NyssaW/github-issues huggingface dataset:
Update items in the dataset without `map`

### Feature request

I would like to be able to update items in my dataset without affecting all rows. At least if there was a range option, I would be able to process those items, save the dataset, and then continue.

If I am supposed to split the dataset first, that is not clear, since the docs suggest that any of those functions returns a new object, so I don't think I can do that.

### Motivation

I am applying an extremely time-consuming function to each item in my `Dataset`. Unfortunately, datasets only supports updating values via `map`, so if my computer dies in the middle of this long-running process, I lose all progress. This is far from ideal. I would like to use `datasets` throughout this processing, but this limitation is now forcing me to write my own dataset format just to do this intermediary operation.

It would be less intuitive but I suppose I could split and then concatenate the dataset before saving? But this feels very inefficient.

### Your contribution

I can test the feature.

Comments:
Hello!

Have you looked at `Dataset.shard`? [Docs](https://huggingface.co/docs/datasets/en/process#shard)

Using this method you could break your dataset in N shards. Apply `map` on each shard and concatenate them back.
num_proc parallelization works only for first ~10s.

### Describe the bug

When I try to load an already downloaded dataset with num_proc=64, the speed is very high for the first 10-20 seconds acheiving 30-40K samples / s, and 100% utilization for all cores but it soon drops to <= 1000 with almost 0% utilization for most cores. 

### Steps to reproduce the bug

```
// download dataset with cli
!huggingface-cli download --repo-type dataset timm/imagenet-1k-wds --max-workers 32

from datasets import load_dataset
ds = load_dataset("timm/imagenet-1k-wds", num_proc=64)
```


### Expected behavior

100% core utilization throughout. 

### Environment info

Azure A100-80GB, 16 cores VM

![Image](https://github.com/user-attachments/assets/69d00fe3-d720-4474-9439-21e046d85034)

Comments:
Hi, can you check if the processes are still alive ? It's a bit weird because `datasets` does check if processes crash and return an error in that case

Thank you for reverting quickly. I digged a bit, and realized my disk's IOPS is also limited - which is causing this. will check further and report if it's an issue of hf datasets' side or mine.
Image Feature in Datasets Library Fails to Handle bytearray Objects from Spark DataFrames

### Describe the bug

When using `IterableDataset.from_spark()` with a Spark DataFrame containing image data, the `Image` feature class fails to properly process this data type, causing an `AttributeError: 'bytearray' object has no attribute 'get'`

### Steps to reproduce the bug

1. Create a Spark DataFrame with a column containing image data as bytearray objects
2. Define a Feature schema with an Image feature
3. Create an IterableDataset using `IterableDataset.from_spark()`
4. Attempt to iterate through the dataset

```
from pyspark.sql import SparkSession
from datasets import Dataset, IterableDataset, Features, Image, Value

# initialize spark
spark = SparkSession.builder.appName("MinimalRepro").getOrCreate()

# create spark dataframe
data = [(0, open("image.png", "rb").read())]
df = spark.createDataFrame(data, "idx: int, image: binary")

# convert to dataset
features = Features({"idx": Value("int64"), "image": Image()})
ds = Dataset.from_spark(df, features=features)
ds_iter = IterableDataset.from_spark(df, features=features)

# iterate
print(next(iter(ds)))
print(next(iter(ds_iter)))
```

### Expected behavior

The features should work on `IterableDataset` the same way they work on `Dataset`

### Environment info

- `datasets` version: 3.5.0
- Platform: macOS-15.3.2-arm64-arm-64bit
- Python version: 3.12.7
- `huggingface_hub` version: 0.30.2
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0


Comments:
Hi ! The `Image()` type accepts either
- a `bytes` object containing the image bytes
- a `str` object containing the image path
- a `PIL.Image` object

but it doesn't support `bytearray`, maybe you can convert to `bytes` beforehand ?

Hi @lhoestq, 
converting to bytes is certainly possible and would work around the error. However, the core issue is that `Dataset` and `IterableDataset` behave differently with the features.

I’d be happy to work on a fix for this issue.

I see, that's an issue indeed. Feel free to ping me if I can help with reviews or any guidance

If it can help, the code that takes a Spark DataFrame and iterates on the rows for `IterableDataset` is here: 

https://github.com/huggingface/datasets/blob/6a96bf313085d7538a999b929a550e14e1d406c9/src/datasets/packaged_modules/spark/spark.py#L49-L53

#self-assign
unsloth/DeepSeek-R1-Distill-Qwen-32B server error

### Describe the bug

hfhubhttperror: 500 server error: internal server error for url: https://huggingface.co/api/models/unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit/commits/main (request id: root=1-67fe23fa-3a2150eb444c2a823c388579;de3aed68-c397-4da5-94d4-6565efd3b919) internal error - we're working hard to fix this as soon as possible!

