Minimartzz/github-issues huggingface dataset:
Creating a HF Dataset from lakeFS with S3 storage takes too much time!
State: closed

Hi,

I’m new to HF dataset and I tried to create datasets based on data versioned in **lakeFS** _(**MinIO** S3 bucket as storage backend)_

Here I’m using ±30000 PIL image from MNIST data however it is taking around 12min to execute, which is a lot!

From what I understand, it is loading the images into cache then building the dataset.
– Please find bellow the execution screenshot –

Is there a way to optimize this or am I doing something wrong?

Thanks!

![Image](https://github.com/user-attachments/assets/c79257c8-f023-42a9-9e6f-0898b3ea93fe)

Comments:
### > Update

The bottleneck, from what I understand, was making one network request per file

For 30k images, this meant 30k separate GET requests to the MinIO server through the S3 API, and that was killing the performance

Using webDataset to transform the large number of files to few .tar files and passing “webdataset” instead of “imagefolder” to the load_dataset function worked perfectly (took only ~11s)
feat(map): reuse unchanged columns when input_columns specified to reduce disk usage (#6013)
State: closed

## Summary

This PR addresses [#6013](https://github.com/huggingface/datasets/issues/6013) by reusing unchanged columns from the original dataset in the `map()` method when `input_columns` is specified.

## What’s Implemented

- Injected logic at the end of `Dataset.map()` to:
  - Identify untouched columns not in `input_columns` or `remove_columns`
  - Select those columns from the original dataset
  - Concatenate them with the transformed result using `pyarrow.concat_tables`

## Example Behavior

```python
ds = Dataset.from_dict({"a": [1, 2], "b": [3, 4]})
ds2 = ds.map(lambda x: {"c": x["a"] + 10}, input_columns=["a"], remove_columns=["a"])
print(ds2.column_names)  # Output: ['b', 'c']
````

Column `b` is reused from the original dataset.

## Notes

* This keeps disk usage and caching minimal by avoiding full dataset duplication.
* Only triggered when `input_columns` is set.

---

cc @lhoestq @mariosasko for review 🙂


Comments:
feat: Add h5folder dataset loader for HDF5 support
State: open

### Related Issue
Closes #3113

### What does this PR do?

This PR introduces a new dataset loader module called **`h5folder`** to support loading datasets stored in **HDF5 (.h5)** format.

It allows users to do:

```python
from datasets import load_dataset
dataset = load_dataset("h5folder", data_dir="path/to/")
````

### 🧩 Design Overview

* Implemented inside `datasets/packaged_modules/h5folder/h5folder.py`
* Based on the `GeneratorBasedBuilder` API
* Uses `h5py` to read HDF5 files and yield examples
* Expects datasets such as `id`, `data`, and `label` inside `data.h5`
* Converts numpy arrays to Python types before yielding

### 🧪 Example `.h5` Structure (for local testing)

```python
import h5py
import numpy as np

with h5py.File("data.h5", "w") as f:
    f.create_dataset("id", data=np.arange(100))
    f.create_dataset("data", data=np.random.randn(100, 10))
    f.create_dataset("label", data=np.random.randint(0, 2, size=100))
```

### ✅ Testing

- The loader logic follows the structure of existing modules like `imagefolder`
- Will rely on Hugging Face CI to validate integration
- Manually testing planned once merged or during feedback

### 📁 Files Added

* `datasets/src/datasets/packaged_modules/h5folder/h5folder.py`

### 📌 Component(s) Affected

* `area/datasets`
* `area/load`

### 📦 Release Note Classification

* `rn/feature` – Adds support for loading `.h5` datasets via `load_dataset("h5folder", ...)`

---

Let me know if any changes or improvements are needed — happy to iterate. Thanks for reviewing!


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7625). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

I guess test failed cause import os, import h5py, and import datasets lines are not alphabetically sorted, or not grouped properly.

![image](https://github.com/user-attachments/assets/ab73f8f9-da50-4ba8-9b2d-b7c30fce94f5)


This commit was accidental - `[Merge branch 'main' into patch-4]`. The 
`[chore: fix import order in h5folder.py to satisfy linter]` should solve the import order issue.
#Dataset Make "image" column appear first in dataset preview UI
State: closed

Hi!

