Minimartzz/github-issues huggingface dataset:
Probabilistic High Memory Usage and Freeze on Python 3.10
State: open

### Describe the bug

A probabilistic issue encountered when processing datasets containing PIL.Image columns using the huggingface/datasets library on Python 3.10. The process occasionally experiences a sudden and significant memory spike, reaching 100% utilization, leading to a complete freeze. During this freeze, the process becomes unresponsive, cannot be forcefully terminated, and does not throw any exceptions.

I have attempted to mitigate this issue by setting `datasets.config.IN_MEMORY_MAX_SIZE`, but it had no effect. In fact, based on the document of `load_dataset`, I suspect that setting `IN_MEMORY_MAX_SIZE` might even have a counterproductive effect.

This bug is not consistently reproducible, but its occurrence rate significantly decreases or disappears entirely when upgrading Python to version 3.11 or higher. Therefore, this issue also serves to share a potential solution for others who might encounter similar problems.

### Steps to reproduce the bug

Due to the probabilistic nature of this bug, consistent reproduction cannot be guaranteed for every run. However, in my environment, processing large datasets like timm/imagenet-1k-wds(whether reading, casting, or mapping operations) almost certainly triggers the issue at some point.
The probability of the issue occurring drastically increases when num_proc is set to a value greater than 1 during operations.

When the issue occurs, my system logs repeatedly show the following warnings:
```
WARN: very high memory utilization: 57.74GiB / 57.74GiB (100 %)
WARN: container is unhealthy: triggered memory limits (OOM)
WARN: container is unhealthy: triggered memory limits (OOM)
WARN: container is unhealthy: triggered memory limits (OOM)
```

### Expected behavior

The dataset should be read and processed normally without memory exhaustion or freezing. If an unrecoverable error occurs, an appropriate exception should be raised.

I have found that upgrading Python to version 3.11 or above completely resolves this issue. On Python 3.11, when memory usage approaches 100%, it suddenly drops before slowly increasing again. I suspect this behavior is due to an expected memory management action, possibly involving writing to disk cache, which prevents the complete freeze observed in Python 3.10.

### Environment info

- `datasets` version: 4.0.0
- Platform: Linux-5.15.0-71-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.33.4
- PyArrow version: 20.0.0
- Pandas version: 2.3.1
- `fsspec` version: 2025.3.0

Comments:
metric glue breaks with 4.0.0
State: closed

### Describe the bug

worked fine with 3.6.0, and with 4.0.0 `eval_metric = metric.compute()` in HF Accelerate breaks.

The code that fails is:
https://huggingface.co/spaces/evaluate-metric/glue/blob/v0.4.0/glue.py#L84

```
def simple_accuracy(preds, labels):
    print(preds, labels)
    print(f"{preds==labels}")
    return float((preds == labels).mean())
```
data:
```
Column([1, 0, 0, 1, 1]) Column([1, 0, 0, 1, 0])
False
```

```
[rank0]:     return float((preds == labels).mean())
[rank0]:                  ^^^^^^^^^^^^^^^^^^^^^^
[rank0]: AttributeError: 'bool' object has no attribute 'mean'
```

Some behavior has changed in this new major release of `datasets` and requires updating HF accelerate and perhaps the glue metric code, all belong to HF.

### Environment info

datasets=4.0.0

Comments:
I released `evaluate` 0.4.5 yesterday to fix the issue - sorry for the inconvenience:

```
pip install -U evaluate
```

Thanks so much, @lhoestq!
To support decoding audio data, please install 'torchcodec'.
State: closed


In the latest version of datasets==4.0.0, i cannot print the audio data on the Colab notebook. But it works on the 3.6.0 version.

!pip install -q -U datasets huggingface_hub fsspec

from datasets import load_dataset

downloaded_dataset = load_dataset("ymoslem/MediaSpeech", "tr", split="train")
print(downloaded_dataset["audio"][0])

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
[/tmp/ipython-input-4-90623240.py](https://localhost:8080/#) in <cell line: 0>()
----> 1 downloaded_dataset["audio"][0]

10 frames
[/usr/local/lib/python3.11/dist-packages/datasets/features/audio.py](https://localhost:8080/#) in decode_example(self, value, token_per_repo_id)
    170             from ._torchcodec import AudioDecoder
    171         else:
--> 172             raise ImportError("To support decoding audio data, please install 'torchcodec'.")
    173 
    174         if not self.decode:

ImportError: To support decoding audio data, please install 'torchcodec'. 