### Steps to reproduce the bug

unsloth/DeepSeek-R1-Distill-Qwen-32B server error

### Expected behavior

Network repair

### Environment info

The web side is also unavailable

Comments:
`concatenate_datasets` does not preserve Pytorch format for IterableDataset

### Describe the bug

When concatenating datasets with `concatenate_datasets`, I would expect the resulting combined dataset to be in the same format as the inputs (assuming it's consistent). This is indeed the behavior when combining `Dataset`, but not when combining `IterableDataset`. Specifically, when applying `concatenate_datasets` to a list of `IterableDataset` in Pytorch format (i.e. using `.with_format(Pytorch)`), the output `IterableDataset` is not in Pytorch format.

### Steps to reproduce the bug

```
import datasets
ds = datasets.Dataset.from_dict({"a": [1,2,3]})
iterable_ds = ds.to_iterable_dataset()
datasets.concatenate_datasets([ds.with_format("torch")]) # <- this preserves Pytorch format
datasets.concatenate_datasets([iterable_ds.with_format("torch")]) # <- this does NOT preserves Pytorch format
```

### Expected behavior

Pytorch format should be preserved when combining IterableDataset in Pytorch format.

### Environment info

datasets==3.5.0, Python 3.11.11, torch==2.2.2

Comments:
Hi ! Oh indeed it would be cool to return the same format in that case. Would you like to submit a PR ? The function that does the concatenation is here:

https://github.com/huggingface/datasets/blob/90e5bf8a8599b625d6103ee5ac83b98269991141/src/datasets/iterable_dataset.py#L3375-L3380

Thank you for the pointer, @lhoestq ! See #7522
Do not hash `generator` in `BuilderConfig.create_config_id`

`Dataset.from_generator` function passes all of its arguments to `BuilderConfig.create_config_id`, including generator function itself. `BuilderConfig.create_config_id` function tries to hash all the args, and hashing a `generator` can take a large amount of time or even cause MemoryError if the dataset processed in a generator function is large enough.

Maybe we should pop generator from `config_kwargs_to_add_to_suffix` before hashing to avoid it.

There is a more detailed description of the problem this PR solves in #7513 

Comments:
Incompatibile dill version (0.3.9) in datasets 2.18.0 - 3.5.0

### Describe the bug

Datasets 2.18.0 - 3.5.0 has a dependency on dill < 0.3.9. This causes errors with dill >= 0.3.9.

Could you please take a look into it and make it compatible?

### Steps to reproduce the bug

1. Install setuptools >= 2.18.0
2. Install dill >=0.3.9
3. Run pip check
4. Output:
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
datasets 2.18.0 requires dill<0.3.9,>=0.3.0, but you have dill 0.3.9 which is incompatible.

### Expected behavior

Pip install both libraries without any errors

### Environment info

Datasets version: 2.18 - 3.5
Python: 3.11

Comments:
Hi ! We can bump `dill` to 0.3.9 if we make sure it's deterministic and doesn't break the caching mechanism in `datasets`.

Would you be interested in opening a PR ? Then we can run the CI to see if it works

Hi!. Yeah I can do it. Should I make any changes besides dill versions?

There are probably some usage of internal functions from `dill` that we'll need to update in `datasets`

If you run `pytest tests/test_fingerprint.py` you should already have a good idea of what works and what doesn't.
But feel free to open a PR anyway, this way we can run the full CI and see the results


Hi, sorry for no response from my side. I will try to do it today.

Created pull request: [LINK](https://github.com/huggingface/datasets/pull/7535)
Tried to run tests by using command you have send and got few errors:

![Image](https://github.com/user-attachments/assets/acbf1feb-4dd1-416e-a118-c91abe0d188b)

Thanks for running the test ! So it appears we have two issues to fix:
1. 'log' is not defined: it seems an internal `dill` function has disappeared, so we should adapt the `datasets` code that was using it
2. there are some hashes mismatches, which means `dill` doesn't seem to output the same dump when passed the same ipython function twice, or the same function but located at a different line in a python file
Iterating over Image feature columns is extremely slow

We are trying to load datasets where the image column stores `PIL.PngImagePlugin.PngImageFile` images. However, iterating over these datasets is extremely slow. 
What I have found:
1. It is the presence of the image column that causes the slowdown. Removing the column from the dataset results in blazingly fast (as expected) times
2. It is ~2x faster to iterate when the column contains a single image as opposed to a list of images i.e., the feature is a Sequence of Image objects. We often need multiple images per sample, so we need to work with a list of images
3. It is ~17x faster to store paths to PNG files and load them using `PIL.Image.open`, as opposed to iterating over a `Dataset` with an Image column, and ~30x faster compared to `Sequence` of `Image`s. See a simple script below with an openly available dataset.