#Dataset

I’m currently uploading a dataset that includes an `"image"` column (PNG files), along with some metadata columns. The dataset is loaded from a .jsonl file. My goal is to have the "image" column appear as the first column in the dataset card preview UI on the :hugs: Hub.

However, at the moment, the `"image"` column is not the first—in fact, it appears last, which is not ideal for the presentation I’d like to achieve.

I have a couple of questions:

Is there a way to force the dataset card to display the `"image"` column first?
Is there currently any way to control or influence the column order in the dataset preview UI?
Does the order of keys in the .jsonl file or the features argument affect the display order?
Thanks again for your time and help! :blush:

Comments:
Hi ! It should follow the same order as the order of the keys in the metadata file

Hi! Thank you for your answer. 

As you said it, I I forced every key in every JSON to have an order using `collections. OrderedDict` in Python. Now, it works!

TY
fix: raise error in FolderBasedBuilder when data_dir and data_files are missing
State: closed

### Related Issues/PRs

Fixes #6152

---

### What changes are proposed in this pull request?

This PR adds a dedicated validation check in the `_info()` method of the `FolderBasedBuilder` class to ensure that users provide either `data_dir` or `data_files` when loading folder-based datasets (such as `audiofolder`, `imagefolder`, etc.).

---

### Why this change?

Previously, when calling:

```python
load_dataset("audiofolder")
````

without specifying `data_dir` or `data_files`, the loader would silently fallback to the **current working directory**, leading to:

* Long loading times
* Unexpected behavior (e.g., scanning unrelated files)

This behavior was discussed in issue #6152. As suggested by maintainers, the fix has now been implemented directly inside the `FolderBasedBuilder._info()` method — keeping the logic localized to the specific builder instead of a generic loader function.

---

### How is this PR tested?

* ✅ Manually tested by calling `load_dataset("audiofolder")` with no `data_dir` or `data_files` → a `ValueError` is now raised early.
* ✅ Existing functionality (with valid input) remains unaffected.

---

### Does this PR require documentation update?

* [x] No

---

### Release Notes

#### Is this a user-facing change?

* [x] Yes

> Folder-based datasets now raise an explicit error if neither `data_dir` nor `data_files` are specified, preventing unintended fallback to the current working directory.

---

#### What component(s) does this PR affect?

* [x] `area/datasets`
* [x] `area/load`

---

<a name="release-note-category"></a>

#### How should the PR be classified?

* [x] `rn/bug-fix` - A user-facing bug fix

---

#### Should this be included in the next patch release?

* [x] Yes

Comments:
@lhoestq Moved the logic to FolderBasedBuilder._info() as discussed in previous PR (#7618). Let me know if anything else is needed — happy to update!

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7623). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Guard against duplicate builder_kwargs/config_kwargs in load_dataset_…
State: closed

…builder (#4910 )

### What does this PR do?

Fixes edge case in `load_dataset_builder` by raising a `TypeError` if the same key exists in both `builder_kwargs` and `config_kwargs`.

### Implementation details

- Added a guard clause in `load_dataset_builder` to detect duplicate keys between `builder_kwargs` and `config_kwargs`
 
- Wrote a unit test in `tests/test_load_duplicate_keys.py` to verify the exception is raised correctly 

### Fixes

Closes #4910 

### Reviewers

@zach-huggingface
@SunMarc 

Would appreciate your review if you have time - thanks!

Comments:
Hi folks, this PR fixes the duplicate-kwargs edge case and includes a unit test. Would love a review when you have a moment!

@zach-huggingface
@SunMarc
Fixes in docs
State: closed

before release 4.0

(I also did minor improvements to `features` to not show their `id=None` in their `__repr__()`)

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7620). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
fix: raise error when folder-based datasets are loaded without data_dir or data_files
State: open


### Related Issues/PRs

<!-- Uncomment 'Resolve' if this PR can close the linked items. -->
<!-- Resolve --> #6152

---

### What changes are proposed in this pull request?

This PR adds an early validation step for folder-based datasets (like `audiofolder`) to prevent silent fallback behavior.

