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https://api.github.com/repos/huggingface/datasets/issues/7107
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I_kwDODunzps6TP_68
| 7,107
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load_dataset broken in 2.21.0
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### Describe the bug
`eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)`
used to work till 2.20.0 but doesn't work in 2.21.0
In 2.20.0:
in 2.21.0:
### Steps to reproduce the bug
1. Spin up a new google collab
2. `pip install datasets==2.21.0`
3. `import datasets`
4. `eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)`
5. Will throw an error.
### Expected behavior
Try steps 1-5 again but replace datasets version with 2.20.0, it will work
### Environment info
- `datasets` version: 2.21.0
- Platform: Linux-6.1.85+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.5
- PyArrow version: 17.0.0
- Pandas version: 2.1.4
- `fsspec` version: 2024.5.0
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Rename LargeList.dtype to LargeList.feature
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Rename `LargeList.dtype` to `LargeList.feature`.
Note that `dtype` is usually used for NumPy data types ("int64", "float32",...): see `Value.dtype`.
However, `LargeList` attribute (like `Sequence.feature`) expects a `FeatureType` instead.
With this renaming:
- we avoid confusion about the expected type and
- we also align `LargeList` with `Sequence`.
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Use `huggingface_hub` cache
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- use `hf_hub_download()` from `huggingface_hub` for HF files
- `datasets` cache_dir is still used for:
- caching datasets as Arrow files (that back `Dataset` objects)
- extracted archives, uncompressed files
- files downloaded via http (datasets with scripts)
- I removed code that were made for http files (and also the dummy_data / mock_download_manager stuff that happened to rely on them and have been legacy for a while now)
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remove more script docs
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Fix args of feature docstrings
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Fix Args section of feature docstrings.
Currently, some args do not appear in the docs because they are not properly parsed due to the lack of their type (between parentheses).
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| 2,466,893,106
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I_kwDODunzps6TCc0y
| 7,102
|
Slow iteration speeds when using IterableDataset.shuffle with load_dataset(data_files=..., streaming=True)
|
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[] | null | 2024-08-14T21:44:44Z
| 2024-08-15T16:17:31Z
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### Describe the bug
When I load a dataset from a number of arrow files, as in:
```
random_dataset = load_dataset(
"arrow",
data_files={split: shard_filepaths},
streaming=True,
split=split,
)
```
I'm able to get fast iteration speeds when iterating over the dataset without shuffling.
When I shuffle the dataset, the iteration speed is reduced by ~1000x.
It's very possible the way I'm loading dataset shards is not appropriate; if so please advise!
Thanks for the help
### Steps to reproduce the bug
Here's full code to reproduce the issue:
- Generate a random dataset
- Create shards of data independently using Dataset.save_to_disk()
- The below will generate 16 shards (arrow files), of 512 examples each
```
import time
from pathlib import Path
from multiprocessing import Pool, cpu_count
import torch
from datasets import Dataset, load_dataset
split = "train"
split_save_dir = "/tmp/random_split"
def generate_random_example():
return {
'inputs': torch.randn(128).tolist(),
'indices': torch.randint(0, 10000, (2, 20000)).tolist(),
'values': torch.randn(20000).tolist(),
}
def generate_shard_dataset(examples_per_shard: int = 512):
dataset_dict = {
'inputs': [],
'indices': [],
'values': []
}
for _ in range(examples_per_shard):
example = generate_random_example()
dataset_dict['inputs'].append(example['inputs'])
dataset_dict['indices'].append(example['indices'])
dataset_dict['values'].append(example['values'])
return Dataset.from_dict(dataset_dict)
def save_shard(shard_idx, save_dir, examples_per_shard):
shard_dataset = generate_shard_dataset(examples_per_shard)
shard_write_path = Path(save_dir) / f"shard_{shard_idx}"
shard_dataset.save_to_disk(shard_write_path)
return str(Path(shard_write_path) / "data-00000-of-00001.arrow")
def generate_split_shards(save_dir, num_shards: int = 16, examples_per_shard: int = 512):
with Pool(cpu_count()) as pool:
args = [(m, save_dir, examples_per_shard) for m in range(num_shards)]
shard_filepaths = pool.starmap(save_shard, args)
return shard_filepaths
shard_filepaths = generate_split_shards(split_save_dir)
```
Load the dataset as IterableDataset:
```
random_dataset = load_dataset(
"arrow",
data_files={split: shard_filepaths},
streaming=True,
split=split,
)
random_dataset = random_dataset.with_format("numpy")
```
Observe the iterations/second when iterating over the dataset directly, and applying shuffling before iterating:
Without shuffling, this gives ~1500 iterations/second
```
start_time = time.time()
for count, item in enumerate(random_dataset):
if count > 0 and count % 100 == 0:
elapsed_time = time.time() - start_time
iterations_per_second = count / elapsed_time
print(f"Processed {count} items at an average of {iterations_per_second:.2f} iterations/second")
```
```
Processed 100 items at an average of 705.74 iterations/second
Processed 200 items at an average of 1169.68 iterations/second
Processed 300 items at an average of 1497.97 iterations/second
Processed 400 items at an average of 1739.62 iterations/second
Processed 500 items at an average of 1931.11 iterations/second`
```
When shuffling, this gives ~3 iterations/second:
```
random_dataset = random_dataset.shuffle(buffer_size=100,seed=42)
start_time = time.time()
for count, item in enumerate(random_dataset):
if count > 0 and count % 100 == 0:
elapsed_time = time.time() - start_time
iterations_per_second = count / elapsed_time
print(f"Processed {count} items at an average of {iterations_per_second:.2f} iterations/second")
```
```
Processed 100 items at an average of 3.75 iterations/second
Processed 200 items at an average of 3.93 iterations/second
```
### Expected behavior
Iterations per second should be barely affected by shuffling, especially with a small buffer size
### Environment info
Datasets version: 2.21.0
Python 3.10
Ubuntu 22.04
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| 2,466,510,783
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I_kwDODunzps6TA_e_
| 7,101
|
`load_dataset` from Hub with `name` to specify `config` using incorrect builder type when multiple data formats are present
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| 2024-08-18T10:33:38Z
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Following [documentation](https://huggingface.co/docs/datasets/repository_structure#define-your-splits-and-subsets-in-yaml) I had defined different configs for [`Dataception`](https://huggingface.co/datasets/bigdata-pw/Dataception), a dataset of datasets:
```yaml
configs:
- config_name: dataception
data_files:
- path: dataception.parquet
split: train
default: true
- config_name: dataset_5423
data_files:
- path: datasets/5423.tar
split: train
...
- config_name: dataset_721736
data_files:
- path: datasets/721736.tar
split: train
```
The intent was for metadata to be browsable via Dataset Viewer, in addition to each individual dataset, and to allow datasets to be loaded by specifying the config/name to `load_dataset`.
While testing `load_dataset` I encountered the following error:
```python
>>> dataset = load_dataset("bigdata-pw/Dataception", "dataset_7691")
Downloading readme: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 467k/467k [00:00<00:00, 1.99MB/s]
Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 71.0M/71.0M [00:02<00:00, 26.8MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "datasets\load.py", line 2145, in load_dataset
builder_instance.download_and_prepare(
File "datasets\builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "datasets\builder.py", line 1100, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "datasets\packaged_modules\parquet\parquet.py", line 58, in _split_generators
self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f))
^^^^^^^^^^^^^^^^^
File "pyarrow\parquet\core.py", line 2325, in read_schema
file = ParquetFile(
^^^^^^^^^^^^
File "pyarrow\parquet\core.py", line 318, in __init__
self.reader.open(
File "pyarrow\_parquet.pyx", line 1470, in pyarrow._parquet.ParquetReader.open
File "pyarrow\error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
The correct file is downloaded, however the incorrect builder type is detected; `parquet` due to other content of the repository. It would appear that the config needs to be taken into account.
Note that I have removed the additional configs from the repository because of this issue and there is a limit of 3000 configs anyway so the Dataset Viewer doesn't work as I intended. I'll add them back in if it assists with testing.
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I_kwDODunzps6S9P5G
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|
IterableDataset: cannot resolve features from list of numpy arrays
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| 2024-10-03T05:47:23Z
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### Describe the bug
when resolve features of `IterableDataset`, got `pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values` error.
```
Traceback (most recent call last):
File "test.py", line 6
iter_ds = iter_ds._resolve_features()
File "lib/python3.10/site-packages/datasets/iterable_dataset.py", line 2876, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "lib/python3.10/site-packages/datasets/iterable_dataset.py", line 63, in _infer_features_from_batch
pa_table = pa.Table.from_pydict(batch)
File "pyarrow/table.pxi", line 1813, in pyarrow.lib._Tabular.from_pydict
File "pyarrow/table.pxi", line 5339, in pyarrow.lib._from_pydict
File "pyarrow/array.pxi", line 374, in pyarrow.lib.asarray
File "pyarrow/array.pxi", line 344, in pyarrow.lib.array
File "pyarrow/array.pxi", line 42, in pyarrow.lib._sequence_to_array
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values
```
### Steps to reproduce the bug
```python
from datasets import Dataset
import numpy as np
# create list of numpy
iter_ds = Dataset.from_dict({'a': [[[1, 2, 3], [1, 2, 3]]]}).to_iterable_dataset().map(lambda x: {'a': [np.array(x['a'])]})
iter_ds = iter_ds._resolve_features() # errors here
```
### Expected behavior
features can be successfully resolved
### Environment info
- `datasets` version: 2.21.0
- Platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.35
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.4
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0
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I_kwDODunzps6SiQ3B
| 7,097
|
Some of DownloadConfig's properties are always being overridden in load.py
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| 2024-08-09T18:26:37Z
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### Describe the bug
The `extract_compressed_file` and `force_extract` properties of DownloadConfig are always being set to True in the function `dataset_module_factory` in the `load.py` file. This behavior is very annoying because data extracted will just be ignored the next time the dataset is loaded.
See this image below:
### Steps to reproduce the bug
1. Have a local dataset that contains archived files (zip, tar.gz, etc)
2. Build a dataset loading script to download and extract these files
3. Run the load_dataset function with a DownloadConfig that specifically set `force_extract` to False
4. The extraction process will start no matter if the archives was extracted previously
### Expected behavior
The extraction process should not run when the archives were previously extracted and `force_extract` is set to False.
