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2024-01-29T15:41:32Z

PR_kwDODunzps5lVr-t

closed

Disable `tqdm` bars in non-interactive environments

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2024-01-29T15:18:21Z

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CONTRIBUTOR

Replace `disable=False` with `disable=None` in the `tqdm` bars to disable them in non-interactive environments (by default). For more info, see a [similar PR](https://github.com/huggingface/huggingface_hub/pull/2000) in `huggingface_hub`.

2024-01-29T15:47:34Z

https://github.com/huggingface/datasets/pull/6627

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2024-01-29T15:12:18Z

PR_kwDODunzps5lU0I2

closed

Raise error on bad split name

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2024-01-29T13:17:41Z

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e.g. dashes '-' are not allowed in split names This should add an error message on datasets with unsupported split names like https://huggingface.co/datasets/open-source-metrics/test cc @AndreaFrancis

2024-01-29T15:18:25Z

https://github.com/huggingface/datasets/pull/6626

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2024-02-06T09:43:31Z

not_planned

I_kwDODunzps59Z71-

closed

How to download the laion-coco dataset

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2024-01-28T03:56:05Z

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The laion coco dataset is not available now. How to download it https://huggingface.co/datasets/laion/laion-coco

2024-02-06T09:43:31Z

https://github.com/huggingface/datasets/issues/6624

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I_kwDODunzps59ZoKr

open

streaming datasets doesn't work properly with multi-node

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2024-01-27T23:46:13Z

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### Feature request Let’s say I have a dataset with 5 samples with values [1, 2, 3, 4, 5], with 2 GPUs (for DDP) and batch size of 2. This dataset is an `IterableDataset` since I am streaming it. Now I split the dataset using `split_dataset_by_node` to ensure it doesn’t get repeated. And since it’s already splitted, I don’t have to use `DistributedSampler` (also they don't work with iterable datasets anyway)? But in this case I noticed that the: First iteraton: first GPU will get → [1, 2] first GPU will get → [3, 4] Second iteraton: first GPU will get → [5] first GPU will get → Nothing which actually creates an issue since in case of `DistributedSampler`, the samples are repeated internally to ensure non of the GPUs at any iteration is missing any data for gradient sync. So my questions are: 1. Here since splitting is happening before hand, how to make sure each GPU get’s a batch at each iteration to avoid gradient sync issues? 2. Do we need to use `DistributedSampler`? If yes, how? 3. in the docstrings of `split_dataset_by_node`, this is mentioned: *"If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples."* Can you explain the last part here? 4. If `dataset.n_shards % world_size != 0`, is it possible to shard the streaming dataset on the fly to avoid the case where data is missing? ### Motivation Somehow streaming datasets should work with DDP since for big LLMs a lot of data is required and DDP/multi-node is mostly used to train such models and streaming can actually help solve the data part of it. ### Your contribution Yes, I can help in submitting the PR once we get mutual understanding on how it should behave.

2024-03-08T14:27:08Z

https://github.com/huggingface/datasets/issues/6623

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2024-02-08T11:18:21Z

completed

I_kwDODunzps59ZSVZ

closed

multi-GPU map does not work

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2024-01-27T20:06:08Z

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### Describe the bug Here is the code for single-GPU processing: https://pastebin.com/bfmEeK2y Here is the code for multi-GPU processing: https://pastebin.com/gQ7i5AQy Here is the video showing that the multi-GPU mapping does not work as expected (there are so many things wrong here, it's better to watch the 3-minute video than explain here): https://youtu.be/RNbdPkSppc4 ### Steps to reproduce the bug - ### Expected behavior - ### Environment info x2 RTX A4000

2024-02-08T11:18:21Z

https://github.com/huggingface/datasets/issues/6622

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2024-01-27T17:14:43Z

completed

I_kwDODunzps59Y4me

closed

deleted

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2024-01-27T16:59:58Z

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2024-01-27T17:14:43Z

https://github.com/huggingface/datasets/issues/6621

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2024-02-06T09:40:19Z

not_planned

I_kwDODunzps59WuuI

closed

wiki_dpr.py error (ID mismatch between lines {id} and vector {vec_id}

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2024-01-27T01:00:09Z

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### Describe the bug I'm trying to run a rag example, and the dataset is wiki_dpr. wiki_dpr download and extracting have been completed successfully. However, at the generating train split stage, an error from wiki_dpr.py keeps popping up. Especially in "_generate_examples" : 1. The following error occurs in the line **id, text, title = line.strip().split("\t")** ValueError: not enough values ​​to unpack (expected 3, got 2) -> This part handles exceptions so that even if an error occurs, it passes. 2. **ID mismatch between lines {id} and vector {vec_id}** This error seems to occur at the line " assert int(id) == int(vec_id),". After I handled the exception in the split error, generating train split progressed to 80%, but an id mismatch error occurred at about the 16200000th vector id. Debugging is even more difficult because it takes a long time to download and split wiki_dpr. I need help. thank you in advance!! ### Steps to reproduce the bug Occurs in the generating train split step when running the rag example in the transformers repository. Specifically, it is an error in wiki_dpr.py. ### Expected behavior . ### Environment info python 3.8

2024-02-06T09:40:19Z

https://github.com/huggingface/datasets/issues/6620

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2024-01-26T15:47:32Z

PR_kwDODunzps5lK2VY

closed

Migrate from `setup.cfg` to `pyproject.toml`

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2024-01-26T15:27:10Z

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CONTRIBUTOR

Based on https://github.com/huggingface/huggingface_hub/pull/1971 in `hfh`

2024-01-26T15:53:40Z

https://github.com/huggingface/datasets/pull/6619

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2024-02-06T09:25:54Z

not_planned

I_kwDODunzps59R_am

closed

While importing load_dataset from datasets

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2024-01-26T09:21:57Z

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### Describe the bug cannot import name 'DEFAULT_CIPHERS' from 'urllib3.util.ssl_' this is the error i received ### Steps to reproduce the bug from datasets import load_dataset ### Expected behavior No errors ### Environment info python 3.11.5

2024-02-06T10:57:01Z

https://github.com/huggingface/datasets/issues/6618

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2024-01-26T14:50:44Z

PR_kwDODunzps5lEagV

closed

Fix CI: pyarrow 15, pandas 2.2 and sqlachemy

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2024-01-25T13:57:41Z

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this should fix the CI failures on `main` close https://github.com/huggingface/datasets/issues/5477

2024-01-26T14:56:46Z

https://github.com/huggingface/datasets/pull/6617

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2024-01-26T16:19:12Z

PR_kwDODunzps5lDSEL

closed

Use schema metadata only if it matches features

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2024-01-25T11:01:14Z

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e.g. if we use `map` in arrow format and transform the table, the returned table might have new columns but the metadata might be wrong

2024-01-26T16:25:24Z

https://github.com/huggingface/datasets/pull/6616

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2024-01-24T19:40:11Z

not_planned

I_kwDODunzps59G3Tx

closed

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2024-01-24T19:42:30Z

https://github.com/huggingface/datasets/issues/6615

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I_kwDODunzps59Gm-o

open

`datasets/downloads` cleanup tool

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2024-01-24T18:52:10Z

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CONTRIBUTOR

### Feature request Splitting off https://github.com/huggingface/huggingface_hub/issues/1997 - currently `huggingface-cli delete-cache` doesn't take care of cleaning `datasets` temp files e.g. I discovered having millions of files under `datasets/downloads` cache, I had to do: ``` sudo find /data/huggingface/datasets/downloads -type f -mtime +3 -exec rm {} \+ sudo find /data/huggingface/datasets/downloads -type d -empty -delete ``` could the cleanup be integrated into `huggingface-cli` or a different tool provided to keep the folders tidy and not consume inodes and space e.g. there were tens of thousands of `.lock` files - I don't know why they never get removed - lock files should be temporary for the duration of the operation requiring the lock and not remain after the operation finished, IMHO. Also I think one should be able to nuke `datasets/downloads` w/o hurting the cache, but I think there are some datasets that rely on files extracted under this dir - or at least they did in the past - which is very difficult to manage since one has no idea what is safe to delete and what not. Thank you @Wauplin (requested to be tagged)

2024-01-24T18:55:09Z

https://github.com/huggingface/datasets/issues/6614

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2024-02-01T08:14:50Z

completed

I_kwDODunzps59DiIC

closed

cnn_dailymail repeats itself

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2024-01-24T11:38:25Z

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### Describe the bug When I try to load `cnn_dailymail` dataset, it takes longer than usual and when I checked the dataset it's 3x bigger than it's supposed to be. Check https://huggingface.co/datasets/cnn_dailymail: it says 287k rows for train. But when I check length of train split it says 861339. Also I checked data: ``` >>> ds['train']['highlights'][0] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday . Young actor says he has no plans to fritter his cash away . Radcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][0] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday . Young actor says he has no plans to fritter his cash away . Radcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][287113] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\nYoung actor says he has no plans to fritter his cash away .\nRadcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][574226] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\nYoung actor says he has no plans to fritter his cash away .\nRadcliffe's earnings from first five Potter films have been held in trust fund ." ``` The datasets seems to be updated 6 days ago to convert it to Parquet. Probably, there is some issue with backward compatability. ### Steps to reproduce the bug 1. ``` from datasets import load_dataset ds = load_dataset('cnn_dailymail', '3.0.0') len(ds['train']) ``` ### Expected behavior It should not repeat itself. ### Environment info datasets==2.13.2 Python==3.7.13

2024-02-01T08:14:50Z

https://github.com/huggingface/datasets/issues/6612

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I_kwDODunzps587n76

open

`load_from_disk` with large dataset from S3 runs into `botocore.exceptions.ClientError`

