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https://api.github.com/repos/huggingface/datasets/issues/7196 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7196/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7196/comments | https://api.github.com/repos/huggingface/datasets/issues/7196/events | https://github.com/huggingface/datasets/issues/7196 | 2,564,218,566 | I_kwDODunzps6Y1t7G | 7,196 | concatenate_datasets does not preserve shuffling state | {
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After concatenate datasets on an iterable dataset, the shuffling state is destroyed, similar to #7156
This means concatenation cant be used for resolving uneven numbers of samples across devices when using iterable datasets in a distributed setting as discussed in #6623
I also noticed that the number of shards is the same after concatenation, which I found surprising, but I don't understand the internals well enough to know whether this is actually surprising or not
### Steps to reproduce the bug
```python
import datasets
import torch.utils.data
def gen(shards):
yield {"shards": shards}
def main():
dataset1 = datasets.IterableDataset.from_generator(
gen, gen_kwargs={"shards": list(range(25))} # TODO: how to understand this?
)
dataset2 = datasets.IterableDataset.from_generator(
gen, gen_kwargs={"shards": list(range(25, 50))} # TODO: how to understand this?
)
dataset1 = dataset1.shuffle(buffer_size=1)
dataset2 = dataset2.shuffle(buffer_size=1)
print(dataset1.n_shards)
print(dataset2.n_shards)
dataset = datasets.concatenate_datasets(
[dataset1, dataset2]
)
print(dataset.n_shards)
# dataset = dataset1
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=8,
num_workers=0,
)
for i, batch in enumerate(dataloader):
print(batch)
print("\nNew epoch")
dataset = dataset.set_epoch(1)
for i, batch in enumerate(dataloader):
print(batch)
if __name__ == "__main__":
main()
```
### Expected behavior
Shuffling state should be preserved
### Environment info
Latest datasets | null | {
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"maybe related: https://github.com/huggingface/datasets/issues/6388",
"Also look at https://github.com/huggingface/dataset-viewer/blob/f5fd117ceded990a7766e705bba1203fa907d6ad/services/worker/src/worker/job_runners/dataset/modalities.py#L241 which lists the 3D file formats that will assign the 3D modality to a dataset."
] | 2024-10-03T13:27:44 | 2024-10-03T13:32:20 | null | COLLABORATOR | null | See https://huggingface.co/datasets/allenai/objaverse for example | null | {
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"Actually there is no such dataset available, that is why you are getting that error.",
"Fixed with @kdutia in Slack chat. Generating a new token fixed this issue. "
] | 2024-10-03T07:49:36 | 2024-10-03T10:09:28 | 2024-10-03T10:09:28 | NONE | null | ### Describe the bug
The following Python code tries to download a private dataset and fails with the error `datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed.`. Downloading a public dataset doesn't work.
``` py
from datasets import load_dataset
_ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly")
```
This seems to be just an issue with my machine config as the code above works with a colleague's machine. So far I have tried:
- logging back out and in from the Huggingface CLI using `huggingface-cli logout`
- manually removing the token cache at `/Users/kalyan/.cache/huggingface/token` (found using `huggingface-cli env`)
- manually passing a token in `load_dataset`
My output of `huggingface-cli whoami`:
```
kdutia
orgs: ClimatePolicyRadar
```
### Steps to reproduce the bug
```
python
Python 3.12.2 (main, Feb 6 2024, 20:19:44) [Clang 15.0.0 (clang-1500.1.0.2.5)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> _ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 2074, in load_dataset
builder_instance = load_dataset_builder(
^^^^^^^^^^^^^^^^^^^^^
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1795, in load_dataset_builder
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1659, in dataset_module_factory
raise e1 from None
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1597, in dataset_module_factory
raise DatasetNotFoundError(f"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.") from e
datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed.
>>>
```
### Expected behavior
The dataset downloads successfully.
### Environment info
From `huggingface-cli env`:
```
- huggingface_hub version: 0.25.1
- Platform: macOS-14.2.1-arm64-arm-64bit
- Python version: 3.12.2
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Running in Google Colab Enterprise ?: No
- Token path ?: /Users/kalyan/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: kdutia
- Configured git credential helpers: osxkeychain
- FastAI: N/A
- Tensorflow: N/A
- Torch: N/A
- Jinja2: 3.1.4
- Graphviz: N/A
- keras: N/A
- Pydot: N/A
- Pillow: N/A
- hf_transfer: N/A
- gradio: N/A
- tensorboard: N/A
- numpy: 2.1.1
- pydantic: N/A
- aiohttp: 3.10.8
- ENDPOINT: https://huggingface.co
- HF_HUB_CACHE: /Users/kalyan/.cache/huggingface/hub
- HF_ASSETS_CACHE: /Users/kalyan/.cache/huggingface/assets
- HF_TOKEN_PATH: /Users/kalyan/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
- HF_HUB_ETAG_TIMEOUT: 10
- HF_HUB_DOWNLOAD_TIMEOUT: 10
```
from `datasets-cli env`:
```
- `datasets` version: 3.0.1
- Platform: macOS-14.2.1-arm64-arm-64bit
- Python version: 3.12.2
- `huggingface_hub` version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
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"I was curious about the same - since map is applied on the fly I was assuming that setting num_workers>1 in DataLoader would effectively do the map in parallel, have you tried that?"
] | 2024-10-02T18:34:04 | 2024-10-03T09:54:15 | null | NONE | null | ### Feature request
Currently, IterableDataset doesn't support setting num_worker in .map(), which results in slow processing here. Could we add support for it? As .map() can be run in the batch fashion (e.g., batch_size is default to 1000 in datasets), it seems to be doable for IterableDataset as the regular Dataset.
### Motivation
Improving data processing efficiency
### Your contribution
Testing | null | {
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"perhaps concatenate_datasets can already be used to achieve almost the same effect? ",
"`concatenate_datasets` does the job when there is a finite number of repetitions, but in case of `.repeat()` forever we need a new logic in `iterable_dataset.py`"
] | 2024-10-02T17:48:13 | 2024-10-03T12:53:34 | null | NONE | null | ### Feature request
It would be useful to be able to straightforwardly repeat iterable datasets indefinitely, to provide complete control over starting and ending of iteration to the user.
An IterableDataset.repeat(n) function could do this automatically
### Motivation
This feature was discussed in this issue https://github.com/huggingface/datasets/issues/7147, and would resolve the need to use the hack of interleave datasets with probability 0 as a simple way to achieve this functionality.
An additional benefit might be the simplification of the use of iterable datasets in a distributed setting:
If the user can assume that datasets will repeat indefinitely, then issues around different numbers of samples appearing on different devices (e.g. https://github.com/huggingface/datasets/issues/6437, https://github.com/huggingface/datasets/issues/6594, https://github.com/huggingface/datasets/issues/6623, https://github.com/huggingface/datasets/issues/6719) can potentially be straightforwardly resolved by simply doing:
ids.repeat(None).take(n_samples_per_epoch)
### Your contribution
I'm not familiar enough with the codebase to assess how straightforward this would be to implement.
If it might be very straightforward, I could possibly have a go. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7191 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7191/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7191/comments | https://api.github.com/repos/huggingface/datasets/issues/7191/events | https://github.com/huggingface/datasets/pull/7191 | 2,562,206,949 | PR_kwDODunzps59Z1zy | 7,191 | Solution to issue: #7080 Modified load_dataset function, so that it prompts the user to select a dataset when subdatasets or splits (train, test) are available | {
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### This PR is raised because of issue: https://github.com/huggingface/datasets/issues/7080
### This PR gives solution to https://github.com/huggingface/datasets/issues/7080
1. Checking whether the dataset has splits or subdatasets.
2. Printing the available splits/subdatasets.
3. Asking the user to choose which one to load.
4. Loading only the selected dataset based on the user's input.
### Key Changes:
1. Available Splits/Subdatasets: The code checks for available splits/subdatasets using builder_instance.info.splits.keys().
2. User Prompt: If splits are found, it prints them out and prompts the user to select one.
3. Loading Based on User Input: The dataset is loaded based on the user's choice.
This way, the dataset loading function will interactively prompt the user to select which subdataset or split they want to load instead of automatically loading all of them.
| null | {
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} | [] | open | false | null | [] | null | [] | 2024-10-02T16:43:46 | 2024-10-02T16:43:46 | null | NONE | null | ### Describe the bug
Installing both in latest versions are not possible
`pip install "datasets==3.0.1" "fsspec==2024.9.0"`
But using older version of datasets is ok
`pip install "datasets==1.24.4" "fsspec==2024.9.0"`
### Steps to reproduce the bug
`pip install "datasets==3.0.1" "fsspec==2024.9.0"`
### Expected behavior
install both versions.
### Environment info
debian 11.
python 3.10.15 | null | {
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Huggingface has quite a comprehensive set of guides for [audio datasets](https://huggingface.co/docs/datasets/en/audio_dataset). It seems, however, all these guides assume the audio array data to be decoded/inserted into a HF dataset always originates from individual files. The [Audio-dataclass](https://github.com/huggingface/datasets/blob/3.0.1/src/datasets/features/audio.py#L20) appears designed with this assumption in mind. Looking at its source code it returns a dictionary with the keys `path`, `array` and `sampling_rate`.
However, sometimes users may have different pipelines where they themselves decode the audio array. This feature request has to do with wishing some clarification in guides on whether it is possible, and in such case how users can insert already decoded audio array data into datasets (pandas DataFrame, HF dataset or whatever) that are later saved as parquet, and still get a functioning audio preview in the dataset viewer.
Do I perhaps need to write a tempfile of my audio array slice to wav and capture the bytes object with `io.BytesIO` and pass that to `Audio()`?
### Motivation
I'm working with large audio datasets, and my pipeline reads (decodes) audio from larger files, and slices the relevant portions of audio from that larger file based on metadata I have available.
The pipeline is designed this way to avoid having to store multiple copies of data, and to avoid having to store tens of millions of small files.
I tried [test-uploading parquet files](https://huggingface.co/datasets/Lauler/riksdagen_test) where I store the audio array data of decoded slices of audio in an `audio` column with a dictionary with the keys `path`, `array` and `sampling_rate`. But I don't know the secret sauce of what the Huggingface Hub expects and requires to be able to display audio previews correctly.
### Your contribution
I could contribute a tool agnostic guide of creating HF audio datasets directly as parquet to the HF documentation if there is an interest. Provided you help me figure out the secret sauce of what the dataset viewer expects to display the preview correctly. | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7188). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-02T05:40:18 | 2024-10-02T06:08:25 | 2024-10-02T06:08:23 | MEMBER | null | Pin multiprocess<0.70.1 to align with dill<0.3.9.
Note that multiprocess-0.70.1 requires dill-0.3.9: https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17
Fix #7186. | {
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```
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 238, in __iter__
[rank0]: for key_example in islice(self.generate_examples_fn(**gen_kwags), shard_example_idx_start, None):
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/packaged_modules/generator/generator.py", line 32, in _generate_examples
[rank0]: for idx, ex in enumerate(self.config.generator(**gen_kwargs)):
[rank0]: File "/home/qinghao/workdir/doremi/doremi/dataloader.py", line 337, in take_data_generator
[rank0]: for ex in ds:
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1791, in __iter__
[rank0]: yield from self._iter_pytorch()
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1704, in _iter_pytorch
[rank0]: ex_iterable = ex_iterable.shard_data_sources(worker_id=worker_info.id, num_workers=worker_info.num_workers)
[rank0]: TypeError: UpdatableRandomlyCyclingMultiSourcesExamplesIterable.shard_data_sources() got an unexpected keyword argument 'worker_id'
```
### Steps to reproduce the bug
IterableDataset cannot use
### Expected behavior
can work on datasets==2.10, but will raise error for later versions.
### Environment info
datasets==3.0.1 | null | {
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] | null | [] | 2024-10-01T22:29:32 | 2024-10-02T06:08:24 | 2024-10-02T06:08:24 | NONE | null | ### Describe the bug
The [latest `multiprocess` release](https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17) requires `dill>=0.3.9` which causes issues when installing `datasets` without backtracking during package version resolution. Is it possible to add a pin for multiprocess so something like `multiprocess<=0.70.16` so that the `dill` version is compatible?
