tf_dataset
|
### 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
|
OPEN
| 2023-09-13T03:19:44
| 2023-09-18T01:11:21
| null |
https://github.com/huggingface/datasets/issues/6236
|
EthanRock
| 3
|
[
"enhancement"
] |
6,235
|
Support multiprocessing for download/extract nestedly
|
### 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
|
OPEN
| 2023-09-12T21:51:08
| 2023-09-12T21:51:08
| null |
https://github.com/huggingface/datasets/issues/6235
|
hgt312
| 0
|
[
"enhancement"
] |
6,229
|
Apply inference on all images in the dataset
|
### 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
|
CLOSED
| 2023-09-10T08:36:12
| 2023-09-20T16:11:53
| 2023-09-20T16:11:52
|
https://github.com/huggingface/datasets/issues/6229
|
andysingal
| 3
|
[] |
6,225
|
Conversion from RGB to BGR in Object Detection tutorial
|
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.
|
CLOSED
| 2023-09-08T06:49:19
| 2023-09-08T17:52:18
| 2023-09-08T17:52:17
|
https://github.com/huggingface/datasets/issues/6225
|
samokhinv
| 1
|
[] |
6,221
|
Support saving datasets with custom formatting
|
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?
|
OPEN
| 2023-09-06T16:03:32
| 2023-09-06T18:32:07
| null |
https://github.com/huggingface/datasets/issues/6221
|
mariosasko
| 1
|
[] |
6,217
|
`Dataset.to_dict()` ignore `decode=True` with Image feature
|
### 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
|
OPEN
| 2023-09-06T09:26:16
| 2023-09-08T17:08:52
| null |
https://github.com/huggingface/datasets/issues/6217
|
qgallouedec
| 1
|
[] |
6,214
|
Unpin fsspec < 2023.9.0
|
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
|
CLOSED
| 2023-09-05T11:02:58
| 2023-09-26T15:32:52
| 2023-09-26T15:32:52
|
https://github.com/huggingface/datasets/issues/6214
|
albertvillanova
| 0
|
[
"enhancement"
] |
6,212
|
Tilde (~) is not supported for data_files
|
### 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`
|
OPEN
| 2023-09-04T14:23:49
| 2023-09-05T08:28:39
| null |
https://github.com/huggingface/datasets/issues/6212
|
exs-avianello
| 2
|
[] |
6,209
|
CI is broken with AssertionError: 3 failed, 12 errors
|
Our CI is broken: 3 failed, 12 errors
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) =
```
|
CLOSED
| 2023-09-04T06:47:05
| 2023-09-04T07:30:01
| 2023-09-04T07:30:01
|
https://github.com/huggingface/datasets/issues/6209
|
albertvillanova
| 0
|
[
"bug"
] |
6,207
|
No-script datasets with ZIP files do not load
|
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
|
CLOSED
| 2023-09-04T05:50:27
| 2023-09-04T09:13:33
| 2023-09-04T09:13:33
|
https://github.com/huggingface/datasets/issues/6207
|
albertvillanova
| 0
|
[
"bug"
] |
6,206
|
When calling load_dataset, raise error: pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
|
### 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
|
CLOSED
| 2023-09-04T04:14:00
| 2024-04-17T15:53:29
| 2023-09-04T06:05:49
|
https://github.com/huggingface/datasets/issues/6206
|
aihao2000
| 2
|
[] |
6,203
|
Support loading from a DVC remote repository
|
### 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`?
|
CLOSED
| 2023-09-01T14:04:52
| 2023-09-15T15:11:27
| 2023-09-15T15:11:27
|
https://github.com/huggingface/datasets/issues/6203
|
bilelomrani1
| 4
|
[
"enhancement"
] |
6,202
|
avoid downgrading jax version
|
### 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.
|
CLOSED
| 2023-09-01T02:57:57
| 2023-10-12T16:28:59
| 2023-10-12T16:28:59
|
https://github.com/huggingface/datasets/issues/6202
|
chrisflesher
| 1
|
[
"enhancement"
] |
6,199
|
Use load_dataset for local json files, but it not works
|
### 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
|
OPEN
| 2023-08-31T09:42:34
| 2023-08-31T19:05:07
| null |
https://github.com/huggingface/datasets/issues/6199
|
Garen-in-bush
| 2
|
[] |
6,197
|
ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns'
|
### 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`
|
CLOSED
| 2023-08-31T08:51:50
| 2023-09-01T10:35:10
| 2023-08-31T10:24:40
|
https://github.com/huggingface/datasets/issues/6197
|
exs-avianello
| 3
|
[] |
6,196
|
Split order is not preserved
|
I have noticed that in some cases the split order is not preserved.
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]
|
CLOSED
| 2023-08-31T08:47:16
| 2023-08-31T13:48:43
| 2023-08-31T13:48:43
|
https://github.com/huggingface/datasets/issues/6196
|
albertvillanova
| 0
|
[
"bug"
] |
6,195
|
Force to reuse cache at given path
|
### Describe the bug
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
|
CLOSED
| 2023-08-30T18:44:54
| 2023-11-03T10:14:21
| 2023-08-30T19:00:45
|
https://github.com/huggingface/datasets/issues/6195
|
Luosuu
| 2
|
[] |
6,194
|
Support custom fingerprinting with `Dataset.from_generator`
|
### 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.
|
OPEN
| 2023-08-29T22:43:13
| 2024-12-22T01:14:39
| null |
https://github.com/huggingface/datasets/issues/6194
|
bilelomrani1
| 7
|
[
"enhancement"
] |
6,193
|
Dataset loading script method does not work with .pyc file
|
### 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
|
OPEN
| 2023-08-29T19:35:06
| 2023-08-31T19:47:29
| null |
https://github.com/huggingface/datasets/issues/6193
|
riteshkumarumassedu
| 3
|
[] |
6,190
|
`Invalid user token` even when correct user token is passed!
|
### 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
|
CLOSED
| 2023-08-29T12:37:03
| 2023-08-29T13:01:10
| 2023-08-29T13:01:09
|
https://github.com/huggingface/datasets/issues/6190
|
Vaibhavs10
| 2
|
[] |
6,188
|
[Feature Request] Check the length of batch before writing so that empty batch is allowed
|
### 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
|
CLOSED
| 2023-08-29T06:37:34
| 2023-09-19T21:55:38
| 2023-09-19T21:55:37
|
https://github.com/huggingface/datasets/issues/6188
|
namespace-Pt
| 1
|
[] |
6,187
|
Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory
|
### 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
|
OPEN
| 2023-08-29T05:49:56
| 2023-08-29T16:21:45
| null |
https://github.com/huggingface/datasets/issues/6187
|
andysingal
| 1
|
[] |
6,186
|
Feature request: add code example of multi-GPU processing
|
### 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 :)
|
CLOSED
| 2023-08-28T10:00:59
| 2024-10-07T09:39:51
| 2023-11-22T15:42:20
|
https://github.com/huggingface/datasets/issues/6186
|
NielsRogge
| 18
|
[
"documentation",
"enhancement"
] |
6,185
|
Error in saving the PIL image into *.arrow files using datasets.arrow_writer
|
### 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
|
OPEN
| 2023-08-26T12:15:57
| 2023-08-29T14:49:58
| null |
https://github.com/huggingface/datasets/issues/6185
|
HaozheZhao
| 1
|
[] |
6,184
|
Map cache does not detect function changes in another module
|
```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.
|
CLOSED
| 2023-08-25T22:59:14
| 2023-08-29T20:57:07
| 2023-08-29T20:56:49
|
https://github.com/huggingface/datasets/issues/6184
|
jonathanasdf
| 2
|
[
"duplicate"
] |
6,183
|
Load dataset with non-existent file
|
### 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
ipywidgets 8.0.4 py310h06a4308_0
jedi 0.18.1 py310h06a4308_1
jinja2 3.1.2 pypi_0 pypi
jsonschema 4.19.0 pypi_0 pypi
jsonschema-specifications 2023.7.1 pypi_0 pypi
jupyter_client 8.1.0 py310h06a4308_0
jupyter_core 5.3.0 py310h06a4308_0
jupyterlab_widgets 3.0.5 py310h06a4308_0
kiwisolver 1.4.4 pypi_0 pypi
ld_impl_linux-64 2.38 h1181459_1
libffi 3.3 he6710b0_2
libgcc-ng 11.2.0 h1234567_1
libgomp 11.2.0 h1234567_1
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
nbformat 4.2.0 pypi_0 pypi
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
peft 0.4.0 pypi_0 pypi
pexpect 4.8.0 pyhd3eb1b0_3
pickleshare 0.7.5 pyhd3eb1b0_1003
pillow 10.0.0 pypi_0 pypi
pip 23.2.1 py310h06a4308_0
platformdirs 2.5.2 py310h06a4308_0
plotly 5.16.1 pypi_0 pypi
prompt-toolkit 3.0.36 py310h06a4308_0
protobuf 4.24.0 pypi_0 pypi
psutil 5.9.0 py310h5eee18b_0
ptyprocess 0.7.0 pyhd3eb1b0_2
pure_eval 0.2.2 pyhd3eb1b0_0
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
xxhash 3.3.0 pypi_0 pypi
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
```
|
CLOSED
| 2023-08-25T22:21:22
| 2023-08-29T13:26:22
| 2023-08-29T13:26:22
|
https://github.com/huggingface/datasets/issues/6183
|
freQuensy23-coder
| 2
|
[] |
6,182
|
Loading Meteor metric in HF evaluate module crashes due to datasets import issue
|
### 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
|
CLOSED
| 2023-08-25T14:54:06
| 2023-09-04T16:41:11
| 2023-08-31T14:38:23
|
https://github.com/huggingface/datasets/issues/6182
|
dsashulya
| 4
|
[] |
6,179
|
Map cache with tokenizer
|
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"))))
```
|
OPEN
| 2023-08-25T12:55:18
| 2023-08-31T15:17:24
| null |
https://github.com/huggingface/datasets/issues/6179
|
jonathanasdf
| 4
|
[] |
6,178
|
'import datasets' throws "invalid syntax error"
|
### 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.
|
CLOSED
| 2023-08-25T08:35:14
| 2023-09-27T17:33:39
| 2023-09-27T17:33:39
|
https://github.com/huggingface/datasets/issues/6178
|
elia-ashraf
| 1
|
[] |
6,176
|
how to limit the size of memory mapped file?
