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https://api.github.com/repos/huggingface/datasets/issues/7503 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7503/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7503/comments | https://api.github.com/repos/huggingface/datasets/issues/7503/events | https://github.com/huggingface/datasets/issues/7503 | 2,978,512,625 | I_kwDODunzps6xiH7x | 7,503 | Inconsistency between load_dataset and load_from_disk functionality | {
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} | ## 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~ | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7502 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7502/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7502/comments | https://api.github.com/repos/huggingface/datasets/issues/7502/events | https://github.com/huggingface/datasets/issues/7502 | 2,977,453,814 | I_kwDODunzps6xeFb2 | 7,502 | `load_dataset` of size 40GB creates a cache of >720GB | {
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} | 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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https://api.github.com/repos/huggingface/datasets/issues/7501 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7501/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7501/comments | https://api.github.com/repos/huggingface/datasets/issues/7501/events | https://github.com/huggingface/datasets/issues/7501 | 2,976,721,014 | I_kwDODunzps6xbSh2 | 7,501 | 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."
] | 1,744,029,339,000 | 1,744,029,784,000 | 1,744,029,783,000 | NONE | {
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} | ### 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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https://api.github.com/repos/huggingface/datasets/issues/7500 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7500/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7500/comments | https://api.github.com/repos/huggingface/datasets/issues/7500/events | https://github.com/huggingface/datasets/issues/7500 | 2,974,841,921 | I_kwDODunzps6xUHxB | 7,500 | Make `with_format` correctly indicate that a `Dataset` is compatible with PyTorch's `Dataset` class | {
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} | ### 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. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7499 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7499/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7499/comments | https://api.github.com/repos/huggingface/datasets/issues/7499/events | https://github.com/huggingface/datasets/pull/7499 | 2,973,489,126 | PR_kwDODunzps6Rd4Zp | 7,499 | Added cache dirs to load and file_utils | {
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} | When adding "cache_dir" to datasets.load_dataset, the cache_dir gets lost in the function calls, changing the cache dir to the default path. This fixes a few of these instances. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7498 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7498/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7498/comments | https://api.github.com/repos/huggingface/datasets/issues/7498/events | https://github.com/huggingface/datasets/issues/7498 | 2,969,218,273 | I_kwDODunzps6w-qzh | 7,498 | Extreme memory bandwidth. | {
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} | ### 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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"total": 0,
"completed": 0,
"percent_completed": 0
} | ### 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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https://api.github.com/repos/huggingface/datasets/issues/7496 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7496/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7496/comments | https://api.github.com/repos/huggingface/datasets/issues/7496/events | https://github.com/huggingface/datasets/issues/7496 | 2,967,345,522 | I_kwDODunzps6w3hly | 7,496 | Json builder: Allow features to override problematic Arrow types | {
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"total": 0,
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} | ### 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. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7495 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7495/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7495/comments | https://api.github.com/repos/huggingface/datasets/issues/7495/events | https://github.com/huggingface/datasets/issues/7495 | 2,967,034,060 | I_kwDODunzps6w2VjM | 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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"total": 0,
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} | ### 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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https://api.github.com/repos/huggingface/datasets/issues/7494 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7494/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7494/comments | https://api.github.com/repos/huggingface/datasets/issues/7494/events | https://github.com/huggingface/datasets/issues/7494 | 2,965,347,685 | I_kwDODunzps6wv51l | 7,494 | Broken links in pdf loading documentation | {
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"total": 0,
"completed": 0,
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} | ### 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) | null | {
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"total": 0,
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} | ### 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 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7492 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7492/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7492/comments | https://api.github.com/repos/huggingface/datasets/issues/7492/events | https://github.com/huggingface/datasets/pull/7492 | 2,959,088,568 | PR_kwDODunzps6QtCQM | 7,492 | Closes #7457 | {
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"This PR fixes issue #7457"
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https://api.github.com/repos/huggingface/datasets/issues/7490 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7490/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7490/comments | https://api.github.com/repos/huggingface/datasets/issues/7490/events | https://github.com/huggingface/datasets/pull/7490 | 2,958,826,222 | PR_kwDODunzps6QsPUI | 7,490 | (refactor) remove redundant logic in _check_valid_index_key | {
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} | This PR contributes a minor refactor, in a small function in `src/datasets/formatting/formatting.py`. No change in logic.
