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I_kwDODunzps6xljLR
| 7,504
|
BuilderConfig ParquetConfig(...) doesn't have a 'use_auth_token' key.
|
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[
"I encountered the same error, have you resolved it?",
"Hi ! `use_auth_token` has been deprecated and removed some time ago. You should use `token` instead in `load_dataset()`",
"Hi @lhoestq, I'd like to take this up.\n\nAs discussed in #7504, the issue arises when `use_auth_token` is passed to `load_dataset`, which forwards it to the config's `__init__`, where it's no longer a valid key.\n\nTo address this, I’ll intercept and strip `use_auth_token` inside `load_dataset()` (similar to how we handle `trust_remote_code`). A warning will be logged, and users will be encouraged to use `token` instead.\n\nThis avoids breaking older scripts that still use `use_auth_token`."
] | 2025-04-08T10:55:03
| 2025-06-28T09:18:09
| null |
NONE
| null | null | null | null |
### Describe the bug
Trying to run the following fine-tuning script (based on this page [here](https://github.com/huggingface/instruction-tuned-sd)):
```
! accelerate launch /content/instruction-tuned-sd/finetune_instruct_pix2pix.py \
--pretrained_model_name_or_path=${MODEL_ID} \
--dataset_name=${DATASET_NAME} \
--use_ema \
--enable_xformers_memory_efficient_attention \
--resolution=512 --random_flip \
--train_batch_size=2 --gradient_accumulation_steps=4 --gradient_checkpointing \
--max_train_steps=500 \
--checkpointing_steps=25 --checkpoints_total_limit=1 \
--learning_rate=5e-05 --max_grad_norm=1 --lr_warmup_steps=20 \
--conditioning_dropout_prob=0.1 \
--mixed_precision=fp16 \
--seed=42 \
--output_dir=${OUTPUT_DIR} \
--original_image_column=before \
--edit_prompt=prompt \
--edited_image=after
```
but I keep getting the following error:
```
Traceback (most recent call last):
File "/content/instruction-tuned-sd/finetune_instruct_pix2pix.py", line 1137, in <module>
main()
File "/content/instruction-tuned-sd/finetune_instruct_pix2pix.py", line 652, in main
dataset = load_dataset(
^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/load.py", line 2129, in load_dataset
builder_instance = load_dataset_builder(
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/load.py", line 1886, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 342, in __init__
self.config, self.config_id = self._create_builder_config(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 590, in _create_builder_config
raise ValueError(f"BuilderConfig {builder_config} doesn't have a '{key}' key.")
ValueError: BuilderConfig ParquetConfig(name='default', version=0.0.0, data_dir=None, data_files={'train': ['data/train-*']}, description=None, batch_size=None, columns=None, features=None, filters=None) doesn't have a 'use_auth_token' key.
Traceback (most recent call last):
File "/usr/local/bin/accelerate", line 10, in <module>
sys.exit(main())
^^^^^^
```
Any ideas? `datasets` version should be `3.2.0`.
### Steps to reproduce the bug
Just running the script above.
### Expected behavior
No errors
### Environment info
Python 3.11.11
datasets==3.2.0
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I_kwDODunzps6xiH7x
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Inconsistency between load_dataset and load_from_disk functionality
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[
"Hi ! you can find more info here: https://github.com/huggingface/datasets/issues/5044#issuecomment-1263714347\n\n> What's the recommended approach for this use case? Should I manually process my gsm8k-new dataset to make it compatible with load_dataset? Is there a standard way to convert between these formats?\n\nYou can use push_to_hub() or to_parquet() for example",
"Hi @zzzzzec & @lhoestq 👋\n\nThanks for raising and discussing this — I've submitted a patch that improves this exact scenario."
] | 2025-04-08T03:46:22
| 2025-06-28T08:51:16
| null |
NONE
| null | null | null | null |
## Issue Description
I've encountered confusion when using `load_dataset` and `load_from_disk` in the datasets library. Specifically, when working offline with the gsm8k dataset, I can load it using a local path:
```python
import datasets
ds = datasets.load_dataset('/root/xxx/datasets/gsm8k', 'main')
```
output:
```text
DatasetDict({
train: Dataset({
features: ['question', 'answer'],
num_rows: 7473
})
test: Dataset({
features: ['question', 'answer'],
num_rows: 1319
})
})
```
This works as expected. However, after processing the dataset (converting answer format from #### to \boxed{})
```python
import datasets
ds = datasets.load_dataset('/root/xxx/datasets/gsm8k', 'main')
ds_train = ds['train']
ds_test = ds['test']
import re
def convert(sample):
solution = sample['answer']
solution = re.sub(r'####\s*(\S+)', r'\\boxed{\1}', solution)
sample = {
'problem': sample['question'],
'solution': solution
}
return sample
ds_train = ds_train.map(convert, remove_columns=['question', 'answer'])
ds_test = ds_test.map(convert,remove_columns=['question', 'answer'])
```
I saved it using save_to_disk:
```python
from datasets.dataset_dict import DatasetDict
data_dict = DatasetDict({
'train': ds_train,
'test': ds_test
})
data_dict.save_to_disk('/root/xxx/datasets/gsm8k-new')
```
But now I can only load it using load_from_disk:
```python
new_ds = load_from_disk('/root/xxx/datasets/gsm8k-new')
```
output:
```text
DatasetDict({
train: Dataset({
features: ['problem', 'solution'],
num_rows: 7473
})
test: Dataset({
features: ['problem', 'solution'],
num_rows: 1319
})
})
```
Attempting to use load_dataset produces unexpected results:
```python
new_ds = load_dataset('/root/xxx/datasets/gsm8k-new')
```
output:
```text
DatasetDict({
train: Dataset({
features: ['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split'],
num_rows: 1
})
test: Dataset({
features: ['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split'],
num_rows: 1
})
})
```
Questions
1. Why is it designed such that after using `save_to_disk`, the dataset cannot be loaded with `load_dataset`? For small projects with limited code, it might be relatively easy to change all instances of `load_dataset` to `load_from_disk`. However, for complex frameworks like TRL or lighteval, diving into the framework code to change `load_dataset` to `load_from_disk` is extremely tedious and error-prone.
Additionally, `load_from_disk` cannot load datasets directly downloaded from the hub, which means that if you need to modify a dataset, you have to choose between using `load_from_disk` or `load_dataset`. This creates an unnecessary dichotomy in the API and complicates workflow when working with modified datasets.
2. What's the recommended approach for this use case? Should I manually process my gsm8k-new dataset to make it compatible with load_dataset? Is there a standard way to convert between these formats?
thanks~
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|
I_kwDODunzps6xeFb2
| 7,502
|
`load_dataset` of size 40GB creates a cache of >720GB
|
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[
"Hi ! Parquet is a compressed format. When you load a dataset, it uncompresses the Parquet data into Arrow data on your disk. That's why you can indeed end up with 720GB of uncompressed data on disk. The uncompression is needed to enable performant dataset objects (especially for random access).\n\nTo save some storage you can instead load the dataset with `streaming=True`. This way you get an `IterableDataset` that reads the Parquet data iteratively without ever writing to disk.\n\nPS: `ReadInstruction` might not be implemented for `streaming=True`, if it's the case you can use `ds.take()` and `ds.skip()` instead",
"Hi @lhoestq, thanks a lot for your answer. This makes perfect sense. I will try using the streaming mode. Closing the issue."
] | 2025-04-07T16:52:34
| 2025-04-15T15:22:12
| 2025-04-15T15:22:11
|
NONE
| null | null | null | null |
Hi there,
I am trying to load a dataset from the Hugging Face Hub and split it into train and validation splits. Somehow, when I try to do it with `load_dataset`, it exhausts my disk quota. So, I tried manually downloading the parquet files from the hub and loading them as follows:
```python
ds = DatasetDict(
{
"train": load_dataset(
"parquet",
data_dir=f"{local_dir}/{tok}",
cache_dir=cache_dir,
num_proc=min(12, os.cpu_count()), # type: ignore
split=ReadInstruction("train", from_=0, to=NUM_TRAIN, unit="abs"), # type: ignore
),
"validation": load_dataset(
"parquet",
data_dir=f"{local_dir}/{tok}",
cache_dir=cache_dir,
num_proc=min(12, os.cpu_count()), # type: ignore
split=ReadInstruction("train", from_=NUM_TRAIN, unit="abs"), # type: ignore
)
}
)
```
which still strangely creates 720GB of cache. In addition, if I remove the raw parquet file folder (`f"{local_dir}/{tok}"` in this example), I am not able to load anything. So, I am left wondering what this cache is doing. Am I missing something? Is there a solution to this problem?
Thanks a lot in advance for your help!
A related issue: https://github.com/huggingface/transformers/issues/10204#issue-809007443.
---
Python: 3.11.11
datasets: 3.5.0
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I_kwDODunzps6xbSh2
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|
Nested Feature raises ArrowNotImplementedError: Unsupported cast using function cast_struct
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[
"Solved by the default `load_dataset(features)` parameters. Do not use `Sequence` for the `list` in `list[any]` json schema, just simply use `[]`. For example, `\"b\": Sequence({...})` fails but `\"b\": [{...}]` works fine."
] | 2025-04-07T12:35:39
| 2025-04-07T12:43:04
| 2025-04-07T12:43:03
|
NONE
| null | null | null | null |
### Describe the bug
`datasets.Features` seems to be unable to handle json file that contains fields of `list[dict]`.
### Steps to reproduce the bug
```json
// test.json
{"a": 1, "b": [{"c": 2, "d": 3}, {"c": 4, "d": 5}]}
{"a": 5, "b": [{"c": 7, "d": 8}, {"c": 9, "d": 10}]}
```
```python
import json
from datasets import Dataset, Features, Value, Sequence, load_dataset
annotation_feature = Features({
"a": Value("int32"),
"b": Sequence({
"c": Value("int32"),
"d": Value("int32"),
}),
})
annotation_dataset = load_dataset(
"json",
data_files="test.json",
features=annotation_feature
)
```
```
ArrowNotImplementedError: Unsupported cast from list<item: struct<c: int32, d: int32>> to struct using function cast_struct
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Cell In[46], line 11
2 from datasets import Dataset, Features, Value, Sequence, load_dataset
4 annotation_feature = Features({
5 "a": Value("int32"),
6 "b": Sequence({
(...) 9 }),
10 })
---> 11 annotation_dataset = load_dataset(
12 "json",
13 data_files="test.json",
14 features=annotation_feature
15 )
```
### Expected behavior
A `datasets.Datasets` instance should be initialized.
### Environment info
- `datasets` version: 3.5.0
- Platform: Linux-6.11.0-21-generic-x86_64-with-glibc2.39
- Python version: 3.11.11
- `huggingface_hub` version: 0.30.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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I_kwDODunzps6xUHxB
| 7,500
|
Make `with_format` correctly indicate that a `Dataset` is compatible with PyTorch's `Dataset` class
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[
"Does the torch `DataLoader` really require the dataset to be a subclass of `torch.utils.data.Dataset` ? Or is there a simpler type we could use ?\n\nPS: also note that a dataset without `with_format()` can also be used in a torch `DataLoader` . Calling `with_format(\"torch\")` simply makes the output of the dataset torch Tensors in an efficient way."
] | 2025-04-06T09:56:09
| 2025-04-15T12:57:39
| null |
NONE
| null | null | null | null |
### Feature request
Currently `datasets` does not correctly indicate to the Python type-checker (e.g. `pyright` / `Pylance`) that the output of `with_format` is compatible with PyTorch's `Dataloader` since it does not indicate that the HuggingFace `Dataset` is compatible with the PyTorch `Dataset` class. It would be great if we could get the typing to work nicely.
### Motivation
To avoid casting types in our Python code.
### Your contribution
I would be happy to contribute a PR if this is something that may be accepted and could work with the current approach.
This doesn't have to be for just PyTorch, but I imagine that the same thing would be useful for `tensorflow` and such, but we only have a need for PyTorch at this stage.
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I_kwDODunzps6w-qzh
| 7,498
|
Extreme memory bandwidth.
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[] | 2025-04-03T11:09:08
| 2025-04-03T11:11:22
| null |
NONE
| null | null | null | null |
### Describe the bug
When I use hf datasets on 4 GPU with 40 workers I get some extreme memory bandwidth of constant ~3GB/s.
However, if I wrap the dataset in `IterableDataset`, this issue is gone and the data also loads way faster (4x faster training on 1 worker).
It seems like the workers don't share memory and basically duplicate the data 4x40.
### Steps to reproduce the bug
Trainer arguments:
```
dataloader_pin_memory=True,
dataloader_num_workers=40,
dataloader_prefetch_factor=2,
dataloader_persistent_workers=True,
```
Call trainer:
```
trainer = Trainer(
model=model,
args=train_args,
train_dataset=load_from_disk('..').with_fromat('torch'),
)
```
The dataset has 600GB and consists of 1225 files.
### Expected behavior
The optimal bandwidth should be 100MB/s to keep up with GPU.
### Environment info
Linux
Python 3.11
datasets==3.2.0
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|
How to convert videos to images?
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"Hi ! there is some documentation here on how to read video frames: https://huggingface.co/docs/datasets/video_load"
] | 2025-04-03T07:08:39
| 2025-04-15T12:35:15
| null |
NONE
| null | null | null | null |
### Feature request
Does someone know how to return the images from videos?
### Motivation
I am trying to use openpi(https://github.com/Physical-Intelligence/openpi) to finetune my Lerobot dataset(V2.0 and V2.1). I find that although the codedaset is v2.0, they are different. It seems like Lerobot V2.0 has two version, one is data include images infos and another one is separate to data and videos.
Does someone know how to return the images from videos?
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| 7,496
|
Json builder: Allow features to override problematic Arrow types
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"Hi ! It would be cool indeed, currently the JSON data are generally loaded here: \n\nhttps://github.com/huggingface/datasets/blob/90e5bf8a8599b625d6103ee5ac83b98269991141/src/datasets/packaged_modules/json/json.py#L137-L140\n\nMaybe we can pass a Arrow `schema` to avoid errors ?"
] | 2025-04-02T19:27:16
| 2025-04-15T13:06:09
| null |
NONE
| null | null | null | null |
### Feature request
In the JSON builder, use explicitly requested feature types before or while converting to Arrow.
### Motivation
Working with JSON datasets is really hard because of Arrow. At the very least, it seems like it should be possible to work-around these problems by explicitly setting problematic columns's types. But it seems like this is not possible because the features are only used *after* converting to arrow.
Here's a simple example where the Arrow error could potentially be avoided by converting the column to a string: https://colab.research.google.com/drive/16QHRdbUwKSrpwVfGwu8V8AHr8v2dv0dt?usp=sharing
### Your contribution
Maybe with some guidance. I'm not very familiar with arrow or pandas.
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| 7,495
|
Columns in the dataset obtained though load_dataset do not correspond to the one in the dataset viewer since 3.4.0
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[
"Hi, the dataset viewer shows all the possible columns and their types, but `load_dataset()` iterates through all the columns that you defined. It seems that you only have one column (‘audio’) defined in your dataset because when I ran `print(ds.column_names)`, the only name I got was “audio”. You need to clearly define all the other features of the dataset as columns to enable your original code to work. Furthermore, you can run this code to print out all the features of your dataset: \n```python\nfrom datasets import load_dataset_builder\nds_builder = load_dataset_builder(\"BrunoHays/Accueil_UBS\")\nprint(ds_builder.info.features)\n```\n",
"@phoebecd \nGood catch, even in datasets<3.4.0, the only feature is \"audio\".\nThis datasets follows the [audio folder](https://huggingface.co/docs/datasets/en/audio_dataset#audiofolder) structure with metadata.csv.\nMaybe I missed something or there is a bug when having and audio_folder with a metadata file\n\nWhat do you think @lhoestq ?",
"I opened a PR to fix the issue :) https://huggingface.co/datasets/BrunoHays/Accueil_UBS/discussions/2\n\nWe expect the metadata file to be in the <split>/ folder now to allow one CSV metadata file per split. But in the PR I just added a manual configuration instead of moving the file and updating all the relative paths it contains."
] | 2025-04-02T17:01:11
| 2025-07-02T23:24:57
| 2025-07-02T23:24:57
|
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
I have noticed that on my dataset named [BrunoHays/Accueil_UBS](https://huggingface.co/datasets/BrunoHays/Accueil_UBS), since the version 3.4.0, every column except audio is missing when I load the dataset.
Interestingly, the dataset viewer still shows the correct columns
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("BrunoHays/Accueil_UBS", streaming=True)
print(next(iter(ds["test"])).keys())
```
With datasets >= 3.4.0:
-> dict_keys(['audio'])
With datasets == 3.3.2:
-> dict_keys(['audio', 'id', 'speaker', 'sentence', 'raw_sentence', 'start_timestamp', 'end_timestamp', 'overlap'])
### Expected behavior
All the columns should be present
### Environment info
- `datasets` version: 3.3.2
- Platform: macOS-14.6.1-x86_64-i386-64bit
- Python version: 3.10.15
- `huggingface_hub` version: 0.30.1
- PyArrow version: 16.1.0
- Pandas version: 1.5.3
- `fsspec` version: 2023.10.0
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Broken links in pdf loading documentation
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[
"thanks for reporting ! I fixed the links, the docs will be updated in the next release"
] | 2025-04-02T06:45:22
| 2025-04-15T13:36:25
| 2025-04-15T13:36:04
|
NONE
| null | null | null | null |
### Describe the bug
Hi, just a couple of small issues I ran into while reading the docs for [loading pdf data](https://huggingface.co/docs/datasets/main/en/document_load):
1. The link for the [`Create a pdf dataset`](https://huggingface.co/docs/datasets/main/en/document_load#pdffolder) points to https://huggingface.co/docs/datasets/main/en/pdf_dataset instead of https://huggingface.co/docs/datasets/main/en/document_dataset and hence gives a 404 error.
2. At the top of the page, it's mentioned that to work with pdf datasets we need to have the `pdfplumber` package installed but the link to its installation guide points to `pytorch/vision` [installation instructions](https://github.com/pytorch/vision#installation) instead of `pdfplumber`'s [guide](https://github.com/jsvine/pdfplumber#installation)
I love the work on enabling pdf dataset support and these small tweaks would help everyone navigate the docs better. Thanks!
### Steps to reproduce the bug
The issue is on the [Load Document Data](https://huggingface.co/docs/datasets/main/en/document_load) page of the datasets docs.
### Expected behavior
1. For solving the first issue, I went through the [source .mdx code](https://github.com/huggingface/datasets/blob/main/docs/source/document_load.mdx?plain=1#L188) of the datasets docs and found that the link is pointing to `./pdf_dataset` instead of `./document_dataset`
2. For the second issue, I went through the [source .mdx code](https://github.com/huggingface/datasets/blob/main/docs/source/document_load.mdx?plain=1#L13) of the datasets docs and found that the link is `pytorch/vision` [installation instructions](https://github.com/pytorch/vision#installation) instead of `pdfplumber`'s [guide](https://github.com/jsvine/pdfplumber#installation)
Just replacing these two links should fix the bugs
### Environment info
datasets v3.5.0 (main at the time of writing)
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I_kwDODunzps6wq29b
| 7,493
|
push_to_hub does not upload videos
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[
"Hi ! the `Video` type is still experimental, and in particular `push_to_hub` doesn't upload videos at the moment (only the paths).\n\nThere is an open question to either upload the videos inside the Parquet files, or rather have them as separate files (which is great to enable remote seeking/streaming)",
"im having the same issue (btw i mistook this to be xet error https://huggingface.co/spaces/xet-team/README/discussions/4 )\n\n@jsulz suggested me to use `upload_folder` but it exceeds hf limits (>10k files per folder and >100k files in total)\n\nfrom my reading of the docs, in my case i have to save as either parquet or webdataset and then use `upload_folder`\n\ni tried `ds.to_parquet(\"...\")` but the parquet file also doesnt contain video, as of `datasets` v4.0\n\nso i think the only workaround for my case is webdataset",
"in that case you can create a VideoFolder dataset instead, see the docs at http://huggingface.co/docs/datasets/video_dataset#videofolder"
] | 2025-04-01T17:00:20
| 2025-09-02T10:32:36
| null |
NONE
| null | null | null | null |
### Describe the bug
Hello,
I would like to upload a video dataset (some .mp4 files and some segments within them), i.e. rows correspond to subsequences from videos. Videos might be referenced by several rows.
I created a dataset locally and it references the videos and the video readers can read them correctly. I use push_to_hub() to upload the dataset to the hub.
Expectation: A user uses `load_dataset` and can load the videos.
However, the videos seem to be just referenced via paths on the computer and not uploaded to the hub. Therefore a target user cannot load the videos in the dataset.
### Steps to reproduce the bug
1. create a video dataset with paths e.g. { ["videos"]: [path1, path2, ...]}
2. dataset.push_to_hub
3. on a different computer (or same pc if relative paths are used in a different folder):
```
dataset = load_dataset("siplab/egosim", split="train")
video = dataset[0]["video_head"]
```
3. will fail
### Expected behavior
Expectation: A user uses `load_dataset` and can load the videos.
### Environment info
datasets 3.1.0
Python 3.8.18
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`shared_datadir` fixture is missing
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[
"OK I was missing the `pytest-datadir` package. Sorry for the noise!"
] | 2025-03-27T18:17:12
| 2025-03-27T19:49:11
| 2025-03-27T19:49:10
|
NONE
| null | null | null | null |
### Describe the bug
Running the tests for the latest release fails due to missing `shared_datadir` fixture.
### Steps to reproduce the bug
Running `pytest` while building a package for Arch Linux leads to these errors:
```
==================================== ERRORS ====================================
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>1] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>2] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>3] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>4] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>5] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>6] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_______________ ERROR at setup of test_dataset_with_pdf_feature ________________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 34
@require_pdfplumber
def test_dataset_with_pdf_feature(shared_datadir):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:34
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>0] _________
[gw46] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
```
### Expected behavior
All fixtures used in tests should be available.
### Environment info
Arch Linux build system, building the [python-datasets](https://gitlab.archlinux.org/archlinux/packaging/packages/python-datasets) package.
There are actually [many deselected tests](https://gitlab.archlinux.org/archlinux/packaging/packages/python-datasets/-/blob/6f97957f0c326cc7b3da6b7f12326305bcaef374/PKGBUILD#L66-148) which were failing on previous releases, but these errors popped up in 3.5.0.
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https://api.github.com/repos/huggingface/datasets/issues/7481
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| 2,950,692,971
|
I_kwDODunzps6v4ABr
| 7,481
|
deal with python `10_000` legal number in slice syntax
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[
"should be an easy fix, I opened a PR"
] | 2025-03-26T20:10:54
| 2025-03-28T16:20:44
| 2025-03-28T16:20:44
|
NONE
| null | null | null | null |
### Feature request
```
In [6]: ds = datasets.load_dataset("HuggingFaceH4/ultrachat_200k", split="train_sft[:1000]")
In [7]: ds = datasets.load_dataset("HuggingFaceH4/ultrachat_200k", split="train_sft[:1_000]")
[dozens of frames skipped]
File /usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py:444, in _str_to_read_instruction(spec)
442 res = _SUB_SPEC_RE.match(spec)
443 if not res:
--> 444 raise ValueError(f"Unrecognized instruction format: {spec}")
ValueError: Unrecognized instruction format: train_sft[:1_000]
```
It took me a while to understand what the problem was. But apparently `pyarrow` doesn't allow python numbers that may include `_` as in `1_000`. The `_` aids readability since `10_000_000` vs `10000000` is obviously easier to grasp of what the actual number is.
Feature request:
ideally `datasets` being a python module will do the right thing and convert python numbers into whatever pyarrow supports - in this case stripping `_`s.
Second best it'd err and tell the user that using numbers with `_` in split slices is not acceptible, so that the user won't have to deal with a huge pyarrow assert they know nothing about.
Thank you!
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| 2,950,315,214
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I_kwDODunzps6v2jzO
| 7,480
|
HF_DATASETS_CACHE ignored?
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[
"FWIW, it does eventually write to /tmp/roller/datasets when generating the final version.",
"Hey, I’d love to work on this issue but I am a beginner, can I work it with you?",
"Hi @lhoestq,\nI'd like to look into this issue but I'm still learning. Could you share any quick pointers on the HF_DATASETS_CACHE behavior here? Thanks!",
"Hi ! `HF_DATASETS_CACHE` is only for the cache files of the `datasets` library, not for the `huggingface_hub` cache for files downloaded from the Hugging Face Hub.\n\nYou should either specify `HF_HOME` (parent cache path for everything HF) or both `HF_DATASETS_CACHE` and `HF_HUB_CACHE`",
"\n\nThanks for clarifying, @lhoestq! To make sure I’ve got it right:\n\n1. **HF_DATASETS_CACHE** only controls where the **datasets** library writes its own cache files (e.g. processed shards, Arrow files, etc.).\n2. Anything downloaded via **huggingface_hub** (models, tokenizers, raw files) still goes into the Hub cache (by default `~/.cache/huggingface/hub`), unless you set **HF_HUB_CACHE** or the parent **HF_HOME**.\n\nSo if you want everything off NFS and onto local disk you have two options:\n\n- **Set both** \n ```bash\n export HF_DATASETS_CACHE=/tmp/roller/datasets \n export HF_HUB_CACHE=/tmp/roller/hub\n ```\n- **Or set** \n ```bash\n export HF_HOME=/tmp/roller\n ```\n which will apply to both subdirectories.\n\nIs that correct? And would it make sense to add a note to the docs clarifying the distinction (or even support S3 for the Hub cache in the future)? I’m happy to draft a small docs PR if that would help.",
"Hi, yes that's correct, thanks for the clarification ! A note in the docs would be welcome, thanks",
"I'm downloading a HF model through vLLM, setting both HF_HUB_CACHE and HF_HOME, and still my `hub` and `xet` end up in $HOME. Anyone an idea what gives?",
"If you set HF_HOME you should be fine\n\nEven HF_XET_CACHE is placed in HF_HOME by default according to the [docs](https://huggingface.co/docs/huggingface_hub/en/package_reference/environment_variables#hfxetcache):\n\n> **HF_XET_CACHE**\n> To configure where Xet chunks (byte ranges from files managed by Xet backend) are cached locally.\n> Defaults to \"$HF_HOME/xet\" (e.g. \"~/.cache/huggingface/xet\" by default).\n\nCan you check you don't have HF_XET_CACHE or HF_HUB_CACHE that would override HF_HOME ?"
] | 2025-03-26T17:19:34
| 2025-10-23T15:59:18
| null |
NONE
| null | null | null | null |
### Describe the bug
I'm struggling to get things to respect HF_DATASETS_CACHE.
Rationale: I'm on a system that uses NFS for homedir, so downloading to NFS is expensive, slow, and wastes valuable quota compared to local disk. Instead, it seems to rely mostly on HF_HUB_CACHE.
Current version: 3.2.1dev. In the process of testing 3.4.0
### Steps to reproduce the bug
[Currently writing using datasets 3.2.1dev. Will follow up with 3.4.0 results]
dump.py:
```python
from datasets import load_dataset
dataset = load_dataset("HuggingFaceFW/fineweb", name="sample-100BT", split="train")
```
Repro steps
```bash
# ensure no cache
$ mv ~/.cache/huggingface ~/.cache/huggingface.bak
$ export HF_DATASETS_CACHE=/tmp/roller/datasets
$ rm -rf ${HF_DATASETS_CACHE}
$ env | grep HF | grep -v TOKEN
HF_DATASETS_CACHE=/tmp/roller/datasets
$ python dump.py
# (omitted for brevity)
# (while downloading)
$ du -hcs ~/.cache/huggingface/hub
18G hub
18G total
# (after downloading)
$ du -hcs ~/.cache/huggingface/hub
```
It's a shame because datasets supports s3 (which I could really use right now) but hub does not.
### Expected behavior
* ~/.cache/huggingface/hub stays empty
* /tmp/roller/datasets becomes full of stuff
### Environment info
[Currently writing using datasets 3.2.1dev. Will follow up with 3.4.0 results]
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I_kwDODunzps6v2QUE
| 7,479
|
Features.from_arrow_schema is destructive
|
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[] | 2025-03-26T16:46:43
| 2025-03-26T16:46:58
| null |
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
I came across this, perhaps niche, bug where `Features` does not/cannot account for pyarrow's `nullable=False` option in Fields. Interestingly, I found that in regular "flat" fields this does not necessarily lead to conflicts, but when a non-nullable field is in a struct, an incompatibility arises.
It's not easy to explain in words, so the minimal example below should help I hope.
Note that I suggest a solution in the comments in the code, simply allowing `Dataset.to_parquet` to allow for a `schema` argument which, when provided, will override the default ds.features.arrow_schema.
### Steps to reproduce the bug
```python
import os
from datasets import Dataset, Features
import pyarrow as pa
import pyarrow.parquet as pq
# HF datasets is destructive when you call Features.from_arrow_schema(schema) on a schema
# because it will not account for nullable and non-nullable fields in structs (it will always allow nullable)
# Reloading the same dataset with the original schema will raise an error because the schema is not the same anymore
non_nullable_schema = pa.schema(
[
pa.field("text", pa.string(), nullable=False),
pa.field("meta",
pa.struct(
[
pa.field("date", pa.list_(pa.string()), nullable=False),
],
),
),
]
)
print("ORIGINAL SCHEMA")
print(non_nullable_schema)
print()
feats = Features.from_arrow_schema(non_nullable_schema)
print("FEATUR-IZED SCHEMA (nullable-restrictions are gone)")
print(feats.arrow_schema)
print()
ds = Dataset.from_dict(
{
"text": ["a", "b", "c"],
"meta": [{"date": ["2021-01-01"]}, {"date": ["2021-01-02"]}, {"date": ["2021-01-03"]}],
},
features=feats,
)
fname = "tmp.parquet"
# This is not possible: TypeError: pyarrow.parquet.core.ParquetWriter() got multiple values for keyword argument 'schema'
# Though I believe this would be the easiest fix: allow schema to be passed to to_parquet and overwrite the schema in the dataset
# ds.to_parquet(fname, schema=non_nullable_schema)
ds.to_parquet(fname)
try:
_ = pq.read_table(fname, schema=non_nullable_schema)
finally:
os.unlink(fname)
```
### Expected behavior
- Non-destructive behavior when converting an arrow schema to Features; or
- the ability to override the default arrow schema with a custom one
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.14.0-427.20.1.el9_4.x86_64-x86_64-with-glibc2.34
- Python version: 3.11.10
- `huggingface_hub` version: 0.27.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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| 2,947,169,460
|
I_kwDODunzps6vqjy0
| 7,477
|
What is the canonical way to compress a Dataset?
|
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[
"I saw this post by @lhoestq: https://discuss.huggingface.co/t/increased-arrow-table-size-by-factor-of-2/26561/4 suggesting that there is at least some internal code for writing sharded parquet datasets non-concurrently. This appears to be that code: https://github.com/huggingface/datasets/blob/94ccd1b4fada8a92cea96dc8df4e915041d695b6/src/datasets/arrow_dataset.py#L5380-L5397\n\nIs there any fundamental reason (e.g. race conditions) that this kind of operation couldn't exist as a utility or method on a `Dataset` with a `num_proc` argument? I am not seeing any other issues explicitly for that ask. \n",
"We simply haven't implemented a method to save as sharded parquet locally yet ^^'\n\nRight now the only sharded parquet export method is `push_to_hub()` which writes to HF. But we can have a local one as well. \n\nIn the meantime the easiest way to export as sharded parquet locally is to `.shard()` and `.to_parquet()` (see code from my comment [here](https://github.com/huggingface/datasets/issues/7047#issuecomment-2233163406))",
"> In the meantime the easiest way to export as sharded parquet locally is to .shard() and .to_parquet()\n\nMakes sense, BUT how can it be done concurrently? I could of course use multiprocessing myself or a dozen other libraries for parallelizing single-node/local operations like that.\n\nWhat I'm asking though is, what is the way to do this that is most canonical for `datasets` specifically? I.e. what is least likely to causing pickling or other issues because it is used frequently internally by `datasets` and already likely tests for a lot of library-native edge-cases?",
"Everything in `datasets` is picklable :) and even better: since the data are memory mapped from disk, pickling in one process and unpickling in another doesn't do any copy - it instantaneously reloads the memory map.\n\nSo feel free to use the library you prefer to parallelize your operations.\n\n(it's another story in distributed setups though, because in that case you either need to copy and send the data or setup a distributed filesystem)"
] | 2025-03-25T16:47:51
| 2025-04-03T09:13:11
| null |
NONE
| null | null | null | null |
Given that Arrow is the preferred backend for a Dataset, what is a user supposed to do if they want concurrent reads, concurrent writes AND on-disk compression for a larger dataset?