### Steps to reproduce the bug
NA
### Expected behavior
NA
### Environment info
NA | {
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] | open | false | null | [] | null | [] | 2024-10-01T08:16:08 | 2024-10-01T08:16:08 | null | MEMBER | null | Since Aug 30, 2024, CI benchmarks are broken: https://github.com/huggingface/datasets/actions/runs/11108421214/job/30861323975
```
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https://api.github.com/repos/huggingface/datasets/issues/7184 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7184/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7184/comments | https://api.github.com/repos/huggingface/datasets/issues/7184/events | https://github.com/huggingface/datasets/pull/7184 | 2,556,855,150 | PR_kwDODunzps59IPEf | 7,184 | Pin dill<0.3.9 to fix CI | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7184). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-30T14:26:25 | 2024-09-30T14:38:59 | 2024-09-30T14:38:57 | MEMBER | null | Pin dill<0.3.9 to fix CI for deps-latest.
Note that dill-0.3.9 was released yesterday Sep 29, 2024:
- https://pypi.org/project/dill/0.3.9/
- https://github.com/uqfoundation/dill/releases/tag/0.3.9
Fix #7183. | {
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https://api.github.com/repos/huggingface/datasets/issues/7183 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7183/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7183/comments | https://api.github.com/repos/huggingface/datasets/issues/7183/events | https://github.com/huggingface/datasets/issues/7183 | 2,556,789,055 | I_kwDODunzps6YZYE_ | 7,183 | CI is broken for deps-latest | {
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} | [] | closed | false | null | [] | null | [] | 2024-09-30T14:02:07 | 2024-09-30T14:38:58 | 2024-09-30T14:38:58 | MEMBER | null | See: https://github.com/huggingface/datasets/actions/runs/11106149906/job/30853879890
```
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_caching_on_disk - AssertionError: Lists differ: [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}] != [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}]
First differing element 1:
{'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}
{'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}
[{'filename': '/tmp/tmp6xcyyjs4/dataset0.arrow'},
- {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}]
? ^^^^^ --------
+ {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}]
? ++++++++++ ^^ +
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - AssertionError: Lists differ: [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}] != [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}]
First differing element 0:
{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}
{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}
- [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}]
? ^^ -----------
+ [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}]
? +++++++++++ ^^
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_regex - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ignores_line_definition_of_function - AssertionError: '52e56ee04ad92499' != '0a4f75cec280f634'
- 52e56ee04ad92499
+ 0a4f75cec280f634
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ipython_function - AssertionError: 'a6bd2041ca63d6c0' != '517bf36b7eecdef5'
- a6bd2041ca63d6c0
+ 517bf36b7eecdef5
FAILED tests/test_fingerprint.py::HashingTest::test_hash_tiktoken_encoding - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_compiled_module - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_generator - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_tensor - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_doesnt_depend_on_order - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_stable - NameError: name 'log' is not defined
ERROR tests/test_iterable_dataset.py::test_iterable_dataset_from_file - NameError: name 'log' is not defined
= 11 failed, 2850 passed, 3 skipped, 23 warnings, 1 error in 191.06s (0:03:11) =
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https://api.github.com/repos/huggingface/datasets/issues/7182 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7182/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7182/comments | https://api.github.com/repos/huggingface/datasets/issues/7182/events | https://github.com/huggingface/datasets/pull/7182 | 2,556,333,671 | PR_kwDODunzps59GdLS | 7,182 | Support features in metadata configs | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7182). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"The CI issue is unrelated:\r\n- #7183"
] | 2024-09-30T11:14:53 | 2024-10-01T13:34:40 | null | MEMBER | null | Support features in metadata configs, like:
```
configs:
- config_name: default
features:
- name: id
dtype: int64
- name: name
dtype: string
- name: score
dtype: float64
```
This will allow to avoid inference of data types.
Currently, we allow passing this information in the `dataset_info` (instead of `configs`) field, but this is not intuitive and it is not properly documented.
TODO:
- [ ] Document usage | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7181 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7181/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7181/comments | https://api.github.com/repos/huggingface/datasets/issues/7181/events | https://github.com/huggingface/datasets/pull/7181 | 2,554,917,019 | PR_kwDODunzps59Br4E | 7,181 | [WIP] Fix datasets export to JSON | {
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"Linked Issue: #7037\r\nIdeas: #7039 ",
"@albertvillanova / @lhoestq any early feedback?\r\n\r\nAFAIK there is no param `orient` in `load_dataset()`. So for orientations other than \"records\", the loading isn't very accurate. Any thoughts?"
] | 2024-09-29T12:45:20 | 2024-09-30T06:36:47 | null | CONTRIBUTOR | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7180 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7180/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7180/comments | https://api.github.com/repos/huggingface/datasets/issues/7180/events | https://github.com/huggingface/datasets/issues/7180 | 2,554,244,750 | I_kwDODunzps6YPq6O | 7,180 | Memory leak when wrapping datasets into PyTorch Dataset without explicit deletion | {
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"> I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.\r\n\r\nDatasets are memory mapped so they work like SWAP memory. In particular as long as you have RAM available the data will stay in RAM, and get paged out once your system needs RAM for something else (no OOM).\r\n\r\nrelated: https://github.com/huggingface/datasets/issues/4883"
] | 2024-09-28T14:00:47 | 2024-09-30T12:07:56 | 2024-09-30T12:07:56 | NONE | null | ### Describe the bug
I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.
### Steps to reproduce the bug
Steps to reproduce:
Create a PyTorch Dataset wrapper for 'nebula/cc12m':
````
from torch.utils.data import Dataset
from tqdm import tqdm
from datasets import load_dataset
from torchvision import transforms
Image.MAX_IMAGE_PIXELS = None
class CC12M(Dataset):
def __init__(self, path_or_name='nebula/cc12m', split='train', transform=None, single_caption=True):
self.raw_dataset = load_dataset(path_or_name)[split]
if transform is None:
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711]
)
])
else:
self.transform = transforms.Compose(transform)
self.single_caption = single_caption
self.length = len(self.raw_dataset)
def __len__(self):
return self.length
def __getitem__(self, index):
item = self.raw_dataset[index]
caption = item['txt']
with io.BytesIO(item['webp']) as buffer:
image = Image.open(buffer).convert('RGB')
if self.transform:
image = self.transform(image)
# del item # Uncomment this line to prevent the memory leak
return image, caption
````
Iterate through the dataset without the del item line in __getitem__.
Observe RAM usage increasing constantly.
Add del item at the end of __getitem__:
```
def __getitem__(self, index):
item = self.raw_dataset[index]
caption = item['txt']
with io.BytesIO(item['webp']) as buffer:
image = Image.open(buffer).convert('RGB')
if self.transform:
image = self.transform(image)
del item # This line prevents the memory leak
return image, caption
```
Iterate through the dataset again and observe that RAM usage remains stable.
### Expected behavior
Expected behavior:
RAM usage should remain stable during iteration without needing to explicitly delete items.
Actual behavior:
RAM usage constantly increases unless items are explicitly deleted after use
### Environment info
- `datasets` version: 2.21.0
- Platform: Linux-4.18.0-513.5.1.el8_9.x86_64-x86_64-with-glibc2.28
- Python version: 3.12.4
- `huggingface_hub` version: 0.24.6
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7179). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-27T08:55:44 | 2024-09-27T08:58:00 | null | MEMBER | null | Support Python 3.11.
Fix #7178. | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7177). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-27T08:47:01 | 2024-09-27T08:57:35 | 2024-09-27T08:57:32 | MEMBER | null | Fix release instructions.
During last release, I had to make this additional update. | {
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"Is this `FSTimeoutError` due to download network issue from remote resource (from where it is being accessed)?",
"It seems to happen for all datasets, not just a specific one, and especially for versions after 3.0. (3.0.0, 3.0.1 have this problem)\r\n\r\nI had the same error on a different dataset, but after downgrading to datasets==2.21.0, the problem was solved.",
"Same as https://github.com/huggingface/datasets/issues/7164\r\n\r\nThis dataset is made of a python script that downloads data from elsewhere than HF, so availability depends on the original host. Ultimately it would be nice to host the files of this dataset on HF\r\n\r\nin `datasets` <3.0 there were lots of mechanisms that got removed after the decision to make datasets with python loading scripts legacy for security and maintenance reasons (we only do very basic support now)",
"@lhoestq Thank you for the clarification! Closing the issue."
] | 2024-09-26T15:42:29 | 2024-09-30T17:28:35 | 2024-09-30T17:28:35 | NONE | null | ### Describe the bug
When using `load_dataset`to load [HuggingFaceM4/VQAv2](https://huggingface.co/datasets/HuggingFaceM4/VQAv2), I am getting `FSTimeoutError`.
### Error
```
TimeoutError:
The above exception was the direct cause of the following exception:
FSTimeoutError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/fsspec/asyn.py](https://klh9mr78js-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240924-060116_RC00_678132060#) in sync(loop, func, timeout, *args, **kwargs)
99 if isinstance(return_result, asyncio.TimeoutError):
100 # suppress asyncio.TimeoutError, raise FSTimeoutError
--> 101 raise FSTimeoutError from return_result
102 elif isinstance(return_result, BaseException):
103 raise return_result
FSTimeoutError:
```
It usually fails around 5-6 GB.
<img width="847" alt="Screenshot 2024-09-26 at 9 10 19 PM" src="https://github.com/user-attachments/assets/ff91995a-fb55-4de6-8214-94025d6c8470">
### Steps to reproduce the bug
To reproduce it, run this in colab notebook:
```
!pip install -q -U datasets
from datasets import load_dataset
ds = load_dataset('HuggingFaceM4/VQAv2', split="train[:10%]")
```
### Expected behavior
It should download properly.
### Environment info
Using Colab Notebook. | {
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] | 2024-09-26T07:45:55 | 2024-09-26T08:12:12 | 2024-09-26T08:05:40 | MEMBER | null | Add `torchdata` as a regular test dependency.
Note that previously, `torchdata` was installed from their repo and current main branch (0.10.0.dev) requires Python>=3.9.
Also note they made a recent release: 0.8.0 on Jul 31, 2024.
Fix #7171. | {
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```
Run uv pip install --system -r additional-tests-requirements.txt --no-deps
× No solution found when resolving dependencies:
╰─▶ Because the current Python version (3.8.18) does not satisfy Python>=3.9
and torchdata==0.10.0a0+1a98f21 depends on Python>=3.9, we can conclude
that torchdata==0.10.0a0+1a98f21 cannot be used.
And because only torchdata==0.10.0a0+1a98f21 is available and
you require torchdata, we can conclude that your requirements are
unsatisfiable.
Error: Process completed with exit code 1.
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] | 2024-09-25T05:08:15 | 2024-09-26T06:42:09 | 2024-09-26T06:42:07 | MEMBER | null | Support JSON lines with missing columns.
Fix #7169.