|
### Describe the bug
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
|
OPEN
| 2023-08-24T05:33:45
| 2023-10-11T06:00:10
| null |
https://github.com/huggingface/datasets/issues/6176
|
williamium3000
| 6
|
[] |
6,173
|
Fix CI for pyarrow 13.0.0
|
pyarrow 13.0.0 just came out
```
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 ?
|
CLOSED
| 2023-08-23T14:11:20
| 2023-08-25T13:06:53
| 2023-08-25T13:06:53
|
https://github.com/huggingface/datasets/issues/6173
|
lhoestq
| 0
|
[] |
6,172
|
Make Dataset streaming queries retryable
|
### Feature request
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.
|
OPEN
| 2023-08-23T13:15:38
| 2023-11-06T13:54:16
| null |
https://github.com/huggingface/datasets/issues/6172
|
rojagtap
| 4
|
[
"enhancement"
] |
6,169
|
Configurations in yaml not working
|
### 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
|
OPEN
| 2023-08-23T00:13:22
| 2023-08-23T15:35:31
| null |
https://github.com/huggingface/datasets/issues/6169
|
tsor13
| 4
|
[] |
6,163
|
Error type: ArrowInvalid Details: Failed to parse string: '[254,254]' as a scalar of type int32
|
### Describe the bug
I am getting the following error while I am trying to upload the CSV sheet to train a model. My CSV sheet content is exactly same as shown in the example CSV file in the Auto Train page. Attaching screenshot of error for reference. I have also tried converting the index of the answer that are integer into string by placing inverted commas and also without inverted commas.
Can anyone please help me out?
FYI : I am using Chrome browser.
Error type: ArrowInvalid
Details: Failed to parse string: '[254,254]' as a scalar of type int32
### Steps to reproduce the bug
Kindly let me know how to fix this?
### Expected behavior
Kindly let me know how to fix this?
### Environment info
Kindly let me know how to fix this?
|
OPEN
| 2023-08-19T11:34:40
| 2025-07-22T12:04:46
| null |
https://github.com/huggingface/datasets/issues/6163
|
shishirCTC
| 2
|
[] |
6,162
|
load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields
|
### Describe the bug
When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**.
When deleting that line the loading is successful.
We also tried loading this file with the discrepancy using this function and it is successful
```python
os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T"
ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train']
```
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. Load one jsonl from the redpajama-data-1T
```bash
wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl
```
2.Load dataset will give error:
```python
from datasets import load_dataset
ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl')
```
_TypeError: Couldn't cast array of type
Struct
<content_hash: string,
timestamp: string,
source: string,
line_count: int64,
max_line_length: int64,
avg_line_length: double,
alnum_prop: double,
repo_name: string,
id: string,
size: string,
binary: bool,
copies: string,
ref: string,
path: string,
mode: string,
license: string,
language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>**
to
{'content_hash': Value(dtype='string', id=None),
'timestamp': Value(dtype='string', id=None),
'source': Value(dtype='string', id=None),
'line_count': Value(dtype='int64', id=None),
'max_line_length': Value(dtype='int64', id=None),
'avg_line_length': Value(dtype='float64', id=None),
'alnum_prop': Value(dtype='float64', id=None),
'repo_name': Value(dtype='string', id=None),
'id': Value(dtype='string', id=None),
'size': Value(dtype='string', id=None),
'binary': Value(dtype='bool', id=None),
'copies': Value(dtype='string', id=None),
'ref': Value(dtype='string', id=None),
'path': Value(dtype='string', id=None),
'mode': Value(dtype='string', id=None),
'license': Value(dtype='string', id=None),
'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_
3. To remove the line causing the problem that includes the **symlink_target: string>** do:
```bash
sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl
```
4. Rerun the loading function now is succesful:
```python
from datasets import load_dataset
ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl')
```
### Expected behavior
Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out.
### Environment info
- `datasets` version: 2.14.1
- Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.17
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
|
OPEN
| 2023-08-18T07:19:39
| 2023-08-18T17:00:35
| null |
https://github.com/huggingface/datasets/issues/6162
|
rbrugaro
| 4
|
[] |
6,159
|
Add `BoundingBox` feature
|
... to make working with object detection datasets easier. Currently, `Sequence(int_or_float, length=4)` can be used to represent this feature optimally (in the storage backend), so I only see this feature being useful if we make it work with the viewer. Also, bounding boxes usually come in 4 different formats (explained [here](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/)), so we need to decide which one to support (or maybe all of them).
cc @NielsRogge @severo
|
OPEN
| 2023-08-17T20:49:51
| 2024-11-18T17:58:43
| null |
https://github.com/huggingface/datasets/issues/6159
|
mariosasko
| 1
|
[
"enhancement"
] |
6,157
|
DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'
|
### Describe the bug
When I was in load_dataset, it said "DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'". The second time I ran it, there was no error and the dataset object worked
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[3], line 1
----> 1 dataset = load_dataset(
2 "/home/aihao/workspace/DeepLearningContent/datasets/manga",
3 data_dir="/home/aihao/workspace/DeepLearningContent/datasets/manga",
4 split="train",
5 )
File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
2142 # Build dataset for splits
2143 keep_in_memory = (
2144 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2145 )
-> 2146 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
2147 # Rename and cast features to match task schema
2148 if task is not None:
2149 # To avoid issuing the same warning twice
File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190), in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)
1187 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS)
1189 # Create a dataset for each of the given splits
-> 1190 datasets = map_nested(
1191 partial(
1192 self._build_single_dataset,
...
File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379), in DatasetInfo.copy(self)
378 def copy(self) -> "DatasetInfo":
--> 379 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
TypeError: DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'
```
### Steps to reproduce the bug
/home/aihao/workspace/DeepLearningContent/datasets/images/images.py
```python
from logging import config
import datasets
import os
from PIL import Image
import csv
import json
class ImagesConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(ImagesConfig, self).__init__(**kwargs)
class Images(datasets.GeneratorBasedBuilder):
def _split_generators(self, dl_manager: datasets.DownloadManager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"split": datasets.Split.TRAIN},
)
]
BUILDER_CONFIGS = [
ImagesConfig(
name="similar_pairs",
description="simliar pair dataset,item is a pair of similar images",
),
ImagesConfig(
name="image_prompt_pairs",
description="image prompt pairs",
),
]
def _info(self):
if self.config.name == "similar_pairs":
return datasets.Features(
{
"image1": datasets.features.Image(),
"image2": datasets.features.Image(),
"similarity": datasets.Value("float32"),
}
)
elif self.config.name == "image_prompt_pairs":
return datasets.Features(
{"image": datasets.features.Image(), "prompt": datasets.Value("string")}
)
def _generate_examples(self, split):
data_path = os.path.join(self.config.data_dir, "data")
if self.config.name == "similar_pairs":
prompts = {}
with open(os.path.join(data_path ,"prompts.json"), "r") as f:
prompts = json.load(f)
with open(os.path.join(data_path, "similar_pairs.csv"), "r") as f:
reader = csv.reader(f)
for row in reader:
image1_path, image2_path, similarity = row
yield image1_path + ":" + image2_path + ":", {
"image1": Image.open(image1_path),
"prompt1": prompts[image1_path],
"image2": Image.open(image2_path),
"prompt2": prompts[image2_path],
"similarity": float(similarity),
}
```
Code that indicates an error:
```python
from datasets import load_dataset
import json
import csv
import ast
import torch
data_dir = "/home/aihao/workspace/DeepLearningContent/datasets/images"
dataset = load_dataset(data_dir, data_dir=data_dir, name="similar_pairs")
```
### Expected behavior
The first execution gives an error, but it works fine
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-6.2.0-26-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
|
CLOSED
| 2023-08-17T15:48:11
| 2023-09-27T17:36:14
| 2023-09-27T17:36:14
|
https://github.com/huggingface/datasets/issues/6157
|
aihao2000
| 13
|
[] |
6,156
|
Why not use self._epoch as seed to shuffle in distributed training with IterableDataset
|
### Describe the bug
Currently, distributed training with `IterableDataset` needs to pass fixed seed to shuffle to keep each node use the same seed to avoid overlapping.
https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1174-L1177
My question is why not directly use `self._epoch` which is set by `set_epoch` as seed? It's almost the same across nodes.
https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1790-L1801
If not using `self._epoch` as shuffling seed, what does this method do to prepare an epoch seeded generator?
https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1206
### Steps to reproduce the bug
As mentioned above.
### Expected behavior
As mentioned above.
### Environment info
Not related
|
CLOSED
| 2023-08-17T10:58:20
| 2023-08-17T14:33:15
| 2023-08-17T14:33:14
|
https://github.com/huggingface/datasets/issues/6156
|
npuichigo
| 3
|
[] |
6,152
|
FolderBase Dataset automatically resolves under current directory when data_dir is not specified
|
### Describe the bug
FolderBase Dataset automatically resolves under current directory when data_dir is not specified.
For example:
```
load_dataset("audiofolder")
```
takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59
### Steps to reproduce the bug
```
load_dataset("audiofolder")
```
### Expected behavior
Error report
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17
- Python version: 3.8.15
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3
|
CLOSED
| 2023-08-16T04:38:09
| 2025-06-18T14:18:42
| 2025-06-18T14:18:42
|
https://github.com/huggingface/datasets/issues/6152
|
npuichigo
| 19
|
[
"good first issue"
] |
6,151
|
Faster sorting for single key items
|
### Feature request
A faster way to sort a dataset which contains a large number of rows.
### Motivation
The current sorting implementations took significantly longer than expected when I was running on a dataset trying to sort by timestamps.