In the original code, there are separate if-conditionals for `isinstance(key, range)` and `isinstance(key, Iterable)`, with essentially the same logic.
This PR combines these two using a single if statement.
**Considerations**
1. Although range in python is guaranteed to have integers, internally calling `int()` on an object that is already an int is negligible. (In python it returns the original object. It doesn't create a new integer object or perform any actual conversion)
2. Technically a range is already an Iterable, and we could just do `isinstance(key, Iterable)` but I explicitly did `isinstance(key, (range, Iterable))` just to be super obvious and consistent that both cases are handled because I see `slice, range, Iterable` everywhere in this `formatting.py`
3. This PR removes the `if len(key)>0` conditional. I think it is cleaner to have it this way for three reasons.
- There was originally no else statement and the code would have failed silently anyway.
- The if len(key)>0 should be caught much earlier, rather than in `formatting.py`.
- There are actually multiple cases where this would fail, if len(key)>0, if key is non numeric or float, or if key is a list of lists. It's clunky to state all this and the error be thrown during max or indexing.
**Previous PR and Issues Checks**
1. No known PR or Issues (both closed or open) in hf datasets repository
**Tests**
1. Tested using Dataset (load_dataset("wikitext", "wikitext-103-raw-v1")), Pytorch DataLoader, with a Pytorch BatchSampler (list of indexes returned instead of single index).
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https://api.github.com/repos/huggingface/datasets/issues/7489 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7489/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7489/comments | https://api.github.com/repos/huggingface/datasets/issues/7489/events | https://github.com/huggingface/datasets/pull/7489 | 2,958,204,763 | PR_kwDODunzps6QqSRD | 7,489 | fix: loading of datasets from Disk(#7373) | {
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"@nepfaff Could you confirm if this fixes the issue for you? I checked Memray, and everything looked good on my end.\r\n\r\nInstall: `pip install git+https://github.com/sam-hey/datasets.git@fix/concatenate_datasets`\r\n",
"Will aim to get to this soon. I don't have a rapid testing pipeline setup but need to wait for some AWS nodes to become free",
"I now set up a small experiment:\r\n\r\n```python\r\n# Log initial RAM usage\r\n process = psutil.Process(os.getpid())\r\n initial_ram = process.memory_info().rss / (1024 * 1024) # Convert to MB\r\n logging.info(f\"Initial RAM usage: {initial_ram:.2f} MB\")\r\n\r\n chunk_datasets = [\r\n Dataset.load_from_disk(dataset_path, keep_in_memory=False) for _ in range(N)\r\n ]\r\n combined_dataset = concatenate_datasets(chunk_datasets)\r\n\r\n # Log final RAM usage\r\n final_ram = process.memory_info().rss / (1024 * 1024) # Convert to MB\r\n ram_diff = final_ram - initial_ram\r\n logging.info(f\"Final RAM usage: {final_ram:.2f} MB\")\r\n logging.info(f\"RAM usage increase: {ram_diff:.2f} MB\")\r\n```\r\n\r\nThe RAM usage is linearly correlated with `N` on datasets master!\r\n\r\nFor my test dataset:\r\n- N=5 => RAM usage increase: 26302.91 MB\r\n- N=10 => RAM usage increase: 52315.18 MB\r\n- N=20 => RAM usage increase: 104510.65 MB\r\n- N=40 => RAM usage increase: 209166.30 MB\r\n\r\nUnfortunately, your patch doesn't seem to change this:\r\n```bash\r\npip install git+https://github.com/sam-hey/datasets.git@fix/concatenate_datasets\r\npip list | grep datasets\r\ndatasets 3.5.1.dev0\r\n```\r\nGives exactly the same RAM statistics.\r\n\r\n**Edit:** The results are a bit flawed as the memory increase all seems to come from `Dataset.load_from_disk(dataset_path, keep_in_memory=False)` here (which I don't think should happen either?) and not from `concatenate_datasets`. This seems different from my large-scale setup that runs out of memory during `concatenate_datasets` but I don't seem to be able to replicate this here...",
"Thanks a lot, @nepfaff, for taking a look at this! It seems that `concatenate_datasets()` is fixed with this PR. I can also confirm that loading a large number of files requires significant memory. However, as I understand it, this is expected/a bug since the memory consumption stems from `pa.memory_map()`, which returns a memory-mapped file.\r\n\r\nThis behavior might be related to this bug: https://github.com/apache/arrow/issues/34423 \r\n\r\n<img width=\"1728\" alt=\"Screenshot 2025-04-03 at 16 01 11\" src=\"https://github.com/user-attachments/assets/475691d8-3aba-4d7e-b8ef-5e7552c70b14\" />\r\n"
] | 1,743,265,378,000 | 1,743,688,939,000 | null | NONE | {
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} | Fixes dataset loading from disk by ensuring that memory maps and streams are properly closed.