**Before this fix**:
- When `data_dir` or `data_files` were not provided, the loader defaulted to the current working directory.
- This caused unexpected behavior like:
  - Long loading times
  - Scanning unintended local files

**Now**:
- If both `data_dir` and `data_files` are missing, a `ValueError` is raised early with a helpful message.

---

### How is this PR tested?

- [x] Manual test via `load_dataset("audiofolder")` with missing `data_dir`
- [ ] Existing unit tests (should not break any)
- [ ] New tests (if needed, maintainers can guide)

---

### Does this PR require documentation update?

- [x] No. You can skip the rest of this section.

---

### Release Notes

#### Is this a user-facing change?

- [x] Yes. Give a description of this change to be included in the release notes for users.

> Adds early error handling for folder-based datasets when neither `data_dir` nor `data_files` is specified, avoiding unintended resolution to the current directory.

#### What component(s), interfaces, languages, and integrations does this PR affect?

Components:
- [x] `area/datasets`
- [x] `area/load`

---

<a name="release-note-category"></a>

#### How should the PR be classified in the release notes? Choose one:

- [x] `rn/bug-fix` - A user-facing bug fix worth mentioning in the release notes

---

#### Should this PR be included in the next patch release?

- [x] Yes (this PR will be cherry-picked and included in the next patch release)


Comments:
Great ! Since this logic is specific to one builder class maybe this check can be in the class definition ? I think you can put it in FolderBasedBuilder's `_info()` method.
Unwanted column padding in nested lists of dicts
State: closed

```python
from datasets import Dataset

dataset = Dataset.from_dict({
    "messages": [
        [
            {"a": "...",},
            {"b": "...",},
        ],
    ]
})
print(dataset[0])
```

What I get:
```
{'messages': [{'a': '...', 'b': None}, {'a': None, 'b': '...'}]}
```

What I want:

```
{'messages': [{'a': '...'}, {'b': '...'}]}
```

Is there an easy way to automatically remove these auto-filled null/none values?

If not, I probably need a recursive none exclusion function, don't I?

Datasets 3.6.0

Comments:
Answer from @lhoestq:

> No
> This is because Arrow and Parquet a columnar format: they require a fixed type for each column. So if you have nested dicts, each item should have the same subfields

The way around I found is the handle it after sampling with this function:

```python
def remove_padding(example):
    if isinstance(example, list):
        return [remove_padding(value) if isinstance(value, (dict, list)) else value for value in example]
    elif isinstance(example, Mapping):
        return {
            key: remove_padding(value) if isinstance(value, (dict, list)) else value
            for key, value in example.items()
            if value is not None
        }
    else:
        raise TypeError("Input must be a list or a dictionary.")

# Example:
example = next(iter(dataset))
example = remove_padding(example)
```
Lazy column
State: closed

Same as https://github.com/huggingface/datasets/pull/7564 but for `Dataset`, cc @TopCoder2K FYI

e.g. `ds[col]` now returns a lazy Column instead of a list

This way calling `ds[col][idx]` only loads the required data in memory

(bonus: also supports subfields access with `ds[col][subcol][idx]`)

the breaking change will be for the next major release, which also includes removal of dataset scripts support

close https://github.com/huggingface/datasets/issues/4180

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7614). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Code example for dataset.add_column() does not reflect correct way to use function
State: closed

https://github.com/huggingface/datasets/blame/38d4d0e11e22fdbc4acf373d2421d25abeb43439/src/datasets/arrow_dataset.py#L5925C10-L5925C10

The example seems to suggest that dataset.add_column() can add column inplace, however, this is wrong -- it cannot. It returns a new dataset with the column added to it.

Comments:
Hi @shaily99 

Thanks for pointing this out — you're absolutely right!

The current example in the docstring for add_column() implies in-place modification, which is misleading since add_column() actually returns a new dataset.

