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I_kwDODunzps5v6D-S

Support loading from a DVC remote repository

open

NONE

### Feature request Adding support for loading a file from a DVC repository, tracked remotely on a SCM. ### Motivation DVC is a popular version control system to version and manage datasets. The files are stored on a remote object storage platform, but they are tracked using Git. Integration with DVC is possible through the `DVCFileSystem`. I have a Gitlab repository where multiple files are tracked using DVC and stored in a GCP bucket. I would like to be able to load these files using `datasets` directly using an URL. My goal is to write a generic code that abstracts the storage layer, such that my users will only have to pass in an `fsspec`-compliant URL and the corresponding files will be loaded. ### Your contribution I managed to instantiate a `DVCFileSystem` pointing to a Gitlab repo from a `fsspec` chained URL in [this pull request](https://github.com/iterative/dvc/pull/9903) to DVC. ```python from fsspec.core import url_to_fs fs, _ = url_to_fs("dvc::https://gitlab.com/repository/group/my-repo") ``` From now I'm not sure how to continue, it seems that `datasets` expects the URL to be fully qualified like so: `dvc::https://gitlab.com/repository/group/my-repo/my-folder/my-file.json` but this fails because `DVCFileSystem` expects the URL to point to the root of an SCM repo. Is there a way to make this work with `datasets`?

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I_kwDODunzps5v2xtP

avoid downgrading jax version

open

NONE

### Feature request Whenever I `pip install datasets[jax]` it downgrades jax to version 0.3.25. I seem to be able to install this library first then upgrade jax back to version 0.4.13. ### Motivation It would be nice to not overwrite currently installed version of jax if possible. ### Your contribution I would be willing to beta test. Or maybe write some code if I could get pointed in the right direction, I'm not super familiar with this codebase.

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https://github.com/huggingface/datasets/pull/6201

PR_kwDODunzps5ZOVbV

Fix to_json ValueError and remove pandas pin

open

MEMBER

This PR fixes the root cause of the issue: - #6197 This PR also removes the temporary pin of `pandas` introduced by: - #6200 Note that for orient in ['records', 'values'], index value is ignored but - in `pandas` < 2.1.0, a ValueError is raised if not index and orient not in ['split', 'table'] - for orient = 'records', we need index = True - default index value is True - in `pandas` = 2.1.0, a ValueError is raised if index is True and orient in ['records', 'values'] - for orient = 'records', we need index = False or None - default index value is None This PR fixes the issue by not passing index and thus using default index value (valid for all pandas versions), unless orient is 'split' or 'table' (where we pass index = False, as it was done before this fix).

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PR_kwDODunzps5ZOCee

Temporarily pin pandas < 2.1.0

closed

MEMBER

Temporarily pin `pandas` < 2.1.0 until permanent solution is found. Hot fix #6197.

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I_kwDODunzps5vxMAB

Use load_dataset for local json files, but it not works

open

NONE

### Describe the bug when I use load_dataset to load my local datasets，it always goes to Hugging Face to download the data instead of loading the local dataset. ### Steps to reproduce the bug `raw_datasets = load_dataset( ‘json’, data_files=data_files)` ### Expected behavior  ### Environment info python version 3.8.5 datasets version 2.12 os version unbuntu 18.04

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https://github.com/huggingface/datasets/pull/6198

PR_kwDODunzps5ZNyBq

Preserve split order in DataFilesDict

closed

MEMBER

After investigation, I have found that this copy forces the splits to be sorted alphabetically: https://github.com/huggingface/datasets/blob/029227a116c14720afca71b9b22e78eb2a1c09a6/src/datasets/builder.py#L556 This PR removes the alphabetically sort of `DataFilesDict` keys. - Note that for a `dict`, the order of keys is relevant when hashing: ```python hash1 = Hasher.hash({'train': 'train.csv', 'test': 'test.csv'}) hash2 = Hasher.hash({'test': 'test.csv', 'train': 'train.csv'}) assert hash1 != hash2 ``` - The `DataFilesDict` is a subclass of `dict`, thus the order should be relevant as well ```python hash1 = Hasher.hash(DataFilesDict({'train': 'train.csv', 'test': 'test.csv'})) hash2 = Hasher.hash(DataFilesDict({'test': 'test.csv', 'train': 'train.csv'})) assert hash1 != hash2 ``` Fix #6196.

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I_kwDODunzps5vw2wL

ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns'

closed

NONE

### Describe the bug Saving a dataset `.to_json()` fails with a `ValueError` since the latest `pandas` [release](https://pandas.pydata.org/docs/dev/whatsnew/v2.1.0.html) (`2.1.0`) In their latest release we have: > Improved error handling when using [DataFrame.to_json()](https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json) with incompatible index and orient arguments ([GH 52143](https://github.com/pandas-dev/pandas/issues/52143)) i.e. an error is now raised for invalid combinations of `index` and `orient`. This means that unfortunately the custom logic at this line might sometimes lead to contradictions: https://github.com/huggingface/datasets/blob/029227a116c14720afca71b9b22e78eb2a1c09a6/src/datasets/io/json.py#L96 e.g. for the default case `orient=records` leads to `index=True`, which now raises a `ValueError` ### Steps to reproduce the bug ```python import datasets if __name__ == '__main__': dataset = datasets.Dataset.from_dict({"A": [1, 2, 3], "B": [4, 5, 6]}) dataset.to_json("dataset.json") ``` ```shell >>> ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns'. ``` ### Expected behavior The dataset is successfully saved as `.json` ### Environment info `python >= 3.9` `pandas >= 2.1.0`

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I_kwDODunzps5vw0_8

Split order is not preserved

closed

MEMBER

I have noticed that in some cases the split order is not preserved. For example, consider a no-script dataset with configs: ```yaml configs: - config_name: default data_files: - split: train path: train.csv - split: test path: test.csv ``` - Note the defined split order is [train, test] Once the dataset is loaded, the split order is not preserved: ```python In [16]: ds Out[16]: DatasetDict({ test: Dataset({ features: ['text', 'label'], num_rows: 1 }) train: Dataset({ features: ['text', 'label'], num_rows: 2 }) }) ``` - Note the obtained split order is [test, train]

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I_kwDODunzps5vtfSB

Force to reuse cache at given path

closed

NONE

### Describe the bug I have run the official example of MLM like: ```bash python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name togethercomputer/RedPajama-Data-1T \ --dataset_config_name arxiv \ --per_device_train_batch_size 10 \ --preprocessing_num_workers 20 \ --validation_split_percentage 0 \ --cache_dir /project/huggingface_cache/datasets \ --line_by_line \ --do_train \ --pad_to_max_length \ --output_dir /project/huggingface_cache/test-mlm ``` it successfully runs and at my cache folder has `cache-1982fea76aa54a13_00001_of_00020.arrow`..... `cache-1982fea76aa54a13_00020_of_00020.arrow ` as tokenization cache of `map` method. And the cache works fine every time I run the command above. However, when I switched to jupyter notebook (since I do not want to load datasets every time when I changed other parameters not related to the dataloading). It is not recognizing the cache files and starts to re-run the entire tokenization process. I changed my code to ```python tokenized_datasets = raw_datasets["train"].map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[text_column_name], load_from_cache_file=True, desc="Running tokenizer on dataset line_by_line", # cache_file_names= {"train": "cache-1982fea76aa54a13.arrow"} cache_file_name="cache-1982fea76aa54a13.arrow", new_fingerprint="1982fea76aa54a13" ) ``` it still does not recognize the previously cached files and trying to re-run the tokenization process. ### Steps to reproduce the bug use jupyter notebook for dataset map function. ### Expected behavior the map function accepts the given cache_file_name and new_fingerprint then load the previously cached files. ### Environment info - `datasets` version: 2.14.4.dev0 - Platform: Linux-3.10.0-1160.59.1.el7.x86_64-x86_64-with-glibc2.10 - Python version: 3.8.8 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3

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I_kwDODunzps5vnZTP

Support custom fingerprinting with `Dataset.from_generator`

open

NONE

### Feature request When using `Dataset.from_generator`, the generator is hashed when building the fingerprint. Similar to `.map`, it would be interesting to let the user bypass this hashing by accepting a `fingerprint` argument to `.from_generator`. ### Motivation Using the `.from_generator` constructor with a non-picklable generator fails. By accepting a `fingerprint` argument to `.from_generator`, the user would have the opportunity to manually fingerprint the dataset and thus bypass the crash. ### Your contribution If validated, I can try to submit a PR for this.