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2024-01-23T12:37:57Z

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NONE

### Describe the bug When loading a large dataset (>1000GB) from S3 I run into the following error: ``` Traceback (most recent call last): File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 113, in _error_wrapper return await func(*args, **kwargs) File "/home/alp/.local/lib/python3.10/site-packages/aiobotocore/client.py", line 383, in _make_api_call raise error_class(parsed_response, operation_name) botocore.exceptions.ClientError: An error occurred (RequestTimeTooSkewed) when calling the GetObject operation: The difference between the request time and the current time is too large. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/alp/phoneme-classification.monorepo/aws_sagemaker/data_processing/inspect_final_dataset.py", line 13, in <module> dataset = load_from_disk("s3://speech-recognition-processed-data/whisper/de/train_data/", storage_options=storage_options) File "/home/alp/.local/lib/python3.10/site-packages/datasets/load.py", line 1902, in load_from_disk return Dataset.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) File "/home/alp/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1686, in load_from_disk fs.download(src_dataset_path, [dest_dataset_path.as](http://dest_dataset_path.as/)_posix(), recursive=True) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1480, in download return self.get(rpath, lpath, recursive=recursive, **kwargs) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper return sync(self.loop, func, *args, **kwargs) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync raise return_result File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner result[0] = await coro File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 604, in _get return await _run_coros_in_chunks( File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 257, in _run_coros_in_chunks await asyncio.gather(*chunk, return_exceptions=return_exceptions), File "/usr/lib/python3.10/asyncio/tasks.py", line 408, in wait_for return await fut File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1193, in _get_file body, content_length = await _open_file(range=0) File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1184, in _open_file resp = await self._call_s3( File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 348, in _call_s3 return await _error_wrapper( File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 140, in _error_wrapper raise err PermissionError: The difference between the request time and the current time is too large. ``` The usual problem for this error is that the time on my local machine is out of sync with the current time. However, this is not the case here. I checked the time and even reset it with no success. See resources here: - https://stackoverflow.com/questions/4770635/s3-error-the-difference-between-the-request-time-and-the-current-time-is-too-la - https://stackoverflow.com/questions/25964491/aws-s3-upload-fails-requesttimetooskewed The error does not appear when loading a smaller dataset (e.g. our test set) from the same s3 path. ### Steps to reproduce the bug 1. Create large dataset 2. Try loading it from s3 using: ``` dataset = load_from_disk("s3://...", storage_options=storage_options) ``` ### Expected behavior Load dataset without running into this error. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.19.3 - PyArrow version: 12.0.1 - Pandas version: 2.0.3

2024-01-23T12:37:57Z

https://github.com/huggingface/datasets/issues/6611

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2024-01-25T02:15:23Z

completed

I_kwDODunzps586Pw_

closed

cast_column to Sequence(subfeatures_dict) has err

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2024-01-23T09:32:32Z

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NONE

### Describe the bug I am working with the following demo code: ``` from datasets import load_dataset from datasets.features import Sequence, Value, ClassLabel, Features ais_dataset = load_dataset("/data/ryan.gao/ais_dataset_cache/raw/1978/") ais_dataset = ais_dataset["train"] def add_class(example): example["my_labeled_bbox"] = {"bbox": [100,100,200,200], "label": "cat"} return example ais_dataset = ais_dataset.map(add_class, batched=False, num_proc=32) ais_dataset = ais_dataset.cast_column("my_labeled_bbox", Sequence( { "bbox": Sequence(Value(dtype="int64")), "label": ClassLabel(names=["cat", "dog"]) })) print(ais_dataset[0]) ``` However, executing this code results in an error: ``` File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type int64 to Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None) ``` Upon examining the source code in datasets/table.py at line 2035: ``` if isinstance(feature, Sequence) and isinstance(feature.feature, dict): feature = { name: Sequence(subfeature, length=feature.length) for name, subfeature in feature.feature.items() } ``` I noticed that if subfeature is of type Sequence, the code results in Sequence(Sequence(...), ...) and Sequence(ClassLabel(...), ...), which appears to be the source of the error. ### Steps to reproduce the bug run my demo code ### Expected behavior no exception ### Environment info python 3.9 datasets: 2.16.1

2024-01-25T02:15:23Z

https://github.com/huggingface/datasets/issues/6610

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2024-02-06T17:21:25Z

completed

I_kwDODunzps584HhS

closed

Wrong path for cache directory in offline mode

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2024-01-23T01:47:19Z

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### Describe the bug Dear huggingfacers, I'm trying to use a subset of the-stack dataset. When I run the command the first time ``` dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' ) ``` It downloads the files and caches them normally. Nevertheless, since my compute nodes are not online (`HF_DATASETS_OFFLINE=1`) . Whenever I try to run the command again, the library is passing the wrong cache path: `Cache directory for the-stack doesn't exist at /Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data%2Ffortran-data_dir=data%2Ffortran` when the right path is: `'/Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data\%2Ffortran` Not sure why those redundancies are included in the path. If I try adding the correct path through the the cache_dir argument it throws an error: ConnectionError: Couldn't reach the Hugging Face Hub for dataset 'bigcode/the-stack': Offline mode is enabled. Your help with this issue is greatly appreciated. Thanks a lot for the great work. ### Steps to reproduce the bug 1: `dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' )` 2: `HF_DATASETS_OFFLINE=1` 3: `dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' )` ### Expected behavior being able to use the cached data ### Environment info several different systems

2024-02-06T17:21:25Z

https://github.com/huggingface/datasets/issues/6609

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2024-01-29T16:36:53Z

PR_kwDODunzps5ku_lN

closed

Add `with_rank` param to `Dataset.filter`

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2024-01-22T15:19:16Z

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CONTRIBUTOR

Fix #6564

2024-01-29T16:43:11Z

https://github.com/huggingface/datasets/pull/6608

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PR_kwDODunzps5knGse

open

Update features.py to avoid bfloat16 unsupported error

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2024-01-20T00:39:44Z

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NONE

Fixes https://github.com/huggingface/datasets/issues/6566 Let me know if there's any tests I need to clear.

2024-03-01T16:11:31Z

https://github.com/huggingface/datasets/pull/6607

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2024-01-26T15:05:34Z

PR_kwDODunzps5kk3KB

closed

Dedicated RNG object for fingerprinting

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2024-01-19T18:34:47Z

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CONTRIBUTOR

Closes https://github.com/huggingface/datasets/issues/6604, closes https://github.com/huggingface/datasets/issues/2775

2024-01-26T15:11:38Z

https://github.com/huggingface/datasets/pull/6606

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2024-02-01T17:58:22Z

completed

I_kwDODunzps58lb5Y

closed

ELI5 no longer available, but referenced in example code

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2024-01-19T10:21:52Z

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Here, an example code is given: https://huggingface.co/docs/transformers/tasks/language_modeling This code + article references the ELI5 dataset. ELI5 is no longer available, as the ELI5 dataset page states: https://huggingface.co/datasets/eli5 "Defunct: Dataset "eli5" is defunct and no longer accessible due to unavailability of the source data. Reddit recently [changed the terms of access](https://www.reddit.com/r/reddit/comments/12qwagm/an_update_regarding_reddits_api/) to its API, making the source data for this dataset unavailable. " Please change the example code to use a different dataset.

2024-02-01T17:58:23Z

https://github.com/huggingface/datasets/issues/6605

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2024-01-26T15:05:35Z

completed

I_kwDODunzps58joEZ

closed

Transform fingerprint collisions due to setting fixed random seed

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2024-01-19T06:32:25Z

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NONE

### Describe the bug The transform fingerprinting logic relies on the `random` library for random bits when the function is not hashable (e.g. bound methods as used in `trl`: https://github.com/huggingface/trl/blob/main/trl/trainer/dpo_trainer.py#L356). This causes collisions when the training code sets a fixed random seed, which is common practice: https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/sft/config_full.yaml#L45. This results in fingerprint collisions which leads to silently loading incorrect cache files corresponding to completely different datasets. ### Steps to reproduce the bug n/a ### Expected behavior Use `uuid` v4 instead of `random.getrandbits()` ### Environment info `datasets` main branch

2024-01-26T15:05:35Z

https://github.com/huggingface/datasets/issues/6604

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I_kwDODunzps58hyGu

open

datasets map `cache_file_name` does not work

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2024-01-18T23:08:30Z

https://api.github.com/repos/huggingface/datasets/issues/6603

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NONE

### Describe the bug In the documentation `datasets.Dataset.map` arg `cache_file_name` is said to be a string, but it doesn't work. ### Steps to reproduce the bug 1. pick a dataset 2. write a map function 3. do `ds.map(..., cache_file_name='some_filename')` 4. it crashes ### Expected behavior It will tell you the filename you specified does not exist or it will generate a new file and tell you the filename does not exist. ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-5.10.201-168.748.amzn2int.x86_64-x86_64-with-glibc2.26 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.12.2

2024-01-28T04:01:15Z

https://github.com/huggingface/datasets/issues/6603

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I_kwDODunzps58hu3L

open

Index error when data is large

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2024-01-18T23:00:47Z

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NONE

### Describe the bug At `save_to_disk` step, the `max_shard_size` by default is `500MB`. However, one row of the dataset might be larger than `500MB` then the saving will throw an index error. Without looking at the source code, the bug is due to wrong calculation of number of shards which i think is `total_size / min(max_shard_size, row_size)` which should be `total_size / max(max_shard_size, row_size)` The fix is setting a larger `max_shard_size` ### Steps to reproduce the bug 1. create a dataset with large dense tensors per row 2. set a small `max_shard_size` say 1MB 3. `save_to_disk` ### Expected behavior ``` raise IndexError(f"Index {index} out of range for dataset of size {size}.") IndexError: Index 10 out of range for dataset of size 10. ``` ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-5.10.201-168.748.amzn2int.x86_64-x86_64-with-glibc2.26 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.12.2

2024-01-18T23:00:47Z

https://github.com/huggingface/datasets/issues/6602

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PR_kwDODunzps5kcWN0

open

add safety checks when using only part of dataset

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2024-01-18T16:16:59Z

https://api.github.com/repos/huggingface/datasets/issues/6601

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NONE

Added some checks to prevent errors that arrise when using evaluate.py on only a portion of the squad 2.0 dataset.