The implemented test raised:
```
datasets.table.CastError: Couldn't cast
age: int64
to
{'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)}
because column names don't match
```
Related to:
- #7160
- #7162 | {
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> CastError: Couldn't cast ... to ... because column names don't match
Related to:
- #7159
- #7161 | {
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https://api.github.com/repos/huggingface/datasets/issues/7168 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7168/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7168/comments | https://api.github.com/repos/huggingface/datasets/issues/7168/events | https://github.com/huggingface/datasets/issues/7168 | 2,546,710,631 | I_kwDODunzps6Xy7hn | 7,168 | sd1.5 diffusers controlnet training script gives new error | {
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"not sure why the issue is formatting oddly",
"I guess this is a dupe of\r\n\r\nhttps://github.com/huggingface/datasets/issues/7071",
"this turned out to be because of a bad image in dataset"
] | 2024-09-25T01:42:49 | 2024-09-30T05:24:03 | 2024-09-30T05:24:02 | NONE | null | ### Describe the bug
This will randomly pop up during training now
```
Traceback (most recent call last):
File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1192, in <module>
main(args)
File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1041, in main
for step, batch in enumerate(train_dataloader):
File "/usr/local/lib/python3.11/dist-packages/accelerate/data_loader.py", line 561, in __iter__
next_batch = next(dataloader_iter)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 630, in __next__
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 673, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/_utils/fetch.py", line 50, in fetch
data = self.dataset.__getitems__(possibly_batched_index)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2746, in __getitems__
batch = self.__getitem__(keys)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2742, in __getitem__
return self._getitem(key)
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2727, in _getitem
formatted_output = format_table(
^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 639, in format_table
return formatter(pa_table, query_type=query_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 407, in __call__
return self.format_batch(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 521, in format_batch
batch = self.python_features_decoder.decode_batch(batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 228, in decode_batch
return self.features.decode_batch(batch) if self.features else batch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2084, in decode_batch
[
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2085, in <listcomp>
decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 1403, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/features/image.py", line 188, in decode_example
image.load() # to avoid "Too many open files" errors
```
### Steps to reproduce the bug
Train on diffusers sd1.5 controlnet example script
This will pop up randomly, you can see in wandb below when i manually resume run everytime this error appears
### Expected behavior
Training to continue without above error
### Environment info
- datasets version: 3.0.0
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- huggingface_hub version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- fsspec version: 2024.6.1
Training on 4090 | {
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https://api.github.com/repos/huggingface/datasets/issues/7167 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7167/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7167/comments | https://api.github.com/repos/huggingface/datasets/issues/7167/events | https://github.com/huggingface/datasets/issues/7167 | 2,546,708,014 | I_kwDODunzps6Xy64u | 7,167 | Error Mapping on sd3, sdxl and upcoming flux controlnet training scripts in diffusers | {
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"this is happening on large datasets, if anyone happens upon this i was able to fix by changing\r\n\r\n```\r\ntrain_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)\r\n```\r\n\r\nto\r\n\r\n```\r\ntrain_dataset = train_dataset.map(compute_embeddings_fn, batched=True, batch_size=16, new_fingerprint=new_fingerprint)\r\n```"
] | 2024-09-25T01:39:51 | 2024-09-30T05:28:15 | 2024-09-30T05:28:04 | NONE | null | ### Describe the bug
```
Map: 6%|██████ | 8000/138120 [19:27<5:16:36, 6.85 examples/s]
Traceback (most recent call last):
File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1416, in <module>
main(args)
File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1132, in main
train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 560, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3035, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3461, in _map_single
writer.write_batch(batch)
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 567, in write_batch
self.write_table(pa_table, writer_batch_size)
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 579, in write_table
pa_table = pa_table.combine_chunks()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 4387, in pyarrow.lib.Table.combine_chunks
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
Traceback (most recent call last):
File "/usr/local/bin/accelerate", line 8, in <module>
sys.exit(main())
^^^^^^
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/accelerate_cli.py", line 48, in main
args.func(args)
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 1174, in launch_command
simple_launcher(args)
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 769, in simple_launcher
```
### Steps to reproduce the bug
The dataset has no problem training on sd1.5 controlnet train script
### Expected behavior
Script not randomly erroing with error above
### Environment info
- `datasets` version: 3.0.0
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7166). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-24T14:39:54 | 2024-09-24T14:42:41 | 2024-09-24T14:40:14 | MEMBER | null | close https://github.com/huggingface/datasets/issues/7163 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7165). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I tested a few load_dataset and they do show up in download stats now",
"Thanks for having noticed and fixed."
] | 2024-09-24T10:14:40 | 2024-09-24T17:31:07 | 2024-09-24T13:48:00 | MEMBER | null | it was failing since 3.0 and therefore not updating download counts on HF or in our dashboard | {
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https://api.github.com/repos/huggingface/datasets/issues/7164 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7164/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7164/comments | https://api.github.com/repos/huggingface/datasets/issues/7164/events | https://github.com/huggingface/datasets/issues/7164 | 2,544,757,297 | I_kwDODunzps6Xreox | 7,164 | fsspec.exceptions.FSTimeoutError when downloading dataset | {
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"Hi ! If you check the dataset loading script [here](https://huggingface.co/datasets/openslr/librispeech_asr/blob/main/librispeech_asr.py) you'll see that it downloads the data from OpenSLR, and apparently their storage has timeout issues. It would be great to ultimately host the dataset on Hugging Face instead.\r\n\r\nIn the meantime I can only recommend to try again later :/",
"Ok, still many thanks!"
] | 2024-09-24T08:45:05 | 2024-09-24T15:35:57 | null | NONE | null | ### Describe the bug
I am trying to download the `librispeech_asr` `clean` dataset, which results in a `FSTimeoutError` exception after downloading around 61% of the data.
### Steps to reproduce the bug
```
import datasets
datasets.load_dataset("librispeech_asr", "clean")
```
The output is as follows:
> Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:06, 13.2MB/s]Traceback (most recent call last):
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 56, in _runner
> result[0] = await coro
> ^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/implementations/http.py", line 262, in _get_file
> chunk = await r.content.read(chunk_size)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 393, in read
> await self._wait("read")
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 311, in _wait
> with self._timer:
> ^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/helpers.py", line 713, in __exit__
> raise asyncio.TimeoutError from None
> TimeoutError
>
> The above exception was the direct cause of the following exception:
>
> Traceback (most recent call last):
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/load_dataset.py", line 3, in <module>
> datasets.load_dataset("librispeech_asr", "clean")
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/load.py", line 2096, in load_dataset
> builder_instance.download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 924, in download_and_prepare
> self._download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1647, in _download_and_prepare
> super()._download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 977, in _download_and_prepare
> split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/2712a8f82f0d20807a56faadcd08734f9bdd24c850bb118ba21ff33ebff0432f/librispeech_asr.py", line 115, in _split_generators
> archive_path = dl_manager.download(_DL_URLS[self.config.name])
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 159, in download
> downloaded_path_or_paths = map_nested(
> ^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 512, in map_nested
> _single_map_nested((function, obj, batched, batch_size, types, None, True, None))
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 380, in _single_map_nested
> return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
> ^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 216, in _download_batched
> self._download_single(url_or_filename, download_config=download_config)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 225, in _download_single
> out = cached_path(url_or_filename, download_config=download_config)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 205, in cached_path
> output_path = get_from_cache(
> ^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 415, in get_from_cache
> fsspec_get(url, temp_file, storage_options=storage_options, desc=download_desc, disable_tqdm=disable_tqdm)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 334, in fsspec_get
> fs.get_file(path, temp_file.name, callback=callback)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 118, in wrapper
> return sync(self.loop, func, *args, **kwargs)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 101, in sync
> raise FSTimeoutError from return_result
> fsspec.exceptions.FSTimeoutError
> Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:09, 13.0MB/s]
### Expected behavior
Complete the download
### Environment info
Python version 3.12.6
Dependencies:
> dependencies = [
> "accelerate>=0.34.2",
> "datasets[audio]>=3.0.0",
> "ipython>=8.18.1",
> "librosa>=0.10.2.post1",
> "torch>=2.4.1",
> "torchaudio>=2.4.1",
> "transformers>=4.44.2",
> ]
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https://api.github.com/repos/huggingface/datasets/issues/7163 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7163/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7163/comments | https://api.github.com/repos/huggingface/datasets/issues/7163/events | https://github.com/huggingface/datasets/issues/7163 | 2,542,361,234 | I_kwDODunzps6XiVqS | 7,163 | Set explicit seed in iterable dataset ddp shuffling example | {
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"thanks for reporting !"
] | 2024-09-23T11:34:06 | 2024-09-24T14:40:15 | 2024-09-24T14:40:15 | NONE | null | ### Describe the bug
In the examples section of the iterable dataset docs https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.IterableDataset
the ddp example shuffles without seeding
```python
from datasets.distributed import split_dataset_by_node
ids = ds.to_iterable_dataset(num_shards=512)
ids = ids.shuffle(buffer_size=10_000) # will shuffle the shards order and use a shuffle buffer when you start iterating
ids = split_dataset_by_node(ds, world_size=8, rank=0) # will keep only 512 / 8 = 64 shards from the shuffled lists of shards when you start iterating
dataloader = torch.utils.data.DataLoader(ids, num_workers=4) # will assign 64 / 4 = 16 shards from this node's list of shards to each worker when you start iterating
for example in ids:
pass
```
This code would - I think - raise an error due to the lack of an explicit seed:
https://github.com/huggingface/datasets/blob/2eb4edb97e1a6af2ea62738ec58afbd3812fc66e/src/datasets/iterable_dataset.py#L1707-L1711
### Steps to reproduce the bug
Run example code
### Expected behavior
Add explicit seeding to example code
### Environment info
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7162). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-23T11:16:12 | 2024-09-23T11:30:08 | 2024-09-23T11:30:06 | MEMBER | null | Support JSON lines with empty struct.
Fix #7161.
Related to:
- #7160 | {
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See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5
> ArrowTypeError: struct fields don't match or are in the wrong order: Input fields: struct<> output fields: struct<pov_count: int64, update_count: int64, citation_needed_count: int64>
Related to:
- #7159 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7160). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-23T08:04:09 | 2024-09-23T11:09:19 | 2024-09-23T11:09:17 | MEMBER | null | Support JSON lines with missing struct fields.
Fix #7159.
The implemented test raised:
```
TypeError: Couldn't cast array of type
struct<age: int64>
to
{'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)}
``` | {
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See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5
One would expect that the struct missing fields are added with null values. | {
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] | 2024-09-21T15:19:14 | 2024-09-24T14:33:54 | 2024-09-24T14:33:54 | MEMBER | null | fix https://github.com/huggingface/datasets/issues/7147 | {
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https://api.github.com/repos/huggingface/datasets/issues/7156 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7156/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7156/comments | https://api.github.com/repos/huggingface/datasets/issues/7156/events | https://github.com/huggingface/datasets/issues/7156 | 2,539,360,617 | I_kwDODunzps6XW5Fp | 7,156 | interleave_datasets resets shuffle state | {
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```
import datasets
import torch.utils.data
def gen(shards):
yield {"shards": shards}
def main():
dataset = datasets.IterableDataset.from_generator(
gen,
gen_kwargs={'shards': list(range(25))}
)
dataset = dataset.shuffle(buffer_size=1)
dataset = datasets.interleave_datasets(
[dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted"
)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=8,
num_workers=8,
)
for i, batch in enumerate(dataloader):
print(batch)
if i >= 10:
break
if __name__ == "__main__":
main()
```
### Steps to reproduce the bug
Run the script, it will output
```
{'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]}
{'shards': [tensor([ 1, 9, 17, 1, 9, 17, 1, 9])]}
{'shards': [tensor([ 2, 10, 18, 2, 10, 18, 2, 10])]}
{'shards': [tensor([ 3, 11, 19, 3, 11, 19, 3, 11])]}
{'shards': [tensor([ 4, 12, 20, 4, 12, 20, 4, 12])]}
{'shards': [tensor([ 5, 13, 21, 5, 13, 21, 5, 13])]}
{'shards': [tensor([ 6, 14, 22, 6, 14, 22, 6, 14])]}
{'shards': [tensor([ 7, 15, 23, 7, 15, 23, 7, 15])]}
{'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]}
{'shards': [tensor([17, 1, 9, 17, 1, 9, 17, 1])]}
{'shards': [tensor([18, 2, 10, 18, 2, 10, 18, 2])]}
```
### Expected behavior
The shards should be shuffled.
### Environment info
- `datasets` version: 3.0.0
- Platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.25.0
- PyArrow version: 17.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.6.0 | null | {
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"I have fixed it! But I would appreciate a new feature wheere I could iterate over and see what each file looks like. "
] | 2024-09-18T12:43:21 | 2024-09-18T13:20:03 | 2024-09-18T13:20:03 | NONE | null | Hello guys,
I have a dataset and I didn't know I couldn't upload more than 32 splits. Now, my dataset viewer is not working. I don't have the dataset locally on my node anymore and recreating would take a week. And I have to publish the dataset coming Monday. I read about the practice, how I can resolve it and avoid this issue in the future. But, at the moment I need a hard fix for two of my datasets.