### Environment info

- `datasets` version: 4.0.0
- Platform: Linux-6.1.123+-x86_64-with-glibc2.35
- Python version: 3.11.13
- `huggingface_hub` version: 0.33.2
- PyArrow version: 18.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2025.3.0

Comments:
Hi ! yes you should `!pip install -U datasets[audio]` to have the required dependencies.

`datasets` 4.0 now relies on `torchcodec` for audio decoding. The `torchcodec` AudioDecoder enables streaming from HF and also allows to decode ranges of audio

Same issues on Colab.

> !pip install -U datasets[audio] 

This works for me. Thanks.
Toxicity fails with datasets 4.0.0
State: closed

### Describe the bug

With the latest 4.0.0 release, huggingface toxicity evaluation module fails with error: `ValueError: text input must be of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).`

### Steps to reproduce the bug

Repro:
```
>>> toxicity.compute(predictions=["This is a response"])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/evaluate/module.py", line 467, in compute
    output = self._compute(**inputs, **compute_kwargs)
  File "/Users/serena.ruan/.cache/huggingface/modules/evaluate_modules/metrics/evaluate-measurement--toxicity/2390290fa0bf6d78480143547c6b08f3d4f8805b249df8c7a8e80d0ce8e3778b/toxicity.py", line 135, in _compute
    scores = toxicity(predictions, self.toxic_classifier, toxic_label)
  File "/Users/serena.ruan/.cache/huggingface/modules/evaluate_modules/metrics/evaluate-measurement--toxicity/2390290fa0bf6d78480143547c6b08f3d4f8805b249df8c7a8e80d0ce8e3778b/toxicity.py", line 103, in toxicity
    for pred_toxic in toxic_classifier(preds):
  File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/pipelines/text_classification.py", line 159, in __call__
    result = super().__call__(*inputs, **kwargs)
  File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/pipelines/base.py", line 1431, in __call__
    return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)
  File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/pipelines/base.py", line 1437, in run_single
    model_inputs = self.preprocess(inputs, **preprocess_params)
  File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/pipelines/text_classification.py", line 183, in preprocess
    return self.tokenizer(inputs, return_tensors=return_tensors, **tokenizer_kwargs)
  File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 2867, in __call__
    encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs)
  File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 2927, in _call_one
    raise ValueError(
ValueError: text input must be of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).
```

### Expected behavior

This works before 4.0.0 release

### Environment info

- `datasets` version: 4.0.0
- Platform: macOS-15.5-arm64-arm-64bit
- Python version: 3.10.16
- `huggingface_hub` version: 0.33.0
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0

Comments:
Hi ! You can fix this by upgrading `evaluate`:

```
pip install -U evaluate
```

Thanks, verified evaluate 0.4.5 works!
Fix double sequence
State: closed

```python
>>> Features({"a": Sequence(Sequence({"c": Value("int64")}))})
{'a': List({'c': List(Value('int64'))})}
```

instead of `{'a': {'c': List(List(Value('int64')))}}`

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7672). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Mapping function not working if the first example is returned as None
State: closed

### Describe the bug

https://github.com/huggingface/datasets/blob/8a19de052e3d79f79cea26821454bbcf0e9dcd68/src/datasets/arrow_dataset.py#L3652C29-L3652C37

Here we can see the writer is initialized on `i==0`. However, there can be cases where in the user mapping function, the first example is filtered out (length constraints, etc).

In this case, the writer would be a `None` type and the code will report `NoneType has no write function`.

A simple fix is available, simply change line 3652 from `if i == 0:` to `if writer is None:`

### Steps to reproduce the bug

Prepare a dataset

have this function

```
import datasets


def make_map_fn(split, max_prompt_tokens=3):
    def process_fn(example, idx):

        question = example['question']
        reasoning_steps = example['reasoning_steps']
        label = example['label']

        answer_format = ""
        for i in range(len(reasoning_steps)):
            system_message = "Dummy"

        all_steps_formatted = []

        content = f"""Dummy"""

        prompt = [
            {"role": "system", "content": system_message},
            {"role": "user", "content": content},
        ]
        tokenized = tokenizer.apply_chat_template(prompt, return_tensors="pt", truncation=False)
        if tokenized.shape[1] > max_prompt_tokens:
            return None  # skip overly long examples

        data = {
            "dummy": "dummy"
        }

        return data

    return process_fn

...
# load your dataset
...
train = train.map(function=make_map_fn('train'), with_indices=True)
```

### Expected behavior

The dataset mapping shall behave even when the first example is filtered out.