Parquet would be the obvious answer except that there is no native support for writing sharded, parquet datasets concurrently [[1](https://github.com/huggingface/datasets/issues/7047)].
Am I missing something?
And if so, why is this not the standard/default way that `Dataset`'s work as they do in Xarray, Ray Data, Composer, etc.?
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I_kwDODunzps6voiq6
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|
IterableDataset's state_dict shard_example_idx is always equal to the number of samples in a shard
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[
"Hey, I’d love to work on this issue but I am a beginner, can I work it with you?",
"Hello. I'm sorry but I don't have much time to get in the details for now.\nHave you managed to reproduce the issue with the code provided ?\nIf you want to work on it, you can self-assign and ask @lhoestq for directions",
"Hi Bruno, I am trying to reproduce it this later in this week and let you know what I found.",
"#self-assign",
"Good catch, I tried and if the dataset is bigger (e.g. `range(9999)`) it returns `\"shard_example_idx\": 1000` with is the `config.DEFAULT_MAX_BATCH_SIZE`\n\nhttps://github.com/huggingface/datasets/blob/94ccd1b4fada8a92cea96dc8df4e915041d695b6/src/datasets/arrow_dataset.py#L5313-L5317\n\nIt looks like the state_dict is incorrect in that case, it should account for this and use the `RebatchedArrowExamplesIterable` which buffers the batch of 1000 rows and counts the iteration within the batch in the state_dict",
"\nHello @lhoestq,\n\nI’ve been debugging the `IterableDataset.state_dict()` behavior and applied a patch to `ArrowExamplesIterable._iter_arrow()` in an attempt to fix the issue described in #7475—specifically, that `shard_example_idx` always equals the number of samples in the shard, even if only a few examples have been consumed.\n\n### What I Tried\n\nI updated `_iter_arrow` to slice off already-consumed rows and increment the state only by the number of actual examples yielded, like this:\n\n```python\nclass ArrowExamplesIterable(_BaseExamplesIterable):\n # ... __init__ and _init_state_dict as before ...\n\n def _iter_arrow(self):\n shard_idx_start = self._state_dict[\"shard_idx\"] if self._state_dict else 0\n\n for gen_kwargs in islice(\n _split_gen_kwargs(self.kwargs, max_num_jobs=self.num_shards),\n shard_idx_start, None\n ):\n shard_example_idx_start = self._state_dict[\"shard_example_idx\"] if self._state_dict else 0\n shard_example_idx = 0\n\n for key, pa_table in self.generate_tables_fn(**gen_kwargs):\n num_rows = len(pa_table)\n next_idx = shard_example_idx + num_rows\n\n if next_idx <= shard_example_idx_start:\n shard_example_idx = next_idx\n continue\n\n offset = max(0, shard_example_idx_start - shard_example_idx)\n sliced_table = pa_table.slice(offset)\n\n if self._state_dict:\n self._state_dict[\"shard_example_idx\"] += len(sliced_table)\n\n yield key, sliced_table\n shard_example_idx = next_idx\n\n if self._state_dict:\n self._state_dict[\"shard_idx\"] += 1\n self._state_dict[\"shard_example_idx\"] = 0\n```\n\nI verified that the updated code was being used, and I added debug prints to confirm the table slicing and counter updates.\n\n### The Issue Still Exists\n\nDespite the changes, the behavior remains the same. Running this minimal repro:\n\n```python\nds = Dataset.from_dict({\"a\": range(6)}).to_iterable_dataset(num_shards=1)\nfor idx, example in enumerate(ds):\n print(example)\n if idx == 2:\n print(\"checkpoint\")\n print(ds.state_dict())\n break\n```\n\nStill outputs:\n\n```bash\n{'a': 0}\n{'a': 1}\n{'a': 2}\ncheckpoint\n{'examples_iterable': {'shard_idx': 0, 'shard_example_idx': 6, 'type': 'ArrowExamplesIterable'}, 'epoch': 0}\n```\n\nEven though only 3 examples were consumed, `shard_example_idx` jumps to 6.\n\n### Questions\n\n- Could there be another place (e.g., in `__iter__`, `RebatchedArrowExamplesIterable`, or the `IterableDataset` wrapper) that's still using the old logic and overriding the state?\n- Is there a better location to intercept and count yielded examples?\n- Would you recommend tracking a new `true_example_idx` to avoid modifying existing behavior?\n\nLet me know your thoughts—happy to iterate further and submit a PR once we align on the right approach. Thanks again for your help and feedback!",
"I found a fix using RebatchedArrowExamplesIterable, let me know if it's all good for you now",
"Hi @lhoestq, thanks for the quick fix and for referencing RebatchedArrowExamplesIterable! 🙌\n\nI just tested your patch locally and can confirm that shard_example_idx is now tracking correctly when only a subset of examples is consumed. This resolves the issue I was seeing in #7475.\n\nReally appreciate the guidance earlier on where to look—it was a great learning opportunity. If there are other parts of the IterableDataset internals that could use cleanup or testing, I’d be happy to help."
] | 2025-03-25T13:58:07
| 2025-05-06T14:22:19
| 2025-05-06T14:05:07
|
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
I've noticed a strange behaviour with Iterable state_dict: the value of shard_example_idx is always equal to the amount of samples in a shard.
### Steps to reproduce the bug
I am reusing the example from the doc
```python
from datasets import Dataset
ds = Dataset.from_dict({"a": range(6)}).to_iterable_dataset(num_shards=1)
state_dict = None
# Iterate through the dataset and print examples
for idx, example in enumerate(ds):
print(example)
if idx == 2:
state_dict = ds.state_dict()
print("checkpoint")
break
print(state_dict)
```
Returns:
```
{'a': 0}
{'a': 1}
checkpoint
{'examples_iterable': {'shard_idx': 0, 'shard_example_idx': 6, 'type': 'ArrowExamplesIterable'}, 'epoch': 0}
```
### Expected behavior
shard_example_idx should be 2 instead of 6
If we run with num_shards=2, then shard_example_idx is 3 instead of 2 and so on.
### Environment info
- `datasets` version: 3.4.1
- Platform: macOS-14.6.1-arm64-arm-64bit
- Python version: 3.12.9
- `huggingface_hub` version: 0.29.3
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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Webdataset data format problem
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[
"I was able to work around it"
] | 2025-03-21T17:23:52
| 2025-03-21T19:19:58
| 2025-03-21T19:19:58
|
NONE
| null | null | null | null |
### Describe the bug
Please see https://huggingface.co/datasets/ejschwartz/idioms/discussions/1
Error code: FileFormatMismatchBetweenSplitsError
All three splits, train, test, and validation, use webdataset. But only the train split has more than one file. How can I force the other two splits to also be interpreted as being the webdataset format? (I don't think there is currently a way, but happy to be told that I am wrong.)
### Steps to reproduce the bug
```
import datasets
datasets.load_dataset("ejschwartz/idioms")
### Expected behavior
The dataset loads. Alternatively, there is a YAML syntax for manually specifying the format.
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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I_kwDODunzps6vGFRo
| 7,472
|
Label casting during `map` process is canceled after the `map` process
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[
"Hi ! By default `map()` tries to keep the types of each column of the dataset, so here it reuses the int type since all your float values can be converted to integers. But I agree it would be nice to store float values as float values and don't try to reuse the same type in this case.\n\nIn the meantime, you can either store the float values in a new column, or pass the output `features=` manually to `map()`",
"Hi @lhoestq \n\nThank you for the answer & suggestion!\n\nCan we add some flag to `map()` function like `reuses_original_type=True` and skip reusing the original type when it's False?\n\nLet me know if it sounds like a reasonable solution. I am happy to submit a PR for this.",
"In general we try to avoid adding new parameters when it's already possible to achieve the same results with existing parameters (here `features=`). But since it's not always convenient to know in advance the `features=` I'm open to contributions to adding this parameter yes",
"Thank you for sharing the context. Good to know that. \n\nI submitted a PR #7483. Could you review the PR?",
"Hi @lhoestq \n\nLet me know if there is something that I should add to [the PR](https://github.com/huggingface/datasets/pull/7483)!",
"Closing this issue as the PR #7483 was merged"
] | 2025-03-21T07:56:22
| 2025-04-10T05:11:15
| 2025-04-10T05:11:14
|
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
When preprocessing a multi-label dataset, I introduced a step to convert int labels to float labels as [BCEWithLogitsLoss](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) expects float labels and forward function of models in transformers package internally use `BCEWithLogitsLoss`
However, the casting was canceled after `.map` process and the label values still use int values, which leads to an error
```
File "/home/yoshitomo/anaconda3/envs/torchdistill/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 1711, in forward
loss = loss_fct(logits, labels)
File "/home/yoshitomo/anaconda3/envs/torchdistill/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/yoshitomo/anaconda3/envs/torchdistill/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/home/yoshitomo/anaconda3/envs/torchdistill/lib/python3.10/site-packages/torch/nn/modules/loss.py", line 819, in forward
return F.binary_cross_entropy_with_logits(
File "/home/yoshitomo/anaconda3/envs/torchdistill/lib/python3.10/site-packages/torch/nn/functional.py", line 3628, in binary_cross_entropy_with_logits
return torch.binary_cross_entropy_with_logits(
RuntimeError: result type Float can't be cast to the desired output type Long
```
This seems like happening only when the original labels are int values (see examples below)
### Steps to reproduce the bug
If the original dataset uses a list of int labels, it will cancel the int->float casting
```python
from datasets import Dataset
data = {
'text': ['text1', 'text2', 'text3', 'text4'],
'labels': [[0, 1, 2], [3], [3, 4], [3]]
}
dataset = Dataset.from_dict(data)
label_set = set([label for labels in data['labels'] for label in labels])
label2idx = {label: idx for idx, label in enumerate(sorted(label_set))}
def multi_labels_to_ids(labels):
ids = [0.0] * len(label2idx)
for label in labels:
ids[label2idx[label]] = 1.0
return ids
def preprocess(examples):
result = {'sentence': [[0, 3, 4] for _ in range(len(examples['labels']))]}
print('"labels" are int', examples['labels'])
result['labels'] = [multi_labels_to_ids(l) for l in examples['labels']]
print('"labels" were converted to multi-label format with float values', result['labels'])
return result
preprocessed_dataset = dataset.map(preprocess, batched=True, remove_columns=['labels', 'text'])
print(preprocessed_dataset[0]['labels'])
# Output: "[1, 1, 1, 0, 0]"
# Expected: "[1.0, 1.0, 1.0, 0.0, 0.0]"
```
If the original dataset uses non-int labels, it works as expected.
```python
from datasets import Dataset
data = {
'text': ['text1', 'text2', 'text3', 'text4'],
'labels': [['label1', 'label2', 'label3'], ['label4'], ['label4', 'label5'], ['label4']]
}
dataset = Dataset.from_dict(data)
label_set = set([label for labels in data['labels'] for label in labels])
label2idx = {label: idx for idx, label in enumerate(sorted(label_set))}
def multi_labels_to_ids(labels):
ids = [0.0] * len(label2idx)
for label in labels:
ids[label2idx[label]] = 1.0
return ids
def preprocess(examples):
result = {'sentence': [[0, 3, 4] for _ in range(len(examples['labels']))]}
print('"labels" are int', examples['labels'])
result['labels'] = [multi_labels_to_ids(l) for l in examples['labels']]
print('"labels" were converted to multi-label format with float values', result['labels'])
return result
preprocessed_dataset = dataset.map(preprocess, batched=True, remove_columns=['labels', 'text'])
print(preprocessed_dataset[0]['labels'])
# Output: "[1.0, 1.0, 1.0, 0.0, 0.0]"
# Expected: "[1.0, 1.0, 1.0, 0.0, 0.0]"
```
Note that the only difference between these two examples is
> 'labels': [[0, 1, 2], [3], [3, 4], [3]]
v.s
> 'labels': [['label1', 'label2', 'label3'], ['label4'], ['label4', 'label5'], ['label4']]
### Expected behavior
Even if the original dataset uses a list of int labels, the int->float casting during `.map` process should not be canceled as shown in the above example
### Environment info
OS Ubuntu 22.04 LTS
Python 3.10.11
datasets v3.4.1
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I_kwDODunzps6vFybV
| 7,471
|
Adding argument to `_get_data_files_patterns`
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[
"Hi ! The pattern can be specified in advance in YAML in the README.md of the dataset :)\n\nFor example\n\n```\n---\nconfigs:\n- config_name: default\n data_files:\n - split: train\n path: \"train/*\"\n - split: test\n path: \"test/*\"\n---\n```\n\nSee the docs at https://huggingface.co/docs/hub/en/datasets-manual-configuration",
"@lhoestq How can we choose in this case ? https://huggingface.co/datasets/datasets-examples/doc-image-5\n",
"choose what ? sorry I didn't get it ^^'"
] | 2025-03-21T07:17:53
| 2025-03-27T12:30:52
| 2025-03-26T07:26:27
|
NONE
| null | null | null | null |
### Feature request
How about adding if the user already know about the pattern?
https://github.com/huggingface/datasets/blob/a256b85cbc67aa3f0e75d32d6586afc507cf535b/src/datasets/data_files.py#L252
### Motivation
While using this load_dataset people might use 10M of images for the local files.
However, due to searching all the appropriate file pattern in fsspec, purely searching this pattern takes more than 10 hours (real use-case).
### Your contribution
Yeah I can make this happen if this seems valid. @lhoestq WDYT?
such like
```
def _get_data_files_patterns(pattern_resolver: Callable[[str], list[str]], patterns: PATTERNS) -> dict[str, list[str]]:
```
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Is it possible to shard a single-sharded IterableDataset?
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[
"Hi ! Maybe you can look for an option in your dataset to partition your data based on a deterministic filter ? For example each worker could stream the data based on `row.id % num_shards` or something like that ?",
"So the recommendation is to start out with multiple shards initially and re-sharding after is not expected to work? :(\n\nWould something like the following work? Some DiskCachingIterableDataset, where worker 0 streams from the datasource, but also writes to disk, and all of the other workers read from what worker 0 wrote? Then that would produce a stream with a deterministic order and we can subsample.",
"To be honest it would be cool to support native multiprocessing in `IterableDataset.map` so you can parallelize any specific processing step without having to rely on a torch Dataloader. What do you think ?\n\nrelated: https://github.com/huggingface/datasets/issues/7193 https://github.com/huggingface/datasets/issues/3444 \noriginal issue: https://github.com/huggingface/datasets/issues/2642\n\nAlternatively the DiskCachingIterableDataset idea works, just note that to make it work with a torch Dataloader with num_workers>0 you'll need:\n1. to make your own `torch.utils.data.IterableDataset` and have rank=0 stream the data and share them with the other workers (either via disk as suggested or IPC)\n2. take into account that`datasets.IterableDataset` will yield 0 examples for ranks with id>0 if there is only one shard, but in your case it's ok since you'd only stream from rank=0",
"Ohh that would be pretty cool!\n\nThanks for the suggestions, as there's no actionable items for this repo I'm going to close this issue now.",
"Another usecase for this resharding:\n\nIf we have a bunch of jsonl files, and we load it as an IterableDataset with multiple dataloader workers, each file gets naively assigned to a worker.\n\nIf the files were not carefully produced to be equally sized, eg if the very last file is significantly shorter, containing just a few examples, and it gets assigned onto a dataloader worker by itself, then the examples in that file will be significantly oversampled.\n\nIt would be nice if datasets had an internal way to rebalance this without requiring offline reprocessing of the data files",
"> Another usecase for this resharding:\n> \n> If we have a bunch of jsonl files, and we load it as an IterableDataset with multiple dataloader workers, each file gets naively assigned to a worker.\n> \n> If the files were not carefully produced to be equally sized, eg if the very last file is significantly shorter, containing just a few examples, and it gets assigned onto a dataloader worker by itself, then the examples in that file will be significantly oversampled.\n> \n> It would be nice if datasets had an internal way to rebalance this without requiring offline reprocessing of the data files\n\nthis is not true right?, unless you're using .repeat(), onces the dataset is exhausted, each worker will eventually terminate or become idlle, shorter file won't end up being oversampled, cmiiw."
] | 2025-03-21T04:33:37
| 2025-11-22T07:55:43
| 2025-03-26T06:49:28
|
NONE
| null | null | null | null |
I thought https://github.com/huggingface/datasets/pull/7252 might be applicable but looking at it maybe not.
Say we have a process, eg. a database query, that can return data in slightly different order each time. So, the initial query needs to be run by a single thread (not to mention running multiple times incurs more cost too). But the results are also big enough that we don't want to materialize it entirely and instead stream it with an IterableDataset.
But after we have the results we want to split it up across workers to parallelize processing.
Is something like this possible to do?
Here's a failed attempt. The end result should be that each of the shards has unique data, but unfortunately with this attempt the generator gets run once in each shard and the results end up with duplicates...
```
import random
import datasets
def gen():
print('RUNNING GENERATOR!')
items = list(range(10))
random.shuffle(items)
yield from items
ds = datasets.IterableDataset.from_generator(gen)
print('dataset contents:')
for item in ds:
print(item)
print()
print('dataset contents (2):')
for item in ds:
print(item)
print()
num_shards = 3
def sharded(shard_id):
for i, example in enumerate(ds):
if i % num_shards in shard_id:
yield example
ds1 = datasets.IterableDataset.from_generator(
sharded, gen_kwargs={'shard_id': list(range(num_shards))}
)
for shard in range(num_shards):
print('shard', shard)
for item in ds1.shard(num_shards, shard):
print(item)
```
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I_kwDODunzps6vCQ2A
| 7,469
|
Custom split name with the web interface
|
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[] | 2025-03-20T20:45:59
| 2025-03-21T07:20:37
| 2025-03-21T07:20:37
|
NONE
| null | null | null | null |
### Describe the bug
According the doc here: https://huggingface.co/docs/hub/datasets-file-names-and-splits#custom-split-name
it should infer the split name from the subdir of data or the beg of the name of the files in data.
When doing this manually through web upload it does not work. it uses "train" as a unique split.
example: https://huggingface.co/datasets/eole-nlp/estimator_chatml
### Steps to reproduce the bug
follow the link above
### Expected behavior
there should be two splits "mlqe" and "1720_da"
### Environment info
website
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| 2,934,094,103
|
I_kwDODunzps6u4rkX
| 7,468
|
function `load_dataset` can't solve folder path with regex characters like "[]"
|
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[] |
open
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[] |
[
"Hi ! Have you tried escaping the glob special characters `[` and `]` ?\n\nbtw note that`AbstractFileSystem.glob` doesn't support regex, instead it supports glob patterns as in the python library [glob](https://docs.python.org/3/library/glob.html)\n"
] | 2025-03-20T05:21:59
| 2025-03-25T10:18:12
| null |
NONE
| null | null | null | null |
### Describe the bug
When using the `load_dataset` function with a folder path containing regex special characters (such as "[]"), the issue occurs due to how the path is handled in the `resolve_pattern` function. This function passes the unprocessed path directly to `AbstractFileSystem.glob`, which supports regular expressions. As a result, the globbing mechanism interprets these characters as regex patterns, leading to a traversal of the entire disk partition instead of confining the search to the intended directory.
### Steps to reproduce the bug
just create a folder like `E:\[D_DATA]\koch_test`, then `load_dataset("parquet", data_dir="E:\[D_DATA]\\test", split="train")`
it will keep searching the whole disk.
I add two `print` in `glob` and `resolve_pattern` to see the path
### Expected behavior
it should load the dataset as in normal folders
### Environment info
- `datasets` version: 3.3.2
- Platform: Windows-10-10.0.22631-SP0
- Python version: 3.10.16
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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I_kwDODunzps6upUaj
| 7,467
|
load_dataset with streaming hangs on parquet datasets
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[
"Hi ! The issue comes from `pyarrow`, I reported it here: https://github.com/apache/arrow/issues/45214 (feel free to comment / thumb up).\n\nAlternatively we can try to find something else than `ParquetFileFragment.to_batches()` to iterate on Parquet data and keep the option the pass `filters=`..."
] | 2025-03-18T23:33:54
| 2025-03-25T10:28:04
| null |
NONE
| null | null | null | null |
### Describe the bug
When I try to load a dataset with parquet files (e.g. "bigcode/the-stack") the dataset loads, but python interpreter can't exit and hangs
### Steps to reproduce the bug
```python3
import datasets
print('Start')
dataset = datasets.load_dataset("bigcode/the-stack", data_dir="data/yaml", streaming=True, split="train")
it = iter(dataset)
next(it)
print('Finish')
```
The program prints finish but doesn't exit and hangs indefinitely.
I tried this on two different machines and several datasets.
### Expected behavior
The program exits successfully
### Environment info
datasets==3.4.1
Python 3.12.9.
MacOS and Ubuntu Linux
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I_kwDODunzps6uYTy7
| 7,461
|
List of images behave differently on IterableDataset and Dataset
|
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[] |
[
"Hi ! Can you try with `datasets` ^3.4 released recently ? on my side it works with IterableDataset on the recent version :)\n\n```python\nIn [20]: def train_iterable_gen():\n ...: images = np.array(load_image(\"https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg\").resize((128, 128)))\n ...: yield {\n ...: \"images\": np.expand_dims(images, axis=0),\n ...: \"messages\": [\n ...: {\n ...: \"role\": \"user\",\n ...: \"content\": [{\"type\": \"image\", \"url\": \"https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg\" }]\n ...: },\n ...: {\n ...: \"role\": \"assistant\",\n ...: \"content\": [{\"type\": \"text\", \"text\": \"duck\" }]\n ...: }\n ...: ]\n ...: }\n ...: \n ...: train_ds = IterableDataset.from_generator(train_iterable_gen,\n ...: features=Features({\n ...: 'images': [datasets.Image(mode=None, decode=True, id=None)],\n ...: 'messages': [{'content': [{'text': datasets.Value(dtype='string', id=None), 'type': datasets.Value(dtype='string', id=None) }],\n ...: 'role': datasets.Value(dtype='string', id=None)}]\n ...: } )\n ...: )\n\n\nIn [21]: \n\nIn [21]: next(iter(train_ds))\n/Users/quentinlhoest/hf/datasets/src/datasets/features/image.py:338: UserWarning: Downcasting array dtype int64 to uint8 to be compatible with 'Pillow'\n warnings.warn(f\"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'\")\nOut[21]: \n{'images': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=128x128>],\n 'messages': [{'content': [{'text': None, 'type': 'image'}], 'role': 'user'},\n {'content': [{'type': 'text', 'text': 'duck'}], 'role': 'assistant'}]}\n```",
"Hm I tried it here and it works as expected, even on datasets 3.3.2. I guess maybe something in the SFTTrainer is doing additional processing on the dataset, I'll have a look there.\n\nThanks @lhoestq!"
] | 2025-03-17T15:59:23
| 2025-03-18T08:57:17
| 2025-03-18T08:57:16
|
NONE
| null | null | null | null |
### Describe the bug
This code:
```python
def train_iterable_gen():
images = np.array(load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg").resize((128, 128)))
yield {
"images": np.expand_dims(images, axis=0),
"messages": [
{
"role": "user",
"content": [{"type": "image", "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" }]
},
{
"role": "assistant",
"content": [{"type": "text", "text": "duck" }]
}
]
}
train_ds = Dataset.from_generator(train_iterable_gen,
features=Features({
'images': [datasets.Image(mode=None, decode=True, id=None)],
'messages': [{'content': [{'text': datasets.Value(dtype='string', id=None), 'type': datasets.Value(dtype='string', id=None) }], 'role': datasets.Value(dtype='string', id=None)}]
} )
)
```
works as I'd expect; if I iterate the dataset then the `images` column returns a `List[PIL.Image.Image]`, i.e. `'images': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=128x128 at 0x77EFB7EF4680>]`.
But if I change `Dataset` to `IterableDataset`, the `images` column changes into `'images': [{'path': None, 'bytes': ..]`
### Steps to reproduce the bug
The code above +
```python
def load_image(url):
response = requests.get(url)
image = Image.open(io.BytesIO(response.content))
return image
```
I'm feeding it to SFTTrainer
### Expected behavior
Dataset and IterableDataset would behave the same
### Environment info
```yaml
requires-python = ">=3.12"
dependencies = [
"av>=14.1.0",
"boto3>=1.36.7",
"datasets>=3.3.2",
"docker>=7.1.0",
"google-cloud-storage>=2.19.0",
"grpcio>=1.70.0",
"grpcio-tools>=1.70.0",
"moviepy>=2.1.2",
"open-clip-torch>=2.31.0",
"opencv-python>=4.11.0.86; sys_platform == 'darwin'",
"opencv-python-headless>=4.11.0.86; sys_platform == 'linux'",
"pandas>=2.2.3",
"pillow>=10.4.0",
"plotly>=6.0.0",
"py-spy>=0.4.0",
"pydantic>=2.10.6",
"pydantic-settings>=2.7.1",
"pymysql>=1.1.1",
"ray[data,default,serve,train,tune]>=2.43.0",
"torch>=2.6.0",
"torchmetrics>=1.6.1",
"torchvision>=0.21.0",
"transformers[torch]@git+https://github.com/huggingface/transformers",
"wandb>=0.19.4",
# https://github.com/Dao-AILab/flash-attention/issues/833
"flash-attn @ https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn-2.7.3+cu12torch2.6cxx11abiFALSE-cp312-cp312-linux_x86_64.whl; sys_platform == 'linux'",
"trl@https://github.com/huggingface/trl.git",
"peft>=0.14.0",
]
```
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| 2,925,403,528
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I_kwDODunzps6uXh2I
| 7,458
|
Loading the `laion/filtered-wit` dataset in streaming mode fails on v3.4.0
|
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[
"thanks for reporting, I released 3.4.1 with a fix"
] | 2025-03-17T14:54:02
| 2025-03-17T16:02:04
| 2025-03-17T15:25:55
|
NONE
| null | null | null | null |
### Describe the bug
Loading https://huggingface.co/datasets/laion/filtered-wit in streaming mode fails after update to `datasets==3.4.0`. The dataset loads fine on v3.3.2.
### Steps to reproduce the bug
Steps to reproduce:
```
pip install datastes==3.4.0
python -c "from datasets import load_dataset; load_dataset('laion/filtered-wit', split='train', streaming=True)"
```
Results in:
```
$ python -c "from datasets import load_dataset; load_dataset('laion/filtered-wit', split='train', streaming=True)"
Repo card metadata block was not found. Setting CardData to empty.
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 560/560 [00:00<00:00, 2280.24it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/nsavel/venvs/tmp/lib/python3.9/site-packages/datasets/load.py", line 2080, in load_dataset
return builder_instance.as_streaming_dataset(split=split)
File "/home/nsavel/venvs/tmp/lib/python3.9/site-packages/datasets/builder.py", line 1265, in as_streaming_dataset
splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
File "/home/nsavel/venvs/tmp/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 49, in _split_generators
data_files = dl_manager.download_and_extract(self.config.data_files)
File "/home/nsavel/venvs/tmp/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 169, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/nsavel/venvs/tmp/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 121, in extract
urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True)
File "/home/nsavel/venvs/tmp/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 496, in map_nested
mapped = [
File "/home/nsavel/venvs/tmp/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 497, in <listcomp>
map_nested(
File "/home/nsavel/venvs/tmp/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 513, in map_nested
mapped = [
File "/home/nsavel/venvs/tmp/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 514, in <listcomp>
_single_map_nested((function, obj, batched, batch_size, types, None, True, None))
File "/home/nsavel/venvs/tmp/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 375, in _single_map_nested
return function(data_struct)
File "/home/nsavel/venvs/tmp/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 131, in _extract
raise NotImplementedError(
NotImplementedError: Extraction protocol for TAR archives like 'hf://datasets/laion/filtered-wit@c38ca7464e9934d9a49f88b3f60f5ad63b245465/data/00000.tar' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
Example usage:
url = dl_manager.download(url)
tar_archive_iterator = dl_manager.iter_archive(url)
for filename, file in tar_archive_iterator:
...
```
### Expected behavior
Dataset loads successfully.
### Environment info
Ubuntu 20.04.6. Python 3.9. Datasets 3.4.0.
pip freeze:
```
aiohappyeyeballs==2.6.1
aiohttp==3.11.14
aiosignal==1.3.2
async-timeout==5.0.1
attrs==25.3.0
certifi==2025.1.31
charset-normalizer==3.4.1
datasets==3.4.0
dill==0.3.8
filelock==3.18.0
frozenlist==1.5.0
fsspec==2024.12.0
huggingface-hub==0.29.3
idna==3.10
multidict==6.1.0
multiprocess==0.70.16
numpy==2.0.2
packaging==24.2
pandas==2.2.3
propcache==0.3.0
pyarrow==19.0.1
python-dateutil==2.9.0.post0
pytz==2025.1
PyYAML==6.0.2
requests==2.32.3
six==1.17.0
tqdm==4.67.1
typing_extensions==4.12.2
tzdata==2025.1
urllib3==2.3.0
xxhash==3.5.0
yarl==1.18.3
```
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I_kwDODunzps6uVjnD
| 7,457
|
Document the HF_DATASETS_CACHE env variable
|
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[
"Strongly agree to this, in addition, I am also suffering to change the cache location similar to other issues (since I changed the environmental variables).\nhttps://github.com/huggingface/datasets/issues/6886",
"`HF_DATASETS_CACHE` should be documented there indeed, feel free to open a PR :) ",
"Hey, I’d love to work on this issue! Could you assign it to me?",
"sure ! you can also comment #self-assign in an issue and a bot assigns you automatically :)"
] | 2025-03-17T12:24:50
| 2025-05-06T15:54:39
| 2025-05-06T15:54:39
|
NONE
| null | null | null | null |
### Feature request
Hello,
I have a use case where my team is sharing models and dataset in shared directory to avoid duplication.
I noticed that the [cache documentation for datasets](https://huggingface.co/docs/datasets/main/en/cache) only mention the `HF_HOME` environment variable but never the `HF_DATASETS_CACHE`.
It should be nice to add `HF_DATASETS_CACHE` to datasets documentation if it's an intended feature.
If it's not, I think a depreciation warning would be appreciated.
### Motivation
This variable is fully working and similar to what `HF_HUB_CACHE` does for models, so it's nice to know that this exists. This seems to be a quick change to implement.
### Your contribution
I could contribute since this is only affecting a small portion of the documentation
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.add_faiss_index and .add_elasticsearch_index returns ImportError at Google Colab
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[
"I can fix this.\nIt's mainly because faiss-gpu requires python<=3.10 but the default python version in colab is 3.11. We just have to downgrade the CPython version down to 3.10 and it should work fine.\n",
"I think I just had no chance to meet with faiss-cpu.\nIt could be import problem? \n_has_faiss gets its value at the beginning of datasets/search.\nI tried to call object before import faiss, so _has_faiss took False. And never updated later. ",
"Yes you can't meet the requirements because faiss-cpu runs only on\r\npython3.10 and lower but the default version for colab is python3.11 which\r\nresults in pip not being able to find wheels for faiss-cpu with python3.11.\r\n\r\nOn Mon, 17 Mar, 2025, 3:56 pm MapleBloom, ***@***.***> wrote:\r\n\r\n> I think I just had no chance to meet with faiss-cpu.\r\n> It could be import problem?\r\n> _has_faiss gets its value at the beginning of datasets/search.\r\n> I tried to call object before import faiss, so _has_faiss took False. And\r\n> never updated later.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMBVD7LEDDUGALOTVN32U2PMBAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRYHE3TKNRXGI>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n> [image: MapleBloom]*MapleBloom* left a comment (huggingface/datasets#7456)\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>\r\n>\r\n> I think I just had no chance to meet with faiss-cpu.\r\n> It could be import problem?\r\n> _has_faiss gets its value at the beginning of datasets/search.\r\n> I tried to call object before import faiss, so _has_faiss took False. And\r\n> never updated later.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMBVD7LEDDUGALOTVN32U2PMBAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRYHE3TKNRXGI>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"> you can't meet the requirements\n\nIt is not the case (or I didn't reach this point) because the same code in notebook\n```importlib.util.find_spec(\"faiss\")```\nfinds faiss. I've mention it.\nI think the problem is in the very moment when _has_faiss takes its value and never try again. \n(or it couldn't find the path that was easily found when started from my code)",
"When you run the first cell containing pip install faiss-cpu does it\r\ninstall it?\r\n\r\nOn Mon, 17 Mar, 2025, 8:01 pm MapleBloom, ***@***.***> wrote:\r\n\r\n> you can't meet the requirements\r\n>\r\n> It is not the case (or I didn't reach this point) because the same code in\r\n> notebook\r\n> importlib.util.find_spec(\"faiss\")\r\n> finds faiss. I've mention it.\r\n> I think the problem is in the very moment when _has_faiss takes its value\r\n> and never try again.\r\n> (or it couldn't find the path that was easily found when started from my\r\n> code)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMCCE6BPZCOVAWXKIY32U3MFVAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRZG4ZTONBRGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n> [image: MapleBloom]*MapleBloom* left a comment (huggingface/datasets#7456)\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>\r\n>\r\n> you can't meet the requirements\r\n>\r\n> It is not the case (or I didn't reach this point) because the same code in\r\n> notebook\r\n> importlib.util.find_spec(\"faiss\")\r\n> finds faiss. I've mention it.\r\n> I think the problem is in the very moment when _has_faiss takes its value\r\n> and never try again.\r\n> (or it couldn't find the path that was easily found when started from my\r\n> code)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMCCE6BPZCOVAWXKIY32U3MFVAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRZG4ZTONBRGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"> When you run the first cell containing pip install faiss-cpu does it\n> install it?\n> […](#)\n\nYes. It was installed succesfully. \nMethods of datasets library that depends on _has_faiss constant didn't start to work."