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I_kwDODunzps5vmM3h

Dataset loading script method does not work with .pyc file

open

NONE

### Describe the bug The huggingface dataset library specifically looks for ‘.py’ file while loading the dataset using loading script approach and it does not work with ‘.pyc’ file. While deploying in production, it becomes an issue when we are restricted to use only .pyc files. Is there any work around for this ? ### Steps to reproduce the bug 1. Create a dataset loading script to read the custom data. 2. compile the code to make sure that .pyc file is created 3. Delete the loading script and re-run the code. Usually, python should make use of complied .pyc files. However, in this case, the dataset library errors out with the message that it's unable to find the data loader loading script. ### Expected behavior The code should make use of .pyc file and run without any error. ### Environment info NA

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PR_kwDODunzps5ZDGnI

Set minimal fsspec version requirement to 2023.1.0

closed

CONTRIBUTOR

Fix https://github.com/huggingface/datasets/issues/6141 Colab installs 2023.6.0, so we should be good 🙂

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PR_kwDODunzps5ZCKmv

Add missing `revision` argument

closed

CONTRIBUTOR

I've noticed that when you're not working on the main branch, there are sometimes errors in the files returned. After some investigation, I realized that the revision was not properly passed everywhere. This PR proposes a fix.

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I_kwDODunzps5vjhPf

`Invalid user token` even when correct user token is passed!

closed

MEMBER

### Describe the bug I'm working on a dataset which comprises other datasets on the hub. URL: https://huggingface.co/datasets/open-asr-leaderboard/datasets-test-only Note: Some of the sub-datasets in this metadataset require explicit access. All the other datasets work fine, except, `common_voice`. ### Steps to reproduce the bug https://github.com/Vaibhavs10/scratchpad/blob/main/cv_datasets_bug_repro.ipynb ### Expected behavior It should work if the provided access token is valid (as it does for all the other datasets) ### Environment info datasets version -> 2.14.4

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PR_kwDODunzps5ZB8Z9

Don't alter input in Features.from_dict

closed

MEMBER

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I_kwDODunzps5vhQF4

[Feature Request] Check the length of batch before writing so that empty batch is allowed

open

NONE

### Use Case I use `dataset.map(process_fn, batched=True)` to process the dataset, with data **augmentations or filtering**. However, when all examples within a batch is filtered out, i.e. **an empty batch is returned**, the following error will be thrown: ``` ValueError: Schema and number of arrays unequal ``` This is because the empty batch does not comply with the schema of other batches. I think an empty batch should be allowed to facilitate coding (one does not need to assign an empty list manually for all keys.) A simple fix is to check the length of `batch` before writing: ``` if len(batch): writer.write_batch(batch) ``` instead of https://github.com/huggingface/datasets/blob/74d60213dcbd7c99484c62ce1d3dfd90a1df0770/src/datasets/arrow_dataset.py#L3493

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I_kwDODunzps5vhDhP

Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory

open

NONE

### Describe the bug ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) [<ipython-input-48-6a7b3e847019>](https://localhost:8080/#) in <cell line: 7>() 5 } 6 ----> 7 csv_datasets_reloaded = load_dataset("tsv", data_files=data_files) 8 csv_datasets_reloaded 2 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1489 raise e1 from None 1490 if isinstance(e1, FileNotFoundError): -> 1491 raise FileNotFoundError( 1492 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1493 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Dataset 'tsv' doesn't exist on the Hub ``` ### Steps to reproduce the bug ``` data_files = { "train": "/content/PUBHEALTH/train.tsv", "validation": "/content/PUBHEALTH/dev.tsv", "test": "/content/PUBHEALTH/test.tsv", } tsv_datasets_reloaded = load_dataset("tsv", data_files=data_files) tsv_datasets_reloaded ``` ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) <ipython-input-48-6a7b3e847019> in <cell line: 7>() 5 } 6 ----> 7 csv_datasets_reloaded = load_dataset("tsv", data_files=data_files) 8 csv_datasets_reloaded 2 frames /usr/local/lib/python3.10/dist-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1489 raise e1 from None 1490 if isinstance(e1, FileNotFoundError): -> 1491 raise FileNotFoundError( 1492 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1493 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Dataset 'tsv' doesn't exist on the Hub ``` ### Expected behavior load the data, push to hub ### Environment info jupyter notebook RTX 3090

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I_kwDODunzps5vbUKh

Feature request: add code example of multi-GPU processing

open

CONTRIBUTOR

### Feature request Would be great to add a code example of how to do multi-GPU processing with 🤗 Datasets in the documentation. cc @stevhliu Currently the docs has a small [section](https://huggingface.co/docs/datasets/v2.3.2/en/process#map) on this saying "your big GPU call goes here", however it didn't work for me out-of-the-box. Let's say you have a PyTorch model that can do translation, and you have multiple GPUs. In that case, you'd like to duplicate the model on each GPU, each processing (translating) a chunk of the data in parallel. Here's how I tried to do that: ``` from datasets import load_dataset from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from multiprocess import set_start_method import torch import os dataset = load_dataset("mlfoundations/datacomp_small") tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") # put model on each available GPU # also, should I do it like this or use nn.DataParallel? model.to("cuda:0") model.to("cuda:1") set_start_method("spawn") def translate_captions(batch, rank): os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count()) texts = batch["text"] inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(model.device) translated_tokens = model.generate( **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["eng_Latn"], max_length=30 ) translated_texts = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True) batch["translated_text"] = translated_texts return batch updated_dataset = dataset.map(translate_captions, with_rank=True, num_proc=2, batched=True, batch_size=256) ``` I've personally tried running this script on a machine with 2 A100 GPUs. ## Error 1 Running the code snippet above from the terminal (python script.py) resulted in the following error: ``` Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 125, in _main prepare(preparation_data) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 236, in prepare _fixup_main_from_path(data['init_main_from_path']) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 289, in run_path return _run_module_code(code, init_globals, run_name, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 96, in _run_module_code _run_code(code, mod_globals, init_globals, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/home/niels/python_projects/datacomp/datasets_multi_gpu.py", line 16, in <module> set_start_method("spawn") File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 247, in set_start_method raise RuntimeError('context has already been set') RuntimeError: context has already been set ``` ## Error 2 Then, based on [this Stackoverflow answer](https://stackoverflow.com/a/71616344/7762882), I put the `set_start_method("spawn")` section in a try: catch block. This resulted in the following error: ``` File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/dataset_dict.py", line 817, in <dictcomp> k: dataset.map( File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2926, in map with Pool(nb_of_missing_shards, initargs=initargs, initializer=initializer) as pool: File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 119, in Pool return Pool(processes, initializer, initargs, maxtasksperchild, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 215, in __init__ self._repopulate_pool() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 306, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 329, in _repopulate_pool_static w.start() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/process.py", line 121, in start self._popen = self._Popen(self) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 288, in _Popen return Popen(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 32, in __init__ super().__init__(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19, in __init__ self._launch(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 42, in _launch prep_data = spawn.get_preparation_data(process_obj._name) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 154, in get_preparation_data _check_not_importing_main() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 134, in _check_not_importing_main raise RuntimeError(''' RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. ``` So then I put the last line under a `if __name__ == '__main__':` block. Then the code snippet seemed to work, but it seemed that it's only leveraging a single GPU (based on monitoring `nvidia-smi`): ``` Mon Aug 28 12:19:24 2023 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 NVIDIA A100-SXM... On | 00000000:01:00.0 Off | 0 | | N/A 55C P0 76W / 275W | 8747MiB / 81920MiB | 0% Default | | | | Disabled | +-------------------------------+----------------------+----------------------+ | 1 NVIDIA A100-SXM... On | 00000000:47:00.0 Off | 0 | | N/A 67C P0 274W / 275W | 59835MiB / 81920MiB | 100% Default | | | | Disabled | ``` Both GPUs should have equal GPU usage, but I've always noticed that the last GPU has way more usage than the other ones. This made me think that `os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count())` might not work inside a Python script, especially if done after importing PyTorch? ### Motivation Would be great to clarify how to do multi-GPU data processing. ### Your contribution If my code snippet can be fixed, I can contribute it to the docs :)