**Code snippet:**
```python
ds = datasets.load_dataset( "json", **{"data_files": {"train": "path-to-jsonlines"}, "split": "train"}, num_proc=os.cpu_count(), keep_in_memory=True)
sorted_ds = ds.sort("pubDate", keep_in_memory=True)
```
However, once I switched to a different method which
1. unpacked to a list of tuples
2. sorted tuples by key
3. run `.select` with the sorted list of indices
It was significantly faster (orders of magnitude, especially with M's of rows)
### Your contribution
I'd be happy to implement a crude single key sorting algorithm so that other users can benefit from this trick. Broadly, this would take a `Dataset` and perform;
```python
# ds is a Dataset object
# key_name is the sorting key
class Dataset:
...
def _sort(key_name: str) -> Dataset:
index_keys = [(i,x) for i,x in enumerate(self[key_name])]
sorted_rows = sorted(row_pubdate, key=lambda x: x[1])
sorted_indicies = [x[0] for x in sorted_rows]
return self.select(sorted_indicies)
```
|
CLOSED
| 2023-08-15T14:02:31
| 2023-08-21T14:38:26
| 2023-08-21T14:38:25
|
https://github.com/huggingface/datasets/issues/6151
|
jackapbutler
| 2
|
[
"enhancement"
] |
6,150
|
Allow dataset implement .take
|
### Feature request
I want to do:
```
dataset.take(512)
```
but it only works with streaming = True
### Motivation
uniform interface to data sets. Really surprising the above only works with streaming = True.
### Your contribution
Should be trivial to copy paste the IterableDataset .take to use the local path in the data (when streaming = False)
|
OPEN
| 2023-08-15T00:17:51
| 2023-08-17T13:49:37
| null |
https://github.com/huggingface/datasets/issues/6150
|
brando90
| 4
|
[
"enhancement"
] |
6,149
|
Dataset.from_parquet cannot load subset of columns
|
### Describe the bug
When using `Dataset.from_parquet(path_or_paths, columns=[...])` and a subset of columns, loading fails with a variant of the following
```
ValueError: Couldn't cast
a: int64
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 273
to
{'a': Value(dtype='int64', id=None), 'b': Value(dtype='int64', id=None)}
because column names don't match
The above exception was the direct cause of the following exception:
```
Looks to be triggered by https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/table.py#L2285-L2286
### Steps to reproduce the bug
```
import pandas as pd
from datasets import Dataset
pd.DataFrame([{"a": 1, "b": 2}]).to_parquet("test.pq")
Dataset.from_parquet("test.pq", columns=["a"])
```
### Expected behavior
A subset of columns should be loaded without error
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.10.0-23-cloud-amd64-x86_64-with-glibc2.2.5
- Python version: 3.8.16
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
|
CLOSED
| 2023-08-14T23:28:22
| 2023-08-17T22:36:05
| 2023-08-17T22:36:05
|
https://github.com/huggingface/datasets/issues/6149
|
dwyatte
| 1
|
[] |
6,147
|
ValueError when running BeamBasedBuilder with GCS path in cache_dir
|
### Describe the bug
When running the BeamBasedBuilder with a GCS path specified in the cache_dir, the following ValueError occurs:
```
ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://my-bucket/huggingface_datasets/my_beam_dataset/default/0.0.0/my_beam_dataset-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite']
```
Same error occurs after running `pip install apache-beam[gcp]` as instructed.
### Steps to reproduce the bug
Put `my_beam_dataset.py`:
```python
import datasets
class MyBeamDataset(datasets.BeamBasedBuilder):
def _info(self):
features = datasets.Features({"value": datasets.Value("int64")})
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager, pipeline):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={})]
def _build_pcollection(self, pipeline):
import apache_beam as beam
return pipeline | beam.Create([{"value": i} for i in range(10)])
```
Run:
```bash
datasets-cli run_beam my_beam_dataset.py --cache_dir=gs://my-bucket/huggingface_datasets/ --beam_pipeline_options="runner=DirectRunner"
```
### Expected behavior
Running the BeamBasedBuilder with a GCS cache path without any errors.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-13.4-arm64-arm-64bit
- Python version: 3.9.17
- Huggingface_hub version: 0.16.4
- PyArrow version: 9.0.0
- Pandas version: 2.0.3
|
CLOSED
| 2023-08-14T03:11:34
| 2024-03-18T16:59:15
| 2024-03-18T16:59:14
|
https://github.com/huggingface/datasets/issues/6147
|
ktrk115
| 2
|
[] |
6,146
|
DatasetGenerationError when load glue benchmark datasets from `load_dataset`
|
### Describe the bug
Package version: datasets-2.14.4
When I run the codes:
```
from datasets import load_dataset
dataset = load_dataset("glue", "ax")
```
I got the following errors:
---------------------------------------------------------------------------
SchemaInferenceError Traceback (most recent call last)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1949, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1948 num_shards = shard_id + 1
-> 1949 num_examples, num_bytes = writer.finalize()
1950 writer.close()
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/arrow_writer.py:598, in ArrowWriter.finalize(self, close_stream)
597 self.stream.close()
--> 598 raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
599 logger.debug(
600 f"Done writing {self._num_examples} {self.unit} in {self._num_bytes} bytes {self._path if self._path else ''}."
601 )
SchemaInferenceError: Please pass `features` or at least one example when writing data
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Cell In[5], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("glue", "ax")
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2136, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
2133 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
2135 # Download and prepare data
-> 2136 builder_instance.download_and_prepare(
2137 download_config=download_config,
2138 download_mode=download_mode,
2139 verification_mode=verification_mode,
2140 try_from_hf_gcs=try_from_hf_gcs,
2141 num_proc=num_proc,
2142 storage_options=storage_options,
2143 )
2145 # Build dataset for splits
2146 keep_in_memory = (
2147 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2148 )
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
952 if num_proc is not None:
953 prepare_split_kwargs["num_proc"] = num_proc
--> 954 self._download_and_prepare(
955 dl_manager=dl_manager,
956 verification_mode=verification_mode,
957 **prepare_split_kwargs,
958 **download_and_prepare_kwargs,
959 )
960 # Sync info
961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
1045 split_dict.add(split_generator.split_info)
1047 try:
1048 # Prepare split will record examples associated to the split
-> 1049 self._prepare_split(split_generator, **prepare_split_kwargs)
1050 except OSError as e:
1051 raise OSError(
1052 "Cannot find data file. "
1053 + (self.manual_download_instructions or "")
1054 + "\nOriginal error:\n"
1055 + str(e)
1056 ) from None
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1811 job_id = 0
1812 with pbar:
-> 1813 for job_id, done, content in self._prepare_split_single(
1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1815 ):
1816 if done:
1817 result = content
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1957 e = e.__context__
-> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
### Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset("glue", "ax")
### Expected behavior
When generating the train split:
Generating train split:
0/0 [00:00<?, ? examples/s]
It raise the error:
DatasetGenerationError: An error occurred while generating the dataset
### Environment info
datasets-2.14.4.
Python 3.10
|
CLOSED
| 2023-08-13T05:17:56
| 2023-08-26T22:09:09
| 2023-08-26T22:09:09
|
https://github.com/huggingface/datasets/issues/6146
|
yusx-swapp
| 4
|
[] |
6,153
|
custom load dataset to hub
|
### System Info
kaggle notebook
i transformed dataset:
```
dataset = load_dataset("Dahoas/first-instruct-human-assistant-prompt")
```
to
formatted_dataset:
```
Dataset({
features: ['message_tree_id', 'message_tree_text'],
num_rows: 33143
})
```
but would like to know how to upload to hub
### Who can help?
@ArthurZucker @younesbelkada
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
shared above
### Expected behavior
load dataset to hub
|
CLOSED
| 2023-08-13T04:42:22
| 2023-11-21T11:50:28
| 2023-10-08T17:04:16
|
https://github.com/huggingface/datasets/issues/6153
|
andysingal
| 5
|
[] |
6,144
|
NIH exporter file not found
|
### Describe the bug
can't use or download the nih exporter pile data.
```
15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights()
16 File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights
17 column_names = next(iter(dataset)).keys()
18 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__
19 for key, example in ex_iterable:
20 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__
21 yield from self.generate_examples_fn(**self.kwargs)
22 File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 236, in _generate_examples
23 with zstd.open(open(files[subset], "rb"), "rt", encoding="utf-8") as f:
24 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py", line 74, in wrapper
25 return function(*args, download_config=download_config, **kwargs)
26 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
27 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
28 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open
29 return self.__enter__()
30 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__
31 f = self.fs.open(self.path, mode=mode)
32 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open
33 f = self._open(
34 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open
35 size = size or self.info(path, **kwargs)["size"]
36 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper
37 return sync(self.loop, func, *args, **kwargs)
38 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync
39 raise return_result
40 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner
41 result[0] = await coro
42 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info
43 raise FileNotFoundError(url) from exc
44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst
```
### Steps to reproduce the bug
run this:
```
from datasets import load_dataset
path, name = 'EleutherAI/pile', 'nih_exporter'
# -- Get data set
dataset = load_dataset(path, name, streaming=True, split="train").with_format("torch")
batch = dataset.take(512)
print(f'{batch=}')
```
### Expected behavior
print the batch
### Environment info
```
(beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ datasets-cli env
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.14.4
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
```
|
OPEN
| 2023-08-11T19:05:25
| 2023-08-14T23:28:38
| null |
https://github.com/huggingface/datasets/issues/6144
|
brando90
| 6
|
[] |
6,142
|
the-stack-dedup fails to generate
|
### Describe the bug
I'm getting an error generating the-stack-dedup with datasets 2.13.1, and with 2.14.4 nothing happens.