] | 2025-03-16T00:51:49
| 2025-03-17T15:57:19
| null |
NONE
| null | null | null | null |
### Describe the bug
At Google Colab
```!pip install faiss-cpu``` works
```import faiss``` no error
but
```embeddings_dataset.add_faiss_index(column='embeddings')```
returns
```
[/usr/local/lib/python3.11/dist-packages/datasets/search.py](https://localhost:8080/#) in init(self, device, string_factory, metric_type, custom_index)
247 self.faiss_index = custom_index
248 if not _has_faiss:
--> 249 raise ImportError(
250 "You must install Faiss to use FaissIndex. To do so you can run conda install -c pytorch faiss-cpu or conda install -c pytorch faiss-gpu. "
251 "A community supported package is also available on pypi: pip install faiss-cpu or pip install faiss-gpu. "
```
because
```_has_faiss = importlib.util.find_spec("faiss") is not None``` at the beginning of ```datasets/search.py``` returns ```False```
when
the same code at colab notebook returns
```ModuleSpec(name='faiss', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7b7851449f50>, origin='/usr/local/lib/python3.11/dist-packages/faiss/init.py', submodule_search_locations=['/usr/local/lib/python3.11/dist-packages/faiss'])```
But
```
import datasets
datasets.search._has_faiss
```
at ```colab notebook``` also returns ```False```
The same story with ```_has_elasticsearch```
### Steps to reproduce the bug
1. Follow https://huggingface.co/learn/nlp-course/chapter5/6?fw=pt at Google Colab
2. till ```embeddings_dataset.add_faiss_index(column='embeddings')```
3. ```embeddings_dataset.add_elasticsearch_index(column='embeddings')```
4. https://colab.research.google.com/drive/1h2cjuiClblqzbNQgrcoLYOC8zBqTLLcv#scrollTo=3ddzRp72auOF
### Expected behavior
I've only started Tutorial and don't know exactly. But something tells me that ```embeddings_dataset.add_faiss_index(column='embeddings')```
should work without ```Import Error```
### Environment info
Google Colab notebook with default config
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| 2,921,933,250
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I_kwDODunzps6uKSnC
| 7,455
|
Problems with local dataset after upgrade from 3.3.2 to 3.4.0
|
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[
"Hi ! I just released 3.4.1 with a fix, let me know if it's working now !"
] | 2025-03-15T09:22:50
| 2025-03-17T16:20:43
| null |
NONE
| null | null | null | null |
### Describe the bug
I was not able to open a local saved dataset anymore that was created using an older datasets version after the upgrade yesterday from datasets 3.3.2 to 3.4.0
The traceback is
```
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 67, in _generate_tables
batches = pa.ipc.open_stream(f)
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 190, in open_stream
return RecordBatchStreamReader(source, options=options,
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 52, in __init__
self._open(source, options=options, memory_pool=memory_pool)
File "pyarrow/ipc.pxi", line 1006, in pyarrow.lib._RecordBatchStreamReader._open
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Expected to read 538970747 metadata bytes, but only read 2126
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1855, in _prepare_split_single
for _, table in generator:
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 69, in _generate_tables
reader = pa.ipc.open_file(f)
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 234, in open_file
return RecordBatchFileReader(
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 110, in __init__
self._open(source, footer_offset=footer_offset,
File "pyarrow/ipc.pxi", line 1090, in pyarrow.lib._RecordBatchFileReader._open
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Not an Arrow file
```
### Steps to reproduce the bug
Load a dataset from a local folder with
```
dataset = load_dataset(
args.train_data_dir,
cache_dir=args.cache_dir,
)
```
as it is done for example in the training script for SD3 controlnet.
This is the minimal script to test it:
```
from datasets import load_dataset
def main():
dataset = load_dataset(
"local_dataset",
)
print(dataset)
print("Sample data:", dataset["train"][0])
if __name__ == "__main__":
main()
````
### Expected behavior
Work in 3.4.0 like in 3.3.2
### Environment info
- `datasets` version: 3.4.0
- Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.29.3
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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| 2,916,235,092
|
I_kwDODunzps6t0jdU
| 7,449
|
Cannot load data with different schemas from different parquet files
|
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[
"Hi ! `load_dataset` expects all the data_files to have the same schema.\n\nMaybe you can try enforcing certain `features` using:\n\n```python\nfeatures = Features({\"conversations\": {'content': Value('string'), 'role': Value('string',)}})\nds = load_dataset(..., features=features)\n```",
"Thanks! It works if I explicitly specify all nested fields of the data."
] | 2025-03-13T08:14:49
| 2025-03-17T07:27:48
| 2025-03-17T07:27:46
|
NONE
| null | null | null | null |
### Describe the bug
Cannot load samples with optional fields from different files. The schema cannot be correctly derived.
### Steps to reproduce the bug
When I place two samples with an optional field `some_extra_field` within a single parquet file, it can be loaded via `load_dataset`.
```python
import pandas as pd
from datasets import load_dataset
data = [
{'conversations': {'role': 'user', 'content': 'hello'}},
{'conversations': {'role': 'user', 'content': 'hi', 'some_extra_field': 'some_value'}}
]
df = pd.DataFrame(data)
df.to_parquet('data.parquet')
dataset = load_dataset('parquet', data_files='data.parquet', split='train')
print(dataset.features)
```
The schema can be derived. `some_extra_field` is set to None for the first row where it is absent.
```
{'conversations': {'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None), 'some_extra_field': Value(dtype='string', id=None)}}
```
However, when I separate the samples into different files, it cannot be loaded.
```python
import pandas as pd
from datasets import load_dataset
data1 = [{'conversations': {'role': 'user', 'content': 'hello'}}]
pd.DataFrame(data1).to_parquet('data1.parquet')
data2 = [{'conversations': {'role': 'user', 'content': 'hi', 'some_extra_field': 'some_value'}}]
pd.DataFrame(data2).to_parquet('data2.parquet')
dataset = load_dataset('parquet', data_files=['data1.parquet', 'data2.parquet'], split='train')
print(dataset.features)
```
Traceback:
```
Traceback (most recent call last):
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/builder.py", line 1854, in _prepare_split_single
for _, table in generator:
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 106, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 73, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
return cast_table_to_schema(table, schema)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2245, in cast_table_to_schema
arrays = [
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2246, in <listcomp>
cast_array_to_feature(
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2108, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<content: string, role: string, some_extra_field: string>
to
{'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None)}
```
### Expected behavior
Correctly load data with optional fields from different parquet files.
### Environment info
- `datasets` version: 3.3.2
- Platform: Linux-5.10.135.bsk.4-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- `huggingface_hub` version: 0.28.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
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I_kwDODunzps6tzwWi
| 7,448
|
`datasets.disable_caching` doesn't work
|
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[
"cc",
"Yes I have the same issue. It's a confusingly named function. See [here](https://github.com/huggingface/datasets/blob/main/src/datasets/fingerprint.py#L115-L130)\n\n```\n...\nIf disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.\n More precisely, if the caching is disabled:\n - cache files are always recreated\n - cache files are written to a temporary directory that is deleted when session closes\n - cache files are named using a random hash instead of the dataset fingerprint\n```\n\nAlso, unfortunately the member variable `ds.cache_files` is not populated either.\n\nI'll let you know if I find a solution."
] | 2025-03-13T06:40:12
| 2025-03-22T04:37:07
| null |
NONE
| null | null | null | null |
When I use `Dataset.from_generator(my_gen)` to load my dataset, it simply skips my changes to the generator function.
I tried `datasets.disable_caching`, but it doesn't work!
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I_kwDODunzps6twu3g
| 7,447
|
Epochs shortened after resuming mid-epoch with Iterable dataset+StatefulDataloader(persistent_workers=True)
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[
"Thanks for reporting ! Maybe we should store the epoch in the state_dict, and then when the dataset is iterated on again after setting a new epoch it should restart from scratch instead of resuming ? wdyt ?",
"But why does this only happen when `persistent_workers=True`? I would expect it to work correctly even without storing the epoch number in the state_dict of the iterable dataset. ",
"I think persistent_workers=False simply ignores the dataset state_dict when it starts a new epoch, that's why the issue doesn't appear in that case",
"I opened https://github.com/huggingface/datasets/pull/7451 to fix the issue, let me know if it works for you",
"I just released `datasets` 3.4 that includes the fix :)\n\nPS: in your script you probably want to set the epoch like this, otherwise it's still set to 0 after the first epoch:\n\n```diff\n if state_dict is None:\n- ds.set_epoch(epoch)\n epoch += 1\n+ ds.set_epoch(epoch)\n```",
"@lhoestq \nIf I understand correctly, the issue was:\nwhen training saves a checkpoint of dataloader in epoch 1, the resumed training only consumes partial data in epoch 2, 3, etc.\n\nHowever, with the fix we are facing the issue that:\nwhen training saves a checkpoint of dataloader in epoch 2, the resumed training starts from scratch instead of consuming remaining partial data in epoch 2.\n\nThis makes training inconsistent between resuming from a checkpoint vs. original training if continued without a checkpoint."
] | 2025-03-12T21:41:05
| 2025-07-09T23:04:57
| 2025-03-14T10:50:10
|
NONE
| null | null | null | null |
### Describe the bug
When `torchdata.stateful_dataloader.StatefulDataloader(persistent_workers=True)` the epochs after resuming only iterate through the examples that were left in the epoch when the training was interrupted. For example, in the script below training is interrupted on step 124 (epoch 1) when 3 batches are left. Then after resuming, the rest of epochs (2 and 3) only iterate through these 3 batches.
### Steps to reproduce the bug
Run the following script with and with PERSISTENT_WORKERS=true.
```python
# !/usr/bin/env python3
# torch==2.5.1
# datasets==3.3.2
# torchdata>=0.9.0
import datasets
import pprint
from torchdata.stateful_dataloader import StatefulDataLoader
import os
PERSISTENT_WORKERS = (
os.environ.get("PERSISTENT_WORKERS", "False").lower() == "true"
)
# PERSISTENT_WORKERS = True # Incorrect resume
# ds = datasets.load_from_disk("dataset").to_iterable_dataset(num_shards=4)
def generator():
for i in range(128):
yield {"x": i}
ds = datasets.Dataset.from_generator(
generator, features=datasets.Features({"x": datasets.Value("int32")})
).to_iterable_dataset(num_shards=4)
dl = StatefulDataLoader(
ds, batch_size=2, num_workers=2, persistent_workers=PERSISTENT_WORKERS
)
global_step = 0
epoch = 0
ds_state_dict = None
state_dict = None
resumed = False
while True:
if epoch >= 3:
break
if state_dict is not None:
dl.load_state_dict(state_dict)
state_dict = None
ds_state_dict = None
resumed = True
print("resumed")
for i, batch in enumerate(dl):
print(f"epoch: {epoch}, global_step: {global_step}, batch: {batch}")
global_step += 1 # consume datapoint
# simulate error
if global_step == 124 and not resumed:
ds_state_dict = ds.state_dict()
state_dict = dl.state_dict()
print("checkpoint")
print("ds_state_dict")
pprint.pprint(ds_state_dict)
print("dl_state_dict")
pprint.pprint(state_dict)
break
if state_dict is None:
ds.set_epoch(epoch)
epoch += 1
```
The script checkpoints when there are three batches left in the second epoch. After resuming, only the last three batches are repeated in the rest of the epochs.
If it helps, following are the two state_dicts for the dataloader save at the same step with the two settings. The left one is for `PERSISTENT_WORKERS=False`
### Expected behavior
All the elements in the dataset should be iterated through in the epochs following the one where we resumed. The expected behavior can be seen by setting `PERSISTENT_WORKERS=False`.
### Environment info
torch==2.5.1
datasets==3.3.2
torchdata>=0.9.0
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I_kwDODunzps6toZ-4
| 7,446
|
pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int'
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[
"I think the Counter object you used in 'labels' may be the issue, since the {2:1} inside is the dict and 2 is the int",
"> I think the Counter object you used in 'labels' may be the issue, since the {2:1} inside is the dict and 2 is the int我认为您在 'labels' 中使用的 Counter 对象可能是问题所在，因为里面的 {2：1} 是 dict，而 2 是 int\n\nYes, that's the point."
] | 2025-03-12T07:48:37
| 2025-07-04T05:14:45
| 2025-07-04T05:14:45
|
NONE
| null | null | null | null |
### Describe the bug
A dict with its keys are all str but get following error
```python
test_data=[{'input_ids':[1,2,3],'labels':[[Counter({2:1})]]}]
dataset = datasets.Dataset.from_list(test_data)
```
```bash
pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int'
```
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
datasets 3.3.2
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I_kwDODunzps6thWyN
| 7,444
|
Excessive warnings when resuming an IterableDataset+buffered shuffle+DDP.
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[
"I had a similar issue when loading the saved iterable dataset state to fast-forward to the mid-train location before resuming. This happened when I shuffled a concatenated dataset. A `iterable_data_state_dict.json` file was saved during checkpointing in the Trainer with:\n```\ndef _save_rng_state(self, output_dir):\n super()._save_rng_state(output_dir)\n if self.args.should_save:\n with open(os.path.join(output_dir, f'iterable_data_state_dict.json'), 'w', encoding='utf-8') as fo:\n json.dump(self.train_dataset.state_dict(), fo, ensure_ascii=False)\n```\nThen when resuming training, I use `load_state_dict` to get the dataset state:\n```\nif training_args.resume_from_checkpoint:\n if isinstance(training_args.resume_from_checkpoint, bool):\n resume_from_checkpoint = get_last_checkpoint(training_args.output_dir)\n else:\n resume_from_checkpoint = training_args.resume_from_checkpoint\n last_ckpt_iterable_data_state_dict_file_path = os.path.join(resume_from_checkpoint, f'iterable_data_state_dict.json')\n if not training_args.ignore_data_skip:\n raise ValueError(f'Please set `ignore_data_skip`=True to skip tokenization.')\n with open(last_ckpt_iterable_data_state_dict_file_path, 'r', encoding='utf-8') as f:\n train_dataset_state_dict = json.load(f)\n train_dataset.load_state_dict(train_dataset_state_dict)\n print(f'Loaded train_dataset state from {last_ckpt_iterable_data_state_dict_file_path}')\n```\n\nThen code works fine before I shuffled a subset of the training data to:\n```\nmath_dataset = concatenate_datasets([A, B]).to_iterable_dataset()\nshuffled_math_dataset = math_dataset.shuffle(seed=42, buffer_size=1000000)\n```\n\nOther than the warning, a real problem is that the loss bumped after loading a ckpt:\n\n<img width=\"400\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/c8944e81-9df9-4857-82de-6ab9ebc1b066\" />",
"anynews?"
] | 2025-03-11T16:34:39
| 2025-11-22T06:45:25
| null |
NONE
| null | null | null | null |
### Describe the bug
I have a large dataset that I shared into 1024 shards and save on the disk during pre-processing. During training, I load the dataset using load_from_disk() and convert it into an iterable dataset, shuffle it and split the shards to different DDP nodes using the recommended method.
However, when the training is resumed mid-epoch, I get thousands of identical warning messages:
```
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
```
### Steps to reproduce the bug
1. Run a multi-node training job using the following python script and interrupt the training after a few seconds to save a mid-epoch checkpoint.
```python
#!/usr/bin/env python
import os
import time
from typing import Dict, List
import torch
import lightning as pl
from torch.utils.data import DataLoader
from datasets import Dataset
from datasets.distributed import split_dataset_by_node
import datasets
from transformers import AutoTokenizer
from more_itertools import flatten, chunked
from torchdata.stateful_dataloader import StatefulDataLoader
from lightning.pytorch.callbacks.on_exception_checkpoint import (
OnExceptionCheckpoint,
)
datasets.logging.set_verbosity_debug()
def dummy_generator():
# Generate 60 examples: integers from $0$ to $59$
# 64 sequences of different lengths
dataset = [
list(range(3, 10)),
list(range(10, 15)),
list(range(15, 21)),
list(range(21, 27)),
list(range(27, 31)),
list(range(31, 36)),
list(range(36, 45)),
list(range(45, 50)),
]
for i in range(8):
for j, ids in enumerate(dataset):
yield {"token_ids": [idx + i * 50 for idx in ids]}
def group_texts(
examples: Dict[str, List[List[int]]],
block_size: int,
eos_token_id: int,
bos_token_id: int,
pad_token_id: int,
) -> Dict[str, List[List[int]]]:
real_block_size = block_size - 2 # make space for bos and eos
# colapse the sequences into a single list of tokens and then create blocks of real_block_size
input_ids = []
attention_mask = []
for block in chunked(flatten(examples["token_ids"]), real_block_size):
s = [bos_token_id] + list(block) + [eos_token_id]
ls = len(s)
attn = [True] * ls
s += [pad_token_id] * (block_size - ls)
attn += [False] * (block_size - ls)
input_ids.append(s)
attention_mask.append(attn)
return {"input_ids": input_ids, "attention_mask": attention_mask}
def collate_fn(batch):
return {
"input_ids": torch.tensor(
[item["input_ids"] for item in batch], dtype=torch.long
),
"attention_mask": torch.tensor(
[item["attention_mask"] for item in batch], dtype=torch.long
),
}
class DummyModule(pl.LightningModule):
def __init__(self):
super().__init__()
# A dummy linear layer (not used for actual computation)
self.layer = torch.nn.Linear(1, 1)
self.ds = None
self.prepare_data_per_node = False
def on_train_start(self):
# This hook is called once training begins on each process.
print(f"[Rank {self.global_rank}] Training started.", flush=True)
self.data_file = open(f"data_{self.global_rank}.txt", "w")
def on_train_end(self):
self.data_file.close()
def training_step(self, batch, batch_idx):
# Print batch information to verify data loading.
time.sleep(5)
# print("batch", batch, flush=True)
print(
f"\n[Rank {self.global_rank}] Training step, epoch {self.trainer.current_epoch}, batch {batch_idx}: {batch['input_ids']}",
flush=True,
)
self.data_file.write(
f"[Rank {self.global_rank}] Training step, epoch {self.trainer.current_epoch}, batch {batch_idx}: {batch['input_ids']}\n"
)
# Compute a dummy loss (here, simply a constant tensor)
loss = torch.tensor(0.0, requires_grad=True)
return loss
def on_train_epoch_start(self):
epoch = self.trainer.current_epoch
print(
f"[Rank {self.global_rank}] Training epoch {epoch} started.",
flush=True,
)
self.data_file.write(
f"[Rank {self.global_rank}] Training epoch {epoch} started.\n"
)
def configure_optimizers(self):
# Return a dummy optimizer.
return torch.optim.SGD(self.parameters(), lr=0.001)
class DM(pl.LightningDataModule):
def __init__(self):
super().__init__()
self.ds = None
self.prepare_data_per_node = False
def set_epoch(self, epoch: int):
self.ds.set_epoch(epoch)
def prepare_data(self):
# download the dataset
dataset = Dataset.from_generator(dummy_generator)
# save the dataset
dataset.save_to_disk("dataset", num_shards=4)
def setup(self, stage: str):
# load the dataset
ds = datasets.load_from_disk("dataset").to_iterable_dataset(
num_shards=4
)
ds = ds.map(
group_texts,
batched=True,
batch_size=5,
fn_kwargs={
"block_size": 5,
"eos_token_id": 1,
"bos_token_id": 0,
"pad_token_id": 2,
},
remove_columns=["token_ids"],
).shuffle(seed=42, buffer_size=8)
ds = split_dataset_by_node(
ds,
rank=self.trainer.global_rank,
world_size=self.trainer.world_size,
)
self.ds = ds
def train_dataloader(self):
print(
f"[Rank {self.trainer.global_rank}] Preparing train_dataloader...",
flush=True,
)
rank = self.trainer.global_rank
print(
f"[Rank {rank}] Global rank: {self.trainer.global_rank}",
flush=True,
)
world_size = self.trainer.world_size
print(f"[Rank {rank}] World size: {world_size}", flush=True)
return StatefulDataLoader(
self.ds,
batch_size=2,
num_workers=2,
collate_fn=collate_fn,
drop_last=True,
persistent_workers=True,
)
if __name__ == "__main__":
print("Starting Lightning training", flush=True)
# Optionally, print some SLURM environment info for debugging.
print(f"SLURM_NNODES: {os.environ.get('SLURM_NNODES', '1')}", flush=True)
# Determine the number of nodes from SLURM (defaulting to 1 if not set)
num_nodes = int(os.environ.get("SLURM_NNODES", "1"))
model = DummyModule()
dm = DM()
on_exception = OnExceptionCheckpoint(
dirpath="checkpoints",
filename="on_exception",
)
# Configure the Trainer to use distributed data parallel (DDP).
trainer = pl.Trainer(
accelerator="gpu" if torch.cuda.is_available() else "cpu",
devices=1,
strategy=(
"ddp" if num_nodes > 1 else "auto"
), # Use DDP strategy for multi-node training.
num_nodes=num_nodes,
max_epochs=2,
logger=False,
enable_checkpointing=True,
num_sanity_val_steps=0,
enable_progress_bar=False,
callbacks=[on_exception],
)
# resume (uncomment to resume)
# trainer.fit(model, datamodule=dm, ckpt_path="checkpoints/on_exception.ckpt")
# train
trainer.fit(model, datamodule=dm)
```
```bash
#!/bin/bash
#SBATCH --job-name=pl_ddp_test
#SBATCH --nodes=2 # Adjust number of nodes as needed
#SBATCH --ntasks-per-node=1 # One GPU (process) per node
#SBATCH --cpus-per-task=3 # At least as many dataloader workers as required
#SBATCH --gres=gpu:1 # Request one GPU per node
#SBATCH --time=00:10:00 # Job runtime (adjust as needed)
#SBATCH --partition=gpu-preempt # Partition or queue name
#SBATCH -o script.out
# Disable Python output buffering.
export PYTHONUNBUFFERED=1
echo "SLURM job starting on $(date)"
echo "Running on nodes: $SLURM_NODELIST"
echo "Current directory: $(pwd)"
ls -l
# Launch the script using srun so that each process starts the Lightning module.
srun script.py
```
2. Uncomment the "resume" line (second to last) and comment the original `trainer.fit` call (last line).
It will produce the following log.
```
[Rank 0] Preparing train_dataloader...
[Rank 0] Global rank: 0
[Rank 0] World size: 2
[Rank 1] Preparing train_dataloader...
[Rank 1] Global rank: 1
[Rank 1] World size: 2
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Assigning 2 shards (or data sources) of the dataset to each node.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#0 dataloader worker#1, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#0 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#0 dataloader worker#1, ': Finished iterating over 1/1 shards.
node#0 dataloader worker#0, ': Finished iterating over 1/1 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
[Rank 0] Training started.
[Rank 0] Training epoch 0 started.
[Rank 0] Training epoch 1 started.
Assigning 2 shards (or data sources) of the dataset to each node.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#0 dataloader worker#1, ': Starting to iterate over 1/2 shards.
node#0 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#1 dataloader worker#1, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#1 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#0 dataloader worker#1, ': Finished iterating over 1/1 shards.
node#0 dataloader worker#0, ': Finished iterating over 1/1 shards.
`Trainer.fit` stopped: `max_epochs=2` reached.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#1 dataloader worker#1, ': Finished iterating over 1/1 shards.
node#1 dataloader worker#0, ': Finished iterating over 1/1 shards.
[Rank 1] Training started.
[Rank 1] Training epoch 0 started.
[Rank 1] Training epoch 1 started.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#1 dataloader worker#1, ': Starting to iterate over 1/2 shards.
node#1 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#1 dataloader worker#0, ': Finished iterating over 1/1 shards.
node#1 dataloader worker#1, ': Finished iterating over 1/1 shards.
```
I'm also attaching the relevant state_dict to make sure that the state is being checkpointed as expected.
```
{'_iterator_finished': True,
'_snapshot': {'_last_yielded_worker_id': 1,
'_main_snapshot': {'_IterableDataset_len_called': None,
'_base_seed': 3992758080362545099,
'_index_sampler_state': {'samples_yielded': 64},
'_num_workers': 2,
'_sampler_iter_state': None,
'_sampler_iter_yielded': 32,
'_shared_seed': None},
'_snapshot_step': 32,
'_worker_snapshots': {'worker_0': {'dataset_state': {'ex_iterable': {'shard_example_idx': 0,
'shard_idx': 1},
'num_examples_since_previous_state': 0,
'previous_state': {'shard_example_idx': 0,
'shard_idx': 1},
'previous_state_example_idx': 33},
'fetcher_state': {'dataset_iter_state': None,
'fetcher_ended': False},
'worker_id': 0},
'worker_1': {'dataset_state': {'ex_iterable': {'shard_example_idx': 0,
'shard_idx': 1},
'num_examples_since_previous_state': 0,
'previous_state': {'shard_example_idx': 0,
'shard_idx': 1},
'previous_state_example_idx': 33},
'fetcher_state': {'dataset_iter_state': None,
'fetcher_ended': False},
'worker_id': 1}}},
'_steps_since_snapshot': 0}
```
### Expected behavior
Since I'm following all the recommended steps, I don't expect to see any warning when resuming. Am I doing something wrong? Also, can someone explain why I'm seeing 20 identical messages in the log in this reproduction setting? I'm trying to understand why I see thousands of these messages with the actual dataset.
One more surprising thing I noticed in the logs is the change in a number of shards per worker. In the following messages, the denominator changes from 2 to 1.
```
node#1 dataloader worker#1, ': Starting to iterate over 1/2 shards.
...
node#1 dataloader worker#1, ': Finished iterating over 1/1 shards.
```
### Environment info
python: 3.11.10
datasets: 3.3.2
lightning: 2.3.1
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I_kwDODunzps6tXX64
| 7,443
|
index error when num_shards > len(dataset)
|
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[
"Actually, looking at the code a bit more carefully, maybe an even better solution is to explicitly set `num_shards=len(self)` somewhere inside both `push_to_hub()` and `save_to_disk()` when these functions are invoked with `num_shards > len(dataset)`."
] | 2025-03-10T22:40:59
| 2025-03-10T23:43:08
| null |
NONE
| null | null | null | null |
In `ds.push_to_hub()` and `ds.save_to_disk()`, `num_shards` must be smaller than or equal to the number of rows in the dataset, but currently this is not checked anywhere inside these functions. Attempting to invoke these functions with `num_shards > len(dataset)` should raise an informative `ValueError`.
I frequently work with datasets with a small number of rows where each row is pretty large, so I often encounter this issue, where the function runs until the shard index in `ds.shard(num_shards, indx)` goes out of bounds. Ideally, a `ValueError` should be raised before reaching this point (i.e. as soon as `ds.push_to_hub()` or `ds.save_to_disk()` is invoked with `num_shards > len(dataset)`).
It seems that adding something like:
```python
if len(self) < num_shards:
raise ValueError(f"num_shards ({num_shards}) must be smaller than or equal to the number of rows in the dataset ({len(self)}). Please either reduce num_shards or increase max_shard_size to make sure num_shards <= len(dataset).")
```
to the beginning of the definition of the `ds.shard()` function [here](https://github.com/huggingface/datasets/blob/f693f4e93aabafa878470c80fd42ddb10ec550d6/src/datasets/arrow_dataset.py#L4728) would deal with this issue for both `ds.push_to_hub()` and `ds.save_to_disk()`, but I'm not exactly sure if this is the best place to raise the `ValueError` (it seems that a more correct way to do it would be to write separate checks for `ds.push_to_hub()` and `ds.save_to_disk()`). I'd be happy to submit a PR if you think something along these lines would be acceptable.
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| 2,905,543,017
|
I_kwDODunzps6tLxFp
| 7,442
|
Flexible Loader
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[
"Ideally `save_to_disk` should save in a format compatible with load_dataset, wdyt ?",
"> Ideally `save_to_disk` should save in a format compatible with load_dataset, wdyt ?\n\nThat would be perfect if not at least a flexible loader.",
"@lhoestq For now, you can use this small utility library: [nanoml](https://pypi.org/project/nanoml/)\n```python\nfrom nanoml.data import load_dataset_flexible\n```\n\nI actively develop and maintain this utility library. Open to contributors. Please open issues, PR, or feature requests."
] | 2025-03-09T16:55:03
| 2025-03-27T23:58:17
| null |
NONE
| null | null | null | null |
### Feature request
Can we have a utility function that will use `load_from_disk` when given the local path and `load_dataset` if given an HF dataset?
It can be something as simple as this one:
```
def load_hf_dataset(path_or_name):
if os.path.exists(path_or_name):
return load_from_disk(path_or_name)
else:
return load_dataset(path_or_name)
```
### Motivation
This can be done inside the user codebase, too, but in my experience, it becomes repetitive code.
### Your contribution
I can open a pull request.
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I_kwDODunzps6tIj15
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|
`drop_last_batch` does not drop the last batch using IterableDataset + interleave_datasets + multi_worker
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[
"Hi @memray, I’d like to help fix the issue with `drop_last_batch` not working when `num_workers > 1`. I’ll investigate and propose a solution. Thanks!\n",
"Thank you very much for offering to help! I also noticed a problem related to a previous issue and left a comment [here](https://github.com/huggingface/datasets/issues/6565#issuecomment-2708169303) (the code checks the validity before certain columns removed). Can you take a look as well?",
"I looked into this and the problem here seems to be the order of sharding and batching/or how `drop_last_batch` is done (see the potential solutions below if unclear). Since we have 2 workers and 2 shards the data is split into 1-12 on worker 1 and 13-24 on worker 2. Now each of those workers iterates in batches of 10 and drops the last element, therefore worker 1 drops `{11, 12}` and worker 2 `{23, 24}`. There are multiple ways to circumvent that:\n - distribute batches in turns to workers and tell workers if they should drop the batches individually, so that only the last worker drops anything\n- distribute data as right now but telling each worker how many samples to drop individually (but that would require each worker to know how many samples they hold and calculating how many samples are there in total). This could work but is probably way more complex but closer to how this behaves now.\n\nNote that OP's example is just the tip of the iceberg, actually all data can be dropped if we choose shards, workers and batch_sizes accordingly:\n```python\ndef convert_to_str(batch, dataset_name):\n batch[\"a\"] = [f\"{dataset_name}-{e}\" for e in batch[\"a\"]]\n return batch\n\n\nnumber = 16 # 15 samples (1-15)\n\n\ndef gen1():\n for ii in range(1, number):\n yield {\"a\": ii}\n\n\ndef gen2():\n for ii in range(1, number):\n yield {\"a\": ii}\n\nif __name__ == \"__main__\":\n print(\"=\" * 40)\n print(\"num_workers=1\")\n print(\"=\" * 40)\n dataset1 = Dataset.from_generator(gen1).to_iterable_dataset(num_shards=3)\n dataset2 = Dataset.from_generator(gen2).to_iterable_dataset(num_shards=3)\n dataset1 = dataset1.map(\n lambda x: convert_to_str(x, dataset_name=\"a\"), batched=True, batch_size=9, drop_last_batch=True\n )\n dataset2 = dataset2.map(\n lambda x: convert_to_str(x, dataset_name=\"b\"), batched=True, batch_size=9, drop_last_batch=True\n )\n\n from datasets import interleave_datasets\n\n interleaved = interleave_datasets([dataset1, dataset2], stopping_strategy=\"all_exhausted\")\n\n loader = DataLoader(interleaved, batch_size=5, num_workers=1)\n i = 0\n for b in loader:\n print(i, b[\"a\"])\n i += 1\n\n print()\n print(\"=\" * 40)\n print(\"num_workers=3\")\n print(\"=\" * 40)\n dataset1 = Dataset.from_generator(gen1).to_iterable_dataset(num_shards=3)\n dataset2 = Dataset.from_generator(gen2).to_iterable_dataset(num_shards=3)\n dataset1 = dataset1.map(\n lambda x: convert_to_str(x, dataset_name=\"a\"), batched=True, batch_size=9, drop_last_batch=True\n )\n dataset2 = dataset2.map(\n lambda x: convert_to_str(x, dataset_name=\"b\"), batched=True, batch_size=9, drop_last_batch=True\n )\n\n interleaved = interleave_datasets([dataset1, dataset2], stopping_strategy=\"all_exhausted\")\n\n loader = DataLoader(interleaved, batch_size=5, num_workers=3)\n i = 0\n for b in loader:\n print(i, b[\"a\"])\n i += 1\n\n if i == 0:\n print(\"Everything got dropped!\")\n```\n\n```bash\n========================================\nnum_workers=1\n========================================\n0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']\n1 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']\n2 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']\n3 ['b-8', 'a-9', 'b-9']\n\n========================================\nnum_workers=3\n========================================\nEverything got dropped!\n```\nEDIT: I looked into this a bit more and I revert my stance on the solutions. I think solution one is not feasible since we divide into shards before we know the `batch_size`. That leaves only option 2 on the table AFAIS right now."