For more details, see https://github.com/huggingface/datasets/issues/7373. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7488 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7488/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7488/comments | https://api.github.com/repos/huggingface/datasets/issues/7488/events | https://github.com/huggingface/datasets/pull/7488 | 2,956,559,358 | PR_kwDODunzps6QlLmn | 7,488 | Support underscore int read instruction | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7488). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"you rock, Quentin - thank you!"
] | 1,743,177,675,000 | 1,743,178,844,000 | 1,743,178,843,000 | MEMBER | {
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https://api.github.com/repos/huggingface/datasets/issues/7487 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7487/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7487/comments | https://api.github.com/repos/huggingface/datasets/issues/7487/events | https://github.com/huggingface/datasets/pull/7487 | 2,956,533,448 | PR_kwDODunzps6QlF8N | 7,487 | Write pdf in map | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7487). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,743,176,965,000 | 1,743,181,793,000 | 1,743,181,791,000 | MEMBER | {
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} | Fix this error when mapping a PDF dataset
```
pyarrow.lib.ArrowInvalid: Could not convert <pdfplumber.pdf.PDF object at 0x13498ee40> with type PDF: did not recognize Python value type when inferring an Arrow data type
```
and also let map() outputs be lists of images or pdfs | {
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https://api.github.com/repos/huggingface/datasets/issues/7486 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7486/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7486/comments | https://api.github.com/repos/huggingface/datasets/issues/7486/events | https://github.com/huggingface/datasets/issues/7486 | 2,954,042,179 | I_kwDODunzps6wExtD | 7,486 | `shared_datadir` fixture is missing | {
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"OK I was missing the `pytest-datadir` package. Sorry for the noise!"
] | 1,743,099,432,000 | 1,743,104,951,000 | 1,743,104,950,000 | NONE | {
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} | ### 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. | {
"login": "lahwaacz",
"id": 1289205,
"node_id": "MDQ6VXNlcjEyODkyMDU=",
"avatar_url": "https://avatars.githubusercontent.com/u/1289205?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lahwaacz",
"html_url": "https://github.com/lahwaacz",
"followers_url": "https://api.github.com/users/lahwaacz/followers",
"following_url": "https://api.github.com/users/lahwaacz/following{/other_user}",
"gists_url": "https://api.github.com/users/lahwaacz/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lahwaacz/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lahwaacz/subscriptions",
"organizations_url": "https://api.github.com/users/lahwaacz/orgs",
"repos_url": "https://api.github.com/users/lahwaacz/repos",
"events_url": "https://api.github.com/users/lahwaacz/events{/privacy}",
"received_events_url": "https://api.github.com/users/lahwaacz/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7485). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7484). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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https://api.github.com/repos/huggingface/datasets/issues/7483 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7483/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7483/comments | https://api.github.com/repos/huggingface/datasets/issues/7483/events | https://github.com/huggingface/datasets/pull/7483 | 2,951,856,468 | PR_kwDODunzps6QVInB | 7,483 | Support skip_trying_type | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7483). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Cool ! Can you run `make style` to fix code formatting ?\r\n\r\nI was also thinking of naming the argument `try_original_type` and have it `True` by default",
"@lhoestq \r\n\r\nThank you for the suggestion! I renamed the argument with `True` by default and ran `make style`\r\nDoes it look good?",
"Thanks @lhoestq !\r\n\r\nLet me know if there are anything that I can do for this PR. Otherwise, looking forward to seeing this update in the package soon!"