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I_kwDODunzps5vWJq0

Error in saving the PIL image into *.arrow files using datasets.arrow_writer

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### Describe the bug I am using the ArrowWriter from datasets.arrow_writer to save a json-style file as arrow files. Within the dictionary, it contains a feature called "image" which is a list of PIL.Image objects. I am saving the json using the following script: ``` def save_to_arrow(path,temp): with ArrowWriter(path=path,writer_batch_size=20) as writer: writer.write_batch(temp) writer.finalize() ``` However, when I attempt to restore the dataset and use the ```Dataset.from_file(path)``` function to load the arrow file, there seems to be an issue with the PIL.Image object in the dataset. The list of PIL.Images appears as follows rather than a normal PIL.Image object:  ### Steps to reproduce the bug 1. Storing the data json into arrow files: ``` def save_to_arrow(path,temp): with ArrowWriter(path=path,writer_batch_size=20) as writer: writer.write_batch(temp) writer.finalize() save_to_arrow( path, json_file ) ``` 2. try to load the arrow file into the Dataset object using the ```Dataset.from_file(path)``` ### Expected behavior Except to saving the contained "image" feature as a list PIL.Image objects as the arrow file. And I can restore the dataset from the file. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.4.0-150-generic-x86_64-with-glibc2.17 - Python version: 3.8.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.4.4