] | 2025-03-08T10:28:44
| 2025-10-09T10:14:24
| null |
NONE
| null | null | null | null |
### Describe the bug
See the script below
`drop_last_batch=True` is defined using map() for each dataset.
The last batch for each dataset is expected to be dropped, id 21-25.
The code behaves as expected when num_workers=0 or 1.
When using num_workers>1, 'a-11', 'b-11', 'a-12', 'b-12' are gone and instead 21 and 22 are sampled.
### Steps to reproduce the bug
```
from datasets import Dataset
from datasets import interleave_datasets
from torch.utils.data import DataLoader
def convert_to_str(batch, dataset_name):
batch['a'] = [f"{dataset_name}-{e}" for e in batch['a']]
return batch
def gen1():
for ii in range(1, 25):
yield {"a": ii}
def gen2():
for ii in range(1, 25):
yield {"a": ii}
# https://github.com/huggingface/datasets/issues/6565
if __name__ == '__main__':
dataset1 = Dataset.from_generator(gen1).to_iterable_dataset(num_shards=2)
dataset2 = Dataset.from_generator(gen2).to_iterable_dataset(num_shards=2)
dataset1 = dataset1.map(lambda x: convert_to_str(x, dataset_name="a"), batched=True, batch_size=10, drop_last_batch=True)
dataset2 = dataset2.map(lambda x: convert_to_str(x, dataset_name="b"), batched=True, batch_size=10, drop_last_batch=True)
interleaved = interleave_datasets([dataset1, dataset2], stopping_strategy="all_exhausted")
print(f"num_workers=0")
loader = DataLoader(interleaved, batch_size=5, num_workers=0)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=1")
loader = DataLoader(interleaved, batch_size=5, num_workers=1)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=2")
loader = DataLoader(interleaved, batch_size=5, num_workers=2)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=3")
loader = DataLoader(interleaved, batch_size=5, num_workers=3)
i = 0
for b in loader:
print(i, b['a'])
i += 1
```
output is:
```
num_workers=0
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
2 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
3 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
4 ['a-11', 'b-11', 'a-12', 'b-12', 'a-13']
5 ['b-13', 'a-14', 'b-14', 'a-15', 'b-15']
6 ['a-16', 'b-16', 'a-17', 'b-17', 'a-18']
7 ['b-18', 'a-19', 'b-19', 'a-20', 'b-20']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=1
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
2 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
3 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
4 ['a-11', 'b-11', 'a-12', 'b-12', 'a-13']
5 ['b-13', 'a-14', 'b-14', 'a-15', 'b-15']
6 ['a-16', 'b-16', 'a-17', 'b-17', 'a-18']
7 ['b-18', 'a-19', 'b-19', 'a-20', 'b-20']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=2
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['a-13', 'b-13', 'a-14', 'b-14', 'a-15']
2 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
3 ['b-15', 'a-16', 'b-16', 'a-17', 'b-17']
4 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
5 ['a-18', 'b-18', 'a-19', 'b-19', 'a-20']
6 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
7 ['b-20', 'a-21', 'b-21', 'a-22', 'b-22']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=3
Too many dataloader workers: 3 (max is dataset.num_shards=2). Stopping 1 dataloader workers.
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['a-13', 'b-13', 'a-14', 'b-14', 'a-15']
2 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
3 ['b-15', 'a-16', 'b-16', 'a-17', 'b-17']
4 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
5 ['a-18', 'b-18', 'a-19', 'b-19', 'a-20']
6 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
7 ['b-20', 'a-21', 'b-21', 'a-22', 'b-22']
```
### Expected behavior
`'a-21', 'b-21', 'a-22', 'b-22'` should be dropped
### Environment info
- `datasets` version: 3.3.2
- Platform: Linux-5.15.0-1056-aws-x86_64-with-glibc2.31
- Python version: 3.10.16
- `huggingface_hub` version: 0.28.0
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
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IterableDataset raises FileNotFoundError instead of retrying
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[
"I have since been training more models with identical architectures over the same dataset, and it is completely unstable. One has now failed at chunk9/1215, whilst others have gotten past that.\n```python\nFileNotFoundError: zstd://example_train_1215.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk9/example_train_1215.jsonl.zst\n```\nBelow is the full training log, where you can clearly see the intermittent dataset issues. Note again that this model only got to epoch 0.11, whereas I have other models training on the exact same dataset right now that have gotten way beyond that. This is quickly turning into a highly expensive bug which I didn't have issues with in the past half year of using the same setup.\n<details>\n<summary>Training log of failed run</summary>\n\n```python\n 1%| | 64/8192 [56:27<87:25:33, 38.72s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 5ef28452-e903-4bd8-946d-f0c77f558a2a)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk5/example_holdout_4799.jsonl.zst\n 1%| | 64/8192 [56:51<87:25:33, 38.72s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:40:14<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: ba6e4c51-f4a4-407e-9934-3772550b7ce9)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk1/example_holdout_2770.jsonl.zst\n 2%|▏ | 192/8192 [2:40:53<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:40:53<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: bdf2cfaa-7e0b-46a0-bec1-b1e573fa7998)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_4386.jsonl.zst\n 2%|▏ | 192/8192 [2:42:16<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:42:16<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 1dc5e455-8042-4c7b-9b97-5ded33dfea34)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk1/example_holdout_1763.jsonl.zst\n 2%|▏ | 192/8192 [2:42:30<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:42:30<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 9cf29917-8111-41fe-80aa-953df65c5803)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_5509.jsonl.zst\n 2%|▏ | 192/8192 [2:44:31<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:44:31<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 2515a0b0-3d81-409f-940c-e78ed5e2dbf8)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_3093.jsonl.zst\n 2%|▏ | 192/8192 [2:45:13<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:45:13<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: a4c1e0c7-1c7a-4377-bc7e-6f076473072b)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_3422.jsonl.zst\n 2%|▏ | 192/8192 [2:46:26<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:46:26<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: c7b0d366-db86-4d0c-a4e0-be251d26519e)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_2250.jsonl.zst\n 2%|▏ | 192/8192 [2:47:24<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:47:24<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. 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timed out. (read timeout=10)\"), '(Request ID: 0faae356-e828-4cff-9a49-42b397431927)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_185.jsonl.zst\n 9%|▊ | 704/8192 [9:38:28<79:08:04, 38.05s/it]Retrying in 1s [Retry 1/5].\n 9%|▊ | 704/8192 [9:38:28<79:08:04, 38.05s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 9557423f-6937-4f70-b50f-05b0c01f5bf3)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_4035.jsonl.zst\n 9%|▊ | 704/8192 [9:44:58<79:08:04, 38.05s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:28:20<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 939d1d36-c607-4d3c-a0a0-8e447579340b)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_165.jsonl.zst\n 10%|█ | 832/8192 [11:30:25<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:30:25<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 0b99bfd1-07ae-46db-81fa-fc6ef0eabdbc)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_1529.jsonl.zst\n 10%|█ | 832/8192 [11:38:24<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:38:24<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: c208d1bb-5d13-45d2-9a01-1d5a2defa598)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk5/example_holdout_4562.jsonl.zst\n 10%|█ | 832/8192 [11:39:58<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:39:58<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 2bf98b5c-473b-4e00-aca2-b152efddb992)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_4414.jsonl.zst\n 10%|█ | 832/8192 [11:41:00<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 11%|█ | 896/8192 [12:24:54<77:09:28, 38.07s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 3b8321b9-2d88-4bfa-9eca-b201c444cba3)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk5/example_holdout_405.jsonl.zst\n 11%|█ | 896/8192 [12:25:55<77:09:28, 38.07s/it]Retrying in 1s [Retry 1/5].\n 11%|█ | 896/8192 [12:25:55<77:09:28, 38.07s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: a98a238a-c0a4-4295-8502-316a89a7ae29)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk1/example_holdout_2524.jsonl.zst\n 11%|█ | 896/8192 [12:33:14<77:09:28, 38.07s/it]Retrying in 1s [Retry 1/5].\n 11%|█▏ | 922/8192 [12:52:49<76:09:46, 37.71s/it]'(ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 36a7cc72-4605-416a-8742-59488d719150)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_5267.jsonl.zst\n 11%|█▏ | 922/8192 [12:52:59<76:09:46, 37.71s/it]Retrying in 1s [Retry 1/5].\n 12%|█▏ | 943/8192 [13:06:07<76:15:57, 37.88s/it]\n{'loss': 3.7796, 'grad_norm': 0.4774172008037567, 'learning_rate': 0.001, 'epoch': 0.06, 'num_input_tokens_seen': 484442112}\n{'loss': 3.7779, 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\"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py\", line 1378, in _iter_from_urlpaths\n raise FileNotFoundError(urlpath)\nFileNotFoundError: zstd://example_train_1215.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk9/example_train_1215.jsonl.zst\n```\n\n</details>",
"Two more today:\n```python\nFileNotFoundError: zstd://example_holdout_5012.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_5012.jsonl.zst\n```\nand\n```python\nFileNotFoundError: zstd://example_holdout_3073.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk2/example_holdout_3073.jsonl.zst\n```\nboth of which exist on the hub ([here](https://huggingface.co/datasets/cerebras/SlimPajama-627B/blob/main/validation/chunk4/example_holdout_5012.jsonl.zst) and [here](https://huggingface.co/datasets/cerebras/SlimPajama-627B/blob/main/validation/chunk2/example_holdout_3073.jsonl.zst)).",
"I also observe the same thing when using streaming with DCLM dataset with 64 GPUs. I have tried ```export HF_DATASETS_STREAMING_PARALLELISM=1``` but doesn't help.",
"Another error today, this time a 504 gateway timeout `HfHubHTTPError`. I have no idea if this is related, but I suspect that it is considering the setup is identical. Notably though, the two errors I posted yesterday were for evaluation (hence the `holdout` in the URLs) whereas today there was no problem doing that first evaluation, but now the `train` split failed.\n```python\n...\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 2226, in __iter__\n for key, example in ex_iterable:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1499, in __iter__\n for x in self.ex_iterable:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1067, in __iter__\n yield from self._iter()\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1231, in _iter\n for key, transformed_example in iter_outputs():\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1207, in iter_outputs\n for i, key_example in inputs_iterator:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1111, in iter_inputs\n for key, example in iterator:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 371, in __iter__\n for key, pa_table in self.generate_tables_fn(**gen_kwags):\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/packaged_modules/json/json.py\", line 114, in _generate_tables\n with open(file, \"rb\") as f:\n ^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/streaming.py\", line 75, in wrapper\n return function(*args, download_config=download_config, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py\", line 948, in xopen\n file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 147, in open\n return self.__enter__()\n ^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 105, in __enter__\n f = self.fs.open(self.path, mode=mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/spec.py\", line 1301, in open\n f = self._open(\n ^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/filesystems/compression.py\", line 85, in _open\n return self._open_with_fsspec().open()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 147, in open\n return self.__enter__()\n ^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 105, in __enter__\n f = self.fs.open(self.path, mode=mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/spec.py\", line 1301, in open\n f = self._open(\n ^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 234, in _open\n return HfFileSystemFile(self, path, mode=mode, revision=revision, block_size=block_size, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 691, in __init__\n self.details = fs.info(self.resolved_path.unresolve(), expand_info=False)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 524, in info\n self.ls(parent_path, expand_info=False)\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 284, in ls\n out = self._ls_tree(path, refresh=refresh, revision=revision, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 375, in _ls_tree\n for path_info in tree:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_api.py\", line 3080, in list_repo_tree\n for path_info in paginate(path=tree_url, headers=headers, params={\"recursive\": recursive, \"expand\": expand}):\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/utils/_pagination.py\", line 46, in paginate\n hf_raise_for_status(r)\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/utils/_http.py\", line 477, in hf_raise_for_status\n raise _format(HfHubHTTPError, str(e), response) from e\nhuggingface_hub.errors.HfHubHTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/cerebras/SlimPajama-627B/tree/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train%2Fchunk8?recursive=False&expand=False&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5amFIVnVhemd2WlhoaGJYQnNaVjkwY21GcGJsOHpOams0TG1wemIyNXNMbnB6ZENKOTozMDAw\n```",
"Another one today:\n```python\nFileNotFoundError: zstd://example_train_4985.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk5/example_train_4985.jsonl.zst\n```",
"This is a constant issue, and has been for six months, at least. Currently, half of my streaming datasets are failing with errors like this.\n\nMuennighoff/natural-instructions:\n```\n File \"/home/crow/repos/praxis/.venv/lib/python3.13/site-packages/datasets/utils/file_utils.py\", line 1379, in _iter_from_urlpaths\n raise FileNotFoundError(urlpath)\nFileNotFoundError: hf://datasets/Muennighoff/natural-instructions@a29a9757125f4bb1c26445ad0d2ef7d9b2cc9c4c/train/task343_winomt_classification_profession_anti_train.jsonl\n```\nopen-phi/textbooks:\n```\n File \"/home/crow/repos/praxis/.venv/lib/python3.13/site-packages/datasets/utils/file_utils.py\", line 1379, in _iter_from_urlpaths\n raise FileNotFoundError(urlpath)\nFileNotFoundError: hf://datasets/open-phi/textbooks@292aaae99cbecacad50f692d7327887f05dacaf2/data/train-00000-of-00001-b513d9e388d56453.parquet\n```\nHuggingFaceTB/smoltalk:\n```\n File \"/home/crow/repos/praxis/.venv/lib/python3.13/site-packages/datasets/utils/file_utils.py\", line 1379, in _iter_from_urlpaths\n raise FileNotFoundError(urlpath)\nFileNotFoundError: hf://datasets/HuggingFaceTB/smoltalk@5feaf2fd3ffca7c237fc38d1861bc30365d48ffa/data/all/train-00003-of-00009.parquet\n```",
"This line of issues has now been going on since April of 2024. It is now August of 2025. I opened this particular issue almost five months ago. Our training runs are still failing. It is apparently too difficult for `datasets` to reliable fetch some text from some server. This is by far the biggest bottleneck in our research and the amount of time spent on setbacks caused by this is unimaginable.\n\nA week ago:\n```python\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 2361, in __iter__\n generator=generator, features=features, gen_kwargs=gen_kwargs, streaming=True, split=split\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1558, in __iter__\n )\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1107, in __iter__\n # If `batched`, first build the batch, if `batch_size` is None or <=0, then the batch is the whole dataset\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1286, in _iter\n iterator = _convert_to_arrow(\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1267, in iter_outputs\n num_examples_to_skip -= 1\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1156, in iter_inputs\n additional_args = ()\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 397, in __iter__\n shard_example_idx_start = self._state_dict[\"shard_example_idx\"] if self._state_dict else 0\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/packaged_modules/json/json.py\", line 99, in _generate_tables\n for file_idx, file in enumerate(itertools.chain.from_iterable(files)):\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/track.py\", line 49, in __iter__\n for x in self.generator(*self.args):\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py\", line 1359, in _iter_from_urlpaths\n cls, urlpaths: Union[str, list[str]], download_config: Optional[DownloadConfig] = None\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: zstd://example_train_1820.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk2/example_train_1820.jsonl.zst\n```\nToday:\n```python\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 2270, in __iter__\n for key, example in ex_iterable:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1535, in __iter__\n for x in self.ex_iterable:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1084, in __iter__\n yield from self._iter()\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1263, in _iter\n for key, transformed_example in outputs:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1244, in iter_outputs\n for i, key_example in inputs_iterator:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1133, in iter_inputs\n for key, example in iterator:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 374, in __iter__\n for key, pa_table in self.generate_tables_fn(**gen_kwags):\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/packaged_modules/json/json.py\", line 99, in _generate_tables\n for file_idx, file in enumerate(itertools.chain.from_iterable(files)):\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/track.py\", line 49, in __iter__\n for x in self.generator(*self.args):\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py\", line 1379, in _iter_from_urlpaths\n raise FileNotFoundError(urlpath)\nFileNotFoundError: zstd://example_train_5054.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_5054.jsonl.zst\n```\nSeriously?"
] | 2025-03-07T19:14:18
| 2025-07-22T08:15:44
| null |
NONE
| null | null | null | null |
### Describe the bug
In https://github.com/huggingface/datasets/issues/6843 it was noted that the streaming feature of `datasets` is highly susceptible to outages and doesn't back off for long (or even *at all*).
I was training a model while streaming SlimPajama and training crashed with a `FileNotFoundError`. I can only assume that this was due to a momentary outage considering the file in question, `train/chunk9/example_train_3889.jsonl.zst`, [exists like all other files in SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B/blob/main/train/chunk9/example_train_3889.jsonl.zst).
```python
...
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 2226, in __iter__
for key, example in ex_iterable:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1499, in __iter__
for x in self.ex_iterable:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1067, in __iter__
yield from self._iter()
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1231, in _iter
for key, transformed_example in iter_outputs():
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1207, in iter_outputs
for i, key_example in inputs_iterator:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1111, in iter_inputs
for key, example in iterator:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 371, in __iter__
for key, pa_table in self.generate_tables_fn(**gen_kwags):
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/packaged_modules/json/json.py", line 99, in _generate_tables
for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/track.py", line 50, in __iter__
for x in self.generator(*self.args):
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py", line 1378, in _iter_from_urlpaths
raise FileNotFoundError(urlpath)
FileNotFoundError: zstd://example_train_3889.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk9/example_train_3889.jsonl.zst
```
That final `raise` is at the bottom of the following snippet:
https://github.com/huggingface/datasets/blob/f693f4e93aabafa878470c80fd42ddb10ec550d6/src/datasets/utils/file_utils.py#L1354-L1379
So clearly, something choked up in `xisfile`.
### Steps to reproduce the bug
This happens when streaming a dataset and iterating over it. In my case, that iteration is done in Trainer's `inner_training_loop`, but this is not relevant to the iterator.
```python
File "/miniconda3/envs/draft/lib/python3.11/site-packages/accelerate/data_loader.py", line 835, in __iter__
next_batch, next_batch_info = self._fetch_batches(main_iterator)
```
### Expected behavior
This bug and the linked issue have one thing in common: *when streaming fails to retrieve an example, the entire program gives up and crashes*. As users, we cannot even protect ourselves from this: when we are iterating over a dataset, we can't make `datasets` skip over a bad example or wait a little longer to retry the iteration, because when a Python generator/iterator raises an error, it loses all its context.
In other words: if you have something that looks like `for b in a: for c in b: for d in c:`, errors in the innermost loop can only be caught by a `try ... except` in `c.__iter__()`. There should be such exception handling in `datasets` and it should have a **configurable exponential back-off**: first wait and retry after 1 minute, then 2 minutes, then 4 minutes, then 8 minutes, ... and after a given amount of retries, **skip the bad example**, and **only after** skipping a given amount of examples, give up and crash. This was requested in https://github.com/huggingface/datasets/issues/6843 too, since currently there is only linear backoff *and* it is clearly not applied to `xisfile`.
### Environment info
- `datasets` version: 3.3.2 *(the latest version)*
- Platform: Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28
- Python version: 3.11.7
- `huggingface_hub` version: 0.26.5
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2024.10.0
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`Dataset.map` ignores existing caches and remaps when ran with different `num_proc`
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[
"This feels related: https://github.com/huggingface/datasets/issues/3044",
"@lhoestq This comment specifically, I agree:\n\n* https://github.com/huggingface/datasets/issues/3044#issuecomment-1239877570\n\n> Almost a year later and I'm in a similar boat. Using custom fingerprints and when using multiprocessing the cached datasets are saved with a template at the end of the filename (something like \"000001_of_000008\" for every process of num_proc). So if in the next time you run the script you set num_proc to a different number, the cache cannot be used.\n> \n> Is there any way to get around this? I am processing a huge dataset so I do the processing on one machine and then transfer the processed data to another in its cache dir but currently that's not possible due to num_proc mismatch.\n\n"
] | 2025-03-03T05:51:26
| 2025-05-12T15:14:09
| 2025-05-12T15:14:09
|
NONE
| null | null | null | null |
### Describe the bug
If you `map` a dataset and save it to a specific `cache_file_name` with a specific `num_proc`, and then call map again with that same existing `cache_file_name` but a different `num_proc`, the dataset will be re-mapped.
### Steps to reproduce the bug
1. Download a dataset
```python
import datasets
dataset = datasets.load_dataset("ylecun/mnist")
```
```
Generating train split: 100%|██████████| 60000/60000 [00:00<00:00, 116429.85 examples/s]
Generating test split: 100%|██████████| 10000/10000 [00:00<00:00, 103310.27 examples/s]
```
2. `map` and cache it with a specific `num_proc`
```python
cache_file_name="./cache/train.map"
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=2)
```
```
Map (num_proc=2): 100%|██████████| 60000/60000 [00:01<00:00, 53764.03 examples/s]
```
3. `map` it with a different `num_proc` and the same `cache_file_name` as before
```python
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=3)
```
```
Map (num_proc=3): 100%|██████████| 60000/60000 [00:00<00:00, 65377.12 examples/s]
```
### Expected behavior
If I specify an existing `cache_file_name`, I don't expect using a different `num_proc` than the one that was used to generate it to cause the dataset to have be be re-mapped.
### Environment info
```console
$ datasets-cli env
- `datasets` version: 3.3.2
- Platform: Linux-5.15.0-131-generic-x86_64-with-glibc2.35
- Python version: 3.10.16
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
```
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Issues with large Datasets
|
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[
"what's the error message ?",
"This was the final error message that it was giving pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0",
"Here is the list of errors:\n\nTraceback (most recent call last):\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 160, in _generate_tables\n df = pandas_read_json(f)\n ^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 38, in pandas_read_json\n return pd.read_json(path_or_buf, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 815, in read_json\n return json_reader.read()\n ^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1025, in read\n obj = self._get_object_parser(self.data)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1051, in _get_object_parser\n obj = FrameParser(json, **kwargs).parse()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1187, in parse\n self._parse()\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1402, in _parse\n self.obj = DataFrame(\n ^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 778, in __init__\n mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 503, in dict_to_mgr\n return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 114, in arrays_to_mgr\n index = _extract_index(arrays)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 677, in _extract_index\n raise ValueError(\"All arrays must be of the same length\")\nValueError: All arrays must be of the same length\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1854, in _prepare_split_single\n for _, table in generator:\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 163, in _generate_tables\n raise e\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 137, in _generate_tables\n pa_table = paj.read_json(\n ^^^^^^^^^^^^^^\n File \"pyarrow/_json.pyx\", line 308, in pyarrow._json.read_json\n File \"pyarrow/error.pxi\", line 155, in pyarrow.lib.pyarrow_internal_check_status\n File \"pyarrow/error.pxi\", line 92, in pyarrow.lib.check_status\npyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to number in row 0\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"run_object_detection.py\", line 582, in <module>\n main()\n File \"run_object_detection.py\", line 407, in main\n dataset = load_dataset(\n ^^^^^^^^^^^^^\n File \"venv/lib/python3.12/site-packages/datasets/load.py\", line 2151, in load_dataset\n builder_instance.download_and_prepare(\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 924, in download_and_prepare\n self._download_and_prepare(\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1000, in _download_and_prepare\n self._prepare_split(split_generator, **prepare_split_kwargs)\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1741, in _prepare_split\n for job_id, done, content in self._prepare_split_single(\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1897, in _prepare_split_single\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\ndatasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset",
"`datasets` is based on Arrow which expects all the lists inside the data to be of fixed type. Arrow can't load lists that contain a mix of integers and strings for example. In your case it looks like one of the lists contains numbers and JSON objects.\n\nI'd suggest you to reformat the data to end up with list of fixed types, otherwise you won't be able to load the data in `datasets`"
] | 2025-02-28T14:05:22
| 2025-03-04T15:02:26
| null |
NONE
| null | null | null | null |
### Describe the bug
If the coco annotation file is too large the dataset will not be able to load it, not entirely sure were the issue is but I am guessing it is due to the code trying to load it all as one line into a dataframe. This was for object detections.
My current work around is the following code but would be nice to be able to do it without worrying about it also probably there is a better way of doing it:
`
dataset_dict = json.load(open("./local_data/annotations/train.json"))
df = pd.DataFrame(columns=['images', 'annotations', 'categories'])
df = df._append({'images': dataset_dict['images'], 'annotations': dataset_dict['annotations'], 'categories': dataset_dict['categories']}, ignore_index=True)
train=Dataset.from_pandas(df)
dataset_dict = json.load(open("./local_data/annotations/validation.json"))
df = pd.DataFrame(columns=['images', 'annotations', 'categories'])
df = df._append({'images': dataset_dict['images'], 'annotations': dataset_dict['annotations'],
'categories': dataset_dict['categories']}, ignore_index=True)
val = Dataset.from_pandas(df)
dataset_dict = json.load(open("./local_data/annotations/test.json"))
df = pd.DataFrame(columns=['images', 'annotations', 'categories'])
df = df._append({'images': dataset_dict['images'], 'annotations': dataset_dict['annotations'],
'categories': dataset_dict['categories']}, ignore_index=True)
test = Dataset.from_pandas(df)
dataset = DatasetDict({'train': train, 'validation': val, 'test': test})
`
### Steps to reproduce the bug
1) step up directory in and have the json files in coco format
-local_data
|-images
|---1.jpg
|---2.jpg
|---....
|---n.jpg
|-annotations
|---test.json
|---train.json
|---validation.json
2) try to load local_data into a dataset if the file is larger than about 300kb it will cause an error.
### Expected behavior
That it loads the jsons preferably in the same format as it has done with a smaller size.
### Environment info
- `datasets` version: 3.3.3.dev0
- Platform: Linux-6.11.0-17-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.29.0
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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I_kwDODunzps6sEvFN
| 7,430
|
Error in code "Time to slice and dice" from course "NLP Course"
|
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[
"You should open an issue in the NLP course website / github page. I'm closing this issue if you don't mind",
"ok, i don't mind, i'll mark the error there"
] | 2025-02-28T11:36:10
| 2025-03-05T11:32:47
| 2025-03-03T17:52:15
|
NONE
| null | null | null | null |
### Describe the bug
When we execute code
```
frequencies = (
train_df["condition"]
.value_counts()
.to_frame()
.reset_index()
.rename(columns={"index": "condition", "condition": "frequency"})
)
frequencies.head()
```
answer should be like this
condition | frequency
birth control | 27655
depression | 8023
acne | 5209
anxiety | 4991
pain | 4744
but he is different
frequency | count
birth control | 27655
depression | 8023
acne | 5209
anxiety | 4991
pain | 4744
this is not correct, correct code
```
frequencies = (
train_df["condition"]
.value_counts()
.to_frame()
.reset_index()
.rename(columns={"index": "condition", "count": "frequency"})
)
````
### Steps to reproduce the bug
```
frequencies = (
train_df["condition"]
.value_counts()
.to_frame()
.reset_index()
.rename(columns={"index": "condition", "condition": "frequency"})
)
frequencies.head()
```
### Expected behavior
condition | frequency
birth control | 27655
depression | 8023
acne | 5209
anxiety | 4991
pain | 4744
### Environment info
Google Colab
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Error splitting the input into NAL units.
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[
"First time I see this error :/ maybe it's an issue with your version of `multiprocess` and `dill` ? Make sure they are compatible with `datasets`",
"> First time I see this error :/ maybe it's an issue with your version of `multiprocess` and `dill` ? Make sure they are compatible with `datasets`\n\nany recommendation for `multiprocess` and `dill`"
] | 2025-02-28T02:30:15
| 2025-03-04T01:40:28
| null |
NONE
| null | null | null | null |
### Describe the bug
I am trying to finetune qwen2.5-vl on 16 * 80G GPUS, and I use `LLaMA-Factory` and set `preprocessing_num_workers=16`. However, I met the following error and the program seem to got crush. It seems that the error come from `datasets` library
The error logging is like following:
```text
Converting format of dataset (num_proc=16): 100%|█████████▉| 19265/19267 [11:44<00:00, 5.88 examples/s]
Converting format of dataset (num_proc=16): 100%|█████████▉| 19266/19267 [11:44<00:00, 5.02 examples/s]
Converting format of dataset (num_proc=16): 100%|██████████| 19267/19267 [11:44<00:00, 5.44 examples/s]
Converting format of dataset (num_proc=16): 100%|██████████| 19267/19267 [11:44<00:00, 27.34 examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [00:00<?, ? examples/s]
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
```
### Others
_No response_
### Steps to reproduce the bug
None
### Expected behavior
excpect to run successfully
### Environment info
```
transformers==4.49.0
datasets==3.2.0
accelerate==1.2.1
peft==0.12.0
trl==0.9.6
tokenizers==0.21.0
gradio>=4.38.0,<=5.18.0
pandas>=2.0.0
scipy
einops
sentencepiece
tiktoken
protobuf
uvicorn
pydantic
fastapi
sse-starlette
matplotlib>=3.7.0
fire
packaging
pyyaml
numpy<2.0.0
av
librosa
tyro<0.9.0
openlm-hub
qwen-vl-utils
```
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I_kwDODunzps6r4YlO
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load_dataset("livecodebench/code_generation_lite", version_tag="release_v2") TypeError: 'NoneType' object is not callable
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[
"> datasets\n\nHi, have you solved this bug? Today I also met the same problem about `livecodebench/code_generation_lite` when evaluating the `Open-R1` repo. I am looking forward to your reply!\n\n",
"Hey guys,\nI tried to reproduce the issue and it works fine. I used google colab as enviroment.\n\n",
"> Hey guys, I tried to reproduce the issue and it works fine. I used google colab as enviroment.\n> \n> \n\nThanks for your kind reply! I wonder which Python version do you use? My Python version is 3.11.11 and datasets version is 3.3.2 but I still met this bug.\n\n<img width=\"1121\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/7c2c5007-ee55-4030-94b9-01fcdea0bf4a\" />",
"@zwxandy It's Python 3.11.11",
"@Serzhanov @zwxandy I have met the same problem, have this problem be solved?",
"> [@Serzhanov](https://github.com/Serzhanov) [@zwxandy](https://github.com/zwxandy) I have met the same problem, have this problem be solved?\n\nI try to downgrade datasets version to 2.20.0，and it works for me @Serzhanov @dshwei , hope this work for you too :)",
"> > datasets\n> \n> Hi, have you solved this bug? Today I also met the same problem about `livecodebench/code_generation_lite` when evaluating the `Open-R1` repo. I am looking forward to your reply!\n> \n> \n\nHi, have you resolved this problem? I meet the same bug when evaluating the ’Open-R1’, too. Looking forward to your reply!",
"> > [@Serzhanov](https://github.com/Serzhanov) [@zwxandy](https://github.com/zwxandy) I have met the same problem, have this problem be solved?\n> \n> I try to downgrade datasets version to 2.20.0，and it works for me [@Serzhanov](https://github.com/Serzhanov) [@dshwei](https://github.com/dshwei) , hope this work for you too :)\n\nI still met the same bug after downgrading datasets version to 2.20.0. Moreover, it is not friendly to Open-R1 since there can be another bug: `open-r1 0.1.0.dev0 requires datasets>=3.2.0` with datasets==2.20.0",
"> > > datasets\n> > \n> > \n> > Hi, have you solved this bug? Today I also met the same problem about `livecodebench/code_generation_lite` when evaluating the `Open-R1` repo. I am looking forward to your reply!\n> > \n> \n> Hi, have you resolved this problem? I meet the same bug when evaluating the ’Open-R1’, too. Looking forward to your reply!\n\nHi, I still cannot solve this bug introduced from datasets version. Downgrading datasets version to 2.20.0 cannot work for me and it introduces another problem `open-r1 0.1.0.dev0 requires datasets>=3.2.0` in Open-R1.\n\nLuckily, there is a tricky way to enable you to run Open-R1. You can remove or comment the code related to `lcb` in `~/anaconda3/envs/openr1/lib/python3.11/site-packages/lighteval/tasks/extended/__init__.py`. I have reproduce the results of DeepSeek-R1-Distill-Qwen-1.5B and 7B on MATH-500, GPQA, and AIME24.\n\nYou can have a try~",
"The issue was resolved .\nbecause the file` livecodebench/code_generation_lite/code_generation_lite.py `was not downloaded. Manually downloading it fixed the problem."