] | 1,743,059,240,000 | 1,744,090,669,000 | null | NONE | {
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cc: @lhoestq | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7482 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7482/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7482/comments | https://api.github.com/repos/huggingface/datasets/issues/7482/events | https://github.com/huggingface/datasets/pull/7482 | 2,950,890,368 | PR_kwDODunzps6QRyY6 | 7,482 | Implement capability to restore non-nullability in Features | {
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"Interestingly, this does not close #7479. The Features are not correctly maintained when calling `from_dict` with the custom Features.",
"Unfortunately this PR does not fix the reported issue. After more digging:\r\n\r\n- when the dataset is created, nullability information is lost in Features;\r\n- even with this PR, it will get lost eventually because of internal copying/recreation of the Features object without accounting for the nullable fields;\r\n- even if that is also fixed, and Features.arrow_schema correctly holds the nullability info, [casting the arrow Table](https://github.com/huggingface/datasets/blob/5f8d2ad9a1b0bccfd962d998987228addfd5be9f/src/datasets/arrow_dataset.py#L677) with a less strict schema to a more strict one (with nullability) will fail (only on deeper structs, not on flat fields). \r\n\r\nInterestingly, passing custom Features does not immediately load the underlying data with the right arrow_schema. Instead, the workflow is like this:\r\n\r\n- load pyarrow table with any of the methods (from_dict, from_pandas, etc.), which will always AUTO INFER rather than use a provided schema\r\n- the loaded table with auto-schema will be used to initialize the `Dataset` class, and only during construction will [CAST](https://github.com/huggingface/datasets/blob/5f8d2ad9a1b0bccfd962d998987228addfd5be9f/src/datasets/arrow_dataset.py#L677) the table to the user-provided schema if needed, if it differs from the auto-inferred one.\r\n\r\nSo I figured, since many/all of the pyarrow [`Table.from_*`](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html) methods have a `schema=` argument, we should already load the Table with the correct schema to begin with. As an example, I tried changing this line:\r\n\r\nhttps://github.com/huggingface/datasets/blob/5f8d2ad9a1b0bccfd962d998987228addfd5be9f/src/datasets/arrow_dataset.py#L940\r\n\r\nto include the arrow_schema, if provided:\r\n\r\n```python\r\npa_table = InMemoryTable.from_pydict(mapping=mapping, schema=features.arrow_schema if features is not None else None)\r\n```\r\n\r\nBut that leads to:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/ampere/vanroy/datasets/scratch.py\", line 33, in <module>\r\n ds = Dataset.from_dict(\r\n ^^^^^^^^^^^^^^^^^^\r\n File \"/home/local/vanroy/datasets/src/datasets/arrow_dataset.py\", line 957, in from_dict\r\n pa_table = InMemoryTable.from_pydict(mapping=mapping, schema=features.arrow_schema if features is not None else None)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/local/vanroy/datasets/src/datasets/table.py\", line 758, in from_pydict\r\n return cls(pa.Table.from_pydict(*args, **kwargs))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"pyarrow/table.pxi\", line 1968, in pyarrow.lib._Tabular.from_pydict\r\n File \"pyarrow/table.pxi\", line 6354, in pyarrow.lib._from_pydict\r\n File \"pyarrow/array.pxi\", line 402, in pyarrow.lib.asarray\r\n File \"pyarrow/array.pxi\", line 252, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 114, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/home/local/vanroy/datasets/src/datasets/arrow_writer.py\", line 201, in __arrow_array__\r\n raise ValueError(\"TypedSequence is supposed to be used with pa.array(typed_sequence, type=None)\")\r\nValueError: TypedSequence is supposed to be used with pa.array(typed_sequence, type=None)\r\n```\r\n\r\nand I am not too familiar with pyarrow to solve this.\r\n\r\nSo ultimately I'm a bit at a loss here. I *think*, if we'd want to do this right, the automatic casting in init should be removed in favor of handling the logic inside `Dataset.from_*`, by passing the schema explicitly to `pa.Table.from_*(..., schema=schema)`. But I lack the knowledge of pyarrow to go further than what I've written about above.\r\n",
"It's indeed a bit more work to support nullable since in addition to your comments, there are unclear behavior when it comes to concatenating nullable with non-nullable, and maybe how to handle non-nullable lists and nested data.\r\n\r\nBut yup I agree having the `Dataset.from_*` function pass the `schema` to the `pa.Table.from*` would be the way.\r\n\r\nJust one comment about this error: \r\n\r\n```\r\nValueError: TypedSequence is supposed to be used with pa.array(typed_sequence, type=None)\r\n```\r\n\r\nThis happens because `Dataset.from_dict` uses `OptimizedTypedSequence` by default, which should only be used if the user doesn't specify a schema"
] | 1,743,027,369,000 | 1,743,080,870,000 | null | CONTRIBUTOR | {
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} | This PR attempts to keep track of non_nullable pyarrow fields when converting a `pa.Schema` to `Features`. At the same time, when outputting the `arrow_schema`, the original non-nullable fields are restored. This allows for more consistent behavior and avoids breaking behavior as illustrated in #7479.