2024-02-08T14:33:10Z

https://github.com/huggingface/datasets/pull/6601

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I_kwDODunzps58eymx

open

Loading CSV exported dataset has unexpected format

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2024-01-18T14:48:27Z

https://api.github.com/repos/huggingface/datasets/issues/6600

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NONE

### Describe the bug I wanted to be able to save a HF dataset for translations and load it again in another script, but I'm a bit confused with the documentation and the result I've got so I'm opening this issue to ask if this behavior is as expected. ### Steps to reproduce the bug The documentation I've mainly consulted is https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/loading_methods#datasets.load_dataset and https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset (where I've found `.to_csv()`) ```python # Load a dataset of translations test_dataset = load_dataset("opus100", name="en-fr", split="test") # Save with .to_csv() test_csv_path = "try_testset_save.csv" test_dataset.to_csv(test_csv_path) # Load dataset from the CSV loaded_dataset = load_dataset("csv", data_files=test_csv_path) print(test_dataset_fromfile[0]["translation"]) print(test_dataset_fromfile[0]["translation"]["en"]) ``` ``` Creating CSV from Arrow format: 100% 2/2 [00:00<00:00, 47.99ba/s] Downloading data files: 100% 1/1 [00:00<00:00, 65.33it/s] Extracting data files: 100% 1/1 [00:00<00:00, 42.10it/s] Generating train split: 2000/0 [00:00<00:00, 47486.09 examples/s] {'en': "She wasn't going to vaccinate her kid against polio, no way.", 'fr': 'Elle ne vaccinerait pas son enfant contre la polio. Pas question.'} --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[29], line 11 9 loaded_dataset = load_dataset("csv", data_files=test_csv_path) 10 print(test_dataset_fromfile[0]["translation"]) ---> 11 print(test_dataset_fromfile[0]["translation"]["en"]) TypeError: string indices must be integers, not 'str' ``` ### Expected behavior Each translation was saved as a stringified dict like `"{'en': ""She wasn't going to vaccinate her kid against polio, no way."", 'fr': 'Elle ne vaccinerait pas son enfant contre la polio. Pas question.'}"` where I would have expected 2 columns (1st with English segments, and 2nd with French segments), and I was expecting `load_dataset` to infer the type of feature automatically as I haven't seen anything about it in the documentation. Do you have an example of how to effectively save and load datasets of translations ? ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-3.10.0-1160.36.2.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.11.5 - `huggingface_hub` version: 0.16.4 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

2024-01-23T14:42:32Z

https://github.com/huggingface/datasets/issues/6600

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2024-01-22T15:35:49Z

not_planned

I_kwDODunzps58YEf4

closed

Easy way to segment into 30s snippets given an m4a file and a vtt file

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2024-01-17T17:51:40Z

https://api.github.com/repos/huggingface/datasets/issues/6599

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### Feature request Uploading datasets is straightforward thanks to the ability to push Audio to hub. However, it would be nice if the data (text and audio) could be segmented when being pushed (if not possible already). ### Motivation It's easy to create a vtt file from an audio file. If there could be auto-segmenting, this would make the creation of datasets much faster. ### Your contribution I have made a custom script to do this but it's not all that clean - uses librosa and pydub.

2024-01-23T10:42:17Z

https://github.com/huggingface/datasets/issues/6599

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I_kwDODunzps58Ou09

open

Unexpected keyword argument 'hf' when downloading CSV dataset from S3

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2024-01-16T15:16:01Z

https://api.github.com/repos/huggingface/datasets/issues/6598

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NONE

### Describe the bug I receive this error message when using `load_dataset` with "csv" path and `dataset_files=s3://...`: ``` TypeError: Session.__init__() got an unexpected keyword argument 'hf' ``` I found a similar issue here: https://stackoverflow.com/questions/77596258/aws-issue-load-dataset-from-s3-fails-with-unexpected-keyword-argument-error-in Full stacktrace: ``` .../site-packages/datasets/load.py:2549: in load_dataset builder_instance.download_and_prepare( .../site-packages/datasets/builder.py:1005: in download_and_prepare self._download_and_prepare( .../site-packages/datasets/builder.py:1078: in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) .../site-packages/datasets/packaged_modules/csv/csv.py:147: in _split_generators data_files = dl_manager.download_and_extract(self.config.data_files) .../site-packages/datasets/download/download_manager.py:562: in download_and_extract return self.extract(self.download(url_or_urls)) .../site-packages/datasets/download/download_manager.py:426: in download downloaded_path_or_paths = map_nested( .../site-packages/datasets/utils/py_utils.py:466: in map_nested mapped = [ .../site-packages/datasets/utils/py_utils.py:467: in <listcomp> _single_map_nested((function, obj, types, None, True, None)) .../site-packages/datasets/utils/py_utils.py:387: in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] .../site-packages/datasets/utils/py_utils.py:387: in <listcomp> mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] .../site-packages/datasets/utils/py_utils.py:370: in _single_map_nested return function(data_struct) .../site-packages/datasets/download/download_manager.py:451: in _download out = cached_path(url_or_filename, download_config=download_config) .../site-packages/datasets/utils/file_utils.py:188: in cached_path output_path = get_from_cache( ...1/site-packages/datasets/utils/file_utils.py:511: in get_from_cache response = fsspec_head(url, storage_options=storage_options) .../site-packages/datasets/utils/file_utils.py:316: in fsspec_head fs, _, paths = fsspec.get_fs_token_paths(url, storage_options=storage_options) .../site-packages/fsspec/core.py:622: in get_fs_token_paths fs = filesystem(protocol, **inkwargs) .../site-packages/fsspec/registry.py:290: in filesystem return cls(**storage_options) .../site-packages/fsspec/spec.py:79: in __call__ obj = super().__call__(*args, **kwargs) .../site-packages/s3fs/core.py:187: in __init__ self.s3 = self.connect() _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <s3fs.core.S3FileSystem object at 0x1500a1310>, refresh = True def connect(self, refresh=True): """ Establish S3 connection object. Parameters ---------- refresh : bool Whether to create new session/client, even if a previous one with the same parameters already exists. If False (default), an existing one will be used if possible """ if refresh is False: # back compat: we store whole FS instance now return self.s3 anon, key, secret, kwargs, ckwargs, token, ssl = ( self.anon, self.key, self.secret, self.kwargs, self.client_kwargs, self.token, self.use_ssl) if not self.passed_in_session: > self.session = botocore.session.Session(**self.kwargs) E TypeError: Session.__init__() got an unexpected keyword argument 'hf' ``` ### Steps to reproduce the bug 1. Assuming a valid CSV file located at `s3://bucket/data.csv` 2. Run the below code: ``` storage_options = { "key": "...", "secret": "...", "client_kwargs": { "endpoint_url": "...", } } load_dataset("csv", data_files="s3://bucket/data.csv", storage_options=storage_options) ``` Encountered in version `2.16.1` but also reproduced in `2.16.0` and `2.15.0`. Note: I encountered this in a unit test using a `moto` mock for S3, however since the error occurs before the session is instantiated, it should not be the issue. ### Expected behavior No exception is raised, the boto3 session is created successfully, and the CSV file is downloaded successfully and returned as a dataset. === After some research I found that `DownloadConfig` has a `__post_init__` method that always forces this value to be set in its `storage_options`, even though in case of an S3 location the storage options get passed on to the S3 Session which does not expect this parameter. I assume this parameter is needed when reading from the huggingface hub and should not be set in this context. Unfortunately there is nothing the user can do to work around it. Even if you manually do something like: ``` download_config = DownloadConfig() del download_config.storage_options["hf"] load_dataset("csv", data_files="s3://bucket/data.csv", download_config=download_config) ``` the library will still reinsert this parameter when `download_config = self.download_config.copy()` in line 418 of `download_manager.py` (`DownloadManager.download`). Therefore `load_dataset` currently cannot be used to read a dataset in CSV format from an S3 location. ### Environment info - `datasets` version: 2.16.1 - Platform: macOS-14.2.1-arm64-arm-64bit - Python version: 3.11.7 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

2024-03-09T02:32:01Z

https://github.com/huggingface/datasets/issues/6598

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2024-02-05T12:29:37Z

completed

I_kwDODunzps58Mt5p

closed

Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace

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2024-01-16T11:27:07Z

https://api.github.com/repos/huggingface/datasets/issues/6597

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MEMBER

While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace. ## Steps to reproduce the bug The command: ```python commit_info = ds.push_to_hub( "caner", config_name="default", commit_message="Convert dataset to Parquet", commit_description="Convert dataset to Parquet.", create_pr=True, token=token, ) ``` creates the additional dataset `albertvillanova/caner`.

2024-02-05T12:29:37Z

https://github.com/huggingface/datasets/issues/6597

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2024-01-16T17:05:52Z

PR_kwDODunzps5kJceH

closed

Drop redundant None guard.

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2024-01-16T06:31:54Z

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CONTRIBUTOR

`xxx if xxx is not None else None` is no-op.

2024-01-16T17:16:16Z

https://github.com/huggingface/datasets/pull/6596

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2024-01-26T02:28:32Z

completed

I_kwDODunzps58JnkU

closed

Loading big dataset raises pyarrow.lib.ArrowNotImplementedError 2

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2024-01-16T02:03:09Z

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NONE

### Describe the bug I'm aware of the issue #5695 . I'm using a modified SDXL trainer: https://github.com/kopyl/diffusers/blob/5e70f604155aeecee254a5c63c5e4236ad4a0d3d/examples/text_to_image/train_text_to_image_sdxl.py#L1027C16-L1027C16 So i 1. Map dataset 2. Save to disk 3. Try to upload: ``` import datasets from datasets import load_from_disk dataset = load_from_disk("ds") datasets.config.DEFAULT_MAX_BATCH_SIZE = 1 dataset.push_to_hub("kopyl/ds", private=True, max_shard_size="500MB") ``` And i get this error: `pyarrow.lib.ArrowNotImplementedError: Unhandled type for Arrow to Parquet schema conversion: halffloat` Full traceback: ``` >>> dataset.push_to_hub("kopyl/3M_icons_monochrome_only_no_captioning_mapped-for-SDXL-2", private=True, max_shard_size="500MB") Map: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1451/1451 [00:00<00:00, 6827.40 examples/s] Uploading the dataset shards: 0%| | 0/2099 [00:00<?, ?it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py", line 1705, in push_to_hub split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py", line 5208, in _push_parquet_shards_to_hub shard.to_parquet(buffer) File "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py", line 4931, in to_parquet return ParquetDatasetWriter(self, path_or_buf, batch_size=batch_size, **parquet_writer_kwargs).write() File "/usr/local/lib/python3.10/dist-packages/datasets/io/parquet.py", line 129, in write written = self._write(file_obj=self.path_or_buf, batch_size=batch_size, **self.parquet_writer_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/io/parquet.py", line 141, in _write writer = pq.ParquetWriter(file_obj, schema=schema, **parquet_writer_kwargs) File "/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/core.py", line 1016, in __init__ self.writer = _parquet.ParquetWriter( File "pyarrow/_parquet.pyx", line 1869, in pyarrow._parquet.ParquetWriter.__cinit__ File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Unhandled type for Arrow to Parquet schema conversion: halffloat ``` Smaller datasets with the same way of saving and pushing work wonders. Big ones are not. I'm currently trying to upload dataset like this: `HfApi().upload_folder...` But i'm not sure that in this case "load_dataset" would work well. This setting num_shards does not help too: ``` dataset.push_to_hub("kopyl/3M_icons_monochrome_only_no_captioning_mapped-for-SDXL-2", private=True, num_shards={'train': 500}) ``` Tried 3000, 500, 478, 100 Also do you know if it's possible to push a dataset with multiple processes? It would take an eternity pushing 1TB... ### Steps to reproduce the bug Described above ### Expected behavior Should be able to upload... ### Environment info Total dataset size: 978G Amount of `.arrow` files: 2101 Each `.arrow` file size: 477M (i know 477 megabytes * 2101 does not equal 978G, but i just checked the size of a couple `.arrow` files, i don't know if some might have different size) Some files: - "ds/train/state.json": https://pastebin.com/tJ3ZLGAg - "ds/train/dataset_info.json": https://pastebin.com/JdXMQ5ih