#self-assign
i cant confirm email
State: open

### Describe the bug

This is dificult, I cant confirm email because I'm not get any email!
I cant post forum because I cant confirm email!
I can send help desk because... no exist on web page.

paragraph 44

### Steps to reproduce the bug

rthjrtrt

### Expected behavior

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### Environment info

sdgfswdegfwe

Comments:
Will you please clarify the issue by some screenshots or more in-depth explanation?

![Image](https://github.com/user-attachments/assets/ebe58239-72ef-43f6-a849-35736878fbf3)
This is clarify answer. I have not received a letter.

**The graphic at the top shows how I don't get any letter. Can you show in a clear way how you don't get a letter from me?**
Update `_dill.py` to use `co_linetable` for Python 3.10+ in place of `co_lnotab`
State: closed

Not 100% about this one, but it seems to be recommended.

```
/fsx/qgallouedec/miniconda3/envs/trl/lib/python3.12/site-packages/datasets/utils/_dill.py:385: DeprecationWarning: co_lnotab is deprecated, use co_lines instead.
```
Tests pass locally. And the warning is gone with this change.

https://peps.python.org/pep-0626/#backwards-compatibility

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7609). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

not 100% sure either, I tried removing unnecessary checks - let me know if they sound good to you otherwise I'll revert

I can't reproduce the warning anymore... 🤦🏻‍♂️


Ah now I can reproduce!, and I can confirm that the warning is gone when you apply the change in this PR
Tests typing and fixes for push_to_hub
State: closed

todo:
- [x] fix TestPushToHub.test_push_dataset_dict_to_hub_iterable_num_proc

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7608). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Make `push_to_hub` atomic (#7600)
State: closed


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7605). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Hi ! unfortunately we can't allow atomic commits for commits with hundreds of files additions (HF would time out)

Maybe an alternative would be to retry if there was a commit in between ? this could be the default behavior as well

Thanks for taking a look – much appreciated!

I've verified that commits with up to 20,000 files don't time out and the commit time scales linearly with the number of operations enqueued. It took just under 2 minutes to complete (successfully) the 20k file commit.

The fundamental issue I'm trying to tackle here is dataset corruption: getting into a state where a dataset on the hub cannot be used when downloaded. Non-atomic commits won't get us there, I think. If, for example, 3 of 5 commits complete and the machine/process calling `push_to_hub` has a network, hardware, or other failure that prevents it from completing the rest of the commits (even with retries) we'll now have some pointer files pointing to the new data and others pointing to the old data => corrupted. While this may seem like an unlikely scenario, it's a regular occurrence at scale.

If you still feel strongly that atomic commits are not the right way to go, I can either set it to not be the default or remove it entirely from this PR.

As for retries, it's a good idea. In a non-atomic world, the logic gets more complicated:
- keep an explicit queue of pending add/delete operations
- chunkwise pop from queue and commit with `parent_commit` set to previous chunked commit hash
- if `create_commit` fails:
  - re-fetch README and set `parent_commit` to latest hash for `revision`
  - re-generate dataset card content
  - swap old `CommitOperationAdd` with new one for README in the pending queue
- resume chunkwise committing from the queue as above

Entirely doable, but more involved than I signed up for with this PR.

Just to clarify – setting the `parent_commit` can be separated from making the commit atomic (which is what I'm suggesting by either atomic commits not the default or removing it from this PR). It's crucial to set the parent commit to avoid the read-modify-write race condition on the README schema.
Docs and more methods for IterableDataset: push_to_hub, to_parquet...
State: closed

to_csv, to_json, to_sql, to_pandas, to_polars, to_dict, to_list

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7604). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Enhance error handling and input validation across multiple modules
State: open

This PR improves the robustness and user experience by:

1. **Audio Module**:
   - Added clear error messages when required fields ('path' or 'bytes') are missing in audio encoding

2. **DatasetDict**:
   - Enhanced key access error messages to show available splits when an invalid key is accessed

3. **NonMutableDict**:
   - Added input validation for the update() method to ensure proper mapping types

4. **Arrow Reader**:
   - Improved error messages for small dataset percentage splits with suggestions for alternatives

5. **FaissIndex**:
   - Strengthened input validation with descriptive error messages
   - Added proper type checking and shape validation for search queries

These changes make the code more maintainable and user-friendly by providing actionable feedback when issues arise.