I am by no means a pyarrow expert so some logic in `find_non_nullable_fields` may not perfect. Not sure if more logic (type checks) are needed for deep-checking a given schema. Maybe there are other pyarrow structures that need to be covered?
Tests are added, but again, these may not have sufficient coverage in terms of pyarrow structure types.
closes #7479 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7481 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7481/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7481/comments | https://api.github.com/repos/huggingface/datasets/issues/7481/events | https://github.com/huggingface/datasets/issues/7481 | 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"
] | 1,743,019,854,000 | 1,743,178,844,000 | 1,743,178,844,000 | NONE | {
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} | ### 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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https://api.github.com/repos/huggingface/datasets/issues/7480 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7480/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7480/comments | https://api.github.com/repos/huggingface/datasets/issues/7480/events | https://github.com/huggingface/datasets/issues/7480 | 2,950,315,214 | 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`"
] | 1,743,009,574,000 | 1,744,046,640,000 | null | NONE | {
"total": 0,
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} | ### 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] | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7479 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7479/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7479/comments | https://api.github.com/repos/huggingface/datasets/issues/7479/events | https://github.com/huggingface/datasets/issues/7479 | 2,950,235,396 | I_kwDODunzps6v2QUE | 7,479 | Features.from_arrow_schema is destructive | {
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"total": 0,
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"percent_completed": 0
} | ### 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 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7478 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7478/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7478/comments | https://api.github.com/repos/huggingface/datasets/issues/7478/events | https://github.com/huggingface/datasets/pull/7478 | 2,948,993,461 | PR_kwDODunzps6QLPe3 | 7,478 | update fsspec 2025.3.0 | {
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"Sorry for tagging you @lhoestq but since you merged the linked PR, I wondered if you might be able to help me get this triaged so it can be reviewed/rejected etc. 🙏🏼 ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7478). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,742,982,785,000 | 1,743,189,354,000 | 1,743,177,115,000 | CONTRIBUTOR | {
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} | It appears there have been two releases of fsspec since this dependency was last updated, it would be great if Datasets could be updated so that it didn't hold back the usage of newer fsspec versions in consuming projects.