] | 2025-02-27T07:36:02
| 2025-03-27T05:05:33
| null |
NONE
| null | null | null | null |
### Describe the bug
from datasets import load_dataset
lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2")
or
configs = get_dataset_config_names("livecodebench/code_generation_lite", trust_remote_code=True)
both error:
Traceback (most recent call last):
File "", line 1, in
File "/workspace/miniconda/envs/grpo/lib/python3.10/site-packages/datasets/load.py", line 2131, in load_dataset
builder_instance = load_dataset_builder(
File "/workspace/miniconda/envs/grpo/lib/python3.10/site-packages/datasets/load.py", line 1888, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
TypeError: 'NoneType' object is not callable
### Steps to reproduce the bug
from datasets import get_dataset_config_names
configs = get_dataset_config_names("livecodebench/code_generation_lite", trust_remote_code=True)
OR
lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2")
### Expected behavior
load datasets livecodebench/code_generation_lite
### Environment info
import datasets
version '3.3.2'
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Row indexing a dataset with numpy integers
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"Would be cool to be consistent when it comes to indexing with numpy objects, if we do accept numpy arrays we should indeed accept numpy integers. Your idea sounds reasonable, I'd also be in favor of adding a simple test as well"
] | 2025-02-25T18:44:45
| 2025-07-28T02:23:17
| 2025-07-28T02:23:17
|
CONTRIBUTOR
| null | null | null | null |
### Feature request
Allow indexing datasets with a scalar numpy integer type.
### Motivation
Indexing a dataset with a scalar numpy.int* object raises a TypeError. This is due to the test in `datasets/formatting/formatting.py:key_to_query_type`
``` python
def key_to_query_type(key: Union[int, slice, range, str, Iterable]) -> str:
if isinstance(key, int):
return "row"
elif isinstance(key, str):
return "column"
elif isinstance(key, (slice, range, Iterable)):
return "batch"
_raise_bad_key_type(key)
```
In the row case, it checks if key is an int, which returns false when key is integer like but not a builtin python integer type. This is counterintuitive because a numpy array of np.int64s can be used for the batch case.
For example:
``` python
import numpy as np
import datasets
dataset = datasets.Dataset.from_dict({"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})
# Regular indexing
dataset[0]
dataset[:2]
# Indexing with numpy data types (expect same results)
idx = np.asarray([0, 1])
dataset[idx] # Succeeds when using an array of np.int64 values
dataset[idx[0]] # Fails with TypeError when using scalar np.int64
```
For the user, this can be solved by wrapping `idx[0]` in `int` but the test could also be changed in `key_to_query_type` to accept a less strict definition of int.
``` diff
+import numbers
+
def key_to_query_type(key: Union[int, slice, range, str, Iterable]) -> str:
+ if isinstance(key, numbers.Integral):
- if isinstance(key, int):
return "row"
elif isinstance(key, str):
return "column"
elif isinstance(key, (slice, range, Iterable)):
return "batch"
_raise_bad_key_type(key)
```
Looking at how others do it, pandas has an `is_integer` definition that it checks which uses `is_integer_object` defined in `pandas/_libs/utils.pxd`:
``` cython
cdef inline bint is_integer_object(object obj) noexcept:
"""
Cython equivalent of
`isinstance(val, (int, np.integer)) and not isinstance(val, (bool, np.timedelta64))`
Parameters
----------
val : object
Returns
-------
is_integer : bool
Notes
-----
This counts np.timedelta64 objects as integers.
"""
return (not PyBool_Check(obj) and isinstance(obj, (int, cnp.integer))
and not is_timedelta64_object(obj))
```
This would be less flexible as it explicitly checks for numpy integer, but worth noting that they had the need to ensure the key is not a bool.
### Your contribution
I can submit a pull request with the above changes after checking that indexing succeeds with the numpy integer type. Or if there is a different integer check that would be preferred I could add that.
If there is a reason not to want this behavior that is fine too.
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DVC integration broken
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"Unfortunately `url` is a reserved argument in `fsspec.url_to_fs`, so ideally file system implementations like DVC should use another argument name to avoid this kind of errors"
] | 2025-02-25T13:14:31
| 2025-03-03T17:42:02
| null |
NONE
| null | null | null | null |
### Describe the bug
The DVC integration seems to be broken.
Followed this guide: https://dvc.org/doc/user-guide/integrations/huggingface
### Steps to reproduce the bug
#### Script to reproduce
~~~python
from datasets import load_dataset
dataset = load_dataset(
"csv",
data_files="dvc://workshop/satellite-data/jan_train.csv",
storage_options={"url": "https://github.com/iterative/dataset-registry.git"},
)
print(dataset)
~~~
#### Error log
~~~
Traceback (most recent call last):
File "C:\tmp\test\load.py", line 3, in <module>
dataset = load_dataset(
^^^^^^^^^^^^^
File "C:\tmp\test\.venv\Lib\site-packages\datasets\load.py", line 2151, in load_dataset
builder_instance.download_and_prepare(
File "C:\tmp\test\.venv\Lib\site-packages\datasets\builder.py", line 808, in download_and_prepare
fs, output_dir = url_to_fs(output_dir, **(storage_options or {}))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: url_to_fs() got multiple values for argument 'url'
~~~
### Expected behavior
Integration would work and the indicated file is downloaded and opened.
### Environment info
#### Python version
~~~
python --version
Python 3.11.10
~~~
#### Venv (pip install datasets dvc):
~~~
Package Version
---------------------- -----------
aiohappyeyeballs 2.4.6
aiohttp 3.11.13
aiohttp-retry 2.9.1
aiosignal 1.3.2
amqp 5.3.1
annotated-types 0.7.0
antlr4-python3-runtime 4.9.3
appdirs 1.4.4
asyncssh 2.20.0
atpublic 5.1
attrs 25.1.0
billiard 4.2.1
celery 5.4.0
certifi 2025.1.31
cffi 1.17.1
charset-normalizer 3.4.1
click 8.1.8
click-didyoumean 0.3.1
click-plugins 1.1.1
click-repl 0.3.0
colorama 0.4.6
configobj 5.0.9
cryptography 44.0.1
datasets 3.3.2
dictdiffer 0.9.0
dill 0.3.8
diskcache 5.6.3
distro 1.9.0
dpath 2.2.0
dulwich 0.22.7
dvc 3.59.1
dvc-data 3.16.9
dvc-http 2.32.0
dvc-objects 5.1.0
dvc-render 1.0.2
dvc-studio-client 0.21.0
dvc-task 0.40.2
entrypoints 0.4
filelock 3.17.0
flatten-dict 0.4.2
flufl-lock 8.1.0
frozenlist 1.5.0
fsspec 2024.12.0
funcy 2.0
gitdb 4.0.12
gitpython 3.1.44
grandalf 0.8
gto 1.7.2
huggingface-hub 0.29.1
hydra-core 1.3.2
idna 3.10
iterative-telemetry 0.0.10
kombu 5.4.2
markdown-it-py 3.0.0
mdurl 0.1.2
multidict 6.1.0
multiprocess 0.70.16
networkx 3.4.2
numpy 2.2.3
omegaconf 2.3.0
orjson 3.10.15
packaging 24.2
pandas 2.2.3
pathspec 0.12.1
platformdirs 4.3.6
prompt-toolkit 3.0.50
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pyarrow 19.0.1
pycparser 2.22
pydantic 2.10.6
pydantic-core 2.27.2
pydot 3.0.4
pygit2 1.17.0
pygments 2.19.1
pygtrie 2.5.0
pyparsing 3.2.1
python-dateutil 2.9.0.post0
pytz 2025.1
pywin32 308
pyyaml 6.0.2
requests 2.32.3
rich 13.9.4
ruamel-yaml 0.18.10
ruamel-yaml-clib 0.2.12
scmrepo 3.3.10
semver 3.0.4
setuptools 75.8.0
shellingham 1.5.4
shortuuid 1.0.13
shtab 1.7.1
six 1.17.0
smmap 5.0.2
sqltrie 0.11.2
tabulate 0.9.0
tomlkit 0.13.2
tqdm 4.67.1
typer 0.15.1
typing-extensions 4.12.2
tzdata 2025.1
urllib3 2.3.0
vine 5.1.0
voluptuous 0.15.2
wcwidth 0.2.13
xxhash 3.5.0
yarl 1.18.3
zc-lockfile 3.0.post1
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better correspondence between cached and saved datasets created using from_generator
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[] | 2025-02-24T22:14:37
| 2025-02-26T03:10:22
| null |
CONTRIBUTOR
| null | null | null | null |
### Feature request
At the moment `.from_generator` can only create a dataset that lives in the cache. The cached dataset cannot be loaded with `load_from_disk` because the cache folder is missing `state.json`. So the only way to convert this cached dataset to a regular is to use `save_to_disk` which needs to create a copy of the cached dataset. For large datasets this can end up wasting a lot of space. In my case the saving operation failed so I am stuck with a large cached dataset and no clear way to convert to a `Dataset` that I can use. The requested feature is to provide a way to be able to load a cached dataset using `.load_from_disk`. Alternatively `.from_generator` can create the dataset at a specified location so that it can be loaded from there with `.load_from_disk`.
### Motivation
I have the following workflow which has exposed some awkwardness about the Datasets saving/caching.
1. I created a cached dataset using `.from_generator` which was cached in a folder. This dataset is rather large (~600GB) with many shards.
2. I tried to save this dataset using `.save_to_disk` to another location so that I can use later as a `Dataset`. This essentially creates another copy (for a total of 1.2TB!) of what is already in the cache... In my case the saving operation keeps dying for some reason and I am stuck with a cached dataset and no copy.
3. Now I am trying to "save" the existing cached dataset but it is not clear how to access the cached files after `.from_generator` has finished e.g. from a different process. I should not be even looking at the cache but I really do not want to waste another 2hr to generate the set so that if fails agains (I already did this couple of times).
- I tried `.load_from_disk` but it does not work with cached files and complains that this is not a `Dataset` (!).
- I looked at `.from_file` which takes one file but the cached file has many (shards) so I am not sure how to make this work.
- I tried `.load_dataset` but this seems to either try to "download" a copy (of a file which is already in the local file system!) which I will then need to save or I need to use `streaming=False` to create an `IterableDataset `which then I need to convert (using the cache) to `Dataset` so that I can save it. With both options I will end up with 3 copies of the same dataset for a total of ~2TB! I am hoping here is another way to do this...
Maybe I am missing something here: I looked at docs and forums but no luck. I have a bunch of arrow files cached by `Dataset.from_generator` and no clean way to make them into a `Dataset` that I can use.
This all could be so much easer if `load_from_disk` can recognize the cached files and produce a `Dataset`: after the cache is created I would not have to "save" it again and I can just load it when I need. At the moment `load_from_disk` needs `state.json` which is lacking in the cache folder. So perhaps `.from_generator` could be made to "finalize" (e.g. create `state.json`) the dataset once it is done so that it can be loaded easily. Or provide `.from_generator` with a `save_to_dir` parameter in addition to `cache_dir` which can be used for the whole process including creating the `state.json` at the end.
As a proof of concept I just created `state.json` by hand and `load_from_disk` worked using the cache! So it seems to be the missing piece here.
### Your contribution
Time permitting I can look into `.from_generator` to see if adding `state.json` is feasible.
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Import order crashes script execution
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[] | 2025-02-24T17:03:43
| 2025-02-24T17:03:43
| null |
NONE
| null | null | null | null |
### Describe the bug
Hello,
I'm trying to convert an HF dataset into a TFRecord so I'm importing `tensorflow` and `datasets` to do so.
Depending in what order I'm importing those librairies, my code hangs forever and is unkillable (CTRL+C doesn't work, I need to kill my shell entirely).
Thank you for your help
🙏
### Steps to reproduce the bug
If you run the following script, this will hang forever :
```python
import tensorflow as tf
import datasets
dataset = datasets.load_dataset("imagenet-1k", split="validation", streaming=True)
print(next(iter(dataset)))
```
however running the following will work fine (I just changed the order of the imports) :
```python
import datasets
import tensorflow as tf
dataset = datasets.load_dataset("imagenet-1k", split="validation", streaming=True)
print(next(iter(dataset)))
```
### Expected behavior
I'm expecting the script to reach the end and my case print the content of the first item in the dataset
```
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=408x500 at 0x70C646A03110>, 'label': 91}
```
### Environment info
```
$ datasets-cli env
- `datasets` version: 3.3.2
- Platform: Linux-6.8.0-1017-aws-x86_64-with-glibc2.35
- Python version: 3.11.7
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
```
I'm also using `tensorflow==2.18.0`.
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pyarrow.lib.arrowinvalid: cannot mix list and non-list, non-null values with map function
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[
"@lhoestq ",
"Can you try passing text: None for the image object ? Pyarrow expects all the objects to have the exact same type, in particular the dicttionaries in \"content\" should all have the keys \"type\" and \"text\"",
"The following modification on system prompt works, but it is different from the usual way to use it.\n```\ndef make_conversation(example):\n prompt = []\n\n prompt.append({\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": system_prompt}]})\n prompt.append(\n {\n \"role\": \"user\", \n \"content\": [\n {\"type\": \"image\"},\n {\"type\": \"text\", \"text\": example[\"problem\"]},\n ]\n }\n )\n return {\"prompt\": prompt}\n```",
"Good to know ! But yes Arrow / Parquet have this typing limitation (which is great to ensure data integrity, but constraining at the same time). It's is really blocking you, feel free to ping the arrow team / community if they plan to have a Union type or a JSON type",
"I encounter exactly the similar problem when using pyarrow. This issue truly helps a lot."
] | 2025-02-21T10:58:06
| 2025-07-11T13:06:10
| null |
NONE
| null | null | null | null |
### Describe the bug
Encounter pyarrow.lib.arrowinvalid error with map function in some example when loading the dataset
### Steps to reproduce the bug
```
from datasets import load_dataset
from PIL import Image, PngImagePlugin
dataset = load_dataset("leonardPKU/GEOQA_R1V_Train_8K")
system_prompt="You are a helpful AI Assistant"
def make_conversation(example):
prompt = []
prompt.append({"role": "system", "content": system_prompt})
prompt.append(
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": example["problem"]},
]
}
)
return {"prompt": prompt}
def check_data_types(example):
for key, value in example.items():
if key == 'image':
if not isinstance(value, PngImagePlugin.PngImageFile):
print(value)
if key == "problem" or key == "solution":
if not isinstance(value, str):
print(value)
return example
dataset = dataset.map(check_data_types)
dataset = dataset.map(make_conversation)
```
### Expected behavior
Successfully process the dataset with map
### Environment info
datasets==3.3.1
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I_kwDODunzps6q0D_S
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Shard Dataset at specific indices
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[
"Hi ! if it's an option I'd suggest to have one sequence per row instead.\n\nOtherwise you'd have to make your own save/load mechanism",
"Saving one sequence per row is very difficult and heavy and makes all the optimizations pointless. How would a custom save/load mechanism look like?",
"You can use `pyarrow` for example to save/load individual arrow or parquet files and control what they contain"
] | 2025-02-20T10:43:10
| 2025-02-24T11:06:45
| null |
NONE
| null | null | null | null |
I have a dataset of sequences, where each example in the sequence is a separate row in the dataset (similar to LeRobotDataset). When running `Dataset.save_to_disk` how can I provide indices where it's possible to shard the dataset such that no episode spans more than 1 shard. Consequently, when I run `Dataset.load_from_disk`, how can I load just a subset of the shards to save memory and time on different ranks?
I guess an alternative to this would be, given a loaded `Dataset`, how can I run `Dataset.shard` such that sharding doesn't split any episode across shards?
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I_kwDODunzps6qhph-
| 7,413
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Documentation on multiple media files of the same type with WebDataset
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[
"Yes this is correct and it works with huggingface datasets as well ! Feel free to include an example here: https://github.com/huggingface/datasets/blob/main/docs/source/video_dataset.mdx"
] | 2025-02-18T16:13:20
| 2025-02-20T14:17:54
| null |
NONE
| null | null | null | null |
The [current documentation](https://huggingface.co/docs/datasets/en/video_dataset) on a creating a video dataset includes only examples with one media file and one json. It would be useful to have examples where multiple files of the same type are included. For example, in a sign language dataset, you may have a base video and a video annotation of the extracted pose. According to the WebDataset documentation, this should be able to be done with period separated filenames. For example:
```e39871fd9fd74f55.base.mp4
e39871fd9fd74f55.pose.mp4
e39871fd9fd74f55.json
f18b91585c4d3f3e.base.mp4
f18b91585c4d3f3e.pose.mp4
f18b91585c4d3f3e.json
...
```
If you can confirm that this method of including multiple media files works with huggingface datasets and include an example in the documentation, I'd appreciate it.
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I_kwDODunzps6qb37u
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|
Index Error Invalid Ket is out of bounds for size 0 for code-search-net/code_search_net dataset
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[] | 2025-02-18T05:58:33
| 2025-02-18T06:42:07
| null |
NONE
| null | null | null | null |
### Describe the bug
I am trying to do model pruning on sentence-transformers/all-mini-L6-v2 for the code-search-net/code_search_net dataset using INCTrainer class
However I am getting below error
```
raise IndexError(f"Invalid Key: {key is our of bounds for size {size}")
IndexError: Invalid key: 1840208 is out of bounds for size 0
```
### Steps to reproduce the bug
Model pruning on the above dataset using the below guide
https://huggingface.co/docs/optimum/en/intel/neural_compressor/optimization#pruning
### Expected behavior
The modsl should be successfully pruned
### Environment info
Torch version: 2.4.1
Python version: 3.8.10
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| 7,406
|
Adding Core Maintainer List to CONTRIBUTING.md
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"@lhoestq",
"there is no per-module maintainer and the list is me alone nowadays ^^'",
"@lhoestq \nOh... I feel for you. \nWhat are your criteria for choosing a core maintainer? \nIt seems like it's too much work for you to manage all this code by yourself.\n\nAlso, if you don't mind, can you check this PR for me?\n#7368 I'd like this to be added as soon as possible because I need it."
] | 2025-02-17T00:32:40
| 2025-03-24T10:57:54
| 2025-03-24T10:57:54
|
CONTRIBUTOR
| null | null | null | null |
### Feature request
I propose adding a core maintainer list to the `CONTRIBUTING.md` file.
### Motivation
The Transformers and Liger-Kernel projects maintain lists of core maintainers for each module.
However, the Datasets project doesn't have such a list.
### Your contribution
I have nothing to add here.
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Lazy loading of environment variables
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[
"Many python packages out there, including `huggingface_hub`, do load the environment variables on import.\nYou should `load_dotenv()` before importing the libraries.\n\nFor example you can move all you imports inside your `main()` function"
] | 2025-02-16T22:31:41
| 2025-02-17T15:17:18
| null |
NONE
| null | null | null | null |
### Describe the bug
Loading a `.env` file after an `import datasets` call does not correctly use the environment variables.
This is due the fact that environment variables are read at import time:
https://github.com/huggingface/datasets/blob/de062f0552a810c52077543c1169c38c1f0c53fc/src/datasets/config.py#L155C1-L155C80
### Steps to reproduce the bug
```bash
# make tmp dir
mkdir -p /tmp/debug-env
# make .env file
echo HF_HOME=/tmp/debug-env/data > /tmp/debug-env/.env
# first load dotenv, downloads to /tmp/debug-env/data
uv run --with datasets,python-dotenv python3 -c \
'import dotenv; dotenv.load_dotenv("/tmp/debug-env/.env"); import datasets; datasets.load_dataset("Anthropic/hh-rlhf")'
# first import datasets, downloads to `~/.cache/huggingface`
uv run --with datasets,python-dotenv python3 -c \
'import datasets; import dotenv; dotenv.load_dotenv("/tmp/debug-env/.env"); datasets.load_dataset("Anthropic/hh-rlhf")'
```
### Expected behavior
I expect that setting environment variables with something like this:
```python3
if __name__ == "__main__":
load_dotenv()
main()
```
works correctly.
### Environment info
"datasets>=3.3.0",
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Performance regression in `dataset.filter`
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[
"Thanks for reporting, I'll fix the regression today",
"I just released `datasets` 3.3.1 with a fix, let me know if it's good now :)",
"@lhoestq it fixed the issue.\n\nThis was (very) fast, thank you very much!"
] | 2025-02-16T22:19:14
| 2025-02-17T17:46:06
| 2025-02-17T14:28:48
|
NONE
| null | null | null | null |
### Describe the bug
We're filtering dataset of ~1M (small-ish) records. At some point in the code we do `dataset.filter`, before (including 3.2.0) it was taking couple of seconds, and now it takes 4 hours.
We use 16 threads/workers, and stack trace at them look as follows:
```
Traceback (most recent call last):
File "/python/lib/python3.12/site-packages/multiprocess/process.py", line 314, in _bootstrap
self.run()
File "/python/lib/python3.12/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/python/lib/python3.12/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 678, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3511, in _map_single
for i, batch in iter_outputs(shard_iterable):
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3461, in iter_outputs
yield i, apply_function(example, i, offset=offset)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3390, in apply_function
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 6416, in get_indices_from_mask_function
indices_array = indices_mapping.column(0).take(indices_array)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 1079, in pyarrow.lib.ChunkedArray.take
File "/python/lib/python3.12/site-packages/pyarrow/compute.py", line 458, in take
def take(data, indices, *, boundscheck=True, memory_pool=None):
```
### Steps to reproduce the bug
1. Save dataset of 1M records in arrow
2. Filter it with 16 threads
3. Watch it take too long
### Expected behavior
Filtering done fast
### Environment info
datasets 3.3.0, python 3.12
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Synchronize parameters for various datasets
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[
"Hi ! the `desc` parameter is only available for Dataset / DatasetDict for the progress bar of `map()``\n\nSince IterableDataset only runs the map functions when you iterate over the dataset, there is no progress bar and `desc` is useless. We could still add the argument for parity but it wouldn't be used for anything",
"I think you should add it. It doesn't hurt. The reason I ran into it was because I re-wrote a pipeline to use either a stream or a fully loaded dataset. Of course I can simply remove it but it is nice to have on the memory loaded dataset. "
] | 2025-02-14T09:15:11
| 2025-02-19T11:50:29
| null |
NONE
| null | null | null | null |
### Describe the bug
[IterableDatasetDict](https://huggingface.co/docs/datasets/v3.2.0/en/package_reference/main_classes#datasets.IterableDatasetDict.map) map function is missing the `desc` parameter. You can see the equivalent map function for [Dataset here](https://huggingface.co/docs/datasets/v3.2.0/en/package_reference/main_classes#datasets.Dataset.map).
There might be other parameters missing - I haven't checked.
### Steps to reproduce the bug
from datasets import Dataset, IterableDataset, IterableDatasetDict
ds = IterableDatasetDict({"train": Dataset.from_dict({"a": range(6)}).to_iterable_dataset(num_shards=3),
"validate": Dataset.from_dict({"a": range(6)}).to_iterable_dataset(num_shards=3)})
for d in ds["train"]:
print(d)
ds = ds.map(lambda x: {k: v+1 for k, v in x.items()}, desc="increment")
for d in ds["train"]:
print(d)
### Expected behavior
The description parameter should be available for all datasets (or none).
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-6.1.85+-x86_64-with-glibc2.35
- Python version: 3.11.11
- `huggingface_hub` version: 0.28.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.9.0
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I_kwDODunzps6qEGV9
| 7,400
|
504 Gateway Timeout when uploading large dataset to Hugging Face Hub
|
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"I transferred to the `datasets` repository. Is there any retry mechanism in `datasets` @lhoestq ?\n\nAnother solution @hotchpotch if you want to get your dataset pushed to the Hub in a robust way is to save it to a local folder first and then use `huggingface-cli upload-large-folder` (see https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-large-folder). It has better retry mechanism in case of failure.",
"There is no retry mechanism for `api.preupload_lfs_files` in `push_to_hub()` but we can definitely add one here\n\nhttps://github.com/huggingface/datasets/blob/de062f0552a810c52077543c1169c38c1f0c53fc/src/datasets/arrow_dataset.py#L5372",
"@Wauplin \n\nThank you! I believe that to use load_dataset() to read data from Hugging Face, we need to first save the markdown metadata and parquet files in our local filesystem, then upload them using upload-large-folder. If you know how to do this, could you please let me know?\n\n",
"@lhoestq \n\nI see, so adding a retry mechanism there would solve it. If I continue to have issues, I'll consider implementing that kind of solution."
] | 2025-02-14T02:18:35
| 2025-02-14T23:48:36
| null |
NONE
| null | null | null | null |
### Description
I encountered consistent 504 Gateway Timeout errors while attempting to upload a large dataset (approximately 500GB) to the Hugging Face Hub. The upload fails during the process with a Gateway Timeout error.
I will continue trying to upload. While it might succeed in future attempts, I wanted to report this issue in the meantime.
### Reproduction
- I attempted the upload 3 times
- Each attempt resulted in the same 504 error during the upload process (not at the start, but in the middle of the upload)
- Using `dataset.push_to_hub()` method
### Environment Information
```
- huggingface_hub version: 0.28.0
- Platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.39
- Python version: 3.11.10
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Running in Google Colab Enterprise ?: No
- Token path ?: /home/hotchpotch/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: hotchpotch
- Configured git credential helpers: store
- FastAI: N/A
- Tensorflow: N/A
- Torch: 2.5.1
- Jinja2: 3.1.5
- Graphviz: N/A
- keras: N/A
- Pydot: N/A
- Pillow: 10.4.0
- hf_transfer: N/A
- gradio: N/A
- tensorboard: N/A
- numpy: 1.26.4
- pydantic: 2.10.6
- aiohttp: 3.11.11
- ENDPOINT: https://huggingface.co
- HF_HUB_CACHE: /home/hotchpotch/.cache/huggingface/hub
- HF_ASSETS_CACHE: /home/hotchpotch/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/hotchpotch/.cache/huggingface/token
- HF_STORED_TOKENS_PATH: /home/hotchpotch/.cache/huggingface/stored_tokens
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
- HF_HUB_ETAG_TIMEOUT: 10
- HF_HUB_DOWNLOAD_TIMEOUT: 10
```
### Full Error Traceback
```python
Traceback (most recent call last):
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_http.py", line 406, in hf_raise_for_status
response.raise_for_status()
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/requests/models.py", line 1024, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/datasets/hotchpotch/fineweb-2-edu-japanese.git/info/lfs/objects/batch
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/create_edu_japanese_ds/upload_edu_japanese_ds.py", line 12, in <module>
ds.push_to_hub("hotchpotch/fineweb-2-edu-japanese", private=True)
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/datasets/dataset_dict.py", line 1665, in push_to_hub
split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 5301, in _push_parquet_shards_to_hub
api.preupload_lfs_files(
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/hf_api.py", line 4215, in preupload_lfs_files
_upload_lfs_files(
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/_commit_api.py", line 395, in _upload_lfs_files
batch_actions_chunk, batch_errors_chunk = post_lfs_batch_info(
^^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/lfs.py", line 168, in post_lfs_batch_info
hf_raise_for_status(resp)
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_http.py", line 477, in hf_raise_for_status
raise _format(HfHubHTTPError, str(e), response) from e
huggingface_hub.errors.HfHubHTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/datasets/hotchpotch/fineweb-2-edu-japanese.git/info/lfs/objects/batch
```
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I_kwDODunzps6ptGYT
| 7,394
|
Using load_dataset with data_files and split arguments yields an error
|
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[
"Hi, \nI want to work on this issue involving adding a verification test for the **`HuggingFaceM4/InterleavedWebDocuments`** dataset. \nThis will be my first contribution to `datasets`, \nI plan to add a simple loading and basic structure verification test like other recent dataset tests. \nYou can expect a PR in the next few hours or days. \nThanks for the good first issue! "
] | 2025-02-12T04:50:11
| 2025-11-21T14:05:23
| null |
NONE
| null | null | null | null |
### Describe the bug
It seems the list of valid splits recorded by the package becomes incorrectly overwritten when using the `data_files` argument.
If I run
```python
from datasets import load_dataset
load_dataset("allenai/super", split="all_examples", data_files="tasks/expert.jsonl")
```
then I get the error
```
ValueError: Unknown split "all_examples". Should be one of ['train'].
```
However, if I run
```python
from datasets import load_dataset
load_dataset("allenai/super", split="train", name="Expert")
```
then I get
```
ValueError: Unknown split "train". Should be one of ['all_examples'].
```
### Steps to reproduce the bug
Run
```python
from datasets import load_dataset
load_dataset("allenai/super", split="all_examples", data_files="tasks/expert.jsonl")
```
### Expected behavior
No error.
### Environment info
Python = 3.12
datasets = 3.2.0
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I_kwDODunzps6po_bD
| 7,392
|
push_to_hub payload too large error when using large ClassLabel feature
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"See also <https://discuss.huggingface.co/t/datasetdict-push-to-hub-failing-with-payload-to-large/140083/8>\n"
] | 2025-02-11T17:51:34
| 2025-02-11T18:01:31
| null |
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
When using `datasets.DatasetDict.push_to_hub` an `HfHubHTTPError: 413 Client Error: Payload Too Large for url` is raised if the dataset contains a large `ClassLabel` feature. Even if the total size of the dataset is small.
### Steps to reproduce the bug
``` python
import random
import sys
import datasets
random.seed(42)
def random_str(sz):
return "".join(chr(random.randint(ord("a"), ord("z"))) for _ in range(sz))
data = datasets.DatasetDict(
{
str(i): datasets.Dataset.from_dict(
{
"label": [list(range(3)) for _ in range(10)],
"abstract": [random_str(10_000) for _ in range(10)],
},
)
for i in range(3)
}
)
features = data["1"].features.copy()
features["label"] = datasets.Sequence(
datasets.ClassLabel(names=[str(i) for i in range(50_000)])
)
data = data.map(lambda examples: {}, features=features)
feat_size = sys.getsizeof(data["1"].features["label"].feature.names)
print(f"Size of ClassLabel names: {feat_size}")
# Size of ClassLabel names: 444376
data.push_to_hub("dconnell/pubtator3_test")
```
Note that this succeeds if `ClassLabel` has fewer names or if `ClassLabel` is replaced with `Value("int64")`
### Expected behavior
Should push the dataset to hub.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 3.2.0
- Platform: Linux-5.15.0-126-generic-x86_64-with-glibc2.35
- Python version: 3.12.8
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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I_kwDODunzps6plhL8
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AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'
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[] | 2025-02-11T12:02:26
| 2025-02-11T12:02:26
| null |
NONE
| null | null | null | null |
pyarrow 尝试了若干个版本都不可以
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| 2,843,813,365
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I_kwDODunzps6pgSX1
| 7,390
|
Re-add py.typed
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"A similar issue was fixed for the `transformers` package, too: https://github.com/huggingface/transformers/pull/37022"
] | 2025-02-10T22:12:52
| 2025-08-10T00:51:17
| null |
CONTRIBUTOR
| null | null | null | null |
### Feature request
The motivation for removing py.typed no longer seems to apply. Would a solution like [this one](https://github.com/huggingface/huggingface_hub/pull/2752) work here?
### Motivation
MyPy support is broken. As more type checkers come out, such as RedKnot, these may also be broken. It would be good to be PEP 561 compliant as long as it's not too onerous.
### Your contribution
I can re-add py.typed, but I don't know how to make sur all of the `__all__` files are provided (although you may not need to with modern PyRight).