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I_kwDODunzps5vU9l_

Map cache does not detect function changes in another module

closed

NONE

```python # dataset.py import os import datasets if not os.path.exists('/tmp/test.json'): with open('/tmp/test.json', 'w') as file: file.write('[{"text": "hello"}]') def transform(example): text = example['text'] # text += ' world' return {'text': text} data = datasets.load_dataset('json', data_files=['/tmp/test.json'], split='train') data = data.map(transform) ``` ```python # test.py import dataset print(next(iter(dataset.data))) ``` Initialize cache ``` python3 test.py # {'text': 'hello'} ``` Edit dataset.py and uncomment the commented line, run again ``` python3 test.py # {'text': 'hello'} # expected: {'text': 'hello world'} ``` Clear cache and run again ``` rm -rf ~/.cache/huggingface/datasets/* python3 test.py # {'text': 'hello world'} ``` If instead the two files are combined, then changes to the function are detected correctly. But it's expected when working on any realistic codebase that things will be modularized into separate files.

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I_kwDODunzps5vU4As

Load dataset with non-existent file

closed

NONE

### Describe the bug When load a dataset from datasets and pass a wrong path to json with the data, error message does not contain something abount "wrong path" or "file do not exist" - ```SchemaInferenceError: Please pass `features` or at least one example when writing data``` ### Steps to reproduce the bug ```python from datasets import load_dataset load_dataset('json', data_files='/home/alexey/unreal_file.json') ``` ### Expected behavior Raise os FileNotFound error or custom error with informative message ### Environment info ``` # packages in environment at /home/alexey/.conda/envs/alex_LoRA: # # Name Version Build Channel _libgcc_mutex 0.1 main _openmp_mutex 5.1 1_gnu accelerate 0.21.0 pypi_0 pypi aiohttp 3.8.5 pypi_0 pypi aiosignal 1.3.1 pypi_0 pypi antlr4-python3-runtime 4.9.3 pypi_0 pypi appdirs 1.4.4 pypi_0 pypi asttokens 2.0.5 pyhd3eb1b0_0 async-timeout 4.0.3 pypi_0 pypi attrs 23.1.0 pypi_0 pypi backcall 0.2.0 pyhd3eb1b0_0 bitsandbytes 0.41.1 pypi_0 pypi bzip2 1.0.8 h7b6447c_0 ca-certificates 2023.05.30 h06a4308_0 certifi 2023.7.22 pypi_0 pypi charset-normalizer 3.2.0 pypi_0 pypi click 8.1.6 pypi_0 pypi cmake 3.27.2 pypi_0 pypi comm 0.1.2 py310h06a4308_0 contourpy 1.1.0 pypi_0 pypi cycler 0.11.0 pypi_0 pypi datasets 2.14.4 pypi_0 pypi debugpy 1.6.7 py310h6a678d5_0 decorator 5.1.1 pyhd3eb1b0_0 dill 0.3.7 pypi_0 pypi docker-pycreds 0.4.0 pypi_0 pypi executing 0.8.3 pyhd3eb1b0_0 filelock 3.12.2 pypi_0 pypi fire 0.5.0 pypi_0 pypi fonttools 4.42.0 pypi_0 pypi frozenlist 1.4.0 pypi_0 pypi fsspec 2023.6.0 pypi_0 pypi gitdb 4.0.10 pypi_0 pypi gitpython 3.1.32 pypi_0 pypi huggingface-hub 0.16.4 pypi_0 pypi idna 3.4 pypi_0 pypi ipykernel 6.25.0 py310h2f386ee_0 ipython 8.12.2 py310h06a4308_0 ipython-genutils 0.2.0 pypi_0 pypi ipywidgets 8.0.4 py310h06a4308_0 jedi 0.18.1 py310h06a4308_1 jinja2 3.1.2 pypi_0 pypi jsonschema 4.19.0 pypi_0 pypi jsonschema-specifications 2023.7.1 pypi_0 pypi jupyter_client 8.1.0 py310h06a4308_0 jupyter_core 5.3.0 py310h06a4308_0 jupyterlab_widgets 3.0.5 py310h06a4308_0 kiwisolver 1.4.4 pypi_0 pypi ld_impl_linux-64 2.38 h1181459_1 libffi 3.3 he6710b0_2 libgcc-ng 11.2.0 h1234567_1 libgomp 11.2.0 h1234567_1 libsodium 1.0.18 h7b6447c_0 libstdcxx-ng 11.2.0 h1234567_1 libuuid 1.41.5 h5eee18b_0 lightning-utilities 0.9.0 pypi_0 pypi lit 16.0.6 pypi_0 pypi markupsafe 2.1.3 pypi_0 pypi matplotlib 3.7.2 pypi_0 pypi matplotlib-inline 0.1.6 py310h06a4308_0 mpmath 1.3.0 pypi_0 pypi multidict 6.0.4 pypi_0 pypi multiprocess 0.70.15 pypi_0 pypi nbformat 4.2.0 pypi_0 pypi ncurses 6.4 h6a678d5_0 nest-asyncio 1.5.6 py310h06a4308_0 networkx 3.1 pypi_0 pypi numpy 1.25.2 pypi_0 pypi nvidia-cublas-cu11 11.10.3.66 pypi_0 pypi nvidia-cuda-cupti-cu11 11.7.101 pypi_0 pypi nvidia-cuda-nvrtc-cu11 11.7.99 pypi_0 pypi nvidia-cuda-runtime-cu11 11.7.99 pypi_0 pypi nvidia-cudnn-cu11 8.5.0.96 pypi_0 pypi nvidia-cufft-cu11 10.9.0.58 pypi_0 pypi nvidia-curand-cu11 10.2.10.91 pypi_0 pypi nvidia-cusolver-cu11 11.4.0.1 pypi_0 pypi nvidia-cusparse-cu11 11.7.4.91 pypi_0 pypi nvidia-nccl-cu11 2.14.3 pypi_0 pypi nvidia-nvtx-cu11 11.7.91 pypi_0 pypi omegaconf 2.3.0 pypi_0 pypi openssl 1.1.1v h7f8727e_0 packaging 23.0 py310h06a4308_0 pandas 2.0.3 pypi_0 pypi parso 0.8.3 pyhd3eb1b0_0 pathtools 0.1.2 pypi_0 pypi peft 0.4.0 pypi_0 pypi pexpect 4.8.0 pyhd3eb1b0_3 pickleshare 0.7.5 pyhd3eb1b0_1003 pillow 10.0.0 pypi_0 pypi pip 23.2.1 py310h06a4308_0 platformdirs 2.5.2 py310h06a4308_0 plotly 5.16.1 pypi_0 pypi prompt-toolkit 3.0.36 py310h06a4308_0 protobuf 4.24.0 pypi_0 pypi psutil 5.9.0 py310h5eee18b_0 ptyprocess 0.7.0 pyhd3eb1b0_2 pure_eval 0.2.2 pyhd3eb1b0_0 pyarrow 12.0.1 pypi_0 pypi pygments 2.15.1 py310h06a4308_1 pyparsing 3.0.9 pypi_0 pypi python 3.10.0 h12debd9_5 python-dateutil 2.8.2 pyhd3eb1b0_0 pytorch-lightning 2.0.6 pypi_0 pypi pytz 2023.3 pypi_0 pypi pyyaml 6.0.1 pypi_0 pypi pyzmq 25.1.0 py310h6a678d5_0 readline 8.2 h5eee18b_0 referencing 0.30.2 pypi_0 pypi regex 2023.8.8 pypi_0 pypi requests 2.31.0 pypi_0 pypi rpds-py 0.9.2 pypi_0 pypi safetensors 0.3.2 pypi_0 pypi scipy 1.11.1 pypi_0 pypi sentencepiece 0.1.99 pypi_0 pypi sentry-sdk 1.29.2 pypi_0 pypi setproctitle 1.3.2 pypi_0 pypi setuptools 68.0.0 py310h06a4308_0 six 1.16.0 pyhd3eb1b0_1 smmap 5.0.0 pypi_0 pypi sqlite 3.41.2 h5eee18b_0 stack_data 0.2.0 pyhd3eb1b0_0 sympy 1.12 pypi_0 pypi tenacity 8.2.3 pypi_0 pypi termcolor 2.3.0 pypi_0 pypi tk 8.6.12 h1ccaba5_0 tokenizers 0.13.3 pypi_0 pypi torch 2.0.1 pypi_0 pypi torchmetrics 1.0.3 pypi_0 pypi tornado 6.3.2 py310h5eee18b_0 tqdm 4.66.1 pypi_0 pypi traitlets 5.7.1 py310h06a4308_0 transformers 4.31.0 pypi_0 pypi triton 2.0.0 pypi_0 pypi typing-extensions 4.7.1 pypi_0 pypi tzdata 2023.3 pypi_0 pypi urllib3 2.0.4 pypi_0 pypi wandb 0.15.8 pypi_0 pypi wcwidth 0.2.5 pyhd3eb1b0_0 wheel 0.38.4 py310h06a4308_0 widgetsnbextension 4.0.5 py310h06a4308_0 xxhash 3.3.0 pypi_0 pypi xz 5.4.2 h5eee18b_0 yarl 1.9.2 pypi_0 pypi zeromq 4.3.4 h2531618_0 zlib 1.2.13 h5eee18b_0 active environment : None user config file : /home/alexey/.condarc populated config files : conda version : 23.1.0 conda-build version : 3.22.0 python version : 3.9.13.final.0 virtual packages : __archspec=1=x86_64 __cuda=12.0=0 __glibc=2.35=0 __linux=5.19.0=0 __unix=0=0 base environment : /opt/anaconda/anaconda3 (read only) conda av data dir : /opt/anaconda/anaconda3/etc/conda conda av metadata url : None channel URLs : https://repo.anaconda.com/pkgs/main/linux-64 https://repo.anaconda.com/pkgs/main/noarch https://repo.anaconda.com/pkgs/r/linux-64 https://repo.anaconda.com/pkgs/r/noarch package cache : /opt/anaconda/anaconda3/pkgs /home/alexey/.conda/pkgs envs directories : /home/alexey/.conda/envs /opt/anaconda/anaconda3/envs platform : linux-64 user-agent : conda/23.1.0 requests/2.31.0 CPython/3.9.13 Linux/5.19.0-46-generic ubuntu/22.04.2 glibc/2.35 UID:GID : 1009:1009 netrc file : /home/alexey/.netrc offline mode : False ```

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I_kwDODunzps5vS0I7

Loading Meteor metric in HF evaluate module crashes due to datasets import issue

closed

NONE

### Describe the bug When using python3.9 and ```evaluate``` module loading Meteor metric crashes at a non-existent import from ```datasets.config``` in ```datasets v2.14``` ### Steps to reproduce the bug ``` from evaluate import load meteor = load("meteor") ``` produces the following error: ``` from datasets.config import importlib_metadata, version ImportError: cannot import name 'importlib_metadata' from 'datasets.config' (<path_to_project>/venv/lib/python3.9/site-packages/datasets/config.py) ``` ### Expected behavior ```datasets``` of v2.10 has the following workaround in ```config.py```: ``` if PY_VERSION < version.parse("3.8"): import importlib_metadata else: import importlib.metadata as importlib_metadata ``` However, it's absent in v2.14 which might be the cause of the issue. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.5-arm64-arm-64bit - Python version: 3.9.6 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - Evaluate version: 0.4.0

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PR_kwDODunzps5Yy2VO

Fix import in `image_load` doc

closed

CONTRIBUTOR

Reported on [Discord](https://discord.com/channels/879548962464493619/1144295822209581168/1144295822209581168)

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PR_kwDODunzps5Yy1r-

Use `hf-internal-testing` repos for hosting test dataset repos

closed

CONTRIBUTOR

Use `hf-internal-testing` for hosting instead of the maintainers' dataset repos.

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I_kwDODunzps5vSEv4

Map cache with tokenizer

open

NONE

Similar issue to https://github.com/huggingface/datasets/issues/5985, but across different sessions rather than two calls in the same session. Unlike that issue, explicitly calling tokenizer(my_args) before the map() doesn't help, because the tokenizer was created with a different hash to begin with... setup ``` from transformers import AutoTokenizer AutoTokenizer.from_pretrained('bert-base-uncased').save_pretrained("tok") ``` this prints different value each time ``` from transformers import AutoTokenizer from datasets.utils.py_utils import dumps # Huggingface datasets print(hash(dumps(AutoTokenizer.from_pretrained("tok")))) ```

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I_kwDODunzps5vQjW2

'import datasets' throws "invalid syntax error"

open

NONE

### Describe the bug Hi, I have been trying to import the datasets library but I keep gtting this error. `Traceback (most recent call last): File /opt/local/jupyterhub/lib64/python3.9/site-packages/IPython/core/interactiveshell.py:3508 in run_code exec(code_obj, self.user_global_ns, self.user_ns) Cell In[2], line 1 import datasets File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/__init__.py:22 from .arrow_dataset import Dataset File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_dataset.py:67 from .arrow_writer import ArrowWriter, OptimizedTypedSequence File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_writer.py:27 from .features import Features, Image, Value File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/__init__.py:17 from .audio import Audio File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/audio.py:11 from ..download.streaming_download_manager import xopen, xsplitext File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/__init__.py:10 from .streaming_download_manager import StreamingDownloadManager File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/streaming_download_manager.py:18 from aiohttp.client_exceptions import ClientError File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/__init__.py:7 from .connector import * # noqa File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/connector.py:12 from .client import ClientRequest File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/client.py:144 yield from asyncio.async(resp.release(), loop=loop) ^ SyntaxError: invalid syntax` I have simply used these commands: `import datasets` and `from datasets import load_dataset` ### Environment info The library has been installed a virtual machine on JupyterHub. Although I have used this library multiple times (on the same VM) before, to train/test an ASR or other ML models, I had never encountered this error.

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PR_kwDODunzps5Ytky-

