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1,603,535,704
PR_kwDODunzps5K9K1i
5,589
Revert "pass the dataset features to the IterableDataset.from_generator"
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2023-02-28T17:52:04Z
2023-09-24T10:07:33Z
2023-03-21T14:18:18Z
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This reverts commit b91070b9c09673e2e148eec458036ab6a62ac042 (temporarily) It hurts iterable dataset performance a lot (e.g. x4 slower because it encodes+decodes images unnecessarily). I think we need to fix this before re-adding it cc @mariosasko @Hubert-Bonisseur
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5,588
Flatten dataset on the fly in `save_to_disk`
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2023-02-28T15:37:46Z
2023-02-28T17:28:35Z
2023-02-28T17:21:17Z
COLLABORATOR
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Flatten a dataset on the fly in `save_to_disk` instead of doing it with `flatten_indices` to avoid creating an additional cache file. (this is one of the sub-tasks in https://github.com/huggingface/datasets/issues/5507)
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https://api.github.com/repos/huggingface/datasets/issues/5587
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PR_kwDODunzps5K70pp
5,587
Fix `sort` with indices mapping
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2023-02-28T14:05:08Z
2023-02-28T17:28:57Z
2023-02-28T17:21:58Z
COLLABORATOR
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null
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Fixes the `key` range in the `query_table` call in `sort` to account for an indices mapping Fix #5586
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1,602,961,544
I_kwDODunzps5fi0CI
5,586
.sort() is broken when used after .filter(), only in 2.10.0
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2023-02-28T12:18:09Z
2023-02-28T18:17:26Z
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### Describe the bug Hi, thank you for your support! It seems like the addition of multiple key sort (#5502) in 2.10.0 broke the `.sort()` method. After filtering a dataset with `.filter()`, the `.sort()` seems to refer to the query_table index of the previous unfiltered dataset, resulting in an IndexError. This only happens with the 2.10.0 release. ### Steps to reproduce the bug ```Python from datasets import load_dataset # dataset with length of 1104 ds = load_dataset('glue', 'ax')['test'] ds = ds.filter(lambda x: x['idx'] > 1100) ds.sort('premise') print('Done') ``` File "/home/dongkeun/datasets_test/test.py", line 5, in <module> ds.sort('premise') File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 528, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, *args, **kwargs) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3959, in sort sort_table = query_table( File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 588, in query_table _check_valid_index_key(key, size) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 537, in _check_valid_index_key _check_valid_index_key(max(key), size=size) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 531, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 1103 is out of bounds for size 3 ### Expected behavior It should sort the dataset and print "Done". Which it does on 2.9.0. ### Environment info - `datasets` version: 2.10.0 - Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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Cache is not transportable
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2023-02-28T21:26:52Z
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### Describe the bug I would like to share cache between two machines (a Windows host machine and a WSL instance). I run most my code in WSL. I have just run out of space in the virtual drive. Rather than expand the drive size, I plan to move to cache to the host Windows machine, thereby sharing the downloads. I'm hoping that I can just copy/paste the cache files, but I notice that a lot of the file names start with the path name, e.g. `_home_davidg_.cache_huggingface_datasets_conll2003_default-451...98.lock` where `home/davidg` is where the cache is in WSL. This seems to suggest that the cache is not portable/cannot be centralised or shared. Is this the case, or are the files that start with path names not integral to the caching mechanism? Because copying the cache files _seems_ to work, but I'm not filled with confidence that something isn't going to break. A related issue, when trying to load a dataset that should come from cache (running in WSL, pointing to cache on the Windows host) it seemed to work fine, but it still uses a WSL directory for `.cache\huggingface\modules\datasets_modules`. I see nothing in the docs about this, or how to point it to a different place. I have asked a related question on the forum: https://discuss.huggingface.co/t/is-datasets-cache-operating-system-agnostic/32656 ### Steps to reproduce the bug View the cache directory in WSL/Windows. ### Expected behavior Cache can be shared between (virtual) machines and be transportable. It would be nice to have a simple way to say "Dear Hugging Face packages, please put ALL your cache in `blah/de/blah`" and have all the Hugging Face packages respect that single location. ### Environment info ``` - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 - ```
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1,601,821,808
I_kwDODunzps5fedxw
5,584
Unable to load coyo700M dataset
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2023-02-28T07:27:59Z
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### Describe the bug Seeing this error when downloading https://huggingface.co/datasets/kakaobrain/coyo-700m: ```ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.``` Full stack trace ```Downloading and preparing dataset parquet/kakaobrain--coyo-700m to /root/.cache/huggingface/datasets/kakaobrain___parquet/kakaobrain--coyo-700m-ae729692ae3e0073/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec... Downloading data files: 100% 1/1 [00:00<00:00, 63.35it/s] Extracting data files: 100% 1/1 [00:00<00:00, 5.00it/s] --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) [/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1859 _time = time.time() -> 1860 for _, table in generator: 1861 if max_shard_size is not None and writer._num_bytes > max_shard_size: 9 frames ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file. The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1891 e = e.__context__ -> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1893 1894 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 hf_dataset = load_dataset("kakaobrain/coyo-700m") ``` ### Expected behavior The above commands load the dataset successfully. Or handles exception and continue loading the remainder. ### Environment info colab. any
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PR_kwDODunzps5K2mIz
5,583
Do no write index by default when exporting a dataset
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2023-02-27T17:04:46Z
2023-02-28T13:52:15Z
2023-02-28T13:44:04Z
COLLABORATOR
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Ensures all the writers that use Pandas for conversion (JSON, CSV, SQL) do not export `index` by default (https://github.com/huggingface/datasets/pull/5490 only did this for CSV)
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5,582
Add column_names to IterableDataset
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2023-02-27T10:50:07Z
2023-03-13T19:10:22Z
2023-03-13T19:03:32Z
CONTRIBUTOR
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null
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This PR closes #5383 * Add column_names property to IterableDataset * Add multiple tests for this new property
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I_kwDODunzps5faF6h
5,581
[DOC] Mistaken docs on set_format
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2023-02-27T08:03:09Z
2023-02-28T19:19:17Z
2023-02-28T19:19:17Z
CONTRIBUTOR
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### Describe the bug https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/main_classes#datasets.Dataset.set_format <img width="700" alt="image" src="https://user-images.githubusercontent.com/36224762/221506973-ae2e3991-60a7-4d4e-99f8-965c6eb61e59.png"> While actually running it will result in: <img width="1094" alt="image" src="https://user-images.githubusercontent.com/36224762/221507032-007dab82-8781-4319-b21a-e6e4d40d97b3.png"> ### Steps to reproduce the bug _ ### Expected behavior _ ### Environment info - `datasets` version: 2.10.0 - Platform: Linux-5.10.147+-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.3.5
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Support cloud storage in load_dataset via fsspec
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2023-02-27T04:06:05Z
2024-11-27T01:25:39Z
2023-03-11T00:55:40Z
CONTRIBUTOR
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Closes https://github.com/huggingface/datasets/issues/5281 This PR uses fsspec to support datasets on cloud storage (tested manually with GCS). ETags are currently unsupported for cloud storage. In general, a much larger refactor could be done to just use fsspec for all schemes (ftp, http/s, s3, gcs) to unify the interfaces here, but I ultimately opted to leave that out of this PR I didn't create a GCS filesystem class in `datasets.filesystems` since the S3 one appears to be a wrapper around `s3fs.S3FileSystem` and mainly used to generate docs.
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5,579
Add instructions to create `DataLoader` from augmented dataset in object detection guide
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2023-02-25T14:53:17Z
2023-03-23T19:24:59Z
2023-03-23T19:24:50Z
CONTRIBUTOR
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The following adds instructions on how to create a `DataLoader` from the guide on how to use object detection with augmentations (#4710). I am open to hearing any suggestions for improvement !
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Add `huggingface_hub` version to env cli command
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2023-02-24T15:37:43Z
2023-02-27T17:28:25Z
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Add the `huggingface_hub` version to the `env` command's output.
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Cannot load `the_pile_openwebtext2`
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2023-02-24T14:01:09Z
2023-02-24T14:01:09Z
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### Describe the bug I met the same bug mentioned in #3053 which is never fixed. Because several `reddit_scores` are larger than `int8` even `int16`. https://huggingface.co/datasets/the_pile_openwebtext2/blob/main/the_pile_openwebtext2.py#L62 ### Steps to reproduce the bug ```python3 from datasets import load_dataset dataset = load_dataset("the_pile_openwebtext2") ``` ### Expected behavior load as normal. ### Environment info - `datasets` version: 2.10.0 - Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31 - Python version: 3.9.2 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers.
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2023-02-24T12:58:31Z
2023-02-24T12:58:18Z
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I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers. I worked around this by downloading the `the_pile_openwebtext2.py` and editing it to use local files and drop reddit scores as a column (not needed for my purposes). _Originally posted by @tc-wolf in https://github.com/huggingface/datasets/issues/3053#issuecomment-1281392422_
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Metadata for each column
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2023-02-24T10:53:44Z
2024-01-05T21:48:35Z
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### Feature request Being able to put some metadata for each column as a string or any other type. ### Motivation I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata ### Your contribution Maybe we could map another relational like database as the metadata?