### Steps to reproduce the bug
My code:
```
import os
import datasets as ds
MY_CACHE_DIR = "/home/ubuntu/the-stack-dedup-local"
MY_TOKEN="my-token"
the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="train", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, use_auth_token=MY_TOKEN, num_proc=64)
```
The exception:
```
Generating train split: 233248251 examples [54:31, 57280.00 examples/s]
multiprocess.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build
er.py", line 1879, in _prepare_split_single
for _, table in generator:
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa
ged_modules/parquet/parquet.py", line 82, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa
ged_modules/parquet/parquet.py", line 61, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table
.py", line 2324, in table_cast
return cast_table_to_schema(table, schema)
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table
.py", line 2282, in cast_table_to_schema
raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nb
ecause column names don't match")
ValueError: Couldn't cast
hexsha: string
size: int64
ext: string
lang: string
max_stars_repo_path: string
max_stars_repo_name: string
max_stars_repo_head_hexsha: string
max_stars_repo_licenses: list<item: string>
child 0, item: string
max_stars_count: int64
max_stars_repo_stars_event_min_datetime: string
max_stars_repo_stars_event_max_datetime: string
max_issues_repo_path: string
max_issues_repo_name: string
max_issues_repo_head_hexsha: string
max_issues_repo_licenses: list<item: string>
child 0, item: string
max_issues_count: int64
max_issues_repo_issues_event_min_datetime: string
max_issues_repo_issues_event_max_datetime: string
max_forks_repo_path: string
max_forks_repo_name: string
max_forks_repo_head_hexsha: string
max_forks_repo_licenses: list<item: string>
child 0, item: string
max_forks_count: int64
max_forks_repo_forks_event_min_datetime: string
max_forks_repo_forks_event_max_datetime: string
content: string
avg_line_length: double
max_line_length: int64
alphanum_fraction: double
__id__: int64
-- schema metadata --
huggingface: '{"info": {"features": {"hexsha": {"dtype": "string", "_type' + 1979
to
{'hexsha': Value(dtype='string', id=None), 'size': Value(dtype='int64', id=None), 'ext': Value(dtype='string', id=None), 'lang': Value(dtype='string', id=None), 'max_stars_repo_path': Value(dtype='string', id=None), 'max_stars_repo_name': Value(dtype='string', id=None), 'max_stars_repo_head_hexsha': Value(dtype='string', id=None), 'max_stars_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_stars_count': Value(dtype='int64', id=None), 'max_stars_repo_stars_event_min_datetime': Value(dtype='string', id=None), 'max_stars_repo_stars_event_max_datetime': Value(dtype='string', id=None), 'max_issues_repo_path': Value(dtype='string', id=None), 'max_issues_repo_name': Value(dtype='string', id=None), 'max_issues_repo_head_hexsha': Value(dtype='string', id=None), 'max_issues_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_issues_count': Value(dtype='int64', id=None), 'max_issues_repo_issues_event_min_datetime': Value(dtype='string', id=None), 'max_issues_repo_issues_event_max_datetime': Value(dtype='string', id=None), 'max_forks_repo_path': Value(dtype='string', id=None), 'max_forks_repo_name': Value(dtype='string', id=None), 'max_forks_repo_head_hexsha': Value(dtype='string', id=None), 'max_forks_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_forks_count': Value(dtype='int64', id=None), 'max_forks_repo_forks_event_min_datetime': Value(dtype='string', id=None), 'max_forks_repo_forks_event_max_datetime': Value(dtype='string', id=None), 'content': Value(dtype='string', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'alphanum_fraction': Value(dtype='float64', id=None)}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p
ool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils
/py_utils.py", line 1328, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build
er.py", line 1912, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating th
e dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while genera
ting the dataset
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/ubuntu/download_the_stack.py", line 7, in <module>
the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="tr
ain", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, us
e_auth_token=MY_TOKEN, num_proc=64)
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/load.
py", line 1809, in load_dataset
builder_instance.download_and_prepare(
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build
er.py", line 909, in download_and_prepare
self._download_and_prepare(
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build
er.py", line 1004, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build
er.py", line 1796, in _prepare_split
for job_id, done, content in iflatmap_unordered(
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils
/py_utils.py", line 1354, in iflatmap_unordered
[async_result.get(timeout=0.05) for async_result in async_results]
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils
/py_utils.py", line 1354, in <listcomp>
[async_result.get(timeout=0.05) for async_result in async_results]
File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p
ool.py", line 774, in get
raise self._value
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
The dataset downloads properly. @lhoestq @loub
### Environment info
Datasets 2.13.1, large VM with 2TB RAM, Ubuntu 20.04
|
CLOSED
| 2023-08-11T05:10:49
| 2023-08-17T09:26:13
| 2023-08-17T09:26:13
|
https://github.com/huggingface/datasets/issues/6142
|
michaelroyzen
| 4
|
[] |
6,141
|
TypeError: ClientSession._request() got an unexpected keyword argument 'https'
|
### Describe the bug
Hello, when I ran the [code snippet](https://huggingface.co/docs/datasets/v2.14.4/en/loading#json) on the document, I encountered the following problem:
```
Python 3.10.9 (main, Mar 1 2023, 18:23:06) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
>>> dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 2112, in load_dataset
builder_instance = load_dataset_builder(
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1798, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1413, in dataset_module_factory
).get_module()
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 949, in get_module
data_files = DataFilesDict.from_patterns(
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 672, in from_patterns
DataFilesList.from_patterns(
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 578, in from_patterns
resolve_pattern(
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 340, in resolve_pattern
for filepath, info in fs.glob(pattern, detail=True).items()
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 113, in wrapper
return sync(self.loop, func, *args, **kwargs)
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 98, in sync
raise return_result
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 53, in _runner
result[0] = await coro
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 449, in _glob
elif await self._exists(path):
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 306, in _exists
r = await session.get(self.encode_url(path), **kw)
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/aiohttp/client.py", line 922, in get
self._request(hdrs.METH_GET, url, allow_redirects=allow_redirects, **kwargs)
TypeError: ClientSession._request() got an unexpected keyword argument 'https'
```
### Steps to reproduce the bug
```
from datasets import load_dataset
base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data")
```
### Expected behavior
able to load normally
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.4.54-2-x86_64-with-glibc2.27
- Python version: 3.10.9
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3
|
CLOSED
| 2023-08-11T02:40:32
| 2023-08-30T13:51:33
| 2023-08-30T13:51:33
|
https://github.com/huggingface/datasets/issues/6141
|
q935970314
| 1
|
[] |
6,140
|
Misalignment between file format specified in configs metadata YAML and the inferred builder
|
There is a misalignment between the format of the `data_files` specified in the configs metadata YAML (CSV):
```yaml
configs:
- config_name: default
data_files:
- split: train
path: data.csv
```
and the inferred builder (JSON). Note there are multiple JSON files in the repo, but they do not appear in the configs metadata YAML.
See: https://huggingface.co/datasets/freddyaboulton/chatinterface_with_image_csv/discussions/1
CC: @freddyaboulton @polinaeterna
|
CLOSED
| 2023-08-10T15:07:34
| 2023-08-17T20:37:20
| 2023-08-17T20:37:20
|
https://github.com/huggingface/datasets/issues/6140
|
albertvillanova
| 0
|
[
"bug"
] |
6,139
|
Offline dataset viewer
|
### Feature request
The dataset viewer feature is very nice. It enables to the user to easily view the dataset. However, when working for private companies we cannot always upload the dataset to the hub. Is there a way to create dataset viewer offline? I.e. to run a code that will open some kind of html or something that makes it easy to view the dataset.
### Motivation
I want to easily view my dataset even when it is hosted locally.
### Your contribution
N.A.
|
CLOSED
| 2023-08-10T11:30:00
| 2024-09-24T18:36:35
| 2023-09-29T13:10:22
|
https://github.com/huggingface/datasets/issues/6139
|
yuvalkirstain
| 7
|
[
"enhancement",
"dataset-viewer"
] |
6,137
|
(`from_spark()`) Unable to connect HDFS in pyspark YARN setting
|
### Describe the bug
related issue: https://github.com/apache/arrow/issues/37057#issue-1841013613
---
Hello. I'm trying to interact with HDFS storage from a driver and workers of pyspark YARN cluster. Precisely I'm using **huggingface's `datasets`** ([link](https://github.com/huggingface/datasets)) library that relies on pyarrow to communicate with HDFS. The `from_spark()` ([link](https://huggingface.co/docs/datasets/use_with_spark#load-from-spark)) is what I'm invoking in my script.
Below is the error I'm encountering. Note that I've masked sensitive paths. My code is sent to worker containers (docker) from driver container then executed. I confirmed that in both driver and worker images I can connect to HDFS using pyarrow since the envs and required jars are properly set, but strangely that becomes impossible when the same image runs as remote worker process.
These are some peculiarities in my environment that might caused this issue.
* **Cluster requires kerberos authentication**
* But I think the error message implies that's not the problem in this case
* **The user that runs the worker process is different from that built the docker image**
* To avoid permission-related issues I made all directories that are accessed from the script accessible to everyone
* **Pyspark-part of my code has no problem interacting with HDFS.**
* Even pyarrow doesn't experience problem when I run the code in interactive session of the same docker images (driver, worker)
* The problem occurs only when it runs as cluster's worker runtime
Hope I could get some help. Thanks.
```bash
2023-08-08 18:51:19,638 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2023-08-08 18:51:20,280 WARN shortcircuit.DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.
23/08/08 18:51:22 WARN TaskSetManager: Lost task 0.0 in stage 142.0 (TID 9732) (ac3bax2062.bdp.bdata.ai executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
23/08/08 18:51:24 WARN TaskSetManager: Lost task 0.1 in stage 142.0 (TID 9733) (ac3iax2079.bdp.bdata.ai executor 2): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
23/08/08 18:51:38 WARN TaskSetManager: Lost task 0.2 in stage 142.0 (TID 9734) (<MASKED> executor 4): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
### Steps to reproduce the bug
Use `from_spark()` function in pyspark YARN setting. I set `cache_dir` to HDFS path.
### Expected behavior
Work as described in document
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.17
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
|
OPEN
| 2023-08-10T11:03:08
| 2023-08-10T11:03:08
| null |
https://github.com/huggingface/datasets/issues/6137
|
kyoungrok0517
| 0
|
[] |
6,136
|
CI check_code_quality error: E721 Do not compare types, use `isinstance()`
|
After latest release of `ruff` (https://pypi.org/project/ruff/0.0.284/), we get the following CI error:
```
src/datasets/utils/py_utils.py:689:12: E721 Do not compare types, use `isinstance()`
```
|
CLOSED
| 2023-08-10T10:19:50
| 2023-08-10T11:22:58
| 2023-08-10T11:22:58
|
https://github.com/huggingface/datasets/issues/6136
|
albertvillanova
| 0
|
[
"maintenance"
] |
6,134
|
`datasets` cannot be installed alongside `apache-beam`
|
### Describe the bug
If one installs `apache-beam` alongside `datasets` (which is required for the [wikipedia](https://huggingface.co/datasets/wikipedia#dataset-summary) dataset) in certain environments (such as a Google Colab notebook), they appear to install successfully, however, actually trying to do something such as importing the `load_dataset` method from `datasets` results in a crashing error.
I think the problem is that `apache-beam` version 2.49.0 requires `dill>=0.3.1.1,<0.3.2`, but the latest version of `multiprocess` (0.70.15) (on which `datasets` depends) requires `dill>=0.3.7,`, so this is causing the dependency resolver to use an older version of `multiprocess` which leads to the `datasets` crashing since it doesn't actually appear to be compatible with older versions.