Use object detection images from `huggingface/documentation-images`

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I_kwDODunzps5vIQq4

how to limit the size of memory mapped file?

open

NONE

### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE（Sun Grid Engine）

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PR_kwDODunzps5YnKlx

PyArrow 13 CI fixes

closed

CONTRIBUTOR

Fixes: * bumps the PyArrow version check in the `cast_array_to_feature` to avoid the offset bug (still not fixed) * aligns the Pandas formatting tests with the Numpy ones (the current test fails due to https://github.com/apache/arrow/pull/35656, which requires `.to_pandas(coerce_temporal_nanoseconds=True)` to always return `datetime [ns]` objects) Fix #6173

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I_kwDODunzps5vEZBx

Fix CI for pyarrow 13.0.0

closed

MEMBER

pyarrow 13.0.0 just came out ``` FAILED tests/test_formatting.py::ArrowExtractorTest::test_pandas_extractor - AssertionError: Attributes of Series are different Attribute "dtype" are different [left]: datetime64[us, UTC] [right]: datetime64[ns, UTC] ``` ``` FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type fixed_size_list<item: int32>[3] to Sequence(feature=Value(dtype='int64', id=None), length=3, id=None) ``` e.g. in https://github.com/huggingface/datasets/actions/runs/5952253963/job/16143847230 first error may be related to https://github.com/apache/arrow/issues/33321 second one maybe because `feature.length * len(array) == len(array_values)` is not satisfied anymore somehow ?

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I_kwDODunzps5vD_oL

Make Dataset streaming queries retryable

open

NONE

### Feature request Streaming datasets, as intended, do not load the entire dataset in memory or disk. However, while querying the next data chunk from the remote, sometimes it is possible that the service is down or there might be other issues that may cause the query to fail. In such a scenario, it would be nice to make these queries retryable (perhaps with a backoff strategy). ### Motivation I was working on a model and the model checkpoints after every 1000 steps. At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch `dataloader`. Given the size of my model and data, it took around 2 hours to reach 1800 steps and now it will take about an hour to recover the lost 800. It would be better to get a retryable querying strategy. ### Your contribution It would be better if someone having experience in this area takes this up as this would require some testing.

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PR_kwDODunzps5Yk4AS

Fix typo in about_mapstyle_vs_iterable.mdx

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PR_kwDODunzps5YkJOV

feat: Return the name of the currently loaded file

open

NONE

Added an optional parameter return_file_name in the load_dataset function. When it is set to True, the function will include the name of the file corresponding to the current line as a feature in the returned output. I added this here https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/json/json.py#L92. fixes #5806

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I_kwDODunzps5vAVyH

Configurations in yaml not working

open

NONE

### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1

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PR_kwDODunzps5YhT7Y

Fix ArrayXD YAML conversion

open

CONTRIBUTOR

Replace the `shape` tuple with a list in the `ArrayXD` YAML conversion. Fix #6112

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PR_kwDODunzps5Yf9-t

Allow hyphen in split name

closed

CONTRIBUTOR

To fix https://discuss.huggingface.co/t/error-when-setting-up-the-dataset-viewer-streamingrowserror/51276.

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PR_kwDODunzps5YfOt0

Document BUILDER_CONFIG_CLASS

closed

MEMBER

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PR_kwDODunzps5YexBL

Fix multiprocessing with spawn in iterable datasets

closed

CONTRIBUTOR

The "Spawn" method is preferred when multiprocessing on macOS or Windows systems, instead of the "Fork" method on linux systems. This causes some methods of Iterable Datasets to break when using a dataloader with more than 0 workers. I fixed the issue by replacing lambda and local methods which are not pickle-able. See the example below: ```python from datasets import load_dataset from torch.utils.data import DataLoader if __name__ == "__main__": dataset = load_dataset("lhoestq/demo1", split="train") dataset = dataset.to_iterable_dataset(num_shards=3) dataset = dataset.remove_columns(["package_name"]) dataset = dataset.rename_columns({ "review": "review1" }) dataset = dataset.rename_column("date", "date1") for sample in DataLoader(dataset, batch_size=None, num_workers=3): print(sample) ``` To notice the fix on a linux system, adding these lines should do the trick: ```python import multiprocessing multiprocessing.set_start_method('spawn') ``` I also removed what looks like code duplication between rename_colums and rename_column

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PR_kwDODunzps5YZZAJ

Fix: Missing a MetadataConfigs init when the repo has a `datasets_info.json` but no README

closed

CONTRIBUTOR

When I try to push to an arrow repo (can provide the link on Slack), it uploads the files but fails to update the metadata, with ``` File "app.py", line 123, in add_new_eval eval_results[level].push_to_hub(my_repo, token=TOKEN, split=SPLIT) File "blabla_my_env_path/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5501, in push_to_hub if not metadata_configs: UnboundLocalError: local variable 'metadata_configs' referenced before assignment ``` This fixes it.

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I_kwDODunzps5uuftB

Error type: ArrowInvalid Details: Failed to parse string: '[254,254]' as a scalar of type int32

open

NONE

### Describe the bug I am getting the following error while I am trying to upload the CSV sheet to train a model. My CSV sheet content is exactly same as shown in the example CSV file in the Auto Train page. Attaching screenshot of error for reference. I have also tried converting the index of the answer that are integer into string by placing inverted commas and also without inverted commas. Can anyone please help me out? FYI : I am using Chrome browser. Error type: ArrowInvalid Details: Failed to parse string: '[254,254]' as a scalar of type int32  ### Steps to reproduce the bug Kindly let me know how to fix this? ### Expected behavior Kindly let me know how to fix this? ### Environment info Kindly let me know how to fix this?

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I_kwDODunzps5uo1bG

load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields

open

NONE

### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>** to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the **symlink_target: string>** do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3

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PR_kwDODunzps5YM0g7

Fix protocol prefix for Beam

open

CONTRIBUTOR

Fix #6147

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PR_kwDODunzps5YMtLQ

Fix Parquet loading with `columns`

closed

CONTRIBUTOR

Fix #6149

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I_kwDODunzps5um5r4

Add `BoundingBox` feature

open

CONTRIBUTOR

... to make working with object detection datasets easier. Currently, `Sequence(int_or_float, length=4)` can be used to represent this feature optimally (in the storage backend), so I only see this feature being useful if we make it work with the viewer. Also, bounding boxes usually come in 4 different formats (explained [here](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/)), so we need to decide which one to support (or maybe all of them). cc @NielsRogge @severo

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PR_kwDODunzps5YLZBf

[docs] Complete `to_iterable_dataset`

closed

MEMBER

Finishes the `to_iterable_dataset` documentation by adding it to the relevant sections in the tutorial and guide.

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I_kwDODunzps5ulRt_

DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'

open

NONE

### Describe the bug When I was in load_dataset, it said "DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'". The second time I ran it, there was no error and the dataset object worked ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[3], line 1 ----> 1 dataset = load_dataset( 2 "/home/aihao/workspace/DeepLearningContent/datasets/manga", 3 data_dir="/home/aihao/workspace/DeepLearningContent/datasets/manga", 4 split="train", 5 ) File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2142 # Build dataset for splits 2143 keep_in_memory = ( 2144 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2145 ) -> 2146 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) 2147 # Rename and cast features to match task schema 2148 if task is not None: 2149 # To avoid issuing the same warning twice File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190), in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory) 1187 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS) 1189 # Create a dataset for each of the given splits -> 1190 datasets = map_nested( 1191 partial( 1192 self._build_single_dataset, ... File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379), in DatasetInfo.copy(self) 378 def copy(self) -> "DatasetInfo": --> 379 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) TypeError: DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding' ``` ### Steps to reproduce the bug /home/aihao/workspace/DeepLearningContent/datasets/images/images.py ```python from logging import config import datasets import os from PIL import Image import csv import json class ImagesConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(ImagesConfig, self).__init__(**kwargs) class Images(datasets.GeneratorBasedBuilder): def _split_generators(self, dl_manager: datasets.DownloadManager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"split": datasets.Split.TRAIN}, ) ] BUILDER_CONFIGS = [ ImagesConfig( name="similar_pairs", description="simliar pair dataset,item is a pair of similar images", ), ImagesConfig( name="image_prompt_pairs", description="image prompt pairs", ), ] def _info(self): if self.config.name == "similar_pairs": return datasets.Features( { "image1": datasets.features.Image(), "image2": datasets.features.Image(), "similarity": datasets.Value("float32"), } ) elif self.config.name == "image_prompt_pairs": return datasets.Features( {"image": datasets.features.Image(), "prompt": datasets.Value("string")} ) def _generate_examples(self, split): data_path = os.path.join(self.config.data_dir, "data") if self.config.name == "similar_pairs": prompts = {} with open(os.path.join(data_path ,"prompts.json"), "r") as f: prompts = json.load(f) with open(os.path.join(data_path, "similar_pairs.csv"), "r") as f: reader = csv.reader(f) for row in reader: image1_path, image2_path, similarity = row yield image1_path + ":" + image2_path + ":", { "image1": Image.open(image1_path), "prompt1": prompts[image1_path], "image2": Image.open(image2_path), "prompt2": prompts[image2_path], "similarity": float(similarity), } ``` Code that indicates an error: ```python from datasets import load_dataset import json import csv import ast import torch data_dir = "/home/aihao/workspace/DeepLearningContent/datasets/images" dataset = load_dataset(data_dir, data_dir=data_dir, name="similar_pairs") ``` ### Expected behavior The first execution gives an error, but it works fine ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3

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I_kwDODunzps5ujYXq