### Environment info
datasets==2.20.0
python3.9
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PR_kwDODunzps535Xkr
| 7,096
|
Automatically create `cache_dir` from `cache_file_name`
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| 2024-08-15T17:25:26Z
| 2024-08-15T10:13:22Z
|
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You get a pretty unhelpful error message when specifying a `cache_file_name` in a directory that doesn't exist, e.g. `cache_file_name="./cache/data.map"`
```python
import datasets
cache_file_name="./cache/train.map"
dataset = datasets.load_dataset("ylecun/mnist")
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name)
```
```
FileNotFoundError: [Errno 2] No such file or directory: '/.../cache/tmp48r61siw'
```
It is simple enough to create and I was expecting that this would have been the case.
cc: @albertvillanova @lhoestq
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PR_kwDODunzps53w2b7
| 7,094
|
Add Arabic Docs to Datasets
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Translate Docs into Arabic issue-number : #7093
[Arabic Docs](https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx)
[English Docs](https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/en/index.mdx)
@stevhliu
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I_kwDODunzps6SS18S
| 7,093
|
Add Arabic Docs to datasets
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[] | null | 2024-08-07T21:48:05Z
| 2024-08-07T21:48:05Z
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### Feature request
Add Arabic Docs to datasets
[Datasets Arabic](https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx)
### Motivation
@AhmedAlmaghz
https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx
### Your contribution
@AhmedAlmaghz
https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx
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| 7,092
|
load_dataset with multiple jsonlines files interprets datastructure too early
|
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| 2024-08-08T16:35:01Z
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### Describe the bug
likely related to #6460
using `datasets.load_dataset("json", data_dir= ... )` with multiple `.jsonl` files will error if one of the files (maybe the first file?) contains a full column of empty data.
### Steps to reproduce the bug
real world example:
data is available in this [PR-branch](https://github.com/Vipitis/shadertoys-dataset/pull/3/commits/cb1e7157814f74acb09d5dc2f1be3c0a868a9933). Because my files are chunked by months, some months contain all empty data for some columns, just by chance - these are `[]`. Otherwise it's all the same structure.
```python
from datasets import load_dataset
ds = load_dataset("json", data_dir="./data/annotated/api")
```
you get a long error trace, where in the middle it says something like
```cs
TypeError: Couldn't cast array of type struct<id: int64, src: string, ctype: string, channel: int64, sampler: struct<filter: string, wrap: string, vflip: string, srgb: string, internal: string>, published: int64> to null
```
toy example: (on request)
### Expected behavior
Some suggestions
1. give a better error message to the user
2. consider all files before deciding on a data structure for a given column.
3. if you encounter a new structure, and can't cast that to null, replace the null-hypothesis. (maybe something for pyarrow)
as a workaround I have lazily implemented the following (essentially step 2)
```python
import os
import jsonlines
import datasets
api_files = os.listdir("./data/annotated/api")
api_files = [f"./data/annotated/api/{f}" for f in api_files]
api_file_contents = []
for f in api_files:
with jsonlines.open(f) as reader:
for obj in reader:
api_file_contents.append(obj)
ds = datasets.Dataset.from_list(api_file_contents)
```
this works fine for my usecase, but is potentially slower and less memory efficient for really large datasets (where this is unlikely to happen in the first place).
### Environment info
- `datasets` version: 2.20.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.4
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2023.10.0
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I_kwDODunzps6SA3Ki
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|
The test test_move_script_doesnt_change_hash fails because it runs the 'python' command while the python executable has a different name
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| 2024-08-06T00:35:05Z
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### Describe the bug
Tests should use the same pythin path as they are launched with, which in the case of FreeBSD is /usr/local/bin/python3.11
Failure:
```
if err_filename is not None:
> raise child_exception_type(errno_num, err_msg, err_filename)
E FileNotFoundError: [Errno 2] No such file or directory: 'python'
```
### Steps to reproduce the bug
regular test run using PyTest
### Expected behavior
n/a
### Environment info
FreeBSD 14.1
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Missing pyspark dependency causes the testsuite to error out, instead of a few tests to be skipped
|
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[] | null | 2024-08-05T21:05:11Z
| 2024-08-05T21:05:11Z
| null |
NONE
| null | null | null | null |
### Describe the bug
see the subject
### Steps to reproduce the bug
regular tests
### Expected behavior
n/a
### Environment info
version 2.20.0
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| 2,447,383,940
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I_kwDODunzps6R4B2E
| 7,088
|
Disable warning when using with_format format on tensors
|
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[] | null | 2024-08-05T00:45:50Z
| 2024-08-05T00:45:50Z
| null |
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| null | null | null | null |
### Feature request
If we write this code:
```python
"""Get data and define datasets."""
from enum import StrEnum
from datasets import load_dataset
from torch.utils.data import DataLoader
from torchvision import transforms
class Split(StrEnum):
"""Describes what type of split to use in the dataloader"""
TRAIN = "train"
TEST = "test"
VAL = "validation"
class ImageNetDataLoader(DataLoader):
"""Create an ImageNetDataloader"""
_preprocess_transform = transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
]
)
def __init__(self, batch_size: int = 4, split: Split = Split.TRAIN):
dataset = (
load_dataset(
"imagenet-1k",
split=split,
trust_remote_code=True,
streaming=True,
)
.with_format("torch")
.map(self._preprocess)
)
super().__init__(dataset=dataset, batch_size=batch_size)
def _preprocess(self, data):
if data["image"].shape[0] < 3:
data["image"] = data["image"].repeat(3, 1, 1)
data["image"] = self._preprocess_transform(data["image"].float())
return data
if __name__ == "__main__":
dataloader = ImageNetDataLoader(batch_size=2)
for batch in dataloader:
print(batch["image"])
break
```
This will trigger an user warning :
```bash
datasets\formatting\torch_formatter.py:85: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})
```
### Motivation
This happens because the the way the formatted tensor is returned in `TorchFormatter._tensorize`.
This function handle values of different types, according to some tests it seems that possible value types are `int`, `numpy.ndarray` and `torch.Tensor`.
In particular this warning is triggered when the value type is `torch.Tensor`, because is not the suggested Pytorch way of doing it:
- https://stackoverflow.com/questions/55266154/pytorch-preferred-way-to-copy-a-tensor
- https://discuss.pytorch.org/t/it-is-recommended-to-use-source-tensor-clone-detach-or-sourcetensor-clone-detach-requires-grad-true/101218#:~:text=The%20warning%20points%20to%20wrapping%20a%20tensor%20in%20torch.tensor%2C%20which%20is%20not%20recommended.%0AInstead%20of%20torch.tensor(outputs)%20use%20outputs.clone().detach()%20or%20the%20same%20with%20.requires_grad_(True)%2C%20if%20necessary.
### Your contribution
A solution that I found to be working is to change the current way of doing it:
```python
return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})
```
To:
```python
if (isinstance(value, torch.Tensor)):
tensor = value.clone().detach()
if self.torch_tensor_kwargs.get('requires_grad', False):
tensor.requires_grad_()
return tensor
else:
return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})
```
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I_kwDODunzps6R3K1z
| 7,087
|
Unable to create dataset card for Lushootseed language
|
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| 2024-08-06T06:59:23Z
| 2024-08-06T06:59:22Z
|
NONE
| null | null | null | null |
### Feature request
While I was creating the dataset which contained all documents from the Lushootseed Wikipedia, the dataset card asked me to enter which language the dataset was in. Since Lushootseed is a critically endangered language, it was not available as one of the options. Is it possible to allow entering languages that aren't available in the options?
### Motivation
I'd like to add more information about my dataset in the dataset card, and the language is one of the most important pieces of information, since the entire dataset is primarily concerned collecting Lushootseed documents.
### Your contribution
I can submit a pull request
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load_dataset ignores cached datasets and tries to hit HF Hub, resulting in API rate limit errors
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[] | null | 2024-08-02T18:12:23Z
| 2025-06-16T18:43:29Z
| null |
NONE
| null | null | null | null |
### Describe the bug
I have been running lm-eval-harness a lot which has results in an API rate limit. This seems strange, since all of the data should be cached locally. I have in fact verified this.
### Steps to reproduce the bug
1. Be Me
2. Run `load_dataset("TAUR-Lab/MuSR")`
3. Hit rate limit error
4. Dataset is in .cache/huggingface/datasets
5. ???
### Expected behavior
We should not run into API rate limits if we have cached the dataset
### Environment info
datasets 2.16.0
python 3.10.4
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|
[Regression] IterableDataset is broken on 2.20.0
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| 2024-08-22T14:49:37Z
| 2024-08-22T14:49:07Z
|
NONE
| null | null | null | null |
### Describe the bug
In the latest version of datasets there is a major regression, after creating an `IterableDataset` from a generator and applying a few operations (`map`, `select`), you can no longer iterate through the dataset multiple times.
The issue seems to stem from the recent addition of "resumable IterableDatasets" (#6658) (@lhoestq). It seems like it's keeping state when it shouldn't.
### Steps to reproduce the bug
Minimal Reproducible Example (comparing `datasets==2.17.0` and `datasets==2.20.0`)
```
#!/bin/bash
# List of dataset versions to test
versions=("2.17.0" "2.20.0")
# Loop through each version
for version in "${versions[@]}"; do
# Install the specific version of the datasets library
pip3 install -q datasets=="$version" 2>/dev/null
# Run the Python script
python3 - <<EOF
from datasets import IterableDataset
from datasets.features.features import Features, Value
def test_gen():
yield from [{"foo": i} for i in range(10)]
features = Features([("foo", Value("int64"))])
d = IterableDataset.from_generator(test_gen, features=features)
mapped = d.map(lambda row: {"foo": row["foo"] * 2})
column = mapped.select_columns(["foo"])
print("Version $version - Iterate Once:", list(column))
print("Version $version - Iterate Twice:", list(column))
EOF
done
```
The output looks like this:
```
Version 2.17.0 - Iterate Once: [{'foo': 0}, {'foo': 2}, {'foo': 4}, {'foo': 6}, {'foo': 8}, {'foo': 10}, {'foo': 12}, {'foo': 14}, {'foo': 16}, {'foo': 18}]
Version 2.17.0 - Iterate Twice: [{'foo': 0}, {'foo': 2}, {'foo': 4}, {'foo': 6}, {'foo': 8}, {'foo': 10}, {'foo': 12}, {'foo': 14}, {'foo': 16}, {'foo': 18}]
Version 2.20.0 - Iterate Once: [{'foo': 0}, {'foo': 2}, {'foo': 4}, {'foo': 6}, {'foo': 8}, {'foo': 10}, {'foo': 12}, {'foo': 14}, {'foo': 16}, {'foo': 18}]
Version 2.20.0 - Iterate Twice: []
```
### Expected behavior
The expected behavior is it version 2.20.0 should behave the same as 2.17.0.
### Environment info
`datasets==2.20.0` on any platform.
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More easily support streaming local files
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### Feature request
Simplify downloading and streaming datasets locally. Specifically, perhaps add an option to `load_dataset(..., streaming="download_first")` or add better support for streaming symlinked or arrow files.