It would be great to understand the standard practices for storing and loading multimodal datasets (image + text). 

https://huggingface.co/docs/datasets/en/image_load seems a bit underdeveloped? (e.g., `dataset.decode` only works with `IterableDataset`, but it's not clear from the doc)

Thanks!

```python
from datasets import load_dataset, load_from_disk
from PIL import Image
from pathlib import Path

ds = load_dataset("getomni-ai/ocr-benchmark")

for idx, sample in enumerate(ds["test"]):
    image = sample["image"]
    image.save(f"/tmp/ds_files/images/image_{idx}.png")

ds.save_to_disk("/tmp/ds_columns")

# Remove the 'image' column
ds["test"] = ds["test"].remove_columns(["image"])

# Create image paths for each sample
image_paths = [f"images/image_{idx}.png" for idx in range(len(ds["test"]))]

# Add the 'image_path' column to the dataset
ds["test"] = ds["test"].add_column("image_path", image_paths)

# Save the updated dataset
ds.save_to_disk("/tmp/ds_files")
files_path = Path("/tmp/ds_files")
column_path = Path("/tmp/ds_columns")

# load and benchmark
ds_file = load_from_disk(files_path)
ds_column = load_from_disk(column_path)
import time


images_files = []
start = time.time()
for idx in range(len(ds_file["test"])):
    image_path = files_path / ds_file["test"][idx]["image_path"]
    image = Image.open(image_path)
    images_files.append(image)
end = time.time()
print(f"Time taken to load images from files: {end - start} seconds")

# Time taken to load images from files: 1.2364635467529297 seconds


images_column = []
start = time.time()
for idx in range(len(ds_column["test"])):
    images_column.append(ds_column["test"][idx]["image"])
end = time.time()
print(f"Time taken to load images from columns: {end - start} seconds")

# Time taken to load images from columns: 20.49347186088562 seconds
```

Comments:
Hi ! Could it be because the `Image()` type in dataset does `image  = Image.open(image_path)` and also `image.load()` which actually loads the image data in memory ? This is needed to avoid too many open files issues, see https://github.com/huggingface/datasets/issues/3985

Yes, that seems to be it. For my purposes, I've cast the column to `Image(decode=False)`, and only load the images when necessary, which is much much faster
Front-end statistical data quantity deviation

### Describe the bug

While browsing the dataset at https://huggingface.co/datasets/NeuML/wikipedia-20250123, I noticed that a dataset with nearly 7M entries was estimated to be only 4M in size—almost half the actual amount. According to the post-download loading and the dataset_info (https://huggingface.co/datasets/NeuML/wikipedia-20250123/blob/main/train/dataset_info.json), the true data volume is indeed close to 7M. This significant discrepancy could mislead users when sorting datasets by row count. Why not directly retrieve this information from dataset_info?

Not sure if this is the right place to report this bug, but leaving it here for the team's awareness.

Comments:
Hi ! the format of this dataset is not supported by the Dataset Viewer. It looks like this dataset was saved using `save_to_disk()` which is meant for local storage / easy reload without compression, not for sharing online.
`load_dataset` of size 40GB creates a cache of >720GB

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


Comments:
Hi ! Parquet is a compressed format. When you load a dataset, it uncompresses the Parquet data into Arrow data on your disk. That's why you can indeed end up with 720GB of uncompressed data on disk. The uncompression is needed to enable performant dataset objects (especially for random access).

To save some storage you can instead load the dataset with `streaming=True`. This way you get an `IterableDataset` that reads the Parquet data iteratively without ever writing to disk.

PS: `ReadInstruction` might not be implemented for `streaming=True`, if it's the case you can use `ds.take()` and `ds.skip()` instead

Hi @lhoestq, thanks a lot for your answer. This makes perfect sense. I will try using the streaming mode. Closing the issue.
Nested Feature raises ArrowNotImplementedError: Unsupported cast using function cast_struct

### 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

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.
Make `with_format` correctly indicate that a `Dataset` is compatible with PyTorch's `Dataset` class

### 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.

Comments:
Does the torch `DataLoader` really require the dataset to be a subclass of `torch.utils.data.Dataset` ? Or is there a simpler type we could use ?