### Environment info

I am using `datasets==3.6.0` but I have observed this issue in the github repo too: https://github.com/huggingface/datasets/blob/8a19de052e3d79f79cea26821454bbcf0e9dcd68/src/datasets/arrow_dataset.py#L3652C29-L3652C37

Comments:
Hi, map() always expect an output.

If you wish to filter examples, you should use filter(), in your case it could be something like this:

```python
ds = ds.map(my_processing_function).filter(ignore_long_prompts)
```

Realized this! Thanks a lot, I will close this issue then.
How can I add my custom data to huggingface datasets
State: open

I want to add my custom dataset in huggingface dataset. Please guide me how to achieve that.

Comments:
Hey @xiagod 

The easiest way to add your custom data to Hugging Face Datasets is to use the built-in load_dataset function with your local files. Some examples include:

CSV files:
from datasets import load_dataset
dataset = load_dataset("csv", data_files="my_file.csv")

JSON or JSONL files:
from datasets import load_dataset
dataset = load_dataset("json", data_files="my_file.json")


Images stored in folders (e.g. data/train/cat/, data/train/dog/):
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="/path/to/pokemon")


These methods let you quickly create a custom dataset without needing to write a full script.

More information can be found in Hugging Face's tutorial "Create a dataset" or "Load" documentation here: 

https://huggingface.co/docs/datasets/create_dataset 

https://huggingface.co/docs/datasets/loading#local-and-remote-files



If you want to submit your dataset to the Hugging Face Datasets GitHub repo so others can load it follow this guide: 

https://huggingface.co/docs/datasets/upload_dataset
Broken EXIF crash the whole program
State: open

### Describe the bug

When parsing this image in the ImageNet1K dataset, the `datasets` crashs whole training process just because unable to parse an invalid EXIF tag.
![Image](https://github.com/user-attachments/assets/3c840203-ac8c-41a0-9cf7-45f64488037d)

### Steps to reproduce the bug

Use the `datasets.Image.decode_example` method to decode the aforementioned image could reproduce the bug.
The decoding function will throw an unhandled exception at the `image.getexif()` method call due to invalid utf-8 stream in EXIF tags.
```
File lib/python3.12/site-packages/datasets/features/image.py:188, in Image.decode_example(self, value, token_per_repo_id)
    186     image = PIL.Image.open(BytesIO(bytes_))
    187 image.load()  # to avoid "Too many open files" errors
--> 188 if image.getexif().get(PIL.Image.ExifTags.Base.Orientation) is not None:
    189     image = PIL.ImageOps.exif_transpose(image)
    190 if self.mode and self.mode != image.mode:

File lib/python3.12/site-packages/PIL/Image.py:1542, in Image.getexif(self)
   1540 xmp_tags = self.info.get("XML:com.adobe.xmp")
   1541 if not xmp_tags and (xmp_tags := self.info.get("xmp")):
-> 1542     xmp_tags = xmp_tags.decode("utf-8")
   1543 if xmp_tags:
   1544     match = re.search(r'tiff:Orientation(="|>)([0-9])', xmp_tags)

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa8 in position 4312: invalid start byte
```

### Expected behavior

The invalid EXIF tag should simply be ignored or issue a warning message, instead of crash the whole program at once.

### Environment info

- `datasets` version: 3.6.0
- Platform: Linux-6.5.0-18-generic-x86_64-with-glibc2.35
- Python version: 3.12.11
- `huggingface_hub` version: 0.33.0
- PyArrow version: 20.0.0
- Pandas version: 2.3.0
- `fsspec` version: 2025.3.0

Comments:
There are other discussions about error handling for images decoding here : https://github.com/huggingface/datasets/issues/7632 https://github.com/huggingface/datasets/issues/7612

and a PR here: https://github.com/huggingface/datasets/pull/7638 (would love your input on the proposed solution !)
Function load_dataset() misinterprets string field content as part of dataset schema when dealing with `.jsonl` files
State: closed

### Describe the bug

When loading a `.jsonl` file using `load_dataset("json", data_files="data.jsonl", split="train")`, the function misinterprets the content of a string field  as if it were part of the dataset schema. 