### Motivation
I have downloaded FineWeb-edu locally and currently trying to stream the dataset from the local files. I have both the raw parquet files using `hugginface-cli download --repo-type dataset HuggingFaceFW/fineweb-edu` and the processed arrow files using `load_dataset("HuggingFaceFW/fineweb-edu")`.
Streaming the files locally does not work well for both file types for two different reasons.
**Arrow files**
When running `load_dataset("arrow", data_files={"train": "~/.cache/huggingface/datasets/HuggingFaceFW___fineweb-edu/default/0.0.0/5b89d1ea9319fe101b3cbdacd89a903aca1d6052/fineweb-edu-train-*.arrow"})` resolving the data files is fast, but because `arrow` is not included in the known [extensions file list](https://github.com/huggingface/datasets/blob/ce4a0c573920607bc6c814605734091b06b860e7/src/datasets/utils/file_utils.py#L738) , all files are opened and scanned to determine the compression type. Adding `arrow` to the known extension types resolves this issue.
**Parquet files**
When running `load_dataset("arrow", data_files={"train": "~/.cache/huggingface/hub/dataset-HuggingFaceFW___fineweb-edu/snapshots/5b89d1ea9319fe101b3cbdacd89a903aca1d6052/data/CC-MAIN-*/train-*.parquet"})` the paths do not get resolved because the parquet files are symlinked from the blobs (which contain all files in case there are different versions). This occurs because the [pattern matching](https://github.com/huggingface/datasets/blob/ce4a0c573920607bc6c814605734091b06b860e7/src/datasets/data_files.py#L389) checks if the path is a file and does not check for symlinks. Symlinks (at least on my machine) are of type "other".
### Your contribution
I have created a PR for fixing arrow file streaming and symlinks. However, I have not checked locally if the tests work or new tests need to be added.
IMO, the easiest option would be to add a `streaming=download_first` option, but I'm afraid that exceeds my current knowledge of how the datasets library works. https://github.com/huggingface/datasets/pull/7083
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fix streaming from arrow files
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Support HTTP authentication in non-streaming mode
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Support HTTP authentication in non-streaming mode, by support passing HTTP storage_options in non-streaming mode.
- Note that currently, HTTP authentication is supported only in streaming mode.
For example, this is necessary if a remote HTTP host requires authentication to download the data.
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Set load_from_disk path type as PathLike
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Set `load_from_disk` path type as `PathLike`. This way it is aligned with `save_to_disk`.
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Generating train split takes a long time
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| 2024-10-02T15:31:22Z
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NONE
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### Describe the bug
Loading a simple webdataset takes ~45 minutes.
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("PixArt-alpha/SAM-LLaVA-Captions10M")
```
### Expected behavior
The dataset should load immediately as it does when loaded through a normal indexed WebDataset loader. Generating splits should be optional and there should be a message showing how to disable it.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.14
- `huggingface_hub` version: 0.24.1
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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| 2024-09-20T13:26:25Z
| 2024-07-27T19:52:30Z
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NONE
| null | null | null | null |
### Describe the bug
newly uploaded datasets, since yesterday, yields an error.
old datasets, works fine.
Seems like the datasets api server returns a 500
I'm getting the same error, when I invoke `load_dataset` with my dataset.
Long discussion about it here, but I'm not sure anyone from huggingface have seen it.
https://discuss.huggingface.co/t/hfhubhttperror-500-server-error-internal-server-error-for-url/99580/1
### Steps to reproduce the bug
this api url:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
respond with:
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Expected behavior
return no error with newer datasets.
With older datasets I can load the datasets fine.
### Environment info
# Browser
When I access the api in the browser:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Request headers
```
Accept text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8
Accept-Encoding gzip, deflate, br, zstd
Accept-Language en-US,en;q=0.5
Connection keep-alive
Host huggingface.co
Priority u=1
Sec-Fetch-Dest document
Sec-Fetch-Mode navigate
Sec-Fetch-Site cross-site
Upgrade-Insecure-Requests 1
User-Agent Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:127.0) Gecko/20100101 Firefox/127.0
```
### Response headers
```
X-Firefox-Spdy h2
access-control-allow-origin https://huggingface.co
access-control-expose-headers X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,X-Total-Count,ETag,Link,Accept-Ranges,Content-Range
content-length 80
content-type application/json; charset=utf-8
cross-origin-opener-policy same-origin
date Fri, 26 Jul 2024 19:09:45 GMT
etag W/"50-9qrwU+BNI4SD0Fe32p/nofkmv0c"
referrer-policy strict-origin-when-cross-origin
vary Origin
via 1.1 1624c79cd07e6098196697a6a7907e4a.cloudfront.net (CloudFront)
x-amz-cf-id SP8E7n5qRaP6i9c9G83dNAiOzJBU4GXSrDRAcVNTomY895K35H0nJQ==
x-amz-cf-pop CPH50-C1
x-cache Error from cloudfront
x-error-message Internal Error - We're working hard to fix this as soon as possible!
x-powered-by huggingface-moon
x-request-id Root=1-66a3f479-026417465ef42f49349fdca1
```
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Fix CI test_convert_to_parquet
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Fix `test_convert_to_parquet` by patching `HfApi.preupload_lfs_files` and revert temporary fix:
- #7074
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I_kwDODunzps6Q-qWR
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column_names ignored by load_dataset() when loading CSV file
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| 2024-07-30T07:52:26Z
| null |
NONE
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### Describe the bug
load_dataset() ignores the column_names kwarg when loading a CSV file. Instead, it uses whatever values are on the first line of the file.
### Steps to reproduce the bug
Call `load_dataset` to load data from a CSV file and specify `column_names` kwarg.
### Expected behavior
The resulting dataset should have the specified column names **and** the first line of the file should be considered as data values.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.10.0-30-cloud-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- `huggingface_hub` version: 0.24.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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🧪 Do not mock create_commit
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Update required soxr version from pre-release to release
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Fix CI by temporarily marking test_convert_to_parquet as expected to fail
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| 2024-07-26T09:23:33Z
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As a hotfix for CI, temporarily mark test_convert_to_parquet as expected to fail.
Fix #7073.
Revert once root cause is fixed.
|
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CI is broken for convert_to_parquet: Invalid rev id: refs/pr/1 404 error causes RevisionNotFoundError
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See: https://github.com/huggingface/datasets/actions/runs/10095313567/job/27915185756
```
FAILED tests/test_hub.py::test_convert_to_parquet - huggingface_hub.utils._errors.RevisionNotFoundError: 404 Client Error. (Request ID: Root=1-66a25839-31ce7b475e70e7db1e4d44c2;b0c8870f-d5ef-4bf2-a6ff-0191f3df0f64)
Revision Not Found for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5188a8-17219154347516/preupload/refs%2Fpr%2F1.
Invalid rev id: refs/pr/1
```
```
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/hub.py:86: in convert_to_parquet
dataset.push_to_hub(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/dataset_dict.py:1722: in push_to_hub
split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/arrow_dataset.py:5511: in _push_parquet_shards_to_hub
api.preupload_lfs_files(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/hf_api.py:4231: in preupload_lfs_files
_fetch_upload_modes(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:118: in _inner_fn
return fn(*args, **kwargs)
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/_commit_api.py:507: in _fetch_upload_modes
hf_raise_for_status(resp)
```
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nm
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NONE
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|
Filter hangs
|
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[] | null | 2024-07-25T15:29:05Z
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### Describe the bug
When trying to filter my custom dataset, the process hangs, regardless of the lambda function used. It appears to be an issue with the way the Images are being handled. The dataset in question is a preprocessed version of https://huggingface.co/datasets/danaaubakirova/patfig where notably, I have converted the data to the Parquet format.
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('lcolonn/patfig', split='test')
ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y')
```
Eventually I ctrl+C and I obtain this stack trace:
```
>>> ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y')
Filter: 0%| | 0/998 [00:00<?, ? examples/s]Filter: 0%| | 0/998 [00:35<?, ? examples/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/fingerprint.py", line 482, in wrapper
out = func(dataset, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3714, in filter
indices = self.map(
^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 602, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3161, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3552, in _map_single
batch = apply_function_on_filtered_inputs(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3421, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 6478, in get_indices_from_mask_function
num_examples = len(batch[next(iter(batch.keys()))])
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 273, in __getitem__
value = self.format(key)
^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 376, in format
return self.formatter.format_column(self.pa_table.select([key]))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 443, in format_column
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 219, in decode_column
return self.features.decode_column(column, column_name) if self.features else column
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in decode_column
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in <listcomp>
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 1351, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/image.py", line 188, in decode_example
image.load() # to avoid "Too many open files" errors
^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/PIL/ImageFile.py", line 293, in load
n, err_code = decoder.decode(b)
^^^^^^^^^^^^^^^^^
KeyboardInterrupt
```
Warning! This can even seem to cause some computers to crash.
### Expected behavior
Should return the filtered dataset
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.24.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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I_kwDODunzps6Q2zWz
| 7,070
|
how set_transform affects batch size?
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[] | null | 2024-07-25T15:19:34Z
| 2024-07-25T15:19:34Z
| null |
NONE
| null | null | null | null |
### Describe the bug
I am trying to fine-tune w2v-bert for ASR task. Since my dataset is so big, I preferred to use the on-the-fly method with set_transform. So i change the preprocessing function to this:
```
def prepare_dataset(batch):
input_features = processor(batch["audio"], sampling_rate=16000).input_features[0]
input_length = len(input_features)
labels = processor.tokenizer(batch["text"], padding=False).input_ids
batch = {
"input_features": [input_features],
"input_length": [input_length],
"labels": [labels]
}
return batch
train_ds.set_transform(prepare_dataset)
val_ds.set_transform(prepare_dataset)
```
After this, I also had to change the DataCollatorCTCWithPadding class like this:
```
@dataclass
class DataCollatorCTCWithPadding:
processor: Wav2Vec2BertProcessor
padding: Union[bool, str] = True
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# Separate input_features and labels
input_features = [{"input_features": feature["input_features"][0]} for feature in features]
labels = [feature["labels"][0] for feature in features]
# Pad input features
batch = self.processor.pad(
input_features,
padding=self.padding,
return_tensors="pt",
)
# Pad and process labels
label_features = self.processor.tokenizer.pad(
{"input_ids": labels},
padding=self.padding,
return_tensors="pt",
)
labels = label_features["input_ids"]
attention_mask = label_features["attention_mask"]
# Replace padding with -100 to ignore these tokens during loss calculation
labels = labels.masked_fill(attention_mask.ne(1), -100)
batch["labels"] = labels
return batch
```
But now a strange thing is happening, no matter how much I increase the batch size, the amount of V-RAM GPU usage does not change, while the number of total steps in the progress-bar (logging) changes. Is this normal or have I made a mistake?
### Steps to reproduce the bug
i can share my code if needed
### Expected behavior
Equal to the batch size value, the set_transform function is applied to the dataset and given to the model as a batch.