PR based on https://github.com/huggingface/datasets/pull/7352 | {
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https://api.github.com/repos/huggingface/datasets/issues/7477 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7477/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7477/comments | https://api.github.com/repos/huggingface/datasets/issues/7477/events | https://github.com/huggingface/datasets/issues/7477 | 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)"
] | 1,742,921,271,000 | 1,743,671,591,000 | null | NONE | {
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} | 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.? | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7476 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7476/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7476/comments | https://api.github.com/repos/huggingface/datasets/issues/7476/events | https://github.com/huggingface/datasets/pull/7476 | 2,946,997,924 | PR_kwDODunzps6QEbmO | 7,476 | Priotitize json | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7476). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,742,917,471,000 | 1,742,917,620,000 | 1,742,917,500,000 | MEMBER | {
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https://api.github.com/repos/huggingface/datasets/issues/7475 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7475/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7475/comments | https://api.github.com/repos/huggingface/datasets/issues/7475/events | https://github.com/huggingface/datasets/issues/7475 | 2,946,640,570 | I_kwDODunzps6voiq6 | 7,475 | 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"
] | 1,742,911,087,000 | 1,744,046,942,000 | null | CONTRIBUTOR | {
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} | ### 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
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https://api.github.com/repos/huggingface/datasets/issues/7474 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7474/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7474/comments | https://api.github.com/repos/huggingface/datasets/issues/7474/events | https://github.com/huggingface/datasets/pull/7474 | 2,945,066,258 | PR_kwDODunzps6P91lM | 7,474 | Remove conditions for Python < 3.9 | {
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"I was able to work around it"
] | 1,742,577,832,000 | 1,742,584,798,000 | 1,742,584,798,000 | NONE | {
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} | ### 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
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https://api.github.com/repos/huggingface/datasets/issues/7472 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7472/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7472/comments | https://api.github.com/repos/huggingface/datasets/issues/7472/events | https://github.com/huggingface/datasets/issues/7472 | 2,937,607,272 | 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)!"
] | 1,742,543,782,000 | 1,743,998,812,000 | null | NONE | {
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} | ### 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 | null | {
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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 ^^'"
] | 1,742,541,473,000 | 1,743,078,652,000 | 1,742,973,987,000 | NONE | {
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} | ### 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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https://api.github.com/repos/huggingface/datasets/issues/7470 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7470/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7470/comments | https://api.github.com/repos/huggingface/datasets/issues/7470/events | https://github.com/huggingface/datasets/issues/7470 | 2,937,236,323 | I_kwDODunzps6vEqtj | 7,470 | 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."
] | 1,742,531,617,000 | 1,742,981,686,000 | 1,742,971,768,000 | NONE | {
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} | 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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https://api.github.com/repos/huggingface/datasets/issues/7469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7469/comments | https://api.github.com/repos/huggingface/datasets/issues/7469/events | https://github.com/huggingface/datasets/issues/7469 | 2,936,606,080 | I_kwDODunzps6vCQ2A | 7,469 | Custom split name with the web interface | {
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} | [] | closed | false | null | [] | [] | 1,742,503,559,000 | 1,742,541,637,000 | 1,742,541,637,000 | NONE | {
"total": 0,
"completed": 0,
"percent_completed": 0
} | ### 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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https://api.github.com/repos/huggingface/datasets/issues/7468 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7468/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7468/comments | https://api.github.com/repos/huggingface/datasets/issues/7468/events | https://github.com/huggingface/datasets/issues/7468 | 2,934,094,103 | I_kwDODunzps6u4rkX | 7,468 | function `load_dataset` can't solve folder path with regex characters like "[]" | {
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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"
] | 1,742,448,119,000 | 1,742,897,892,000 | null | NONE | {
"total": 0,
"completed": 0,
"percent_completed": 0
} | ### 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 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7467 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7467/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7467/comments | https://api.github.com/repos/huggingface/datasets/issues/7467/events | https://github.com/huggingface/datasets/issues/7467 | 2,930,067,107 | 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=`..."
] | 1,742,340,834,000 | 1,742,898,484,000 | null | NONE | {
"total": 0,
"completed": 0,
"percent_completed": 0
} | ### 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 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7466 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7466/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7466/comments | https://api.github.com/repos/huggingface/datasets/issues/7466/events | https://github.com/huggingface/datasets/pull/7466 | 2,928,661,327 | PR_kwDODunzps6PHQyp | 7,466 | Fix local pdf loading | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7466). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,742,306,946,000 | 1,742,307,112,000 | 1,742,306,961,000 | MEMBER | {
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} | fir this error when accessing a local pdf
```
File ~/.pyenv/versions/3.12.2/envs/hf-datasets/lib/python3.12/site-packages/pdfminer/psparser.py:220, in PSBaseParser.seek(self, pos)
218 """Seeks the parser to the given position."""