PS: also note that a dataset without `with_format()` can also be used in a torch `DataLoader` . Calling `with_format("torch")` simply makes the output of the dataset torch Tensors in an efficient way.
Added cache dirs to load and file_utils

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.

Comments:
hi ! the `hf_hub_download` cache_dir is a different cache directory than the one for `datasets`.

`hf_hub_download` uses the `huggingface_hub` cache which is located in by default in `~/.cache/huggingface/hub`, while `datasets` uses a different cache for Arrow files and map() results `~/.cache/huggingface/datasets`

Is there a way to change the default cache directory for both of these on calling load_dataset? Currently, cache_dir makes dealing with where I want files to go a bit confusing as the documentation doesn't mention it only relocates.../datasets and not .../hub.

You can set `HF_HOME` which is the common parent directory for those two caches. Or individually `HF_DATASETS_CACHE` and `HF_HUB_CACHE`

Got it. Can this be added to the documentation for load_dataset and related functions to avoid confusion with cache_dir?

done in https://github.com/huggingface/datasets/pull/7532 :)
Extreme memory bandwidth.

### 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



Comments:
How to convert videos to images?

### 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?





Comments:
Hi ! there is some documentation here on how to read video frames: https://huggingface.co/docs/datasets/video_load
Json builder: Allow features to override problematic Arrow types

### 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.

Comments:
Hi ! It would be cool indeed, currently the JSON data are generally loaded here: 

https://github.com/huggingface/datasets/blob/90e5bf8a8599b625d6103ee5ac83b98269991141/src/datasets/packaged_modules/json/json.py#L137-L140

Maybe we can pass a Arrow `schema` to avoid errors ?
Columns in the dataset obtained though load_dataset do not correspond to the one in the dataset viewer since 3.4.0

### 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


Comments:
Hi, the dataset viewer shows all the possible columns and their types, but `load_dataset()` iterates through all the columns that you defined. It seems that you only have one column (‘audio’) defined in your dataset because when I ran `print(ds.column_names)`, the only name I got was “audio”. You need to clearly define all the other features of the dataset as columns to enable your original code to work. Furthermore, you can run this code to print out all the features of your dataset: 
```python
from datasets import load_dataset_builder
ds_builder = load_dataset_builder("BrunoHays/Accueil_UBS")
print(ds_builder.info.features)
```


@phoebecd 
Good catch, even in datasets<3.4.0, the only feature is "audio".
This datasets follows the [audio folder](https://huggingface.co/docs/datasets/en/audio_dataset#audiofolder) structure with metadata.csv.
Maybe I missed something or there is a bug when having and audio_folder with a metadata file

What do you think @lhoestq ?

I opened a PR to fix the issue :) https://huggingface.co/datasets/BrunoHays/Accueil_UBS/discussions/2

We expect the metadata file to be in the <split>/ folder now to allow one CSV metadata file per split. But in the PR I just added a manual configuration instead of moving the file and updating all the relative paths it contains.
Broken links in pdf loading documentation

### 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)

Comments:
thanks for reporting ! I fixed the links, the docs will be updated in the next release
push_to_hub does not upload videos

### 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

Comments:
Hi ! the `Video` type is still experimental, and in particular `push_to_hub` doesn't upload videos at the moment (only the paths).

There is an open question to either upload the videos inside the Parquet files, or rather have them as separate files (which is great to enable remote seeking/streaming)

im having the same issue (btw i mistook this to be xet error https://huggingface.co/spaces/xet-team/README/discussions/4 )

@jsulz suggested me to use `upload_folder` but it exceeds hf limits (>10k files per folder and >100k files in total)

from my reading of the docs, in my case i have to save as either parquet or webdataset and then use `upload_folder`

i tried `ds.to_parquet("...")` but the parquet file also doesnt contain video, as of `datasets` v4.0

so i think the only workaround for my case is webdataset

in that case you can create a VideoFolder dataset instead, see the docs at http://huggingface.co/docs/datasets/video_dataset#videofolder

I just added support in push_to_hub() for videos :)

Note a small discrepancy for video datasets I hope to fix soon:

- `load_dataset("username/my-folder-of-videos", streaming=True)` -> videos are lazy loaded one by one when iterating, and only actually downloaded when accessing frames in torchcodec (only the requested frames are downloaded)
- `load_dataset("username/my-video-dataset-in-parquet-saved-with-push_to_hub", streaming=True)` -> videos are downloaded one by one when iterating. Even if no frame is accessed in torchcodec, the videos are downloaded completely
(refactor) remove redundant logic in _check_valid_index_key

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). 


Comments:
fix: loading of datasets from Disk(#7373)

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.

Comments:
@nepfaff Could you confirm if this fixes the issue for you? I checked Memray, and everything looked good on my end.