### Steps to reproduce the bug
See this [Google Colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing) to easily reproduce the bug.
In some environments, I have been able to reproduce the bug by running the following in Bash:
```bash
$ pip install datasets apache-beam
```
then the following in a Python shell:
```python
from datasets import load_dataset
```
Here is my stacktrace from running on Google Colab:
<details>
<summary>stacktrace</summary>
```
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
20 __version__ = "2.14.4"
21
---> 22 from .arrow_dataset import Dataset
23 from .arrow_reader import ReadInstruction
24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
64
65 from . import config
---> 66 from .arrow_reader import ArrowReader
67 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
68 from .data_files import sanitize_patterns
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module>
28 import pyarrow.parquet as pq
29
---> 30 from .download.download_config import DownloadConfig
31 from .naming import _split_re, filenames_for_dataset_split
32 from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables
[/usr/local/lib/python3.10/dist-packages/datasets/download/__init__.py](https://localhost:8080/#) in <module>
7
8 from .download_config import DownloadConfig
----> 9 from .download_manager import DownloadManager, DownloadMode
10 from .streaming_download_manager import StreamingDownloadManager
[/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py](https://localhost:8080/#) in <module>
33 from ..utils.info_utils import get_size_checksum_dict
34 from ..utils.logging import get_logger, is_progress_bar_enabled, tqdm
---> 35 from ..utils.py_utils import NestedDataStructure, map_nested, size_str
36 from .download_config import DownloadConfig
37
[/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <module>
38 import dill
39 import multiprocess
---> 40 import multiprocess.pool
41 import numpy as np
42 from packaging import version
[/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in <module>
607 #
608
--> 609 class ThreadPool(Pool):
610
611 from .dummy import Process
[/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in ThreadPool()
609 class ThreadPool(Pool):
610
--> 611 from .dummy import Process
612
613 def __init__(self, processes=None, initializer=None, initargs=()):
[/usr/local/lib/python3.10/dist-packages/multiprocess/dummy/__init__.py](https://localhost:8080/#) in <module>
85 #
86
---> 87 class Condition(threading._Condition):
88 # XXX
89 if sys.version_info < (3, 0):
AttributeError: module 'threading' has no attribute '_Condition'
```
</details>
I've also found that attempting to install these `datasets` and `apache-beam` in certain environments (e.g. via pip inside a conda env) simply causes pip to hang indefinitely.
### Expected behavior
I would expect to be able to import methods from `datasets` without crashing. I have tested that this is possible as long as I do not attempt to install `apache-beam`.
### Environment info
Google Colab
|
CLOSED
| 2023-08-10T06:54:32
| 2023-09-01T03:19:49
| 2023-08-10T15:22:10
|
https://github.com/huggingface/datasets/issues/6134
|
boyleconnor
| 1
|
[] |
6,133
|
Dataset is slower after calling `to_iterable_dataset`
|
### Describe the bug
Can anyone explain why looping over a dataset becomes slower after calling `to_iterable_dataset` to convert to `IterableDataset`
### Steps to reproduce the bug
Any dataset after converting to `IterableDataset`
### Expected behavior
Maybe it should be faster on big dataset? I only test on small dataset
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17
- Python version: 3.8.15
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3
|
OPEN
| 2023-08-10T06:36:23
| 2023-08-16T09:18:54
| null |
https://github.com/huggingface/datasets/issues/6133
|
npuichigo
| 2
|
[] |
6,132
|
to_iterable_dataset is missing in document
|
### Describe the bug
to_iterable_dataset is missing in document
### Steps to reproduce the bug
to_iterable_dataset is missing in document
### Expected behavior
document enhancement
### Environment info
unrelated
|
CLOSED
| 2023-08-09T15:15:03
| 2023-08-16T04:43:36
| 2023-08-16T04:43:29
|
https://github.com/huggingface/datasets/issues/6132
|
npuichigo
| 1
|
[] |
6,130
|
default config name doesn't work when config kwargs are specified.
|
### Describe the bug
https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522
If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs.
### Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('/dataset/with/multiple/config'') # Ok
datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err
```
### Expected behavior
Default config behavior should be consistent.
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17
- Python version: 3.8.15
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3
|
CLOSED
| 2023-08-09T12:43:15
| 2023-11-22T11:50:49
| 2023-11-22T11:50:48
|
https://github.com/huggingface/datasets/issues/6130
|
npuichigo
| 15
|
[] |
6,128
|
IndexError: Invalid key: 88 is out of bounds for size 0
|
### Describe the bug
This bug generates when I use torch.compile(model) in my code, which seems to raise an error in datasets lib.
### Steps to reproduce the bug
I use the following code to fine-tune Falcon on my private dataset.
```python
import transformers
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
AutoConfig,
DataCollatorForSeq2Seq,
Trainer,
Seq2SeqTrainer,
HfArgumentParser,
Seq2SeqTrainingArguments,
BitsAndBytesConfig,
)
from peft import (
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
prepare_model_for_int8_training,
set_peft_model_state_dict,
)
import torch
import os
import evaluate
import functools
from datasets import load_dataset
import bitsandbytes as bnb
import logging
import json
import copy
from typing import Dict, Optional, Sequence
from dataclasses import dataclass, field
# Lora settings
LORA_R = 8
LORA_ALPHA = 16
LORA_DROPOUT= 0.05
LORA_TARGET_MODULES = ["query_key_value"]
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(default="Salesforce/codegen2-7B")
@dataclass
class DataArguments:
data_path: str = field(default=None, metadata={"help": "Path to the training data."})
train_file: str = field(default=None, metadata={"help": "Path to the evaluation data."})
eval_file: str = field(default=None, metadata={"help": "Path to the evaluation data."})
cache_path: str = field(default=None, metadata={"help": "Path to the cache directory."})
num_proc: int = field(default=4, metadata={"help": "Number of processes to use for data preprocessing."})
@dataclass
class TrainingArguments(transformers.TrainingArguments):
# cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
model_max_length: int = field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
)
is_lora: bool = field(default=True, metadata={"help": "Whether to use LORA."})
def tokenize(text, tokenizer, max_seq_len=512, add_eos_token=True):
result = tokenizer(
text,
truncation=True,
max_length=max_seq_len,
padding=False,
return_tensors=None,
)
if (
result["input_ids"][-1] != tokenizer.eos_token_id
and len(result["input_ids"]) < max_seq_len
and add_eos_token
):
result["input_ids"].append(tokenizer.eos_token_id)
result["attention_mask"].append(1)
if add_eos_token and len(result["input_ids"]) >= max_seq_len:
result["input_ids"][max_seq_len - 1] = tokenizer.eos_token_id
result["attention_mask"][max_seq_len - 1] = 1
result["labels"] = result["input_ids"].copy()
return result
def main():
parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
config = AutoConfig.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
trust_remote_code=True,
)
if training_args.is_lora:
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
torch_dtype=torch.float16,
trust_remote_code=True,
load_in_8bit=True,
quantization_config=BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0
),
)
model = prepare_model_for_int8_training(model)
config = LoraConfig(
r=LORA_R,
lora_alpha=LORA_ALPHA,
target_modules=LORA_TARGET_MODULES,
lora_dropout=LORA_DROPOUT,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, config)
else:
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
torch_dtype=torch.float16,
cache_dir=data_args.cache_path,
trust_remote_code=True,
)
model.config.use_cache = False
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
print_trainable_parameters(model)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
model_max_length=training_args.model_max_length,
padding_side="left",
use_fast=True,
trust_remote_code=True,
)
tokenizer.pad_token = tokenizer.eos_token
# Load dataset
def generate_and_tokenize_prompt(sample):
input_text = sample["input"]
target_text = sample["output"] + tokenizer.eos_token
full_text = input_text + target_text
tokenized_full_text = tokenize(full_text, tokenizer, max_seq_len=512)
tokenized_input_text = tokenize(input_text, tokenizer, max_seq_len=512)
input_len = len(tokenized_input_text["input_ids"]) - 1 # -1 for eos token
tokenized_full_text["labels"] = [-100] * input_len + tokenized_full_text["labels"][input_len:]
return tokenized_full_text
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.eval_file is not None:
data_files["eval"] = data_args.eval_file
dataset = load_dataset(data_args.data_path, data_files=data_files)
train_dataset = dataset["train"]
eval_dataset = dataset["eval"]
train_dataset = train_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc)
eval_dataset = eval_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc)
data_collator = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True)
# Evaluation metrics
def compute_metrics(eval_preds, tokenizer):
metric = evaluate.load('exact_match')
preds, labels = eval_preds
# In case the model returns more than the prediction logits
if isinstance(preds, tuple):
preds = preds[0]
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=False)
# Replace -100s in the labels as we can't decode them
labels[labels == -100] = tokenizer.pad_token_id
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False)
# Some simple post-processing
decoded_preds = [pred.strip() for pred in decoded_preds]
decoded_labels = [label.strip() for label in decoded_labels]
result = metric.compute(predictions=decoded_preds, references=decoded_labels)
return {'exact_match': result['exact_match']}
compute_metrics_fn = functools.partial(compute_metrics, tokenizer=tokenizer)
model = torch.compile(model)
# Training
trainer = Trainer(
model=model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
args=training_args,
data_collator=data_collator,
compute_metrics=compute_metrics_fn,
)
trainer.train()
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)
tokenizer.save_pretrained(save_directory=training_args.output_dir)
if __name__ == "__main__":
main()
```
When I didn't use `torch.cpmpile(model)`, my code worked well. But when I added this line to my code, It produced the following error:
```
Traceback (most recent call last):
File "falcon_sft.py", line 230, in <module>
main()
File "falcon_sft.py", line 223, in main
trainer.train()
File "python3.10/site-packages/transformers/trainer.py", line 1539, in train
return inner_training_loop(
File "python3.10/site-packages/transformers/trainer.py", line 1787, in _inner_training_loop
for step, inputs in enumerate(epoch_iterator):
File "python3.10/site-packages/accelerate/data_loader.py", line 384, in __iter__
current_batch = next(dataloader_iter)
File "python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
data = self._next_data()
File "python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = self.dataset.__getitems__(possibly_batched_index)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__
batch = self.__getitem__(keys)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__
return self._getitem(key)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem
pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table
_check_valid_index_key(key, size)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key
_check_valid_index_key(int(max(key)), size=size)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 88 is out of bounds for size 0
```
So I'm confused about why this error was generated, and how to fix it. Is this error produced by datasets or `torch.compile`?