In my case there is a field `body:` with a string value 
```
"### Describe the bug (...) ,action: string, datetime: timestamp[s], author: string, (...) Pandas version: 1.3.4"
```
As a result, I got an exception 
```
"TypeError: Couldn't cast array of type timestamp[s] to null". 
```
Full stack-trace in the attached file below.

I also attach a minimized dataset (data.json, a single entry) that reproduces the error.

**Observations**(on the minimal example): 
- if I remove _all fields before_ `body`, a different error appears,
- if I remove _all fields after_ `body`, yet another error appears,
- if `body` is _the only field_, the error disappears.

So this might be one complex bug or several edge cases interacting. I haven’t dug deeper.

Also changing the file extension to `.json` or `.txt` avoids the problem. This suggests **a possible workaround** for the general case: convert `.jsonl` to `.json`. Though I haven’t verified correctness of that workaround yet.

Anyway my understanding is that `load_dataset` with first argument set to "json" should properly handle `.jsonl` files. Correct me if I'm wrong.

[stack_trace.txt](https://github.com/user-attachments/files/21004153/stack_trace.txt)
[data.json](https://github.com/user-attachments/files/21004164/data.json)

P.S.
I discovered this while going through the HuggingFace tutorial. Specifically [this part](https://huggingface.co/learn/llm-course/chapter5/5?fw=pt).I will try to inform the tutorial team about this issue, as it can be a showstopper for young 🤗 adepts.

### Steps to reproduce the bug

1. Download attached [data.json](https://github.com/user-attachments/files/21004164/data.json) file.
2. Run the following code which should work correctly:
```
from datasets import load_dataset
load_dataset("json", data_files="data.json", split="train")
```
3. Change extension of the `data` file to `.jsonl` and run:
```
from datasets import load_dataset
load_dataset("json", data_files="data.jsonl", split="train")
```

This will trigger an error like the one in the attached [stack_trace.txt](https://github.com/user-attachments/files/21004153/stack_trace.txt).

One can also try removing fields before the `body` field and after it. These actions give different errors.


### Expected behavior

Parsing data in `.jsonl` format should yield the same result as parsing the same data in `.json` format. In any case, the content of a string field should never be interpreted as part of the dataset schema.

### Environment info

datasets version: _3.6.0_
pyarrow version: _20.0.0_
Python version: _3.11.9_
platform version: _macOS-15.5-arm64-arm-64bit_

Comments:
Somehow I created the issue twice🙈 This one is an exact duplicate of #7664.
Custom metadata filenames
State: closed

example: https://huggingface.co/datasets/lhoestq/overlapping-subsets-imagefolder/tree/main

To make multiple subsets for an imagefolder (one metadata file per subset), e.g.

```yaml
configs:
  - config_name: default
    metadata_filenames:
      - metadata.csv
  - config_name: other
    metadata_filenames:
      - metadata2.csv
```

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7663). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
AttributeError: type object 'tqdm' has no attribute '_lock'
State: open

### Describe the bug

`AttributeError: type object 'tqdm' has no attribute '_lock'`

It occurs when I'm trying to load datasets in thread pool.

Issue https://github.com/huggingface/datasets/issues/6066 and PR https://github.com/huggingface/datasets/pull/6067 https://github.com/huggingface/datasets/pull/6068 tried to fix this. 


### Steps to reproduce the bug

Will have to try several times to reproduce the error because it is concerned with threads.
 