Install: `pip install git+https://github.com/sam-hey/datasets.git@fix/concatenate_datasets`


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:

```python
# Log initial RAM usage
    process = psutil.Process(os.getpid())
    initial_ram = process.memory_info().rss / (1024 * 1024)  # Convert to MB
    logging.info(f"Initial RAM usage: {initial_ram:.2f} MB")

    chunk_datasets = [
        Dataset.load_from_disk(dataset_path, keep_in_memory=False) for _ in range(N)
    ]
    combined_dataset = concatenate_datasets(chunk_datasets)

    # Log final RAM usage
    final_ram = process.memory_info().rss / (1024 * 1024)  # Convert to MB
    ram_diff = final_ram - initial_ram
    logging.info(f"Final RAM usage: {final_ram:.2f} MB")
    logging.info(f"RAM usage increase: {ram_diff:.2f} MB")
```

The RAM usage is linearly correlated with `N` on datasets master!

For my test dataset:
- N=5 => RAM usage increase: 26302.91 MB
- N=10 => RAM usage increase: 52315.18 MB
- N=20 => RAM usage increase: 104510.65 MB
- N=40 => RAM usage increase: 209166.30 MB

Unfortunately, your patch doesn't seem to change this:
```bash
pip install git+https://github.com/sam-hey/datasets.git@fix/concatenate_datasets
pip list | grep datasets
datasets                 3.5.1.dev0
```
Gives exactly the same RAM statistics.

**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.

This behavior might be related to this bug: https://github.com/apache/arrow/issues/34423 

<img width="1728" alt="Screenshot 2025-04-03 at 16 01 11" src="https://github.com/user-attachments/assets/475691d8-3aba-4d7e-b8ef-5e7552c70b14" />


Great ! have you tested that it also fixes the memory issue in your case @iamollas ?

Happy to know that it works for you @sam-hey ! Looking forward to merging this

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7489). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Write pdf in map

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

Comments:
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.
Support skip_trying_type 

This PR addresses Issue #7472

cc: @lhoestq 

Comments:
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 ?

I was also thinking of naming the argument `try_original_type` and have it `True` by default

@lhoestq 

Thank you for the suggestion! I renamed the argument with `True` by default and ran `make style`
Does it look good?

Thanks @lhoestq !

Let me know if there are anything that I can do for this PR. Otherwise, looking forward to seeing this update in the package soon!

CI failures are unrelated, merging :)

Great, thanks for your support!
I can't wait for the next release :)
deal with python `10_000` legal number in slice syntax

### 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!

Comments:
should be an easy fix, I opened a PR
Features.from_arrow_schema is destructive

### 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

Comments:
update fsspec 2025.3.0

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

Comments:
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.
What is the canonical way to compress a Dataset?

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.?

Comments:
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

Is 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. 


We simply haven't implemented a method to save as sharded parquet locally yet ^^'

Right now the only sharded parquet export method is `push_to_hub()` which writes to HF. But we can have a local one as well. 

In 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()

Makes 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.

What 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.

So feel free to use the library you prefer to parallelize your operations.

(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)
Remove conditions for Python < 3.9

This PR remove conditions for Python < 3.9.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7474). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Thanks ! can you run `make style` to fix code formatting ? then we can merge

@lhoestq Done
Webdataset data format problem

### Describe the bug

Please see https://huggingface.co/datasets/ejschwartz/idioms/discussions/1

Error code: FileFormatMismatchBetweenSplitsError

All three splits, train, test, and validation, use webdataset. But only the train split has more than one file. How can I force the other two splits to also be interpreted as being the webdataset format?  (I don't think there is currently a way, but happy to be told that I am wrong.)

### Steps to reproduce the bug

```
import datasets
datasets.load_dataset("ejschwartz/idioms")

### Expected behavior

The dataset loads.  Alternatively, there is a YAML syntax for manually specifying the format.

### Environment info

- `datasets` version: 3.2.0
- Platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0

Comments:
I was able to work around it
Adding argument to `_get_data_files_patterns`

### 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]]:
```

Comments:
Hi ! The pattern can be specified in advance in YAML in the README.md of the dataset :)

For example

```
---
configs:
- config_name: default
  data_files:
  - split: train
    path: "train/*"
  - split: test
    path: "test/*"
---
```

See 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


choose what ? sorry I didn't get it ^^'
Custom split name with the web interface

### 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

Comments:
function `load_dataset`  can't solve folder path with regex characters like "[]"

### 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

Comments:
Hi ! Have you tried escaping the glob special characters `[` and `]` ?

btw 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)