### Expected behavior
I want to use `torch.compile` in my code.
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.8
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
|
CLOSED
| 2023-08-08T15:32:08
| 2023-12-26T07:51:57
| 2023-08-11T13:35:09
|
https://github.com/huggingface/datasets/issues/6128
|
TomasAndersonFang
| 5
|
[] |
6,126
|
Private datasets do not load when passing token
|
### Describe the bug
Since the release of `datasets` 2.14, private/gated datasets do not load when passing `token`: they raise `EmptyDatasetError`.
This is a non-planned backward incompatible breaking change.
Note that private datasets do load if instead `download_config` is passed:
```python
from datasets import DownloadConfig, load_dataset
ds = load_dataset("albertvillanova/tmp-private", split="train", download_config=DownloadConfig(token="<MY-TOKEN>"))
ds
```
gives
```
Dataset({
features: ['text'],
num_rows: 4
})
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>")
```
gives
```
---------------------------------------------------------------------------
EmptyDatasetError Traceback (most recent call last)
[<ipython-input-2-25b48732107a>](https://localhost:8080/#) in <cell line: 3>()
1 from datasets import load_dataset
2
----> 3 ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>")
5 frames
[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
2107
2108 # Create a dataset builder
-> 2109 builder_instance = load_dataset_builder(
2110 path=path,
2111 name=name,
[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs)
1793 download_config = download_config.copy() if download_config else DownloadConfig()
1794 download_config.storage_options.update(storage_options)
-> 1795 dataset_module = dataset_module_factory(
1796 path,
1797 revision=revision,
[/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)
1484 raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
1485 if isinstance(e1, EmptyDatasetError):
-> 1486 raise e1 from None
1487 if isinstance(e1, FileNotFoundError):
1488 raise FileNotFoundError(
[/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)
1474 download_config=download_config,
1475 download_mode=download_mode,
-> 1476 ).get_module()
1477 except (
1478 Exception
[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self)
1030 sanitize_patterns(self.data_files)
1031 if self.data_files is not None
-> 1032 else get_data_patterns(base_path, download_config=self.download_config)
1033 )
1034 data_files = DataFilesDict.from_patterns(
[/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in get_data_patterns(base_path, download_config)
457 return _get_data_files_patterns(resolver)
458 except FileNotFoundError:
--> 459 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
460
461
EmptyDatasetError: The directory at hf://datasets/albertvillanova/tmp-private@79b9e4fe79670a9a050d6ebc385464891915a71d doesn't contain any data files
```
### Expected behavior
The dataset should load.
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-5.15.109+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.16.4
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
|
CLOSED
| 2023-08-07T15:06:47
| 2023-08-08T15:16:23
| 2023-08-08T15:16:23
|
https://github.com/huggingface/datasets/issues/6126
|
albertvillanova
| 4
|
[
"bug"
] |
6,125
|
Reinforcement Learning and Robotics are not task categories in HF datasets metadata
|
### Describe the bug
In https://huggingface.co/models there are task categories for RL and robotics but none in https://huggingface.co/datasets
Our lab is currently moving our datasets over to hugging face and would like to be able to add those 2 tags
Moreover we see some older datasets that do have that tag, but we can't seem to add it ourselves.
### Steps to reproduce the bug
1. Create a new dataset on Hugging face
2. Try to type reinforcemement-learning or robotics into the tasks categories, it does not allow you to commit
### Expected behavior
Expected to be able to add RL and robotics as task categories as some previous datasets have these tags
### Environment info
N/A
|
CLOSED
| 2023-08-05T23:59:42
| 2023-08-18T12:28:42
| 2023-08-18T12:28:42
|
https://github.com/huggingface/datasets/issues/6125
|
StoneT2000
| 0
|
[] |
6,124
|
Datasets crashing runs due to KeyError
|
### Describe the bug
Hi all,
I have been running into a pretty persistent issue recently when trying to load datasets.
```python
train_dataset = load_dataset(
'llama-2-7b-tokenized',
split = 'train'
)
```
I receive a KeyError which crashes the runs.
```
Traceback (most recent call last):
main()
train_dataset = load_dataset(
^^^^^^^^^^^^^
builder_instance = load_dataset_builder(
^^^^^^^^^^^^^^^^^^^^^
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
raise e1 from None
).get_module()
^^^^^^^^^^^^
else get_data_patterns(base_path, download_config=self.download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
return _get_data_files_patterns(resolver)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
data_files = pattern_resolver(pattern)
^^^^^^^^^^^^^^^^^^^^^^^^^
fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
paths = [f for f in sorted(fs.glob(paths)) if not fs.isdir(f)]
^^^^^^^^^^^^^^
allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
for _, dirs, files in self.walk(path, maxdepth, detail=True, **kwargs):
listing = self.ls(path, detail=True, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
"last_modified": parse_datetime(tree_item["lastCommit"]["date"]),
~~~~~~~~~^^^^^^^^^^^^^^
KeyError: 'lastCommit'
```
Any help would be greatly appreciated.
Thank you,
Enrico
### Steps to reproduce the bug
Load the dataset from the Huggingface hub.
```python
train_dataset = load_dataset(
'llama-2-7b-tokenized',
split = 'train'
)
```
### Expected behavior
Loads the dataset.
### Environment info
datasets-2.14.3
CUDA 11.8
Python 3.11
|
CLOSED
| 2023-08-05T17:48:56
| 2023-11-30T16:28:57
| 2023-11-30T16:28:57
|
https://github.com/huggingface/datasets/issues/6124
|
conceptofmind
| 7
|
[] |
6,123
|
Inaccurate Bounding Boxes in "wildreceipt" Dataset
|
### Describe the bug
I would like to bring to your attention an issue related to the accuracy of bounding boxes within the "wildreceipt" dataset, which is made available through the Hugging Face API. Specifically, I have identified a discrepancy between the bounding boxes generated by the dataset loading commands, namely `load_dataset("Theivaprakasham/wildreceipt")` and `load_dataset("jinhybr/WildReceipt")`, and the actual labels and corresponding bounding boxes present in the dataset.
To illustrate this divergence, I've provided two examples in the form of screenshots. These screenshots highlight the contrasting outcomes between my personal implementation of the dataloader and the implementation offered by Hugging Face:
**Example 1:**
**Example 2:**
It's important to note that my dataloader implementation is based on the same dataset files as utilized in the Hugging Face implementation. For your reference, you can access the dataset files through this link: [wildreceipt dataset files](https://download.openmmlab.com/mmocr/data/wildreceipt.tar).
This inconsistency in bounding box accuracy warrants investigation and rectification for maintaining the integrity of the "wildreceipt" dataset. Your attention and assistance in addressing this matter would be greatly appreciated.
### Steps to reproduce the bug
```python
import matplotlib.pyplot as plt
from datasets import load_dataset
# Define functions to convert bounding box formats
def convert_format1(box):
x, y, w, h = box
x2, y2 = x + w, y + h
return [x, y, x2, y2]
def convert_format2(box):
x1, y1, x2, y2 = box
return [x1, y1, x2, y2]
def plot_cropped_image(image, box, title):
cropped_image = image.crop(box)
plt.imshow(cropped_image)
plt.title(title)
plt.axis('off')
plt.savefig(title+'.png')
plt.show()
doc_index = 1
word_index = 3
dataset = load_dataset("Theivaprakasham/wildreceipt")['train']
bbox_hugging_face = dataset[doc_index]['bboxes'][word_index]
text_unit_face = dataset[doc_index]['words'][word_index]
common_box_hugface_1 = convert_format1(bbox_hugging_face)
common_box_hugface_2 = convert_format2(bbox_hugging_face)
plot_cropped_image(image_hugging, common_box_hugface_1,
f'Hugging Face Bouding boxes (x,y,w,h format) \n its associated text unit: {text_unit_face}')
plot_cropped_image(image_hugging, common_box_hugface_2,
f'Hugging Face Bouding boxes (x1,y1,x2, y2 format) \n its associated text unit: {text_unit_face}')
```
### Expected behavior
The bounding boxes generated by the "wildreceipt" dataset in HuggingFace implementation loading commands should accurately match the actual labels and bounding boxes of the dataset.
### Environment info
- Python version: 3.8
- Hugging Face datasets version: 2.14.2
- Dataset file taken from this link: https://download.openmmlab.com/mmocr/data/wildreceipt.tar
|
CLOSED
| 2023-08-05T14:34:13
| 2023-08-17T14:25:27
| 2023-08-17T14:25:26
|
https://github.com/huggingface/datasets/issues/6123
|
HamzaGbada
| 1
|
[] |
6,122
|
Upload README via `push_to_hub`
|
### Feature request
`push_to_hub` now allows users to upload datasets programmatically. However, based on the latest doc, we still need to open the dataset page to add readme file manually.
However, I do discover snippets to intialize a README for every `push_to_hub`:
```
dataset_card = (
DatasetCard(
"---\n"
+ str(dataset_card_data)
+ "\n---\n"
+ f'# Dataset Card for "{repo_id.split("/")[-1]}"\n\n[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)'
)
if dataset_card is None
else dataset_card
)
HfApi(endpoint=config.HF_ENDPOINT).upload_file(
path_or_fileobj=str(dataset_card).encode(),
path_in_repo="README.md",
repo_id=repo_id,
token=token,
repo_type="dataset",
revision=branch,
)
```
So, if we can enable `push_to_hub` to upload a readme file by ourselves instead of using the auto generated ones, it can save ton of time, and will definitely alleviate the current "lack-of-dataset-card" situation.
### Motivation
as elabrated above.
### Your contribution
I might be able to make a pr.
|
CLOSED
| 2023-08-04T21:00:27
| 2023-08-21T18:18:54
| 2023-08-21T18:18:54
|
https://github.com/huggingface/datasets/issues/6122
|
liyucheng09
| 1
|
[
"enhancement"
] |
6,120
|
Lookahead streaming support?
|
### Feature request
From what I understand, streaming dataset currently pulls the data, and process the data as it is requested.
This can introduce significant latency delays when data is loaded into the training process, needing to wait for each segment.
While the delays might be dataset specific (or even mapping instruction/tokenizer specific)
Is it possible to introduce a `streaming_lookahead` parameter, which is used for predictable workloads (even shuffled dataset with fixed seed). As we can predict in advance what the next few datasamples will be. And fetch them while the current set is being trained.