2024-01-27T18:26:33Z

https://github.com/huggingface/datasets/issues/6595

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I_kwDODunzps58JDdz

open

IterableDataset sharding logic needs improvement

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2024-01-15T22:22:36Z

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NONE

### Describe the bug The sharding of IterableDatasets with respect to distributed and dataloader worker processes appears problematic with significant performance traps and inconsistencies wrt to distributed train processes vs worker processes. Splitting across num_workers (per train process loader processes) and world_size (distributed training processes) appears inconsistent. * worker split: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/iterable_dataset.py#L1266-L1283 * distributed split: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/iterable_dataset.py#L1335-L1356 In the case of the distributed split, there is a modulus check that flips between two very different behaviours, why is this different than splitting across the data loader workers? For IterableDatasets the DataLoaders worker processes are independent, so whether it's workers within one train process or across a distributed world the shards should be distributed the same, across `world_size * num_worker` independent workers in either case... Further, the fallback case when the `n_shards % world_size == 0` check fails is a rather extreme change. I argue it is not desirable to do that implicitly, it should be an explicit case for specific scenarios (ie reliable validation). A train scenario would likely be much better handled with improved wrapping / stopping behaviour to eg also fix #6437. Changing from stepping shards to stepping samples means that every single process reads ALL of the shards. This was never an intended default for sharded training, shards gain their performance advantage in large scale distributed training by explicitly avoiding the need to have every process overlapping in the data they read, by default, only the data allocated to each process via their assigned shards should be read in each pass of the dataset. Using a large scale CLIP example, some of the larger datasets have 10-20k shards across 100+TB of data. Training with 1000 GPUs we are switching between reading 100 terabytes per epoch to 100 petabytes if say change 20k % 1000 and drop one gpu-node to 20k % 992. The 'step over samples' case might be worth the overhead in specific validation scenarios where gaurantees of at least/most once samples seen are more important and do not make up a significant portion of train time or are done in smaller world sizes outside of train. ### Steps to reproduce the bug N/A ### Expected behavior We have an iterable dataset with N shards, to split across workers * shuffle shards (same seed across all train processes) * step shard iterator across distributed processes * step shard iterator across dataloader worker processes * shuffle samples in every worker via shuffle buffer (different seed in each worker, but ideally controllable (based on base seed + worker id + epoch). * end up with (possibly uneven) number of shards per worker but each shard only ever accessed by 1 worker per pass (epoch) ### Environment info N/A

2024-01-15T22:25:10Z

https://github.com/huggingface/datasets/issues/6594

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2024-02-12T17:35:21Z

not_planned

I_kwDODunzps58Hw8R

closed

Logs are delayed when doing .map when `docker logs`

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2024-01-15T17:05:21Z

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NONE

### Describe the bug When I run my SD training in a Docker image and then listen to logs like `docker logs train -f`, the progress bar is delayed. It's updating every few percent. When you have a large dataset that has to be mapped (like 1+ million samples), it's crucial to see the updates in real-time, not every couple hours to make sure nothing got frozen or broken ### Steps to reproduce the bug 1. Run any huge dataset processing as a Docker image 2. `docker logs image_name` to it ### Expected behavior ... ### Environment info ...

2024-02-12T17:35:21Z

https://github.com/huggingface/datasets/issues/6592

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2024-01-22T23:18:09Z

completed

I_kwDODunzps58HpTN

closed

The datasets models housed in Dropbox can't support a lot of users downloading them

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2024-01-15T16:43:38Z

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NONE

### Describe the bug I'm using the datasets ``` from datasets import load_dataset, Audio dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` And it seems that sometimes when I imagine a lot of users are accessing the same resources, the Dropbox host fails: `raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach https://www.dropbox.com/s/e2us0hcs3ilr20e/MInDS-14.zip?dl=1 (error 429)` My question is if we can somehow host these files elsewhere or can you change the limit of simultaneous users accessing those resources or any other solution? Also, has anyone had this issue before? Thanks ### Steps to reproduce the bug 1: Create a python script like so: ``` from datasets import load_dataset, Audio dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` 2: Execute this by a certain number of users at the same time ### Expected behavior I woudl expect that this shouldnt happen unless its a huge amount of users, which it is not the case ### Environment info This was done in an Ubuntu 22 environment.

2024-01-22T23:18:09Z

https://github.com/huggingface/datasets/issues/6591

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I_kwDODunzps58GMzU

open

Feature request: Multi-GPU dataset mapping for SDXL training

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2024-01-15T13:06:06Z

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### Feature request We need to speed up SDXL dataset pre-process. Please make it possible to use multiple GPUs for the [official SDXL trainer](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py) :) ### Motivation Pre-computing 3 million of images takes around 2 days. Would be nice to be able to be able to do multi-GPU (or even better – multi-GPU + multi-node) vae and embedding precompute... ### Your contribution I'm not sure i can wrap my head around the multi-GPU mapping... Plus it's too expensive for me to take x2 A100 and spend a day just figuring out the staff since I don't have a job right now.

2024-01-15T13:07:07Z

https://github.com/huggingface/datasets/issues/6590

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2024-01-30T15:28:38Z

completed

I_kwDODunzps58DwMb

closed

After `2.16.0` version, there are `PermissionError` when users use shared cache_dir

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2024-01-15T06:46:27Z

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NONE

### Describe the bug - We use shared `cache_dir` using `HF_HOME="{shared_directory}"` - After dataset version 2.16.0, datasets uses `filelock` package for file locking #6445 - But, `filelock` package make `.lock` file with `644` permission - Dataset is not available to other users except the user who created the lock file via `load_dataset`. ### Steps to reproduce the bug 1. `pip install datasets==2.16.0` 2. `export HF_HOME="{shared_directory}"` 3. download dataset with `load_dataset` 4. logout and login another user 5. `pip install datasets==2.16.0` 6. `export HF_HOME="{shared_directory}"` 7. download dataset with `load_dataset` 8. `PermissionError` occurs ### Expected behavior - Users can share `cache_dir` using environment variable `HF_HOME` ### Environment info - python == 3.9.10 - datasets == 2.16.0 - ubuntu 22.04 - shared_directory has ACL  - users are same group (developers)

2024-02-02T07:55:38Z

https://github.com/huggingface/datasets/issues/6589

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2024-01-24T10:08:29Z

completed

I_kwDODunzps58DeCd

closed

fix os.listdir return name is empty string

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2024-01-15T05:34:36Z

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CONTRIBUTOR

### Describe the bug xlistdir return name is empty string Overloaded os.listdir ### Steps to reproduce the bug ```python from datasets.download.streaming_download_manager import xjoin from datasets.download.streaming_download_manager import xlistdir config = DownloadConfig(storage_options=options) manger = StreamingDownloadManager("ILSVRC2012",download_config=config) input_path = "lakefs://datalab/main/imagenet/ILSVRC2012.zip" download_files = manger.download_and_extract(input_path) current_dir = xjoin(download_files,"ILSVRC2012/Images/ILSVRC2012_img_train") folder_list = xlistdir(current_dir) ``` in xlistdir function Obj ["name"] ends with "/" last return "" ### Expected behavior Obj ["name"] ends with "/" return folder name ### Environment info no

2024-01-24T10:08:29Z

https://github.com/huggingface/datasets/issues/6588

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2024-02-08T14:38:32Z

PR_kwDODunzps5kAT_5

closed

Allow concatenation of datasets with mixed structs

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2024-01-13T15:33:20Z

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CONTRIBUTOR

Fixes #6466 The idea is to do a recursive check for structs. PyArrow handles it well enough. For a demo you can do: ```python from datasets import Dataset, concatenate_datasets ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]}) ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]}) print(concatenate_datasets([ds, ds2]).features) print(concatenate_datasets([ds, ds2]).to_dict()) ``` Now both the features and the rows are fixed. I note that Sequence suffers from the same problem, so I can fix that in a future PR once this one is merged.