Why not use self._epoch as seed to shuffle in distributed training with IterableDataset

closed

CONTRIBUTOR

### Describe the bug Currently, distributed training with `IterableDataset` needs to pass fixed seed to shuffle to keep each node use the same seed to avoid overlapping. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1174-L1177 My question is why not directly use `self._epoch` which is set by `set_epoch` as seed? It's almost the same across nodes. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1790-L1801 If not using `self._epoch` as shuffling seed, what does this method do to prepare an epoch seeded generator? https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1206 ### Steps to reproduce the bug As mentioned above. ### Expected behavior As mentioned above. ### Environment info Not related

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PR_kwDODunzps5YI8Pc

Raise FileNotFoundError when passing data_files that don't exist

closed

MEMBER

e.g. when running `load_dataset("parquet", data_files="doesnt_exist.parquet")`

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PR_kwDODunzps5YItlH

Use yaml instead of get data patterns when possible

closed

MEMBER

This would make the data files resolution faster: no need to list all the data files to infer the dataset builder to use. fix https://github.com/huggingface/datasets/issues/6140

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I_kwDODunzps5uatM2

FolderBase Dataset automatically resolves under current directory when data_dir is not specified

open

CONTRIBUTOR

### Describe the bug FolderBase Dataset automatically resolves under current directory when data_dir is not specified. For example: ``` load_dataset("audiofolder") ``` takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59 ### Steps to reproduce the bug ``` load_dataset("audiofolder") ``` ### Expected behavior Error report ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3

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I_kwDODunzps5uW51a

Faster sorting for single key items

closed

NONE

### Feature request A faster way to sort a dataset which contains a large number of rows. ### Motivation The current sorting implementations took significantly longer than expected when I was running on a dataset trying to sort by timestamps. **Code snippet:** ```python ds = datasets.load_dataset( "json", **{"data_files": {"train": "path-to-jsonlines"}, "split": "train"}, num_proc=os.cpu_count(), keep_in_memory=True) sorted_ds = ds.sort("pubDate", keep_in_memory=True) ``` However, once I switched to a different method which 1. unpacked to a list of tuples 2. sorted tuples by key 3. run `.select` with the sorted list of indices It was significantly faster (orders of magnitude, especially with M's of rows) ### Your contribution I'd be happy to implement a crude single key sorting algorithm so that other users can benefit from this trick. Broadly, this would take a `Dataset` and perform; ```python # ds is a Dataset object # key_name is the sorting key class Dataset: ... def _sort(key_name: str) -> Dataset: index_keys = [(i,x) for i,x in enumerate(self[key_name])] sorted_rows = sorted(row_pubdate, key=lambda x: x[1]) sorted_indicies = [x[0] for x in sorted_rows] return self.select(sorted_indicies) ```

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I_kwDODunzps5uUA7o

Allow dataset implement .take

open

NONE

### Feature request I want to do: ``` dataset.take(512) ``` but it only works with streaming = True ### Motivation uniform interface to data sets. Really surprising the above only works with streaming = True. ### Your contribution Should be trivial to copy paste the IterableDataset .take to use the local path in the data (when streaming = False)

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I_kwDODunzps5uT3NQ

Dataset.from_parquet cannot load subset of columns

closed

CONTRIBUTOR

### Describe the bug When using `Dataset.from_parquet(path_or_paths, columns=[...])` and a subset of columns, loading fails with a variant of the following ``` ValueError: Couldn't cast a: int64 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 273 to {'a': Value(dtype='int64', id=None), 'b': Value(dtype='int64', id=None)} because column names don't match The above exception was the direct cause of the following exception: ``` Looks to be triggered by https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/table.py#L2285-L2286 ### Steps to reproduce the bug ``` import pandas as pd from datasets import Dataset pd.DataFrame([{"a": 1, "b": 2}]).to_parquet("test.pq") Dataset.from_parquet("test.pq", columns=["a"]) ``` ### Expected behavior A subset of columns should be loaded without error ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.10.0-23-cloud-amd64-x86_64-with-glibc2.2.5 - Python version: 3.8.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3

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PR_kwDODunzps5X3oqv

Ignore parallel warning in map_nested

closed

MEMBER

This warning message was shown every time you pass num_proc to `load_dataset` because of `map_nested` ``` parallel_map is experimental and might be subject to breaking changes in the future ``` This PR removes it for `map_nested`. If someone uses another parallel backend they're already warned when `parallel_backend` is called anyway

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I_kwDODunzps5uNDOO

ValueError when running BeamBasedBuilder with GCS path in cache_dir

open

NONE

### Describe the bug When running the BeamBasedBuilder with a GCS path specified in the cache_dir, the following ValueError occurs: ``` ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://my-bucket/huggingface_datasets/my_beam_dataset/default/0.0.0/my_beam_dataset-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite'] ``` Same error occurs after running `pip install apache-beam[gcp]` as instructed. ### Steps to reproduce the bug Put `my_beam_dataset.py`: ```python import datasets class MyBeamDataset(datasets.BeamBasedBuilder): def _info(self): features = datasets.Features({"value": datasets.Value("int64")}) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager, pipeline): return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={})] def _build_pcollection(self, pipeline): import apache_beam as beam return pipeline | beam.Create([{"value": i} for i in range(10)]) ``` Run: ```bash datasets-cli run_beam my_beam_dataset.py --cache_dir=gs://my-bucket/huggingface_datasets/ --beam_pipeline_options="runner=DirectRunner" ``` ### Expected behavior Running the BeamBasedBuilder with a GCS cache path without any errors. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 2.0.3

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I_kwDODunzps5uLJxW

DatasetGenerationError when load glue benchmark datasets from `load_dataset`

closed

NONE

### Describe the bug Package version: datasets-2.14.4 When I run the codes: ``` from datasets import load_dataset dataset = load_dataset("glue", "ax") ``` I got the following errors: --------------------------------------------------------------------------- SchemaInferenceError Traceback (most recent call last) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1949, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1948 num_shards = shard_id + 1 -> 1949 num_examples, num_bytes = writer.finalize() 1950 writer.close() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/arrow_writer.py:598, in ArrowWriter.finalize(self, close_stream) 597 self.stream.close() --> 598 raise SchemaInferenceError("Please pass `features` or at least one example when writing data") 599 logger.debug( 600 f"Done writing {self._num_examples} {self.unit} in {self._num_bytes} bytes {self._path if self._path else ''}." 601 ) SchemaInferenceError: Please pass `features` or at least one example when writing data The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[5], line 3 1 from datasets import load_dataset ----> 3 dataset = load_dataset("glue", "ax") File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2136, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2133 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2135 # Download and prepare data -> 2136 builder_instance.download_and_prepare( 2137 download_config=download_config, 2138 download_mode=download_mode, 2139 verification_mode=verification_mode, 2140 try_from_hf_gcs=try_from_hf_gcs, 2141 num_proc=num_proc, 2142 storage_options=storage_options, 2143 ) 2145 # Build dataset for splits 2146 keep_in_memory = ( 2147 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2148 ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 **prepare_split_kwargs, 958 **download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1045 split_dict.add(split_generator.split_info) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, **prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( 1052 "Cannot find data file. " 1053 + (self.manual_download_instructions or "") 1054 + "\nOriginal error:\n" 1055 + str(e) 1056 ) from None File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): 1816 if done: 1817 result = content File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("glue", "ax") ### Expected behavior When generating the train split: Generating train split: 0/0 [00:00<?, ? examples/s] It raise the error: DatasetGenerationError: An error occurred while generating the dataset ### Environment info datasets-2.14.4. Python 3.10

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I_kwDODunzps5ubOQ6

custom load dataset to hub

open

NONE

### System Info kaggle notebook i transformed dataset: ``` dataset = load_dataset("Dahoas/first-instruct-human-assistant-prompt") ``` to formatted_dataset: ``` Dataset({ features: ['message_tree_id', 'message_tree_text'], num_rows: 33143 }) ``` but would like to know how to upload to hub ### Who can help? @ArthurZucker @younesbelkada ### Information - [ ] The official example scripts - [ ] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction shared above ### Expected behavior load dataset to hub

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PR_kwDODunzps5Xx5If

Export to_iterable_dataset to document

closed

CONTRIBUTOR

Fix the export of a missing method of `Dataset`

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I_kwDODunzps5uG4LH

NIH exporter file not found

open

NONE