And I don't want to mess or change anything and allow everyone in public to see the dataset and interact with it. Can you please help me?
https://huggingface.co/datasets/laion/Wikipedia-X
https://huggingface.co/datasets/laion/Wikipedia-X-Full | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7154). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Thanks for your review, @severo.\r\n\r\nYes, I was aware of this. From internal conversation:\r\n> Please note that although NDJSON was planned to be submitted as an RFC standard spec, it is no longer maintained:\r\n> - See note from the author: https://github.com/ndjson/ndjson-spec/issues/35#issuecomment-1285673417\r\n> - See that their official website domain has expired: https://ndjson.org/ \r\n\r\nThe purpose of this PR is just supporting datasets with ndjson data files (e.g. Wikimedia Enterprise data files), but it should not imply any recommendation or endorsement of this format from our part."
] | 2024-09-18T06:10:10 | 2024-09-19T11:25:17 | 2024-09-19T11:25:14 | MEMBER | null | Support `ndjson` (Newline Delimited JSON) data files.
Fix #7153. | {
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] | null | [] | 2024-09-18T05:54:45 | 2024-09-19T11:25:15 | 2024-09-19T11:25:15 | MEMBER | null | ### Feature request
Support data files with `.ndjson` extension.
### Motivation
We already support data files with `.jsonl` extension.
### Your contribution
I am opening a PR. | {
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This PR uses the same `base_plus_ext` function as the one used by the `webdataset` library.
Fix #7150.
Related to #7144. | {
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https://api.github.com/repos/huggingface/datasets/issues/7150 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7150/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7150/comments | https://api.github.com/repos/huggingface/datasets/issues/7150/events | https://github.com/huggingface/datasets/issues/7150 | 2,527,571,175 | I_kwDODunzps6Wp6zn | 7,150 | WebDataset loader splits keys differently than WebDataset library | {
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] | null | [] | 2024-09-16T06:02:47 | 2024-09-16T15:26:35 | 2024-09-16T15:26:35 | MEMBER | null | As reported by @ragavsachdeva (see discussion here: https://github.com/huggingface/datasets/pull/7144#issuecomment-2348307792), our webdataset loader is not aligned with the `webdataset` library when splitting keys from filenames.
For example, we get a different key splitting for filename `/some/path/22.0/1.1.png`:
- datasets library: `/some/path/22` and `0/1.1.png`
- webdataset library: `/some/path/22.0/1`, `1.png`
```python
import webdataset as wds
wds.tariterators.base_plus_ext("/some/path/22.0/1.1.png")
# ('/some/path/22.0/1', '1.png')
```
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https://api.github.com/repos/huggingface/datasets/issues/7149 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7149/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7149/comments | https://api.github.com/repos/huggingface/datasets/issues/7149/events | https://github.com/huggingface/datasets/issues/7149 | 2,524,497,448 | I_kwDODunzps6WeMYo | 7,149 | Datasets Unknown Keyword Argument Error - task_templates | {
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"Thanks, for reporting.\r\n\r\nWe have been fixing most Hub datasets to remove the deprecated (and now non-supported) task templates, but we missed the \"facebook/winoground\".\r\n\r\nIt is fixed now: https://huggingface.co/datasets/facebook/winoground/discussions/8\r\n\r\n"
] | 2024-09-13T10:30:57 | 2024-09-13T14:10:48 | 2024-09-13T14:10:48 | NONE | null | ### Describe the bug
Issue
```python
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
```
Gives error
```
TypeError: DatasetInfo.__init__() got an unexpected keyword argument 'task_templates'
```
A simple downgrade to lower `datasets v 2.21.0` solves it.
### Steps to reproduce the bug
1. `pip install datsets`
2.
```python
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
```
### Expected behavior
Should load the dataset correctly.
### Environment info
- Datasets version `3.0.0`
- `transformers` version: 4.45.0.dev0
- Platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.35
- Python version: 3.12.4
- Huggingface_hub version: 0.24.6
- Safetensors version: 0.4.5
- Accelerate version: 0.35.0.dev0
- Accelerate config: not found
- PyTorch version (GPU?): 2.4.1+cu121 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
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https://api.github.com/repos/huggingface/datasets/issues/7148 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7148/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7148/comments | https://api.github.com/repos/huggingface/datasets/issues/7148/events | https://github.com/huggingface/datasets/issues/7148 | 2,523,833,413 | I_kwDODunzps6WbqRF | 7,148 | Bug: Error when downloading mteb/mtop_domain | {
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"Could you please try with `force_redownload` instead?\r\nEDIT:\r\n```python\r\ndata = load_dataset(\"mteb/mtop_domain\", \"en\", download_mode=\"force_redownload\")\r\n```",
"Seems the error is still there",
"I am not able to reproduce the issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: data = load_dataset(\"mteb/mtop_domain\", \"en\")\r\n\r\nIn [3]: data\r\nOut[3]: DatasetDict({\r\n train: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 15667\r\n })\r\n validation: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 2235\r\n })\r\n test: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 4386\r\n })\r\n})\r\n```",
"Just solved this by reinstall Huggingface Hub and datasets. Thanks for your help!"
] | 2024-09-13T04:09:39 | 2024-09-14T15:11:35 | 2024-09-14T15:11:35 | NONE | null | ### Describe the bug
When downloading the dataset "mteb/mtop_domain", ran into the following error:
```
Traceback (most recent call last):
File "/share/project/xzy/test/test_download.py", line 3, in <module>
data = load_dataset("mteb/mtop_domain", "en", trust_remote_code=True)
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2606, in load_dataset
builder_instance = load_dataset_builder(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2277, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1923, in dataset_module_factory
raise e1 from None
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1896, in dataset_module_factory
).get_module()
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1507, in get_module
local_path = self.download_loading_script()
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1467, in download_loading_script
return cached_path(file_path, download_config=download_config)
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 211, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 689, in get_from_cache
fsspec_get(
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 395, in fsspec_get
fs.get_file(path, temp_file.name, callback=callback)
File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 648, in get_file
http_get(
File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 578, in http_get
raise EnvironmentError(
OSError: Consistency check failed: file should be of size 2191 but has size 2190 ((…)ets/mteb/mtop_domain@main/mtop_domain.py).
We are sorry for the inconvenience. Please retry with `force_download=True`.
If the issue persists, please let us know by opening an issue on https://github.com/huggingface/huggingface_hub.
```
Try to download through HF datasets directly but got the same error as above.
```python
from datasets import load_dataset
data = load_dataset("mteb/mtop_domain", "en")
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
data = load_dataset("mteb/mtop_domain", "en", force_download=True)
```
With and without `force_download=True` both ran into the same error.
### Expected behavior
Should download the dataset successfully.
### Environment info
- datasets version: 2.21.0
- huggingface-hub version: 0.24.6 | {
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"Yes `interleave_datasets` seems to have an issue with shuffling, could you open a new issue on this ?\r\n\r\nThen regarding the deadlock, it has to do with interleave_dataset with probabilities=[1, 0] with workers that may contain an empty dataset in first position (it can be empty since you distribute 1024 shard to 8 workers, so some workers may not have an example that satisfies your condition `if shard < 25`). It creates an infinite loop, trying to get samples from empty datasets with probability 1.",
"Opened https://github.com/huggingface/datasets/issues/7156\r\n\r\nCan the deadlock be fixed somehow? The point of IterableDataset is so we don't need to preload the entire dataset, which loses some meaning if we need to see how many examples are in the dataset in order to set shards correctly.",
"~~And it is kinda strange that `Commenting out the final shuffle avoids the issue` since if the infinite loop is inside interleave_datasets you'd expect that to happen regardless of the additional shuffle call?~~\r\n\r\nEdit: oh I guess without the shuffle it's guaranteed every worker gets something, but the shuffle makes it so some workers could have nothing\r\n\r\n~~Edit2: maybe the shuffle can be changed so initially it gives one example to each worker, and only starts the random shuffle after that~~ wait it's not about the workers not getting any shards, it's about a worker getting shards but all of the shards it gets are empty shards\r\n\r\nEdit3: If it's trying to get samples from empty datasets, it should be getting back a StopIteration -- and \"all_exhausted\" should mean it eventually discovers all its datasets are empty, and then it should just raise a StopIteration itself. So it seems like there is a reasonable behavior result for this?",
"well the second dataset passed to interleave_datasets is never exhausted, since it's never sampled. But we could also state that the stream of examples from the second dataset is empty if it has probability 0, so I opened https://github.com/huggingface/datasets/pull/7157 to fix the infinite loop issue by ignoring datasets with probability 0, let me know what you think !",
"Thanks for taking a look!\r\n\r\nI think you're right that this is ultimately an issue that the user opts into by specifying a dataset with probability 0, because the user is basically saying \"I want to force this `interleave_datasets` call to run forever\" and yet one of the workers can end up having only empty shards to mix...\r\n\r\nThat said it's probably not a good idea to randomly change the behavior of `interleave_datasets` with probability 0, I can't be the only one that uses it to repeat many different datasets (since there is no `datasets.repeat()` function). https://xkcd.com/1172/\r\n\r\nI think just the knowledge that filtering out probability 0 datasets fixes the deadlock is good enough for me. I can filter it out on my side and add a restart loop around the dataloader instead.\r\n\r\nThanks again for investigating.",
"Ok I see ! We can also add .repeat() as well"
] | 2024-09-12T18:59:33 | 2024-09-23T09:32:27 | 2024-09-21T17:37:34 | NONE | null | ### Describe the bug
```
import datasets
import torch.utils.data
num_shards = 1024
def gen(shards):
for shard in shards:
if shard < 25:
yield {"shard": shard}
def main():
dataset = datasets.IterableDataset.from_generator(
gen,
gen_kwargs={"shards": list(range(num_shards))},
)
dataset = dataset.shuffle(buffer_size=1)
dataset = datasets.interleave_datasets(
[dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted"
)
dataset = dataset.shuffle(buffer_size=1)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=8,
num_workers=8,
)
for i, batch in enumerate(dataloader):
print(batch)
if i >= 10:
break
print()
if __name__ == "__main__":
for _ in range(100):
main()
```
### Steps to reproduce the bug
Running the script above, at some point it will freeze.
- Changing `num_shards` from 1024 to 25 avoids the issue
- Commenting out the final shuffle avoids the issue
- Commenting out the interleave_datasets call avoids the issue
As an aside, if you comment out just the final shuffle, the output from interleave_datasets is not shuffled at all even though there's the shuffle before it. So something about that shuffle config is not being propagated to interleave_datasets.
### Expected behavior
The script should not freeze.
### Environment info
- `datasets` version: 3.0.0
- Platform: macOS-14.6.1-arm64-arm-64bit
- Python version: 3.12.5
- `huggingface_hub` version: 0.24.7
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
I observed this with 2.21.0 initially, then tried upgrading to 3.0.0 and could still repro. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7146). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-11T13:53:27 | 2024-09-12T04:34:08 | 2024-09-12T04:34:06 | MEMBER | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7196 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7196/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7196/comments | https://api.github.com/repos/huggingface/datasets/issues/7196/events | https://github.com/huggingface/datasets/issues/7196 | 2,564,218,566 | I_kwDODunzps6Y1t7G | 7,196 | concatenate_datasets does not preserve shuffling state | {
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} | [] | open | false | null | [] | null | [] | 2024-10-03T14:30:38 | 2024-10-03T15:47:48 | null | NONE | null | ### Describe the bug
After concatenate datasets on an iterable dataset, the shuffling state is destroyed, similar to #7156
This means concatenation cant be used for resolving uneven numbers of samples across devices when using iterable datasets in a distributed setting as discussed in #6623
I also noticed that the number of shards is the same after concatenation, which I found surprising, but I don't understand the internals well enough to know whether this is actually surprising or not
### Steps to reproduce the bug
```python
import datasets
import torch.utils.data
def gen(shards):
yield {"shards": shards}
def main():
dataset1 = datasets.IterableDataset.from_generator(
gen, gen_kwargs={"shards": list(range(25))} # TODO: how to understand this?