219 log.debug("seek: %r", pos)
--> 220 self.fp.seek(pos)
221 # reset the status for nextline()
222 self.bufpos = pos
ValueError: seek of closed file
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7464 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7464/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7464/comments | https://api.github.com/repos/huggingface/datasets/issues/7464/events | https://github.com/huggingface/datasets/pull/7464 | 2,926,478,838 | PR_kwDODunzps6PABJa | 7,464 | Minor fix for metadata files in extension counter | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7464). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,742,248,631,000 | 1,742,311,303,000 | 1,742,311,301,000 | MEMBER | {
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"Hi ! I'mn wondering if this shouldn't this be an `Image()` type and decoded as a `PIL.Image` ?\r\n\r\nThis would make it easier to integrate with the rest of the HF ecosystem, and you could still get a numpy array using `ds = ds.with_format(\"numpy\")` which sets all the images to be formatted as numpy arrays",
"@lhoestq do you mean to add the decoder, and exr extension to the image format? Yes, that probably would be better ",
"yes exactly"
] | 1,742,233,360,000 | 1,743,597,219,000 | null | NONE | {
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} | This PR adds the EXR feature to store depth images (or can be normals, etc) in float32.
It relies on [openexr_numpy](https://github.com/martinResearch/openexr_numpy/tree/main) to manipulate EXR images.
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7462). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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End of preview. Expand
in Data Studio
Dataset Card for [GitHub issues and comments dataset]
Dataset Summary
[This dataset was created by collecting GitHub issues and their associated comments from the 🤗 Datasets repository using the GitHub API. The dataset serves as the foundation for building a semantic search engine that helps users find answers to their most pressing questions about the library.
The dataset includes metadata fields such as issue titles, descriptions (`body`), timestamps, labels, user associations, and the full list of comments per issue.
It can be used to experiment with tasks like text similarity, semantic search, and information retrieval.
For the tutorial on how this dataset was created, refer to the Hugging Face course:
https://huggingface.co/learn/llm-course/en/chapter5/5?fw=pt#creating-a-dataset-card]
Supported Tasks and Leaderboards
[More Information Needed]
Languages
[More Information Needed]
Dataset Structure
Data Instances
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'title': 'Nested Feature raises ArrowNotImplementedError: Unsupported cast using function cast_struct',
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'comments': ['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.'],
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'body': '### Describe the bug\n\ndatasets.Features seems to be unable to handle json file that contains fields of list[dict].\n\n### Steps to reproduce the bug\n\njson\n// test.json\n{"a": 1, "b": [{"c": 2, "d": 3}, {"c": 4, "d": 5}]}\n{"a": 5, "b": [{"c": 7, "d": 8}, {"c": 9, "d": 10}]}\n\n\npython\nimport json\nfrom datasets import Dataset, Features, Value, Sequence, load_dataset\n\nannotation_feature = Features({\n "a": Value("int32"),\n "b": Sequence({\n "c": Value("int32"),\n "d": Value("int32"),\n }),\n})\nannotation_dataset = load_dataset(\n "json", \n data_files="test.json",\n features=annotation_feature\n)\n\n\n\nArrowNotImplementedError: Unsupported cast from list<item: struct<c: int32, d: int32>> to struct using function cast_struct\n\nThe above exception was the direct cause of the following exception:\n\nDatasetGenerationError Traceback (most recent call last)\nCell In[46], line 11\n 2 from datasets import Dataset, Features, Value, Sequence, load_dataset\n 4 annotation_feature = Features({\n 5 "a": Value("int32"),\n 6 "b": Sequence({\n (...) 9 }),\n 10 })\n---> 11 annotation_dataset = load_dataset(\n 12 "json", \n 13 data_files="test.json",\n 14 features=annotation_feature\n 15 )\n\n\n### Expected behavior\n\nA datasets.Datasets instance should be initialized.\n\n### Environment info\n\n- datasets version: 3.5.0\n- Platform: Linux-6.11.0-21-generic-x86_64-with-glibc2.39\n- Python version: 3.11.11\n- huggingface_hub version: 0.30.1\n- PyArrow version: 19.0.1\n- Pandas version: 2.2.3\n- fsspec version: 2024.12.0',
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Data Fields
[More Information Needed]
Data Splits
[More Information Needed]
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
[More Information Needed]
Contributions
Thanks to @github-username for adding this dataset.
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