### Describe the bug can't use or download the nih exporter pile data. ``` 15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights() 16 File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights 17 column_names = next(iter(dataset)).keys() 18 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__ 19 for key, example in ex_iterable: 20 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__ 21 yield from self.generate_examples_fn(**self.kwargs) 22 File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 236, in _generate_examples 23 with zstd.open(open(files[subset], "rb"), "rt", encoding="utf-8") as f: 24 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py", line 74, in wrapper 25 return function(*args, download_config=download_config, **kwargs) 26 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen 27 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() 28 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open 29 return self.__enter__() 30 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__ 31 f = self.fs.open(self.path, mode=mode) 32 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open 33 f = self._open( 34 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open 35 size = size or self.info(path, **kwargs)["size"] 36 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper 37 return sync(self.loop, func, *args, **kwargs) 38 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync 39 raise return_result 40 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner 41 result[0] = await coro 42 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info 43 raise FileNotFoundError(url) from exc 44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst ``` ### Steps to reproduce the bug run this: ``` from datasets import load_dataset path, name = 'EleutherAI/pile', 'nih_exporter' # -- Get data set dataset = load_dataset(path, name, streaming=True, split="train").with_format("torch") batch = dataset.take(512) print(f'{batch=}') ``` ### Expected behavior print the batch ### Environment info ``` (beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.4 - Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ```

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I_kwDODunzps5uCtsg

the-stack-dedup fails to generate

closed

NONE

### Describe the bug I'm getting an error generating the-stack-dedup with datasets 2.13.1, and with 2.14.4 nothing happens. ### Steps to reproduce the bug My code: ``` import os import datasets as ds MY_CACHE_DIR = "/home/ubuntu/the-stack-dedup-local" MY_TOKEN="my-token" the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="train", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, use_auth_token=MY_TOKEN, num_proc=64) ``` The exception: ``` Generating train split: 233248251 examples [54:31, 57280.00 examples/s] multiprocess.pool.RemoteTraceback: """ Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1879, in _prepare_split_single for _, table in generator: File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 82, in _generate_tables yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 61, in _cast_table pa_table = table_cast(pa_table, self.info.features.arrow_schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2324, in table_cast return cast_table_to_schema(table, schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2282, in cast_table_to_schema raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nb ecause column names don't match") ValueError: Couldn't cast hexsha: string size: int64 ext: string lang: string max_stars_repo_path: string max_stars_repo_name: string max_stars_repo_head_hexsha: string max_stars_repo_licenses: list<item: string> child 0, item: string max_stars_count: int64 max_stars_repo_stars_event_min_datetime: string max_stars_repo_stars_event_max_datetime: string max_issues_repo_path: string max_issues_repo_name: string max_issues_repo_head_hexsha: string max_issues_repo_licenses: list<item: string> child 0, item: string max_issues_count: int64 max_issues_repo_issues_event_min_datetime: string max_issues_repo_issues_event_max_datetime: string max_forks_repo_path: string max_forks_repo_name: string max_forks_repo_head_hexsha: string max_forks_repo_licenses: list<item: string> child 0, item: string max_forks_count: int64 max_forks_repo_forks_event_min_datetime: string max_forks_repo_forks_event_max_datetime: string content: string avg_line_length: double max_line_length: int64 alphanum_fraction: double __id__: int64 -- schema metadata -- huggingface: '{"info": {"features": {"hexsha": {"dtype": "string", "_type' + 1979 to {'hexsha': Value(dtype='string', id=None), 'size': Value(dtype='int64', id=None), 'ext': Value(dtype='string', id=None), 'lang': Value(dtype='string', id=None), 'max_stars_repo_path': Value(dtype='string', id=None), 'max_stars_repo_name': Value(dtype='string', id=None), 'max_stars_repo_head_hexsha': Value(dtype='string', id=None), 'max_stars_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_stars_count': Value(dtype='int64', id=None), 'max_stars_repo_stars_event_min_datetime': Value(dtype='string', id=None), 'max_stars_repo_stars_event_max_datetime': Value(dtype='string', id=None), 'max_issues_repo_path': Value(dtype='string', id=None), 'max_issues_repo_name': Value(dtype='string', id=None), 'max_issues_repo_head_hexsha': Value(dtype='string', id=None), 'max_issues_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_issues_count': Value(dtype='int64', id=None), 'max_issues_repo_issues_event_min_datetime': Value(dtype='string', id=None), 'max_issues_repo_issues_event_max_datetime': Value(dtype='string', id=None), 'max_forks_repo_path': Value(dtype='string', id=None), 'max_forks_repo_name': Value(dtype='string', id=None), 'max_forks_repo_head_hexsha': Value(dtype='string', id=None), 'max_forks_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_forks_count': Value(dtype='int64', id=None), 'max_forks_repo_forks_event_min_datetime': Value(dtype='string', id=None), 'max_forks_repo_forks_event_max_datetime': Value(dtype='string', id=None), 'content': Value(dtype='string', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'alphanum_fraction': Value(dtype='float64', id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1328, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating th e dataset") from e datasets.builder.DatasetGenerationError: An error occurred while genera ting the dataset """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/download_the_stack.py", line 7, in <module> the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="tr ain", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, us e_auth_token=MY_TOKEN, num_proc=64) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/load. py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1796, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 774, in get raise self._value datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior The dataset downloads properly. @lhoestq @loub ### Environment info Datasets 2.13.1, large VM with 2TB RAM, Ubuntu 20.04

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I_kwDODunzps5uCYVh

TypeError: ClientSession._request() got an unexpected keyword argument 'https'

closed

NONE

### Describe the bug Hello, when I ran the [code snippet](https://huggingface.co/docs/datasets/v2.14.4/en/loading#json) on the document, I encountered the following problem: ``` Python 3.10.9 (main, Mar 1 2023, 18:23:06) [GCC 11.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" >>> dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 2112, in load_dataset builder_instance = load_dataset_builder( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1798, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1413, in dataset_module_factory ).get_module() File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 949, in get_module data_files = DataFilesDict.from_patterns( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 672, in from_patterns DataFilesList.from_patterns( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 578, in from_patterns resolve_pattern( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 340, in resolve_pattern for filepath, info in fs.glob(pattern, detail=True).items() File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 113, in wrapper return sync(self.loop, func, *args, **kwargs) File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 98, in sync raise return_result File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 53, in _runner result[0] = await coro File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 449, in _glob elif await self._exists(path): File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 306, in _exists r = await session.get(self.encode_url(path), **kw) File "/home/liushuai/anaconda3/lib/python3.10/site-packages/aiohttp/client.py", line 922, in get self._request(hdrs.METH_GET, url, allow_redirects=allow_redirects, **kwargs) TypeError: ClientSession._request() got an unexpected keyword argument 'https' ``` ### Steps to reproduce the bug ``` from datasets import load_dataset base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data") ``` ### Expected behavior able to load normally ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.4.54-2-x86_64-with-glibc2.27 - Python version: 3.10.9 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3

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I_kwDODunzps5t_lYI

Misalignment between file format specified in configs metadata YAML and the inferred builder

closed

MEMBER

There is a misalignment between the format of the `data_files` specified in the configs metadata YAML (CSV): ```yaml configs: - config_name: default data_files: - split: train path: data.csv ``` and the inferred builder (JSON). Note there are multiple JSON files in the repo, but they do not appear in the configs metadata YAML. See: https://huggingface.co/datasets/freddyaboulton/chatinterface_with_image_csv/discussions/1 CC: @freddyaboulton @polinaeterna

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I_kwDODunzps5t-FZf

Offline dataset viewer

open

NONE

### Feature request The dataset viewer feature is very nice. It enables to the user to easily view the dataset. However, when working for private companies we cannot always upload the dataset to the hub. Is there a way to create dataset viewer offline? I.e. to run a code that will open some kind of html or something that makes it easy to view the dataset. ### Motivation I want to easily view my dataset even when it is hosted locally. ### Your contribution N.A.

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https://github.com/huggingface/datasets/pull/6138

PR_kwDODunzps5XoH2V

Ignore CI lint rule violation in Pickler.memoize

closed

MEMBER

This PR ignores the violation of the lint rule E721 in `Pickler.memoize`. The lint rule violation was introduced in this PR: - #3182 @lhoestq is there a reason you did not use `isinstance` instead? As a hotfix, we just ignore the violation of the lint rule. Fix #6136.

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I_kwDODunzps5t97z4

(`from_spark()`) Unable to connect HDFS in pyspark YARN setting

open

NONE