)
dataset2 = datasets.IterableDataset.from_generator(
gen, gen_kwargs={"shards": list(range(25, 50))} # TODO: how to understand this?
)
dataset1 = dataset1.shuffle(buffer_size=1)
dataset2 = dataset2.shuffle(buffer_size=1)
print(dataset1.n_shards)
print(dataset2.n_shards)
dataset = datasets.concatenate_datasets(
[dataset1, dataset2]
)
print(dataset.n_shards)
# dataset = dataset1
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=8,
num_workers=0,
)
for i, batch in enumerate(dataloader):
print(batch)
print("\nNew epoch")
dataset = dataset.set_epoch(1)
for i, batch in enumerate(dataloader):
print(batch)
if __name__ == "__main__":
main()
```
### Expected behavior
Shuffling state should be preserved
### Environment info
Latest datasets | null | {
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"maybe related: https://github.com/huggingface/datasets/issues/6388",
"Also look at https://github.com/huggingface/dataset-viewer/blob/f5fd117ceded990a7766e705bba1203fa907d6ad/services/worker/src/worker/job_runners/dataset/modalities.py#L241 which lists the 3D file formats that will assign the 3D modality to a dataset."
] | 2024-10-03T13:27:44 | 2024-10-03T13:32:20 | null | COLLABORATOR | null | See https://huggingface.co/datasets/allenai/objaverse for example | null | {
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"Actually there is no such dataset available, that is why you are getting that error.",
"Fixed with @kdutia in Slack chat. Generating a new token fixed this issue. "
] | 2024-10-03T07:49:36 | 2024-10-03T10:09:28 | 2024-10-03T10:09:28 | NONE | null | ### Describe the bug
The following Python code tries to download a private dataset and fails with the error `datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed.`. Downloading a public dataset doesn't work.
``` py
from datasets import load_dataset
_ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly")
```
This seems to be just an issue with my machine config as the code above works with a colleague's machine. So far I have tried:
- logging back out and in from the Huggingface CLI using `huggingface-cli logout`
- manually removing the token cache at `/Users/kalyan/.cache/huggingface/token` (found using `huggingface-cli env`)
- manually passing a token in `load_dataset`
My output of `huggingface-cli whoami`:
```
kdutia
orgs: ClimatePolicyRadar
```
### Steps to reproduce the bug
```
python
Python 3.12.2 (main, Feb 6 2024, 20:19:44) [Clang 15.0.0 (clang-1500.1.0.2.5)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> _ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 2074, in load_dataset
builder_instance = load_dataset_builder(
^^^^^^^^^^^^^^^^^^^^^
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1795, in load_dataset_builder
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1659, in dataset_module_factory
raise e1 from None
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1597, in dataset_module_factory
raise DatasetNotFoundError(f"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.") from e
datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed.
>>>
```
### Expected behavior
The dataset downloads successfully.
### Environment info
From `huggingface-cli env`:
```
- huggingface_hub version: 0.25.1
- Platform: macOS-14.2.1-arm64-arm-64bit
- Python version: 3.12.2
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Running in Google Colab Enterprise ?: No
- Token path ?: /Users/kalyan/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: kdutia
- Configured git credential helpers: osxkeychain
- FastAI: N/A
- Tensorflow: N/A
- Torch: N/A
- Jinja2: 3.1.4
- Graphviz: N/A
- keras: N/A
- Pydot: N/A
- Pillow: N/A
- hf_transfer: N/A
- gradio: N/A
- tensorboard: N/A
- numpy: 2.1.1
- pydantic: N/A
- aiohttp: 3.10.8
- ENDPOINT: https://huggingface.co
- HF_HUB_CACHE: /Users/kalyan/.cache/huggingface/hub
- HF_ASSETS_CACHE: /Users/kalyan/.cache/huggingface/assets
- HF_TOKEN_PATH: /Users/kalyan/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
- HF_HUB_ETAG_TIMEOUT: 10
- HF_HUB_DOWNLOAD_TIMEOUT: 10
```
from `datasets-cli env`:
```
- `datasets` version: 3.0.1
- Platform: macOS-14.2.1-arm64-arm-64bit
- Python version: 3.12.2
- `huggingface_hub` version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
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https://api.github.com/repos/huggingface/datasets/issues/7193 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7193/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7193/comments | https://api.github.com/repos/huggingface/datasets/issues/7193/events | https://github.com/huggingface/datasets/issues/7193 | 2,562,392,887 | I_kwDODunzps6YuwM3 | 7,193 | Support of num_workers (multiprocessing) in map for IterableDataset | {
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"I was curious about the same - since map is applied on the fly I was assuming that setting num_workers>1 in DataLoader would effectively do the map in parallel, have you tried that?"
] | 2024-10-02T18:34:04 | 2024-10-03T09:54:15 | null | NONE | null | ### Feature request
Currently, IterableDataset doesn't support setting num_worker in .map(), which results in slow processing here. Could we add support for it? As .map() can be run in the batch fashion (e.g., batch_size is default to 1000 in datasets), it seems to be doable for IterableDataset as the regular Dataset.
### Motivation
Improving data processing efficiency
### Your contribution
Testing | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7192 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7192/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7192/comments | https://api.github.com/repos/huggingface/datasets/issues/7192/events | https://github.com/huggingface/datasets/issues/7192 | 2,562,289,642 | I_kwDODunzps6YuW_q | 7,192 | Add repeat() for iterable datasets | {
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"perhaps concatenate_datasets can already be used to achieve almost the same effect? ",
"`concatenate_datasets` does the job when there is a finite number of repetitions, but in case of `.repeat()` forever we need a new logic in `iterable_dataset.py`"
] | 2024-10-02T17:48:13 | 2024-10-03T12:53:34 | null | NONE | null | ### Feature request
It would be useful to be able to straightforwardly repeat iterable datasets indefinitely, to provide complete control over starting and ending of iteration to the user.
An IterableDataset.repeat(n) function could do this automatically
### Motivation
This feature was discussed in this issue https://github.com/huggingface/datasets/issues/7147, and would resolve the need to use the hack of interleave datasets with probability 0 as a simple way to achieve this functionality.
An additional benefit might be the simplification of the use of iterable datasets in a distributed setting:
If the user can assume that datasets will repeat indefinitely, then issues around different numbers of samples appearing on different devices (e.g. https://github.com/huggingface/datasets/issues/6437, https://github.com/huggingface/datasets/issues/6594, https://github.com/huggingface/datasets/issues/6623, https://github.com/huggingface/datasets/issues/6719) can potentially be straightforwardly resolved by simply doing:
ids.repeat(None).take(n_samples_per_epoch)
### Your contribution
I'm not familiar enough with the codebase to assess how straightforward this would be to implement.
If it might be very straightforward, I could possibly have a go. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7191 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7191/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7191/comments | https://api.github.com/repos/huggingface/datasets/issues/7191/events | https://github.com/huggingface/datasets/pull/7191 | 2,562,206,949 | PR_kwDODunzps59Z1zy | 7,191 | Solution to issue: #7080 Modified load_dataset function, so that it prompts the user to select a dataset when subdatasets or splits (train, test) are available | {
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### This PR is raised because of issue: https://github.com/huggingface/datasets/issues/7080
### This PR gives solution to https://github.com/huggingface/datasets/issues/7080
1. Checking whether the dataset has splits or subdatasets.
2. Printing the available splits/subdatasets.
3. Asking the user to choose which one to load.
4. Loading only the selected dataset based on the user's input.
### Key Changes:
1. Available Splits/Subdatasets: The code checks for available splits/subdatasets using builder_instance.info.splits.keys().
2. User Prompt: If splits are found, it prints them out and prompts the user to select one.
3. Loading Based on User Input: The dataset is loaded based on the user's choice.
This way, the dataset loading function will interactively prompt the user to select which subdataset or split they want to load instead of automatically loading all of them.
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/7190 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7190/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7190/comments | https://api.github.com/repos/huggingface/datasets/issues/7190/events | https://github.com/huggingface/datasets/issues/7190 | 2,562,162,725 | I_kwDODunzps6Yt4Al | 7,190 | Datasets conflicts with fsspec 2024.9 | {
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} | [] | open | false | null | [] | null | [] | 2024-10-02T16:43:46 | 2024-10-02T16:43:46 | null | NONE | null | ### Describe the bug
Installing both in latest versions are not possible
`pip install "datasets==3.0.1" "fsspec==2024.9.0"`
But using older version of datasets is ok
`pip install "datasets==1.24.4" "fsspec==2024.9.0"`
### Steps to reproduce the bug
`pip install "datasets==3.0.1" "fsspec==2024.9.0"`
### Expected behavior
install both versions.
### Environment info
debian 11.
python 3.10.15 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7189 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7189/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7189/comments | https://api.github.com/repos/huggingface/datasets/issues/7189/events | https://github.com/huggingface/datasets/issues/7189 | 2,562,152,845 | I_kwDODunzps6Yt1mN | 7,189 | Audio preview in dataset viewer for audio array data without a path/filename | {
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] | open | false | null | [] | null | [] | 2024-10-02T16:38:38 | 2024-10-02T17:01:40 | null | NONE | null | ### Feature request
Huggingface has quite a comprehensive set of guides for [audio datasets](https://huggingface.co/docs/datasets/en/audio_dataset). It seems, however, all these guides assume the audio array data to be decoded/inserted into a HF dataset always originates from individual files. The [Audio-dataclass](https://github.com/huggingface/datasets/blob/3.0.1/src/datasets/features/audio.py#L20) appears designed with this assumption in mind. Looking at its source code it returns a dictionary with the keys `path`, `array` and `sampling_rate`.
However, sometimes users may have different pipelines where they themselves decode the audio array. This feature request has to do with wishing some clarification in guides on whether it is possible, and in such case how users can insert already decoded audio array data into datasets (pandas DataFrame, HF dataset or whatever) that are later saved as parquet, and still get a functioning audio preview in the dataset viewer.
Do I perhaps need to write a tempfile of my audio array slice to wav and capture the bytes object with `io.BytesIO` and pass that to `Audio()`?
### Motivation
I'm working with large audio datasets, and my pipeline reads (decodes) audio from larger files, and slices the relevant portions of audio from that larger file based on metadata I have available.
The pipeline is designed this way to avoid having to store multiple copies of data, and to avoid having to store tens of millions of small files.
I tried [test-uploading parquet files](https://huggingface.co/datasets/Lauler/riksdagen_test) where I store the audio array data of decoded slices of audio in an `audio` column with a dictionary with the keys `path`, `array` and `sampling_rate`. But I don't know the secret sauce of what the Huggingface Hub expects and requires to be able to display audio previews correctly.
### Your contribution
I could contribute a tool agnostic guide of creating HF audio datasets directly as parquet to the HF documentation if there is an interest. Provided you help me figure out the secret sauce of what the dataset viewer expects to display the preview correctly. | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7188). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-02T05:40:18 | 2024-10-02T06:08:25 | 2024-10-02T06:08:23 | MEMBER | null | Pin multiprocess<0.70.1 to align with dill<0.3.9.