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Getting statistics about filtered examples
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"You can actually track a running sum in map() or filter() :)\n\n```python\nnum_filtered = 0\n\ndef f(x):\n global num_filtered\n condition = len(x[\"text\"]) < 1000\n if not condition:\n num_filtered += 1\n return condition\n\nds = ds.filter(f)\nprint(num_filtered)\n```\n\nand if you want to use multiprocessing, make sure to use a variable that is shared across processes\n\n\n```python\nfrom multiprocess import Manager\n\nmanager = Manager()\nnum_filtered = manager.Value('i', 0)\n\ndef f(x):\n global num_filtered\n condition = len(x[\"text\"]) < 1000\n if not condition:\n num_filtered.value += 1\n return condition\n\nds = ds.filter(f, num_proc=4)\nprint(num_filtered.value)\n```\n\nPS: `datasets` uses `multiprocess` instead of the `multiprocessing` package to support lambda functions in map() and filter()",
"Oh that's great to know!\n\nI guess this value would not be exactly synced with the batch in cases of pre-fetch and shuffle buffers and so on, but that's probably fine. Thanks!"
] | 2025-02-10T20:48:29
| 2025-02-11T20:44:15
| 2025-02-11T20:44:13
|
NONE
| null | null | null | null |
@lhoestq wondering if the team has thought about this and if there are any recommendations?
Currently when processing datasets some examples are bound to get filtered out, whether it's due to bad format, or length is too long, or any other custom filters that might be getting applied. Let's just focus on the filter by length for now, since that would be something that gets applied dynamically for each training run. Say we want to show a graph in W&B with the running total of the number of filtered examples so far.
What would be a good way to go about hooking this up? Because the map/filter operations happen before the DataLoader batches are created, at training time if we're just grabbing batches from the DataLoader then we won't know how many things have been filtered already. But there's not really a good way to include a 'num_filtered' key into the dataset itself either because dataset map/filter process examples independently and don't have a way to track a running sum.
The only approach I can kind of think of is having a 'is_filtered' key in the dataset, and then creating a custom batcher/collator that reads that and tracks the metric?
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OSError: [Errno 22] Invalid argument forbidden character
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"You can probably copy the dataset in your HF account and rename the files (without having to download them to your disk). Or alternatively feel free to open a Pull Request to this dataset with the renamed file",
"Thank you, that will help me work around this problem"
] | 2025-02-10T17:46:31
| 2025-02-11T13:42:32
| 2025-02-11T13:42:30
|
NONE
| null | null | null | null |
### Describe the bug
I'm on Windows and i'm trying to load a datasets but i'm having title error because files in the repository are named with charactere like < >which can't be in a name file. Could it be possible to load this datasets but removing those charactere ?
### Steps to reproduce the bug
load_dataset("CATMuS/medieval") on Windows
### Expected behavior
Making the function to erase the forbidden character to allow loading the datasets who have those characters.
### Environment info
- `datasets` version: 3.2.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.12.2
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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Dynamic adjusting dataloader sampling weight
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[
"You mean based on a condition that has to be checked on-the-fly during training ? Otherwise if you know in advance after how many samples you need to change the sampling you can simply concatenate the two mixes",
"Yes, like during training, if one data sample's prediction is consistently wrong, its sampling weight gets higher and higher, and if one data sample's prediction is already correct, then we rarely sample it",
"it's not possible to use `interleave_datasets()` and modify the probabilities while iterating on the dataset at the moment, so you'd have to implement your own torch `Sampler` or your own`IterableDataset` to implement this logic"
] | 2025-02-10T03:18:47
| 2025-03-07T14:06:54
| null |
NONE
| null | null | null | null |
Hi,
Thanks for your wonderful work! I'm wondering is there a way to dynamically adjust the sampling weight of each data in the dataset during training? Looking forward to your reply, thanks again.
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Add bookfolder Dataset Builder for Digital Book Formats
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"On second thought, probably not a good idea."
] | 2025-02-08T14:27:55
| 2025-02-08T14:30:10
| 2025-02-08T14:30:09
|
NONE
| null | null | null | null |
### Feature request
This feature proposes adding a new dataset builder called bookfolder to the datasets library. This builder would allow users to easily load datasets consisting of various digital book formats, including: AZW, AZW3, CB7, CBR, CBT, CBZ, EPUB, MOBI, and PDF.
### Motivation
Currently, loading datasets of these digital book files requires manual effort. This would also lower the barrier to entry for working with these formats, enabling more diverse and interesting datasets to be used within the Hugging Face ecosystem.
### Your contribution
This feature is rather simple as it will be based on the folder-based builder, similar to imagefolder. I'm willing to contribute to this feature by submitting a PR
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Iterating over values of a column in the IterableDataset
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"I'd be in favor of that ! I saw many people implementing their own iterables that wrap a dataset just to iterate on a single column, that would make things more practical.\n\nKinda related: https://github.com/huggingface/datasets/issues/5847",
"(For anyone's information, I'm going on vacation for the next 3 weeks, so the work is postponed. If anyone can implement this feature within the next 4 weeks, go ahead :) )\n\nUPD from 04/06/25:\nI'm planning to start work on the feature in early May.",
"#self-assign",
"# Preliminary discussion\n\nIdeally, I would like to be able to operate on a column with [map](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.IterableDataset.map), [filter](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.IterableDataset.filter), [batch](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.IterableDataset.batch) and probably some other `IterableDataset`'s methods, however, the same results can be achieved by using the methods on an `IterableDataset` object and utilizing `__getitem__()` afterwards. Thus, one may not support these methods at first and try to make the implementation as simple as possible.\n\n# Implementation\n\nBased on the preliminary discussion, one can do the following:\n```python\nclass IterableColumn:\n def __init__(self, dataset: \"IterableDataset\", column_name: str):\n self.dataset = dataset\n self.column_name = column_name\n\n def __iter__(self) -> Iterator[Any]:\n for example in self.dataset:\n yield example[self.column_name]\n\n\nclass IterableDataset(DatasetInfoMixin):\n ...\n def __getitem__(self, column_name: str) -> IterableColumn:\n return IterableColumn(self, column_name)\n ...\n```\n\n# Testing\n\nIt works as expected in our simple test:\n```python\ndef gen():\n yield {\"text\": \"Good\", \"label\": 0}\n yield {\"text\": \"Bad\", \"label\": 1}\n\nds = IterableDataset.from_generator(gen)\n\ntexts = ds[\"text\"] # `texts` is an IterableColumn object\nfor v in texts:\n print(v) # Prints \"Good\" and \"Bad\"\nfor v in texts:\n print(v) # Prints \"Good\" and \"Bad\" again\n```\n\n# Questions\n\n1. What do you think about the implementation, @lhoestq?\n2. How to properly test the implementation? I've found [test_iterable_dataset.py](https://github.com/huggingface/datasets/blob/main/tests/test_iterable_dataset.py) but 1) I haven't found any guidelines for testing, 2) the script tests a lot of things while I'd like to test only my feature.",
"Sounds great !\n\nRegarding testing, it's actually possible to have your test function in test_iterable_dataset.py, which you can run using\n\n```python\npytest tests/test_iterable_dataset.py::my_function\n```",
"> Regarding testing, it's actually possible to have your test function in test_iterable_dataset.py, which you can run using\n\nI hoped not to run `pip install -e \".[dev]\"`, but your answer implies that I should. The problem is that I was unable to install the dependencies with Python 3.13 due to `tensorflow` and with Python 3.11-3.12 due to \"there are no versions of pyav\" [¬º-°]¬ Therefore, I had to test in a separate script file to avoid importing optional dependencies. Anyway, I've opened a PR: https://github.com/huggingface/datasets/pull/7564. Please, take a look (there are questions about the documentation).\n\nMoreover, I want to note that `make style` and `pre-commit` give different results for `test_iterable_dataset.py` (and a couple of files). Example:\n```python\n assert skip_ex_iterable.shuffle_data_sources(np.random.default_rng(42)) is skip_ex_iterable, (\n \"skip examples makes the shards order fixed\"\n )\n```\nvs\n```python\n assert (\n skip_ex_iterable.shuffle_data_sources(np.random.default_rng(42)) is skip_ex_iterable\n ), \"skip examples makes the shards order fixed\"\n```\n ¯\\\\_(ツ)_/¯\n\n> Kinda related: https://github.com/huggingface/datasets/issues/5847\n\nI had forgotten about this, but I've looked at it by now. [This comment](https://github.com/huggingface/datasets/issues/5847#issuecomment-1549799951) implies that `IterableColumn` should support chained indexing, so thank you for pointing this out! Did you mean anything else by referencing the issue?",
"> I hoped not to run pip install -e \".[dev]\", but your answer implies that I should. The problem is that I was unable to install the dependencies with Python 3.13 due to tensorflow and with Python 3.11-3.12 due to \"there are no versions of pyav\" [¬º-°]¬ Therefore, I had to test in a separate script file to avoid importing optional dependencies. Anyway, I've opened a PR: https://github.com/huggingface/datasets/pull/7564. Please, take a look (there are questions about the documentation).\n\nwe try to not not require optional dependencies when running tests, so you can try running the tests only with `pytest`, `pytest-datadir` and `pytest-xdist`\n\n> I had forgotten about this, but I've looked at it by now. https://github.com/huggingface/datasets/issues/5847#issuecomment-1549799951 implies that IterableColumn should support chained indexing, so thank you for pointing this out! Did you mean anything else by referencing the issue?\n\nNo I simply referenced the issue because it will enable `pipe(ds[\"column_name\"])`, but no need to support nested fields access in a first step - we can see that later as it's uncommon and would add complexity to the contribution",
"> we try to not not require optional dependencies when running tests, so you can try running the tests only with `pytest`, `pytest-datadir` and `pytest-xdist`\n\nUnderstood. If it's necessary to run the tests again, I'll try to install only the mentioned libraries, thank you!\n\n> No I simply referenced the issue because it will enable pipe(ds[\"column_name\"]), but no need to support nested fields access in a first step - we can see that later as it's uncommon and would add complexity to the contribution\n\nAh, I see. Anyway, I've already implemented chained indexing (it was relatively easy).\n\n@lhoestq, could you please take a look at the PR and answer [questions](https://github.com/huggingface/datasets/pull/7564#issuecomment-2863391781) there?",
"> so you can try running the tests only with pytest, pytest-datadir and pytest-xdist\n\nYes, they are sufficient. There was one more problem with Python 3.12 and `distutils` that were removed, but I just downgraded to 3.11 and successfully ran `test_iterable_dataset.py`.",
"@lhoestq, could you write in the [discussion](https://discuss.huggingface.co/t/how-to-iterate-over-values-of-a-column-in-the-iterabledataset/135649) for people coming there from the Internet that the feature has been implemented? I could do it by myself but the topic is closed to me.",
"done, thanks you !"
] | 2025-01-28T13:17:36
| 2025-05-22T18:00:04
| 2025-05-22T18:00:04
|
CONTRIBUTOR
| null | null | null | null |
### Feature request
I would like to be able to iterate (and re-iterate if needed) over a column of an `IterableDataset` instance. The following example shows the supposed API:
```python
def gen():
yield {"text": "Good", "label": 0}
yield {"text": "Bad", "label": 1}
ds = IterableDataset.from_generator(gen)
texts = ds["text"]
for v in texts:
print(v) # Prints "Good" and "Bad"
for v in texts:
print(v) # Prints "Good" and "Bad" again
```
### Motivation
In the real world problems, huge NNs like Transformer are not always the best option, so there is a need to conduct experiments with different methods. While 🤗Datasets is perfectly adapted to 🤗Transformers, it may be inconvenient when being used with other libraries. The ability to retrieve a particular column is the case (e.g., gensim's FastText [requires](https://radimrehurek.com/gensim/models/fasttext.html#gensim.models.fasttext.FastText.train) only lists of strings, not dictionaries).
While there are ways to achieve the desired functionality, they are not good ([forum](https://discuss.huggingface.co/t/how-to-iterate-over-values-of-a-column-in-the-iterabledataset/135649)). It would be great if there was a built-in solution.
### Your contribution
Theoretically, I can submit a PR, but I have very little knowledge of the internal structure of 🤗Datasets, so some help may be needed.
Moreover, I can only work on weekends, since I have a full-time job. However, the feature does not seem to be popular, so there is no need to implement it as fast as possible.
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| 114 days, 4:42:28
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| 2,802,957,388
|
I_kwDODunzps6nEbxM
| 7,378
|
Allow pushing config version to hub
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"Hi ! This sounds reasonable to me, feel free to open a PR :)"
] | 2025-01-21T22:35:07
| 2025-01-30T13:56:56
| null |
NONE
| null | null | null | null |
### Feature request
Currently, when datasets are created, they can be versioned by passing the `version` argument to `load_dataset(...)`. For example creating `outcomes.csv` on the command line
```
echo "id,value\n1,0\n2,0\n3,1\n4,1\n" > outcomes.csv
```
and creating it
```
import datasets
dataset = datasets.load_dataset(
"csv",
data_files ="outcomes.csv",
keep_in_memory = True,
version = '1.0.0')
```
The version info is stored in the `info` and can be accessed e.g. by `next(iter(dataset.values())).info.version`
This dataset can be uploaded to the hub with `dataset.push_to_hub(repo_id = "maomlab/example_dataset")`. This will create a dataset on the hub with the following in the `README.md`, but it doesn't upload the version information:
```
---
dataset_info:
features:
- name: id
dtype: int64
- name: value
dtype: int64
splits:
- name: train
num_bytes: 64
num_examples: 4
download_size: 1332
dataset_size: 64
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
```
However, when I download from the hub, the version information is missing:
```
dataset_from_hub_no_version = datasets.load_dataset("maomlab/example_dataset")
next(iter(dataset.values())).info.version
```
I can add the version information manually to the hub, by appending it to the end of config section:
```
...
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
version: 1.0.0
---
```
And then when I download it, the version information is correct.
### Motivation
### Why adding version information for each config makes sense
1. The version information is already recorded in the dataset config info data structure and is able to parse it correctly, so it makes sense to sync it with `push_to_hub`.
2. Keeping the version info in at the config level is different from version info at the branch level. As the former relates to the version of the specific dataset the config refers to rather than the version of the dataset curation itself.
## A explanation for the current behavior:
In [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1
), the `_INCLUDED_INFO_IN_YAML` variable doesn't include `"version"`.
If my reading of the code is right, adding `"version"` to `_INCLUDED_INFO_IN_YAML`, would allow the version information to be uploaded to the hub.
### Your contribution
Request: add `"version"` to `_INCLUDE_INFO_IN_YAML` in [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1
)
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| 7,377
|
Support for sparse arrays with the Arrow Sparse Tensor format?
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[
"Hi ! Unfortunately the Sparse Tensor structure in Arrow is not part of the Arrow format (yes it's confusing...), so it's not possible to use it in `datasets`. It's a separate structure that doesn't correspond to any type or extension type in Arrow.\n\nThe Arrow community recently added an extension type for fixed shape tensors at https://arrow.apache.org/docs/format/CanonicalExtensions.html#fixed-shape-tensor, it should be possible to contribute an extension type for sparse tensors as well."
] | 2025-01-21T20:14:35
| 2025-01-30T14:06:45
| null |
NONE
| null | null | null | null |
### Feature request
AI in biology is becoming a big thing. One thing that would be a huge benefit to the field that Huggingface Datasets doesn't currently have is native support for **sparse arrays**.
Arrow has support for sparse tensors.
https://arrow.apache.org/docs/format/Other.html#sparse-tensor
It would be a big deal if Hugging Face Datasets supported sparse tensors as a feature type, natively.
### Motivation
This is important for example in the field of transcriptomics (modeling and understanding gene expression), because a large fraction of the genes are not expressed (zero). More generally, in science, sparse arrays are very common, so adding support for them would be very benefitial, it would make just using Hugging Face Dataset objects a lot more straightforward and clean.
### Your contribution
We can discuss this further once the team comments of what they think about the feature, and if there were previous attempts at making it work, and understanding their evaluation of how hard it would be. My intuition is that it should be fairly straightforward, as the Arrow backend already supports it.
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vllm批量推理报错
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"Make sure you have installed a recent version of `soundfile`"
] | 2025-01-21T03:22:23
| 2025-01-30T14:02:40
| null |
NONE
| null | null | null | null |
### Describe the bug
### Steps to reproduce the bug
### Expected behavior
### Environment info
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|
Excessive RAM Usage After Dataset Concatenation concatenate_datasets
|
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[
"\n\n\n\nAdding a img from memray\nhttps://gist.github.com/sam-hey/00c958f13fb0f7b54d17197fe353002f",
"I'm having the same issue where concatenation seems to use a huge amount of RAM.\n\n```python\n# Load all chunks and concatenate them into a final dataset.\n chunk_datasets = [\n Dataset.load_from_disk(file, keep_in_memory=False)\n for file in tqdm(chunk_files, desc=\"Loading chunk datasets\")\n ]\n logging.info(\"Concatenating chunk datasets...\")\n final_dataset = concatenate_datasets(chunk_datasets)\n```\n\nThis is a real issue for me as the final dataset is a few terabytes in size. I'm using datasets version `3.1.0`. Also tested with version `3.4.1`",
"I did have a short look, the error seems to be from `memory_map` and the stream not being closed. \n\nhttps://github.com/huggingface/datasets/blob/5f8d2ad9a1b0bccfd962d998987228addfd5be9f/src/datasets/table.py#L48-L50\n\n\nDid not have the time to test jet: https://github.com/sam-hey/datasets/tree/fix/concatenate_datasets\n\nI will probably have a better look in a couple of days. \n\n"
] | 2025-01-16T16:33:10
| 2025-03-27T17:40:59
| null |
NONE
| null | null | null | null |
### Describe the bug
When loading a dataset from disk, concatenating it, and starting the training process, the RAM usage progressively increases until the kernel terminates the process due to excessive memory consumption.
https://github.com/huggingface/datasets/issues/2276
### Steps to reproduce the bug
```python
from datasets import DatasetDict, concatenate_datasets
dataset = DatasetDict.load_from_disk("data")
...
...
combined_dataset = concatenate_datasets(
[dataset[split] for split in dataset]
)
#start SentenceTransformer training
```
### Expected behavior
I would not expect RAM utilization to increase after concatenation. Removing the concatenation step resolves the issue
### Environment info
sentence-transformers==3.1.1
datasets==3.2.0
python3.10
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I_kwDODunzps6mZuRI
| 7,372
|
Inconsistent Behavior Between `load_dataset` and `load_from_disk` When Loading Sharded Datasets
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[] | 2025-01-16T05:47:20
| 2025-01-16T05:47:20
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NONE
| null | null | null | null |
### Description
I encountered an inconsistency in behavior between `load_dataset` and `load_from_disk` when loading sharded datasets. Here is a minimal example to reproduce the issue:
#### Code 1: Using `load_dataset`
```python
from datasets import Dataset, load_dataset
# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Load the DatasetDict
loaded_datasetdict = load_dataset("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 1350 samples.
- `test` has 150 samples.
#### Code 2: Using `load_from_disk`
```python
from datasets import Dataset, load_from_disk
# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Load the DatasetDict
loaded_datasetdict = load_from_disk("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 450 samples.
- `test` has 50 samples.
### Expected Behavior
I expected both `load_dataset` and `load_from_disk` to load the same dataset, as they are pointing to the same directory. However, the results differ significantly:
- `load_dataset` seems to merge all shards, resulting in a combined dataset.
- `load_from_disk` only loads the last saved dataset, ignoring previous shards.
### Questions
1. Is this behavior intentional? If so, could you clarify the difference between `load_dataset` and `load_from_disk` in the documentation?
2. If this is not intentional, could this be considered a bug?
3. What is the recommended way to handle cases where multiple datasets are saved to the same directory?
Thank you for your time and effort in maintaining this great library! I look forward to your feedback.
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I_kwDODunzps6mVGmB
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|
500 Server error with pushing a dataset
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[
"EDIT: seems to be all good now. I'll add a comment if the error happens again within the next 48 hours. If it doesn't, I'll just close the topic."
] | 2025-01-15T18:23:02
| 2025-01-15T20:06:05
| null |
NONE
| null | null | null | null |
### Describe the bug
Suddenly, I started getting this error message saying it was an internal error.
`Error creating/pushing dataset: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/ll4ma-lab/grasp-dataset/commit/main (Request ID: Root=1-6787f0b7-66d5bd45413e481c4c2fb22d;670d04ff-65f5-4741-a353-2eacc47a3928)
Internal Error - We're working hard to fix this as soon as possible!
Traceback (most recent call last):
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 406, in hf_raise_for_status
response.raise_for_status()
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/requests/models.py", line 1024, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/ll4ma-lab/grasp-dataset/commit/main
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/uufs/chpc.utah.edu/common/home/u1295595/grasp_dataset_converter/src/grasp_dataset_converter/main.py", line 142, in main
subset_train.push_to_hub(dataset_name, split='train')
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5624, in push_to_hub
commit_info = api.create_commit(
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 1518, in _inner
return fn(self, *args, **kwargs)
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 4087, in create_commit
hf_raise_for_status(commit_resp, endpoint_name="commit")
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 477, in hf_raise_for_status
raise _format(HfHubHTTPError, str(e), response) from e
huggingface_hub.errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/ll4ma-lab/grasp-dataset/commit/main (Request ID: Root=1-6787f0b7-66d5bd45413e481c4c2fb22d;670d04ff-65f5-4741-a353-2eacc47a3928)
Internal Error - We're working hard to fix this as soon as possible!`
### Steps to reproduce the bug
I am pushing a Dataset in a loop via push_to_hub API
### Expected behavior
It worked fine until it stopped working suddenly.
Expected behavior: It should start working again
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-4.18.0-477.15.1.el8_8.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.0
- `huggingface_hub` version: 0.27.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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|
Importing dataset gives unhelpful error message when filenames in metadata.csv are not found in the directory
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[
"I'd prefer even more verbose errors; like `\"file123.mp3\" is referenced in metadata.csv, but not found in the data directory '/path/to/audiofolder' ! (and 100+ more missing files)` Or something along those lines."
] | 2025-01-14T13:53:21
| 2025-01-14T15:05:51
| null |
NONE
| null | null | null | null |
### Describe the bug
While importing an audiofolder dataset, where the names of the audiofiles don't correspond to the filenames in the metadata.csv, we get an unclear error message that is not helpful for the debugging, i.e.
```
ValueError: Instruction "train" corresponds to no data!
```
### Steps to reproduce the bug
Assume an audiofolder with audiofiles, filename1.mp3, filename2.mp3 etc and a file metadata.csv which contains the columns file_name and sentence. The file_names are formatted like filename1.mp3, filename2.mp3 etc.
Load the audio
```
from datasets import load_dataset
load_dataset("audiofolder", data_dir='/path/to/audiofolder')
```
When the file_names in the csv are not in sync with the filenames in the audiofolder, then we get an Error message:
```
File /opt/conda/lib/python3.12/site-packages/datasets/arrow_reader.py:251, in BaseReader.read(self, name, instructions, split_infos, in_memory)
249 if not files:
250 msg = f'Instruction "{instructions}" corresponds to no data!'
--> 251 raise ValueError(msg)
252 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
ValueError: Instruction "train" corresponds to no data!
```
load_dataset has a default value for the argument split = 'train'.
### Expected behavior
It would be better to get an error report something like:
```
The metadata.csv file has different filenames than the files in the datadirectory.
```
It would have saved me 4 hours of debugging.
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.14.0-427.40.1.el9_4.x86_64-x86_64-with-glibc2.39
- Python version: 3.12.8
- `huggingface_hub` version: 0.27.0
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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Dataset.from_dict() can't handle large dict
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[] | 2025-01-11T02:05:21
| 2025-01-11T02:05:21
| null |
NONE
| null | null | null | null |
### Describe the bug
I have 26,000,000 3-tuples. When I use Dataset.from_dict() to load, neither. py nor Jupiter notebook can run successfully. This is my code:
```
# len(example_data) is 26,000,000, 'diff' is a text
diff1_list = [example_data[i].texts[0] for i in range(len(example_data))]
diff2_list = [example_data[i].texts[1] for i in range(len(example_data))]
label_list = [example_data[i].label for i in range(len(example_data))]
embedding_dataset = Dataset.from_dict({
"diff1": diff1_list,
"diff2": diff2_list,
"label": label_list
})
```
### Steps to reproduce the bug
1. Initialize a large 3-tuple, e.g. 26,000,000
2. Use Dataset.from_dict() to load
### Expected behavior
Dataset.from_dict() run successfully
### Environment info
sentence-transformers 3.3.1
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A parameter is specified but not used in datasets.arrow_dataset.Dataset.from_pandas()
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[] | 2025-01-10T13:39:33
| 2025-01-10T13:39:33
| null |
NONE
| null | null | null | null |
### Describe the bug
I am interested in creating train, test and eval splits from a pandas Dataframe, therefore I was looking at the possibilities I can follow. I noticed the split parameter and was hopeful to use it in order to generate the 3 at once, however, while trying to understand the code, i noticed that it has no added value (correct me if I am wrong or misunderstood the code).
from_pandas function code :
```python
if info is not None and features is not None and info.features != features:
raise ValueError(
f"Features specified in `features` and `info.features` can't be different:\n{features}\n{info.features}"
)
features = features if features is not None else info.features if info is not None else None
if info is None:
info = DatasetInfo()
info.features = features
table = InMemoryTable.from_pandas(
df=df,
preserve_index=preserve_index,
)
if features is not None:
# more expensive cast than InMemoryTable.from_pandas(..., schema=features.arrow_schema)
# needed to support the str to Audio conversion for instance
table = table.cast(features.arrow_schema)
return cls(table, info=info, split=split)
```
### Steps to reproduce the bug
```python
from datasets import Dataset
# Filling the split parameter with whatever causes no harm at all
data = Dataset.from_pandas(self.raw_data, split='egiojegoierjgoiejgrefiergiuorenvuirgurthgi')
```
### Expected behavior
Would be great if there is no split parameter (if it isn't working), or to add a concrete example of how it can be used.
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.15.0-127-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.27.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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I_kwDODunzps6lhJP0
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|
API endpoints for gated dataset access requests
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[
"Looks like a [similar feature request](https://github.com/huggingface/huggingface_hub/issues/1198) was made to the HF Hub team. Is handling this at the Hub level more appropriate?\r\n\r\n(As an aside, I've gotten the [HTTP-based solution](https://github.com/huggingface/huggingface_hub/issues/1198#issuecomment-1905774983) proposed in that forum to work for simple cases.)",
"yes it's more for https://github.com/huggingface/huggingface_hub cc @hanouticelina ",
"yes i think @Wauplin's comment on that thread is still what we recommend"
] | 2025-01-09T06:21:20
| 2025-01-09T11:17:40
| 2025-01-09T11:17:20
|
NONE
| null | null | null | null |
### Feature request
I would like a programatic way of requesting access to gated datasets. The current solution to gain access forces me to visit a website and physically click an "agreement" button (as per the [documentation](https://huggingface.co/docs/hub/en/datasets-gated#access-gated-datasets-as-a-user)).
An ideal approach would be HF API download methods that negotiate access on my behalf based on information from my CLI login and/or token. I realise that may be naive given the various types of access semantics available to dataset authors (automatic versus manual approval, for example) and complexities it might add to existing methods, but something along those lines would be nice.
Perhaps using the `*_access_request` methods available to dataset authors can be a precedent; see [`reject_access_request`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/hf_api#huggingface_hub.HfApi.reject_access_request) for example.
### Motivation
When trying to download files from a gated dataset, I'm met with a `GatedRepoError` and instructed to visit the repository's website to gain access:
```
Cannot access gated repo for url https://huggingface.co/datasets/open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details/resolve/main/meta-llama__Meta-Llama-3.1-70B-Instruct/samples_leaderboard_math_precalculus_hard_2024-07-19T18-47-29.522341.jsonl.
Access to dataset open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details is restricted and you are not in the authorized list. Visit https://huggingface.co/datasets/open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details to ask for access.
```
This makes task automation extremely difficult. For example, I'm interested in studying sample-level responses of models on the LLM leaderboard -- how they answered particular questions on a given evaluation framework. As I come across more and more participants that gate their data, it's becoming unwieldy to continue my work (there over 2,000 participants, so in the worst case that's the number of website visits I'd need to manually undertake).
One approach is use Selenium to react to the `GatedRepoError`, but that seems like overkill; and a potential violation HF terms of service (?).
As mentioned in the previous section, there seems to be an [API for gated dataset owners](https://huggingface.co/docs/hub/en/datasets-gated#via-the-api) to managed access requests, and thus some appetite for allowing automated management of gating. This feature request is to extend that to dataset users.
### Your contribution
Whether I can help depends on a few things; one being the complexity of the underlying gated access design. If this feature request is accepted I am open to being involved in discussions and testing, and even development under the right time-outcome tradeoff.
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ImportError: To support decoding images, please install 'Pillow'.
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[
"what's your `pip show Pillow` output",
"same issue.. my pip show Pillow output as below:\n\n```\nName: pillow\nVersion: 11.1.0\nSummary: Python Imaging Library (Fork)\nHome-page: https://python-pillow.github.io/\nAuthor: \nAuthor-email: \"Jeffrey A. Clark\" <aclark@aclark.net>\nLicense: MIT-CMU\nLocation: [/opt/homebrew/lib/python3.10/site-packages](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/lib/python3.10/site-packages)\nRequires: \nRequired-by:\n```",
"I encountered the same problem on Ubuntu system, my pip show Pillow output as below:\n\n```\nName: pillow\nVersion: 10.4.0\nSummary: Python Imaging Library (Fork)\nHome-page: https://python-pillow.org/\nAuthor: \nAuthor-email: \"Jeffrey A. Clark\" <[aclark@aclark.net](mailto:aclark@aclark.net)>\nLicense: HPND\nLocation: /home/shunying/.local/lib/python3.8/site-packages\nRequires: \nRequired-by: \n```\n\nWell, solved this by specifying the pip version to my conda virtual environment :)",
"I have also encountered this. It's a strange thing that's happening.\n\nChecking the code `datasets` it uses `importlib.util.find_spec(\"PIL\")` to verify if `PIL` is installed. While both `pip show` and `importlib` work correctly, I still got the error.\n\nIn my case, restarting and redoing the `datasets` import helped. Seems weird to me."
] | 2025-01-08T02:22:57
| 2025-05-28T14:56:53
| null |
NONE
| null | null | null | null |
### Describe the bug
Following this tutorial locally using a macboko and VSCode: https://huggingface.co/docs/diffusers/en/tutorials/basic_training
This line of code: for i, image in enumerate(dataset[:4]["image"]):
throws: ImportError: To support decoding images, please install 'Pillow'.
Pillow is installed.
### Steps to reproduce the bug
Run the tutorial
### Expected behavior
Images should be rendered
### Environment info
MacBook, VSCode
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HuggingFace CLI dataset download raises error
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[
"I got the same error and was able to resolve it by upgrading from 2.15.0 to 3.2.0.",
"> I got the same error and was able to resolve it by upgrading from 2.15.0 to 3.2.0.\r\n\r\nWhat is needed is upgrading `huggingface-hub==0.27.1`. `datasets` does not appear to have anything to do with the error. The upgrade is a workaround, if the workaround works for your use case. Otherwise, this issue breaks all existing Python clients not using some minimum version of `huggingface-hub`. ",
"Correct, this has to do with `huggingface_hub`, not `datasets`. Some old versions of `huggingface_hub` are unfortunately not robust to recent changes on HF. Updating `huggingface_hub` fixes the issue :)\r\n\r\nClosing this issue since it's not directly related to `datasets`"
] | 2025-01-07T21:03:30
| 2025-01-08T15:00:37
| 2025-01-08T14:35:52
|
NONE
| null | null | null | null |
### Describe the bug
Trying to download Hugging Face datasets using Hugging Face CLI raises error. This error only started after December 27th, 2024. For example:
```
huggingface-cli download --repo-type dataset gboleda/wikicorpus
Traceback (most recent call last):
File "/home/ubuntu/test_venv/bin/huggingface-cli", line 8, in <module>
sys.exit(main())
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/commands/huggingface_cli.py", line 51, in main
service.run()
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/commands/download.py", line 146, in run
print(self._download()) # Print path to downloaded files
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/commands/download.py", line 180, in _download
return snapshot_download(
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/_snapshot_download.py", line 164, in snapshot_download
repo_info = api.repo_info(repo_id=repo_id, repo_type=repo_type, revision=revision, token=token)
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2491, in repo_info
return method(
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2366, in dataset_info
return DatasetInfo(**data)
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 799, in __init__
self.tags = kwargs.pop("tags")
KeyError: 'tags'
```
### Steps to reproduce the bug
```
1. huggingface-cli download --repo-type dataset gboleda/wikicorpus
```
### Expected behavior
There should be no error.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-6.8.0-1015-aws-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.5
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.3.1
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I_kwDODunzps6lNZHu
| 7,360
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error when loading dataset in Hugging Face: NoneType error is not callable
|
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[
"Hi ! I couldn't reproduce on my side, can you try deleting your cache at `~/.cache/huggingface/modules/datasets_modules/datasets/InstaDeepAI--nucleotide_transformer_downstream_tasks_revised` and try again ? For some reason `datasets` wasn't able to find the DatasetBuilder class in the python script of this dataset",
"I've met the same problem when importing [LongBench-v1](https://github.com/THUDM/LongBench/blob/main/LongBench/README.md). the debugger reports `dataset_module.builder_configs_parameters.builder_configs` as `None` so that no `builder_cls` gets created:\n\n<img width=\"711\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/b62bdea7-442b-47dc-b892-87f4d235e324\" />\n\ndoes this mean that I need to downgrade `datasets`?",
"I tried downgrading `datasets` to v2.20.0 and it works fine now...\n\nI think there might be some compatibility issues during code updates between `v2.20.0` and `v3.0.0` 🤔 \n\nalso I suggest @nanu23333 to see if downgrading works.",
"Found the same problem. When I tried to downgrade the datasets to version below v3.0.0, another problem was raised: `UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb5 in position 1: invalid start byte`",
"\nwhen I use the pip install datasets==3.3, I come across the error。Then I \n```\npip uninstall datasets\npip install datasets==2.21.0\n```\nIt is OK now"
] | 2025-01-07T02:11:36
| 2025-02-24T13:32:52
| null |
NONE
| null | null | null | null |
### Describe the bug
I met an error when running a notebook provide by Hugging Face, and met the error.