2024-02-15T15:20:06Z

https://github.com/huggingface/datasets/pull/6587

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2024-01-26T15:53:28Z

PR_kwDODunzps5j8aJn

closed

keep more info in DatasetInfo.from_merge #6585

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2024-01-12T16:08:16Z

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CONTRIBUTOR

* try not to merge DatasetInfos if they're equal * fixes losing DatasetInfo during parallel Dataset.map

2024-01-26T15:59:35Z

https://github.com/huggingface/datasets/pull/6586

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I_kwDODunzps576RmV

open

losing DatasetInfo in Dataset.map when num_proc > 1

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2024-01-12T13:39:19Z

https://api.github.com/repos/huggingface/datasets/issues/6585

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CONTRIBUTOR

### Describe the bug Hello and thanks for developing this package! When I process a Dataset with the map function using multiple processors some set attributes of the DatasetInfo get lost and are None in the resulting Dataset. ### Steps to reproduce the bug ```python from datasets import Dataset, DatasetInfo def run_map(num_proc): dataset = Dataset.from_dict( {"col1": [0, 1], "col2": [3, 4]}, info=DatasetInfo( dataset_name="my_dataset", ), ) ds = dataset.map(lambda x: x, num_proc=num_proc) print(ds.info.dataset_name) run_map(1) run_map(2) ``` This puts out: ```bash Map: 100%|██████████| 2/2 [00:00<00:00, 724.66 examples/s] my_dataset Map (num_proc=2): 100%|██████████| 2/2 [00:00<00:00, 18.25 examples/s] None ``` ### Expected behavior I expect the DatasetInfo to be kept as it was and there should be no difference in the output of running map with num_proc=1 and num_proc=2. Expected output: ```bash Map: 100%|██████████| 2/2 [00:00<00:00, 724.66 examples/s] my_dataset Map (num_proc=2): 100%|██████████| 2/2 [00:00<00:00, 18.25 examples/s] my_dataset ``` ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.17 - Python version: 3.8.18 - `huggingface_hub` version: 0.20.2 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - `fsspec` version: 2023.9.2

2024-01-12T14:08:24Z

https://github.com/huggingface/datasets/issues/6585

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I_kwDODunzps574rRe

open

np.fromfile not supported

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2024-01-12T09:46:17Z

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CONTRIBUTOR

How to do np.fromfile to use it like np.load ```python def xnumpy_fromfile(filepath_or_buffer, *args, download_config: Optional[DownloadConfig] = None, **kwargs): import numpy as np if hasattr(filepath_or_buffer, "read"): return np.fromfile(filepath_or_buffer, *args, **kwargs) else: filepath_or_buffer = str(filepath_or_buffer) return np.fromfile(xopen(filepath_or_buffer, "rb", download_config=download_config).read(), *args, **kwargs) ``` this is not work

2024-01-15T05:20:50Z

https://github.com/huggingface/datasets/issues/6584

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2024-01-11T16:09:24Z

PR_kwDODunzps5j1DzY

closed

remove eli5 test

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2024-01-11T16:05:20Z

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MEMBER

since the dataset is defunct

2024-01-11T16:15:34Z

https://github.com/huggingface/datasets/pull/6583

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2024-03-01T19:02:33Z

PR_kwDODunzps5jxpry

closed

Fix for Incorrect ex_iterable used with multi num_worker

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2024-01-11T08:49:43Z

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CONTRIBUTOR

Corrects an issue where `self._ex_iterable` was erroneously used instead of `ex_iterable`, when both Distributed Data Parallel (DDP) and multi num_worker are used concurrently. This improper usage led to the generation of incorrect `shards_indices`, subsequently causing issues with the control flow responsible for worker creation. The fix ensures the appropriate iterable is used, thus providing a more accurate determination of whether a new worker should be instantiated or not.

2024-03-01T19:09:14Z

https://github.com/huggingface/datasets/pull/6582

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2024-01-24T10:08:28Z

PR_kwDODunzps5jxIbt

closed

fix os.listdir return name is empty string

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2024-01-11T07:10:55Z

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CONTRIBUTOR

fix #6588 xlistdir return name is empty string for example： ` from datasets.download.streaming_download_manager import xjoin from datasets.download.streaming_download_manager import xlistdir config = DownloadConfig(storage_options=options) manger = StreamingDownloadManager("ILSVRC2012",download_config=config) input_path = "lakefs://datalab/main/imagenet/ILSVRC2012.zip" download_files = manger.download_and_extract(input_path) current_dir = xjoin(download_files,"ILSVRC2012/Images/ILSVRC2012_img_train") folder_list = xlistdir(current_dir) `

2024-01-24T10:14:43Z

https://github.com/huggingface/datasets/pull/6581

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2024-01-20T12:46:16Z

completed

I_kwDODunzps57t9Ry

closed

dataset cache only stores one config of the dataset in parquet dir, and uses that for all other configs resulting in showing same data in all configs.

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2024-01-11T03:14:18Z

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### Describe the bug ds = load_dataset("ai2_arc", "ARC-Easy"), i have tried to force redownload, delete cache and changing the cache dir. ### Steps to reproduce the bug dataset = [] dataset_name = "ai2_arc" possible_configs = [ 'ARC-Challenge', 'ARC-Easy' ] for config in possible_configs: dataset_slice = load_dataset(dataset_name, config,ignore_verifications=True,cache_dir='ai2_arc_files') dataset.append(dataset_slice) ### Expected behavior all configs should get saved in cache with their respective names. ### Environment info ai2_arc

2024-01-20T12:46:16Z

https://github.com/huggingface/datasets/issues/6580

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2024-01-11T09:19:17Z

not_planned

I_kwDODunzps57tDRx

closed

Unable to load `eli5` dataset with streaming

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2024-01-10T23:44:20Z

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### Describe the bug Unable to load `eli5` dataset with streaming. ### Steps to reproduce the bug This fails with FileNotFoundError: https://files.pushshift.io/reddit/submissions ``` from datasets import load_dataset load_dataset("eli5", streaming=True) ``` This works correctly. ``` from datasets import load_dataset load_dataset("eli5") ``` ### Expected behavior - Loading `eli5` dataset should not raise an error under the streaming mode. - Or at the very least, show a warning that streaming mode is not supported with `eli5` dataset. ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-6.2.0-39-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - `fsspec` version: 2023.6.0

2024-01-11T09:19:18Z

https://github.com/huggingface/datasets/issues/6579

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2024-01-30T18:39:51Z

PR_kwDODunzps5jtthB

closed

Faster webdataset streaming

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2024-01-10T18:18:09Z

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requests.get(..., streaming=True) is faster than using HTTP range requests when streaming large TAR files it can be enabled using block_size=0 in fsspec cc @rwightman

2024-01-30T18:46:02Z

https://github.com/huggingface/datasets/pull/6578

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2024-01-15T16:05:44Z

completed

I_kwDODunzps57qsvA

closed

502 Server Errors when streaming large dataset

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2024-01-10T16:59:36Z

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### Describe the bug When streaming a [large ASR dataset](https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set) from the Hug (~3TB) I often encounter 502 Server Errors seemingly randomly during streaming: ``` huggingface_hub.utils._errors.HfHubHTTPError: 502 Server Error: Bad Gateway for url: https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set/resolve/7d2acc5c59de848e456e951a76e805304d6fb350/train/train-00288-of-07135.parquet ``` This is despite the parquet file definitely existing on the Hub: https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set/blob/main/train/train-00228-of-07135.parquet And having the correct commit id: [7d2acc5c59de848e456e951a76e805304d6fb350](https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set/commits/main/train) I’m wondering whether this is coming from datasets? Or from the Hub side? ### Steps to reproduce the bug Reproducer: ```python from datasets import load_dataset from torch.utils.data import DataLoader from tqdm import tqdm NUM_EPOCHS = 20 dataset = load_dataset("sanchit-gandhi/concatenated-train-set", "train", streaming=True) dataset = dataset.with_format("torch") dataloader = DataLoader(dataset["train"], batch_size=256, drop_last=True, pin_memory=True, num_workers=16) for epoch in tqdm(range(NUM_EPOCHS), desc="Epoch", position=0): for batch in tqdm(dataloader, desc="Batch", position=1): continue ``` Running the above script tends to fail within about 2 hours with a traceback like the following: <details> <summary> Traceback: </summary> ```python 1029 for batch in train_loader: 1030 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ 1031 data = self._next_data() 1032 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1325, in _next_data 1033 return self._process_data(data) 1034 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data 1035 data.reraise() 1036 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/_utils.py", line 694, in reraise 1037 raise exception 1038 huggingface_hub.utils._errors.HfHubHTTPError: Caught HfHubHTTPError in DataLoader worker process 10. 1039 Original Traceback (most recent call last): 1040 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py", line 286, in hf_raise_for_status 1041 response.raise_for_status() 1042 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/requests/models.py", line 1021, in raise_for_status 1043 raise HTTPError(http_error_msg, response=self) 1044 requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set/resolve/7d2acc5c59de848e456e951a76e805304d6fb350/train/train-00288-of-07135.parquet 1045 The above exception was the direct cause of the following exception: 1046 Traceback (most recent call last): 1047 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop 1048 data = fetcher.fetch(index) 1049 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch 1050 data.append(next(self.dataset_iter)) 1051 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 1363, in __iter__ 1052 yield from self._iter_pytorch() 1053 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 1298, in _iter_pytorch 1054 for key, example in ex_iterable: 1055 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 983, in __iter__ 1056 for x in self.ex_iterable: 1057 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 863, in __iter__ 1058 yield from self._iter() 1059 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 900, in _iter 1060 for key, example in iterator: 1061 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 679, in __iter__ 1062 yield from self._iter() 1063 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 741, in _iter 1064 for key, example in iterator: 1065 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 863, in __iter__ 1066 yield from self._iter() 1067 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 900, in _iter 1068 for key, example in iterator: 1069 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 1115, in __iter__ 1070 for key, example in self.ex_iterable: 1071 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 679, in __iter__ 1072 yield from self._iter() 1073 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 741, in _iter 1074 for key, example in iterator: 1075 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 1115, in __iter__ 1076 for key, example in self.ex_iterable: 1077 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 282, in __iter__ 1078 for key, pa_table in self.generate_tables_fn(**self.kwargs): 1079 File "/home/sanchitgandhi/datasets/src/datasets/packaged_modules/parquet/parquet.py", line 87, in _generate_tables 1080 for batch_idx, record_batch in enumerate( 1081 File "pyarrow/_parquet.pyx", line 1367, in iter_batches 1082 File "pyarrow/types.pxi", line 88, in pyarrow.lib._datatype_to_pep3118 1083 File "/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py", line 341, in read_with_retries 1084 out = read(*args, **kwargs) 1085 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/fsspec/spec.py", line 1856, in read 1086 out = self.cache._fetch(self.loc, self.loc + length) 1087 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/fsspec/caching.py", line 189, in _fetch 1088 self.cache = self.fetcher(start, end) # new block replaces old 1089 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/huggingface_hub/hf_file_system.py", line 626, in _fetch_range 1090 hf_raise_for_status(r) 1091 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py", line 333, in hf_raise_for_status 1092 raise HfHubHTTPError(str(e), response=response) from e 1093 huggingface_hub.utils._errors.HfHubHTTPError: 502 Server Error: Bad Gateway for url: https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set/resolve/7d2acc5c59de848e456e951a76e805304d6fb350/train/train-00288-of-07135.parquet ``` </details> ### Expected behavior Should be able to stream the dataset without any 502 error. ### Environment info - `datasets` version: 2.16.2.dev0 - Platform: Linux-5.13.0-1023-gcp-x86_64-with-glibc2.29 - Python version: 3.8.10 - `huggingface_hub` version: 0.20.1 - PyArrow version: 14.0.2 - Pandas version: 2.0.3 - `fsspec` version: 2023.10.0