Note that multiprocess-0.70.1 requires dill-0.3.9: https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17
Fix #7186. | {
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https://api.github.com/repos/huggingface/datasets/issues/7187 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7187/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7187/comments | https://api.github.com/repos/huggingface/datasets/issues/7187/events | https://github.com/huggingface/datasets/issues/7187 | 2,560,501,308 | I_kwDODunzps6YniY8 | 7,187 | shard_data_sources() got an unexpected keyword argument 'worker_id' | {
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} | [] | open | false | null | [] | null | [] | 2024-10-02T01:26:35 | 2024-10-02T01:26:35 | null | NONE | null | ### Describe the bug
```
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 238, in __iter__
[rank0]: for key_example in islice(self.generate_examples_fn(**gen_kwags), shard_example_idx_start, None):
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/packaged_modules/generator/generator.py", line 32, in _generate_examples
[rank0]: for idx, ex in enumerate(self.config.generator(**gen_kwargs)):
[rank0]: File "/home/qinghao/workdir/doremi/doremi/dataloader.py", line 337, in take_data_generator
[rank0]: for ex in ds:
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1791, in __iter__
[rank0]: yield from self._iter_pytorch()
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1704, in _iter_pytorch
[rank0]: ex_iterable = ex_iterable.shard_data_sources(worker_id=worker_info.id, num_workers=worker_info.num_workers)
[rank0]: TypeError: UpdatableRandomlyCyclingMultiSourcesExamplesIterable.shard_data_sources() got an unexpected keyword argument 'worker_id'
```
### Steps to reproduce the bug
IterableDataset cannot use
### Expected behavior
can work on datasets==2.10, but will raise error for later versions.
### Environment info
datasets==3.0.1 | null | {
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] | null | [] | 2024-10-01T22:29:32 | 2024-10-02T06:08:24 | 2024-10-02T06:08:24 | NONE | null | ### Describe the bug
The [latest `multiprocess` release](https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17) requires `dill>=0.3.9` which causes issues when installing `datasets` without backtracking during package version resolution. Is it possible to add a pin for multiprocess so something like `multiprocess<=0.70.16` so that the `dill` version is compatible?
### Steps to reproduce the bug
NA
### Expected behavior
NA
### Environment info
NA | {
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] | open | false | null | [] | null | [] | 2024-10-01T08:16:08 | 2024-10-01T08:16:08 | null | MEMBER | null | Since Aug 30, 2024, CI benchmarks are broken: https://github.com/huggingface/datasets/actions/runs/11108421214/job/30861323975
```
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...
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7184). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-30T14:26:25 | 2024-09-30T14:38:59 | 2024-09-30T14:38:57 | MEMBER | null | Pin dill<0.3.9 to fix CI for deps-latest.
Note that dill-0.3.9 was released yesterday Sep 29, 2024:
- https://pypi.org/project/dill/0.3.9/
- https://github.com/uqfoundation/dill/releases/tag/0.3.9
Fix #7183. | {
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https://api.github.com/repos/huggingface/datasets/issues/7183 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7183/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7183/comments | https://api.github.com/repos/huggingface/datasets/issues/7183/events | https://github.com/huggingface/datasets/issues/7183 | 2,556,789,055 | I_kwDODunzps6YZYE_ | 7,183 | CI is broken for deps-latest | {
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```
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_caching_on_disk - AssertionError: Lists differ: [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}] != [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}]
First differing element 1:
{'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}
{'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}
[{'filename': '/tmp/tmp6xcyyjs4/dataset0.arrow'},
- {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}]
? ^^^^^ --------
+ {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}]
? ++++++++++ ^^ +
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - AssertionError: Lists differ: [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}] != [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}]
First differing element 0:
{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}
{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}
- [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}]
? ^^ -----------
+ [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}]
? +++++++++++ ^^
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_regex - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ignores_line_definition_of_function - AssertionError: '52e56ee04ad92499' != '0a4f75cec280f634'
- 52e56ee04ad92499
+ 0a4f75cec280f634
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ipython_function - AssertionError: 'a6bd2041ca63d6c0' != '517bf36b7eecdef5'
- a6bd2041ca63d6c0
+ 517bf36b7eecdef5
FAILED tests/test_fingerprint.py::HashingTest::test_hash_tiktoken_encoding - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_compiled_module - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_generator - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_tensor - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_doesnt_depend_on_order - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_stable - NameError: name 'log' is not defined
ERROR tests/test_iterable_dataset.py::test_iterable_dataset_from_file - NameError: name 'log' is not defined
= 11 failed, 2850 passed, 3 skipped, 23 warnings, 1 error in 191.06s (0:03:11) =
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https://api.github.com/repos/huggingface/datasets/issues/7182 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7182/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7182/comments | https://api.github.com/repos/huggingface/datasets/issues/7182/events | https://github.com/huggingface/datasets/pull/7182 | 2,556,333,671 | PR_kwDODunzps59GdLS | 7,182 | Support features in metadata configs | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7182). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"The CI issue is unrelated:\r\n- #7183"
] | 2024-09-30T11:14:53 | 2024-10-01T13:34:40 | null | MEMBER | null | Support features in metadata configs, like:
```
configs:
- config_name: default
features:
- name: id
dtype: int64
- name: name
dtype: string
- name: score
dtype: float64
```
This will allow to avoid inference of data types.
Currently, we allow passing this information in the `dataset_info` (instead of `configs`) field, but this is not intuitive and it is not properly documented.
TODO:
- [ ] Document usage | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7181 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7181/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7181/comments | https://api.github.com/repos/huggingface/datasets/issues/7181/events | https://github.com/huggingface/datasets/pull/7181 | 2,554,917,019 | PR_kwDODunzps59Br4E | 7,181 | [WIP] Fix datasets export to JSON | {
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"Linked Issue: #7037\r\nIdeas: #7039 ",
"@albertvillanova / @lhoestq any early feedback?\r\n\r\nAFAIK there is no param `orient` in `load_dataset()`. So for orientations other than \"records\", the loading isn't very accurate. Any thoughts?"
] | 2024-09-29T12:45:20 | 2024-09-30T06:36:47 | null | CONTRIBUTOR | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7180 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7180/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7180/comments | https://api.github.com/repos/huggingface/datasets/issues/7180/events | https://github.com/huggingface/datasets/issues/7180 | 2,554,244,750 | I_kwDODunzps6YPq6O | 7,180 | Memory leak when wrapping datasets into PyTorch Dataset without explicit deletion | {
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"> I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.\r\n\r\nDatasets are memory mapped so they work like SWAP memory. In particular as long as you have RAM available the data will stay in RAM, and get paged out once your system needs RAM for something else (no OOM).\r\n\r\nrelated: https://github.com/huggingface/datasets/issues/4883"
] | 2024-09-28T14:00:47 | 2024-09-30T12:07:56 | 2024-09-30T12:07:56 | NONE | null | ### Describe the bug
I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.
### Steps to reproduce the bug
Steps to reproduce:
Create a PyTorch Dataset wrapper for 'nebula/cc12m':
````
from torch.utils.data import Dataset
from tqdm import tqdm
from datasets import load_dataset
from torchvision import transforms
Image.MAX_IMAGE_PIXELS = None
class CC12M(Dataset):
def __init__(self, path_or_name='nebula/cc12m', split='train', transform=None, single_caption=True):
self.raw_dataset = load_dataset(path_or_name)[split]
if transform is None:
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711]
)
])
else:
self.transform = transforms.Compose(transform)
self.single_caption = single_caption
self.length = len(self.raw_dataset)
def __len__(self):
return self.length
def __getitem__(self, index):
item = self.raw_dataset[index]
caption = item['txt']
with io.BytesIO(item['webp']) as buffer:
image = Image.open(buffer).convert('RGB')
if self.transform:
image = self.transform(image)
# del item # Uncomment this line to prevent the memory leak
return image, caption
````
Iterate through the dataset without the del item line in __getitem__.
Observe RAM usage increasing constantly.
Add del item at the end of __getitem__:
```
def __getitem__(self, index):
item = self.raw_dataset[index]
caption = item['txt']
with io.BytesIO(item['webp']) as buffer:
image = Image.open(buffer).convert('RGB')
if self.transform:
image = self.transform(image)
del item # This line prevents the memory leak
return image, caption
```
Iterate through the dataset again and observe that RAM usage remains stable.
### Expected behavior
Expected behavior:
RAM usage should remain stable during iteration without needing to explicitly delete items.
Actual behavior:
RAM usage constantly increases unless items are explicitly deleted after use
### Environment info
- `datasets` version: 2.21.0
- Platform: Linux-4.18.0-513.5.1.el8_9.x86_64-x86_64-with-glibc2.28
- Python version: 3.12.4
- `huggingface_hub` version: 0.24.6
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7179). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-27T08:55:44 | 2024-09-27T08:58:00 | null | MEMBER | null | Support Python 3.11.
Fix #7178. | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7177). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-27T08:47:01 | 2024-09-27T08:57:35 | 2024-09-27T08:57:32 | MEMBER | null | Fix release instructions.
During last release, I had to make this additional update. | {
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https://api.github.com/repos/huggingface/datasets/issues/7175 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7175/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7175/comments | https://api.github.com/repos/huggingface/datasets/issues/7175/events | https://github.com/huggingface/datasets/issues/7175 | 2,550,957,337 | I_kwDODunzps6YDIUZ | 7,175 | [FSTimeoutError] load_dataset | {
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"Is this `FSTimeoutError` due to download network issue from remote resource (from where it is being accessed)?",
"It seems to happen for all datasets, not just a specific one, and especially for versions after 3.0. (3.0.0, 3.0.1 have this problem)\r\n\r\nI had the same error on a different dataset, but after downgrading to datasets==2.21.0, the problem was solved.",
"Same as https://github.com/huggingface/datasets/issues/7164\r\n\r\nThis dataset is made of a python script that downloads data from elsewhere than HF, so availability depends on the original host. Ultimately it would be nice to host the files of this dataset on HF\r\n\r\nin `datasets` <3.0 there were lots of mechanisms that got removed after the decision to make datasets with python loading scripts legacy for security and maintenance reasons (we only do very basic support now)",
"@lhoestq Thank you for the clarification! Closing the issue."
] | 2024-09-26T15:42:29 | 2024-09-30T17:28:35 | 2024-09-30T17:28:35 | NONE | null | ### Describe the bug
When using `load_dataset`to load [HuggingFaceM4/VQAv2](https://huggingface.co/datasets/HuggingFaceM4/VQAv2), I am getting `FSTimeoutError`.
### Error
```
TimeoutError:
The above exception was the direct cause of the following exception:
FSTimeoutError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/fsspec/asyn.py](https://klh9mr78js-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240924-060116_RC00_678132060#) in sync(loop, func, timeout, *args, **kwargs)
99 if isinstance(return_result, asyncio.TimeoutError):
100 # suppress asyncio.TimeoutError, raise FSTimeoutError
--> 101 raise FSTimeoutError from return_result
102 elif isinstance(return_result, BaseException):
103 raise return_result
FSTimeoutError:
```
It usually fails around 5-6 GB.
<img width="847" alt="Screenshot 2024-09-26 at 9 10 19 PM" src="https://github.com/user-attachments/assets/ff91995a-fb55-4de6-8214-94025d6c8470">
### Steps to reproduce the bug
To reproduce it, run this in colab notebook:
```
!pip install -q -U datasets
from datasets import load_dataset
ds = load_dataset('HuggingFaceM4/VQAv2', split="train[:10%]")
```
### Expected behavior
It should download properly.
### Environment info
Using Colab Notebook. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7174). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7173). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7172). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-26T07:45:55 | 2024-09-26T08:12:12 | 2024-09-26T08:05:40 | MEMBER | null | Add `torchdata` as a regular test dependency.
Note that previously, `torchdata` was installed from their repo and current main branch (0.10.0.dev) requires Python>=3.9.
Also note they made a recent release: 0.8.0 on Jul 31, 2024.
Fix #7171. | {
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```
Run uv pip install --system -r additional-tests-requirements.txt --no-deps
× No solution found when resolving dependencies:
╰─▶ Because the current Python version (3.8.18) does not satisfy Python>=3.9
and torchdata==0.10.0a0+1a98f21 depends on Python>=3.9, we can conclude
that torchdata==0.10.0a0+1a98f21 cannot be used.
And because only torchdata==0.10.0a0+1a98f21 is available and
you require torchdata, we can conclude that your requirements are
unsatisfiable.
Error: Process completed with exit code 1.
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7170). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-25T05:08:15 | 2024-09-26T06:42:09 | 2024-09-26T06:42:07 | MEMBER | null | Support JSON lines with missing columns.
Fix #7169.