### Environment info
all updated versions
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PR_kwDODunzps52betB
| 7,069
|
Fix push_to_hub by not calling create_branch if PR branch
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[] | null | 2024-07-25T07:50:04Z
| 2024-07-31T07:10:07Z
| 2024-07-30T10:51:01Z
|
MEMBER
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Fix push_to_hub by not calling create_branch if PR branch (e.g. `refs/pr/1`).
Note that currently create_branch raises a 400 Bad Request error if the user passes a PR branch (e.g. `refs/pr/1`).
EDIT:
~~Fix push_to_hub by not calling create_branch if branch exists.~~
Note that currently create_branch raises a 403 Forbidden error even if all these conditions are met:
- exist_ok is passed
- the branch already exists
- the user does not have WRITE permission
Fix #7067.
Related issue:
- https://github.com/huggingface/huggingface_hub/issues/2419
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PR_kwDODunzps52SwXS
| 7,068
|
Fix prepare_single_hop_path_and_storage_options
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[] | null | 2024-07-24T05:52:34Z
| 2024-07-29T07:02:07Z
| 2024-07-29T06:56:15Z
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Fix `_prepare_single_hop_path_and_storage_options`:
- Do not pass HF authentication headers and HF user-agent to non-HF HTTP URLs
- Do not overwrite passed `storage_options` nested values:
- Before, when passed
```DownloadConfig(storage_options={"https": {"client_kwargs": {"raise_for_status": True}}})```,
it was overwritten to
```{"https": {"client_kwargs": {"trust_env": True}}}```
- Now, the result combines both:
```{"https": {"client_kwargs": {"trust_env": True, "raise_for_status": True}}}```
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Convert_to_parquet fails for datasets with multiple configs
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| 2024-07-30T10:51:02Z
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NONE
| null | null | null | null |
If the dataset has multiple configs, when using the `datasets-cli convert_to_parquet` command to avoid issues with the data viewer caused by loading scripts, the conversion process only successfully converts the data corresponding to the first config. When it starts converting the second config, it throws an error:
```
Traceback (most recent call last):
File "/opt/anaconda3/envs/dl/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/datasets_cli.py", line 41, in main
service.run()
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/convert_to_parquet.py", line 83, in run
dataset.push_to_hub(
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/dataset_dict.py", line 1713, in push_to_hub
api.create_branch(repo_id, branch=revision, token=token, repo_type="dataset", exist_ok=True)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 5503, in create_branch
hf_raise_for_status(response)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 358, in hf_raise_for_status
raise BadRequestError(message, response=response) from e
huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-669fc665-7c2e80d75f4337496ee95402;731fcdc7-0950-4eec-99cf-ce047b8d003f)
Bad request:
Invalid reference for a branch: refs/pr/1
```
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I_kwDODunzps6QjHko
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|
One subset per file in repo ?
|
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[] | null | 2024-07-23T12:43:59Z
| 2025-06-26T08:24:50Z
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Right now we consider all the files of a dataset to be the same data, e.g.
```
single_subset_dataset/
├── train0.jsonl
├── train1.jsonl
└── train2.jsonl
```
but in cases like this, each file is actually a different subset of the dataset and should be loaded separately
```
many_subsets_dataset/
├── animals.jsonl
├── trees.jsonl
└── metadata.jsonl
```
It would be nice to detect those subsets automatically using a simple heuristic. For example we can group files together if their paths names are the same except some digits ?
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Cannot get item after loading from disk and then converting to iterable.
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### Describe the bug
The dataset generated from local file works fine.
```py
root = "/home/data/train"
file_list1 = glob(os.path.join(root, "*part1.flac"))
file_list2 = glob(os.path.join(root, "*part2.flac"))
ds = (
Dataset.from_dict({"part1": file_list1, "part2": file_list2})
.cast_column("part1", Audio(sampling_rate=None, mono=False))
.cast_column("part2", Audio(sampling_rate=None, mono=False))
)
ids = ds.to_iterable_dataset(128)
ids = ids.shuffle(buffer_size=10000, seed=42)
dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True)
for batch in dataloader:
break
```
But after saving it to disk and then loading it from disk, I cannot get data as expected.
```py
root = "/home/data/train"
file_list1 = glob(os.path.join(root, "*part1.flac"))
file_list2 = glob(os.path.join(root, "*part2.flac"))
ds = (
Dataset.from_dict({"part1": file_list1, "part2": file_list2})
.cast_column("part1", Audio(sampling_rate=None, mono=False))
.cast_column("part2", Audio(sampling_rate=None, mono=False))
)
ds.save_to_disk("./train")
ds = datasets.load_from_disk("./train")
ids = ds.to_iterable_dataset(128)
ids = ids.shuffle(buffer_size=10000, seed=42)
dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True)
for batch in dataloader:
break
```
After a long time waiting, an error occurs:
```
Loading dataset from disk: 100%|█████████████████████████████████████████████████████████████████████████| 165/165 [00:00<00:00, 6422.18it/s]
Traceback (most recent call last):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1133, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/queues.py", line 113, in get
if not self._poll(timeout):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 257, in poll
return self._poll(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 424, in _poll
r = wait([self], timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 931, in wait
ready = selector.select(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/selectors.py", line 416, in select
fd_event_list = self._selector.poll(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 3490529) is killed by signal: Killed.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module>
cli.main()
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
run()
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
runpy.run_path(target, run_name="__main__")
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
exec(code, run_globals)
File "/home/hanzerui/workspace/NetEase/test/test_datasets.py", line 60, in <module>
for batch in dataloader:
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
data = self._next_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1329, in _next_data
idx, data = self._get_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1295, in _get_data
success, data = self._try_get_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1146, in _try_get_data
raise RuntimeError(f'DataLoader worker (pid(s) {pids_str}) exited unexpectedly') from e
RuntimeError: DataLoader worker (pid(s) 3490529) exited unexpectedly
```
It seems that streaming is not supported by `laod_from_disk`, so does that mean I cannot convert it to iterable?
### Steps to reproduce the bug
1. Create a `Dataset` from local files with `from_dict`
2. Save it to disk with `save_to_disk`
3. Load it from disk with `load_from_disk`
4. Convert to iterable with `to_iterable_dataset`
5. Loop the dataset
### Expected behavior
Get items faster than the original dataset generated from dict.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.23.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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This PR introduces a new `batch` method to the `Dataset` class, aligning its functionality with the `IterableDataset.batch()` method (implemented in #7054). The implementation uses as well the existing `map` method for efficient batching of examples.
Key changes:
- Add `batch` method to `Dataset` class in `arrow_dataset.py`
- Utilize `map` method for batching
Closes #7063
Once the approach is approved, i will create the tests and update the documentation.
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Add `batch` method to `Dataset`
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CONTRIBUTOR
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### Feature request
Add a `batch` method to the Dataset class, similar to the one recently implemented for `IterableDataset` in PR #7054.
### Motivation
A batched iteration speeds up data loading significantly (see e.g. #6279)
### Your contribution
I plan to open a PR to implement this.
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Avoid calling http_head for non-HTTP URLs
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Avoid calling `http_head` for non-HTTP URLs, by adding and `else` statement.
Currently, it makes an unnecessary HTTP call (which adds latency) for non-HTTP protocols, like FTP, S3,...
I discovered this while working in an unrelated issue.
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Custom Dataset | Still Raise Error while handling errors in _generate_examples
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[] | null | 2024-07-22T21:18:12Z
| 2024-09-09T14:48:07Z
| null |
NONE
| null | null | null | null |
### Describe the bug
I follow this [example](https://discuss.huggingface.co/t/error-handling-in-iterabledataset/72827/3) to handle errors in custom dataset. I am writing a dataset script which read jsonl files and i need to handle errors and continue reading files without raising exception and exit the execution.
```
def _generate_examples(self, filepaths):
errors=[]
id_ = 0
for filepath in filepaths:
try:
with open(filepath, 'r') as f:
for line in f:
json_obj = json.loads(line)
yield id_, json_obj
id_ += 1
except Exception as exc:
logger.error(f"error occur at filepath: {filepath}")
errors.append(error)
```
seems the logger.error is printed but still exception is raised the the run is exit.
```
Downloading and preparing dataset custom_dataset/default to /home/myuser/.cache/huggingface/datasets/custom_dataset/default-a14cdd566afee0a6/1.0.0/acfcc9fb9c57034b580c4252841
ERROR: datasets_modules.datasets.custom_dataset.acfcc9fb9c57034b580c4252841bb890a5617cbd28678dd4be5e52b81188ad02.custom_dataset: 2024-07-22 10:47:42,167: error occur at filepath: '/home/myuser/ds/corrupted-file.jsonl
Traceback (most recent call last):
File "/home/myuser/.cache/huggingface/modules/datasets_modules/datasets/custom_dataset/ac..2/custom_dataset.py", line 48, in _generate_examples
json_obj = json.loads(line)
File "myenv/lib/python3.8/json/__init__.py", line 357, in loads
return _default_decoder.decode(s)
File "myenv/lib/python3.8/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "myenv/lib/python3.8/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Invalid control character at: line 1 column 4 (char 3)
Generating train split: 0 examples [00:06, ? examples/s]>
RemoteTraceback:
"""
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1637, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "myenv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 594, in finalize
raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "myenv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 1353, in
_write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1646, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
"""
The above exception was the direct cause of the following exception:
│ │
│ myenv/lib/python3.8/site-packages/datasets/utils/py_utils. │
│ py:1377 in <listcomp> │
│ │
│ 1374 │ │ │ │ if all(async_result.ready() for async_result in async_results) and queue │
│ 1375 │ │ │ │ │ break │
│ 1376 │ │ # we get the result in case there's an error to raise │
│ ❱ 1377 │ │ [async_result.get() for async_result in async_results] │
│ 1378 │
│ │
│ ╭──────────────────────────────── locals ─────────────────────────────────╮ │
│ │ .0 = <list_iterator object at 0x7f2cc1f0ce20> │ │
│ │ async_result = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │
│ ╰─────────────────────────────────────────────────────────────────────────╯ │
│ │
│ myenv/lib/python3.8/site-packages/multiprocess/pool.py:771 │
│ in get │
│ │
│ 768 │ │ if self._success: │
│ 769 │ │ │ return self._value │
│ 770 │ │ else: │
│ ❱ 771 │ │ │ raise self._value │
│ 772 │ │
│ 773 │ def _set(self, i, obj): │
│ 774 │ │ self._success, self._value = obj │
│ │
│ ╭────────────────────────────── locals ──────────────────────────────╮ │
│ │ self = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │
│ │ timeout = None │ │
│ ╰────────────────────────────────────────────────────────────────────╯ │
DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
same as above
### Expected behavior
should handle error and continue reading remaining files
### Environment info
python 3.9
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WebDataset BuilderConfig
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[] | null | 2024-07-22T15:41:07Z
| 2024-07-23T13:28:44Z
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NONE
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|
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This PR adds `WebDatasetConfig`.