2024-02-12T11:46:03Z

https://github.com/huggingface/datasets/issues/6577

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2024-01-17T14:01:31Z

completed

I_kwDODunzps57mk4s

closed

document page 404 not found after redirection

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2024-01-10T06:48:14Z

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### Describe the bug The redirected page encountered 404 not found. ### Steps to reproduce the bug 1. In this tutorial: https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt original md: https://github.com/huggingface/course/blob/2c733c2246b8b7e0e6f19a9e5d15bb12df43b2a3/chapters/en/chapter5/4.mdx#L49 ``` By default, 🤗 Datasets will decompress the files needed to load a dataset. If you want to preserve hard drive space, you can pass `DownloadConfig(delete_extracted=True)` to the `download_config` argument of `load_dataset()`. See the [documentation](https://huggingface.co/docs/datasets/package_reference/builder_classes.html?#datasets.utils.DownloadConfig) for more details. ``` The documentation points to `https://huggingface.co/docs/datasets/package_reference/builder_classes.html?#datasets.utils.DownloadConfig` it shows `The documentation page PACKAGE_REFERENCE/BUILDER_CLASSES.HTML doesn’t exist in v2.16.1, but exists on the main version. Click [here](https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes.html) to redirect to the main version of the documentation.` But the redirected website `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes.html` is 404 not found. ### Expected behavior I Guess the redirected webisite should be `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes` (without `.html`) or `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes#datasets.DownloadConfig`. ### Environment info Datasets main

2024-01-17T14:01:31Z

https://github.com/huggingface/datasets/issues/6576

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2024-01-11T16:10:30Z

PR_kwDODunzps5jl1V6

closed

[IterableDataset] Fix `drop_last_batch`in map after shuffling or sharding

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2024-01-09T15:35:31Z

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It was not taken into account e.g. when passing to a DataLoader with num_workers>0 Fix https://github.com/huggingface/datasets/issues/6565

2024-01-11T16:16:54Z

https://github.com/huggingface/datasets/pull/6575

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2024-01-09T16:05:13Z

PR_kwDODunzps5jltBC

closed

Fix tests based on datasets that used to have scripts

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2024-01-09T15:16:16Z

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...now that `squad` and `paws` don't have a script anymore

2024-01-09T16:11:33Z

https://github.com/huggingface/datasets/pull/6574

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2024-01-11T16:11:04Z

PR_kwDODunzps5jlnaj

closed

[WebDataset] Audio support and bug fixes

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2024-01-09T15:03:04Z

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- Add audio support - Fix an issue where user-provided features with additional fields are not taken into account Close https://github.com/huggingface/datasets/issues/6569

2024-01-11T16:17:28Z

https://github.com/huggingface/datasets/pull/6573

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2024-02-25T08:13:01Z

PR_kwDODunzps5jlCO5

closed

Adding option for multipart achive download

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2024-01-09T13:35:44Z

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Right now we can only download multiple separate archives or a single file archive, but not multipart archives, such as those produced by `tar --multi-volume`. This PR allows for downloading and extraction of archives split into multiple parts. With the new `multi_part` field of the `DownloadConfig` set, the downloader will first retrieve all the files and attempt to concatenate them before starting extraction. This will obviously fail if files retrieved are actually multiple separate archives, so the option is set to `False` by default. Tests and docs incoming.

2024-02-25T08:13:01Z

https://github.com/huggingface/datasets/pull/6572

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I_kwDODunzps57geeY

open

Make DatasetDict.column_names return a list instead of dict

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2024-01-09T10:45:17Z

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Currently, `DatasetDict.column_names` returns a dict, with each split name as keys and the corresponding list of column names as values. However, by construction, all splits have the same column names. I think it makes more sense to return a single list with the column names, which is the same for all the split keys.

2024-01-09T10:45:17Z

https://github.com/huggingface/datasets/issues/6571

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2024-01-09T16:45:50Z

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No online docs for 2.16 release

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2024-01-09T07:43:30Z

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We do not have the online docs for the latest minor release 2.16 (2.16.0 nor 2.16.1). In the online docs, the latest version appearing is 2.15.0: https://huggingface.co/docs/datasets/index 

2024-01-09T16:45:50Z

https://github.com/huggingface/datasets/issues/6570

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2024-01-11T16:11:05Z

completed

I_kwDODunzps57ZYZy

closed

WebDataset ignores features defined in YAML or passed to load_dataset

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2024-01-08T11:24:21Z

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we should not override if the features exist already https://github.com/huggingface/datasets/blob/d26abadce0b884db32382b92422d8a6aa997d40a/src/datasets/packaged_modules/webdataset/webdataset.py#L78-L85

2024-01-11T16:11:06Z

https://github.com/huggingface/datasets/issues/6569

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I_kwDODunzps57YIFn

open

keep_in_memory=True does not seem to work

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2024-01-08T08:03:58Z

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UPD: [Fixed](https://github.com/huggingface/datasets/issues/6568#issuecomment-1880817794) . But a new issue came up :(

2024-01-13T04:53:04Z

https://github.com/huggingface/datasets/issues/6568

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2024-01-08T10:03:17Z

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closed

AttributeError: 'str' object has no attribute 'to'

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### Describe the bug ``` -------------------------------------------------------------------------- AttributeError Traceback (most recent call last) [<ipython-input-6-80c6086794e8>](https://localhost:8080/#) in <cell line: 10>() 8 report_to="wandb") 9 ---> 10 trainer = Trainer( 11 model=model, 12 args=training_args, 1 frames [/usr/local/lib/python3.10/dist-packages/transformers/trainer.py](https://localhost:8080/#) in _move_model_to_device(self, model, device) 688 689 def _move_model_to_device(self, model, device): --> 690 model = model.to(device) 691 # Moving a model to an XLA device disconnects the tied weights, so we have to retie them. 692 if self.args.parallel_mode == ParallelMode.TPU and hasattr(model, "tie_weights"): AttributeError: 'str' object has no attribute 'to' ``` ### Steps to reproduce the bug here is the notebook: ``` https://colab.research.google.com/drive/10JDBNsLlYrQdnI2FWfDK3F5M8wvVUDXG?usp=sharing ``` ### Expected behavior run the Training ### Environment info Colab Notebook , T4

2024-01-08T11:56:19Z

https://github.com/huggingface/datasets/issues/6567

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open

I train controlnet_sdxl in bf16 datatype, got unsupported ERROR in datasets

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2024-01-08T02:37:03Z

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### Describe the bug ``` Traceback (most recent call last): File "train_controlnet_sdxl.py", line 1252, in <module> main(args) File "train_controlnet_sdxl.py", line 1013, in main train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint) File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3093, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3489, in _map_single writer.write_batch(batch) File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_writer.py", line 557, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 248, in pyarrow.lib.array File "pyarrow/array.pxi", line 113, in pyarrow.lib._handle_arrow_array_protocol File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_writer.py", line 191, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/features/features.py", line 447, in cast_to_python_objects return _cast_to_python_objects( File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/features/features.py", line 324, in _cast_to_python_objects for x in obj.detach().cpu().numpy() TypeError: Got unsupported ScalarType BFloat16 ``` ### Steps to reproduce the bug Here is my train script I use BF16 type，I use diffusers train my model ``` export MODEL_DIR="/home/mhh/sd_models/stable-diffusion-xl-base-1.0" export OUTPUT_DIR="./control_net" export VAE_NAME="/home/mhh/sd_models/sdxl-vae-fp16-fix" accelerate launch train_controlnet_sdxl.py \ --pretrained_model_name_or_path=$MODEL_DIR \ --output_dir=$OUTPUT_DIR \ --pretrained_vae_model_name_or_path=$VAE_NAME \ --dataset_name=/home/mhh/sd_datasets/fusing/fill50k \ --mixed_precision="bf16" \ --resolution=1024 \ --learning_rate=1e-5 \ --max_train_steps=200 \ --validation_image "/home/mhh/sd_datasets/controlnet_image/conditioning_image_1.png" "/home/mhh/sd_datasets/controlnet_image/conditioning_image_2.png" \ --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \ --validation_steps=50 \ --train_batch_size=1 \ --gradient_accumulation_steps=4 \ --report_to="wandb" \ --seed=42 \ ``` ### Expected behavior When I changed the data type to fp16, it worked. ### Environment info datasets 2.16.1 numpy 1.24.4

2024-01-20T00:29:18Z

https://github.com/huggingface/datasets/issues/6566

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2024-01-11T16:10:31Z

completed

I_kwDODunzps57UYOW

closed

`drop_last_batch=True` for IterableDataset map function is ignored with multiprocessing DataLoader

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2024-01-07T02:46:50Z

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NONE

### Describe the bug Scenario: - Interleaving two iterable datasets of unequal lengths (`all_exhausted`), followed by a batch mapping with batch size 2 to effectively merge the two datasets and get a sample from each dataset in a single batch, with `drop_last_batch=True` to skip the last batch in case it doesn't have two samples. What works: - Using DataLoader with `num_workers=0` What does not work: - Using DataLoader with `num_workers=1`, errors in the last batch. Basically, `drop_last_batch=True` is ignored when using multiple dataloading workers. Please take a look at the minimal repro script below. ### Steps to reproduce the bug ```python from datasets import Dataset, interleave_datasets from torch.utils.data import DataLoader def merge_samples(batch): assert len(batch['a']) == 2, "Batch size must be 2" batch['c'] = [batch['a'][0]] batch['d'] = [batch['a'][1]] return batch def gen1(): for ii in range(1, 8385): yield {"a": ii} def gen2(): for ii in range(1, 5302): yield {"a": ii} if __name__ == '__main__': dataset1 = Dataset.from_generator(gen1).to_iterable_dataset(num_shards=1024) dataset2 = Dataset.from_generator(gen2).to_iterable_dataset(num_shards=1024) interleaved = interleave_datasets([dataset1, dataset2], stopping_strategy="all_exhausted") mapped = interleaved.map(merge_samples, batched=True, batch_size=2, remove_columns=interleaved.column_names, drop_last_batch=True) # Works loader = DataLoader(mapped, batch_size=32, num_workers=0) i = 0 for b in loader: print(i, b['c'].shape, b['d'].shape) i += 1 print("DataLoader with num_workers=0 works") # Doesn't work loader = DataLoader(mapped, batch_size=32, num_workers=1) i = 0 for b in loader: print(i, b['c'].shape, b['d'].shape) i += 1 ``` ### Expected behavior `drop_last_batch=True` should have same behaviour for `num_workers=0` and `num_workers>=1` ### Environment info - `datasets` version: 2.16.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.10.12 - `huggingface_hub` version: 0.20.2 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - `fsspec` version: 2023.6.0 I have also tested on Linux and got the same behavior.