### Describe the bug related issue: https://github.com/apache/arrow/issues/37057#issue-1841013613 --- Hello. I'm trying to interact with HDFS storage from a driver and workers of pyspark YARN cluster. Precisely I'm using **huggingface's `datasets`** ([link](https://github.com/huggingface/datasets)) library that relies on pyarrow to communicate with HDFS. The `from_spark()` ([link](https://huggingface.co/docs/datasets/use_with_spark#load-from-spark)) is what I'm invoking in my script. Below is the error I'm encountering. Note that I've masked sensitive paths. My code is sent to worker containers (docker) from driver container then executed. I confirmed that in both driver and worker images I can connect to HDFS using pyarrow since the envs and required jars are properly set, but strangely that becomes impossible when the same image runs as remote worker process. These are some peculiarities in my environment that might caused this issue. * **Cluster requires kerberos authentication** * But I think the error message implies that's not the problem in this case * **The user that runs the worker process is different from that built the docker image** * To avoid permission-related issues I made all directories that are accessed from the script accessible to everyone * **Pyspark-part of my code has no problem interacting with HDFS.** * Even pyarrow doesn't experience problem when I run the code in interactive session of the same docker images (driver, worker) * The problem occurs only when it runs as cluster's worker runtime Hope I could get some help. Thanks. ```bash 2023-08-08 18:51:19,638 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 2023-08-08 18:51:20,280 WARN shortcircuit.DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded. 23/08/08 18:51:22 WARN TaskSetManager: Lost task 0.0 in stage 142.0 (TID 9732) (ac3bax2062.bdp.bdata.ai executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 830, in main process() File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 820, in process out_iter = func(split_index, iterator) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func File "/root/spark/python/pyspark/rdd.py", line 828, in func File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe open(probe_file, "a") File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper return function(*args, download_config=download_config, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open out = open_files( ^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files fs, fs_token, paths = get_fs_token_paths( ^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths fs = filesystem(protocol, **inkwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem return cls(**storage_options) ^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__ obj = super().__call__(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__ fs = HadoopFileSystem( ^^^^^^^^^^^^^^^^^ File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__ File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: HDFS connection failed at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:366) at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358) at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345) at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92) at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161) at org.apache.spark.scheduler.Task.run(Task.scala:139) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) 23/08/08 18:51:24 WARN TaskSetManager: Lost task 0.1 in stage 142.0 (TID 9733) (ac3iax2079.bdp.bdata.ai executor 2): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 830, in main process() File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 820, in process out_iter = func(split_index, iterator) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func File "/root/spark/python/pyspark/rdd.py", line 828, in func File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe open(probe_file, "a") File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper return function(*args, download_config=download_config, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open out = open_files( ^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files fs, fs_token, paths = get_fs_token_paths( ^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths fs = filesystem(protocol, **inkwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem return cls(**storage_options) ^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__ obj = super().__call__(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__ fs = HadoopFileSystem( ^^^^^^^^^^^^^^^^^ File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__ File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: HDFS connection failed at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:366) at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358) at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345) at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92) at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161) at org.apache.spark.scheduler.Task.run(Task.scala:139) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) 23/08/08 18:51:38 WARN TaskSetManager: Lost task 0.2 in stage 142.0 (TID 9734) (<MASKED> executor 4): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 830, in main process() File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 820, in process out_iter = func(split_index, iterator) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func File "/root/spark/python/pyspark/rdd.py", line 828, in func File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe open(probe_file, "a") File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper return function(*args, download_config=download_config, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open out = open_files( ^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files fs, fs_token, paths = get_fs_token_paths( ^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths fs = filesystem(protocol, **inkwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem return cls(**storage_options) ^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__ obj = super().__call__(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__ fs = HadoopFileSystem( ^^^^^^^^^^^^^^^^^ File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__ File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: HDFS connection failed at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:366) at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358) at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345) at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92) at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161) at org.apache.spark.scheduler.Task.run(Task.scala:139) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) ``` ### Steps to reproduce the bug Use `from_spark()` function in pyspark YARN setting. I set `cache_dir` to HDFS path. ### Expected behavior Work as described in document ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.17 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 10.0.1 - Pandas version: 1.5.3

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I_kwDODunzps5t9sE6

CI check_code_quality error: E721 Do not compare types, use `isinstance()`

closed

MEMBER

After latest release of `ruff` (https://pypi.org/project/ruff/0.0.284/), we get the following CI error: ``` src/datasets/utils/py_utils.py:689:12: E721 Do not compare types, use `isinstance()` ```

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PR_kwDODunzps5Xn2AT

Remove unused allowed_extensions param

closed

MEMBER

This PR removes unused `allowed_extensions` parameter from `create_builder_configs_from_metadata_configs`.

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I_kwDODunzps5t8V9m

`datasets` cannot be installed alongside `apache-beam`

closed

NONE

### Describe the bug If one installs `apache-beam` alongside `datasets` (which is required for the [wikipedia](https://huggingface.co/datasets/wikipedia#dataset-summary) dataset) in certain environments (such as a Google Colab notebook), they appear to install successfully, however, actually trying to do something such as importing the `load_dataset` method from `datasets` results in a crashing error. I think the problem is that `apache-beam` version 2.49.0 requires `dill>=0.3.1.1,<0.3.2`, but the latest version of `multiprocess` (0.70.15) (on which `datasets` depends) requires `dill>=0.3.7,`, so this is causing the dependency resolver to use an older version of `multiprocess` which leads to the `datasets` crashing since it doesn't actually appear to be compatible with older versions. ### Steps to reproduce the bug See this [Google Colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing) to easily reproduce the bug. In some environments, I have been able to reproduce the bug by running the following in Bash: ```bash $ pip install datasets apache-beam ``` then the following in a Python shell: ```python from datasets import load_dataset ``` Here is my stacktrace from running on Google Colab: <details> <summary>stacktrace</summary> ``` [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.14.4" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 64 65 from . import config ---> 66 from .arrow_reader import ArrowReader 67 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 68 from .data_files import sanitize_patterns [/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module> 28 import pyarrow.parquet as pq 29 ---> 30 from .download.download_config import DownloadConfig 31 from .naming import _split_re, filenames_for_dataset_split 32 from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables [/usr/local/lib/python3.10/dist-packages/datasets/download/__init__.py](https://localhost:8080/#) in <module> 7 8 from .download_config import DownloadConfig ----> 9 from .download_manager import DownloadManager, DownloadMode 10 from .streaming_download_manager import StreamingDownloadManager [/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py](https://localhost:8080/#) in <module> 33 from ..utils.info_utils import get_size_checksum_dict 34 from ..utils.logging import get_logger, is_progress_bar_enabled, tqdm ---> 35 from ..utils.py_utils import NestedDataStructure, map_nested, size_str 36 from .download_config import DownloadConfig 37 [/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <module> 38 import dill 39 import multiprocess ---> 40 import multiprocess.pool 41 import numpy as np 42 from packaging import version [/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in <module> 607 # 608 --> 609 class ThreadPool(Pool): 610 611 from .dummy import Process [/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in ThreadPool() 609 class ThreadPool(Pool): 610 --> 611 from .dummy import Process 612 613 def __init__(self, processes=None, initializer=None, initargs=()): [/usr/local/lib/python3.10/dist-packages/multiprocess/dummy/__init__.py](https://localhost:8080/#) in <module> 85 # 86 ---> 87 class Condition(threading._Condition): 88 # XXX 89 if sys.version_info < (3, 0): AttributeError: module 'threading' has no attribute '_Condition' ``` </details> I've also found that attempting to install these `datasets` and `apache-beam` in certain environments (e.g. via pip inside a conda env) simply causes pip to hang indefinitely. ### Expected behavior I would expect to be able to import methods from `datasets` without crashing. I have tested that this is possible as long as I do not attempt to install `apache-beam`. ### Environment info Google Colab

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I_kwDODunzps5t8QMf

Dataset is slower after calling `to_iterable_dataset`

open

CONTRIBUTOR

### Describe the bug Can anyone explain why looping over a dataset becomes slower after calling `to_iterable_dataset` to convert to `IterableDataset` ### Steps to reproduce the bug Any dataset after converting to `IterableDataset` ### Expected behavior Maybe it should be faster on big dataset? I only test on small dataset ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3

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I_kwDODunzps5t4XDM

to_iterable_dataset is missing in document

closed

CONTRIBUTOR

### Describe the bug to_iterable_dataset is missing in document ### Steps to reproduce the bug to_iterable_dataset is missing in document ### Expected behavior document enhancement ### Environment info unrelated

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I_kwDODunzps5t3F8-

default config name doesn't work when config kwargs are specified.

open