Closes #7055
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I_kwDODunzps6QaWt0
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|
None values are skipped when reading jsonl in subobjects
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[] | null | 2024-07-22T13:02:42Z
| 2024-07-22T13:02:53Z
| null |
NONE
| null | null | null | null |
### Describe the bug
I have been fighting against my machine since this morning only to find out this is some kind of a bug.
When loading a dataset composed of `metadata.jsonl`, if you have nullable values (Optional[str]), they can be ignored by the parser, shifting things around.
E.g., let's take this example
Here are two version of a same dataset:
[not-buggy.tar.gz](https://github.com/user-attachments/files/16333532/not-buggy.tar.gz)
[buggy.tar.gz](https://github.com/user-attachments/files/16333553/buggy.tar.gz)
### Steps to reproduce the bug
1. Load the `buggy.tar.gz` dataset
2. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
3. Load the `not-buggy.tar.gz` dataset
4. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
### Expected behavior
Both should have 4 baseline entries:
1. Buggy should have None followed by three lists
2. Non-Buggy should have four lists, and the first one should be an empty list.
One does not work, 2 works. Despite accepting None in another position than the first one.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
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New feature type: Document
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It would be useful for PDF.
https://github.com/huggingface/dataset-viewer/issues/2991#issuecomment-2242656069
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Update load_hub.mdx
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PR_kwDODunzps52DgOu
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Make `BufferShuffledExamplesIterable` resumable
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[] | null | 2024-07-22T07:50:02Z
| 2025-01-31T05:34:20Z
| 2025-01-31T05:34:19Z
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CONTRIBUTOR
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This PR aims to implement a resumable `BufferShuffledExamplesIterable`.
Instead of saving the entire buffer content, which is very memory-intensive, the newly implemented `BufferShuffledExamplesIterable` saves only the minimal state necessary for recovery, e.g., the random generator states and the state of the first example in the buffer dict.
The idea is that since the buffer size is limited, even if the entire buffer is discarded, we can rebuild it as long as the state of the oldest example is recorded. For buffer size $B$, the expected distance between when an example is pushed and when it is yielded is
$d = \sum_{k=1}^{\infty} k\frac{1}{B} (1 - \frac{1}{B} )^{k-1} =B$.
Simulation experiments support these claims:
```py
from random import randint
BUFFER_SIZE = 1024
dists = []
buffer = []
for i in range(10000000):
if i < BUFFER_SIZE:
buffer.append(i)
else:
index = randint(0, BUFFER_SIZE - 1)
dists.append(i - buffer[index])
buffer[index] = i
print(f"MIN DIST: {min(dists)}\nMAX DIST: {max(dists)}\nAVG DIST: {sum(dists) / len(dists):.2f}\n")
```
which produces the following output:
```py
MIN DIST: 1
MAX DIST: 15136
AVG DIST: 1023.95
```
The overall time for reconstructing the buffer and recovery should not be too long.
The following code mimics the cases of resuming online tokenization by `datasets` and `StatefulDataLoader` under distributed scenarios,
```py
import pickle
import time
from itertools import chain
from typing import Any, Dict, List
import torch
from datasets import load_dataset
from torchdata.stateful_dataloader import StatefulDataLoader
from tqdm import tqdm
from transformers import AutoTokenizer, DataCollatorForLanguageModeling
tokenizer = AutoTokenizer.from_pretrained('fla-hub/gla-1.3B-100B')
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
torch.manual_seed(42)
def tokenize(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]:
input_ids = tokenizer(examples['text'])['input_ids']
input_ids = list(chain(*input_ids))
total_length = len(input_ids)
chunk_size = 2048
total_length = (total_length // chunk_size) * chunk_size
# the last chunk smaller than chunk_size will be discarded
return {'input_ids': [input_ids[i: i+chunk_size] for i in range(0, total_length, chunk_size)]}
batch_size = 16
num_workers = 5
context_length = 2048
rank = 1
world_size = 32
prefetch_factor = 2
steps = 2048
path = 'fla-hub/slimpajama-test'
dataset = load_dataset(
path=path,
split='train',
streaming=True,
trust_remote_code=True
)
dataset = dataset.map(tokenize, batched=True, remove_columns=next(iter(dataset)).keys())
dataset = dataset.shuffle(seed=42)
loader = StatefulDataLoader(dataset=dataset,
batch_size=batch_size,
collate_fn=data_collator,
num_workers=num_workers,
persistent_workers=False,
prefetch_factor=prefetch_factor)
start = time.time()
for i, batch in tqdm(enumerate(loader)):
if i == 0:
print(f'{i}\n{batch["input_ids"]}')
if i == steps - 1:
print(f'{i}\n{batch["input_ids"]}')
state_dict = loader.state_dict()
if i == steps:
print(f'{i}\n{batch["input_ids"]}')
break
print(f"{time.time() - start:.2f}s elapsed")
print(f"{len(pickle.dumps(state_dict)) / 1024**2:.2f}MB states in total")
for worker in state_dict['_snapshot']['_worker_snapshots'].keys():
print(f"{worker} {len(pickle.dumps(state_dict['_snapshot']['_worker_snapshots'][worker])) / 1024**2:.2f}MB")
print(state_dict['_snapshot']['_worker_snapshots']['worker_0']['dataset_state'])
loader = StatefulDataLoader(dataset=dataset,
batch_size=batch_size,
collate_fn=data_collator,
num_workers=num_workers,
persistent_workers=False,
prefetch_factor=prefetch_factor)
print("Loading state dict")
loader.load_state_dict(state_dict)
start = time.time()
for batch in loader:
print(batch['input_ids'])
break
print(f"{time.time() - start:.2f}s elapsed")
```
and the outputs are
```py
0
tensor([[ 909, 395, 19082, ..., 13088, 16232, 395],
[ 601, 28705, 28770, ..., 28733, 923, 288],
[21753, 15071, 13977, ..., 9369, 28723, 415],
...,
[21763, 28751, 20300, ..., 28781, 28734, 4775],
[ 354, 396, 10214, ..., 298, 429, 28770],
[ 333, 6149, 28768, ..., 2773, 340, 351]])
2047
tensor([[28723, 415, 3889, ..., 272, 3065, 2609],
[ 403, 3214, 3629, ..., 403, 21163, 16434],
[28723, 13, 28749, ..., 28705, 28750, 28734],
...,
[ 2778, 2251, 28723, ..., 354, 684, 429],
[ 5659, 298, 1038, ..., 5290, 297, 22153],
[ 938, 28723, 1537, ..., 9123, 28733, 12154]])
2048
tensor([[ 769, 278, 12531, ..., 28721, 19309, 28739],
[ 415, 23347, 622, ..., 3937, 2426, 28725],
[28745, 4345, 28723, ..., 338, 28725, 583],
...,
[ 1670, 28709, 5809, ..., 28734, 28760, 393],
[ 340, 1277, 624, ..., 325, 28790, 1329],
[ 523, 1144, 3409, ..., 359, 359, 17422]])
65.97s elapsed
0.00MB states in total
worker_0 0.00MB
worker_1 0.00MB
worker_2 0.00MB
worker_3 0.00MB
worker_4 0.00MB
{'ex_iterable': {'ex_iterable': {'shard_idx': 0, 'shard_example_idx': 14000}, 'num_examples_since_previous_state': 166, 'previous_state_example_idx': 7394, 'previous_state': {'shard_idx': 0, 'shard_example_idx': 13000}}, 'num_taken': 6560, 'global_example_idx': 7560, 'buffer_state_dict': {'num_taken': 6560, 'global_example_idx': 356, 'index_offset': 0, 'first_state': {'ex_iterable': {'shard_idx': 0, 'shard_example_idx': 1000}, 'num_examples_since_previous_state': 356, 'previous_state_example_idx': 0, 'previous_state': {'shard_idx': 0, 'shard_example_idx': 0}}, 'bit_generator_state': {'state': {'state': 274674114334540486603088602300644985544, 'inc': 332724090758049132448979897138935081983}, 'bit_generator': 'PCG64', 'has_uint32': 0, 'uinteger': 0}}}
Loading state dict
tensor([[ 769, 278, 12531, ..., 28721, 19309, 28739],
[ 415, 23347, 622, ..., 3937, 2426, 28725],
[28745, 4345, 28723, ..., 338, 28725, 583],
...,
[ 1670, 28709, 5809, ..., 28734, 28760, 393],
[ 340, 1277, 624, ..., 325, 28790, 1329],
[ 523, 1144, 3409, ..., 359, 359, 17422]])
24.60s elapsed
```
Not sure if this PR complies with the `datasets` code style. Looking for your help @lhoestq, also very willing to further improve the code if any suggestions are given.
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I_kwDODunzps6QWFhb
| 7,055
|
WebDataset with different prefixes are unsupported
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[] | null | 2024-07-22T01:14:19Z
| 2024-07-24T13:26:30Z
| 2024-07-23T13:28:46Z
|
NONE
| null | null | null | null |
### Describe the bug
Consider a WebDataset with multiple images for each item where the number of images may vary: [example](https://huggingface.co/datasets/bigdata-pw/fashion-150k)
Due to this [code](https://github.com/huggingface/datasets/blob/87f4c2088854ff33e817e724e75179e9975c1b02/src/datasets/packaged_modules/webdataset/webdataset.py#L76-L80) an error is given.
```
The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
```
The purpose of this check is unclear because PyArrow supports different keys.
Removing the check allows the dataset to be loaded and there's no issue when iterating through the dataset.
```
>>> from datasets import load_dataset
>>> path = "shards/*.tar"
>>> dataset = load_dataset("webdataset", data_files={"train": path}, split="train", streaming=True)
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 152/152 [00:00<00:00, 56458.93it/s]
>>> dataset
IterableDataset({
features: ['__key__', '__url__', '1.jpg', '2.jpg', '3.jpg', '4.jpg', 'json'],
n_shards: 152
})
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("bigdata-pw/fashion-150k")
```
### Expected behavior
Dataset loads without error
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.14.0-467.el9.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.19
- `huggingface_hub` version: 0.23.4
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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PR_kwDODunzps514T1f
| 7,054
|
Add batching to `IterableDataset`
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[] | null | 2024-07-19T10:11:47Z
| 2024-07-23T13:25:13Z
| 2024-07-23T10:34:28Z
|
CONTRIBUTOR
| null | null | 0
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I've taken a try at implementing a batched `IterableDataset` as requested in issue #6279. This PR adds a new `BatchedExamplesIterable` class and a `.batch()` method to the `IterableDataset` class.