With enough CPU & bandwidth to keep up with the training process, and a sufficiently large lookahead, this will reduce the various latency involved while waiting for the dataset to be ready between batches.
### Motivation
Faster streaming performance, while training over extra large TB sized datasets
### Your contribution
I currently use HF dataset, with pytorch lightning trainer for RWKV project, and would be able to help test this feature if supported.
|
OPEN
| 2023-08-04T04:01:52
| 2023-08-17T17:48:42
| null |
https://github.com/huggingface/datasets/issues/6120
|
PicoCreator
| 1
|
[
"enhancement"
] |
6,118
|
IterableDataset.from_generator() fails with pickle error when provided a generator or iterator
|
### Describe the bug
**Description**
Providing a generator in an instantiation of IterableDataset.from_generator() fails with `TypeError: cannot pickle 'generator' object` when the generator argument is supplied with a generator.
**Code example**
```
def line_generator(files: List[Path]):
if isinstance(files, str):
files = [Path(files)]
for file in files:
if isinstance(file, str):
file = Path(file)
yield from open(file,'r').readlines()
...
model_training_files = ['file1.txt', 'file2.txt', 'file3.txt']
train_dataset = IterableDataset.from_generator(generator=line_generator(model_training_files))
```
**Traceback**
Traceback (most recent call last):
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/contextlib.py", line 135, in __exit__
self.gen.throw(type, value, traceback)
File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 691, in _no_cache_fields
yield
File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 701, in dumps
dump(obj, file)
File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 676, in dump
Pickler(file, recurse=True).dump(obj)
File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 394, in dump
StockPickler.dump(self, obj)
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 487, in dump
self.save(obj)
File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 971, in save_dict
self._batch_setitems(obj.items())
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 997, in _batch_setitems
save(v)
File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 578, in save
rv = reduce(self.proto)
TypeError: cannot pickle 'generator' object
### Steps to reproduce the bug
1. Create a set of text files to iterate over.
2. Create a generator that returns the lines in each file until all files are exhausted.
3. Instantiate the dataset over the generator by instantiating an IterableDataset.from_generator().
4. Wait for the explosion.
### Expected behavior
I would expect that since the function claims to accept a generator that there would be no crash. Instead, I would expect the dataset to return all the lines in the files as queued up in the `line_generator()` function.
### Environment info
datasets.__version__ == '2.13.1'
Python 3.9.6
Platform: Darwin WE35261 22.5.0 Darwin Kernel Version 22.5.0: Thu Jun 8 22:22:22 PDT 2023; root:xnu-8796.121.3~7/RELEASE_X86_64 x86_64
|
OPEN
| 2023-08-04T01:45:04
| 2025-11-18T16:07:04
| null |
https://github.com/huggingface/datasets/issues/6118
|
finkga
| 4
|
[] |
6,116
|
[Docs] The "Process" how-to guide lacks description of `select_columns` function
|
### Feature request
The [how to process dataset guide](https://huggingface.co/docs/datasets/main/en/process) currently does not mention the [`select_columns`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.select_columns) function. It would be nice to include it in the guide.
### Motivation
This function is a commonly requested feature (see this [forum thread](https://discuss.huggingface.co/t/how-to-create-a-new-dataset-from-another-dataset-and-select-specific-columns-and-the-data-along-with-the-column/15120) and #5468 #5474). However, it has not been included in the guide since its implementation by PR #5480.
Mentioning it in the guide would help future users discover this added feature.
### Your contribution
I could submit a PR to add a brief description of the function to said guide.
|
CLOSED
| 2023-08-03T13:45:10
| 2023-08-16T10:02:53
| 2023-08-16T10:02:53
|
https://github.com/huggingface/datasets/issues/6116
|
unifyh
| 1
|
[
"enhancement"
] |
6,114
|
Cache not being used when loading commonvoice 8.0.0
|
### Describe the bug
I have commonvoice 8.0.0 downloaded in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. The folder contains all the arrow files etc, and was used as the cached version last time I touched the ec2 instance I'm working on. Now, with the same command that downloaded it initially:
```
dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")
```
it tries to redownload the dataset to `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/05bdc7940b0a336ceeaeef13470c89522c29a8e4494cbeece64fb472a87acb32`
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")```
2. dataset is updated by maintainers
3. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")```
### Expected behavior
I expect that it uses the already downloaded data in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`.
Not sure what's happening in 2. but if, say it's an issue with the dataset referenced by "mozilla-foundation/common_voice_8_0" being modified by the maintainers, how would I force datasets to point to the original version I downloaded?
EDIT: It was indeed that the maintainers had updated the dataset (v 8.0.0). However I still cant load the dataset from disk instead of redownloading, with for example:
```
load_dataset(".cache/huggingface/datasets/downloads/extracted/<hash>/cv-corpus-8.0-2022-01-19/en/", "en")
> ...
> File [~/miniconda3/envs/aa_torch2/lib/python3.10/site-packages/datasets/table.py:1938](.../ python3.10/site-packages/datasets/table.py:1938), in cast_array_to_feature(array, feature, allow_number_to_str)
1937 elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1938 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
...
1794 e = e.__context__
-> 1795 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1797 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Environment info
datasets==2.7.0
python==3.10.8
OS: AWS Linux
|
CLOSED
| 2023-08-02T23:18:11
| 2023-08-18T23:59:00
| 2023-08-18T23:59:00
|
https://github.com/huggingface/datasets/issues/6114
|
clabornd
| 2
|
[] |
6,113
|
load_dataset() fails with streamlit caching inside docker
|
### Describe the bug
When calling `load_dataset` in a streamlit application running within a docker container, get a failure with the error message:
EmptyDatasetError: The directory at hf://datasets/fetch-rewards/inc-rings-2000@bea27cf60842b3641eae418f38864a2ec4cde684 doesn't contain any data files
Traceback:
File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "/home/user/app/app.py", line 62, in <module>
dashboard()
File "/home/user/app/app.py", line 47, in dashboard
feat_dict, path_gml = load_data(hf_repo, model_gml_dict[selected_model], hf_token)
File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 211, in wrapper
return cached_func(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 240, in __call__
return self._get_or_create_cached_value(args, kwargs)
File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 266, in _get_or_create_cached_value
return self._handle_cache_miss(cache, value_key, func_args, func_kwargs)
File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 320, in _handle_cache_miss
computed_value = self._info.func(*func_args, **func_kwargs)
File "/home/user/app/hf_interface.py", line 16, in load_data
hf_dataset = load_dataset(repo_id, use_auth_token=hf_token)
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2109, in load_dataset
builder_instance = load_dataset_builder(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1795, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1486, in dataset_module_factory
raise e1 from None
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1476, in dataset_module_factory
).get_module()
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1032, in get_module
else get_data_patterns(base_path, download_config=self.download_config)
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 458, in get_data_patterns
raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
### Steps to reproduce the bug
```python
@st.cache_resource
def load_data(repo_id: str, hf_token=None):
"""Load data from HuggingFace Hub
"""
hf_dataset = load_dataset(repo_id, use_auth_token=hf_token)
hf_dataset = hf_dataset.map(lambda x: json.loads(x["ground_truth"]), remove_columns=["ground_truth"])
return hf_dataset
```
### Expected behavior
Expect to load.
Note: works fine with datasets==2.13.1
### Environment info
datasets==2.14.2,
Ubuntu bionic-based Docker container.
|
CLOSED
| 2023-08-02T20:20:26
| 2023-08-21T18:18:27
| 2023-08-21T18:18:27
|
https://github.com/huggingface/datasets/issues/6113
|
fierval
| 1
|
[] |
6,112
|
yaml error using push_to_hub with generated README.md
|
### Describe the bug
When I construct a dataset with the following features:
```
features = Features(
{
"pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)),
"input_ids": Sequence(feature=Value(dtype="int64")),
"attention_mask": Sequence(Value(dtype="int64")),
"tokens": Sequence(Value(dtype="string")),
"bbox": Array2D(dtype="int64", shape=(512, 4)),
}
)
```
and run `push_to_hub`, the individual `*.parquet` files are pushed, but when trying to upload the auto-generated README, I run into the following error:
```
Traceback (most recent call last):
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 261, in hf_raise_for_status
response.raise_for_status()
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/requests/models.py", line 1021, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/looppayments/multitask_document_classification_dataset/commit/main
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 297, in <module>
build_dataset()
File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 290, in build_dataset
push_to_hub(dataset, "multitask_document_classification_dataset")
File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 135, in push_to_hub
dataset.push_to_hub(f"looppayments/{dataset_name}", private=True)
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5577, in push_to_hub
HfApi(endpoint=config.HF_ENDPOINT).upload_file(
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner
return fn(self, *args, **kwargs)
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file
commit_info = self.create_commit(
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner
return fn(self, *args, **kwargs)
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2728, in create_commit
hf_raise_for_status(commit_resp, endpoint_name="commit")
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 299, in hf_raise_for_status
raise BadRequestError(message, response=response) from e
huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-64ca9c3d-2d2bbef354e102482a9a168e;bc00371c-8549-4859-9f41-43ff140ad36e)
Bad request for commit endpoint:
Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple> (10:9)
7 | - 3
8 | - 224
9 | - 224
10 | dtype: float64
--------------^
11 | - name: input_ids
12 | sequence: int64
```
My guess is that the auto-generated yaml is unable to be parsed for some reason.
### Steps to reproduce the bug
The description contains most of what's needed to reproduce the issue, but I've added a shortened code snippet:
```
from datasets import Array2D, Array3D, ClassLabel, Dataset, Features, Sequence, Value
from PIL import Image
from transformers import AutoProcessor
features = Features(
{
"pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)),
"input_ids": Sequence(feature=Value(dtype="int64")),
"attention_mask": Sequence(Value(dtype="int64")),
"tokens": Sequence(Value(dtype="string")),
"bbox": Array2D(dtype="int64", shape=(512, 4)),
}
)
processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
def preprocess_dataset(rows):
# Get images
images = [
Image.open(png_filename).convert("RGB") for png_filename in rows["png_filename"]
]
encoding = processor(
images,
rows["tokens"],
boxes=rows["bbox"],
truncation=True,
padding="max_length",
)
encoding["tokens"] = rows["tokens"]
return encoding
dataset = dataset.map(
preprocess_dataset,
batched=True,
batch_size=5,
features=features,
)
```
### Expected behavior
Using datasets==2.11.0, I'm able to succesfully push_to_hub, no issues, but with datasets==2.14.2, I run into the above error.