The implemented test raised:
```
datasets.table.CastError: Couldn't cast
age: int64
to
{'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)}
because column names don't match
```
Related to:
- #7160
- #7162 | {
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] | null | [] | 2024-09-25T04:43:28 | 2024-09-26T06:42:08 | 2024-09-26T06:42:08 | MEMBER | null | JSON lines with missing columns raise CastError:
> CastError: Couldn't cast ... to ... because column names don't match
Related to:
- #7159
- #7161 | {
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https://api.github.com/repos/huggingface/datasets/issues/7168 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7168/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7168/comments | https://api.github.com/repos/huggingface/datasets/issues/7168/events | https://github.com/huggingface/datasets/issues/7168 | 2,546,710,631 | I_kwDODunzps6Xy7hn | 7,168 | sd1.5 diffusers controlnet training script gives new error | {
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"not sure why the issue is formatting oddly",
"I guess this is a dupe of\r\n\r\nhttps://github.com/huggingface/datasets/issues/7071",
"this turned out to be because of a bad image in dataset"
] | 2024-09-25T01:42:49 | 2024-09-30T05:24:03 | 2024-09-30T05:24:02 | NONE | null | ### Describe the bug
This will randomly pop up during training now
```
Traceback (most recent call last):
File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1192, in <module>
main(args)
File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1041, in main
for step, batch in enumerate(train_dataloader):
File "/usr/local/lib/python3.11/dist-packages/accelerate/data_loader.py", line 561, in __iter__
next_batch = next(dataloader_iter)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 630, in __next__
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 673, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/_utils/fetch.py", line 50, in fetch
data = self.dataset.__getitems__(possibly_batched_index)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2746, in __getitems__
batch = self.__getitem__(keys)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2742, in __getitem__
return self._getitem(key)
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2727, in _getitem
formatted_output = format_table(
^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 639, in format_table
return formatter(pa_table, query_type=query_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 407, in __call__
return self.format_batch(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 521, in format_batch
batch = self.python_features_decoder.decode_batch(batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 228, in decode_batch
return self.features.decode_batch(batch) if self.features else batch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2084, in decode_batch
[
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2085, in <listcomp>
decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 1403, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/features/image.py", line 188, in decode_example
image.load() # to avoid "Too many open files" errors
```
### Steps to reproduce the bug
Train on diffusers sd1.5 controlnet example script
This will pop up randomly, you can see in wandb below when i manually resume run everytime this error appears
### Expected behavior
Training to continue without above error
### Environment info
- datasets version: 3.0.0
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- huggingface_hub version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- fsspec version: 2024.6.1
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https://api.github.com/repos/huggingface/datasets/issues/7167 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7167/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7167/comments | https://api.github.com/repos/huggingface/datasets/issues/7167/events | https://github.com/huggingface/datasets/issues/7167 | 2,546,708,014 | I_kwDODunzps6Xy64u | 7,167 | Error Mapping on sd3, sdxl and upcoming flux controlnet training scripts in diffusers | {
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"this is happening on large datasets, if anyone happens upon this i was able to fix by changing\r\n\r\n```\r\ntrain_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)\r\n```\r\n\r\nto\r\n\r\n```\r\ntrain_dataset = train_dataset.map(compute_embeddings_fn, batched=True, batch_size=16, new_fingerprint=new_fingerprint)\r\n```"
] | 2024-09-25T01:39:51 | 2024-09-30T05:28:15 | 2024-09-30T05:28:04 | NONE | null | ### Describe the bug
```
Map: 6%|██████ | 8000/138120 [19:27<5:16:36, 6.85 examples/s]
Traceback (most recent call last):
File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1416, in <module>
main(args)
File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1132, in main
train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 560, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3035, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3461, in _map_single
writer.write_batch(batch)
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 567, in write_batch
self.write_table(pa_table, writer_batch_size)
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 579, in write_table
pa_table = pa_table.combine_chunks()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 4387, in pyarrow.lib.Table.combine_chunks
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
Traceback (most recent call last):
File "/usr/local/bin/accelerate", line 8, in <module>
sys.exit(main())
^^^^^^
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/accelerate_cli.py", line 48, in main
args.func(args)
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 1174, in launch_command
simple_launcher(args)
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 769, in simple_launcher
```
### Steps to reproduce the bug
The dataset has no problem training on sd1.5 controlnet train script
### Expected behavior
Script not randomly erroing with error above
### Environment info
- `datasets` version: 3.0.0
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
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https://api.github.com/repos/huggingface/datasets/issues/7166 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7166/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7166/comments | https://api.github.com/repos/huggingface/datasets/issues/7166/events | https://github.com/huggingface/datasets/pull/7166 | 2,545,608,736 | PR_kwDODunzps58h8pd | 7,166 | fix docstring code example for distributed shuffle | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7166). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-24T14:39:54 | 2024-09-24T14:42:41 | 2024-09-24T14:40:14 | MEMBER | null | close https://github.com/huggingface/datasets/issues/7163 | {
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https://api.github.com/repos/huggingface/datasets/issues/7165 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7165/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7165/comments | https://api.github.com/repos/huggingface/datasets/issues/7165/events | https://github.com/huggingface/datasets/pull/7165 | 2,544,972,541 | PR_kwDODunzps58fva1 | 7,165 | fix increase_load_count | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7165). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I tested a few load_dataset and they do show up in download stats now",
"Thanks for having noticed and fixed."
] | 2024-09-24T10:14:40 | 2024-09-24T17:31:07 | 2024-09-24T13:48:00 | MEMBER | null | it was failing since 3.0 and therefore not updating download counts on HF or in our dashboard | {
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https://api.github.com/repos/huggingface/datasets/issues/7164 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7164/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7164/comments | https://api.github.com/repos/huggingface/datasets/issues/7164/events | https://github.com/huggingface/datasets/issues/7164 | 2,544,757,297 | I_kwDODunzps6Xreox | 7,164 | fsspec.exceptions.FSTimeoutError when downloading dataset | {
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"Hi ! If you check the dataset loading script [here](https://huggingface.co/datasets/openslr/librispeech_asr/blob/main/librispeech_asr.py) you'll see that it downloads the data from OpenSLR, and apparently their storage has timeout issues. It would be great to ultimately host the dataset on Hugging Face instead.\r\n\r\nIn the meantime I can only recommend to try again later :/",
"Ok, still many thanks!"
] | 2024-09-24T08:45:05 | 2024-09-24T15:35:57 | null | NONE | null | ### Describe the bug
I am trying to download the `librispeech_asr` `clean` dataset, which results in a `FSTimeoutError` exception after downloading around 61% of the data.
### Steps to reproduce the bug
```
import datasets
datasets.load_dataset("librispeech_asr", "clean")
```
The output is as follows:
> Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:06, 13.2MB/s]Traceback (most recent call last):
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 56, in _runner
> result[0] = await coro
> ^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/implementations/http.py", line 262, in _get_file
> chunk = await r.content.read(chunk_size)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 393, in read
> await self._wait("read")
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 311, in _wait
> with self._timer:
> ^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/helpers.py", line 713, in __exit__
> raise asyncio.TimeoutError from None
> TimeoutError
>
> The above exception was the direct cause of the following exception:
>
> Traceback (most recent call last):
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/load_dataset.py", line 3, in <module>
> datasets.load_dataset("librispeech_asr", "clean")
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/load.py", line 2096, in load_dataset
> builder_instance.download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 924, in download_and_prepare
> self._download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1647, in _download_and_prepare
> super()._download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 977, in _download_and_prepare
> split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/2712a8f82f0d20807a56faadcd08734f9bdd24c850bb118ba21ff33ebff0432f/librispeech_asr.py", line 115, in _split_generators
> archive_path = dl_manager.download(_DL_URLS[self.config.name])
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 159, in download
> downloaded_path_or_paths = map_nested(
> ^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 512, in map_nested
> _single_map_nested((function, obj, batched, batch_size, types, None, True, None))
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 380, in _single_map_nested
> return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
> ^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 216, in _download_batched
> self._download_single(url_or_filename, download_config=download_config)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 225, in _download_single
> out = cached_path(url_or_filename, download_config=download_config)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 205, in cached_path
> output_path = get_from_cache(
> ^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 415, in get_from_cache
> fsspec_get(url, temp_file, storage_options=storage_options, desc=download_desc, disable_tqdm=disable_tqdm)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 334, in fsspec_get
> fs.get_file(path, temp_file.name, callback=callback)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 118, in wrapper
> return sync(self.loop, func, *args, **kwargs)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 101, in sync
> raise FSTimeoutError from return_result
> fsspec.exceptions.FSTimeoutError
> Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:09, 13.0MB/s]
### Expected behavior
Complete the download
### Environment info
Python version 3.12.6
Dependencies:
> dependencies = [
> "accelerate>=0.34.2",
> "datasets[audio]>=3.0.0",
> "ipython>=8.18.1",
> "librosa>=0.10.2.post1",
> "torch>=2.4.1",
> "torchaudio>=2.4.1",
> "transformers>=4.44.2",
> ]
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"thanks for reporting !"
] | 2024-09-23T11:34:06 | 2024-09-24T14:40:15 | 2024-09-24T14:40:15 | NONE | null | ### Describe the bug
In the examples section of the iterable dataset docs https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.IterableDataset
the ddp example shuffles without seeding
```python
from datasets.distributed import split_dataset_by_node
ids = ds.to_iterable_dataset(num_shards=512)
ids = ids.shuffle(buffer_size=10_000) # will shuffle the shards order and use a shuffle buffer when you start iterating
ids = split_dataset_by_node(ds, world_size=8, rank=0) # will keep only 512 / 8 = 64 shards from the shuffled lists of shards when you start iterating
dataloader = torch.utils.data.DataLoader(ids, num_workers=4) # will assign 64 / 4 = 16 shards from this node's list of shards to each worker when you start iterating
for example in ids:
pass
```
This code would - I think - raise an error due to the lack of an explicit seed:
https://github.com/huggingface/datasets/blob/2eb4edb97e1a6af2ea62738ec58afbd3812fc66e/src/datasets/iterable_dataset.py#L1707-L1711
### Steps to reproduce the bug
Run example code
### Expected behavior
Add explicit seeding to example code
### Environment info
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7162). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-23T11:16:12 | 2024-09-23T11:30:08 | 2024-09-23T11:30:06 | MEMBER | null | Support JSON lines with empty struct.
Fix #7161.
Related to:
- #7160 | {
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See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5
> ArrowTypeError: struct fields don't match or are in the wrong order: Input fields: struct<> output fields: struct<pov_count: int64, update_count: int64, citation_needed_count: int64>
Related to:
- #7159 | {
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] | 2024-09-23T08:04:09 | 2024-09-23T11:09:19 | 2024-09-23T11:09:17 | MEMBER | null | Support JSON lines with missing struct fields.
Fix #7159.
The implemented test raised:
```
TypeError: Couldn't cast array of type
struct<age: int64>
to
{'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)}
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] | null | [] | 2024-09-23T07:57:58 | 2024-09-25T04:43:37 | 2024-09-23T11:09:18 | MEMBER | null | JSON lines with missing struct fields raise TypeError: Couldn't cast array of type.
See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5
One would expect that the struct missing fields are added with null values. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7157). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-21T15:19:14 | 2024-09-24T14:33:54 | 2024-09-24T14:33:54 | MEMBER | null | fix https://github.com/huggingface/datasets/issues/7147 | {
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} | [] | open | false | null | [] | null | [] | 2024-09-20T17:57:54 | 2024-09-20T17:57:54 | null | NONE | null | ### Describe the bug
```
import datasets
import torch.utils.data
def gen(shards):
yield {"shards": shards}
def main():
dataset = datasets.IterableDataset.from_generator(
gen,
gen_kwargs={'shards': list(range(25))}
)
dataset = dataset.shuffle(buffer_size=1)
dataset = datasets.interleave_datasets(
[dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted"
)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=8,
num_workers=8,
)
for i, batch in enumerate(dataloader):
print(batch)
if i >= 10:
break
if __name__ == "__main__":
main()
```
### Steps to reproduce the bug
Run the script, it will output
```
{'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]}
{'shards': [tensor([ 1, 9, 17, 1, 9, 17, 1, 9])]}
{'shards': [tensor([ 2, 10, 18, 2, 10, 18, 2, 10])]}
{'shards': [tensor([ 3, 11, 19, 3, 11, 19, 3, 11])]}
{'shards': [tensor([ 4, 12, 20, 4, 12, 20, 4, 12])]}
{'shards': [tensor([ 5, 13, 21, 5, 13, 21, 5, 13])]}
{'shards': [tensor([ 6, 14, 22, 6, 14, 22, 6, 14])]}
{'shards': [tensor([ 7, 15, 23, 7, 15, 23, 7, 15])]}
{'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]}
{'shards': [tensor([17, 1, 9, 17, 1, 9, 17, 1])]}
{'shards': [tensor([18, 2, 10, 18, 2, 10, 18, 2])]}
```
### Expected behavior
The shards should be shuffled.