The main changes are:
1. A new `BatchedExamplesIterable` that groups examples into batches.
2. A `.batch()` method for `IterableDataset` to easily create batched versions.
3. Support for shuffling and sharding to work with PyTorch DataLoader and multiple workers.
I'm not sure if this is exactly what you had in mind and also have not fully tested it atm, so I'd really appreciate your feedback. Does this seem like it's heading in the right direction? I'm happy to make any changes or explore different approaches if needed.
Pinging @lhoestq
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Datasets.datafiles resolve_pattern `TypeError: can only concatenate tuple (not "str") to tuple`
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| 2024-07-18T15:17:42Z
| 2024-07-18T15:16:18Z
|
NONE
| null | null | null | null |
### Describe the bug
in data_files.py, line 332,
`fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options)`
If we run the code on AWS, as fs.protocol will be a tuple like: `('file', 'local')`
So, `isinstance(fs.protocol, str) == False` and
`protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else ""` will raise
`TypeError: can only concatenate tuple (not "str") to tuple`.
### Steps to reproduce the bug
Steps to reproduce:
1. Run on a cloud server like AWS,
2. `import datasets.data_files as datafile`
3. datafile.resolve_pattern('path/to/dataset', '.')
4. `TypeError: can only concatenate tuple (not "str") to tuple`
### Expected behavior
Should return path of the dataset, with fs.protocol at the beginning
### Environment info
- `datasets` version: 2.14.0
- Platform: Linux-3.10.0-1160.119.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.19
- Huggingface_hub version: 0.23.5
- PyArrow version: 16.1.0
- Pandas version: 1.1.5
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PR_kwDODunzps51iuop
| 7,052
|
Adding `Music` feature for symbolic music modality (MIDI, abc)
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| 2024-07-29T06:47:55Z
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⚠️ (WIP) ⚠️
### What this PR does
This PR adds a `Music` feature for the symbolic music modality, in particular [MIDI](https://en.wikipedia.org/wiki/Musical_Instrument_Digital_Interface) and [abc](https://en.wikipedia.org/wiki/ABC_notation) files.
### Motivations
These two file formats are widely used in the [Music Information Retrieval (MIR)](https://en.wikipedia.org/wiki/Music_information_retrieval) for tasks such as music generation, music transcription, music synthesis or music transcription. Having a dedicated feature in the datasets library would allow to both encourage researchers to share datasets of this modality as well as making them more easily usable for end users, benefitting from the perks of the library.
These file formats are supported by [symusic](https://github.com/Yikai-Liao/symusic), a lightweight Python library with C bindings (using nanobind) allowing to efficiently read, write and manipulate them. The library is actively developed, and can in the future also implement other file formats such as [musicXML](https://en.wikipedia.org/wiki/MusicXML). As such, this PR relies on it.
The music data can then easily be tokenized with appropriate tokenizers such as [MidiTok](https://github.com/Natooz/MidiTok) or converted to pianorolls matrices by symusic.
**Jul 16th 2024:**
* the tests for the `Music` feature are currently failing due to non-supported access to the LazyBatch in `test_dataset_with_music_feature_map` and `test_dataset_with_music_feature_map_resample_music` (see TODOs). I am a beginner with pyArrow, I'll take any advice to make this work;
* additional tests including the `Music` feature with parquet and WebDataset should be implemented. As of right now, I am waiting for your feedback before taking further steps;
* a `MusicFolder` should also be implemented to comply with the usages of the `Image` and `Audio` features, waiting for your feedback too.
CCing @lhoestq and @albertvillanova
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I_kwDODunzps6Pm9LJ
| 7,051
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How to set_epoch with interleave_datasets?
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Let's say I have dataset A which has 100k examples, and dataset B which has 100m examples.
I want to train on an interleaved dataset of A+B, with stopping_strategy='all_exhausted' so dataset B doesn't repeat any examples. But every time A is exhausted I want it to be reshuffled (eg. calling set_epoch)
Of course I want to interleave as IterableDatasets / streaming mode so B doesn't have to get tokenized completely at the start.
How could I achieve this? I was thinking something like, if I wrap dataset A in some new IterableDataset with from_generator() and manually call set_epoch before interleaving it? But I'm not sure how to keep the number of shards in that dataset...
Something like
```
dataset_a = load_dataset(...)
dataset_b = load_dataset(...)
def epoch_shuffled_dataset(ds):
# How to make this maintain the number of shards in ds??
for epoch in itertools.count():
ds.set_epoch(epoch)
yield from iter(ds)
shuffled_dataset_a = IterableDataset.from_generator(epoch_shuffled_dataset, gen_kwargs={'ds': dataset_a})
interleaved = interleave_datasets([shuffled_dataset_a, dataset_b], probs, stopping_strategy='all_exhausted')
```
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PR_kwDODunzps51Z1Yp
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add checkpoint and resume title in docs
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MEMBER
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(minor) just to make it more prominent in the docs page for the soon-to-be-released new torchdata
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I_kwDODunzps6PjwM-
| 7,049
|
Save nparray as list
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NONE
| null | null | null | null |
### Describe the bug
When I use the `map` function to convert images into features, datasets saves nparray as a list. Some people use the `set_format` function to convert the column back, but doesn't this lose precision?
### Steps to reproduce the bug
the map function
```python
def convert_image_to_features(inst, processor, image_dir):
image_file = inst["image_url"]
file = image_file.split("/")[-1]
image_path = os.path.join(image_dir, file)
image = Image.open(image_path)
image = image.convert("RGBA")
inst["pixel_values"] = processor(images=image, return_tensors="np")["pixel_values"]
return inst
```
main function
```python
map_fun = partial(
convert_image_to_features, processor=processor, image_dir=image_dir
)
ds = ds.map(map_fun, batched=False, num_proc=20)
print(type(ds[0]["pixel_values"])
```
### Expected behavior
(type < list>)
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-4.19.91-009.ali4000.alios7.x86_64-x86_64-with-glibc2.35
- Python version: 3.11.5
- `huggingface_hub` version: 0.23.4
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0
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ImportError: numpy.core.multiarray when using `filter`
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| 2024-07-16T10:11:25Z
| 2024-07-16T10:11:25Z
|
NONE
| null | null | null | null |
### Describe the bug
I can't apply the filter method on my dataset.
### Steps to reproduce the bug
The following snippet generates a bug:
```python
from datasets import load_dataset
ami = load_dataset('kamilakesbi/ami', 'ihm')
ami['train'].filter(
lambda example: example["file_name"] == 'EN2001a'
)
```
I get the following error:
`ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).`
### Expected behavior
It should work properly!
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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I_kwDODunzps6PcDNs
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|
Save Dataset as Sharded Parquet
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[] | null | 2024-07-12T23:47:51Z
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### Feature request
`to_parquet` currently saves the dataset as one massive, monolithic parquet file, rather than as several small parquet files. It should shard large datasets automatically.
### Motivation
This default behavior makes me very sad because a program I ran for 6 hours saved its results using `to_parquet`, putting the entire billion+ row dataset into a 171 GB *single shard parquet file* which pyarrow, apache spark, etc. all cannot work with without completely exhausting the memory of my system. I was previously able to work with larger-than-memory parquet files, but not this one. I *assume* the reason why this is happening is because it is a single shard. Making sharding the default behavior puts datasets in parity with other frameworks, such as spark, which automatically shard when a large dataset is saved as parquet.
### Your contribution
I could change the logic here https://github.com/huggingface/datasets/blob/bf6f41e94d9b2f1c620cf937a2e85e5754a8b960/src/datasets/io/parquet.py#L109-L158
to use `pyarrow.dataset.write_dataset`, which seems to support sharding, or periodically open new files. We would only shard if the user passed in a path rather than file handle.
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Support librosa and numpy 2.0 for Python 3.10
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Support librosa and numpy 2.0 for Python 3.10 by installing soxr 0.4.0b1 pre-release:
- https://github.com/dofuuz/python-soxr/releases/tag/v0.4.0b1
- https://github.com/dofuuz/python-soxr/issues/28
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Fix tensorflow min version depending on Python version
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Fix tensorflow min version depending on Python version.
Related to:
- #6991
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Mark tests that require librosa
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Mark tests that require `librosa`.
Note that `librosa` is an optional dependency (installed with `audio` option) and we should be able to test environments without that library installed. This is the case if we want to test Numpy 2.0, which is currently incompatible with `librosa` due to its dependency on `soxr`:
- https://github.com/dofuuz/python-soxr/issues/28
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Add decorator as explicit test dependency
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Add decorator as explicit test dependency.
We use `decorator` library in our CI test since PR:
- #4845
However we did not add it as an explicit test requirement, and we depended on it indirectly through other libraries' dependencies.
I discovered this while testing Numpy 2.0 and removing incompatible libraries.
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Improved the tutorial by adding a link for loading datasets
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CONTRIBUTOR
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Improved the tutorial by letting readers know about loading datasets with common files and including a link. I left the local files section alone because the methods were already listed with code snippets.
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I_kwDODunzps6PUusm
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|
`sort` after `filter` unreasonably slow
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| 2025-04-29T09:49:25Z
| 2025-04-29T09:49:25Z
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NONE
| null | null | null | null |
### Describe the bug
as the tittle says ...
### Steps to reproduce the bug
`sort` seems to be normal.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
but `sort` after `filter` is extremely slow.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
ds = ds.filter(lambda x:x > 100, input_columns="k")
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
### Expected behavior
Is this a bug, or is it a misuse of the `sort` function?
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-3.10.0-1127.19.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2023.10.0
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I_kwDODunzps6POZ-_
| 7,040
|
load `streaming=True` dataset with downloaded cache
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[] | null | 2024-07-11T11:14:13Z
| 2024-07-11T14:11:56Z
| null |
NONE
| null | null | null | null |
### Describe the bug
We build a dataset which contains several hdf5 files and write a script using `h5py` to generate the dataset. The hdf5 files are large and the processed dataset cache takes more disk space. So we hope to try streaming iterable dataset. Unfortunately, `h5py` can't convert a remote URL into a hdf5 file descriptor. So we use `fsspec` as an interface like below:
```python
def _generate_examples(self, filepath, split):
for file in filepath:
with fsspec.open(file, "rb") as fs:
with h5py.File(fs, "r") as fp:
# for event_id in sorted(list(fp.keys())):
event_ids = list(fp.keys())
......
```
### Steps to reproduce the bug
The `fsspec` works, but it takes 10+ min to print the first 10 examples, which is even longer than the downloading time. I'm not sure if it just caches the whole hdf5 file and generates the examples.
### Expected behavior
So does the following make sense so far?
1. download the files
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True)
```
2. load the iterable dataset faster (using the raw file cache at path `.cache/huggingface/datasets/downloads`)
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True, streaming=true)
```
I made some tests, but the code above can't get the expected result. I'm not sure if this is supported. I also find the issue #6327 . It seemed similar to mine, but I couldn't find a solution.
### Environment info
- `datasets` = 2.18.0
- `h5py` = 3.10.0
- `fsspec` = 2023.10.0
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PR_kwDODunzps51DgCY
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Fix export to JSON when dataset larger than batch size
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Fix export to JSON (`lines=False`) when dataset larger than batch size.
Fix #7037.
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I_kwDODunzps6PEAej
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|
A bug of Dataset.to_json() function
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| 2024-09-22T13:16:07Z
| null |
NONE
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### Describe the bug
When using the Dataset.to_json() function, an unexpected error occurs if the parameter is set to lines=False. The stored data should be in the form of a list, but it actually turns into multiple lists, which causes an error when reading the data again.
The reason is that to_json() writes to the file in several segments based on the batch size. This is not a problem when lines=True, but it is incorrect when lines=False, because writing in several times will produce multiple lists(when len(dataset) > batch_size).
### Steps to reproduce the bug
try this code:
```python
from datasets import load_dataset
import json
train_dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")["train"]
output_path = "./harmless-base_hftojs.json"
print(len(train_dataset))
train_dataset.to_json(output_path, lines=False, force_ascii=False, indent=2)
with open(output_path, encoding="utf-8") as f:
data = json.loads(f.read())
```
it raise error: json.decoder.JSONDecodeError: Extra data: line 4003 column 1 (char 1373709)
Extra square brackets have appeared here:
<img width="265" alt="image" src="https://github.com/huggingface/datasets/assets/26499566/81492332-386d-42e8-88d1-b6d4ae3682cc">
### Expected behavior
The code runs normally.
### Environment info
datasets=2.20.0
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PR_kwDODunzps507bZk
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Fix doc generation when NamedSplit is used as parameter default value
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Fix doc generation when `NamedSplit` is used as parameter default value.
Fix #7035.
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I_kwDODunzps6PDWrp
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Docs are not generated when a parameter defaults to a NamedSplit value
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MEMBER
| null | null | null | null |
While generating the docs, we get an error when some parameter defaults to a `NamedSplit` value, like:
```python
def call_function(split=Split.TRAIN):
...
```
The error is: ValueError: Equality not supported between split train and <class 'inspect._empty'>
See: https://github.com/huggingface/datasets/actions/runs/9869660902/job/27254359863?pr=7015
```
Building the MDX files: 97%|█████████▋| 58/60 [00:00<00:00, 91.94it/s]
Traceback (most recent call last):
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 197, in build_mdx_files
content, new_anchors, source_files, errors = resolve_autodoc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 123, in resolve_autodoc
doc = autodoc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 499, in autodoc
method_doc, check = document_object(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 395, in document_object
signature = format_signature(obj)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 126, in format_signature
if param.default != inspect._empty:
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 136, in __ne__
return not self.__eq__(other)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 379, in __eq__
raise ValueError(f"Equality not supported between split {self} and {other}")
ValueError: Equality not supported between split train and <class 'inspect._empty'>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/datasets/datasets/.venv/bin/doc-builder", line 8, in <module>
sys.exit(main())
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/doc_builder_cli.py", line 47, in main
args.func(args)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/build.py", line 102, in build_command
build_doc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 367, in build_doc
anchors_mapping, source_files_mapping = build_mdx_files(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 230, in build_mdx_files
raise type(e)(f"There was an error when converting {file} to the MDX format.\n" + e.args[0]) from e
ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format.
Equality not supported between split train and <class 'inspect._empty'>
```
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PR_kwDODunzps50y-ya
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chore: fix typos in docs
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I_kwDODunzps6O5bj4
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`from_generator` does not allow to specify the split name
|
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CONTRIBUTOR
| null | null | null | null |
### Describe the bug
I'm building train, dev, and test using `from_generator`; however, in all three cases, the logger prints `Generating train split:`
It's not possible to change the split name since it seems to be hardcoded: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/generator/generator.py
### Steps to reproduce the bug
```
In [1]: from datasets import Dataset
In [2]: def gen():
...: yield {"pokemon": "bulbasaur", "type": "grass"}
...:
In [3]: ds = Dataset.from_generator(gen)
Generating train split: 1 examples [00:00, 133.89 examples/s]
```
### Expected behavior
It should be possible to specify any split name
### Environment info
- `datasets` version: 2.19.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- `huggingface_hub` version: 0.23.3
- PyArrow version: 15.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0
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| 2024-07-12T15:07:03Z
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CONTRIBUTOR
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For example, https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0 dataset files have `.zstd` extension which is currently ignored (only `.zst` is registered).
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I_kwDODunzps6Oxu3c
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CI quality is broken: use ruff check instead
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MEMBER
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CI quality is broken: https://github.com/huggingface/datasets/actions/runs/9838873879/job/27159697027
```
error: `ruff <path>` has been removed. Use `ruff check <path>` instead.
```
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I_kwDODunzps6OqJAv
| 7,030
|
Add option to disable progress bar when reading a dataset ("Loading dataset from disk")
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[] | null | 2024-07-06T05:43:37Z
| 2024-07-13T14:35:59Z
| 2024-07-13T14:35:59Z
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NONE
| null | null | null | null |
### Feature request
Add an option in load_from_disk to disable the progress bar even if the number of files is larger than 16.
### Motivation
I am reading a lot of datasets that it creates lots of logs.
<img width="1432" alt="image" src="https://github.com/huggingface/datasets/assets/57996478/8d4bbf03-6b89-44b6-937c-932f01b4eb2a">
### Your contribution
Seems like an easy fix to make. I can create a PR if necessary.
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I_kwDODunzps6OiVwo
| 7,029
|
load_dataset on AWS lambda throws OSError(30, 'Read-only file system') error
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[] | null | 2024-07-04T19:15:16Z
| 2024-07-17T12:44:03Z
| null |
NONE
| null | null | null | null |
### Describe the bug
I'm using AWS lambda to run a python application. I run the `load_dataset` function with cache_dir="/tmp" and is still throws the OSError(30, 'Read-only file system') error. Is even updated all the HF envs to point to /tmp dir but the issue still persists. I can confirm that the I can write to /tmp directory.
### Steps to reproduce the bug
```python
d = load_dataset(
path=hugging_face_link,
split=split,
token=token,
cache_dir="/tmp/hugging_face_cache",
)
```
### Expected behavior
Everything written to the file system as part of the load_datasets function should be in the /tmp directory.
### Environment info
datasets version: 2.16.1
Platform: Linux-5.10.216-225.855.amzn2.x86_64-x86_64-with-glibc2.26
Python version: 3.11.9
huggingface_hub version: 0.19.4
PyArrow version: 16.1.0
Pandas version: 2.2.2
fsspec version: 2023.10.0
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|
Fix ci
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MEMBER
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...after last pr errors
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Missing line from previous pr
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Fix check_library_imports
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move it to after the `trust_remote_code` check
Note that it only affects local datasets that already exist on disk, not datasets loaded from HF directly
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PR_kwDODunzps50bSyD
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feat: support non streamable arrow file binary format
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CONTRIBUTOR
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Support Arrow files (`.arrow`) that are in non streamable binary file formats.
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I_kwDODunzps6Odqq6
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Streaming dataset not returning data
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NONE
| null | null | null | null |
### Describe the bug
I'm deciding to post here because I'm still not sure what the issue is, or if I am using IterableDatasets wrongly.
I'm following the guide on here https://huggingface.co/learn/cookbook/en/fine_tuning_code_llm_on_single_gpu pretty much to a tee and have verified that it works when I'm fine-tuning on the provided dataset.
However, I'm doing some data preprocessing steps (filtering out entries), when I try to swap out the dataset for mine, it fails to train. However, I eventually fixed this by simply setting `stream=False` in `load_dataset`.
Coud this be some sort of network / firewall issue I'm facing?
### Steps to reproduce the bug
I made a post with greater description about how I reproduced this problem before I found my workaround: https://discuss.huggingface.co/t/problem-with-custom-iterator-of-streaming-dataset-not-returning-anything/94551
Here is the problematic dataset snippet, which works when streaming=False (and with buffer keyword removed from shuffle)
```
commitpackft = load_dataset(
"chargoddard/commitpack-ft-instruct", split="train", streaming=True
).filter(lambda example: example["language"] == "Python")
def form_template(example):
"""Forms a template for each example following the alpaca format for CommitPack"""
example["content"] = (
"### Human: " + example["instruction"] + " " + example["input"] + " ### Assistant: " + example["output"]
)
return example
dataset = commitpackft.map(
form_template,
remove_columns=["id", "language", "license", "instruction", "input", "output"],
).shuffle(
seed=42, buffer_size=10000
) # remove everything since its all inside "content" now
validation_data = dataset.take(4000)
train_data = dataset.skip(4000)
```
The annoying part about this is that it only fails during training and I don't know when it will fail, except that it always fails during evaluation.
### Expected behavior
The expected behavior is that I should be able to get something from the iterator when called instead of getting nothing / stuck in a loop somewhere.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.31
- Python version: 3.11.7
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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PR_kwDODunzps50TDot
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Remove dead code for pyarrow < 15.0.0
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Remove dead code for pyarrow < 15.0.0.
Code is dead since the merge of:
- #6892
Fix #7022.
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There is dead code after we require pyarrow >= 15.0.0
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| 2024-07-03T09:17:36Z
| 2024-07-03T09:17:36Z
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MEMBER
| null | null | null | null |
There are code lines specific for pyarrow versions < 15.0.0.
However, we require pyarrow >= 15.0.0 since the merge of PR:
- #6892
Those code lines are now dead code and should be removed.
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PR_kwDODunzps50SlKR
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|
Fix casting list array to fixed size list
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Fix casting list array to fixed size list.
This bug was introduced in [datasets-2.17.0](https://github.com/huggingface/datasets/releases/tag/2.17.0) by PR: https://github.com/huggingface/datasets/pull/6283/files#diff-1cb2b66aa9311d729cfd83013dad56cf5afcda35b39dfd0bfe9c3813a049eab0R1899
- #6283
Fix #7020.
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I_kwDODunzps6OVRZ-
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|
Casting list array to fixed size list raises error
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MEMBER
| null | null | null | null |
When trying to cast a list array to fixed size list, an AttributeError is raised:
> AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'
Steps to reproduce the bug:
```python
import pyarrow as pa
from datasets.table import array_cast
arr = pa.array([[0, 1]])
array_cast(arr, pa.list_(pa.int64(), 2))
```
Stack trace:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-6cb90a1d8216> in <module>
3
4 arr = pa.array([[0, 1]])
----> 5 array_cast(arr, pa.list_(pa.int64(), 2))
~/huggingface/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
1802 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1803 else:
-> 1804 return func(array, *args, **kwargs)
1805
1806 return wrapper
~/huggingface/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str)
1920 else:
1921 array_values = array.values[
-> 1922 array.offset * pa_type.length : (array.offset + len(array)) * pa_type.length
1923 ]
1924 return pa.FixedSizeListArray.from_arrays(_c(array_values, pa_type.value_type), pa_type.list_size)
AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'
```
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PR_kwDODunzps50LMjW
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|
Support pyarrow large_list
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Allow Polars round trip by supporting pyarrow large list.
Fix #6834, fix #6984.
Supersede and close #4800, close #6835, close #6986.
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`load_dataset` fails to load dataset saved by `save_to_disk`
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[] | null | 2024-07-01T12:19:19Z
| 2025-05-24T05:21:12Z
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NONE
| null | null | null | null |
### Describe the bug
This code fails to load the dataset it just saved:
```python
from datasets import load_dataset
from transformers import AutoTokenizer
MODEL = "google-bert/bert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
dataset = load_dataset("yelp_review_full")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
tokenized_datasets.save_to_disk("dataset")
tokenized_datasets = load_dataset("dataset/") # raises
```
It raises `ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})}`.
I believe this bug is caused by the [logic that tries to infer dataset format](https://github.com/huggingface/datasets/blob/9af8dd3de7626183a9a9ec8973cebc672d690400/src/datasets/load.py#L556). It counts the most common file extension. However, a small dataset can fit in a single `.arrow` file and have two JSON metadata files, causing the format to be inferred as JSON:
```shell
$ ls -l dataset/test
-rw-r--r-- 1 sliedes sliedes 191498784 Jul 1 13:55 data-00000-of-00001.arrow
-rw-r--r-- 1 sliedes sliedes 1730 Jul 1 13:55 dataset_info.json
-rw-r--r-- 1 sliedes sliedes 249 Jul 1 13:55 state.json
```
### Steps to reproduce the bug
Execute the code above.
### Expected behavior
The dataset is loaded successfully.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.9.3-arch1-1-x86_64-with-glibc2.39
- Python version: 3.12.4
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
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I_kwDODunzps6ODbOQ
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`drop_duplicates` method
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[] | null | 2024-07-01T09:01:06Z
| 2024-07-20T06:51:58Z
| null |
NONE
| null | null | null | null |
### Feature request
`drop_duplicates` method for huggingface datasets (similiar in simplicity to the `pandas` one)
### Motivation
Ease of use
### Your contribution
I don't think i am good enough to help
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add split argument to Generator
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[] | null | 2024-07-01T08:09:25Z
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CONTRIBUTOR
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## Actual
When creating a multi-split dataset using generators like
```python
datasets.DatasetDict({
"val": datasets.Dataset.from_generator(
generator=generator_val,
features=features
),
"test": datasets.Dataset.from_generator(
generator=generator_test,
features=features,
)
})
```
It displays (for both test and val)
```
Generating train split
```
## Expected
I would like to be able to improve this behavior by doing
```python
datasets.DatasetDict({
"val": datasets.Dataset.from_generator(
generator=generator_val,
features=features,
split="val"
),
"test": datasets.Dataset.from_generator(
generator=generator_test,
features=features,
split="test"
)
})
```
It would display
```
Generating val split
```
and
```
Generating test split
```
## Proposal
Current PR is adding an explicit `split` argument and replace the implicit "train" split in the following classes/function :
* Generator
* from_generator
* AbstractDatasetInputStream
* GeneratorDatasetInputStream
Please share your feedbacks
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Skip faiss tests on Windows to avoid running CI for 360 minutes
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Skip faiss tests on Windows to avoid running CI for 360 minutes.
Fix #7013.
Revert once the underlying issue is fixed.
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MEMBER
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Faiss tests on Windows make the CI run indefinitely until maximum execution time (360 minutes) is reached.
See: https://github.com/huggingface/datasets/actions/runs/9712659783
```
test (integration, windows-latest, deps-minimum)
The job running on runner GitHub Actions 60 has exceeded the maximum execution time of 360 minutes.
test (integration, windows-latest, deps-latest)
The job running on runner GitHub Actions 238 has exceeded the maximum execution time of 360 minutes.
```
```
____________________________ tests/test_search.py _____________________________
[gw1] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw1' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
____________________________ tests/test_search.py _____________________________
[gw2] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw2' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
```
```
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw0] node down: Not properly terminated
[gw0] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw0
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw1] node down: Not properly terminated
[gw1] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw1
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw2] node down: Not properly terminated
[gw2] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
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```
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Fix #7010.
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Note that this can only be done once transformers releases the fix:
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MEMBER
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Support ruff 0.5.0 in CI.
Also revert:
- #7007
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PR_kwDODunzps5z2Q68
| 7,007
|
Fix CI by temporarily pinning ruff < 0.5.0
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| 2024-06-28T05:25:17Z
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As a hotfix for CI, temporarily pin ruff upper version < 0.5.0.
Fix #7006.
Revert once root cause is fixed.
|
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I_kwDODunzps6N1Yhn
| 7,006
|
CI is broken after ruff-0.5.0: E721
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MEMBER
| null | null | null | null |
After ruff-0.5.0 release (https://github.com/astral-sh/ruff/releases/tag/0.5.0), our CI is broken due to E721 rule.
See: https://github.com/huggingface/datasets/actions/runs/9707641618/job/26793170961?pr=6983
> src/datasets/features/features.py:844:12: E721 Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks
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EmptyDatasetError: The directory at /metadata.jsonl doesn't contain any data files
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NONE
| null | null | null | null |
### Describe the bug
while trying to load custom dataset from jsonl file, I get the error: "metadata.jsonl doesn't contain any data files"
### Steps to reproduce the bug
This is my [metadata_v2.jsonl](https://github.com/user-attachments/files/16016011/metadata_v2.json) file. I have this file in the folder with all images mentioned in that json(l) file.
Through below mentioned command I am trying to load_dataset so that I can upload it as mentioned here on the [official website](https://huggingface.co/docs/datasets/en/image_dataset#upload-dataset-to-the-hub).
````
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="path/to/jsonl/metadata.jsonl")
````
error:
````
EmptyDatasetError Traceback (most recent call last)
Cell In[18], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("imagefolder",
4 data_dir="path/to/jsonl/file/metadata.jsonl")
5 dataset[0]["objects"]
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2594, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2589 verification_mode = VerificationMode(
2590 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2591 )
2593 # Create a dataset builder
-> 2594 builder_instance = load_dataset_builder(
2595 path=path,
2596 name=name,
2597 data_dir=data_dir,
2598 data_files=data_files,
2599 cache_dir=cache_dir,
2600 features=features,
2601 download_config=download_config,
2602 download_mode=download_mode,
2603 revision=revision,
2604 token=token,
2605 storage_options=storage_options,
2606 trust_remote_code=trust_remote_code,
2607 _require_default_config_name=name is None,
2608 **config_kwargs,
2609 )
2611 # Return iterable dataset in case of streaming
2612 if streaming:
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2266, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
2264 download_config = download_config.copy() if download_config else DownloadConfig()
2265 download_config.storage_options.update(storage_options)
-> 2266 dataset_module = dataset_module_factory(
2267 path,
2268 revision=revision,
2269 download_config=download_config,
2270 download_mode=download_mode,
2271 data_dir=data_dir,
2272 data_files=data_files,
2273 cache_dir=cache_dir,
2274 trust_remote_code=trust_remote_code,
2275 _require_default_config_name=_require_default_config_name,
2276 _require_custom_configs=bool(config_kwargs),
2277 )
2278 # Get dataset builder class from the processing script
2279 builder_kwargs = dataset_module.builder_kwargs
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1805, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
1782 # We have several ways to get a dataset builder:
1783 #
1784 # - if path is the name of a packaged dataset module
(...)
1796
1797 # Try packaged
1798 if path in _PACKAGED_DATASETS_MODULES:
1799 return PackagedDatasetModuleFactory(
1800 path,
1801 data_dir=data_dir,
1802 data_files=data_files,
1803 download_config=download_config,
1804 download_mode=download_mode,
-> 1805 ).get_module()
1806 # Try locally
1807 elif path.endswith(filename):
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1140, in PackagedDatasetModuleFactory.get_module(self)
1135 def get_module(self) -> DatasetModule:
1136 base_path = Path(self.data_dir or "").expanduser().resolve().as_posix()
1137 patterns = (
1138 sanitize_patterns(self.data_files)
1139 if self.data_files is not None
-> 1140 else get_data_patterns(base_path, download_config=self.download_config)
1141 )
1142 data_files = DataFilesDict.from_patterns(
1143 patterns,
1144 download_config=self.download_config,
1145 base_path=base_path,
1146 )
1147 supports_metadata = self.name in _MODULE_SUPPORTS_METADATA
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/data_files.py:503, in get_data_patterns(base_path, download_config)
501 return _get_data_files_patterns(resolver)
502 except FileNotFoundError:
--> 503 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
EmptyDatasetError: The directory at path/to/jsonl/file/metadata.jsonl doesn't contain any data files`
```
### Expected behavior
It should be able load the whole file in a format of "dataset" inside the dataset variable. But it gives error "The directory at "path/to/jsonl/metadata.jsonl" doesn't contain any data files."
### Environment info
I am using conda environment.
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"events_url": "https://api.github.com/users/Aki1991/events{/privacy}",
"followers_url": "https://api.github.com/users/Aki1991/followers",
"following_url": "https://api.github.com/users/Aki1991/following{/other_user}",
"gists_url": "https://api.github.com/users/Aki1991/gists{/gist_id}",
"gravatar_id": "",
"html_url": "https://github.com/Aki1991",
"id": 117731544,
"login": "Aki1991",
"node_id": "U_kgDOBwRw2A",
"organizations_url": "https://api.github.com/users/Aki1991/orgs",
"received_events_url": "https://api.github.com/users/Aki1991/received_events",
"repos_url": "https://api.github.com/users/Aki1991/repos",
"site_admin": false,
"starred_url": "https://api.github.com/users/Aki1991/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/Aki1991/subscriptions",
"type": "User",
"url": "https://api.github.com/users/Aki1991",
"user_view_type": "public"
}
|
{
"+1": 0,
"-1": 0,
"confused": 0,
"eyes": 0,
"heart": 0,
"hooray": 0,
"laugh": 0,
"rocket": 0,
"total_count": 0,
"url": "https://api.github.com/repos/huggingface/datasets/issues/7005/reactions"
}
|
https://api.github.com/repos/huggingface/datasets/issues/7005/timeline
| null |
completed
|
{
"completed": 0,
"percent_completed": 0,
"total": 0
}
| false
|
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