### Environment info
- `datasets` version: 2.14.2
- Platform: macOS-12.5-arm64-arm-64bit
- Python version: 3.10.12
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3
|
CLOSED
| 2023-08-02T18:21:21
| 2023-12-12T15:00:44
| 2023-12-12T15:00:44
|
https://github.com/huggingface/datasets/issues/6112
|
kevintee
| 1
|
[] |
6,111
|
raise FileNotFoundError("Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." )
|
### Describe the bug
For researchers in some countries or regions, it is usually the case that the download ability of `load_dataset` is disabled due to the complex network environment. People in these regions often prefer to use git clone or other programming tricks to manually download the files to the disk (for example, [How to elegantly download hf models, zhihu zhuanlan](https://zhuanlan.zhihu.com/p/475260268) proposed a crawlder based solution, and [Is there any mirror for hf_hub, zhihu answer](https://www.zhihu.com/question/371644077) provided some cloud based solutions, and [How to avoid pitfalls on Hugging face downloading, zhihu zhuanlan] gave some useful suggestions), and then use `load_from_disk` to get the dataset object.
However, when one finally has the local files on the disk, it is still buggy when trying to load the files into objects.
### Steps to reproduce the bug
Steps to reproduce the bug:
1. Found CIFAR dataset in hugging face: https://huggingface.co/datasets/cifar100/tree/main
2. Click ":" button to show "Clone repository" option, and then follow the prompts on the box:
```bash
cd my_directory_absolute
git lfs install
git clone https://huggingface.co/datasets/cifar100
ls my_directory_absolute/cifar100 # confirm that the directory exists and it is OK.
```
3. Write A python file to try to load the dataset
```python
from datasets import load_dataset, load_from_disk
dataset = load_from_disk("my_directory_absolute/cifar100")
```
Notice that according to issue #3700 , it is wrong to use load_dataset("my_directory_absolute/cifar100"), so we must use load_from_disk instead.
4. Then you will see the error reported:
```log
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[5], line 9
1 from datasets import load_dataset, load_from_disk
----> 9 dataset = load_from_disk("my_directory_absolute/cifar100")
File [~/miniconda3/envs/ai/lib/python3.10/site-packages/datasets/load.py:2232), in load_from_disk(dataset_path, fs, keep_in_memory, storage_options)
2230 return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)
2231 else:
-> 2232 raise FileNotFoundError(
2233 f"Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory."
2234 )
FileNotFoundError: Directory my_directory_absolute/cifar100 is neither a `Dataset` directory nor a `DatasetDict` directory.
```
### Expected behavior
The dataset should be load successfully.
### Environment info
```bash
datasets-cli env
```
-> results:
```txt
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.14.2
- Platform: Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.12
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
```
|
CLOSED
| 2023-08-02T09:17:29
| 2023-08-29T02:00:28
| 2023-08-29T02:00:28
|
https://github.com/huggingface/datasets/issues/6111
|
2catycm
| 3
|
[] |
6,110
|
[BUG] Dataset initialized from in-memory data does not create cache.
|
### Describe the bug
`Dataset` initialized from in-memory data (dictionary in my case, haven't tested with other types) does not create cache when processed with the `map` method, unlike `Dataset` initialized by other methods such as `load_dataset`.
### Steps to reproduce the bug
```python
# below code was run the second time so the map function can be loaded from cache if exists
from datasets import load_dataset, Dataset
dataset = load_dataset("tatsu-lab/alpaca")['train']
dataset = dataset.map(lambda x: {'input': x['input'] + 'hi'}) # some random map
print(len(dataset.cache_files))
# 1
# copy the exact same data but initialize from a dictionary
memory_dataset = Dataset.from_dict({
'instruction': dataset['instruction'],
'input': dataset['input'],
'output': dataset['output'],
'text': dataset['text']})
memory_dataset = memory_dataset.map(lambda x: {'input': x['input'] + 'hi'}) # exact same map
print(len(memory_dataset.cache_files))
# Map: 100%|ββββββββββ| 52002[/52002]
# 0
```
### Expected behavior
The `map` function should create cache regardless of the method the `Dataset` was created.
### Environment info
- `datasets` version: 2.14.2
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
CLOSED
| 2023-08-01T11:58:58
| 2023-08-17T14:03:01
| 2023-08-17T14:03:00
|
https://github.com/huggingface/datasets/issues/6110
|
MattYoon
| 1
|
[] |
6,109
|
Problems in downloading Amazon reviews from HF
|
### Describe the bug
I have a script downloading `amazon_reviews_multi`.
When the download starts, I get
```
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]
Downloading data: 243B [00:00, 1.43MB/s]
Downloading data files: 100%|ββββββββββ| 1/1 [00:01<00:00, 1.54s/it]
Extracting data files: 100%|ββββββββββ| 1/1 [00:00<00:00, 842.40it/s]
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]
Downloading data: 243B [00:00, 928kB/s]
Downloading data files: 100%|ββββββββββ| 1/1 [00:01<00:00, 1.42s/it]
Extracting data files: 100%|ββββββββββ| 1/1 [00:00<00:00, 832.70it/s]
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]
Downloading data: 243B [00:00, 1.81MB/s]
Downloading data files: 100%|ββββββββββ| 1/1 [00:01<00:00, 1.40s/it]
Extracting data files: 100%|ββββββββββ| 1/1 [00:00<00:00, 1294.14it/s]
Generating train split: 0%| | 0/200000 [00:00<?, ? examples/s]
```
the file is clearly too small to contain the requested dataset, in fact it contains en error message:
```
<?xml version="1.0" encoding="UTF-8"?>
<Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>AGJWSY3ZADT2QVWE</RequestId><HostId>Gx1O2KXnxtQFqvzDLxyVSTq3+TTJuTnuVFnJL3SP89Yp8UzvYLPTVwd1PpniE4EvQzT3tCaqEJw=</HostId></Error>
```
obviously the script fails:
```
> raise DatasetGenerationError("An error occurred while generating the dataset") from e
E datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
1. load_dataset("amazon_reviews_multi", name="en", split="train", cache_dir="ADDYOURPATHHERE")
### Expected behavior
I would expect the dataset to be downloaded and processed
### Environment info
* The problem is present with both datasets 2.12.0 and 2.14.2
* python version 3.10.12
|
CLOSED
| 2023-08-01T08:38:29
| 2025-07-18T17:47:30
| 2023-08-02T07:12:07
|
https://github.com/huggingface/datasets/issues/6109
|
610v4nn1
| 3
|
[] |
6,108
|
Loading local datasets got strangely stuck
|
### Describe the bug
I try to use `load_dataset()` to load several local `.jsonl` files as a dataset. Every line of these files is a json structure only containing one key `text` (yeah it is a dataset for NLP model). The code snippet is as:
```python
ds = load_dataset("json", data_files=LIST_OF_FILE_PATHS, num_proc=16)['train']
```
However, I found that the loading process can get stuck -- the progress bar `Generating train split` no more proceed. When I was trying to find the cause and solution, I found a really strange behavior. If I load the dataset in this way:
```python
dlist = list()
for _ in LIST_OF_FILE_PATHS:
dlist.append(load_dataset("json", data_files=_)['train'])
ds = concatenate_datasets(dlist)
```
I can actually successfully load all the files despite its slow speed. But if I load them in batch like above, things go wrong. I did try to use Control-C to trace the stuck point but the program cannot be terminated in this way when `num_proc` is set to `None`. The only thing I can do is use Control-Z to hang it up then kill it. If I use more than 2 cpus, a Control-C would simply cause the following error:
```bash
^C
Process ForkPoolWorker-1:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 314, in _bootstrap
self.run()
File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 114, in worker
task = get()
File "/usr/local/lib/python3.10/dist-packages/multiprocess/queues.py", line 368, in get
res = self._reader.recv_bytes()
File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 224, in recv_bytes
buf = self._recv_bytes(maxlength)
File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes
buf = self._recv(4)
File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv
chunk = read(handle, remaining)
KeyboardInterrupt
Generating train split: 92431 examples [01:23, 1104.25 examples/s]
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1373, in iflatmap_unordered
yield queue.get(timeout=0.05)
File "<string>", line 2, in get
File "/usr/local/lib/python3.10/dist-packages/multiprocess/managers.py", line 818, in _callmethod
kind, result = conn.recv()
File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 258, in recv
buf = self._recv_bytes()
File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes
buf = self._recv(4)
File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv
chunk = read(handle, remaining)
KeyboardInterrupt
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/mnt/data/liyongyuan/source/batch_load.py", line 11, in <module>
a = load_dataset(
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2133, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 954, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1049, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1842, in _prepare_split
for job_id, done, content in iflatmap_unordered(
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered
[async_result.get(timeout=0.05) for async_result in async_results]
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in <listcomp>
[async_result.get(timeout=0.05) for async_result in async_results]
File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 770, in get
raise TimeoutError
multiprocess.context.TimeoutError
```
I have validated the basic correctness of these `.jsonl` files. They are correctly formatted (or they cannot be loaded singly by `load_dataset`) though some of the json may contain too long text (more than 1e7 characters). I do not know if this could be the problem. And there should not be any bottleneck in system's resource. The whole dataset is ~300GB, and I am using a cloud server with plenty of storage and 1TB ram.
Thanks for your efforts and patience! Any suggestion or help would be appreciated.
### Steps to reproduce the bug
1. use load_dataset() with `data_files = LIST_OF_FILES`
### Expected behavior
All the files should be smoothly loaded.
### Environment info
- Datasets: A private dataset. ~2500 `.jsonl` files. ~300GB in total. Each json structure only contains one key: `text`. Format checked.
- `datasets` version: 2.14.2
- Platform: Linux-4.19.91-014.kangaroo.alios7.x86_64-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.15.1
- PyArrow version: 10.0.1.dev0+ga6eabc2b.d20230609
- Pandas version: 1.5.2
|
OPEN
| 2023-08-01T02:28:06
| 2024-12-31T16:01:00
| null |
https://github.com/huggingface/datasets/issues/6108
|
LoveCatc
| 7
|
[] |
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