2024-01-11T16:10:31Z

https://github.com/huggingface/datasets/issues/6565

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2024-01-29T16:36:54Z

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I_kwDODunzps57UM4K

closed

`Dataset.filter` missing `with_rank` parameter

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2024-01-06T23:48:13Z

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### Describe the bug The issue shall be open: https://github.com/huggingface/datasets/issues/6435 When i try to pass `with_rank` to `Dataset.filter()`, i get this: `Dataset.filter() got an unexpected keyword argument 'with_rank'` ### Steps to reproduce the bug Run notebook: https://colab.research.google.com/drive/1WUNKph8BdP0on5ve3gQnh_PE0cFLQqTn?usp=sharing ### Expected behavior Should work? ### Environment info NVIDIA RTX 4090

2024-01-29T16:36:55Z

https://github.com/huggingface/datasets/issues/6564

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2024-01-06T16:13:27Z

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I_kwDODunzps57R8pC

closed

`ImportError`: cannot import name 'insecure_hashlib' from 'huggingface_hub.utils' (.../huggingface_hub/utils/__init__.py)

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2024-01-06T02:28:54Z

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NONE

### Describe the bug Yep its not [there](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/utils/__init__.py) anymore. ```text + python /home/trainer/sft_train.py --model_name cognitivecomputations/dolphin-2.2.1-mistral-7b --dataset_name wasertech/OneOS --load_in_4bit --use_peft --batch_size 4 --num_train_epochs 1 --learning_rate 1.41e-5 --gradient_accumulation_steps 8 --seq_length 4096 --output_dir output --log_with wandb Traceback (most recent call last): File "/home/trainer/sft_train.py", line 22, in <module> from datasets import load_dataset File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/__init__.py", line 22, in <module> from .arrow_dataset import Dataset File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 66, in <module> from .arrow_reader import ArrowReader File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/arrow_reader.py", line 30, in <module> from .download.download_config import DownloadConfig File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/download/__init__.py", line 9, in <module> from .download_manager import DownloadManager, DownloadMode File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/download/download_manager.py", line 31, in <module> from ..utils import tqdm as hf_tqdm File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/utils/__init__.py", line 19, in <module> from .info_utils import VerificationMode File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 5, in <module> from huggingface_hub.utils import insecure_hashlib ImportError: cannot import name 'insecure_hashlib' from 'huggingface_hub.utils' (/home/trainer/llm-train/lib/python3.8/site-packages/huggingface_hub/utils/__init__.py) ``` ### Steps to reproduce the bug Using `datasets==2.16.1` and `huggingface_hub== 0.17.3`, load a dataset with `load_dataset`. ### Expected behavior The dataset should be (downloaded - if needed - and) returned. ### Environment info ```text trainer@a311ae86939e:/mnt$ pip show datasets Name: datasets Version: 2.16.1 Summary: HuggingFace community-driven open-source library of datasets Home-page: https://github.com/huggingface/datasets Author: HuggingFace Inc. Author-email: thomas@huggingface.co License: Apache 2.0 Location: /home/trainer/llm-train/lib/python3.8/site-packages Requires: packaging, pyyaml, multiprocess, pyarrow-hotfix, pandas, pyarrow, xxhash, dill, numpy, aiohttp, tqdm, fsspec, requests, filelock, huggingface-hub Required-by: trl, lm-eval, evaluate trainer@a311ae86939e:/mnt$ pip show huggingface_hub Name: huggingface-hub Version: 0.17.3 Summary: Client library to download and publish models, datasets and other repos on the huggingface.co hub Home-page: https://github.com/huggingface/huggingface_hub Author: Hugging Face, Inc. Author-email: julien@huggingface.co License: Apache Location: /home/trainer/llm-train/lib/python3.8/site-packages Requires: requests, pyyaml, packaging, typing-extensions, tqdm, filelock, fsspec Required-by: transformers, tokenizers, peft, evaluate, datasets, accelerate trainer@a311ae86939e:/mnt$ huggingface-cli env Copy-and-paste the text below in your GitHub issue. - huggingface_hub version: 0.17.3 - Platform: Linux-6.5.13-7-MANJARO-x86_64-with-glibc2.29 - Python version: 3.8.10 - Running in iPython ?: No - Running in notebook ?: No - Running in Google Colab ?: No - Token path ?: /home/trainer/.cache/huggingface/token - Has saved token ?: True - Who am I ?: wasertech - Configured git credential helpers: - FastAI: N/A - Tensorflow: N/A - Torch: 2.1.2 - Jinja2: 3.1.2 - Graphviz: N/A - Pydot: N/A - Pillow: 10.2.0 - hf_transfer: N/A - gradio: N/A - tensorboard: N/A - numpy: 1.24.4 - pydantic: N/A - aiohttp: 3.9.1 - ENDPOINT: https://huggingface.co - HUGGINGFACE_HUB_CACHE: /home/trainer/.cache/huggingface/hub - HUGGINGFACE_ASSETS_CACHE: /home/trainer/.cache/huggingface/assets - HF_TOKEN_PATH: /home/trainer/.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 ```

2024-01-06T21:05:37Z

https://github.com/huggingface/datasets/issues/6563

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open

datasets.DownloadMode.FORCE_REDOWNLOAD use cache to download dataset features with load_dataset function

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2024-01-05T19:10:25Z

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NONE

### Describe the bug I have updated my dataset by adding a new feature, and push it to the hub. When I want to download it on my machine which contain the old version by using `datasets.load_dataset("your_dataset_name", download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD)` I get an error (paste bellow). Seems that the load_dataset function still use the old features schema instead of downloading everything new from the HUB. I find a way to go around this issue by manually deleting the old dataset cache. But from my understanding of `datasets.DownloadMode.FORCE_REDOWNLOAD` option, the dataset cache should be ignored. ### Steps to reproduce the bug 1. Download your dataset in your machine using `datasets.load_dataset` 2. Create a new feature in your dataset and push it to the hub 3. On the same machine redownload your dataset using `datasets.load_dataset("your_dataset_name", download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD)` ### Expected behavior ` ValueError: Couldn't cast id: string level: string context: list<element: string> child 0, element: string type: string answer: string question: string supporting_facts: list<element: string> child 0, element: string fra_answer: string fra_question: string -- schema metadata -- huggingface: '{"info": {"features": {"id": {"dtype": "string", "_type": "' + 490 to {'id': Value(dtype='string', id=None), 'level': Value(dtype='string', id=None), 'context': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'type': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'supporting_facts': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError ... DatasetGenerationError: An error occurred while generating the dataset` ### Environment info datasets-2.16.1 huggingface-hub-0.20.2

2024-01-05T19:10:25Z

https://github.com/huggingface/datasets/issues/6562

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open

Document YAML configuration with "data_dir"

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2024-01-05T14:03:33Z

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CONTRIBUTOR

See https://huggingface.co/datasets/uonlp/CulturaX/discussions/15#6597e83f185db94370d6bf50 for reference

2024-01-05T14:06:18Z

https://github.com/huggingface/datasets/issues/6561

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open

Support Video

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2024-01-04T13:10:58Z

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### Feature request HF datasets are awesome in supporting text and images. Will be great to see such a support in videos :) ### Motivation Video generation :) ### Your contribution Will probably be limited to raising this feature request ;)

2024-01-04T13:10:58Z

https://github.com/huggingface/datasets/issues/6560

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2024-01-05T01:26:25Z

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closed

Latest version 2.16.1, when load dataset error occurs. ValueError: BuilderConfig 'allenai--c4' not found. Available: ['default']

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2024-01-04T07:04:48Z

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NONE

### Describe the bug python script is: ``` from datasets import load_dataset cache_dir = 'path/to/your/cache/directory' dataset = load_dataset('allenai/c4','allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', use_auth_token=False, cache_dir=cache_dir) ``` the script success when datasets version is 2.14.7. when using 2.16.1, error occurs ` ValueError: BuilderConfig 'allenai--c4' not found. Available: ['default']` ### Steps to reproduce the bug 1. pip install datasets==2.16.1 2. run python script: ``` from datasets import load_dataset cache_dir = 'path/to/your/cache/directory' dataset = load_dataset('allenai/c4','allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', use_auth_token=False, cache_dir=cache_dir) ``` ### Expected behavior the dataset should be loaded successful in the latest version. ### Environment info datasets 2.16.1