### Environment info
- `datasets` version: 3.0.0
- Platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.25.0
- PyArrow version: 17.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.6.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7155 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7155/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7155/comments | https://api.github.com/repos/huggingface/datasets/issues/7155/events | https://github.com/huggingface/datasets/issues/7155 | 2,533,641,870 | I_kwDODunzps6XBE6O | 7,155 | Dataset viewer not working! Failure due to more than 32 splits. | {
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"I have fixed it! But I would appreciate a new feature wheere I could iterate over and see what each file looks like. "
] | 2024-09-18T12:43:21 | 2024-09-18T13:20:03 | 2024-09-18T13:20:03 | NONE | null | Hello guys,
I have a dataset and I didn't know I couldn't upload more than 32 splits. Now, my dataset viewer is not working. I don't have the dataset locally on my node anymore and recreating would take a week. And I have to publish the dataset coming Monday. I read about the practice, how I can resolve it and avoid this issue in the future. But, at the moment I need a hard fix for two of my datasets.
And I don't want to mess or change anything and allow everyone in public to see the dataset and interact with it. Can you please help me?
https://huggingface.co/datasets/laion/Wikipedia-X
https://huggingface.co/datasets/laion/Wikipedia-X-Full | {
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https://api.github.com/repos/huggingface/datasets/issues/7154 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7154/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7154/comments | https://api.github.com/repos/huggingface/datasets/issues/7154/events | https://github.com/huggingface/datasets/pull/7154 | 2,532,812,323 | PR_kwDODunzps572hBd | 7,154 | Support ndjson data files | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7154). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Thanks for your review, @severo.\r\n\r\nYes, I was aware of this. From internal conversation:\r\n> Please note that although NDJSON was planned to be submitted as an RFC standard spec, it is no longer maintained:\r\n> - See note from the author: https://github.com/ndjson/ndjson-spec/issues/35#issuecomment-1285673417\r\n> - See that their official website domain has expired: https://ndjson.org/ \r\n\r\nThe purpose of this PR is just supporting datasets with ndjson data files (e.g. Wikimedia Enterprise data files), but it should not imply any recommendation or endorsement of this format from our part."
] | 2024-09-18T06:10:10 | 2024-09-19T11:25:17 | 2024-09-19T11:25:14 | MEMBER | null | Support `ndjson` (Newline Delimited JSON) data files.
Fix #7153. | {
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] | null | [] | 2024-09-18T05:54:45 | 2024-09-19T11:25:15 | 2024-09-19T11:25:15 | MEMBER | null | ### Feature request
Support data files with `.ndjson` extension.
### Motivation
We already support data files with `.jsonl` extension.
### Your contribution
I am opening a PR. | {
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This PR uses the same `base_plus_ext` function as the one used by the `webdataset` library.
Fix #7150.
Related to #7144. | {
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For example, we get a different key splitting for filename `/some/path/22.0/1.1.png`:
- datasets library: `/some/path/22` and `0/1.1.png`
- webdataset library: `/some/path/22.0/1`, `1.png`
```python
import webdataset as wds
wds.tariterators.base_plus_ext("/some/path/22.0/1.1.png")
# ('/some/path/22.0/1', '1.png')
```
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"Thanks, for reporting.\r\n\r\nWe have been fixing most Hub datasets to remove the deprecated (and now non-supported) task templates, but we missed the \"facebook/winoground\".\r\n\r\nIt is fixed now: https://huggingface.co/datasets/facebook/winoground/discussions/8\r\n\r\n"
] | 2024-09-13T10:30:57 | 2024-09-13T14:10:48 | 2024-09-13T14:10:48 | NONE | null | ### Describe the bug
Issue
```python
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
```
Gives error
```
TypeError: DatasetInfo.__init__() got an unexpected keyword argument 'task_templates'
```
A simple downgrade to lower `datasets v 2.21.0` solves it.
### Steps to reproduce the bug
1. `pip install datsets`
2.
```python
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
```
### Expected behavior
Should load the dataset correctly.
### Environment info
- Datasets version `3.0.0`
- `transformers` version: 4.45.0.dev0
- Platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.35
- Python version: 3.12.4
- Huggingface_hub version: 0.24.6
- Safetensors version: 0.4.5
- Accelerate version: 0.35.0.dev0
- Accelerate config: not found
- PyTorch version (GPU?): 2.4.1+cu121 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
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"Could you please try with `force_redownload` instead?\r\nEDIT:\r\n```python\r\ndata = load_dataset(\"mteb/mtop_domain\", \"en\", download_mode=\"force_redownload\")\r\n```",
"Seems the error is still there",
"I am not able to reproduce the issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: data = load_dataset(\"mteb/mtop_domain\", \"en\")\r\n\r\nIn [3]: data\r\nOut[3]: DatasetDict({\r\n train: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 15667\r\n })\r\n validation: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 2235\r\n })\r\n test: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 4386\r\n })\r\n})\r\n```",
"Just solved this by reinstall Huggingface Hub and datasets. Thanks for your help!"
] | 2024-09-13T04:09:39 | 2024-09-14T15:11:35 | 2024-09-14T15:11:35 | NONE | null | ### Describe the bug
When downloading the dataset "mteb/mtop_domain", ran into the following error:
```
Traceback (most recent call last):
File "/share/project/xzy/test/test_download.py", line 3, in <module>
data = load_dataset("mteb/mtop_domain", "en", trust_remote_code=True)
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2606, in load_dataset
builder_instance = load_dataset_builder(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2277, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1923, in dataset_module_factory
raise e1 from None
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1896, in dataset_module_factory
).get_module()
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1507, in get_module
local_path = self.download_loading_script()
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1467, in download_loading_script
return cached_path(file_path, download_config=download_config)
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 211, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 689, in get_from_cache
fsspec_get(
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 395, in fsspec_get
fs.get_file(path, temp_file.name, callback=callback)
File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 648, in get_file
http_get(
File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 578, in http_get
raise EnvironmentError(
OSError: Consistency check failed: file should be of size 2191 but has size 2190 ((…)ets/mteb/mtop_domain@main/mtop_domain.py).
We are sorry for the inconvenience. Please retry with `force_download=True`.
If the issue persists, please let us know by opening an issue on https://github.com/huggingface/huggingface_hub.
```
Try to download through HF datasets directly but got the same error as above.
```python
from datasets import load_dataset
data = load_dataset("mteb/mtop_domain", "en")
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
data = load_dataset("mteb/mtop_domain", "en", force_download=True)
```
With and without `force_download=True` both ran into the same error.
### Expected behavior
Should download the dataset successfully.
### Environment info
- datasets version: 2.21.0
- huggingface-hub version: 0.24.6 | {
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https://api.github.com/repos/huggingface/datasets/issues/7147 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7147/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7147/comments | https://api.github.com/repos/huggingface/datasets/issues/7147/events | https://github.com/huggingface/datasets/issues/7147 | 2,523,129,465 | I_kwDODunzps6WY-Z5 | 7,147 | IterableDataset strange deadlock | {
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"Yes `interleave_datasets` seems to have an issue with shuffling, could you open a new issue on this ?\r\n\r\nThen regarding the deadlock, it has to do with interleave_dataset with probabilities=[1, 0] with workers that may contain an empty dataset in first position (it can be empty since you distribute 1024 shard to 8 workers, so some workers may not have an example that satisfies your condition `if shard < 25`). It creates an infinite loop, trying to get samples from empty datasets with probability 1.",
"Opened https://github.com/huggingface/datasets/issues/7156\r\n\r\nCan the deadlock be fixed somehow? The point of IterableDataset is so we don't need to preload the entire dataset, which loses some meaning if we need to see how many examples are in the dataset in order to set shards correctly.",
"~~And it is kinda strange that `Commenting out the final shuffle avoids the issue` since if the infinite loop is inside interleave_datasets you'd expect that to happen regardless of the additional shuffle call?~~\r\n\r\nEdit: oh I guess without the shuffle it's guaranteed every worker gets something, but the shuffle makes it so some workers could have nothing\r\n\r\n~~Edit2: maybe the shuffle can be changed so initially it gives one example to each worker, and only starts the random shuffle after that~~ wait it's not about the workers not getting any shards, it's about a worker getting shards but all of the shards it gets are empty shards\r\n\r\nEdit3: If it's trying to get samples from empty datasets, it should be getting back a StopIteration -- and \"all_exhausted\" should mean it eventually discovers all its datasets are empty, and then it should just raise a StopIteration itself. So it seems like there is a reasonable behavior result for this?",
"well the second dataset passed to interleave_datasets is never exhausted, since it's never sampled. But we could also state that the stream of examples from the second dataset is empty if it has probability 0, so I opened https://github.com/huggingface/datasets/pull/7157 to fix the infinite loop issue by ignoring datasets with probability 0, let me know what you think !",
"Thanks for taking a look!\r\n\r\nI think you're right that this is ultimately an issue that the user opts into by specifying a dataset with probability 0, because the user is basically saying \"I want to force this `interleave_datasets` call to run forever\" and yet one of the workers can end up having only empty shards to mix...\r\n\r\nThat said it's probably not a good idea to randomly change the behavior of `interleave_datasets` with probability 0, I can't be the only one that uses it to repeat many different datasets (since there is no `datasets.repeat()` function). https://xkcd.com/1172/\r\n\r\nI think just the knowledge that filtering out probability 0 datasets fixes the deadlock is good enough for me. I can filter it out on my side and add a restart loop around the dataloader instead.\r\n\r\nThanks again for investigating.",
"Ok I see ! We can also add .repeat() as well"
] | 2024-09-12T18:59:33 | 2024-09-23T09:32:27 | 2024-09-21T17:37:34 | NONE | null | ### Describe the bug
```
import datasets
import torch.utils.data
num_shards = 1024
def gen(shards):
for shard in shards:
if shard < 25:
yield {"shard": shard}
def main():
dataset = datasets.IterableDataset.from_generator(
gen,
gen_kwargs={"shards": list(range(num_shards))},
)
dataset = dataset.shuffle(buffer_size=1)
dataset = datasets.interleave_datasets(
[dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted"
)
dataset = dataset.shuffle(buffer_size=1)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=8,
num_workers=8,
)
for i, batch in enumerate(dataloader):
print(batch)
if i >= 10:
break
print()
if __name__ == "__main__":
for _ in range(100):
main()
```
### Steps to reproduce the bug
Running the script above, at some point it will freeze.
- Changing `num_shards` from 1024 to 25 avoids the issue
- Commenting out the final shuffle avoids the issue
- Commenting out the interleave_datasets call avoids the issue
As an aside, if you comment out just the final shuffle, the output from interleave_datasets is not shuffled at all even though there's the shuffle before it. So something about that shuffle config is not being propagated to interleave_datasets.
### Expected behavior
The script should not freeze.
### Environment info
- `datasets` version: 3.0.0
- Platform: macOS-14.6.1-arm64-arm-64bit
- Python version: 3.12.5
- `huggingface_hub` version: 0.24.7
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
I observed this with 2.21.0 initially, then tried upgrading to 3.0.0 and could still repro. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7146). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-11T13:53:27 | 2024-09-12T04:34:08 | 2024-09-12T04:34:06 | MEMBER | null | null | {
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