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[2], line 5
3 # Load the enhancers dataset from the InstaDeep Hugging Face ressources
4 dataset_name = "enhancers_types"
----> 5 train_dataset_enhancers = load_dataset(
6 "InstaDeepAI/nucleotide_transformer_downstream_tasks_revised",
7 dataset_name,
8 split="train",
9 streaming= False,
10 )
11 test_dataset_enhancers = load_dataset(
12 "InstaDeepAI/nucleotide_transformer_downstream_tasks_revised",
13 dataset_name,
14 split="test",
15 streaming= False,
16 )
File /public/home/hhl/miniconda3/envs/transformer/lib/python3.9/site-packages/datasets/load.py:2129, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2124 verification_mode = VerificationMode(
2125 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2126 )
2128 # Create a dataset builder
-> 2129 builder_instance = load_dataset_builder(
2130 path=path,
2131 name=name,
2132 data_dir=data_dir,
2133 data_files=data_files,
2134 cache_dir=cache_dir,
2135 features=features,
2136 download_config=download_config,
2137 download_mode=download_mode,
2138 revision=revision,
2139 token=token,
2140 storage_options=storage_options,
2141 trust_remote_code=trust_remote_code,
2142 _require_default_config_name=name is None,
2143 **config_kwargs,
2144 )
2146 # Return iterable dataset in case of streaming
2147 if streaming:
File /public/home/hhl/miniconda3/envs/transformer/lib/python3.9/site-packages/datasets/load.py:1886, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
1884 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name)
1885 # Instantiate the dataset builder
-> 1886 builder_instance: DatasetBuilder = builder_cls(
1887 cache_dir=cache_dir,
1888 dataset_name=dataset_name,
1889 config_name=config_name,
1890 data_dir=data_dir,
1891 data_files=data_files,
1892 hash=dataset_module.hash,
1893 info=info,
1894 features=features,
1895 token=token,
1896 storage_options=storage_options,
1897 **builder_kwargs,
1898 **config_kwargs,
1899 )
1900 builder_instance._use_legacy_cache_dir_if_possible(dataset_module)
1902 return builder_instance
TypeError: 'NoneType' object is not callable
```
I have checked my internet, it worked well. And the dataset name was just copied from the Hugging Face.
Totally no idea what is wrong!
### Steps to reproduce the bug
To reproduce the bug you may run
```
from datasets import load_dataset, Dataset
# Load the enhancers dataset from the InstaDeep Hugging Face ressources
dataset_name = "enhancers_types"
train_dataset_enhancers = load_dataset(
"InstaDeepAI/nucleotide_transformer_downstream_tasks_revised",
dataset_name,
split="train",
streaming= False,
)
test_dataset_enhancers = load_dataset(
"InstaDeepAI/nucleotide_transformer_downstream_tasks_revised",
dataset_name,
split="test",
streaming= False,
)
```
### Expected behavior
1. what may be the reasons of the error
2. how can I fine which reason lead to the error
3. how can I save the problem
### Environment info
```
- `datasets` version: 3.2.0
- Platform: Linux-5.15.0-117-generic-x86_64-with-glibc2.31
- Python version: 3.9.21
- `huggingface_hub` version: 0.27.0
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
```
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| 2,771,137,842
|
I_kwDODunzps6lLDUy
| 7,359
|
There are multiple 'mteb/arguana' configurations in the cache: default, corpus, queries with HF_HUB_OFFLINE=1
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[
"Related to https://github.com/embeddings-benchmark/mteb/issues/1714"
] | 2025-01-06T17:42:49
| 2025-01-06T17:43:31
| null |
NONE
| null | null | null | null |
### Describe the bug
Hey folks,
I am trying to run this code -
```python
from datasets import load_dataset, get_dataset_config_names
ds = load_dataset("mteb/arguana")
```
with HF_HUB_OFFLINE=1
But I get the following error -
```python
Using the latest cached version of the dataset since mteb/arguana couldn't be found on the Hugging Face Hub (offline mode is enabled).
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[2], line 1
----> 1 ds = load_dataset("mteb/arguana")
File ~/env/lib/python3.10/site-packages/datasets/load.py:2129, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2124 verification_mode = VerificationMode(
2125 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2126 )
2128 # Create a dataset builder
-> 2129 builder_instance = load_dataset_builder(
2130 path=path,
2131 name=name,
2132 data_dir=data_dir,
2133 data_files=data_files,
2134 cache_dir=cache_dir,
2135 features=features,
2136 download_config=download_config,
2137 download_mode=download_mode,
2138 revision=revision,
2139 token=token,
2140 storage_options=storage_options,
2141 trust_remote_code=trust_remote_code,
2142 _require_default_config_name=name is None,
2143 **config_kwargs,
2144 )
2146 # Return iterable dataset in case of streaming
2147 if streaming:
File ~/env/lib/python3.10/site-packages/datasets/load.py:1886, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
1884 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name)
1885 # Instantiate the dataset builder
-> 1886 builder_instance: DatasetBuilder = builder_cls(
1887 cache_dir=cache_dir,
1888 dataset_name=dataset_name,
1889 config_name=config_name,
1890 data_dir=data_dir,
1891 data_files=data_files,
1892 hash=dataset_module.hash,
1893 info=info,
1894 features=features,
1895 token=token,
1896 storage_options=storage_options,
1897 **builder_kwargs,
1898 **config_kwargs,
1899 )
1900 builder_instance._use_legacy_cache_dir_if_possible(dataset_module)
1902 return builder_instance
File ~/env/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py:124, in Cache.__init__(self, cache_dir, dataset_name, config_name, version, hash, base_path, info, features, token, repo_id, data_files, data_dir, storage_options, writer_batch_size, **config_kwargs)
122 config_kwargs["data_dir"] = data_dir
123 if hash == "auto" and version == "auto":
--> 124 config_name, version, hash = _find_hash_in_cache(
125 dataset_name=repo_id or dataset_name,
126 config_name=config_name,
127 cache_dir=cache_dir,
128 config_kwargs=config_kwargs,
129 custom_features=features,
130 )
131 elif hash == "auto" or version == "auto":
132 raise NotImplementedError("Pass both hash='auto' and version='auto' instead")
File ~/env/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py:84, in _find_hash_in_cache(dataset_name, config_name, cache_dir, config_kwargs, custom_features)
72 other_configs = [
73 Path(_cached_directory_path).parts[-3]
74 for _cached_directory_path in glob.glob(os.path.join(cached_datasets_directory_path_root, "*", version, hash))
(...)
81 )
82 ]
83 if not config_id and len(other_configs) > 1:
---> 84 raise ValueError(
85 f"There are multiple '{dataset_name}' configurations in the cache: {', '.join(other_configs)}"
86 f"\nPlease specify which configuration to reload from the cache, e.g."
87 f"\n\tload_dataset('{dataset_name}', '{other_configs[0]}')"
88 )
89 config_name = cached_directory_path.parts[-3]
90 warning_msg = (
91 f"Found the latest cached dataset configuration '{config_name}' at {cached_directory_path} "
92 f"(last modified on {time.ctime(_get_modification_time(cached_directory_path))})."
93 )
ValueError: There are multiple 'mteb/arguana' configurations in the cache: queries, corpus, default
Please specify which configuration to reload from the cache, e.g.
load_dataset('mteb/arguana', 'queries')
```
It works when I run the same code with HF_HUB_OFFLINE=0, but after the data is downloaded, I turn off the HF hub cache with HF_HUB_OFFLINE=1, and then this error appears.
Are there some files I am missing with hub disabled?
### Steps to reproduce the bug
from datasets import load_dataset, get_dataset_config_names
ds = load_dataset("mteb/arguana")
with HF_HUB_OFFLINE=1
(after already running it with HF_HUB_OFFLINE=0 and populating the datasets cache)
### Expected behavior
Dataset loaded successfully as it does with HF_HUB_OFFLINE=1
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.15.148.2-2.cm2-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.27.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
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I_kwDODunzps6lIc4_
| 7,357
|
Python process aborded with GIL issue when using image dataset
|
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[
"The issue seems to come from `pyarrow`, I opened an issue on their side at https://github.com/apache/arrow/issues/45214",
"I \"solved\" this by setting a low batch_size for load_datasets()",
"datasets==3.1.0 works\ndatasets==4.1.1 fails",
"If you want to use latest version over 3.1.0, a temporary fix is to modify datasets/packaged_modules/parquet/parquet.py\n\n```diff\n with open(file, \"rb\") as f:\n- parquet_fragment = ds.ParquetFileFormat().make_fragment(f)\n- if parquet_fragment.row_groups:\n- batch_size = self.config.batch_size or parquet_fragment.row_groups[0].num_rows\n+ parquet_file = pq.ParquetFile(f)\n+ if parquet_file.metadata.num_row_groups > 0:\n+ batch_size = self.config.batch_size or parquet_file.metadata.row_group(0).num_rows\n try:\n for batch_idx, record_batch in enumerate(\n- parquet_fragment.to_batches(\n- batch_size=batch_size,\n- columns=self.config.columns,\n- filter=filter_expr,\n- batch_readahead=0,\n- fragment_readahead=0,\n+ parquet_file.iter_batches(batch_size=batch_size, columns=self.config.columns) \n )\n```\n\n[See this commit for to_batches change](https://github.com/huggingface/datasets/commit/661d7bac29689e2d9eb74fba3d243939d6e9f25b)"
] | 2025-01-06T11:29:30
| 2025-09-30T23:01:53
| null |
NONE
| null | null | null | null |
### Describe the bug
The issue is visible only with the latest `datasets==3.2.0`.
When using image dataset the Python process gets aborted right before the exit with the following error:
```
Fatal Python error: PyGILState_Release: thread state 0x7fa1f409ade0 must be current when releasing
Python runtime state: finalizing (tstate=0x0000000000ad2958)
Thread 0x00007fa33d157740 (most recent call first):
<no Python frame>
Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._boun
ded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, pyarrow.lib, pandas._libs.tslibs.ccalendar, pandas._libs.ts
libs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.t
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das._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join,
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, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, sentencepiece._sentencepiece, sklearn.__check_build._check_build, psutil._psut
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inalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg.cython_blas, scipy.linalg._matfuncs_expm, scipy.linalg._decomp_up
date, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack,
scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flo
w, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial
._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.spatial.transform._rotation, scipy.optimize._group_columns, s
cipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, sc
ipy.optimize._zeros, scipy.optimize._highs.cython.src._highs_wrapper, scipy.optimize._highs._highs_wrapper, scipy.optimize._highs.cython.src._highs_constants, scipy.optimize._highs._highs_constants, scipy.l
inalg._interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integr
ate._lsoda, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._bspl, scipy.interpolate._ppoly, scipy.interpolate.interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._r
gi_cython, scipy.special.cython_special, scipy.stats._stats, scipy.stats._biasedurn, scipy.stats._levy_stable.levyst, scipy.stats._stats_pythran, scipy._lib._uarray._uarray, scipy.stats._ansari_swilk_statis
tics, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._mvn, scipy.stats._rcont.rcont, scipy.stats._unuran.unuran_wrapper, scipy.ndimage._nd_image, _ni_label, scipy.ndimage._ni_label, sklearn.utils._isf
inite, sklearn.utils.sparsefuncs_fast, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.preprocessing._csr_polynomial_expansion, sklearn.p
reprocessing._target_encoder_fast, sklearn.metrics._dist_metrics, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.utils._cython_blas, sklearn.metrics._pairwise_distances_reduction._bas
e, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distanc
es_reduction._argkmin_classmode, sklearn.utils._vector_sentinel, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_distances_reduction._radius_neighbors_classmode, s
klearn.metrics._pairwise_fast, PIL._imagingft, google._upb._message, h5py._errors, h5py.defs, h5py._objects, h5py.h5, h5py.utils, h5py.h5t, h5py.h5s, h5py.h5ac, h5py.h5p, h5py.h5r, h5py._proxy, h5py._conv,
h5py.h5z, h5py.h5a, h5py.h5d, h5py.h5ds, h5py.h5g, h5py.h5i, h5py.h5o, h5py.h5f, h5py.h5fd, h5py.h5pl, h5py.h5l, h5py._selector, _cffi_backend, pyarrow._parquet, pyarrow._fs, pyarrow._azurefs, pyarrow._hdfs
, pyarrow._gcsfs, pyarrow._s3fs, multidict._multidict, propcache._helpers_c, yarl._quoting_c, aiohttp._helpers, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket, frozenlist._frozenlist, xxhash
._xxhash, pyarrow._json, pyarrow._acero, pyarrow._csv, pyarrow._dataset, pyarrow._dataset_orc, pyarrow._parquet_encryption, pyarrow._dataset_parquet_encryption, pyarrow._dataset_parquet, regex._regex, scipy
.io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, PIL._imagingmath, PIL._webp (total: 236)
Aborted (core dumped)
```an
### Steps to reproduce the bug
Install `datasets==3.2.0`
Run the following script:
```python
import datasets
DATASET_NAME = "phiyodr/InpaintCOCO"
NUM_SAMPLES = 10
def preprocess_fn(example):
return {
"prompts": example["inpaint_caption"],
"images": example["coco_image"],
"masks": example["mask"],
}
default_dataset = datasets.load_dataset(
DATASET_NAME, split="test", streaming=True
).filter(lambda example: example["inpaint_caption"] != "").take(NUM_SAMPLES)
test_data = default_dataset.map(
lambda x: preprocess_fn(x), remove_columns=default_dataset.column_names
)
for data in test_data:
print(data["prompts"])
``
### Expected behavior
The script should not hang or crash.
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.15.0-50-generic-x86_64-with-glibc2.31
- Python version: 3.11.0
- `huggingface_hub` version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.2.0
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|
How about adding a feature to pass the key when performing map on DatasetDict?
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[
"@lhoestq \r\nIf it's okay with you, can I work on this?",
"Hi ! Can you give an example of what it would look like to use this new feature ?\r\n\r\nNote that currently you can already do\r\n\r\n```python\r\nds[\"train\"] = ds[\"train\"].map(process_train)\r\nds[\"test\"] = ds[\"test\"].map(process_test)\r\n```",
"@lhoestq \nThanks for the response! \nLet me clarify what I'm looking for with an example:\n\nCurrently, we need to write separate processing functions or call .map() separately:\n```python\n# Current approach\ndef process_train(example):\n # Training-specific processing\n return example\n\ndef process_valid(example):\n # Validation-specific processing\n return example\n\nds[\"train\"] = ds[\"train\"].map(process_train)\nds[\"valid\"] = ds[\"valid\"].map(process_valid)\n```\n\nWhat I'm proposing is to have a single processing function that knows which split it's processing:\n\n```python\n# Proposed feature\ndef process(example, split_key):\n if split_key == \"train\":\n # Training-specific processing\n elif split_key == \"valid\":\n # Validation-specific processing\n return example\n\n# Using with_key=True to pass the split information\nds = ds.map(process, with_key=True)\n```\n\nThis becomes particularly useful when:\n1. The processing logic is heavily shared between splits but needs minor adjustments\n2. You want to maintain the processing logic in one place for better maintainability\n3. The processing function is complex and you want to avoid duplicating code\n\nSo I wanted to request this feature to achieve this kind of functionality. \nI've created a draft PR implementing this: https://github.com/huggingface/datasets/pull/7240/files\n",
"I see ! I think it makes sense, and it's more readable than doing something like this:\r\n```python\r\nfrom functools import partial\r\nds = DatasetDict({key: ds[key].map(partial(process, split_key=key)) for key in ds})\r\n```\r\n\r\nPS: you named the argument `with_key` in your example, but it might be even clearer with it's named `with_split` maybe no ?",
"@lhoestq I agree. \nIt seems better to use `with_split`.\nSo can I open a PR with this change?",
"Sure !"
] | 2025-01-06T08:13:52
| 2025-03-24T10:57:47
| 2025-03-24T10:57:47
|
CONTRIBUTOR
| null | null | null | null |
### Feature request
Add a feature to pass the key of the DatasetDict when performing map
### Motivation
I often preprocess using map on DatasetDict.
Sometimes, I need to preprocess train and valid data differently depending on the task.
So, I thought it would be nice to pass the key (like train, valid) when performing map on DatasetDict.
What do you think?
### Your contribution
I can submit a pull request to add the feature to pass the key of the DatasetDict when performing map.
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|
Not available datasets[audio] on python 3.13
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[
"It looks like an issue with `numba` which can't be installed on 3.13 ? `numba` is a dependency of `librosa`, used to decode audio files",
"There seems that `uv` cannot resolve \n\n```bhas\nuv add -n datasets[audio] huggingface-hub[hf-transfer] transformers\n```\n\nThe problem is again `librosa` which depends on `numba` which has as a transitive dep `llvm-lite`\n\n```bash\nRuntimeError: Cannot install on Python version 3.13.3; only versions >=3.6,<3.10 are supported.\n# Python 3.9 works but is quite old and generates some problems with pytorch and numpy 2.0 ....\n```\n\nThe packaging seems problematic...",
"Seems to be solved on https://github.com/huggingface/datasets/commit/161f99d94a1daf8380eabdb826048a0652510ee6#diff-60f61ab7a8d1910d86d9fda2261620314edcae5894d5aaa236b821c7256badd7L140"
] | 2025-01-04T18:37:08
| 2025-06-28T00:26:19
| null |
NONE
| null | null | null | null |
### Describe the bug
This is the error I got, it seems numba package does not support python 3.13
PS C:\Users\sergi\Documents> pip install datasets[audio]
Defaulting to user installation because normal site-packages is not writeable
Collecting datasets[audio]
Using cached datasets-3.2.0-py3-none-any.whl.metadata (20 kB)
... (OTHER PACKAGES)
Collecting numba>=0.51.0 (from librosa->datasets[audio])
Downloading numba-0.60.0.tar.gz (2.7 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.7/2.7 MB 44.1 MB/s eta 0:00:00
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [24 lines of output]
Traceback (most recent call last):
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 353, in <module>
main()
~~~~^^
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 335, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 118, in get_requires_for_build_wheel
return hook(config_settings)
File "C:\Users\sergi\AppData\Local\Temp\pip-build-env-yauns_qh\overlay\Lib\site-packages\setuptools\build_meta.py", line 334, in get_requires_for_build_wheel
return self._get_build_requires(config_settings, requirements=[])
~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\sergi\AppData\Local\Temp\pip-build-env-yauns_qh\overlay\Lib\site-packages\setuptools\build_meta.py", line 304, in _get_build_requires
self.run_setup()
~~~~~~~~~~~~~~^^
RuntimeError: Cannot install on Python version 3.13.1; only versions >=3.9,<3.13 are supported.
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
### Steps to reproduce the bug
1. install python >=3.13
2. !pip install datasets[audio]
### Expected behavior
I needed datasets[audio] in the python 3.13
### Environment info
python 3.13.1
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A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
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[
"recreated .venv and run this: pip install diffusers[training]==0.11.1"
] | 2025-01-04T18:30:17
| 2025-01-08T02:20:58
| 2025-01-08T02:20:58
|
NONE
| null | null | null | null |
### Describe the bug
Following this tutorial: https://huggingface.co/docs/diffusers/en/tutorials/basic_training and running it locally using VSCode on my MacBook. The first line in the tutorial fails: from datasets import load_dataset
dataset = load_dataset('huggan/smithsonian_butterflies_subset', split="train"). with this error:
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2. and ImportError: numpy.core.multiarray failed to import.
Does from datasets import load_dataset really use NumPy 1.x?
### Steps to reproduce the bug
Open VSCode. create a new venv. Create a new ipynb file. Import pip install diffusers[training] try to run this line of code: from datasets import load_dataset
### Expected behavior
data is loaded
### Environment info
ran this: datasets-cli env
and got A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.
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I_kwDODunzps6khpDj
| 7,347
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Converting Arrow to WebDataset TAR Format for Offline Use
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"Hi,\r\n\r\nI've downloaded an Arrow-formatted dataset offline using the hugggingface's datasets library by:\r\n\r\nimport json\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"pixparse/cc3m-wds\")\r\ndataset.save_to_disk(\"./cc3m_1\")\r\n\r\n\r\nnow I need to convert it to WebDataset's TAR format for offline data ingestion.\r\nIs there a straightforward method to achieve this conversion without an internet connection? Can I simply convert it by\r\n\r\ntar -cvf\r\n\r\n\r\nbtw, when I tried:\r\n\r\nimport webdataset as wds\r\nfrom huggingface_hub import get_token\r\nfrom torch.utils.data import DataLoader\r\n\r\nhf_token = get_token()\r\nurl = \"https://huggingface.co/datasets/timm/imagenet-12k-wds/resolve/main/imagenet12k-train-{{0000..1023}}.tar\"\r\nurl = f\"pipe:curl -s -L {url} -H 'Authorization:Bearer {hf_token}'\"\r\ndataset = wds.WebDataset(url).decode()\r\ndataset.save_to_disk(\"./cc3m_webdataset\")\r\n\r\n\r\nerror occured:\r\n\r\nAttributeError: 'WebDataset' object has no attribute 'save_to_disk'\r\n\r\n\r\nThanks a lot!\r\n\r\nMotivation\r\n\r\nConverting Arrow to WebDataset TAR Format\r\n\r\nYour contribution\r\n\r\nNo clue yet\r\n\r\n\r\nاحصل على Outlook لـ iOS<https://aka.ms/o0ukef>\r\n________________________________\r\nمن: katie312 ***@***.***>\r\nتم الإرسال: Friday, December 27, 2024 4:41:21 AM\r\nإلى: huggingface/datasets ***@***.***>\r\nنسخة: Subscribed ***@***.***>\r\nالموضوع: [huggingface/datasets] Converting Arrow to WebDataset TAR Format for Offline Use (Issue #7347)\r\n\r\n\r\nFeature request\r\n\r\nHi,\r\n\r\nI've downloaded an Arrow-formatted dataset offline using the hugggingface's datasets library by:\r\n\r\nimport json\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"pixparse/cc3m-wds\")\r\ndataset.save_to_disk(\"./cc3m_1\")\r\n\r\n\r\nnow I need to convert it to WebDataset's TAR format for offline data ingestion.\r\nIs there a straightforward method to achieve this conversion without an internet connection? Can I simply convert it by\r\n\r\ntar -cvf\r\n\r\n\r\nbtw, when I tried:\r\n\r\nimport webdataset as wds\r\nfrom huggingface_hub import get_token\r\nfrom torch.utils.data import DataLoader\r\n\r\nhf_token = get_token()\r\nurl = \"https://huggingface.co/datasets/timm/imagenet-12k-wds/resolve/main/imagenet12k-train-{{0000..1023}}.tar\"\r\nurl = f\"pipe:curl -s -L {url} -H 'Authorization:Bearer {hf_token}'\"\r\ndataset = wds.WebDataset(url).decode()\r\ndataset.save_to_disk(\"./cc3m_webdataset\")\r\n\r\n\r\nerror occured:\r\n\r\nAttributeError: 'WebDataset' object has no attribute 'save_to_disk'\r\n\r\n\r\nThanks a lot!\r\n\r\nMotivation\r\n\r\nConverting Arrow to WebDataset TAR Format\r\n\r\nYour contribution\r\n\r\nNo clue yet\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/7347>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AQJDZ2X2RUIIULBJEF5R2HL2HSV4DAVCNFSM6AAAAABUH5QSLCVHI2DSMVQWIX3LMV43ASLTON2WKOZSG43DAMRYGIZTGOI>.\r\nYou are receiving this because you are subscribed to this thread.Message ID: ***@***.***>\r\n",
"> now I need to convert it to WebDataset's TAR format for offline data ingestion.\r\n\r\nyou can directly download the .TAR files from HF using e.g. `huggingface-cli download` and load them in webdataset :)",
"الفله سنه والطبقه يوم\r\n\r\nاحصل على Outlook لـ iOS<https://aka.ms/o0ukef>\r\n________________________________\r\nمن: Quentin Lhoest ***@***.***>\r\nتم الإرسال: Friday, December 27, 2024 4:14:43 PM\r\nإلى: huggingface/datasets ***@***.***>\r\nنسخة: hamad350 ***@***.***>; Comment ***@***.***>\r\nالموضوع: Re: [huggingface/datasets] Converting Arrow to WebDataset TAR Format for Offline Use (Issue #7347)\r\n\r\n\r\nnow I need to convert it to WebDataset's TAR format for offline data ingestion.\r\n\r\nyou can directly download the .TAR files from HF using e.g. huggingface-cli download and load them in webdataset :)\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/7347#issuecomment-2563691570>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AQJDZ2R5M3Z7L2MZZYARYID2HVHEHAVCNFSM6AAAAABUH5QSLCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKNRTGY4TCNJXGA>.\r\nYou are receiving this because you commented.Message ID: ***@***.***>\r\n",
"> > now I need to convert it to WebDataset's TAR format for offline data ingestion.\r\n> \r\n> you can directly download the .TAR files from HF using e.g. `huggingface-cli download` and load them in webdataset :)\r\n\r\nThanks a lot! I completely forgot to use Hugging Face-CLI download. Thanks for the reminding!"
] | 2024-12-27T01:40:44
| 2024-12-31T17:38:00
| 2024-12-28T15:38:03
|
NONE
| null | null | null | null |
### Feature request
Hi,
I've downloaded an Arrow-formatted dataset offline using the hugggingface's datasets library by:
```
import json
from datasets import load_dataset
dataset = load_dataset("pixparse/cc3m-wds")
dataset.save_to_disk("./cc3m_1")
```
now I need to convert it to WebDataset's TAR format for offline data ingestion.
Is there a straightforward method to achieve this conversion without an internet connection? Can I simply convert it by
```
tar -cvf
```
btw, when I tried:
```
import webdataset as wds
from huggingface_hub import get_token
from torch.utils.data import DataLoader
hf_token = get_token()
url = "https://huggingface.co/datasets/timm/imagenet-12k-wds/resolve/main/imagenet12k-train-{{0000..1023}}.tar"
url = f"pipe:curl -s -L {url} -H 'Authorization:Bearer {hf_token}'"
dataset = wds.WebDataset(url).decode()
dataset.save_to_disk("./cc3m_webdataset")
```
error occured:
```
AttributeError: 'WebDataset' object has no attribute 'save_to_disk'
```
Thanks a lot!
### Motivation
Converting Arrow to WebDataset TAR Format
### Your contribution
No clue yet
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OSError: Invalid flatbuffers message.
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[
"Thanks for reporting, it looks like an issue with `pyarrow.ipc.open_stream`\r\n\r\nCan you try installing `datasets` from this pull request and see if it helps ? https://github.com/huggingface/datasets/pull/7348",
"> Thanks for reporting, it looks like an issue with `pyarrow.ipc.open_stream`\r\n> \r\n> Can you try installing `datasets` from this pull request and see if it helps ? #7348\r\n\r\nThank you very much. Here, it also needed to be changed to `except (OSError, pa.lib.ArrowInvalid):`. And then the bug was fixed.\r\nhttps://github.com/huggingface/datasets/blob/2826a040a05e19fca894253b78a932d4fcb4a584/src/datasets/packaged_modules/arrow/arrow.py#L48",
"Cool ! we will do a new release soon :) in the meantime you can use `datasets` from `main`"
] | 2024-12-25T11:38:52
| 2025-01-09T14:25:29
| 2025-01-09T14:25:05
|
NONE
| null | null | null | null |
### Describe the bug
When loading a large 2D data (1000 × 1152) with a large number of (2,000 data in this case) in `load_dataset`, the error message `OSError: Invalid flatbuffers message` is reported.
When only 300 pieces of data of this size (1000 × 1152) are stored, they can be loaded correctly.
When 2,000 2D arrays are stored in each file, about 100 files are generated, each with a file size of about 5-6GB. But when 300 2D arrays are stored in each file, **about 600 files are generated, which is too many files**.
### Steps to reproduce the bug
error：
```python
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
Cell In[2], line 4
1 from datasets import Dataset
2 from datasets import load_dataset
----> 4 real_dataset = load_dataset("arrow", data_files='tensorData/real_ResidueTensor/*', split="train")#.with_format("torch") # , split="train"
5 # sim_dataset = load_dataset("arrow", data_files='tensorData/sim_ResidueTensor/*', split="train").with_format("torch")
6 real_dataset
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/load.py:2151](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/load.py#line=2150), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2148 return builder_instance.as_streaming_dataset(split=split)
2150 # Download and prepare data
-> 2151 builder_instance.download_and_prepare(
2152 download_config=download_config,
2153 download_mode=download_mode,
2154 verification_mode=verification_mode,
2155 num_proc=num_proc,
2156 storage_options=storage_options,
2157 )
2159 # Build dataset for splits
2160 keep_in_memory = (
2161 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2162 )
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py:924](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py#line=923), in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, dl_manager, base_path, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
922 if num_proc is not None:
923 prepare_split_kwargs["num_proc"] = num_proc
--> 924 self._download_and_prepare(
925 dl_manager=dl_manager,
926 verification_mode=verification_mode,
927 **prepare_split_kwargs,
928 **download_and_prepare_kwargs,
929 )
930 # Sync info
931 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py:978](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py#line=977), in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
976 split_dict = SplitDict(dataset_name=self.dataset_name)
977 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 978 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
980 # Checksums verification
981 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/packaged_modules/arrow/arrow.py:47](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/packaged_modules/arrow/arrow.py#line=46), in Arrow._split_generators(self, dl_manager)
45 with open(file, "rb") as f:
46 try:
---> 47 reader = pa.ipc.open_stream(f)
48 except pa.lib.ArrowInvalid:
49 reader = pa.ipc.open_file(f)
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py:190](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py#line=189), in open_stream(source, options, memory_pool)
171 def open_stream(source, *, options=None, memory_pool=None):
172 """
173 Create reader for Arrow streaming format.
174
(...)
188 A reader for the given source
189 """
--> 190 return RecordBatchStreamReader(source, options=options,
191 memory_pool=memory_pool)
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py:52](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py#line=51), in RecordBatchStreamReader.__init__(self, source, options, memory_pool)
50 def __init__(self, source, *, options=None, memory_pool=None):
51 options = _ensure_default_ipc_read_options(options)
---> 52 self._open(source, options=options, memory_pool=memory_pool)
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.pxi:1006](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.pxi#line=1005), in pyarrow.lib._RecordBatchStreamReader._open()
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi:155](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi#line=154), in pyarrow.lib.pyarrow_internal_check_status()
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi:92](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi#line=91), in pyarrow.lib.check_status()
OSError: Invalid flatbuffers message.