1. save some datasets for test
```pythonfrom datasets import Dataset, DatasetDict
import os

os.makedirs("test_dataset_shards", exist_ok=True)

for i in range(10):
    data = Dataset.from_dict({"text": [f"example {j}" for j in range(1000000)]})
    data = DatasetDict({'train': data})
    data.save_to_disk(f"test_dataset_shards/shard_{i}")
```

2. load them in a thread pool 
```python
from datasets import load_from_disk
from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor, as_completed
import glob

datas = glob.glob('test_dataset_shards/shard_*')
with ThreadPoolExecutor(max_workers=10) as pool:
    futures = [pool.submit(load_from_disk, it) for it in datas]
datas = []
for future in tqdm(as_completed(futures), total=len(futures)):
    datas.append(future.result())
```

### Expected behavior

no exception raised

### Environment info

datasets==2.19.0
python==3.10

Comments:
Deleting a class (**not instance**) attribute might be invalid in this case, which is `tqdm` doing in `ensure_lock`.

```python
from tqdm import tqdm as old_tqdm

class tqdm1(old_tqdm):
     def __delattr__(self, attr):
        try:
            super().__delattr__(attr)
        except AttributeError:
            if attr != '_lock':
                print(attr)
                raise

class Meta(type):
    def __delattr__(cls, name):
        if name == "_lock":
            return  
        return super().__delattr__(name)
    
class tqdm2(old_tqdm, metaclass=Meta):
    pass

del tqdm2._lock
del tqdm1._lock # error
```

https://github.com/huggingface/datasets/blob/e71b0b19d79c7531f9b9bea7c09916b5f6157f42/src/datasets/utils/tqdm.py#L104-L122

A cheaper option (seems to work in my case):  
```python
from datasets import tqdm as hf_tqdm
hf_tqdm.set_lock(hf_tqdm.get_lock())
```
Fix: Prevent loss of info.features and column_names in IterableDatasetDict.map when features is None
State: closed

This PR fixes a bug where calling `IterableDatasetDict.map()` or `IterableDataset.map()` with the default `features=None` argument would overwrite the existing `info.features` attribute with `None`. This, in turn, caused the resulting dataset to lose its schema, breaking downstream usage of attributes like `column_names`.

Why
Previously, the code would always set `info.features = features`, even if `features` was `None`. When mapping with removal of columns or other transformations, this led to the destruction of the schema and caused failures in code that relied on the dataset schema being present.

How
We now update `info.features` only if `features` is not `None`. This preserves the original schema unless the user explicitly provides a new one.

Reference
Fixes #7568

Comments:
Hi!
I haven’t included a test for this change, as the fix is quite small and targeted.
Please let me know if you’d like a test for this case or if you’d prefer to handle it during review.
Thanks!

we can't know in advance the `features` after map() (it transforms the data !), so you can reuse the `features` from `info.features`

I'll the patch as suggested — `info.features = features` or `self.info.features` — to ensure schema preservation while keeping the logic simple and explicit. WDYT?


info.features should be None in the general case, and replaced by the user's `features` if it's passed explicitly with `map(..., features=...)`

https://github.com/huggingface/datasets/issues/7568 is not an issue we can fix

> info.features should be None in the general case, and replaced by the user's `features` if it's passed explicitly with `map(..., features=...)`
> 
> #7568 is not an issue we can fix

Thanks for the clarification! Totally makes sense now — I understand that features=None is the expected behavior post-map() unless explicitly passed, and that preserving old schema by default could lead to incorrect assumptions.
Closing this one — appreciate the feedback as always
feat: add subset_name as alias for name in load_dataset
State: open

fixes #7637

This PR introduces subset_name as a user-facing alias for the name (previously `config_name`) argument in load_dataset. It aligns terminology with the Hugging Face Hub UI (which shows “Subset”), reducing confusion for new users.

Supports `subset_name` in `load_dataset()`

Adds `.subset_name` property to DatasetBuilder

Maintains full backward compatibility

Raises clear error if name and `subset_name` conflict

Comments:
fix(iterable): ensure MappedExamplesIterable supports state_dict for resume
State: open

Fixes #7630

### Problem
When calling `.map()` on an `IterableDataset`, resuming from a checkpoint skips a large number of samples. This is because `MappedExamplesIterable` did not implement `state_dict()` or `load_state_dict()`, so checkpointing was not properly delegated to the underlying iterable.

### What This PR Does
This patch adds:
```python
def state_dict(self):
    return self.ex_iterable.state_dict()

def load_state_dict(self, state):
    self.ex_iterable.load_state_dict(state)
```

to MappedExamplesIterable, so the wrapped base iterable's state can be saved and restored as expected.

Result
Using .map() no longer causes sample skipping after checkpoint resume.

Let me know if a dedicated test case is required — happy to add one!

Comments: