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https://api.github.com/repos/huggingface/datasets/issues/6261
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1,913,813,178
I_kwDODunzps5yEni6
6,261
Can't load a dataset
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[ "I believe is due to the fact that doesn't work with .tgz files.", "`JourneyDB/JourneyDB` is a gated dataset, so this error means you are not authenticated to access it, either by using an invalid token or by not agreeing to the terms in the dialog on the dataset page.\r\n\r\n> I believe is due to the fact that doesn't work with .tgz files.\r\n\r\nIndeed, the dataset's data files structure is not supported natively by `datasets`. To load it, one option is to clone the repo (or download it with `huggingface_hub.snapshot_download`) and use `Dataset.from_generator` to process the files.", "> JourneyDB/JourneyDB is a gated dataset, so this error means you are not authenticated to access it, either by using an invalid token or by not agreeing to the terms in the dialog on the dataset page.´\r\n\r\nI did authentication with:\r\n\r\n```\r\nfrom huggingface_hub import notebook_login\r\nnotebook_login()\r\n```\r\n\r\nIsn't that the correct way to do it?\r\n\r\n> Indeed, the dataset's data files structure is not supported natively by datasets. To load it, one option is to clone the repo (or download it with huggingface_hub.snapshot_download) and use Dataset.from_generator to process the files.\r\n\r\nGreat suggestion I will give it a try.", "Have you accepted the terms in the dialog [here](https://huggingface.co/datasets/JourneyDB/JourneyDB)?\r\n\r\nIIRC Kaggle preinstalls an outdated `datasets` version, so it's also a good idea to update it before importing `datasets` (and do the same for `huggingface_hub`)", "Sorry for the late reply. Yes, I did. Thanks for the tip!" ]
2023-09-26T15:46:25
2023-10-05T10:23:23
2023-10-05T10:23:22
NONE
null
null
null
### Describe the bug Can't seem to load the JourneyDB dataset. It throws the following error: ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[15], line 2 1 # If the dataset is gated/private, make sure you have run huggingface-cli login ----> 2 dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True) File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1664, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1661 ignore_verifications = ignore_verifications or save_infos 1663 # Create a dataset builder -> 1664 builder_instance = load_dataset_builder( 1665 path=path, 1666 name=name, 1667 data_dir=data_dir, 1668 data_files=data_files, 1669 cache_dir=cache_dir, 1670 features=features, 1671 download_config=download_config, 1672 download_mode=download_mode, 1673 revision=revision, 1674 use_auth_token=use_auth_token, 1675 **config_kwargs, 1676 ) 1678 # Return iterable dataset in case of streaming 1679 if streaming: File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1490, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs) 1488 download_config = download_config.copy() if download_config else DownloadConfig() 1489 download_config.use_auth_token = use_auth_token -> 1490 dataset_module = dataset_module_factory( 1491 path, 1492 revision=revision, 1493 download_config=download_config, 1494 download_mode=download_mode, 1495 data_dir=data_dir, 1496 data_files=data_files, 1497 ) 1499 # Get dataset builder class from the processing script 1500 builder_cls = import_main_class(dataset_module.module_path) File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1238, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1236 raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1237 if isinstance(e1, FileNotFoundError): -> 1238 raise FileNotFoundError( 1239 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1240 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" 1241 ) from None 1242 raise e1 from None 1243 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/JourneyDB/JourneyDB/JourneyDB.py or any data file in the same directory. Couldn't find 'JourneyDB/JourneyDB' on the Hugging Face Hub either: FileNotFoundError: Unable to find data in dataset repository JourneyDB/JourneyDB with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] ``` ### Steps to reproduce the bug 1) ``` from huggingface_hub import notebook_login notebook_login() ``` 2) ``` !pip install -q datasets from datasets import load_dataset ``` 3) `dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True)` ### Expected behavior Load the dataset ### Environment info Notebook
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6,260
REUSE_DATASET_IF_EXISTS don't work
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[ "Hi! Unfortunately, the current behavior is to delete the downloaded data when this error happens. So, I've opened a PR that removes the problematic import to avoid losing data due to `apache_beam` not being installed (we host the preprocessed version of `natual_questions` on the HF GCS, so requiring `apache_beam` in that case doesn't make sense)", "Thanks for your reply. I met another question that I set `export HF_DATASETS_CACHE=/data/lxy/.cache` , but each time I run load_datasets, the datasets module still looking for NQ in the wrong default cache dir '/home/lxy/.cache' 。How to avoid this incorrect behavior. I am sure HF_DATASETS_CACHE was set correctly since I use echo & to check it.\r\n![image](https://github.com/huggingface/datasets/assets/88258534/e7029f27-b9f9-496c-8948-6234ef695646)\r\nby the way I delete the file in '/home/lxy/.cache' since I found there has some kb size file seems useless.", "You need to set this variable before the `datasets` import. Then, you can use `import datasets; datasets.config.HF_DATASETS_CACHE` to verify the cache location." ]
2023-09-26T03:02:16
2023-09-28T18:23:36
2023-09-28T18:23:36
NONE
null
null
null
### Describe the bug I use the following code to download natural_question dataset. Even though I have completely download it, the next time I run this code, the new download procedure will start and cover the original /data/lxy/NQ config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ') data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS) --- Since I don't have apache_beam installed, it throw a exception. After I pip install apache_beam ,the download restart.. ![image](https://github.com/huggingface/datasets/assets/88258534/f28ce7fe-29ea-4348-b87f-e69182a8bd41) ### Steps to reproduce the bug run this two line code config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ') data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS) ### Expected behavior Download behavior can be correctly follow DownloadMode ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-3.10.0-1160.88.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 11.0.0 - Pandas version: 2.0.3
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I_kwDODunzps5x9kg-
6,259
Duplicated Rows When Loading Parquet Files from Root Directory with Subdirectories
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null
[ "Thanks for reporting this issue! We should be able to avoid this by making our `glob` patterns more precise. In the meantime, you can load the dataset by directly assigning splits to the data files: \r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"parquet\", data_files={\"train\": \"testing123/train/output_train.parquet\", \"validation\": \"testing123/val/output_val.parquet\"})\r\n```" ]
2023-09-25T17:20:54
2024-03-15T15:22:04
2024-03-15T15:22:04
NONE
null
null
null
### Describe the bug When parquet files are saved in "train" and "val" subdirectories under a root directory, and datasets are then loaded using `load_dataset("parquet", data_dir="root_directory")`, the resulting dataset has duplicated rows for both the training and validation sets. ### Steps to reproduce the bug 1. Create a root directory, e.g., "testing123". 2. Under "testing123", create two subdirectories: "train" and "val". 3. Create and save a parquet file with 3 unique rows in the "train" subdirectory. 4. Create and save a parquet file with 4 unique rows in the "val" subdirectory. 5. Load the datasets from the root directory using `load_dataset("parquet", data_dir="testing123")` 6. Iterate through the datasets and print the rows Here's a collab reproducing these steps: https://colab.research.google.com/drive/11NEdImnQ3OqJlwKSHRMhr7jCBesNdLY4?usp=sharing ### Expected behavior - Training set should contain 3 unique rows. - Validation set should contain 4 unique rows. ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.17.2 - PyArrow version: 9.0.0 - Pandas version: 1.5.3
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HfHubHTTPError - exceeded our hourly quotas for action: commit
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[ "how is your dataset structured? (file types, how many commits and files are you trying to push, etc)", "I succeeded in uploading it after several attempts with an hour gap between each attempt (inconvenient but worked). The final dataset is [here](https://huggingface.co/datasets/yuvalkirstain/pickapic_v2), code and context to the dataset can be found [here](https://github.com/yuvalkirstain/PickScore/).\r\nI can close the issue if this behavior is intended, as most users probably do not need to upload large-scale datasets.", "We could fix this by creating a single commit for all the (Parquet) shards in `push_to_hub` instead of one commit per shard, as we currently do. \r\n\r\n@Wauplin Any updates on the 2-step commit process suggested by you that we need to implement this?", "> Any updates on the 2-step commit process suggested by you that we need to implement this?\r\n\r\nRe-prioritizing this, sorry. Will let you know but probably can be done this week." ]
2023-09-25T06:11:43
2023-10-16T13:30:49
2023-10-16T13:30:48
NONE
null
null
null
### Describe the bug I try to upload a very large dataset of images, and get the following error: ``` File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/hf_api.py:2712, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit, run_as_future) 2710 try: 2711 commit_resp = get_session().post(url=commit_url, headers=headers, data=data, params=params) -> 2712 hf_raise_for_status(commit_resp, endpoint_name="commit") 2713 except RepositoryNotFoundError as e: 2714 e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE) File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:301, in hf_raise_for_status(response, endpoint_name) 297 raise BadRequestError(message, response=response) from e 299 # Convert `HTTPError` into a `HfHubHTTPError` to display request information 300 # as well (request id and/or server error message) --> 301 raise HfHubHTTPError(str(e), response=response) from e HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/yuvalkirstain/pickapic_v2/commit/main (Request ID: Root=1-65112399-12d63f7d7f28bfa40a36a0fd) You have exceeded our hourly quotas for action: commit. We invite you to retry later. ``` this makes it much less convenient to host large datasets on HF hub. ### Steps to reproduce the bug Upload a very large dataset of images ### Expected behavior the upload to work well ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-1033-aws-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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6,256
load_dataset() function's cache_dir does not seems to work
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[ "Can you share the error message?\r\n\r\nAlso, it would help if you could check whether `huggingface_hub`'s download behaves the same:\r\n```python\r\nfrom huggingface_hub import snapshot_download\r\nsnapshot_download(\"trec\", repo_type=\"dataset\", cache_dir='/path/to/my/dir)\r\n```\r\n\r\nIn the next major release, we aim to switch to `huggingface_hub` for file download/caching, but we could align the `cache_dir`'s `umask` behavior earlier than this if their solution works for your use case." ]
2023-09-24T15:34:06
2023-09-27T13:40:45
null
NONE
null
null
null
### Describe the bug datasets version: 2.14.5 when trying to run the following command trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir') I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine. It seems the cache_dir parameter cannot change the dataset saving directory from the default what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work ### Steps to reproduce the bug datasets version: 2.14.5 when trying to run the following command trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir') I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine. It seems the cache_dir parameter cannot change the dataset saving directory from the default what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work ### Expected behavior the dataset should be saved to the cache_dir points to ### Environment info datasets version: 2.14.5 macos X: Ventura 13.4.1 (c)
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1,909,672,104
I_kwDODunzps5x00io
6,254
Dataset.from_generator() cost much more time in vscode debugging mode then running mode
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[ "Answered on the forum: https://discuss.huggingface.co/t/dataset-from-generator-cost-much-more-time-in-vscode-debugging-mode-then-running-mode/56005/2" ]
2023-09-23T02:07:26
2023-10-03T14:42:53
2023-10-03T14:42:53
NONE
null
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null
### Describe the bug Hey there, I’m using Dataset.from_generator() to convert a torch_dataset to the Huggingface Dataset. However, when I debug my code on vscode, I find that it runs really slow on Dataset.from_generator() which may even 20 times longer then run the script on terminal. ### Steps to reproduce the bug I write a simple test code : ```python import os from functools import partial from typing import Callable import torch import time from torch.utils.data import Dataset as TorchDataset from datasets import load_from_disk, Dataset as HFDataset import torch from torch.utils.data import Dataset class SimpleDataset(Dataset): def __init__(self, data): self.data = data self.keys = list(data[0].keys()) def __len__(self): return len(self.data) def __getitem__(self, index): sample = self.data[index] return {key: sample[key] for key in self.keys} def TorchDataset2HuggingfaceDataset(torch_dataset: TorchDataset, cache_dir: str = None ) -> HFDataset: """ convert torch dataset to huggingface dataset """ generator : Callable[[], TorchDataset] = lambda: (sample for sample in torch_dataset) return HFDataset.from_generator(generator, cache_dir=cache_dir) if __name__ == '__main__': data = [ {'id': 1, 'name': 'Alice'}, {'id': 2, 'name': 'Bob'}, {'id': 3, 'name': 'Charlie'} ] torch_dataset = SimpleDataset(data) start_time = time.time() huggingface_dataset = TorchDataset2HuggingfaceDataset(torch_dataset) end_time = time.time() print("time: ", end_time - start_time) print(huggingface_dataset) ``` ### Expected behavior this test on my machine report that the running time on terminal is 0.086, however the running time in debugging mode on vscode is 0.25, which I think is much longer than expected. I’d like to know is the anything wrong in the code or just because of debugging? I have traced the code and I find is this func which I get stuck. ```python def create_config_id( self, config_kwargs: dict, custom_features: Optional[Features] = None, ) -> str: ... # stuck in this line suffix = Hasher.hash(config_kwargs_to_add_to_suffix) ``` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31 - Python version: 3.11.3 - Huggingface_hub version: 0.17.2 - PyArrow version: 11.0.0 - Pandas version: 2.0.1
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I_kwDODunzps5xoPrS
6,252
exif_transpose not done to Image (PIL problem)
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[ "Indeed, it makes sense to do this by default. \r\n\r\nIn the meantime, you can use `.with_transform` to transpose the images when accessing them:\r\n\r\n```python\r\nimport PIL.ImageOps\r\n\r\ndef exif_transpose_transform(batch):\r\n batch[\"image\"] = [PIL.ImageOps.exif_transpose(image) for image in batch[\"image\"]]\r\n return batch\r\n\r\ndataset = dataset.with_transform(exif_transpose_transform)\r\n```", "This operation sets some `Image` attributes to `None` (`.format`, `.filename`, etc.), causing our tests to fail, so I think we should wait for Datasets 3.0 to make this change. In version 3.0, storing image paths will be replaced by embedding image bytes, so there will be fewer instances where we use the `.filename` attribute." ]
2023-09-21T08:11:46
2023-09-22T14:07:52
null
NONE
null
null
null
### Feature request I noticed that some of my images loaded using PIL have some metadata related to exif that can rotate them when loading. Since the dataset.features.Image uses PIL for loading, the loaded image may be rotated (width and height will be inverted) thus for tasks as object detection and layoutLM this can create some inconsistencies (between input bboxes and input images). For now there is no option in datasets.features.Image to specify that. We need to do the following when preparing examples (when preparing images for training, test or inference): ``` from PIL import Image, ImageOps pil = ImageOps.exif_transpose(pil) ``` reference: https://stackoverflow.com/a/63950647/5720150 Is it possible to add this by default to the datasets.feature.Image ? or to add the option to do the ImageOps.exif_transpose? Thank you ### Motivation Prevent having inverted data related to exif metadata that may affect object detection tasks ### Your contribution Changing in datasets.featrues.Image I can help with that.
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1,899,848,414
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6,246
Add new column to dataset
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[ "I think it's an issue with the code.\r\n\r\nSpecifically:\r\n```python\r\ndataset = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```\r\n\r\nNow `dataset` is the train set with a new column. \r\nTo fix this, you can do:\r\n\r\n```python\r\ndataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```", "> I think it's an issue with the code.\r\n> \r\n> Specifically:\r\n> \r\n> ```python\r\n> dataset = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n> \r\n> Now `dataset` is the train set with a new column. To fix this, you can do:\r\n> \r\n> ```python\r\n> dataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n\r\nThanks for your response, but i can not access mask images, please let me know why the problem still persists. Here is the notebook for reference: https://colab.research.google.com/drive/10lZ_zLtU4itYVmIVTvIEVbjfOtCZaAZy?usp=sharing ", "I think there is a slight misunderstanding.\r\n```python\r\nnew_column = [\"mask\"] * len(dataset[\"train\"])\r\ndataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```\r\n\r\nadds a column with the string `mask` to your dataset.\r\nIf you're trying to load the images `\"mask_{idx}.png\"` in your dataset, you could try:\r\n\r\n```\r\nfrom datasets import Image\r\n\r\ndataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/workspace/data/mask_{idx}.png\", with_indices=True).cast_column(\"mask\", Image())\r\n```\r\n\r\nWhat this does is that it adds a column to your dataset name `mask` with the path to the mask, then it cast the column as an `Image` feature.\r\n\r\nThis [link](https://huggingface.co/docs/datasets/v2.5.1/en/image_load) explains how to load images.\r\n\r\nHope this helps!", "> I think there is a slight misunderstanding.\r\n> \r\n> ```python\r\n> new_column = [\"mask\"] * len(dataset[\"train\"])\r\n> dataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n> \r\n> adds a column with the string `mask` to your dataset. If you're trying to load the images `\"mask_{idx}.png\"` in your dataset, you could try:\r\n> \r\n> ```\r\n> from datasets import Image\r\n> \r\n> dataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/workspace/data/mask_{idx}.png\", with_indices=True).cast_column(\"mask\", Image())\r\n> ```\r\n> \r\n> What this does is that it adds a column to your dataset name `mask` with the path to the mask, then it cast the column as an `Image` feature.\r\n> \r\n> This [link](https://huggingface.co/docs/datasets/v2.5.1/en/image_load) explains how to load images.\r\n> \r\n> Hope this helps!\r\n\r\nThank you very much, this is really helpful...\r\ni made some changes for it to work:\r\n```\r\ndataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/content/data/mask_{idx}.png\"}, with_indices=True).cast_column(\"mask\", Image())\r\n```\r\nThanks Again @Dref360 " ]
2023-09-17T16:59:48
2023-09-18T16:20:09
2023-09-18T16:20:09
NONE
null
null
null
### Describe the bug ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-9-bd197b36b6a0>](https://localhost:8080/#) in <cell line: 1>() ----> 1 dataset['train']['/workspace/data'] 3 frames [/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_column_key(key, columns) 518 def _check_valid_column_key(key: str, columns: List[str]) -> None: 519 if key not in columns: --> 520 raise KeyError(f"Column {key} not in the dataset. Current columns in the dataset: {columns}") 521 522 KeyError: "Column train not in the dataset. Current columns in the dataset: ['image', '/workspace/data']" ``` ### Steps to reproduce the bug please find the notebook for reference: https://colab.research.google.com/drive/10lZ_zLtU4itYVmIVTvIEVbjfOtCZaAZy?usp=sharing ### Expected behavior add column to the dataset ### Environment info colab pro
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6,242
Data alteration when loading dataset with unspecified inner sequence length
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[ "While this issue may seem specific, it led to a silent problem in my workflow that took days to diagnose. If this feature is not intended to be supported, an error should be raised when encountering this configuration to prevent such issues.", "Thanks for reporting! This is a MRE:\r\n\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets.table import cast_array_to_feature\r\nfrom datasets import Sequence, Value\r\ndata = [\r\n [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],\r\n [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]],\r\n]\r\narr = pa.array(data, pa.list_(pa.list_(pa.float32(), 3)))\r\ncast_array_to_feature(arr, Sequence(Sequence(Value(\"float32\"))))\r\n```\r\n\r\nI've opened a PR with a fix." ]
2023-09-14T16:12:45
2023-09-19T17:53:18
2023-09-19T17:53:18
MEMBER
null
null
null
### Describe the bug When a dataset saved with a specified inner sequence length is loaded without specifying that length, the original data is altered and becomes inconsistent. ### Steps to reproduce the bug ```python from datasets import Dataset, Features, Value, Sequence, load_dataset # Repository ID repo_id = "my_repo_id" # Define features with a specific length of 3 for each inner sequence specified_features = Features({"key": Sequence(Sequence(Value("float32"), length=3))}) # Create a dataset with the specified features data = [ [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]], ] dataset = Dataset.from_dict({"key": data}, features=specified_features) # Push the dataset to the hub dataset.push_to_hub(repo_id) # Define features without specifying the length unspecified_features = Features({"key": Sequence(Sequence(Value("float32")))}) # Load the dataset from the hub with this new feature definition dataset = load_dataset(f"qgallouedec/{repo_id}", split="train", features=unspecified_features) # The obtained data is altered print(dataset.to_dict()) # {'key': [[[1.0], [2.0]], [[3.0], [4.0]]]} ``` ### Expected behavior ```python print(dataset.to_dict()) # {'key': [[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]]} ``` ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-6.2.0-32-generic-x86_64-with-glibc2.35 - Python version: 3.9.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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6,240
Dataloader stuck on multiple GPUs
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[ "What type of dataset are you using in this script? `torch.utils.data.Dataset` or `datasets.Dataset`? Please share the `datasets` package version if it's the latter. Otherwise, it's better to move this issue to the `accelerate` repo.", "Very sorry, I thought I had a repo in `accelerate!`\r\nI will close this issue and repo the issue in the appropriate place." ]
2023-09-14T05:30:30
2023-09-14T23:54:42
2023-09-14T23:54:42
NONE
null
null
null
### Describe the bug I am trying to get CLIP to fine-tuning with my code. When I tried to run it on multiple GPUs using accelerate, I encountered the following phenomenon. - Validation dataloader stuck in 2nd epoch only on multi-GPU Specifically, when the "for inputs in valid_loader:" process is finished, it does not proceed to the next step. train_loader process is completed. Also, both train and valid are working correctly in the first epoch. The accelerate command at that time is as follows. `accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...` - This will not happen when single GPU is used. `CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...` - Setting num_workers=0 in dataloader did not change the result. ### Steps to reproduce the bug 1. The codes for fine-tuning the regular CLIP were updated for accelerate. 2. Run the code with the accelerate command as `accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...` and the above problem will occur. 3. CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...` , it works fine. ### Expected behavior It Should end normally as if it was run on a single GPU. ### Environment info Since `datasets-cli env` did not work, the environment is described below. - OS: Ubuntu 22.04 with Docker - Docker: 24.0.5, build ced0996 - Python: 3.10.12 - torch==2.0.1 - accelerate==0.21.0 - transformers==4.33.1
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1,895,349,382
I_kwDODunzps5w-LyG
6,239
Load local audio data doesn't work
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[ "I think this is the same issue as https://github.com/huggingface/datasets/issues/4776. Maybe installing `ffmpeg` can fix it:\r\n```python\r\nadd-apt-repository -y ppa:savoury1/ffmpeg4\r\napt-get -qq install -y ffmpeg\r\n```\r\n\r\nHowever, the best solution is to use a newer version of `datasets`. In the recent releases, we've replaced `torchaudio` with `soundfile`, which is easier to install and faster.", "@mariosasko \r\nThanks for your help" ]
2023-09-13T22:30:01
2023-09-15T14:32:10
2023-09-15T14:32:10
NONE
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### Describe the bug I get a RuntimeError from the following code: ```python audio_dataset = Dataset.from_dict({"audio": ["/kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3"]}).cast_column("audio", Audio()) audio_dataset[0] ``` ### Traceback <details> ```python RuntimeError Traceback (most recent call last) Cell In[33], line 1 ----> 1 train_dataset[0] File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key) 1762 def __getitem__(self, key): # noqa: F811 1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 1764 return self._getitem( 1765 key, 1766 ) File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs) 1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs) 1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 1749 formatted_output = format_table( 1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1751 ) 1752 return formatted_output File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:312, in PythonFormatter.format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:221, in PythonFeaturesDecoder.decode_row(self, row) 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1386, in Features.decode_example(self, example) 1376 def decode_example(self, example: dict): 1377 """Decode example with custom feature decoding. 1378 1379 Args: (...) 1383 :obj:`dict[str, Any]` 1384 """ -> 1386 return { 1387 column_name: decode_nested_example(feature, value) 1388 if self._column_requires_decoding[column_name] 1389 else value 1390 for column_name, (feature, value) in zip_dict( 1391 {key: value for key, value in self.items() if key in example}, example 1392 ) 1393 } File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1387, in <dictcomp>(.0) 1376 def decode_example(self, example: dict): 1377 """Decode example with custom feature decoding. 1378 1379 Args: (...) 1383 :obj:`dict[str, Any]` 1384 """ 1386 return { -> 1387 column_name: decode_nested_example(feature, value) 1388 if self._column_requires_decoding[column_name] 1389 else value 1390 for column_name, (feature, value) in zip_dict( 1391 {key: value for key, value in self.items() if key in example}, example 1392 ) 1393 } File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1087, in decode_nested_example(schema, obj) 1085 # Object with special decoding: 1086 elif isinstance(schema, (Audio, Image)): -> 1087 return schema.decode_example(obj) if obj is not None else None 1088 return obj File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:103, in Audio.decode_example(self, value) 101 raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.") 102 elif path is not None and path.endswith("mp3"): --> 103 array, sampling_rate = self._decode_mp3(file if file else path) 104 elif path is not None and path.endswith("opus"): 105 if file: File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:241, in Audio._decode_mp3(self, path_or_file) 238 except RuntimeError as err: 239 raise ImportError("To support decoding 'mp3' audio files, please install 'sox'.") from err --> 241 array, sampling_rate = torchaudio.load(path_or_file, format="mp3") 242 if self.sampling_rate and self.sampling_rate != sampling_rate: 243 if not hasattr(self, "_resampler") or self._resampler.orig_freq != sampling_rate: File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:256, in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 254 if ret is not None: 255 return ret --> 256 return _fallback_load(filepath, frame_offset, num_frames, normalize, channels_first, format) File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:30, in _fail_load(filepath, frame_offset, num_frames, normalize, channels_first, format) 22 def _fail_load( 23 filepath: str, 24 frame_offset: int = 0, (...) 28 format: Optional[str] = None, 29 ) -> Tuple[torch.Tensor, int]: ---> 30 raise RuntimeError("Failed to load audio from {}".format(filepath)) RuntimeError: Failed to load audio from /kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3 ``` </details> ### Steps to reproduce the bug 1. - Create a custom dataset using Local files of type mp3. 3. - Try to read the first audio item. ### Expected behavior Expected output ```python audio_dataset[0]["audio"] {'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414, 0. , 0. ], dtype=float32), 'path': 'path/to/audio_1', 'sampling_rate': 16000} ``` ### Environment info N/A
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6,238
`dataset.filter` ALWAYS removes the first item from the dataset when using batched=True
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[ "`filter` treats the function's output as a (selection) mask - `True` keeps the sample, and `False` drops it. In your case, `bool(0)` evaluates to `False`, so dropping the first sample is the correct behavior.", "Oh gosh! 🤦 I totally misunderstood the API! My apologies!" ]
2023-09-13T20:20:37
2023-09-17T07:05:07
2023-09-17T07:05:07
NONE
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### Describe the bug If you call batched=True when calling `filter`, the first item is _always_ filtered out, regardless of the filter condition. ### Steps to reproduce the bug Here's a minimal example: ```python def filter_batch_always_true(batch, indices): print("First index being passed into this filter function: ", indices[0]) return indices # Keep all indices data = {"value": list(range(10))} dataset = Dataset.from_dict(data) filtered_dataset = dataset.filter(filter_batch_always_true, with_indices=True, batched=True) print("Length of original dataset: ", len(dataset)) print("Length of filtered_dataset: ", len(filtered_dataset)) print("Is equal to original? ", len(filtered_dataset) == len(dataset)) print("First item of filtered dataset: ", filtered_dataset[0]) print("Last item of filtered dataset: ", filtered_dataset[-1]) ``` prints: ``` First index being passed into this filter function: 0 Length of original dataset: 10 Length of filtered_dataset: 9 Is equal to original? False First item of filtered dataset: {'value': 1} Last item of filtered dataset: {'value': 9} ``` ### Expected behavior Filter should respect the filter condition. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.5-arm64-arm-64bit - Python version: 3.9.18 - Huggingface_hub version: 0.17.1 - PyArrow version: 10.0.1 - Pandas version: 2.0.2
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I_kwDODunzps5w4W9x
6,237
Tokenization with multiple workers is too slow
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[ "[This](https://huggingface.co/docs/datasets/nlp_process#map) is the most performant way to tokenize a dataset (`batched=True, num_proc=None, return_tensors=\"np\"`) \r\n\r\nIf`tokenizer.is_fast` returns `True`, `num_proc` must be `None/1` to benefit from the fast tokenizers' parallelism (the fast tokenizers are implemented in Rust, and Rust multi-threading doesn't work well with Python multi-processing)" ]
2023-09-13T06:18:34
2023-09-19T21:54:58
2023-09-19T21:54:58
NONE
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I am trying to tokenize a few million documents with multiple workers but the tokenization process is taking forever. Code snippet: ``` raw_datasets.map( encode_function, batched=False, num_proc=args.preprocessing_num_workers, load_from_cache_file=not args.overwrite_cache, remove_columns=[name for name in raw_datasets["train"].column_names if name not in ["input_ids", "labels", "attention_mask"]], desc="Tokenizing data", ) ``` Details: ``` transformers==4.28.0.dev0 datasets==4.28.0.dev0 preprocessing_num_workers==48 ``` tokenizer == decapoda-research/llama-7b-hf
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1,893,648,480
I_kwDODunzps5w3shg
6,236
Support buffer shuffle for to_tf_dataset
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[ "cc @Rocketknight1 ", "Hey! You can implement this yourself, just:\r\n\r\n1) Create the dataset with `to_tf_dataset()` with `shuffle=False`\r\n2) Add an `unbatch()` at the end (or use batch_size=1)\r\n3) Add a `shuffle()` to the resulting dataset with your desired buffer size\r\n4) Add a `batch()` at the end again to re-batch your dataset.\r\n\r\nNote that the way we construct datasets in `to_tf_dataset()`, we don't actually shuffle the entire dataset in-memory, using `tf.data.Dataset.shuffle()`! Instead, we shuffle an index array and then load from the dataset with that. This means that shuffling with `tf.data.Dataset.shuffle()` will probably be slower and use more memory than our approach - I don't think adding the option for smaller shuffle buffers will actually save you memory on this!", "Thanks for your reply! @Rocketknight1 \r\n\"We don't actually shuffle the entire dataset in-memory, using tf.data.Dataset.shuffle()! Instead, we shuffle an index array and then load from the dataset with that.\"\r\nIn such case, there will be random access to dataset data during shuffling. When the dataset is large, the performance can be X10 times slow. I have tried many ways with to_tf_dataset() trying to achieve comparable performance with tf.data.Dataset().shuffle(buffer_size).batch(). But the performance with to_tf_dataset() is still slow. \r\n" ]
2023-09-13T03:19:44
2023-09-18T01:11:21
null
NONE
null
null
null
### Feature request I'm using to_tf_dataset to convert a large dataset to tf.data.Dataset and use Keras fit to train model. Currently, to_tf_dataset only supports full size shuffle, which can be very slow on large dataset. tf.data.Dataset support buffer shuffle by default. shuffle( buffer_size, seed=None, reshuffle_each_iteration=None, name=None ) ### Motivation I'm very frustrated to find the loading with shuffling large dataset is very slow. It seems impossible to shuffle before training Keras with big dataset. ### Your contribution NA
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1,893,337,083
I_kwDODunzps5w2gf7
6,235
Support multiprocessing for download/extract nestedly
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2023-09-12T21:51:08
2023-09-12T21:51:08
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### Feature request Current multiprocessing for download/extract is not done nestedly. For example, when processing SlimPajama, there is only 3 processes (for train/test/val), while there are many files inside these 3 folders ``` Downloading data files #0: 0%| | 0/1 [00:00<?, ?obj/s] Downloading data files #1: 0%| | 0/1 [00:00<?, ?obj/s] Downloading data files #2: 0%| | 0/1 [00:00<?, ?obj/s] Extracting data files #0: 0%| | 0/1 [00:00<?, ?obj/s] Extracting data files #1: 0%| | 0/1 [00:00<?, ?obj/s] Extracting data files #2: 0%| | 0/1 [00:00<?, ?obj/s] ``` ### Motivation speedup dataset loading ### Your contribution I can help test the feature
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1,889,050,954
I_kwDODunzps5wmKFK
6,229
Apply inference on all images in the dataset
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[ "From what I see, `MMSegInferencer` supports NumPy arrays, so replace the line `image_path = example['image']` with `image_path = np.array(example['image'])` to fix the issue (`example[\"image\"]` is a `PIL.Image` object). ", "> From what I see, `MMSegInferencer` supports NumPy arrays, so replace the line `image_path = example['image']` with `image_path = np.array(example['image'])` to fix the issue (`example[\"image\"]` is a `PIL.Image` object).\r\n\r\nThanks @mariosasko for your reply...\r\ni tried :\r\n```\r\n# Define a function to apply the code to each image in the dataset\r\ndef process_image(image_path):\r\n print(\"Processing image:\", image_path)\r\n result = inferencer(image_path)['predictions']\r\n mask = np.where(result == 12, 255, 0).astype('uint8')\r\n return Image.fromarray(mask)\r\n\r\n# Process and save masks for each image in the dataset\r\nfor idx, example in enumerate(dataset['train']):\r\n image_path = np.array(example['image'])\r\n mask_image = process_image(image_path)\r\n mask_image.save(f\"mask_{idx}.png\")\r\n```\r\nand got\r\n```\r\nProcessing image: [[[202 165 87]\r\n [203 166 88]\r\n [207 168 91]\r\n ...\r\n [243 205 122]\r\n [244 202 120]\r\n [242 200 118]]\r\n\r\n [[202 165 87]\r\n [203 166 88]\r\n [207 168 91]\r\n ...\r\n [244 206 123]\r\n [245 203 121]\r\n [243 201 119]]\r\n\r\n [[203 164 87]\r\n [204 165 88]\r\n [207 168 91]\r\n ...\r\n [245 207 126]\r\n [246 204 122]\r\n [245 203 121]]\r\n\r\n ...\r\n\r\n [[154 123 56]\r\n [155 124 57]\r\n [158 125 56]\r\n ...\r\n [ 3 3 1]\r\n [ 3 3 1]\r\n [ 3 3 1]]\r\n\r\n [[154 123 56]\r\n [154 123 56]\r\n [155 124 57]\r\n ...\r\n [ 2 2 0]\r\n [ 2 2 0]\r\n [ 2 2 0]]\r\n\r\n [[152 121 54]\r\n [152 121 54]\r\n [153 122 55]\r\n ...\r\n [ 2 2 0]\r\n [ 2 2 0]\r\n [ 2 2 0]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[ 39 44 40]\r\n [ 39 44 40]\r\n [ 39 43 44]\r\n ...\r\n [187 185 164]\r\n [208 204 175]\r\n [203 198 166]]\r\n\r\n [[ 42 47 43]\r\n [ 40 45 41]\r\n [ 40 44 45]\r\n ...\r\n [188 186 165]\r\n [202 198 169]\r\n [201 196 164]]\r\n\r\n [[ 41 46 42]\r\n [ 39 44 40]\r\n [ 40 44 45]\r\n ...\r\n [187 184 165]\r\n [197 193 166]\r\n [201 196 166]]\r\n\r\n ...\r\n\r\n [[ 29 27 30]\r\n [ 28 26 29]\r\n [ 25 23 26]\r\n ...\r\n [ 48 33 28]\r\n [ 44 31 25]\r\n [ 39 26 20]]\r\n\r\n [[ 34 29 33]\r\n [ 32 27 31]\r\n [ 29 24 28]\r\n ...\r\n [ 30 17 11]\r\n [ 36 23 15]\r\n [ 41 28 20]]\r\n\r\n [[ 35 30 34]\r\n [ 33 28 32]\r\n [ 28 23 27]\r\n ...\r\n [ 28 15 9]\r\n [ 41 28 20]\r\n [ 46 33 25]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[ 65 53 55]\r\n [ 65 53 55]\r\n [ 51 39 41]\r\n ...\r\n [133 127 111]\r\n [150 141 124]\r\n [133 124 107]]\r\n\r\n [[ 58 45 52]\r\n [ 61 48 55]\r\n [ 51 38 45]\r\n ...\r\n [148 141 123]\r\n [178 169 152]\r\n [144 135 118]]\r\n\r\n [[ 79 66 83]\r\n [ 73 60 77]\r\n [ 65 51 66]\r\n ...\r\n [140 131 114]\r\n [142 133 116]\r\n [147 136 118]]\r\n\r\n ...\r\n\r\n [[132 122 133]\r\n [ 95 85 94]\r\n [ 61 51 60]\r\n ...\r\n [ 39 28 42]\r\n [ 46 36 45]\r\n [ 25 16 21]]\r\n\r\n [[150 143 151]\r\n [114 107 115]\r\n [ 64 54 63]\r\n ...\r\n [ 47 35 47]\r\n [ 38 27 35]\r\n [140 129 133]]\r\n\r\n [[145 138 146]\r\n [115 108 116]\r\n [ 69 59 67]\r\n ...\r\n [ 31 19 31]\r\n [128 117 123]\r\n [196 185 189]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[159 151 140]\r\n [171 163 152]\r\n [161 148 142]\r\n ...\r\n [198 184 171]\r\n [189 175 162]\r\n [183 169 156]]\r\n\r\n [[128 118 106]\r\n [138 128 116]\r\n [138 125 116]\r\n ...\r\n [200 186 173]\r\n [190 176 163]\r\n [187 173 160]]\r\n\r\n [[165 153 137]\r\n [170 158 142]\r\n [174 162 148]\r\n ...\r\n [200 187 171]\r\n [188 175 159]\r\n [182 169 153]]\r\n```\r\nHowever , when trying to add to:\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('Andyrasika/cat_kingdom')\r\ndataset\r\n```\r\ni did \r\n```\r\nnew_column = [\"mask\"] * len(dataset[\"train\"])\r\nnew_column\r\ndataset = dataset.add_column(\"/workspace/data\", new_column)\r\n\r\nprint(dataset)\r\n```\r\ngot error:\r\n```\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\nCell In[11], line 3\r\n 1 new_column = [\"mask\"] * len(dataset[\"train\"])\r\n 2 new_column\r\n----> 3 dataset = dataset.add_column(\"/workspace/data\", new_column)\r\n 5 print(dataset)\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'add_column'\r\n```", "https://github.com/huggingface/datasets/issues/6246 resolved the `add_column` error, so I'm closing this issue :) " ]
2023-09-10T08:36:12
2023-09-20T16:11:53
2023-09-20T16:11:52
NONE
null
null
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### Describe the bug ``` --------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last) Cell In[14], line 11 9 for idx, example in enumerate(dataset['train']): 10 image_path = example['image'] ---> 11 mask_image = process_image(image_path) 12 mask_image.save(f"mask_{idx}.png") Cell In[14], line 4, in process_image(image_path) 2 def process_image(image_path): 3 print("Processing image:", image_path) ----> 4 result = inferencer(image_path)['predictions'] 5 mask = np.where(result == 12, 255, 0).astype('uint8') 6 return Image.fromarray(mask) File /usr/local/lib/python3.10/dist-packages/mmseg/apis/mmseg_inferencer.py:183, in MMSegInferencer.__call__(self, inputs, return_datasamples, batch_size, show, wait_time, out_dir, img_out_dir, pred_out_dir, **kwargs) 180 pred_out_dir = '' 181 img_out_dir = '' --> 183 return super().__call__( 184 inputs=inputs, 185 return_datasamples=return_datasamples, 186 batch_size=batch_size, 187 show=show, 188 wait_time=wait_time, 189 img_out_dir=img_out_dir, 190 pred_out_dir=pred_out_dir, 191 **kwargs) File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:221, in BaseInferencer.__call__(self, inputs, return_datasamples, batch_size, **kwargs) 218 inputs = self.preprocess( 219 ori_inputs, batch_size=batch_size, **preprocess_kwargs) 220 preds = [] --> 221 for data in (track(inputs, description='Inference') 222 if self.show_progress else inputs): 223 preds.extend(self.forward(data, **forward_kwargs)) 224 visualization = self.visualize( 225 ori_inputs, preds, 226 **visualize_kwargs) # type: ignore # noqa: E501 File /usr/local/lib/python3.10/dist-packages/rich/progress.py:168, in track(sequence, description, total, auto_refresh, console, transient, get_time, refresh_per_second, style, complete_style, finished_style, pulse_style, update_period, disable, show_speed) 157 progress = Progress( 158 *columns, 159 auto_refresh=auto_refresh, (...) 164 disable=disable, 165 ) 167 with progress: --> 168 yield from progress.track( 169 sequence, total=total, description=description, update_period=update_period 170 ) File /usr/local/lib/python3.10/dist-packages/rich/progress.py:1210, in Progress.track(self, sequence, total, task_id, description, update_period) 1208 if self.live.auto_refresh: 1209 with _TrackThread(self, task_id, update_period) as track_thread: -> 1210 for value in sequence: 1211 yield value 1212 track_thread.completed += 1 File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:291, in BaseInferencer.preprocess(self, inputs, batch_size, **kwargs) 266 """Process the inputs into a model-feedable format. 267 268 Customize your preprocess by overriding this method. Preprocess should (...) 287 Any: Data processed by the ``pipeline`` and ``collate_fn``. 288 """ 289 chunked_data = self._get_chunk_data( 290 map(self.pipeline, inputs), batch_size) --> 291 yield from map(self.collate_fn, chunked_data) File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:588, in BaseInferencer._get_chunk_data(self, inputs, chunk_size) 586 chunk_data = [] 587 for _ in range(chunk_size): --> 588 processed_data = next(inputs_iter) 589 chunk_data.append(processed_data) 590 yield chunk_data File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results) 9 def __call__(self, 10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]: ---> 12 return self.transform(results) File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/wrappers.py:88, in Compose.transform(self, results) 79 """Call function to apply transforms sequentially. 80 81 Args: (...) 85 dict or None: Transformed results. 86 """ 87 for t in self.transforms: ---> 88 results = t(results) # type: ignore 89 if results is None: 90 return None File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results) 9 def __call__(self, 10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]: ---> 12 return self.transform(results) File /usr/local/lib/python3.10/dist-packages/mmseg/datasets/transforms/loading.py:496, in InferencerLoader.transform(self, single_input) 494 inputs = single_input 495 else: --> 496 raise NotImplementedError 498 if 'img' in inputs: 499 return self.from_ndarray(inputs) NotImplementedError: ```` ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset('Andyrasika/cat_kingdom') dataset from mmseg.apis import MMSegInferencer checkpoint_name = 'segformer_mit-b5_8xb2-160k_ade20k-640x640' inferencer = MMSegInferencer(model=checkpoint_name) # Define a function to apply the code to each image in the dataset def process_image(image_path): print("Processing image:", image_path) result = inferencer(image_path)['predictions'] mask = np.where(result == 12, 255, 0).astype('uint8') return Image.fromarray(mask) # Process and save masks for each image in the dataset for idx, example in enumerate(dataset['train']): image_path = example['image'] mask_image = process_image(image_path) mask_image.save(f"mask_{idx}.png") ``` ### Expected behavior create a separate column with masks in the dataset and further shows as a separate column in hub ### Environment info jupyter notebook RTX 3090
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1,887,054,320
I_kwDODunzps5weinw
6,225
Conversion from RGB to BGR in Object Detection tutorial
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[ "Good catch!" ]
2023-09-08T06:49:19
2023-09-08T17:52:18
2023-09-08T17:52:17
NONE
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The [tutorial](https://huggingface.co/docs/datasets/main/en/object_detection) mentions the necessity of conversion the input image from BGR to RGB > albumentations expects the image to be in BGR format, not RGB, so you’ll have to convert the image before applying the transform. [Link to tutorial](https://github.com/huggingface/datasets/blob/0a068dbf3b446417ffd89d32857608394ec699e6/docs/source/object_detection.mdx#L77) However, relevant albumentations' tutorials [on channels conversion](https://albumentations.ai/docs/examples/example/#read-the-image-from-the-disk-and-convert-it-from-the-bgr-color-space-to-the-rgb-color-space) and [on boxes](https://albumentations.ai/docs/examples/example_bboxes/) imply that it's not really true no more. I suggest removing this outdated conversion from the tutorial.
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1,884,324,631
I_kwDODunzps5wUIMX
6,221
Support saving datasets with custom formatting
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[ "Not a fan of pickling this sort of stuff either.\r\nNote that users can also share the code in their dataset documentation." ]
2023-09-06T16:03:32
2023-09-06T18:32:07
null
CONTRIBUTOR
null
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Requested in https://discuss.huggingface.co/t/using-set-transform-on-a-dataset-leads-to-an-exception/53036. I am not sure if supporting this is the best idea for the following reasons: >For this to work, we would have to pickle a custom transform, which means the transform and the objects it references need to be serializable. Also, deserializing these bytes would make `load_from_disk` unsafe, so I'm not sure this is a good idea. @lhoestq WDYT?
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1,883,614,607
I_kwDODunzps5wRa2P
6,217
`Dataset.to_dict()` ignore `decode=True` with Image feature
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[ "We need to implement the `Image` type as a PyArrow extension type (to allow us to override the Python conversion) for this to work as expected. For now, it's best to use your approach indeed." ]
2023-09-06T09:26:16
2023-09-08T17:08:52
null
MEMBER
null
null
null
### Describe the bug `Dataset.to_dict` seems to ignore the decoding instruction passed in features. ### Steps to reproduce the bug ```python import datasets import numpy as np from PIL import Image img = np.random.randint(0, 256, (5, 5, 3), dtype=np.uint8) img = Image.fromarray(img) features = datasets.Features({"image": datasets.Image(decode=True)}) dataset = datasets.Dataset.from_dict({"image": [img]}, features=features) print({key: dataset[key] for key in dataset.column_names}) # {'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=5x5 at 0x7EFBC80E15B0>]} print(dataset.to_dict()) # {'image': [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x05\x00\x00\x00\x05\x08\x02\x00\x00\x00\x02\r\xb1\xb2\x00\x00\x00[IDATx\x9c\x01P\x00\xaf\xff\x01\x13\x1b<7\xe7\xe0\xdc^6\xed\x04\xc7M\xd2\x9f\x00X\x1b\xb0?\x1ba\x15\xc5 o\xd0\x80\xbe\x19/\x01\xec\x95\x1f\x9f\xffj\xfa1\xa7\xc4X\xea\xbe\xa4g\x00\xc4\x15\xdeC\xc7 \xbbaqe\xc8\xb9\xa9q\xe7\x00,?M\xc0)\xdaD`}\xb1\xdci\x1e\xafC\xa9]%.@\xa6\xf0\xb3\x00\x00\x00\x00IEND\xaeB`\x82', 'path': None}]} ``` ### Expected behavior I would expect `{key: dataset[key] for key in dataset.column_names}` and `dataset.to_dict()` to be equivalent. If the previous behavior is expected, then it should be stated [in the doc](https://huggingface.co/docs/datasets/v2.14.4/en/package_reference/main_classes#datasets.Dataset.to_dict). ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35 - Python version: 3.9.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - Pillow 9.5.0 - numpy 1.25.2
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1,881,736,469
I_kwDODunzps5wKQUV
6,214
Unpin fsspec < 2023.9.0
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2023-09-05T11:02:58
2023-09-26T15:32:52
2023-09-26T15:32:52
MEMBER
null
null
null
Once root issue is fixed, remove temporary pin of fsspec < 2023.9.0 introduced by: - #6210 Related to issue: - #6209 After investigation, I think the root issue is related to the new glob behavior with double asterisk `**` they have introduced in: - https://github.com/fsspec/filesystem_spec/pull/1329
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1,880,399,516
I_kwDODunzps5wFJ6c
6,212
Tilde (~) is not supported for data_files
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[ "Hi @exs-avianello, is it really needed? Note you can alternatively use `pathlib.Path` among others as it follows:\r\n\r\n```python\r\nimport datasets\r\nfrom pathlib import Path\r\n\r\n# save a parquet file at ~/path/to/data.parquet\r\n\r\ndata_files = Path.home() / \"path/to/data.parquet\"\r\ndataset = datasets.load_dataset(\"parquet\", data_files=data_files)\r\n```", "Hi @alvarobartt ! \r\n\r\nThis is definitely just a \"nice to have\" and I am personally more than happy to just use absolute paths client-side. I just wanted to flag it up in case it can help improve the package even more 🙌 It might not be immediately obvious from the stack trace that the error is triggered by the `~` in the path" ]
2023-09-04T14:23:49
2023-09-05T08:28:39
null
NONE
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### Describe the bug Attempting to `load_dataset` from a path starting with `~` (as a shorthand for the user's home directory) seems not to be fully working - at least as far as the `parquet` dataset builder is concerned. (the same file can be loaded correctly if providing its absolute path instead) I think that this is very similar to https://github.com/huggingface/datasets/issues/5757, but for `data_files` rather than `data_dir` ### Steps to reproduce the bug ```python import datasets # save a parquet file at ~/path/to/data.parquet data_files = "~/path/to/data.parquet" dataset = datasets.load_dataset("parquet", data_files=data_files) ``` ``` Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 12671.61it/s] Extracting data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 22671.91it/s] Generating train split: 0 examples [00:00, ? examples/s] Traceback (most recent call last): File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1949, in _prepare_split_single num_examples, num_bytes = writer.finalize() ^^^^^^^^^^^^^^^^^ File ".venv/lib/python3.11/site-packages/datasets/arrow_writer.py", line 598, in finalize raise SchemaInferenceError("Please pass `features` or at least one example when writing data") datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data The above exception was the direct cause of the following exception: Traceback (most recent call last): File ".venv/lib/python3.11/site-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1813, in _prepare_split for job_id, done, content in self._prepare_split_single( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1958, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior Can use `~` shorthand in paths when loading local (parquet) datasets. ### Environment info `datasets 2.14.3`
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I_kwDODunzps5wCMFw
6,209
CI is broken with AssertionError: 3 failed, 12 errors
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2023-09-04T06:47:05
2023-09-04T07:30:01
2023-09-04T07:30:01
MEMBER
null
null
null
Our CI is broken: 3 failed, 12 errors See: https://github.com/huggingface/datasets/actions/runs/6069947111/job/16465138041 ``` =========================== short test summary info ============================ FAILED tests/test_load.py::ModuleFactoryTest::test_LocalDatasetModuleFactoryWithoutScript_with_data_dir - AssertionError: assert ({NamedSplit('train'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt'], NamedSplit('test'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt']} is not None and 2 == 1) + where 2 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt']) FAILED tests/test_load.py::test_load_dataset_arrow[False] - AssertionError: assert 20 == 10 + where 20 = Dataset({\n features: ['col_1'],\n num_rows: 20\n}).num_rows FAILED tests/test_load.py::test_load_dataset_arrow[True] - assert 20 == 10 ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl']) ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl']) ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv']) ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv']) = 3 failed, 2383 passed, 26 skipped, 9 warnings, 12 errors in 280.79s (0:04:40) = ```
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1,879,555,234
I_kwDODunzps5wB7yi
6,207
No-script datasets with ZIP files do not load
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2023-09-04T05:50:27
2023-09-04T09:13:33
2023-09-04T09:13:33
MEMBER
null
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null
While investigating an issue on a Hub dataset, I have discovered the no-script datasets containing ZIP files do not load. For example, that no-script dataset containing ZIP files, raises NonMatchingSplitsSizesError: ```python In [2]: ds = load_dataset("sidovic/LearningQ-qg") NonMatchingSplitsSizesError: [ { 'expected': SplitInfo(name='train', num_bytes=0, num_examples=188660, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg') }, { 'expected': SplitInfo(name='validation', num_bytes=0, num_examples=20630, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg') }, { 'expected': SplitInfo(name='test', num_bytes=0, num_examples=18227, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg') } ] ``` As another example, a no-script dataset containing just a (CSV)-ZIP file, raises a DatasetGenerationError: ``` > num_examples, num_bytes = writer.finalize() src/datasets/builder.py:1949: > raise SchemaInferenceError("Please pass `features` or at least one example when writing data") E datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data src/datasets/arrow_writer.py:598: SchemaInferenceError The above exception was the direct cause of the following exception: src/datasets/load.py:2143: in load_dataset builder_instance.download_and_prepare( src/datasets/builder.py:954: in download_and_prepare self._download_and_prepare( src/datasets/builder.py:1049: in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) src/datasets/builder.py:1813: in _prepare_split for job_id, done, content in self._prepare_split_single( > raise DatasetGenerationError("An error occurred while generating the dataset") from e E datasets.builder.DatasetGenerationError: An error occurred while generating the dataset src/datasets/builder.py:1958: DatasetGenerationError ``` After investigating, I think this bug was introduced in this PR: - #5972 Related to: - https://huggingface.co/datasets/sidovic/LearningQ-qg/discussions/1 CC: @lhoestq
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1,879,473,745
I_kwDODunzps5wBn5R
6,206
When calling load_dataset, raise error: pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
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[ "I solved the problem by modifying the \"self DEFAULT_WRITER_BATCH_SIZE\" in \"class MyDataset (datasets. GeneratorBasedBuilder) : __init__\"" ]
2023-09-04T04:14:00
2023-09-04T06:05:50
2023-09-04T06:05:49
NONE
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### Describe the bug When calling load_dataset, raise error ``` Traceback (most recent call last): File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1694, in _pre pare_split_single writer.write(example, key) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 490, in write self.write_examples_on_file() File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 559, in write_batch self.write_table(pa_table, writer_batch_size) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 571, in write_table pa_table = pa_table.combine_chunks() ^^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow/table.pxi", line 3439, in pyarrow.lib.Table.combine_chunks File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays The above exception was the direct cause of the following exception: Traceback (most recent call last): dataset = load_dataset( ^^^^^^^^^^^^^ File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py", line 2133, in load_da taset builder_instance.download_and_prepare( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 954, in downl oad_and_prepare self._download_and_prepare( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _dow nload_and_prepare super()._download_and_prepare( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _dow nload_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _pre pare_split for job_id, done, content in self._prepare_split_single( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1712, in _pre pare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset Setting num_proc from 8 back to 1 for the train split to disable multiprocessing as it only contains one shard. 09/04/2023 12:02:04 - WARNING - datasets.builder - Setting num_proc from 8 back to 1 for the train split to dis able multiprocessing as it only contains one shard. ``` ### Steps to reproduce the bug Call load_dataset with the large image as feature ### Expected behavior no error ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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1,877,491,602
I_kwDODunzps5v6D-S
6,203
Support loading from a DVC remote repository
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[ "(cross-posting from the linked DVC issue)\r\n\r\nI think this should already work out of the box with the current `datasets` and `dvc.api` releases by passing the correct `storage_options` into the datasets calls. `storage_options` is essentially just the kwargs dict that gets passed to the fsspec fs constructor.\r\n\r\nThe main thing to note here is that the fsspec DVCFileSystem URL should be `dvc://folder/file.json` (i.e. this should be the DVCFileSystem path that is relative to the DVC repo root). You cannot use a URL like `https://gitlab.com/user/repo/folder/file.json`.\r\n\r\nI think something like this should work for you (in a venv where both DVC and datasets are installed):\r\n```python\r\nimport datasets\r\n\r\n# load a dataset from Git/DVC repository where Git repo is located at https://gitlab.com/user/repo.git\r\n# and path to dataset (relative to git/dvc repo root) is 'folder/file.json'\r\ndatasets.load_from_disk(\r\n \"dvc://folder/file.json\",\r\n storage_options={\"url\": \"https://gitlab.com/user/repo.git\"},\r\n)\r\n```\r\n\r\nbasically the `dvc://` is what tells fsspec to create a `DVCFileSystem` and it will construct it like\r\n```python\r\nfs = DVCFileSystem(**storage_options)\r\n```\r\n\r\nThen the subsequent calls use the rest of the `dvc://...` URL like \r\n```python\r\nfs.exists(\"folder/file.json\")\r\n```", "Hi @pmrowla Thank you for your help, that's very helpful, I was indeed using `fsspec` incorrectly here. There is still an issue with `datasets`:\r\n\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"json\", data_files=\"dvc://folder/file.jsonl\", storage_options={\"url\": \"https://gitlab.com/repo/folder/\"})\r\n```\r\n\r\nresults in the following exception:\r\n\r\n```\r\nTraceback (most recent call last): \r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/scmrepo/fs.py\", line 217, in info\r\n ret = self.trie.info(key)\r\n ^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/scmrepo/git/objects.py\", line 141, in info\r\n obj = self.trie[key]\r\n ~~~~~~~~~^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/pygtrie.py\", line 937, in __getitem__\r\n node, _ = self._get_node(key_or_slice)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/pygtrie.py\", line 630, in _get_node\r\n raise KeyError(key)\r\nKeyError: ('dvc:', 'datasets', 'spider', 'train.jsonl')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 2129, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 1815, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 1430, in dataset_module_factory\r\n ).get_module()\r\n ^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 958, in get_module\r\n data_files = DataFilesDict.from_patterns(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 674, in from_patterns\r\n DataFilesList.from_patterns(\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 589, in from_patterns\r\n origin_metadata = _get_origin_metadata(data_files, download_config=download_config)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 504, in _get_origin_metadata\r\n return thread_map(\r\n ^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/tqdm/contrib/concurrent.py\", line 69, in thread_map\r\n return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/tqdm/contrib/concurrent.py\", line 51, in _executor_map\r\n return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 619, in result_iterator\r\n yield _result_or_cancel(fs.pop())\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 317, in _result_or_cancel\r\n return fut.result(timeout)\r\n ^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 456, in result\r\n return self.__get_result()\r\n ^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 401, in __get_result\r\n raise self._exception\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/thread.py\", line 58, in run\r\n result = self.fn(*self.args, **self.kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 491, in _get_single_origin_metadata\r\n info = fs.info(data_file)\r\n ^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/dvc/fs/dvc.py\", line 357, in info\r\n return self._info(key, path, ignore_subrepos=ignore_subrepos)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/dvc/fs/dvc.py\", line 377, in _info\r\n fs_info = fs.info(fs_path)\r\n ^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/dvc_objects/fs/base.py\", line 501, in info\r\n return self.fs.info(path, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/scmrepo/fs.py\", line 221, in info\r\n raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), path)\r\nFileNotFoundError: [Errno 2] No such file or directory: '/dvc:/folder/file.jsonl'\r\n```\r\n\r\nSomehow the URL gets turned into `/dvc:/folder/file.jsonl` inside `datasets`. Otherwise I can confirm that using `fsspec` properly with DVC works as expected.\r\n", "For the record, there was a `dvc.api.DVCFileSystem` bug which is fixed in DVC `main` and will be available in the next DVC release.\r\n\r\nTo use DVC with `datasets` you just need to pass the Git/DVC repo `url` in `storage_options` as discussed above.\r\n\r\n(note that this requires having both `datasets` and `dvc` installed in your python environment)\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> load_dataset(\r\n... \"json\",\r\n... data_files=\"dvc://eval/metrics.json\",\r\n... storage_options={\"url\": \"https://github.com/iterative/example-get-started.git\"},\r\n... )\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['avg_prec', 'roc_auc'],\r\n num_rows: 1\r\n })\r\n})\r\n```\r\n\r\nAny additional `DVCFileSystem` args can be passed in the same way, so to get a specific branch/tag/commit from the DVC repo you just need to specify the `rev` in `storage_options` like\r\n```\r\nstorage_options={\"url\": \"https://github.com/iterative/example-get-started.git\", \"rev\": \"main\"}\r\n```\r\n\r\nI think this issue can probably be closed now.", "Thank you for your help, closing." ]