2024-01-05T01:26:26Z

https://github.com/huggingface/datasets/issues/6559

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2024-02-21T00:38:12Z

completed

I_kwDODunzps57E6jg

closed

OSError: image file is truncated (1 bytes not processed) #28323

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### Describe the bug ``` --------------------------------------------------------------------------- OSError Traceback (most recent call last) Cell In[24], line 28 23 return example 25 # Filter the dataset 26 # filtered_dataset = dataset.filter(contains_number) 27 # Add the 'label' field in the dataset ---> 28 labeled_dataset = dataset.filter(contains_number).map(add_label) 29 # View the structure of the updated dataset 30 print(labeled_dataset) File /usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py:975, in DatasetDict.filter(self, function, with_indices, input_columns, batched, batch_size, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, fn_kwargs, num_proc, desc) 972 if cache_file_names is None: 973 cache_file_names = {k: None for k in self} 974 return DatasetDict( --> 975 { 976 k: dataset.filter( 977 function=function, 978 with_indices=with_indices, 979 input_columns=input_columns, 980 batched=batched, 981 batch_size=batch_size, 982 keep_in_memory=keep_in_memory, 983 load_from_cache_file=load_from_cache_file, 984 cache_file_name=cache_file_names[k], 985 writer_batch_size=writer_batch_size, 986 fn_kwargs=fn_kwargs, 987 num_proc=num_proc, 988 desc=desc, 989 ) 990 for k, dataset in self.items() 991 } 992 ) File /usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py:976, in <dictcomp>(.0) 972 if cache_file_names is None: 973 cache_file_names = {k: None for k in self} 974 return DatasetDict( 975 { --> 976 k: dataset.filter( 977 function=function, 978 with_indices=with_indices, 979 input_columns=input_columns, 980 batched=batched, 981 batch_size=batch_size, 982 keep_in_memory=keep_in_memory, 983 load_from_cache_file=load_from_cache_file, 984 cache_file_name=cache_file_names[k], 985 writer_batch_size=writer_batch_size, 986 fn_kwargs=fn_kwargs, 987 num_proc=num_proc, 988 desc=desc, 989 ) 990 for k, dataset in self.items() 991 } 992 ) File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:557, in transmit_format.<locals>.wrapper(*args, **kwargs) 550 self_format = { 551 "type": self._format_type, 552 "format_kwargs": self._format_kwargs, 553 "columns": self._format_columns, 554 "output_all_columns": self._output_all_columns, 555 } 556 # apply actual function --> 557 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 558 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 559 # re-apply format to the output File /usr/local/lib/python3.10/dist-packages/datasets/fingerprint.py:481, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs) 477 validate_fingerprint(kwargs[fingerprint_name]) 479 # Call actual function --> 481 out = func(dataset, *args, **kwargs) 483 # Update fingerprint of in-place transforms + update in-place history of transforms 485 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3623, in Dataset.filter(self, function, with_indices, input_columns, batched, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 3620 if len(self) == 0: 3621 return self -> 3623 indices = self.map( 3624 function=partial( 3625 get_indices_from_mask_function, function, batched, with_indices, input_columns, self._indices 3626 ), 3627 with_indices=True, 3628 features=Features({"indices": Value("uint64")}), 3629 batched=True, 3630 batch_size=batch_size, 3631 remove_columns=self.column_names, 3632 keep_in_memory=keep_in_memory, 3633 load_from_cache_file=load_from_cache_file, 3634 cache_file_name=cache_file_name, 3635 writer_batch_size=writer_batch_size, 3636 fn_kwargs=fn_kwargs, 3637 num_proc=num_proc, 3638 suffix_template=suffix_template, 3639 new_fingerprint=new_fingerprint, 3640 input_columns=input_columns, 3641 desc=desc or "Filter", 3642 ) 3643 new_dataset = copy.deepcopy(self) 3644 new_dataset._indices = indices.data File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:592, in transmit_tasks.<locals>.wrapper(*args, **kwargs) 590 self: "Dataset" = kwargs.pop("self") 591 # apply actual function --> 592 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 593 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 594 for dataset in datasets: 595 # Remove task templates if a column mapping of the template is no longer valid File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:557, in transmit_format.<locals>.wrapper(*args, **kwargs) 550 self_format = { 551 "type": self._format_type, 552 "format_kwargs": self._format_kwargs, 553 "columns": self._format_columns, 554 "output_all_columns": self._output_all_columns, 555 } 556 # apply actual function --> 557 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 558 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 559 # re-apply format to the output File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3093, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 3087 if transformed_dataset is None: 3088 with hf_tqdm( 3089 unit=" examples", 3090 total=pbar_total, 3091 desc=desc or "Map", 3092 ) as pbar: -> 3093 for rank, done, content in Dataset._map_single(**dataset_kwargs): 3094 if done: 3095 shards_done += 1 File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3470, in Dataset._map_single(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset) 3466 indices = list( 3467 range(*(slice(i, i + batch_size).indices(shard.num_rows))) 3468 ) # Something simpler? 3469 try: -> 3470 batch = apply_function_on_filtered_inputs( 3471 batch, 3472 indices, 3473 check_same_num_examples=len(shard.list_indexes()) > 0, 3474 offset=offset, 3475 ) 3476 except NumExamplesMismatchError: 3477 raise DatasetTransformationNotAllowedError( 3478 "Using `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn't create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it." 3479 ) from None File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3349, in Dataset._map_single.<locals>.apply_function_on_filtered_inputs(pa_inputs, indices, check_same_num_examples, offset) 3347 if with_rank: 3348 additional_args += (rank,) -> 3349 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) 3350 if isinstance(processed_inputs, LazyDict): 3351 processed_inputs = { 3352 k: v for k, v in processed_inputs.data.items() if k not in processed_inputs.keys_to_format 3353 } File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:6212, in get_indices_from_mask_function(function, batched, with_indices, input_columns, indices_mapping, *args, **fn_kwargs) 6209 if input_columns is None: 6210 # inputs only contains a batch of examples 6211 batch: dict = inputs[0] -> 6212 num_examples = len(batch[next(iter(batch.keys()))]) 6213 for i in range(num_examples): 6214 example = {key: batch[key][i] for key in batch} File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:272, in LazyDict.__getitem__(self, key) 270 value = self.data[key] 271 if key in self.keys_to_format: --> 272 value = self.format(key) 273 self.data[key] = value 274 self.keys_to_format.remove(key) File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:375, in LazyBatch.format(self, key) 374 def format(self, key): --> 375 return self.formatter.format_column(self.pa_table.select([key])) File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:442, in PythonFormatter.format_column(self, pa_table) 440 def format_column(self, pa_table: pa.Table) -> list: 441 column = self.python_arrow_extractor().extract_column(pa_table) --> 442 column = self.python_features_decoder.decode_column(column, pa_table.column_names[0]) 443 return column File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:218, in PythonFeaturesDecoder.decode_column(self, column, column_name) 217 def decode_column(self, column: list, column_name: str) -> list: --> 218 return self.features.decode_column(column, column_name) if self.features else column File /usr/local/lib/python3.10/dist-packages/datasets/features/features.py:1951, in Features.decode_column(self, column, column_name) 1938 def decode_column(self, column: list, column_name: str): 1939 """Decode column with custom feature decoding. 1940 1941 Args: (...) 1948 `list[Any]` 1949 """ 1950 return ( -> 1951 [decode_nested_example(self[column_name], value) if value is not None else None for value in column] 1952 if self._column_requires_decoding[column_name] 1953 else column 1954 ) File /usr/local/lib/python3.10/dist-packages/datasets/features/features.py:1951, in <listcomp>(.0) 1938 def decode_column(self, column: list, column_name: str): 1939 """Decode column with custom feature decoding. 1940 1941 Args: (...) 1948 `list[Any]` 1949 """ 1950 return ( -> 1951 [decode_nested_example(self[column_name], value) if value is not None else None for value in column] 1952 if self._column_requires_decoding[column_name] 1953 else column 1954 ) File /usr/local/lib/python3.10/dist-packages/datasets/features/features.py:1339, in decode_nested_example(schema, obj, token_per_repo_id) 1336 elif isinstance(schema, (Audio, Image)): 1337 # we pass the token to read and decode files from private repositories in streaming mode 1338 if obj is not None and schema.decode: -> 1339 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) 1340 return obj File /usr/local/lib/python3.10/dist-packages/datasets/features/image.py:185, in Image.decode_example(self, value, token_per_repo_id) 183 else: 184 image = PIL.Image.open(BytesIO(bytes_)) --> 185 image.load() # to avoid "Too many open files" errors 186 return image File /usr/local/lib/python3.10/dist-packages/PIL/ImageFile.py:254, in ImageFile.load(self) 252 break 253 else: --> 254 raise OSError( 255 "image file is truncated " 256 f"({len(b)} bytes not processed)" 257 ) 259 b = b + s 260 n, err_code = decoder.decode(b) OSError: image file is truncated (1 bytes not processed) ``` ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset("mehul7/captioned_military_aircraft") from transformers import AutoImageProcessor checkpoint = "microsoft/resnet-50" image_processor = AutoImageProcessor.from_pretrained(checkpoint) import re from PIL import Image import io def contains_number(example): try: image = Image.open(io.BytesIO(example["image"]['bytes'])) t = image_processor(images=image, return_tensors="pt")['pixel_values'] except Exception as e: print(f"Error processing image：{example['text']}") return False return bool(re.search(r'\d', example['text'])) # Define a function to add the 'label' field def add_label(example): lab = example['text'].split() temp = 'NOT' for item in lab: if str(item[-1]).isdigit(): temp = item break example['label'] = temp return example # Filter the dataset # filtered_dataset = dataset.filter(contains_number) # Add the 'label' field in the dataset labeled_dataset = dataset.filter(contains_number).map(add_label) # View the structure of the updated dataset print(labeled_dataset) ``` ### Expected behavior needs to form labels same as : https://www.kaggle.com/code/jiabaowangts/dataset-air/notebook ### Environment info Kaggle notebook P100

2024-02-21T00:38:12Z

https://github.com/huggingface/datasets/issues/6558

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2024-01-11T17:53:42Z

PR_kwDODunzps5jJ63z

closed

Support standalone yaml

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2024-01-03T16:47:35Z

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see (internal) https://huggingface.slack.com/archives/C02V51Q3800/p1703885853581679

2024-01-11T17:59:51Z

https://github.com/huggingface/datasets/pull/6557

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2024-01-09T13:06:30Z

PR_kwDODunzps5jI0nN

closed

Fix imagefolder with one image

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2024-01-03T13:13:02Z

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A dataset repository with one image and one metadata file was considered a JSON dataset instead of an ImageFolder dataset. This is because we pick the dataset type with the most compatible data file extensions present in the repository and it results in a tie in this case. e.g. for https://huggingface.co/datasets/multimodalart/repro_1_image I fixed this by deprioritizing metadata files in the count. fix https://github.com/huggingface/datasets/issues/6545

2024-02-12T21:57:34Z

https://github.com/huggingface/datasets/pull/6556

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2024-02-02T10:35:28Z

PR_kwDODunzps5jIM79

closed

Do not use Parquet exports if revision is passed

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2024-01-03T11:33:10Z

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