Comments:
`push_to_hub` is not concurrency safe (dataset schema corruption)
State: closed

### Describe the bug

Concurrent processes modifying and pushing a dataset can overwrite each others' dataset card, leaving the dataset unusable.

Consider this scenario:
- we have an Arrow dataset
- there are `N` configs of the dataset
- there are `N` independent processes operating on each of the individual configs (e.g. adding a column, `new_col`)
- each process calls `push_to_hub` on their particular config when they're done processing
- all calls to `push_to_hub` succeed
- the `README.md` now has some configs with `new_col` added and some with `new_col` missing

Any attempt to load a config (using `load_dataset`) where `new_col` is missing will fail because of a schema mismatch between `README.md` and the Arrow files. Fixing the dataset requires updating `README.md` by hand with the correct schema for the affected config. In effect, `push_to_hub` is doing a `git push --force` (I found this behavior quite surprising).

We have hit this issue every time we run processing jobs over our datasets and have to fix corrupted schemas by hand.

Reading through the code, it seems that specifying a [`parent_commit`](https://github.com/huggingface/huggingface_hub/blob/v0.32.4/src/huggingface_hub/hf_api.py#L4587) hash around here https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L5794 would get us to a normal, non-forced git push, and avoid schema corruption. I'm not familiar enough with the code to know how to determine the commit hash from which the in-memory dataset card was loaded.

### Steps to reproduce the bug

See above.

### Expected behavior

Concurrent edits to disjoint configs of a dataset should never corrupt the dataset schema.

### Environment info

- `datasets` version: 2.20.0
- Platform: Linux-5.15.0-118-generic-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.2
- `fsspec` version: 2023.9.0

Comments:
@lhoestq can you please take a look? I've submitted a PR that fixes this issue. Thanks.

Thanks for the ping ! As I said in https://github.com/huggingface/datasets/pull/7605 there is maybe a more general approach using retries :)

Dropping this due to inactivity; we've implemented push_to_hub outside of HF datasets that's concurrency safe. Feel free to use the code I provided as a starting point if there's still interest in addressing this issue.

Exploring another fix here: https://github.com/huggingface/datasets/issues/7600
My already working dataset (when uploaded few months ago) now is ignoring metadata.jsonl
State: closed

### Describe the bug

Hi everyone, I uploaded my dataset https://huggingface.co/datasets/PRAIG/SMB a few months ago while I was waiting for a conference acceptance response. Without modifying anything in the dataset repository now the Dataset viewer is not rendering the metadata.jsonl annotations, neither it is being downloaded when using load_dataset. Can you please help? Thank you in advance.

### Steps to reproduce the bug

from datasets import load_dataset
ds = load_dataset("PRAIG/SMB")
ds = ds["train"]

### Expected behavior

 It is expected to have all the metadata available in the jsonl file. Fields like: "score_id", "original_width", "original_height", "regions"... among others.

### Environment info

datasets==3.6.0, python 3.13.3 (but he problem is already in the huggingface dataset page)

Comments:
Maybe its been a recent update, but i can manage to load the metadata.jsonl separately from the images with:

```
metadata = load_dataset("PRAIG/SMB", split="train", data_files=["*.jsonl"])
images = load_dataset("PRAIG/SMB", split="train")
```
Do you know it this is an expected behaviour? This makes my dataset viewer to only load the images without the labeling of metadata.jsonl.

Thanks

Hi ! this is because we now expect the metadata file to be inside the directory named after the split "train" (this way each split can have its own metadata and can be loaded independently)

You can fix that by configuring it explicitly in the dataset's README.md header:

```yaml
configs:
- config_name: default
  data_files:
  - split: train
    path:
    - "train/**/*.png"
    - "metadata.jsonl"
```

(or by moving the metadata.jsonl in train/ but in this case you also have to modify the content of the JSONL to fix the relative paths to the images)

Thank you very much, dataset viewer is already working as expected!!