```
reproduce：Here is just an example result, the real 2D matrix is the output of the ESM large model, and the matrix size is approximate
```python
import numpy as np
import pyarrow as pa
random_arrays_list = [np.random.rand(1000, 1152) for _ in range(2000)]
table = pa.Table.from_pydict({
'tensor': [tensor.tolist() for tensor in random_arrays_list]
})
import pyarrow.feather as feather
feather.write_feather(table, 'test.arrow')
from datasets import load_dataset
dataset = load_dataset("arrow", data_files='test.arrow', split="train")
```
### Expected behavior
`load_dataset` load the dataset as normal as `feather.read_feather`
```python
import pyarrow.feather as feather
feather.read_feather('tensorData/real_ResidueTensor/real_tensor_1.arrow')
```
Plus `load_dataset("parquet", data_files='test.arrow', split="train")` works fine
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-6.8.0-49-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.26.5
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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[
"Good catch ! Do you think you can open a PR to fix this issue ?"
] | 2024-12-25T07:36:48
| 2025-01-07T11:56:42
| 2025-01-07T11:56:42
|
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
The following code
```python
import datasets as hf
ds1 = hf.Dataset.from_list([{'i': i} for i in [0,1]])
#ds1 = ds1.to_iterable_dataset()
ds2 = ds1.map(
lambda i: {'i': i+1},
input_columns = ['i'],
remove_columns = ['i']
)
list(ds2)
```
produces
```python
[{'i': 1}, {'i': 2}]
```
as expected. If the line that converts `ds1` to iterable is uncommented so that the `ds2` is a map of an `IterableDataset`, the result is
```python
[{},{}]
```
I expected the output to be the same as before. It seems that in the second case the removed column is not added back into the output.
The issue seems to be [here](https://github.com/huggingface/datasets/blob/6c6a82a573f946c4a81069f56446caed15cee9c2/src/datasets/iterable_dataset.py#L1093): the columns are removed after the mapping which is not what we want (or what the [documentation says](https://github.com/huggingface/datasets/blob/6c6a82a573f946c4a81069f56446caed15cee9c2/src/datasets/iterable_dataset.py#L2370)) because we want the columns removed from the transformed example but then added if the map produced them.
This is `datasets==3.2.0` and `python==3.10`
### Steps to reproduce the bug
see above
### Expected behavior
see above
### Environment info
see above
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| 2,754,735,951
|
I_kwDODunzps6kMe9P
| 7,344
|
HfHubHTTPError: 429 Client Error: Too Many Requests for URL when trying to access SlimPajama-627B or c4 on TPUs
|
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[
"Hi ! This is due to your old version of `datasets` which calls HF with `expand=True`, an option that is strongly rate limited.\r\n\r\nRecent versions of `datasets` don't rely on this anymore, you can fix your issue by upgrading `datasets` :)\r\n\r\n```\r\npip install -U datasets\r\n```\r\n\r\nYou can also get maximum HF availability on your compute nodes with HF Enterprise (see [network security features](https://huggingface.co/docs/hub/enterprise-hub-network-security))",
"Upgrading fixed the issue for me. Thanks! "
] | 2024-12-22T16:30:07
| 2025-01-15T05:32:00
| 2025-01-15T05:31:58
|
NONE
| null | null | null | null |
### Describe the bug
I am trying to run some trainings on Google's TPUs using Huggingface's DataLoader on [SlimPajama-627B](https://huggingface.co/datasets/cerebras/SlimPajama-627B) and [c4](https://huggingface.co/datasets/allenai/c4), but I end up running into `429 Client Error: Too Many Requests for URL` error when I call `load_dataset`. The even odder part is that I am able to sucessfully run trainings with the [wikitext dataset](https://huggingface.co/datasets/Salesforce/wikitext). Is there something I need to setup to specifically train with SlimPajama or C4 with TPUs because I am not clear why I am getting these errors.
### Steps to reproduce the bug
These are the commands you could run to produce the error below but you will require a ClearML account (you can create one [here](https://app.clear.ml/login?redirect=%2Fdashboard)) with a queue setup to run on Google TPUs
```bash
git clone https://github.com/clankur/muGPT.git
cd muGPT
python -m train --config-name=slim_v4-32_84m.yaml +training.queue={NAME_OF_CLEARML_QUEUE}
```
The error I see:
```
Traceback (most recent call last):
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/clearml/binding/hydra_bind.py", line 230, in _patched_task_function
return task_function(a_config, *a_args, **a_kwargs)
File "/home/clankur/.clearml/venvs-builds/3.10/task_repository/muGPT.git/train.py", line 1037, in main
main_contained(config, logger)
File "/home/clankur/.clearml/venvs-builds/3.10/task_repository/muGPT.git/train.py", line 840, in main_contained
loader = get_loader("train", config.training_data, config.training.tokens)
File "/home/clankur/.clearml/venvs-builds/3.10/task_repository/muGPT.git/input_loader.py", line 549, in get_loader
return HuggingFaceDataLoader(split, config, token_batch_params)
File "/home/clankur/.clearml/venvs-builds/3.10/task_repository/muGPT.git/input_loader.py", line 395, in __init__
self.dataset = load_dataset(
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/load.py", line 2112, in load_dataset
builder_instance = load_dataset_builder(
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/load.py", line 1798, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/load.py", line 1495, in dataset_module_factory
raise e1 from None
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/load.py", line 1479, in dataset_module_factory
).get_module()
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/load.py", line 1034, in get_module
else get_data_patterns(base_path, download_config=self.download_config)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/data_files.py", line 457, in get_data_patterns
return _get_data_files_patterns(resolver)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/data_files.py", line 248, in _get_data_files_patterns
data_files = pattern_resolver(pattern)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/data_files.py", line 340, in resolve_pattern
for filepath, info in fs.glob(pattern, detail=True).items()
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 409, in glob
return super().glob(path, **kwargs)
File "/home/clankur/.clearml/venvs-builds/3.10/lib/python3.10/site-packages/fsspec/spec.py", line 602, in glob
allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 429, in find
out = self._ls_tree(path, recursive=True, refresh=refresh, revision=resolved_path.revision, **kwargs)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 358, in _ls_tree
self._ls_tree(
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 375, in _ls_tree
for path_info in tree:
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 3080, in list_repo_tree
for path_info in paginate(path=tree_url, headers=headers, params={"recursive": recursive, "expand": expand}):
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/utils/_pagination.py", line 46, in paginate
hf_raise_for_status(r)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 477, in hf_raise_for_status
raise _format(HfHubHTTPError, str(e), response) from e
huggingface_hub.errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/cerebras/SlimPajama-627B/tree/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543?recursive=True&expand=True&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBaWE4wTDJOb2RXNXJNUzlsZUdGdGNHeGxYMmh2YkdSdmRYUmZPVFEzTG1wemIyNXNMbnB6ZENKOTo2MjUw (Request ID: Root=1-67673de9-1413900606ede7712b08ef2c;1304c09c-3e69-4222-be14-f10ee709d49c)
maximum queue size reached
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
```
### Expected behavior
I'd expect the DataLoader to load from the SlimPajama-627B and c4 dataset without issue.
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.8.0-1035-gcp-x86_64-with-glibc2.31
- Python version: 3.10.16
- Huggingface_hub version: 0.26.5
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
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|
[Bug] Inconsistent behavior of data_files and data_dir in load_dataset method.
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[
"Hi ! `data_files` with a list is equivalent to `data_files={\"train\": data_files}` with a train test only.\r\n\r\nWhen no split are specified, they are inferred based on file names, and files with no apparent split are ignored",
"Thanks for your reply!\r\n`files with no apparent split are ignored`. Is there a option that I can choose to ignored it or not as I mention aboved? Thanks!",
"To include all the files, the best way is to pass `data_files` yourself. There is no option to disable split detection at the moment",
"Thanks! I hope you guys can consider adding this option in the future. :)"
] | 2024-12-19T14:31:27
| 2025-01-03T15:54:09
| 2025-01-03T15:54:09
|
NONE
| null | null | null | null |
### Describe the bug
Inconsistent operation of data_files and data_dir in load_dataset method.
### Steps to reproduce the bug
# First
I have three files, named 'train.json', 'val.json', 'test.json'.
Each one has a simple dict `{text:'aaa'}`.
Their path are `/data/train.json`, `/data/val.json`, `/data/test.json`
I load dataset with `data_files` argument:
```py
files = [os.path.join('./data',file) for file in os.listdir('./data')]
ds = load_dataset(
path='json',
data_files=files,)
```
And I get:
```py
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 3
})
})
```
However, If I load dataset with `data_dir` argument:
```py
ds = load_dataset(
path='json',
data_dir='./data',)
```
And I get:
```py
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 1
})
validation: Dataset({
features: ['text'],
num_rows: 1
})
test: Dataset({
features: ['text'],
num_rows: 1
})
})
```
Two results are not the same. Their behaviors are not equal, even if the statement [here](https://github.com/huggingface/datasets/blob/d0c152a979d91cc34b605c0298aebc650ab7dd27/src/datasets/load.py#L1790) said that their behaviors are equal.
# Second
If some filename include 'test' while others do not, `load_dataset` only return `test` dataset and others files are **abandoned**.
Given two files named `test.json` and `1.json`
Each one has a simple dict `{text:'aaa'}`.
I load the dataset using:
```py
ds = load_dataset(
path='json',
data_dir='./data',)
```
Only `test` is returned, `1.json` is missing:
```py
DatasetDict({
test: Dataset({
features: ['text'],
num_rows: 1
})
})
```
Things do not change even I manually set `split='train'`
### Expected behavior
1. Fix the above bugs.
2. Although the document says that load_dataset method will `Find which file goes into which split (e.g. train/test) based on file and directory names or on the YAML configuration`, I hope I can manually decide whether to do so. Sometimes users may accidentally put a `test` string in the filename but they just want a single `train` dataset. If the number of files in `data_dir` is huge, it's not easy to find out what cause the second situation metioned above.
### Environment info
datasets==3.2.0
Ubuntu18.84
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[
"Hmmm I double checked in the source code and I found a contradiction: in the current implementation the metadata file is ignored if it's not in the same archive as the zip image somehow:\r\n\r\nhttps://github.com/huggingface/datasets/blob/caa705e8bf4bedf1a956f48b545283b2ca14170a/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L352-L353\r\n\r\nin the tests suite the metadata file is placed inside the archive:\r\n\r\nhttps://github.com/huggingface/datasets/blob/caa705e8bf4bedf1a956f48b545283b2ca14170a/tests/packaged_modules/test_imagefolder.py#L223-L223\r\n\r\nThanks for reporting this issue, it seems the documentation is wrong and we never implemented the support for zip + metadata outside zip. We might rewrite part of this code soon though to make it more flexible, it can be a good occasion to fix this. In the meantime feel free to open a PR to fix the documentation if you'd like"
] | 2024-12-17T12:58:43
| 2025-01-03T15:28:13
| null |
NONE
| null | null | null | null |
### Describe the bug
ImageFolder with metadata.jsonl error. I downloaded liuhaotian/LLaVA-CC3M-Pretrain-595K locally from Hugging Face. According to the tutorial in https://huggingface.co/docs/datasets/image_dataset#image-captioning, only put images.zip and metadata.jsonl containing information in the same folder. However, after loading, an error was reported: One or several metadata.jsonl were found, but not in the same directory or in a parent directory of.
The data in my jsonl file is as follows:
> {"id": "GCC_train_002448550", "file_name": "GCC_train_002448550.jpg", "conversations": [{"from": "human", "value": "<image>\nProvide a brief description of the given image."}, {"from": "gpt", "value": "a view of a city , where the flyover was proposed to reduce the increasing traffic on thursday ."}]}
### Steps to reproduce the bug
from datasets import load_dataset
image = load_dataset("imagefolder",data_dir='data/opensource_data')
### Expected behavior
success
### Environment info
datasets==3.2.0
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|
I_kwDODunzps6jmYHY
| 7,336
|
Clarify documentation or Create DatasetCard
|
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[] | 2024-12-17T12:01:00
| 2024-12-17T12:01:00
| null |
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| null | null | null | null |
### Feature request
I noticed that you can use a Model Card instead of a Dataset Card when pushing a dataset to the Hub, but this isn’t clearly mentioned in [the docs.](https://huggingface.co/docs/datasets/dataset_card)
- Update the docs to clarify that a Model Card can work for datasets too.
- It might be worth creating a dedicated DatasetCard module, similar to the ModelCard module, for consistency and better support.
Not sure if this belongs here or on the [Hub repo](https://github.com/huggingface/huggingface_hub), but thought I’d bring it up!
### Motivation
I just spent an hour like on [this issue](https://github.com/huggingface/trl/pull/2491) trying to create a `DatasetCard` for a script.
### Your contribution
might later
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Too many open files: '/root/.cache/huggingface/token'
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[] | 2024-12-16T21:30:24
| 2024-12-16T21:30:24
| null |
NONE
| null | null | null | null |
### Describe the bug
I ran this code:
```
from datasets import load_dataset
dataset = load_dataset("common-canvas/commoncatalog-cc-by", cache_dir="/datadrive/datasets/cc", num_proc=1000)
```
And got this error.
Before it was some other file though (lie something...incomplete)
runnting
```
ulimit -n 8192
```
did not help at all.
### Steps to reproduce the bug
Run the code i sent
### Expected behavior
Should be no errors
### Environment info
linux, jupyter lab.
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TypeError: Value.__init__() missing 1 required positional argument: 'dtype'
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[
"same error \n```\ndata = load_dataset('/opt/deepseek_R1_finetune/hf_datasets/openai/gsm8k', 'main')[split] \n```",
"> same error\n> \n> ```\n> data = load_dataset('/opt/deepseek_R1_finetune/hf_datasets/openai/gsm8k', 'main')[split] \n> ```\n\nhttps://github.com/huggingface/open-r1/issues/204 this help me",
"Solved by delete `dataset_infos.json` file in dataset dir, or you can transfer datasets from Hugginface to Modelscope by [hf-ms-transfer](https://github.com/wa008/hf-ms-transfer), which will solve this problem by default. "
] | 2024-12-15T04:08:46
| 2025-10-30T09:05:53
| null |
NONE
| null | null | null | null |
### Describe the bug
ds = load_dataset(
"./xxx.py",
name="default",
split="train",
)
The datasets does not support debugging locally anymore...
### Steps to reproduce the bug
```
from datasets import load_dataset
ds = load_dataset(
"./repo.py",
name="default",
split="train",
)
for item in ds:
print(item)
```
It works fine for "username/repo", but it does not work for "./repo.py" when debugging locally...
Running above code template will report TypeError: Value.__init__() missing 1 required positional argument: 'dtype'
### Expected behavior
fix this bug
### Environment info
python 3.10 datasets==2.21
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|
.map() is not caching and ram goes OOM
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"I have the same issue - any update on this?"
] | 2024-12-13T14:22:56
| 2025-02-10T10:42:38
| null |
NONE
| null | null | null | null |
### Describe the bug
Im trying to run a fairly simple map that is converting a dataset into numpy arrays. however, it just piles up on memory and doesnt write to disk. Ive tried multiple cache techniques such as specifying the cache dir, setting max mem, +++ but none seem to work. What am I missing here?
### Steps to reproduce the bug
```
from pydub import AudioSegment
import io
import base64
import numpy as np
import os
CACHE_PATH = "/mnt/extdisk/cache" # "/root/.cache/huggingface/"#
os.environ["HF_HOME"] = CACHE_PATH
import datasets
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# Create a handler for Jupyter notebook
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
#datasets.config.IN_MEMORY_MAX_SIZE= 1000#*(2**30) #50 gb
print(datasets.config.HF_CACHE_HOME)
print(datasets.config.HF_DATASETS_CACHE)
# Decode the base64 string into bytes
def convert_mp3_to_audio_segment(example):
"""
example = ds['train'][0]
"""
try:
audio_data_bytes = base64.b64decode(example['audio'])
# Use pydub to load the MP3 audio from the decoded bytes
audio_segment = AudioSegment.from_file(io.BytesIO(audio_data_bytes), format="mp3")
# Resample to 24_000
audio_segment = audio_segment.set_frame_rate(24_000)
audio = {'sampling_rate' : audio_segment.frame_rate,
'array' : np.array(audio_segment.get_array_of_samples(), dtype="float")}
del audio_segment
duration = len(audio['array']) / audio['sampling_rate']
except Exception as e:
logger.warning(f"Failed to convert audio for {example['id']}. Error: {e}")
audio = {'sampling_rate' : 0,
'array' : np.array([]), duration : 0}
return {'audio' : audio, 'duration' : duration}
ds = datasets.load_dataset("NbAiLab/nb_distil_speech_noconcat_stortinget", cache_dir=CACHE_PATH, keep_in_memory=False)
#%%
num_proc=32
ds_processed = (
ds
#.select(range(10))
.map(convert_mp3_to_audio_segment, num_proc=num_proc, desc="Converting mp3 to audio segment") #, cache_file_name=f"{CACHE_PATH}/stortinget_audio" # , cache_file_name="test"
)
```
### Expected behavior
the map should write to disk
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-6.8.0-45-generic-x86_64-with-glibc2.39
- Python version: 3.12.7
- `huggingface_hub` version: 0.26.3
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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I_kwDODunzps6jNXJm
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|
Remove upper bound for fsspec
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"Unfortunately `fsspec` versioning allows breaking changes across version and there is no way we can keep it without constrains at the moment. It already broke `datasets` once in the past. Maybe one day once `fsspec` decides on a stable and future proof API but I don't think this will happen anytime soon\r\n\r\nedit: bumped to 2024.10.0 in https://github.com/huggingface/datasets/pull/7352"
] | 2024-12-13T11:35:12
| 2025-01-03T15:34:37
| null |
NONE
| null | null | null | null |
### Describe the bug
As also raised by @cyyever in https://github.com/huggingface/datasets/pull/7296 and @NeilGirdhar in https://github.com/huggingface/datasets/commit/d5468836fe94e8be1ae093397dd43d4a2503b926#commitcomment-140952162 , `datasets` has a problematic version constraint on `fsspec`.
In our case this causes (unnecessary?) troubles due to a race condition bug in that version of the corresponding `gcsfs` plugin, that causes deadlocks: https://github.com/fsspec/gcsfs/pull/643
We just use a version override to ignore the constraint from `datasets`, but imho the version constraint could just be removed in the first place?
The last few PRs bumping the upper bound were basically uneventful:
* https://github.com/huggingface/datasets/pull/7219
* https://github.com/huggingface/datasets/pull/6921
* https://github.com/huggingface/datasets/pull/6747
### Steps to reproduce the bug
-
### Expected behavior
Installing `fsspec>=2024.10.0` along `datasets` should be possible without overwriting constraints.
### Environment info
All recent datasets versions
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[
"Hi ! Since `datasets` 3.x, the `datasets` specific files are in `cache_dir=` and the HF files are cached using `huggingface_hub` and you can set its cache directory using the `HF_HOME` environment variable.\r\n\r\nThey are independent, for example you can delete the Hub cache (containing downloaded files) but still reload your cached datasets from the `datasets` cache (containing prepared datasets in Arrow format)"
] | 2024-12-12T14:03:00
| 2025-01-31T11:34:24
| 2025-01-31T11:34:24
|
NONE
| null | null | null | null |
### Describe the bug
Following the (Cache management)[https://huggingface.co/docs/datasets/en/cache] docu and previous behaviour from datasets version 2.18.0, one is able to change the cache directory. Previously, all downloaded/extracted/etc files were found in this folder. As i have recently update to the latest version this is not the case anymore. Downloaded files are stored in `~/.cache/huggingface/hub`.
Providing the `cache_dir` argument in `load_dataset` the cache directory is created and there are some files but the bulk is still in `~/.cache/huggingface/hub`.
I believe this could be solved by adding the cache_dir argument [here](https://github.com/huggingface/datasets/blob/fdda5585ab18ea1292547f36c969d12c408ab842/src/datasets/utils/file_utils.py#L188)
### Steps to reproduce the bug
For example using https://huggingface.co/datasets/ashraq/esc50:
```python
from datasets import load_dataset
ds = load_dataset("ashraq/esc50", "default", cache_dir="~/custom/cache/path/esc50")
```
### Expected behavior
I would expect the bulk of files related to the dataset to be stored somewhere in `~/custom/cache/path/esc50`, but it seems they are in `~/.cache/huggingface/hub/datasets--ashraq--esc50`.
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.14.0-503.15.1.el9_5.x86_64-x86_64-with-glibc2.34
- Python version: 3.10.14
- `huggingface_hub` version: 0.26.5
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
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ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
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[
"Hi ! `datasets` uses Arrow under the hood which expects each column and array to have fixed types that don't change across rows of a dataset, which is why we get this error. This dataset in particular doesn't have a format compatible with Arrow unfortunately. Don't hesitate to open a discussion or PR on HF to fix the dataset",
"@lhoestq Is it correct to assume that most multimodal datasets with variable number of images across conversations are not compatible with Arrow ? \n\nI’m running into a problem while trying to format multimodal datasets (image + text) using the Hugging Face datasets library. Specifically, I’m working with a structure where conversations include both images and text messages. When I convert my dataset from a Python dict to a Hugging Face Dataset, I’m seeing unexpected None values being inserted for some fields that aren’t relevant for a given message (e.g., \"text\": None in image messages). Here’s what seems to be happening:\n\n🔍 What’s going wrong\n\nHugging Face datasets (backed by Apache Arrow) tries to flatten the schema across all samples. That means it enforces a fixed set of fields across the dataset – even if some of them are None for a given entry. Since my dataset contains heterogeneous conversation messages (some with images, others with text), Arrow is injecting None for whichever attributes don’t exist in each message type to preserve a unified schema.\n\nThis results in a lot of meaningless or misleading Nones across the dataset, and breaks logic further down the pipeline – in particular, it causes the qwen_vl_utils functions (like process_vision_info) to crash or misbehave.\n\n\n🤔 Workarounds considered\n\nI could stick with raw JSON, which preserves the heterogeneous structure properly. But that means giving up all the nice features of datasets and Arrow (e.g. streaming, map/filter, etc.), which feels like a shame.\n\n❓My question\n\nDo you know of a clean way to define a flexible schema with Hugging Face Datasets – maybe using nested structures or dynamic fields – so that each conversation message doesn’t get forced into a flat structure with irrelevant keys? Or should I just stick with JSON for this kind of multimodal case?\n\nLet me know if you’ve run into this before or have any tips!\n",
"Datasets without fixed types are harder to use in many data frameworks unfortunately. You will have to handle the case with None values if you want to use Arrow/`datasets` (or any other framework using Arrow like spark, ray, dask). **IMO the short term solution is to fix qwen_vl_utils / process_vision_info.**\n\nAlternatively we can explore adding the Arrow `Json` type to `datasets`, but doesn't allow having image types in the Json object so I don't think this is the right solution.",
"I totally share the analysis, I am happy to try to help with this! I am currently using Unsloth so it is the `UnslothVisionDataCollator` which has almost the same logic as `qwen_vl_utils.process_vision_info`. Is there a Huggingface class I could patch too ? "
] | 2024-12-11T08:41:39
| 2025-07-15T13:06:55
| null |
NONE
| null | null | null | null |
### Describe the bug
Encountering an error while loading the ```liuhaotian/LLaVA-Instruct-150K dataset```.
### Steps to reproduce the bug
```
from datasets import load_dataset
fw =load_dataset("liuhaotian/LLaVA-Instruct-150K")
```
Error:
```
ArrowInvalid Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py](https://localhost:8080/#) in _generate_tables(self, files)
136 try:
--> 137 pa_table = paj.read_json(
138 io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
20 frames
ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
During handling of the above exception, another exception occurred:
ArrowTypeError Traceback (most recent call last)
ArrowTypeError: ("Expected bytes, got a 'int' object", 'Conversion failed for column id with type object')
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1895 if isinstance(e, DatasetGenerationError):
1896 raise
-> 1897 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1898
1899 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
I have tried loading the dataset both on my own server and on Colab, and encountered errors in both instances.
### Environment info
```
- `datasets` version: 3.2.0
- Platform: Linux-6.1.85+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.26.3
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.9.0
```
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ImportError: cannot import name 'set_caching_enabled' from 'datasets'
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[
"pip install datasets==2.18.0",
"Hi ! I think you need to update axolotl"
] | 2024-12-11T01:58:46
| 2024-12-11T13:32:15
| null |
NONE
| null | null | null | null |
### Describe the bug
Traceback (most recent call last):
File "/usr/local/lib/python3.10/runpy.py", line 187, in _run_module_as_main
mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
File "/usr/local/lib/python3.10/runpy.py", line 110, in _get_module_details
__import__(pkg_name)
File "/home/Medusa/axolotl/src/axolotl/cli/__init__.py", line 23, in <module>
from axolotl.train import TrainDatasetMeta
File "/home/Medusa/axolotl/src/axolotl/train.py", line 23, in <module>
from axolotl.utils.trainer import setup_trainer
File "/home/Medusa/axolotl/src/axolotl/utils/trainer.py", line 13, in <module>
from datasets import set_caching_enabled
ImportError: cannot import name 'set_caching_enabled' from 'datasets' (/usr/local/lib/python3.10/site-packages/datasets/__init__.py)
### Steps to reproduce the bug
1、axolotl
2、accelerate launch -m axolotl.cli.train examples/medusa/qwen_lora_stage1.yml
### Expected behavior
enable datasets
### Environment info
python3.10
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ValueError: You should supply an encoding or a list of encodings to this method that includes input_ids, but you provided ['label']
|
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[
"Now i have other error"
] | 2024-12-10T20:23:11
| 2024-12-10T23:22:23
| 2024-12-10T23:22:23
|
NONE
| null | null | null | null |
### Describe the bug
I am trying to create a PEFT model from DISTILBERT model, and run a training loop. However, the trainer.train() is giving me this error: ValueError: You should supply an encoding or a list of encodings to this method that includes input_ids, but you provided ['label']
Here is my code:
### Steps to reproduce the bug
#Creating a PEFT Config
from peft import LoraConfig
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import get_peft_model
lora_config = LoraConfig(
task_type="SEQ_CLASS",
r=8,
lora_alpha=32,
target_modules=["q_lin", "k_lin", "v_lin"],
lora_dropout=0.01,
)
#Converting a Transformers Model into a PEFT Model
model = AutoModelForSequenceClassification.from_pretrained(
"distilbert-base-uncased",
num_labels=2, #Binary classification, 1 = positive, 0 = negative
)
lora_model = get_peft_model(model, lora_config)
print(lora_model)
Tokenize data set
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
# Load the train and test splits dataset
dataset = load_dataset("fancyzhx/amazon_polarity")
#create a smaller subset for train and test
subset_size = 5000
small_train_dataset = dataset["train"].shuffle(seed=42).select(range(subset_size))
small_test_dataset = dataset["test"].shuffle(seed=42).select(range(subset_size))
#Tokenize data
def tokenize_function(example):
return tokenizer(example["content"], padding="max_length", truncation=True)
tokenized_train_dataset = small_train_dataset.map(tokenize_function, batched=True)
tokenized_test_dataset = small_test_dataset.map(tokenize_function, batched=True)
train_lora = tokenized_train_dataset.rename_column('label', 'labels')
test_lora = tokenized_test_dataset.rename_column('label', 'labels')
print(tokenized_train_dataset.column_names)
print(tokenized_test_dataset.column_names)
#Train the PEFT model
import numpy as np
from transformers import Trainer, TrainingArguments, default_data_collator, DataCollatorWithPadding
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification
def compute_metrics(eval_pred):
predictions, labels = eval_pred
predictions = np.argmax(predictions, axis=1)
return {"accuracy": (predictions == labels).mean()}
trainer = Trainer(
model=lora_model,
args=TrainingArguments(
output_dir=".",
learning_rate=2e-3,
# Reduce the batch size if you don't have enough memory
per_device_train_batch_size=1,
per_device_eval_batch_size=1,
num_train_epochs=3,
weight_decay=0.01,
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
),
train_dataset=tokenized_train_dataset,
eval_dataset=tokenized_test_dataset,
tokenizer=tokenizer,
data_collator=DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt"),
compute_metrics=compute_metrics,
)
trainer.train()
### Expected behavior
Example of output:
[558/558 01:04, Epoch XX]
Epoch | Training Loss | Validation Loss | Accuracy
-- | -- | -- | --
1 | No log | 0.046478 | 0.988341
2 | 0.052800 | 0.048840 | 0.988341
### Environment info
Using python and jupyter notbook
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|
Introduce support for PDFs
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[
"#self-assign",
"Awesome ! Let me know if you have any question or if I can help :)\r\n\r\ncc @AndreaFrancis as well for viz",
"Other candidates libraries for the Pdf type: PyMuPDF pypdf and pdfplumber\r\n\r\nEDIT: Pymupdf looks like a good choice when it comes to maturity + performance + versatility BUT the license is maybe an issue, and pypdf, pypdfium2 or pdfplumber are good options imo",
"Related to https://github.com/huggingface/datasets/issues/7058",
"PyMuPDF is AGPL licensed, so we can't use it. I will move forward with [pdfplumber](https://github.com/jsvine/pdfplumber?tab=readme-ov-file#python-library).",
"Hi both! I have made a pull request with a first basic implementation of the Pdf feature. I followed closely what I saw on the Video and Image features. It is my first time contributing so any comments are very welcomed. I think it would be useful to outline together what additional things we can implement (e.g. enabling parsing of the pdf). Thanks :) "
] | 2024-12-10T16:59:48
| 2024-12-12T18:38:13
| null |
CONTRIBUTOR
| null | null | null | null |
### Feature request
The idea (discussed in the Discord server with @lhoestq ) is to have a Pdf type like Image/Audio/Video. For example [Video](https://github.com/huggingface/datasets/blob/main/src/datasets/features/video.py) was recently added and contains how to decode a video file encoded in a dictionary like {"path": ..., "bytes": ...} as a VideoReader using decord. We want to do the same with pdf and get a [pypdfium2.PdfDocument](https://pypdfium2.readthedocs.io/en/stable/_modules/pypdfium2/_helpers/document.html#PdfDocument).
### Motivation
In many cases PDFs contain very valuable information beyond text (e.g. images, figures). Support for PDFs would help create datasets where all the information is preserved.
### Your contribution
I can start the implementation of the Pdf type :)
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| 7,313
|
Cannot create a dataset with relative audio path
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[
"Hello ! when you `cast_column` you need the paths to be absolute paths or relative paths to your working directory, not the original dataset directory.\r\n\r\nThough I'd recommend structuring your dataset as an AudioFolder which automatically links a metadata.jsonl or csv to the audio files via relative paths **within** the dataset repository: https://huggingface.co/docs/datasets/v3.2.0/en/audio_load#audiofolder",
"@lhoestq thank you, but there are two problems with using AudioFolder:\r\n1. It is said that AudioFolder requires metadata.csv. However, my datset is too large and contains nested and np.ndarray fields, so I can't use csv.\r\n2. It is said that I need to load the dataset with `load_dataset(\"audiofolder\", ...)`. However, if possible, I want my dataset to be loaded as usual with `load_dataset(dataset_name)` after I upload if to HF.",
"You can use metadata.jsonl if you have nested data :)\r\n\r\nAnd actually if you have a dataset structured as an AudioFolder then `load_dataset(dataset_name)` does work after uploading to HF",
"I have created an audio dataset. In my repo, I have explained the steps and structure. An example dataset is also available in the repo. https://github.com/pr0mila/ParquetToHuggingFace "
] | 2024-12-09T07:34:20
| 2025-04-19T07:13:08
| null |
NONE
| null | null | null | null |
### Describe the bug
Hello! I want to create a dataset of parquet files, with audios stored as separate .mp3 files. However, it says "No such file or directory" (see the reproducing code).
### Steps to reproduce the bug
Creating a dataset
```
from pathlib import Path
from datasets import Dataset, load_dataset, Audio
Path('my_dataset/audio').mkdir(parents=True, exist_ok=True)
Path('my_dataset/audio/file.mp3').touch(exist_ok=True)
Dataset.from_list(
[{'audio': {'path': 'audio/file.mp3'}}]
).to_parquet('my_dataset/data.parquet')
```
Result:
```
# my_dataset
# ├── audio
# │ └── file.mp3
# └── data.parquet
```
Trying to load the dataset
```
dataset = (
load_dataset('my_dataset', split='train')
.cast_column('audio', Audio(sampling_rate=16_000))
)
dataset[0]
>>> FileNotFoundError: [Errno 2] No such file or directory: 'audio/file.mp3'
```
### Expected behavior
I expect the dataset to load correctly.
I've found 2 workarounds, but they are not very good:
1. I can specify an absolute path to the audio, however, when I move the folder or upload to HF it will stop working.
2. I can set `'path': 'file.mp3'`, and load with `load_dataset('my_dataset', data_dir='audio')` - it seems to work, but does this mean that anyone from Hugging Face who wants to use this dataset should also pass the `data_dir` argument, otherwise it won't work?
### Environment info
datasets 3.1.0, Ubuntu 24.04.1
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