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c4 dataset streaming fails with `FileNotFoundError`
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### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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1,597,400,836
PR_kwDODunzps5Kop7n
5,573
Use soundfile for mp3 decoding instead of torchaudio
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2023-02-23T19:19:44Z
2023-02-28T20:25:14Z
2023-02-28T20:16:02Z
CONTRIBUTOR
null
null
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I've removed `torchaudio` completely and switched to use `soundfile` for everything. With the new version of `soundfile` package this should work smoothly because the `libsndfile` C library is bundled, in Linux wheels too. Let me know if you think it's too harsh and we should continue to support `torchaudio` decoding. I decided that we can drop it completely because: 1. it's always something wrong with `torchaudio` (for example recently https://github.com/huggingface/datasets/issues/5488 ) 2. the results of mp3 decoding are different depending on `torchaudio` version 3. `soundfile` is slightly faster then the latest `torchaudio` 4. anyway users can pass any custom decoding function with any library they want if needed (worth putting a snippet in the docs). cc @sanchit-gandhi @vaibhavad
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1,597,257,624
I_kwDODunzps5fNDeY
5,572
Datasets 2.10.0 does not reuse the dataset cache
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2023-02-23T17:28:11Z
2023-02-23T18:03:55Z
2023-02-23T18:03:55Z
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### Describe the bug download_mode="reuse_dataset_if_exists" will always consider that a dataset doesn't exist. Specifically, upon losing an internet connection trying to load a dataset for a second time in ten seconds, a connection error results, showing a breakpoint of: ``` File ~/jupyterlab/.direnv/python-3.9.6/lib/python3.9/site-packages/datasets/load.py:1174, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1165 except Exception as e: # noqa: catch any exception of hf_hub and consider that the dataset doesn't exist 1166 if isinstance( 1167 e, 1168 ( (...) 1172 ), 1173 ): -> 1174 raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})") 1175 elif "404" in str(e): 1176 msg = f"Dataset '{path}' doesn't exist on the Hub" ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError) ``` This has been around since at least v2.0. ### Steps to reproduce the bug ``` from datasets import load_dataset import numpy as np tenk = load_dataset("lsb/tenk") # ten thousand integers print(np.average(tenk['train']['a'])) # prints 4999.5 ### now disconnect your internet tenk_too = load_dataset("lsb/tenk", download_mode="reuse_dataset_if_exists") # Raises ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError) ``` ### Expected behavior I expected that I would be able to reuse the dataset I just downloaded. ### Environment info - `datasets` version: 2.10.0 - Platform: macOS-13.1-arm64-arm-64bit - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.5.2
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1,597,198,953
I_kwDODunzps5fM1Jp
5,571
load_dataset fails for JSON in windows
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2023-02-23T16:50:11Z
2023-02-24T13:21:47Z
2023-02-24T13:21:47Z
NONE
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### Describe the bug Steps: 1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method. 2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json" 3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON. 4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py. raise InvalidConfigName( f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. " f"They could create issues when creating a directory for this config on Windows filesystem." 6. When I bring the data to the current directory, it works fine. ### Steps to reproduce the bug Steps: 1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method. 2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json" 3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON. 4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py. raise InvalidConfigName( f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. " f"They could create issues when creating a directory for this config on Windows filesystem." 6. When I bring the data to the current directory, it works fine. ### Expected behavior Should be able to read from a path different than current directory in Windows machine. ### Environment info datasets version: 2.3.1 python version: 3.8 Windows OS
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I_kwDODunzps5fMzMO
5,570
load_dataset gives FileNotFoundError on imagenet-1k if license is not accepted on the hub
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2023-02-23T16:44:32Z
2023-07-24T15:18:50Z
2023-07-24T15:18:50Z
NONE
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### Describe the bug When calling ```load_dataset('imagenet-1k')``` FileNotFoundError is raised, if not logged in and if logged in with huggingface-cli but not having accepted the licence on the hub. There is no error once accepting. ### Steps to reproduce the bug ``` from datasets import load_dataset imagenet = load_dataset("imagenet-1k", split="train", streaming=True) FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub ``` tested on a colab notebook. ### Expected behavior I would expect a specific error indicating that I have to login then accept the dataset licence. I find this bug very relevant as this code is on a guide on the [Huggingface documentation for Datasets](https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable) ### Environment info google colab cpu-only instance
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PR_kwDODunzps5KnwHD
5,569
pass the dataset features to the IterableDataset.from_generator function
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2023-02-24T14:06:37Z
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[5558](https://github.com/huggingface/datasets/issues/5568)
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dataset.to_iterable_dataset() loses useful info like dataset features
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2023-02-23T13:45:33Z
2023-02-24T13:22:36Z
2023-02-24T13:22:36Z
CONTRIBUTOR
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### Describe the bug Hello, I like the new `to_iterable_dataset` feature but I noticed something that seems to be missing. When using `to_iterable_dataset` to transform your map style dataset into iterable dataset, you lose valuable metadata like the features. These metadata are useful if you want to interleave iterable datasets, cast columns etc. ### Steps to reproduce the bug ```python dataset = load_dataset("lhoestq/demo1")["train"] print(dataset.features) # {'id': Value(dtype='string', id=None), 'package_name': Value(dtype='string', id=None), 'review': Value(dtype='string', id=None), 'date': Value(dtype='string', id=None), 'star': Value(dtype='int64', id=None), 'version_id': Value(dtype='int64', id=None)} dataset = dataset.to_iterable_dataset() print(dataset.features) # None ``` ### Expected behavior Keep the relevant information ### Environment info datasets==2.10.0
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5,566
Directly reading parquet files in a s3 bucket from the load_dataset method
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### Feature request Right now, we have to read the get the parquet file to the local storage. So having ability to read given the bucket directly address would be benificial ### Motivation In a production set up, this feature can help us a lot. So we do not need move training datafiles in between storage. ### Your contribution I am willing to help if there's anyway.
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Add writer_batch_size for ArrowBasedBuilder
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This way we can control the size of the record_batches/row_groups of arrow/parquet files. This can be useful for `datasets-server` to keep control of the row groups size which can affect random access performance for audio/image/video datasets Right now having 1,000 examples per row group cause some image datasets to be pretty slow for random access (e.g. 4seconds for `beans` to get 20 rows)
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1,594,625,539
PR_kwDODunzps5KfTUT
5,562
Update csv.py
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2023-02-22T07:56:10Z
2023-02-23T11:07:49Z
2023-02-23T11:00:58Z
CONTRIBUTOR
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Removed mangle_dup_cols=True from BuilderConfig. It triggered following deprecation warning: /usr/local/lib/python3.8/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols' return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs) Further documentation of pandas: https://pandas.pydata.org/docs/whatsnew/v1.4.0.html#mangle-dupe-cols-in-read-csv-no-longer-renames-unique-columns-conflicting-with-target-names At first sight it seems like this flag is resolved internally, it might need some more research.
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1,593,862,388
PR_kwDODunzps5Kcxw_
5,561
Add pre-commit config yaml file to enable automatic code formatting
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2023-02-21T17:35:07Z
2023-02-28T15:37:22Z
2023-02-23T18:23:29Z
CONTRIBUTOR
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@huggingface/datasets do you think it would be useful? Motivation - sometimes PRs are like 30% "fix: style" commits :) If so - I need to double check the config but for me locally it works as expected.
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5,560
Ensure last tqdm update in `map`
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2023-02-21T16:56:17Z
2023-02-21T18:26:23Z
2023-02-21T18:19:09Z
COLLABORATOR
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This PR modifies `map` to: * ensure the TQDM bar gets the last progress update * when a map function fails, avoid throwing a chained exception in the single-proc mode
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5,559
Fix map suffix_template
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2023-02-21T15:26:26Z
2023-02-21T17:21:37Z
2023-02-21T17:14:29Z
MEMBER
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#5455 introduced a small bug that lead `map` to ignore the `suffix_template` argument and not put suffixes to cached files in multiprocessing. I fixed this and also improved a few things: - regarding logging: "Loading cached processed dataset" is now logged only once even in multiprocessing (it used to be logged `num_proc` times) - regarding new_fingerprint: I made sure that the returned dataset satisfies `ds._fingerprint==new_fingerprint` if `new_fingerprint` is passed to `map`
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5,558
Remove instructions for `ffmpeg` system package installation on Colab
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2023-02-21T15:13:36Z
2023-03-01T13:46:04Z
2023-02-23T13:50:27Z
CONTRIBUTOR
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Colab now has Ubuntu 20.04 which already has `ffmpeg` of required (>4) version.
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5,557
Add filter desc
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2023-02-21T14:04:42Z
2023-02-21T14:19:54Z
2023-02-21T14:12:39Z
MEMBER
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Otherwise it would show a `Map` progress bar, since it uses `map` under the hood
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Use default audio resampling type
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2023-02-21T10:45:50Z
2023-02-21T12:49:50Z
2023-02-21T12:42:52Z
MEMBER
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...instead of relying on the optional librosa dependency `resampy`. It was only used for `_decode_non_mp3_file_like` anyway and not for the other ones - removing it fixes consistency between decoding methods (except torchaudio decoding) Therefore I think it is a better solution than adding `resampy` as a dependency in https://github.com/huggingface/datasets/pull/5554 cc @polinaeterna
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I_kwDODunzps5e6ymy
5,555
`.shuffle` throwing error `ValueError: Protocol not known: parent`
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2023-02-20T21:33:45Z
2023-02-27T09:23:34Z
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### Describe the bug ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In [16], line 1 ----> 1 train_dataset = train_dataset.shuffle() File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs) 544 self_format = { 545 "type": self._format_type, 546 "format_kwargs": self._format_kwargs, 547 "columns": self._format_columns, 548 "output_all_columns": self._output_all_columns, 549 } 550 # apply actual function --> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 553 # re-apply format to the output File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs) 476 validate_fingerprint(kwargs[fingerprint_name]) 478 # Call actual function --> 480 out = func(self, *args, **kwargs) 482 # Update fingerprint of in-place transforms + update in-place history of transforms 484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3616, in Dataset.shuffle(self, seed, generator, keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint) 3610 return self._new_dataset_with_indices( 3611 fingerprint=new_fingerprint, indices_cache_file_name=indices_cache_file_name 3612 ) 3614 permutation = generator.permutation(len(self)) -> 3616 return self.select( 3617 indices=permutation, 3618 keep_in_memory=keep_in_memory, 3619 indices_cache_file_name=indices_cache_file_name if not keep_in_memory else None, 3620 writer_batch_size=writer_batch_size, 3621 new_fingerprint=new_fingerprint, 3622 ) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs) 544 self_format = { 545 "type": self._format_type, 546 "format_kwargs": self._format_kwargs, 547 "columns": self._format_columns, 548 "output_all_columns": self._output_all_columns, 549 } 550 # apply actual function --> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 553 # re-apply format to the output File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs) 476 validate_fingerprint(kwargs[fingerprint_name]) 478 # Call actual function --> 480 out = func(self, *args, **kwargs) 482 # Update fingerprint of in-place transforms + update in-place history of transforms 484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3266, in Dataset.select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint) 3263 return self._select_contiguous(start, length, new_fingerprint=new_fingerprint) 3265 # If not contiguous, we need to create a new indices mapping -> 3266 return self._select_with_indices_mapping( 3267 indices, 3268 keep_in_memory=keep_in_memory, 3269 indices_cache_file_name=indices_cache_file_name, 3270 writer_batch_size=writer_batch_size, 3271 new_fingerprint=new_fingerprint, 3272 ) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs) 544 self_format = { 545 "type": self._format_type, 546 "format_kwargs": self._format_kwargs, 547 "columns": self._format_columns, 548 "output_all_columns": self._output_all_columns, 549 } 550 # apply actual function --> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 553 # re-apply format to the output File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs) 476 validate_fingerprint(kwargs[fingerprint_name]) 478 # Call actual function --> 480 out = func(self, *args, **kwargs) 482 # Update fingerprint of in-place transforms + update in-place history of transforms 484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3389, in Dataset._select_with_indices_mapping(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint) 3387 logger.info(f"Caching indices mapping at {indices_cache_file_name}") 3388 tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False) -> 3389 writer = ArrowWriter( 3390 path=tmp_file.name, writer_batch_size=writer_batch_size, fingerprint=new_fingerprint, unit="indices" 3391 ) 3393 indices = indices if isinstance(indices, list) else list(indices) 3395 size = len(self) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_writer.py:315, in ArrowWriter.__init__(self, schema, features, path, stream, fingerprint, writer_batch_size, hash_salt, check_duplicates, disable_nullable, update_features, with_metadata, unit, embed_local_files, storage_options) 312 self._disable_nullable = disable_nullable 314 if stream is None: --> 315 fs_token_paths = fsspec.get_fs_token_paths(path, storage_options=storage_options) 316 self._fs: fsspec.AbstractFileSystem = fs_token_paths[0] 317 self._path = ( 318 fs_token_paths[2][0] 319 if not is_remote_filesystem(self._fs) 320 else self._fs.unstrip_protocol(fs_token_paths[2][0]) 321 ) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:593, in get_fs_token_paths(urlpath, mode, num, name_function, storage_options, protocol, expand) 591 else: 592 urlpath = stringify_path(urlpath) --> 593 chain = _un_chain(urlpath, storage_options or {}) 594 if len(chain) > 1: 595 inkwargs = {} File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:330, in _un_chain(path, kwargs) 328 for bit in reversed(bits): 329 protocol = split_protocol(bit)[0] or "file" --> 330 cls = get_filesystem_class(protocol) 331 extra_kwargs = cls._get_kwargs_from_urls(bit) 332 kws = kwargs.get(protocol, {}) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/registry.py:240, in get_filesystem_class(protocol) 238 if protocol not in registry: 239 if protocol not in known_implementations: --> 240 raise ValueError("Protocol not known: %s" % protocol) 241 bit = known_implementations[protocol] 242 try: ValueError: Protocol not known: parent ``` This is what the `train_dataset` object looks like ``` Dataset({ features: ['label', 'input_ids', 'attention_mask'], num_rows: 364166 }) ``` ### Steps to reproduce the bug The `train_dataset` obj is created by concatenating two datasets And then shuffle is called, but it throws the mentioned error. ### Expected behavior Should shuffle the dataset properly. ### Environment info - `datasets` version: 2.6.1 - Platform: Linux-5.15.0-1022-aws-x86_64-with-glibc2.31 - Python version: 3.9.13 - PyArrow version: 10.0.0 - Pandas version: 1.4.4
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PR_kwDODunzps5KXhZh
5,554
Add resampy dep
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2023-02-20T18:15:43Z
2023-09-24T10:07:29Z
2023-02-21T12:43:38Z
MEMBER
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In librosa 0.10 they removed the `resmpy` dependency and set it to optional. However it is necessary for resampling. I added it to the "audio" extra dependencies.
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improved message error row formatting
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2023-02-20T17:29:14Z
2023-02-21T13:08:25Z
2023-02-21T12:58:12Z
CONTRIBUTOR
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Solves #5539
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5,552
Make tiktoken tokenizers hashable
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2023-02-20T16:50:09Z
2023-02-21T13:20:42Z
2023-02-21T13:13:05Z
COLLABORATOR
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Fix for https://discord.com/channels/879548962464493619/1075729627546406912/1075729627546406912
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1,592,140,836
PR_kwDODunzps5KXCof
5,551
Suggest scikit-learn instead of sklearn
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2023-02-20T16:16:57Z
2023-02-21T13:27:57Z
2023-02-21T13:21:07Z
CONTRIBUTOR
null
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This is kinda unimportant fix but, the suggested `pip install sklearn` does not work. The current error message if sklearn is not installed: ``` ImportError: To be able to use [dataset name], you need to install the following dependency: sklearn. Please install it using 'pip install sklearn' for instance. ```
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PR_kwDODunzps5KUl5i
5,550
Resolve four broken refs in the docs
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2023-02-20T08:52:11Z
2023-02-20T15:16:13Z
2023-02-20T15:09:13Z
MEMBER
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Hello! ## Pull Request overview * Resolve 4 broken references in the docs ## The problems Two broken references [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.class_encode_column): ![image](https://user-images.githubusercontent.com/37621491/220056232-366b64dc-33c9-461b-8f82-1ac4aa570280.png) --- One broken reference [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique): ![image](https://user-images.githubusercontent.com/37621491/220057135-2f249d60-c01d-48b5-82bb-5085a7635198.png) --- One missing reference [here](https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.DatasetDict.class_encode_column): ![image](https://user-images.githubusercontent.com/37621491/220057025-4a8e5556-5041-4ec7-b8d8-ed4fdc266495.png) - Tom Aarsen
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1,590,836,848
PR_kwDODunzps5KSsi3
5,549
Apply ruff flake8-comprehension checks
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2023-02-19T20:09:28Z
2023-02-23T14:06:39Z
2023-02-23T13:59:39Z
CONTRIBUTOR
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Fix #5548 Apply ruff's flake8-comprehension checks for better performance, and more readable code.
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I_kwDODunzps5e0jkX
5,548
Apply flake8-comprehensions to codebase
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2023-02-19T20:05:38Z
2023-02-23T13:59:41Z
2023-02-23T13:59:41Z
CONTRIBUTOR
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### Feature request Apply ruff flake8 comprehension checks to codebase. ### Motivation This should strictly improve the performance / readability of the codebase by removing unnecessary iteration, function calls, etc. This should generate better Python bytecode which should strictly improve performance. I already applied this fixes to PyTorch and Sympy with little issue and have opened PRs to diffusers and transformers todo this as well. ### Your contribution Making a PR.
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Add JAX device selection when formatting
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2023-02-18T20:57:40Z
2023-02-21T16:10:55Z
2023-02-21T16:04:03Z
MEMBER
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## What's in this PR? After exploring for a while the JAX integration in 🤗`datasets`, I found out that, even though JAX prioritizes the TPU and GPU as the default device when available, the `JaxFormatter` doesn't let you specify the device where you want to place the `jax.Array`s in case you don't want to rely on JAX's default array placement. So on, I've included the `device` param in `JaxFormatter` but there are some things to take into consideration: * A formatted `Dataset` is copied with `copy.deepcopy` which means that if one adds the param `device` in `JaxFormatter` as a `jaxlib.xla_extension.Device`, it "fails" because that object cannot be serialized (instead of serializing the param adds a random hash instead). That's the reason why I added a function `_map_devices_to_str` to basically create a mapping of strings to `jaxlib.xla_extension.Device`s so that `self.device` is a string and not a `jaxlib.xla_extension.Device`. * To create a `jax.Array` in a device you need to either create it in the default device and then move it to the desired device with `jax.device_put` or directly create it in the device you want with `jax.default_device()` context manager. * JAX will create an array by default in `jax.devices()[0]` More information on JAX device management is available at https://jax.readthedocs.io/en/latest/faq.html#controlling-data-and-computation-placement-on-devices ## What's missing in this PR? I've tested it both locally in CPU (Mac M2 and Mac M1, as no GPU support for Mac yet), and in GPU and TPU in Google Colab, let me know if you want me to provide you the Notebook for the latter. But I did not implement any integration test as I wanted to get your feedback first.
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1,590,346,349
I_kwDODunzps5eysJt
5,546
Downloaded datasets do not cache at $HF_HOME
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2023-02-18T13:30:35Z
2023-07-24T14:22:43Z
2023-07-24T14:22:43Z
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### Describe the bug In the huggingface course (https://huggingface.co/course/chapter3/2?fw=pt) it said that if we set HF_HOME, downloaded datasets would be cached at specified address but it does not. downloaded models from checkpoint names are downloaded and cached at HF_HOME but this is not the case for datasets, they are still cached at ~/.cache/huggingface/datasets. ### Steps to reproduce the bug Run the following code ``` from datasets import load_dataset raw_datasets = load_dataset("glue", "mrpc") raw_datasets ``` it downloads and store dataset at ~/.cache/huggingface/datasets ### Expected behavior to cache dataset at HF_HOME. ### Environment info python 3.10.6 Kubuntu 22.04 HF_HOME located on a separate partition
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PR_kwDODunzps5KRKct
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Added return methods for URL-references to the pushed dataset
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2023-02-18T11:26:25Z
2023-12-18T16:57:56Z
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Hi, I was missing the ability to easily open the pushed dataset and it seemed like a quick fix. Maybe we also want to log this info somewhere, but let me know if I need to add that too. Cheers, David
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the pile datasets url seems to change back
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2023-02-21T06:37:00Z
2023-02-20T08:41:33Z
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### Describe the bug in #3627, the host url of the pile dataset became `https://mystic.the-eye.eu`. Now the new url is broken, but `https://the-eye.eu` seems to work again. ### Steps to reproduce the bug ```python3 from datasets import load_dataset dataset = load_dataset("bookcorpusopen") ``` shows ```python3 ConnectionError: Couldn't reach https://mystic.the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz (ProxyError(MaxRetryError("HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_pr eliminary_components/books1.tar.gz (Caused by ProxyError('Cannot connect to proxy.', OSError('Tunnel connection failed: 504 Gateway Timeout')))"))) ``` ### Expected behavior Downloading as normal. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31 - Python version: 3.9.2 - PyArrow version: 6.0.1 - Pandas version: 1.5.3
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Avoid saving sparse ChunkedArrays in pyarrow tables
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2023-02-17T01:52:38Z
2023-02-17T19:20:49Z
2023-02-17T11:12:32Z
CONTRIBUTOR
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Fixes https://github.com/huggingface/datasets/issues/5541
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Flattening indices in selected datasets is extremely inefficient
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2023-02-22T13:15:20Z
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### Describe the bug If we perform a `select` (or `shuffle`, `train_test_split`, etc.) operation on a dataset , we end up with a dataset with an `indices_table`. Currently, flattening such dataset consumes a lot of memory and the resulting flat dataset contains ChunkedArrays with as many chunks as there are rows. This is extremely inefficient and slows down the operations on the flat dataset, e.g., saving/loading the dataset to disk becomes really slow. Perhaps more importantly, loading the dataset back from disk basically loads the whole table into RAM, as it cannot take advantage of memory mapping. ### Steps to reproduce the bug The following script reproduces the issue: ```python import gc import os import psutil import tempfile import time from datasets import Dataset DATASET_SIZE = 5000000 def profile(func): def wrapper(*args, **kwargs): mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024) start = time.time() # Run function here out = func(*args, **kwargs) end = time.time() mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024) print(f"{func.__name__} -- RAM memory used: {mem_after - mem_before} MB -- Total time: {end - start:.6f} s") return out return wrapper def main(): ds = Dataset.from_list([{'col': i} for i in range(DATASET_SIZE)]) print(f"Num chunks for original ds: {ds.data['col'].num_chunks}") with tempfile.TemporaryDirectory() as tmpdir: path1 = os.path.join(tmpdir, 'ds1') print("Original ds save/load") profile(ds.save_to_disk)(path1) ds_loaded = profile(Dataset.load_from_disk)(path1) print(f"Num chunks for original ds after reloading: {ds_loaded.data['col'].num_chunks}") print("") ds_select = ds.select(reversed(range(len(ds)))) print(f"Num chunks for selected ds: {ds_select.data['col'].num_chunks}") del ds del ds_loaded gc.collect() # This would happen anyway when we call save_to_disk ds_select = profile(ds_select.flatten_indices)() print(f"Num chunks for selected ds after flattening: {ds_select.data['col'].num_chunks}") print("") path2 = os.path.join(tmpdir, 'ds2') print("Selected ds save/load") profile(ds_select.save_to_disk)(path2) del ds_select gc.collect() ds_select_loaded = profile(Dataset.load_from_disk)(path2) print(f"Num chunks for selected ds after reloading: {ds_select_loaded.data['col'].num_chunks}") if __name__ == '__main__': main() ``` Sample result: ``` Num chunks for original ds: 1 Original ds save/load save_to_disk -- RAM memory used: 0.515625 MB -- Total time: 0.253888 s load_from_disk -- RAM memory used: 42.765625 MB -- Total time: 0.015176 s Num chunks for original ds after reloading: 5000 Num chunks for selected ds: 1 flatten_indices -- RAM memory used: 4852.609375 MB -- Total time: 46.116774 s Num chunks for selected ds after flattening: 5000000 Selected ds save/load save_to_disk -- RAM memory used: 1326.65625 MB -- Total time: 42.309825 s load_from_disk -- RAM memory used: 2085.953125 MB -- Total time: 11.659137 s Num chunks for selected ds after reloading: 5000000 ``` ### Expected behavior Saving/loading the dataset should be much faster and consume almost no extra memory thanks to pyarrow memory mapping. ### Environment info - `datasets` version: 2.9.1.dev0 - Platform: macOS-13.1-arm64-arm-64bit - Python version: 3.10.8 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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Tutorial for creating a dataset
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2023-02-16T22:09:35Z
2023-02-17T18:50:46Z
2023-02-17T18:41:28Z
MEMBER
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A tutorial for creating datasets based on the folder-based builders and `from_dict` and `from_generator` methods. I've also mentioned loading scripts as a next step, but I think we should keep the tutorial focused on the low-code methods. Let me know what you think! 🙂
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I_kwDODunzps5epoAj
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IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
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2023-02-16T16:08:51Z
2023-02-22T10:30:30Z
2023-02-21T13:03:57Z
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### Describe the bug When dataset contains a 0-dim tensor, formatting.py raises a following error and fails. ```bash Traceback (most recent call last): File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 501, in format_row return _unnest(formatted_batch) File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in _unnest return {key: array[0] for key, array in py_dict.items()} File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in <dictcomp> return {key: array[0] for key, array in py_dict.items()} IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number ``` ### Steps to reproduce the bug Load whichever dataset and add transform method to add 0-dim tensor. Or create/find a dataset containing 0-dim tensor. E.g. ```python from datasets import load_dataset import torch dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train') def t(batch): return {"test": torch.tensor(1)} dataset.set_transform(t) d_0 = dataset[0] ``` ### Expected behavior Extractor will correctly get a row from the dataset, even if it contains 0-dim tensor. ### Environment info `datasets==2.8.0`, but it looks like it is also applicable to main branch version (as of 16th February)
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load_dataset in seaborn is not working for me. getting this error.
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2023-02-16T14:01:58Z
2023-02-16T14:44:36Z
2023-02-16T14:44:36Z
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TimeoutError Traceback (most recent call last) ~\anaconda3\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args) 1345 try: -> 1346 h.request(req.get_method(), req.selector, req.data, headers, 1347 encode_chunked=req.has_header('Transfer-encoding')) ~\anaconda3\lib\http\client.py in request(self, method, url, body, headers, encode_chunked) 1278 """Send a complete request to the server.""" -> 1279 self._send_request(method, url, body, headers, encode_chunked) 1280 ~\anaconda3\lib\http\client.py in _send_request(self, method, url, body, headers, encode_chunked) 1324 body = _encode(body, 'body') -> 1325 self.endheaders(body, encode_chunked=encode_chunked) 1326 ~\anaconda3\lib\http\client.py in endheaders(self, message_body, encode_chunked) 1273 raise CannotSendHeader() -> 1274 self._send_output(message_body, encode_chunked=encode_chunked) 1275 ~\anaconda3\lib\http\client.py in _send_output(self, message_body, encode_chunked) 1033 del self._buffer[:] -> 1034 self.send(msg) 1035 ~\anaconda3\lib\http\client.py in send(self, data) 973 if self.auto_open: --> 974 self.connect() 975 else: ~\anaconda3\lib\http\client.py in connect(self) 1440 -> 1441 super().connect() 1442 ~\anaconda3\lib\http\client.py in connect(self) 944 """Connect to the host and port specified in __init__.""" --> 945 self.sock = self._create_connection( 946 (self.host,self.port), self.timeout, self.source_address) ~\anaconda3\lib\socket.py in create_connection(address, timeout, source_address) 843 try: --> 844 raise err 845 finally: ~\anaconda3\lib\socket.py in create_connection(address, timeout, source_address) 831 sock.bind(source_address) --> 832 sock.connect(sa) 833 # Break explicitly a reference cycle TimeoutError: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond During handling of the above exception, another exception occurred: URLError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_12220/2927704185.py in <module> 1 import seaborn as sn ----> 2 iris = sn.load_dataset('iris') ~\anaconda3\lib\site-packages\seaborn\utils.py in load_dataset(name, cache, data_home, **kws) 594 if name not in get_dataset_names(): 595 raise ValueError(f"'{name}' is not one of the example datasets.") --> 596 urlretrieve(url, cache_path) 597 full_path = cache_path 598 else: ~\anaconda3\lib\urllib\request.py in urlretrieve(url, filename, reporthook, data) 237 url_type, path = _splittype(url) 238 --> 239 with contextlib.closing(urlopen(url, data)) as fp: 240 headers = fp.info() 241 ~\anaconda3\lib\urllib\request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context) 212 else: 213 opener = _opener --> 214 return opener.open(url, data, timeout) 215 216 def install_opener(opener): ~\anaconda3\lib\urllib\request.py in open(self, fullurl, data, timeout) 515 516 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method()) --> 517 response = self._open(req, data) 518 519 # post-process response ~\anaconda3\lib\urllib\request.py in _open(self, req, data) 532 533 protocol = req.type --> 534 result = self._call_chain(self.handle_open, protocol, protocol + 535 '_open', req) 536 if result: ~\anaconda3\lib\urllib\request.py in _call_chain(self, chain, kind, meth_name, *args) 492 for handler in handlers: 493 func = getattr(handler, meth_name) --> 494 result = func(*args) 495 if result is not None: 496 return result ~\anaconda3\lib\urllib\request.py in https_open(self, req) 1387 1388 def https_open(self, req): -> 1389 return self.do_open(http.client.HTTPSConnection, req, 1390 context=self._context, check_hostname=self._check_hostname) 1391 ~\anaconda3\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args) 1347 encode_chunked=req.has_header('Transfer-encoding')) 1348 except OSError as err: # timeout error -> 1349 raise URLError(err) 1350 r = h.getresponse() 1351 except: URLError: <urlopen error [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond>
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Increase speed of data files resolution
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2023-02-16T12:11:45Z
2023-12-15T13:12:31Z
2023-12-15T13:12:31Z
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Certain datasets like `bigcode/the-stack-dedup` have so many files that loading them takes forever right from the data files resolution step. `datasets` uses file patterns to check the structure of the repository but it takes too much time to iterate over and over again on all the data files. This comes from `resolve_patterns_in_dataset_repository` which calls `_resolve_single_pattern_in_dataset_repository`, which iterates on all the files at ```python glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)] ``` but calling `glob` on such a dataset is too expensive. Indeed it calls `ls()` in `hffilesystem.py` too many times. Maybe `glob` can be more optimized in `hffilesystem.py`, or the data files resolution can directly be implemented in the filesystem by checking its `dir_cache` ?
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I_kwDODunzps5elqPT
5,536
Failure to hash function when using .map()
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2023-09-08T21:06:01Z
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### Describe the bug _Parameter 'function'=<function process at 0x7f1ec4388af0> of the transform datasets.arrow_dataset.Dataset.\_map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed._ This issue with `.map()` happens for me consistently, as also described in closed issue #4506 Dataset indices can be individually serialized using dill and pickle without any errors. I'm using tiktoken to encode in the function passed to map(). Similarly, indices can be individually encoded without error. ### Steps to reproduce the bug ```py from datasets import load_dataset import tiktoken dataset = load_dataset("stas/openwebtext-10k") enc = tiktoken.get_encoding("gpt2") tokenized = dataset.map( process, remove_columns=['text'], desc="tokenizing the OWT splits", ) def process(example): ids = enc.encode(example['text']) ids.append(enc.eot_token) out = {'ids': ids, 'len': len(ids)} return out ``` ### Expected behavior Should encode simple text objects. ### Environment info Python versions tried: both 3.8 and 3.10.10 `PYTHONUTF8=1` as env variable Datasets tried: - stas/openwebtext-10k - rotten_tomatoes - local text file OS: Ubuntu Linux 20.04 Package versions: - torch 1.13.1 - dill 0.3.4 (if using 0.3.6 - same issue) - datasets 2.9.0 - tiktoken 0.2.0
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1,586,520,369
PR_kwDODunzps5KEb5L
5,535
Add JAX-formatting documentation
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2023-02-15T20:35:11Z
2023-02-20T10:39:42Z
2023-02-20T10:32:39Z
MEMBER
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## What's in this PR? As a follow-up of #5522, I've created this entry in the documentation to explain how to use `.with_format("jax")` and why is it useful. @lhoestq Feel free to drop any feedback and/or suggestion, as probably more useful features can be included there!
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5,534
map() breaks at certain dataset size when using Array3D
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2023-02-15T16:34:25Z
2023-03-03T16:31:33Z
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### Describe the bug `map()` magically breaks when using a `Array3D` feature and mapping it. I created a very simple dummy dataset (see below). When filtering it down to 95 elements I can apply map, but it breaks when filtering it down to just 96 entries with the following exception: ``` Traceback (most recent call last): File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3255, in _map_single writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 581, in finalize self.write_examples_on_file() File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 440, in write_examples_on_file batch_examples[col] = array_concat(arrays) File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1931, in array_concat return _concat_arrays(arrays) File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1901, in _concat_arrays return array_type.wrap_array(_concat_arrays([array.storage for array in arrays])) File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays _concat_arrays([array.values for array in arrays]), File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays _concat_arrays([array.values for array in arrays]), File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1920, in _concat_arrays return pa.ListArray.from_arrays( File "pyarrow/array.pxi", line 1997, in pyarrow.lib.ListArray.from_arrays File "pyarrow/array.pxi", line 1527, in pyarrow.lib.Array.validate File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Negative offsets in list array During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2815, in map return self._map_single( File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 546, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 513, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/fingerprint.py", line 480, in wrapper out = func(self, *args, **kwargs) File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3259, in _map_single writer.finalize() File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 581, in finalize self.write_examples_on_file() File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 440, in write_examples_on_file batch_examples[col] = array_concat(arrays) File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1931, in array_concat return _concat_arrays(arrays) File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1901, in _concat_arrays return array_type.wrap_array(_concat_arrays([array.storage for array in arrays])) File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays _concat_arrays([array.values for array in arrays]), File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays _concat_arrays([array.values for array in arrays]), File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1920, in _concat_arrays return pa.ListArray.from_arrays( File "pyarrow/array.pxi", line 1997, in pyarrow.lib.ListArray.from_arrays File "pyarrow/array.pxi", line 1527, in pyarrow.lib.Array.validate File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Negative offsets in list array ``` ### Steps to reproduce the bug 1. put following dataset loading script into: debug/debug.py ```python import datasets import numpy as np class DEBUG(datasets.GeneratorBasedBuilder): """DEBUG dataset.""" def _info(self): return datasets.DatasetInfo( features=datasets.Features( { "id": datasets.Value("uint8"), "img_data": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"), }, ), supervised_keys=None, ) def _split_generators(self, dl_manager): return [datasets.SplitGenerator(name=datasets.Split.TRAIN)] def _generate_examples(self): for i in range(149): image_np = np.zeros(shape=(3, 224, 224), dtype=np.int8).tolist() yield f"id_{i}", {"id": i, "img_data": image_np} ``` 2. try the following code: ```python import datasets def add_dummy_col(ex): ex["dummy"] = "test" return ex ds = datasets.load_dataset(path="debug", split="train") # works ds_filtered_works = ds.filter(lambda example: example["id"] < 95) print(f"filtered result size: {len(ds_filtered_works)}") # output: # filtered result size: 95 ds_mapped_works = ds_filtered_works.map(add_dummy_col) # fails ds_filtered_error = ds.filter(lambda example: example["id"] < 96) print(f"filtered result size: {len(ds_filtered_error)}") # output: # filtered result size: 96 ds_mapped_error = ds_filtered_error.map(add_dummy_col) ``` ### Expected behavior The example code does not fail. ### Environment info Python 3.9.16 (main, Jan 11 2023, 16:05:54); [GCC 11.2.0] :: Anaconda, Inc. on linux datasets 2.9.0
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5,533
Add reduce function
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2023-02-15T13:44:01Z
2024-11-25T14:33:27Z
2023-02-28T14:46:12Z
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This PR closes #5496 . I tried to imitate the `reduce`-method from `functools`, i.e. the function input must be a binary operation. I assume that the input type has an empty element, i.e. `input_type()` is defined, as the acumulant is instantiated as this object - im not sure that is this a reasonable assumption? If `batched= True` the reduction of each shard is _not_ returned, but the reduction of the entire dataset. I was unsure wether this was an intuitive API, or it would make more sense to return the reduction of each shard?
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I_kwDODunzps5ecaEo
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train_test_split in arrow_dataset does not ensure to keep single classes in test set
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2023-02-14T16:52:29Z
2023-02-15T16:09:19Z
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### Describe the bug When I have a dataset with very few (e.g. 1) examples per class and I call the train_test_split function on it, sometimes the single class will be in the test set. thus will never be considered for training. ### Steps to reproduce the bug ``` import numpy as np from datasets import Dataset data = [ {'label': 0, 'text': "example1"}, {'label': 1, 'text': "example2"}, {'label': 1, 'text': "example3"}, {'label': 1, 'text': "example4"}, {'label': 0, 'text': "example5"}, {'label': 1, 'text': "example6"}, {'label': 2, 'text': "example7"}, {'label': 2, 'text': "example8"} ] for _ in range(10): data_set = Dataset.from_list(data) data_set = data_set.train_test_split(test_size=0.5) data_set["train"] unique_labels_train = np.unique(data_set["train"][:]["label"]) unique_labels_test = np.unique(data_set["test"][:]["label"]) assert len(unique_labels_train) >= len(unique_labels_test) ``` ### Expected behavior I expect to have every available class at least once in my training set. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 11.0.0 - Pandas version: 1.3.5
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5,531
Invalid Arrow data from JSONL
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2023-02-14T15:39:49Z
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This code fails: ```python from datasets import Dataset ds = Dataset.from_json(path_to_file) ds.data.validate() ``` raises ```python ArrowInvalid: Column 2: In chunk 1: Invalid: Struct child array #3 invalid: Invalid: Length spanned by list offsets (4064) larger than values array (length 4063) ``` This causes many issues for @TevenLeScao: - `map` fails because it fails to rewrite invalid arrow arrays ```python ~/Desktop/hf/datasets/src/datasets/arrow_writer.py in write_examples_on_file(self) 438 if all(isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) for row in self.current_examples): 439 arrays = [row[0][col] for row in self.current_examples] --> 440 batch_examples[col] = array_concat(arrays) 441 else: 442 batch_examples[col] = [ ~/Desktop/hf/datasets/src/datasets/table.py in array_concat(arrays) 1885 1886 if not _is_extension_type(array_type): -> 1887 return pa.concat_arrays(arrays) 1888 1889 def _offsets_concat(offsets): ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.concat_arrays() ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowIndexError: array slice would exceed array length ``` - `to_dict()` **segfaults** ⚠️ ```python /Users/runner/work/crossbow/crossbow/arrow/cpp/src/arrow/array/data.cc:99: Check failed: (off) <= (length) Slice offset greater than array length ``` To reproduce: unzip the archive and run the above code using `sanity_oscar_en.jsonl` [sanity_oscar_en.jsonl.zip](https://github.com/huggingface/datasets/files/10734124/sanity_oscar_en.jsonl.zip) PS: reading using pandas and converting to Arrow works though (note that the dataset lives in RAM in this case): ```python ds = Dataset.from_pandas(pd.read_json(path_to_file, lines=True)) ds.data.validate() ```
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Add missing license in `NumpyFormatter`
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2023-02-13T19:33:23Z
2023-02-14T14:40:41Z
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## What's in this PR? As discussed with @lhoestq in https://github.com/huggingface/datasets/pull/5522, the license for `NumpyFormatter` at `datasets/formatting/np_formatter.py` was missing, but present on the rest of the `formatting/*.py` files. So this PR is basically to include it there.
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Fix `datasets.load_from_disk`, `DatasetDict.load_from_disk` and `Dataset.load_from_disk`
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2023-02-13T14:54:55Z
2023-02-23T18:14:32Z
2023-02-23T18:05:26Z
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## What's in this PR? After playing around a little bit with 🤗`datasets` in Google Cloud Storage (GCS), I found out some things that should be fixed IMO in the code: * `datasets.load_from_disk` is not checking whether `state.json` is there too when trying to load a `Dataset`, just `dataset_info.json` is checked * `DatasetDict.load_from_disk` is not checking whether `state.json` is there too when redirecting the user to load it as `datasets.load_from_disk`, just `dataset_info.json` is checked, which is misleading, as it won't be loadable that way either * `Dataset.load_from_disk` is missing the `extract_path_from_uri` call before checking in the `fs` whether `dataset_info.json` and `dataset_dict.json` exist, which when using `gcsfs` leads to 400 error code (not blocking) due to `gcsfs.retry.HttpError: Invalid bucket name: 'gs:', 400` * And, finally, the exception messages are a little bit misleading / incomplete IMO so I've tried to include all the relevant information in the messages to avoid issues when interpreting the exceptions
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Push to hub in a pull request
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2023-02-13T11:43:47Z
2023-10-06T21:58:02Z
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Fixes #5492. Introduce new kwarg `create_pr` in `push_to_hub`, which is passed to `HFapi.upload_file`.
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Fix benchmarks CI - pin protobuf
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2023-02-12T11:51:25Z
2023-02-13T10:29:03Z
2023-02-13T09:24:16Z
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fix https://github.com/huggingface/datasets/actions/runs/4156059127/jobs/7189576331
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PR_kwDODunzps5JwVol
5,526
Allow loading/saving of FAISS index using fsspec
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2023-02-10T23:37:14Z
2023-03-27T15:26:46Z
2023-03-27T15:18:20Z
CONTRIBUTOR
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null
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Fixes #5428 Allow loading/saving of FAISS index using fsspec: 1. Simply use BufferedIOWriter/Reader to Read/Write indices on fsspec stream. 2. Needed `mockfs` in the test, so I took it out of the `TestCase`. Let me know if that makes sense. I can work on the documentation once the code changes are approved.
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I_kwDODunzps5eMh3J
5,525
TypeError: Couldn't cast array of type string to null
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2023-02-10T21:12:36Z
2023-02-14T17:41:08Z
2023-02-14T09:35:49Z
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### Describe the bug Processing a dataset I alredy uploaded to the Hub (https://huggingface.co/datasets/tj-solergibert/Europarl-ST) I found that for some splits and some languages (test split, source_lang = "nl") after applying a map function I get the mentioned error. I alredy tried reseting the shorter strings (reset_cortas function). It only happends with NL, PL, RO and PT. It does not make sense since when processing the other languages I also use the corpus of those that fail and it does not cause any errors. I suspect that the error may be in this direction: We use cast_array_to_feature to support casting to custom types like Audio and Image # Also, when trying type "string", we don't want to convert integers or floats to "string". # We only do it if trying_type is False - since this is what the user asks for. ### Steps to reproduce the bug Here I link a colab notebook to reproduce the error: https://colab.research.google.com/drive/1JCrS7FlGfu_kFqChMrwKZ_bpabnIMqbP?authuser=1#scrollTo=FBAvlhMxIzpA ### Expected behavior Data processing does not fail. A correct example can be seen here: https://huggingface.co/datasets/tj-solergibert/Europarl-ST-processed-mt-en ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.10.147+-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.3.5
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[INVALID PR]
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2023-02-10T19:35:50Z
2023-02-10T19:51:45Z
2023-02-10T19:49:12Z
MEMBER
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Hi to whoever is reading this! 🤗 ## What's in this PR? ~~Basically, I've removed the 🤗`datasets` installation as `python -m pip install ".[quality]" in the `check_code_quality` job in `.github/workflows/ci.yaml`, as we don't need to install the whole package to run the CI, unless that's done on purpose e.g. to check that the Python package installation succeeds before running the tests over the matrix of os?~~ ~~So I just wanted to check whether the time was reduced doing this (which I assume it will), plus whether this is something that can be improved, or just discarded in case you're also using that step to make sure that the package can be installed.~~ ## What's missing? ~~I was just wondering whether you consider replacing `isort` and `flake8` with `ruff` (if possible), since it's way faster, more information at [`ruff`](https://github.com/charliermarsh/ruff). Before creating this PR the average time of the `check_code_quality` job was around 40s.~~ ## Edit Sorry for the inconvenience this may have caused, didn't realise that the config is defined in `setup.cfg` and `pyproject.toml`, so running those without installing the Python package leads to failure, my bad 😞
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I_kwDODunzps5eL9T3
5,523
Checking that split name is correct happens only after the data is downloaded
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2023-02-10T19:13:03Z
2023-02-10T19:14:50Z
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### Describe the bug Verification of split names (=indexing data by split) happens after downloading the data. So when the split name is incorrect, users learn about that only after the data is fully downloaded, for large datasets it might take a lot of time. ### Steps to reproduce the bug Load any dataset with random split name, for example: ```python from datasets import load_dataset load_dataset("mozilla-foundation/common_voice_11_0", "en", split="blabla") ``` and the download will start smoothly, despite there is no split named "blabla". ### Expected behavior Raise error when split name is incorrect. ### Environment info `datasets==2.9.1.dev0`
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PR_kwDODunzps5JvTVp
5,522
Minor changes in JAX-formatting docstrings & type-hints
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2023-02-10T19:05:00Z
2023-02-15T14:48:27Z
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MEMBER
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Hi to whoever is reading this! 🤗 ## What's in this PR? I was exploring the code regarding the `JaxFormatter` implemented in 🤗`datasets`, and found some things that IMO could be changed. Those are mainly regarding the docstrings and the type-hints based on `jax`'s 0.4.1 release where `jax.Array` was introduced as the default type for JAX-arrays (instead of `jnp.DeviceArray`, `jnp.SharedDeviceArray`, and `jnp.GlobalDeviceArray`). Even though `isinstance(..., jax.Array)` also works with lower versions such as e.g. `0.3.25`. More information about the latter at [`jax` v0.4.1 - Release Notes](https://github.com/google/jax/releases/tag/jax-v0.4.1) and [jax.Array migration - JAX documentation](https://jax.readthedocs.io/en/latest/jax_array_migration.html). ## What's missing? * Do you want me to write an entry in the documentation on how to use 🤗`datasets` with JAX as https://huggingface.co/docs/datasets/use_with_pytorch with PyTorch? * Do we need to actually include `pyarrow` under the `TYPE_CHECKING` when needed? I just did it for JAX, but if we are OK with that, I can do that with the rest of the formatters, just LMK. * Should the License header be included in `datasets.formatting.np_formatter`? If so, do I include the one from 2020 e.g. https://github.com/huggingface/datasets/blob/b065547654efa0ec633cf373ac1512884c68b2e1/src/datasets/formatting/tf_formatter.py#L1-L13 * Is there any reason why `jnp.array` is being used instead of `jnp.asarray`? There's no difference between both, just that `jnp.asarray` has `copy=False` as default, even though `numpy` to `jax.numpy` conversion is not zero-copy, but just asking :)
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PR_kwDODunzps5JpWnp
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Fix bug when casting empty array to class labels
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2023-02-09T18:47:59Z
2023-02-13T20:40:48Z
2023-02-12T11:17:17Z
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Fix https://github.com/huggingface/datasets/issues/5520.
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ClassLabel.cast_storage raises TypeError when called on an empty IntegerArray
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2023-02-12T11:17:18Z
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### Describe the bug `ClassLabel.cast_storage` raises `TypeError` when called on an empty `IntegerArray`. ### Steps to reproduce the bug Minimal steps: ```python import pyarrow as pa from datasets import ClassLabel ClassLabel(names=['foo', 'bar']).cast_storage(pa.array([], pa.int64())) ``` In practice, this bug arises in situations like the one below: ```python from datasets import ClassLabel, Dataset, Features, Sequence dataset = Dataset.from_dict({'labels': [[], []]}, features=Features({'labels': Sequence(ClassLabel(names=['foo', 'bar']))})) # this raises TypeError dataset.map(batched=True, batch_size=1) ``` ### Expected behavior `ClassLabel.cast_storage` should return an empty Int64Array. ### Environment info - `datasets` version: 2.9.1.dev0 - Platform: Linux-4.15.0-1032-aws-x86_64-with-glibc2.27 - Python version: 3.10.6 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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Lint code with `ruff`
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2023-02-09T17:50:21Z
2024-06-01T15:35:02Z
2023-02-14T16:18:38Z
COLLABORATOR
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EDIT: Use `ruff` for linting instead of `isort` and `flake8` ~~`black`~~ to be consistent with [`transformers`](https://github.com/huggingface/transformers/pull/21480) and [`hfh`](https://github.com/huggingface/huggingface_hub/pull/1323). TODO: - [x] ~Merge the community contributors' PR to avoid having to run `make style` on their PR branches~ (we have some new PRs, but fixing those shouldn't be too big of a problem)
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Remove py.typed
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2023-02-09T16:22:29Z
2023-02-13T13:55:49Z
2023-02-13T13:48:40Z
COLLABORATOR
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Fix https://github.com/huggingface/datasets/issues/3841
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`with_format("numpy")` silently downcasts float64 to float32 features
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### Describe the bug When I create a dataset with a `float64` feature, then apply numpy formatting the returned numpy arrays are silently downcasted to `float32`. ### Steps to reproduce the bug ```python import datasets dataset = datasets.Dataset.from_dict({'a': [1.0, 2.0, 3.0]}).with_format("numpy") print("feature dtype:", dataset.features['a'].dtype) print("array dtype:", dataset['a'].dtype) ``` output: ``` feature dtype: float64 array dtype: float32 ``` ### Expected behavior ``` feature dtype: float64 array dtype: float64 ``` ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.4.0-135-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 10.0.1 - Pandas version: 1.4.4 ### Suggested Fix Changing [the `_tensorize` function of the numpy formatter](https://github.com/huggingface/datasets/blob/b065547654efa0ec633cf373ac1512884c68b2e1/src/datasets/formatting/np_formatter.py#L32) to ```python def _tensorize(self, value): if isinstance(value, (str, bytes, type(None))): return value elif isinstance(value, (np.character, np.ndarray)) and np.issubdtype(value.dtype, np.character): return value elif isinstance(value, np.number): return value return np.asarray(value, **self.np_array_kwargs) ``` fixes this particular issue for me. Not sure if this would break other tests. This should also avoid unnecessary copying of the array.
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Reload features from Parquet metadata
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2023-02-09T10:52:15Z
2023-02-12T16:00:00Z
2023-02-12T15:57:01Z
CONTRIBUTOR
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Resolves #5482. Attaches feature metadata to parquet files serialised using `Dataset.to_parquet`. This allows retrieving data with "rich" feature types (e.g., `datasets.features.image.Image` or `datasets.features.audio.Audio`) from parquet files without cumbersome casting (for an example, see #5482). @lhoestq It seems that it is sufficient to attach metadata to the schema prior to serialising and features are loaded back with correct types afterwards automatically. I used the following script to test the implementation: ```python from pathlib import Path import datasets dataset_name = "Maysee/tiny-imagenet" ds = datasets.load_dataset(dataset_name, split=datasets.Split.TRAIN) output_directory_path = Path(__file__).parent.joinpath("example_test_outputs", dataset_name.replace("/", "_")) output_directory_path.mkdir(exist_ok=True, parents=True) output_filepath = output_directory_path.joinpath("ds.parquet") ds.to_parquet(str(output_filepath)) reloaded_ds = datasets.load_dataset(str(output_directory_path), split=datasets.Split.TRAIN) assert ds.features == reloaded_ds.features ``` Prior to the change in this PR this script raises an `AssertionError` and the `Image` features lose their type after serialisation. After the change in this PR, the assertion does not raise an error and manual inspection of the features shows type `Image` for the respective columns of `reloaded_ds `. Some open questions: * How/where can I best add new unit tests for this implementation? * What dataset would I best use in the tests? I chose `Maysee/tiny-imagenet` mainly because it is small and contains an ?Image` feature that can be used to test, but I'd be happy for suggestions on a suitable data source to use. * Currently I'm calling `datasets.arrow_writer.ArrowWriter._build_metadata` as I need the same logic. However, I'm not happy with the coupling between `datasets.io.parquet` and `datasets.arrow_writer` it leaves me with. Suggest to factor this common logic out into a helper function and reuse it from both of these. Do you agree and if yes, could you please guide me where I would best place this function? Many thanks in advance and kind regards, MFreidank
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Unify `load_from_cache_file` type and logic
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2023-02-09T10:04:46Z
2023-02-14T15:38:13Z
2023-02-14T14:26:42Z
CONTRIBUTOR
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* Updating type annotations for #`load_from_cache_file` * Added logic for cache checking if needed * Updated documentation following the wording of `Dataset.map`
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Improve inconsistency of `Dataset.map` interface for `load_from_cache_file`
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2023-02-08T16:40:44Z
2023-02-14T14:26:44Z
2023-02-14T14:26:44Z
CONTRIBUTOR
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### Feature request 1. Replace the `load_from_cache_file` default value to `True`. 2. Remove or alter checks from `is_caching_enabled` logic. ### Motivation I stumbled over an inconsistency in the `Dataset.map` interface. The documentation (and source) states for the parameter `load_from_cache_file`: ``` load_from_cache_file (`bool`, defaults to `True` if caching is enabled): If a cache file storing the current computation from `function` can be identified, use it instead of recomputing. ``` 1. `load_from_cache_file` default value is `None`, while being annotated as `bool` 2. It is inconsistent with other method signatures like `filter`, that have the default value `True` 3. The logic is inconsistent, as the `map` method checks if caching is enabled through `is_caching_enabled`. This logic is not used for other similar methods. ### Your contribution I am not fully aware of the logic behind caching checks. If this is just a inconsistency that historically grew, I would suggest to remove the `is_caching_enabled` logic as the "default" logic. Maybe someone can give insights, if environment variables have a higher priority than local variables or vice versa. If this is clarified, I could adjust the source according to the "Feature request" section of this issue.
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Some functions use a param named `type` shouldn't that be avoided since it's a Python reserved name?
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2023-02-08T15:13:46Z
2023-07-24T16:02:18Z
2023-07-24T14:27:59Z
MEMBER
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Hi @mariosasko, @lhoestq, or whoever reads this! :) After going through `ArrowDataset.set_format` I found out that the `type` param is actually named `type` which is a Python reserved name as you may already know, shouldn't that be renamed to `format_type` before the 3.0.0 is released? Just wanted to get your input, and if applicable, tackle this issue myself! Thanks 🤗
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Speed up batched PyTorch DataLoader
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2023-02-08T13:38:59Z
2023-02-19T18:35:09Z
2023-02-19T18:27:29Z
MEMBER
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I implemented `__getitems__` to speed up batched data loading in PyTorch close https://github.com/huggingface/datasets/issues/5505
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Creating a dummy dataset from a bigger one
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2023-02-08T10:18:41Z
2023-12-28T18:21:01Z
2023-02-08T10:35:48Z
CONTRIBUTOR
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### Describe the bug I often want to create a dummy dataset from a bigger dataset for fast iteration when training. However, I'm having a hard time doing this especially when trying to upload the dataset to the Hub. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("lambdalabs/pokemon-blip-captions") dataset["train"] = dataset["train"].select(range(20)) dataset.push_to_hub("patrickvonplaten/dummy_image_data") ``` gives: ``` ~/python_bin/datasets/arrow_dataset.py in _push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, embed_external_files) 4003 base_wait_time=2.0, 4004 max_retries=5, -> 4005 max_wait_time=20.0, 4006 ) 4007 return repo_id, split, uploaded_size, dataset_nbytes ~/python_bin/datasets/utils/file_utils.py in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time) 328 while True: 329 try: --> 330 return func(*func_args, **func_kwargs) 331 except exceptions as err: 332 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes): ~/hf/lib/python3.7/site-packages/huggingface_hub/utils/_validators.py in _inner_fn(*args, **kwargs) 122 ) 123 --> 124 return fn(*args, **kwargs) 125 126 return _inner_fn # type: ignore TypeError: upload_file() got an unexpected keyword argument 'identical_ok' In [2]: ``` ### Expected behavior I would have expected this to work. It's for me the most intuitive way of creating a dummy dataset. ### Environment info ``` - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13 - Python version: 3.7.3 - PyArrow version: 11.0.0 - Pandas version: 1.3.5 ```
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Milvus integration for search
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2023-02-07T23:30:26Z
2023-02-24T16:45:09Z
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Signed-off-by: Filip Haltmayer <[email protected]>
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5,509
Add a static `__all__` to `__init__.py` for typecheckers
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This adds a static `__all__` field to `__init__.py`, allowing typecheckers to know which symbols are accessible from `datasets` at runtime. In particular [Pyright](https://github.com/microsoft/pylance-release/issues/2328#issuecomment-1029381258) seems to rely on this. At this point I have added all (modulo oversight) the symbols mentioned in the Reference part of [the docs](https://huggingface.co/docs/datasets), but that could be adjusted. As a side effect, only these symbols will be imported by `from datasets import *`, which may or may not be a good thing (and if it isn't, that's easy to fix). Another option would be to add a pyi stub, but I think `__all__` should be the most pythonic solution. This should fix #3841.
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5,508
Saving a dataset after setting format to torch doesn't work, but only if filtering
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2023-02-06T21:08:58Z
2023-02-09T14:55:26Z
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### Describe the bug Saving a dataset after setting format to torch doesn't work, but only if filtering ### Steps to reproduce the bug ``` a = Dataset.from_dict({"b": [1, 2]}) a.set_format('torch') a.save_to_disk("test_save") # saves successfully a.filter(None).save_to_disk("test_save_filter") # does not >> [...] TypeError: Provided `function` which is applied to all elements of table returns a `dict` of types [<class 'torch.Tensor'>]. When using `batched=True`, make sure provided `function` returns a `dict` of types like `(<class 'list'>, <class 'numpy.ndarray'>)`. # note: skipping the format change to torch lets this work. ### Expected behavior Saving to work ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-6.1.9-arch1-1-x86_64-with-glibc2.36 - Python version: 3.10.9 - PyArrow version: 9.0.0 - Pandas version: 1.4.4
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5,507
Optimise behaviour in respect to indices mapping
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2023-02-28T18:19:18Z
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COLLABORATOR
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_Originally [posted](https://huggingface.slack.com/archives/C02V51Q3800/p1675443873878489?thread_ts=1675418893.373479&cid=C02V51Q3800) on Slack_ Considering all this, perhaps for Datasets 3.0, we can do the following: * [ ] have `continuous=True` by default in `.shard` (requested in the survey and makes more sense for us since it doesn't create an indices mapping) * [x] allow calling `save_to_disk` on "unflattened" datasets * [ ] remove "hidden" expensive calls in `save_to_disk`, `unique`, `concatenate_datasets`, etc. For instance, instead of silently calling `flatten_indices` where it's needed, it's probably better to be explicit (considering how expensive these ops can be) and raise an error instead
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5,506
IterableDataset and Dataset return different batch sizes when using Trainer with multiple GPUs
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### Describe the bug I am training a Roberta model using 2 GPUs and the `Trainer` API with a batch size of 256. Initially I used a standard `Dataset`, but had issues with slow data loading. After reading [this issue](https://github.com/huggingface/datasets/issues/2252), I swapped to loading my dataset as contiguous shards and passing those to an `IterableDataset`. I observed an unexpected drop in GPU memory utilization, and found the batch size returned from the model had been cut in half. When using `Trainer` with 2 GPUs and a batch size of 256, `Dataset` returns a batch of size 512 (256 per GPU), while `IterableDataset` returns a batch size of 256 (256 total). My guess is `IterableDataset` isn't accounting for multiple cards. ### Steps to reproduce the bug ```python import datasets from datasets import IterableDataset from transformers import RobertaConfig from transformers import RobertaTokenizerFast from transformers import RobertaForMaskedLM from transformers import DataCollatorForLanguageModeling from transformers import Trainer, TrainingArguments use_iterable_dataset = True def gen_from_shards(shards): for shard in shards: for example in shard: yield example dataset = datasets.load_from_disk('my_dataset.hf') if use_iterable_dataset: n_shards = 100 shards = [dataset.shard(num_shards=n_shards, index=i) for i in range(n_shards)] dataset = IterableDataset.from_generator(gen_from_shards, gen_kwargs={"shards": shards}) tokenizer = RobertaTokenizerFast.from_pretrained("./my_tokenizer", max_len=160, use_fast=True) config = RobertaConfig( vocab_size=8248, max_position_embeddings=256, num_attention_heads=8, num_hidden_layers=6, type_vocab_size=1) model = RobertaForMaskedLM(config=config) data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15) training_args = TrainingArguments( per_device_train_batch_size=256 # other args removed for brevity ) trainer = Trainer( model=model, args=training_args, data_collator=data_collator, train_dataset=dataset, ) trainer.train() ``` ### Expected behavior Expected `Dataset` and `IterableDataset` to have the same batch size behavior. If the current behavior is intentional, the batch size printout at the start of training should be updated. Currently, both dataset classes result in `Trainer` printing the same total batch size, even though the batch size sent to the GPUs are different. ### Environment info datasets 2.7.1 transformers 4.25.1
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I_kwDODunzps5dro5u
5,505
PyTorch BatchSampler still loads from Dataset one-by-one
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2023-02-06T01:14:55Z
2023-02-19T18:27:30Z
2023-02-19T18:27:30Z
NONE
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### Describe the bug In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue. I'm not sure if this is a mistake in the docs or the code, but it seems that the only way for a Dataset to be passed a list of indexes by PyTorch (instead of one index at a time) is to define a `__getitems__` method (note the plural) on the Dataset object, and since the HF Dataset doesn't have this, PyTorch executes [this line of code](https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/fetch.py#L58), reverting to fetching one-by-one. ### Steps to reproduce the bug You can put a breakpoint in `Dataset.__getitem__()` or just print the args from there and see that it's called multiple times for a single `next(iter(dataloader))`, even when using the code from the docs: ```py from torch.utils.data.sampler import BatchSampler, RandomSampler batch_sampler = BatchSampler(RandomSampler(ds), batch_size=32, drop_last=False) dataloader = DataLoader(ds, batch_sampler=batch_sampler) ``` ### Expected behavior The expected behaviour would be for it to fetch batches from the dataset, rather than one-by-one. To demonstrate that there is room for improvement: once I have a HF dataset `ds`, if I just add this line: ```py ds.__getitems__ = ds.__getitem__ ``` ...then the time taken to loop over the dataset improves considerably (for wikitext-103, from one minute to 13 seconds with batch size 32). Probably not a big deal in the grand scheme of things, but seems like an easy win. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.3
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PR_kwDODunzps5JPoWy
5,504
don't zero copy timestamps
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2023-02-03T23:39:04Z
2023-02-08T17:28:50Z
2023-02-08T14:33:17Z
CONTRIBUTOR
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null
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Fixes https://github.com/huggingface/datasets/issues/5495 I'm not sure whether we prefer a test here or if timestamps are known to be unsupported (like booleans). The current test at least covers the bug
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5,502
Added functionality: sort datasets by multiple keys
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2023-02-03T16:17:00Z
2023-02-21T14:46:49Z
2023-02-21T14:39:23Z
CONTRIBUTOR
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null
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Added functionality implementation: sort datasets by multiple keys/columns as discussed in https://github.com/huggingface/datasets/issues/5425.
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PR_kwDODunzps5JMTn8
5,501
Increase chunk size for speeding up file downloads
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2023-02-03T10:50:10Z
2023-02-09T11:04:11Z
null
CONTRIBUTOR
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Original fix: https://github.com/huggingface/huggingface_hub/pull/1267 Not sure this function is actually still called though. I haven't done benches on this. Is there a dataset where files are hosted on the hub through cloudfront so we can have the same setup as in `hf_hub` ?
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1,569,257,240
I_kwDODunzps5diPcY
5,500
WMT19 custom download checksum error
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2023-02-03T05:45:37Z
2023-02-03T05:52:56Z
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### Describe the bug I use the following scripts to download data from WMT19: ```python import datasets from datasets import inspect_dataset, load_dataset_builder from wmt19.wmt_utils import _TRAIN_SUBSETS,_DEV_SUBSETS ## this is a must due to: https://discuss.huggingface.co/t/load-dataset-hangs-with-local-files/28034/3 if __name__ == '__main__': dev_subsets,train_subsets = [],[] for subset in _TRAIN_SUBSETS: if subset.target=='en' and 'de' in subset.sources: train_subsets.append(subset.name) for subset in _DEV_SUBSETS: if subset.target=='en' and 'de' in subset.sources: dev_subsets.append(subset.name) inspect_dataset("wmt19", "./wmt19") builder = load_dataset_builder( "./wmt19/wmt_utils.py", language_pair=("de", "en"), subsets={ datasets.Split.TRAIN: train_subsets, datasets.Split.VALIDATION: dev_subsets, }, ) builder.download_and_prepare() ds = builder.as_dataset() ds.to_json("../data/wmt19/ende/data.json") ``` And I got the following error: ``` Traceback (most recent call last): | 0/2 [00:00<?, ?obj/s] File "draft.py", line 26, in <module> builder.download_and_prepare() | 0/1 [00:00<?, ?obj/s] File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/builder.py", line 605, in download_and_prepare self._download_and_prepare(%| | 0/1 [00:00<?, ?obj/s] File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/builder.py", line 1104, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) | 0/1 [00:00<?, ?obj/s] File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/builder.py", line 676, in _download_and_prepare verify_checksums(s #13: 0%| | 0/1 [00:00<?, ?obj/s] File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 35, in verify_checksums raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) | 0/1 [00:00<?, ?obj/s] datasets.utils.info_utils.UnexpectedDownloadedFile: {'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-de.zipporah0-dedup-clean.tgz', 'https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-europarl-v7.zip', 'https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/rapid2016.zip', 'https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/training-parallel-nc-v13.zip', 'https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/training-parallel-nc-v12.zip', 'https://huggingface.co/datasets/wmt/wmt14/resolve/main-zip/training-parallel-nc-v9.zip', 'https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/training-parallel-nc-v10.zip', 'https://huggingface.co/datasets/wmt/wmt16/resolve/main-zip/translation-task/training-parallel-nc-v11.zip'} ``` ### Steps to reproduce the bug see above ### Expected behavior download data successfully ### Environment info datasets==2.1.0 python==3.8
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I_kwDODunzps5dhBRC
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`load_dataset` has ~4 seconds of overhead for cached data
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2023-02-02T23:34:50Z
2023-02-07T19:35:11Z
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### Feature request When loading a dataset that has been cached locally, the `load_dataset` function takes a lot longer than it should take to fetch the dataset from disk (or memory). This is particularly noticeable for smaller datasets. For example, wikitext-2, comparing `load_data` (once cached) and `load_from_disk`, the `load_dataset` method takes 40 times longer. ⏱ 4.84s ⮜ load_dataset ⏱ 119ms ⮜ load_from_disk ### Motivation I assume this is doing something like checking for a newer version. If so, that's an age old problem: do you make the user wait _every single time they load from cache_ or do you do something like load from cache always, _then_ check for a newer version and alert if they have stale data. The decision usually revolves around what percentage of the time the data will have been updated, and how dangerous old data is. For most datasets it's extremely unlikely that there will be a newer version on any given run, so 99% of the time this is just wasted time. Maybe you don't want to make that decision for all users, but at least having the _option_ to not wait for checks would be an improvement. ### Your contribution .
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TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset
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2023-02-02T14:46:49Z
2023-10-08T06:12:47Z
2023-02-04T17:19:36Z
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### Describe the bug Hi, Thanks for the amazing work on the library! **Describe the bug** I think I might have noticed a small bug in the filter method. Having loaded a dataset using `load_dataset`, when I try to filter out empty entries with `batched=True`, I get a TypeError. ### Steps to reproduce the bug ``` train_dataset = train_dataset.filter( function=lambda example: example["image"] is not None, batched=True, batch_size=10) ``` Error message: ``` File .../lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs) 476 validate_fingerprint(kwargs[fingerprint_name]) 478 # Call actual function --> 480 out = func(self, *args, **kwargs) ... -> 5666 indices_array = [i for i, to_keep in zip(indices, mask) if to_keep] 5667 if indices_mapping is not None: 5668 indices_array = pa.array(indices_array, type=pa.uint64()) TypeError: 'bool' object is not iterable ``` **Removing batched=True allows to bypass the issue.** ### Expected behavior According to the doc, "[batch_size corresponds to the] number of examples per batch provided to function if batched = True", so we shouldn't need to remove the batchd=True arg? source: https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.Dataset.filter ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31 - Python version: 3.9.10 - PyArrow version: 10.0.1 - Pandas version: 1.5.3
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Improved error message for gated/private repos
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2023-02-02T08:56:15Z
2023-02-02T11:26:08Z
2023-02-02T11:17:15Z
CONTRIBUTOR
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Using `use_auth_token=True` is not needed anymore. If a user logged in, the token will be automatically retrieved. Also include a mention for gated repos See https://github.com/huggingface/huggingface_hub/pull/1064
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Add a `reduce` method
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2023-02-02T04:30:22Z
2024-11-12T05:58:14Z
2023-07-21T14:24:32Z
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### Feature request Right now the `Dataset` class implements `map()` and `filter()`, but leaves out the third functional idiom popular among Python users: `reduce`. ### Motivation A `reduce` method is often useful when calculating dataset statistics, for example, the occurrence of a particular n-gram or the average line length of a code dataset. ### Your contribution I haven't contributed to `datasets` before, but I don't expect this will be too difficult, since the implementation will closely follow that of `map` and `filter`. I could have a crack over the weekend.
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to_tf_dataset fails with datetime UTC columns even if not included in columns argument
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2023-02-01T20:47:33Z
2023-02-08T14:33:19Z
2023-02-08T14:33:19Z
CONTRIBUTOR
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### Describe the bug There appears to be some eager behavior in `to_tf_dataset` that runs against every column in a dataset even if they aren't included in the columns argument. This is problematic with datetime UTC columns due to them not working with zero copy. If I don't have UTC information in my datetime column, then everything works as expected. ### Steps to reproduce the bug ```python import numpy as np import pandas as pd from datasets import Dataset df = pd.DataFrame(np.random.rand(2, 1), columns=["x"]) # df["dt"] = pd.to_datetime(["2023-01-01", "2023-01-01"]) # works fine df["dt"] = pd.to_datetime(["2023-01-01 00:00:00.00000+00:00", "2023-01-01 00:00:00.00000+00:00"]) df.to_parquet("test.pq") ds = Dataset.from_parquet("test.pq") tf_ds = ds.to_tf_dataset(columns=["x"], batch_size=2, shuffle=True) ``` ``` ArrowInvalid Traceback (most recent call last) Cell In[1], line 12 8 df.to_parquet("test.pq") 11 ds = Dataset.from_parquet("test.pq") ---> 12 tf_ds = ds.to_tf_dataset(columns=["r"], batch_size=2, shuffle=True) File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:411, in TensorflowDatasetMixin.to_tf_dataset(self, batch_size, columns, shuffle, collate_fn, drop_remainder, collate_fn_args, label_cols, prefetch, num_workers) 407 dataset = self 409 # TODO(Matt, QL): deprecate the retention of label_ids and label --> 411 output_signature, columns_to_np_types = dataset._get_output_signature( 412 dataset, 413 collate_fn=collate_fn, 414 collate_fn_args=collate_fn_args, 415 cols_to_retain=cols_to_retain, 416 batch_size=batch_size if drop_remainder else None, 417 ) 419 if "labels" in output_signature: 420 if ("label_ids" in columns or "label" in columns) and "labels" not in columns: File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:254, in TensorflowDatasetMixin._get_output_signature(dataset, collate_fn, collate_fn_args, cols_to_retain, batch_size, num_test_batches) 252 for _ in range(num_test_batches): 253 indices = sample(range(len(dataset)), test_batch_size) --> 254 test_batch = dataset[indices] 255 if cols_to_retain is not None: 256 test_batch = {key: value for key, value in test_batch.items() if key in cols_to_retain} File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2590, in Dataset.__getitem__(self, key) 2588 def __getitem__(self, key): # noqa: F811 2589 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 2590 return self._getitem( 2591 key, 2592 ) File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2575, in Dataset._getitem(self, key, **kwargs) 2573 formatter = get_formatter(format_type, features=self.features, **format_kwargs) 2574 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 2575 formatted_output = format_table( 2576 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 2577 ) 2578 return formatted_output File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:634, in format_table(table, key, formatter, format_columns, output_all_columns) 632 python_formatter = PythonFormatter(features=None) 633 if format_columns is None: --> 634 return formatter(pa_table, query_type=query_type) 635 elif query_type == "column": 636 if key in format_columns: File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:410, in Formatter.__call__(self, pa_table, query_type) 408 return self.format_column(pa_table) 409 elif query_type == "batch": --> 410 return self.format_batch(pa_table) File ~/venv/lib/python3.8/site-packages/datasets/formatting/np_formatter.py:78, in NumpyFormatter.format_batch(self, pa_table) 77 def format_batch(self, pa_table: pa.Table) -> Mapping: ---> 78 batch = self.numpy_arrow_extractor().extract_batch(pa_table) 79 batch = self.python_features_decoder.decode_batch(batch) 80 batch = self.recursive_tensorize(batch) File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:164, in NumpyArrowExtractor.extract_batch(self, pa_table) 163 def extract_batch(self, pa_table: pa.Table) -> dict: --> 164 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:164, in <dictcomp>(.0) 163 def extract_batch(self, pa_table: pa.Table) -> dict: --> 164 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:185, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array) 181 else: 182 zero_copy_only = _is_zero_copy_only(pa_array.type) and all( 183 not _is_array_with_nulls(chunk) for chunk in pa_array.chunks 184 ) --> 185 array: List = [ 186 row for chunk in pa_array.chunks for row in chunk.to_numpy(zero_copy_only=zero_copy_only) 187 ] 188 else: 189 if isinstance(pa_array.type, _ArrayXDExtensionType): 190 # don't call to_pylist() to preserve dtype of the fixed-size array File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:186, in <listcomp>(.0) 181 else: 182 zero_copy_only = _is_zero_copy_only(pa_array.type) and all( 183 not _is_array_with_nulls(chunk) for chunk in pa_array.chunks 184 ) 185 array: List = [ --> 186 row for chunk in pa_array.chunks for row in chunk.to_numpy(zero_copy_only=zero_copy_only) 187 ] 188 else: 189 if isinstance(pa_array.type, _ArrayXDExtensionType): 190 # don't call to_pylist() to preserve dtype of the fixed-size array File ~/venv/lib/python3.8/site-packages/pyarrow/array.pxi:1475, in pyarrow.lib.Array.to_numpy() File ~/venv/lib/python3.8/site-packages/pyarrow/error.pxi:100, in pyarrow.lib.check_status() ArrowInvalid: Needed to copy 1 chunks with 0 nulls, but zero_copy_only was True ``` ### Expected behavior I think there are two potential issues/fixes 1. Proper handling of datetime UTC columns (perhaps there is something incorrect with zero copy handling here) 2. Not eagerly running against every column in a dataset when the columns argument of `to_tf_dataset` specifies a subset of columns (although I'm not sure if this is unavoidable) ### Environment info - `datasets` version: 2.9.0 - Platform: macOS-13.2-x86_64-i386-64bit - Python version: 3.8.12 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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Update audio installation doc page
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2023-02-01T19:07:50Z
2023-03-02T16:08:17Z
2023-03-02T16:08:17Z
CONTRIBUTOR
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Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on all linux versions but there is a custom ubuntu repo for it, we have insctructions in the code: https://github.com/huggingface/datasets/blob/main/src/datasets/features/audio.py#L327 So we should update the doc page. But first investigate [this issue](5488).
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Remove unused `load_from_cache_file` arg from `Dataset.shard()` docstring
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Push_to_hub in a pull request
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2023-10-16T13:30:48Z
2023-10-16T13:30:48Z
MEMBER
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Right now `ds.push_to_hub()` can push a dataset on `main` or on a new branch with `branch=`, but there is no way to open a pull request. Even passing `branch=refs/pr/x` doesn't seem to work: it tries to create a branch with that name cc @nateraw It should be possible to tweak the use of `huggingface_hub` in `push_to_hub` to make it open a PR or push to an existing PR
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[MINOR] Typo
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2023-02-01T14:39:39Z
2023-02-02T07:42:28Z
2023-02-02T07:35:14Z
CONTRIBUTOR
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1,565,842,327
PR_kwDODunzps5I_nz-
5,490
Do not add index column by default when exporting to CSV
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2023-02-01T10:20:55Z
2023-02-09T09:29:08Z
2023-02-09T09:22:23Z
MEMBER
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As pointed out by @merveenoyan, default behavior of `Dataset.to_csv` adds the index as an additional column without name. This PR changes the default behavior, so that now the index column is not written. To add the index column, now you need to pass `index=True` and also `index_label=<name of the index colum>` to name that column. CC: @merveenoyan
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PR_kwDODunzps5I_WPH
5,489
Pin dill lower version
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2023-02-01T09:33:42Z
2023-02-02T07:48:09Z
2023-02-02T07:40:43Z
MEMBER
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Pin `dill` lower version compatible with `datasets`. Related to: - #5487 - #288 Note that the required `dill._dill` module was introduced in dill-2.8.0, however we have heuristically tested that datasets can only be installed with dill>=3.0.0 (otherwise pip hangs indefinitely while preparing metadata for multiprocess-0.70.7)
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1,565,025,262
I_kwDODunzps5dSGPu
5,488
Error loading MP3 files from CommonVoice
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2023-01-31T21:25:33Z
2023-03-02T16:25:14Z
2023-03-02T16:25:13Z
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### Describe the bug When loading a CommonVoice dataset with `datasets==2.9.0` and `torchaudio>=0.12.0`, I get an error reading the audio arrays: ```python --------------------------------------------------------------------------- LibsndfileError Traceback (most recent call last) ~/.local/lib/python3.8/site-packages/datasets/features/audio.py in _decode_mp3(self, path_or_file) 310 try: # try torchaudio anyway because sometimes it works (depending on the os and os packages installed) --> 311 array, sampling_rate = self._decode_mp3_torchaudio(path_or_file) 312 except RuntimeError: ~/.local/lib/python3.8/site-packages/datasets/features/audio.py in _decode_mp3_torchaudio(self, path_or_file) 351 --> 352 array, sampling_rate = torchaudio.load(path_or_file, format="mp3") 353 if self.sampling_rate and self.sampling_rate != sampling_rate: ~/.local/lib/python3.8/site-packages/torchaudio/backend/soundfile_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 204 """ --> 205 with soundfile.SoundFile(filepath, "r") as file_: 206 if file_.format != "WAV" or normalize: ~/.local/lib/python3.8/site-packages/soundfile.py in __init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd) 654 format, subtype, endian) --> 655 self._file = self._open(file, mode_int, closefd) 656 if set(mode).issuperset('r+') and self.seekable(): ~/.local/lib/python3.8/site-packages/soundfile.py in _open(self, file, mode_int, closefd) 1212 err = _snd.sf_error(file_ptr) -> 1213 raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name)) 1214 if mode_int == _snd.SFM_WRITE: LibsndfileError: Error opening <_io.BytesIO object at 0x7fa539462090>: File contains data in an unknown format. ``` I assume this is because there's some issue with the mp3 decoding process. I've verified that I have `ffmpeg>=4` (on a Linux distro), which appears to be the fallback backend for `torchaudio,` (at least according to #4889). ### Steps to reproduce the bug ```python dataset = load_dataset("mozilla-foundation/common_voice_11_0", "be", split="train") dataset[0] ``` ### Expected behavior Similar behavior to `torchaudio<0.12.0`, which doesn't result in a `LibsndfileError` ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 10.0.1 - Pandas version: 1.5.1
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5,487
Incorrect filepath for dill module
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2023-01-31T15:01:08Z
2023-02-24T16:18:36Z
2023-02-24T16:18:36Z
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### Describe the bug I installed the `datasets` package and when I try to `import` it, I get the following error: ``` Traceback (most recent call last): File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module> import datasets File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/__init__.py", line 43, in <module> from .arrow_dataset import Dataset File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 66, in <module> from .arrow_writer import ArrowWriter, OptimizedTypedSequence File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 27, in <module> from .features import Features, Image, Value File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/features/__init__.py", line 17, in <module> from .audio import Audio File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/features/audio.py", line 12, in <module> from ..download.streaming_download_manager import xopen File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/download/__init__.py", line 9, in <module> from .download_manager import DownloadManager, DownloadMode File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/download/download_manager.py", line 36, in <module> from ..utils.py_utils import NestedDataStructure, map_nested, size_str File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 602, in <module> class Pickler(dill.Pickler): File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 605, in Pickler dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy()) AttributeError: module 'dill' has no attribute '_dill' ``` Looking at the github source code for dill, it appears that `datasets` has a bug or is not compatible with the latest `dill`. Specifically, rather than `dill._dill.XXXX` it should be `dill.dill._dill.XXXX`. But given the popularity of `datasets` I feel confused about me being the first person to have this issue, so it makes me wonder if I'm misdiagnosing the issue. ### Steps to reproduce the bug Install `dill` and `datasets` packages and then `import datasets` ### Expected behavior I expect `datasets` to import. ### Environment info - `datasets` version: 2.9.0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.9.13 - PyArrow version: 11.0.0 - Pandas version: 1.4.4
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