2023-09-01T14:04:52
2023-09-15T15:11:27
2023-09-15T15:11:27
NONE
null
null
null
### Feature request Adding support for loading a file from a DVC repository, tracked remotely on a SCM. ### Motivation DVC is a popular version control system to version and manage datasets. The files are stored on a remote object storage platform, but they are tracked using Git. Integration with DVC is possible through the `DVCFileSystem`. I have a Gitlab repository where multiple files are tracked using DVC and stored in a GCP bucket. I would like to be able to load these files using `datasets` directly using an URL. My goal is to write a generic code that abstracts the storage layer, such that my users will only have to pass in an `fsspec`-compliant URL and the corresponding files will be loaded. ### Your contribution I managed to instantiate a `DVCFileSystem` pointing to a Gitlab repo from a `fsspec` chained URL in [this pull request](https://github.com/iterative/dvc/pull/9903) to DVC. ```python from fsspec.core import url_to_fs fs, _ = url_to_fs("dvc::https://gitlab.com/repository/group/my-repo") ``` From now I'm not sure how to continue, it seems that `datasets` expects the URL to be fully qualified like so: `dvc::https://gitlab.com/repository/group/my-repo/my-folder/my-file.json` but this fails because `DVCFileSystem` expects the URL to point to the root of an SCM repo. Is there a way to make this work with `datasets`?
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1,876,630,351
I_kwDODunzps5v2xtP
6,202
avoid downgrading jax version
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[ "https://github.com/huggingface/datasets/blob/main/setup.py#L236\r\nCurrently has the highest version at 0.3.25; Not sure if there is any reason for this, other than that was the tested version?" ]
2023-09-01T02:57:57
2023-10-12T16:28:59
2023-10-12T16:28:59
NONE
null
null
null
### Feature request Whenever I `pip install datasets[jax]` it downgrades jax to version 0.3.25. I seem to be able to install this library first then upgrade jax back to version 0.4.13. ### Motivation It would be nice to not overwrite currently installed version of jax if possible. ### Your contribution I would be willing to beta test. Or maybe write some code if I could get pointed in the right direction, I'm not super familiar with this codebase.
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I_kwDODunzps5vxMAB
6,199
Use load_dataset for local json files, but it not works
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[ "Hugging Face's datasets library may prioritize remote configurations. Make sure there are no conflicting configurations causing the library to prefer downloading data\r\nMay be try debugging\r\nraw_datasets = load_dataset('json', data_files=data_files)\r\nprint(raw_datasets)\r\n", "It doesn't download them but writes them to the local HF cache. The logging could indeed be better. Does loading the dataset succeed? If it doesn't, can you share the error stack trace?" ]
2023-08-31T09:42:34
2023-08-31T19:05:07
null
NONE
null
null
null
### Describe the bug when I use load_dataset to load my local datasets,it always goes to Hugging Face to download the data instead of loading the local dataset. ### Steps to reproduce the bug `raw_datasets = load_dataset( ‘json’, data_files=data_files)` ### Expected behavior ![image](https://github.com/huggingface/datasets/assets/50519434/add3747f-6481-4da7-b374-8f81c5a6472c) ### Environment info python version 3.8.5 datasets version 2.12 os version unbuntu 18.04
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6,197
ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns'
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[ "Thanks for reporting. We are investigating it.", "This issue is caused by latest `pandas` release 2.1.0 (released yesterday Aug 30).\r\n\r\nSee: https://github.com/huggingface/datasets/actions/runs/6035484010/job/16375932085?pr=6198\r\n", "People using previous releases of `datasets` should pin `pandas` in their local environment:\r\n```\r\npython -m pip install 'pandas<2.1.0'\r\n```" ]
2023-08-31T08:51:50
2023-09-01T10:35:10
2023-08-31T10:24:40
NONE
null
null
null
### Describe the bug Saving a dataset `.to_json()` fails with a `ValueError` since the latest `pandas` [release](https://pandas.pydata.org/docs/dev/whatsnew/v2.1.0.html) (`2.1.0`) In their latest release we have: > Improved error handling when using [DataFrame.to_json()](https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json) with incompatible index and orient arguments ([GH 52143](https://github.com/pandas-dev/pandas/issues/52143)) i.e. an error is now raised for invalid combinations of `index` and `orient`. This means that unfortunately the custom logic at this line might sometimes lead to contradictions: https://github.com/huggingface/datasets/blob/029227a116c14720afca71b9b22e78eb2a1c09a6/src/datasets/io/json.py#L96 e.g. for the default case `orient=records` leads to `index=True`, which now raises a `ValueError` ### Steps to reproduce the bug ```python import datasets if __name__ == '__main__': dataset = datasets.Dataset.from_dict({"A": [1, 2, 3], "B": [4, 5, 6]}) dataset.to_json("dataset.json") ``` ```shell >>> ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns'. ``` ### Expected behavior The dataset is successfully saved as `.json` ### Environment info `python >= 3.9` `pandas >= 2.1.0`
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I_kwDODunzps5vw0_8
6,196
Split order is not preserved
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2023-08-31T08:47:16
2023-08-31T13:48:43
2023-08-31T13:48:43
MEMBER
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I have noticed that in some cases the split order is not preserved. For example, consider a no-script dataset with configs: ```yaml configs: - config_name: default data_files: - split: train path: train.csv - split: test path: test.csv ``` - Note the defined split order is [train, test] Once the dataset is loaded, the split order is not preserved: ```python In [16]: ds Out[16]: DatasetDict({ test: Dataset({ features: ['text', 'label'], num_rows: 1 }) train: Dataset({ features: ['text', 'label'], num_rows: 2 }) }) ``` - Note the obtained split order is [test, train]
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6,195
Force to reuse cache at given path
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[ "realized that need to pass the path at `cache_file_name` like\r\n\r\n```python\r\ntokenized_datasets = raw_datasets[\"train\"].map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=data_args.preprocessing_num_workers,\r\n remove_columns=[text_column_name],\r\n load_from_cache_file=True,\r\n desc=\"Running tokenizer on dataset line_by_line\",\r\n # cache_file_names= {\"train\": \"cache-1982fea76aa54a13.arrow\"}\r\n cache_file_name=\"/project/huggingface_cache/datasets/..../cache-1982fea76aa54a13.arrow\",\r\n new_fingerprint=\"1982fea76aa54a13\"\r\n )\r\n```", "Thank you so much! I went through a lot of issues before finding similar experiences here. I have to say that the [docs](https://huggingface.co/docs/datasets/v2.11.0/en/package_reference/main_classes#datasets.Dataset.map) of `.map()` is really misleading, probably making people think that just assigning the file name to cache_file_name is enough." ]
2023-08-30T18:44:54
2023-11-03T10:14:21
2023-08-30T19:00:45
NONE
null
null
null
### Describe the bug I have run the official example of MLM like: ```bash python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name togethercomputer/RedPajama-Data-1T \ --dataset_config_name arxiv \ --per_device_train_batch_size 10 \ --preprocessing_num_workers 20 \ --validation_split_percentage 0 \ --cache_dir /project/huggingface_cache/datasets \ --line_by_line \ --do_train \ --pad_to_max_length \ --output_dir /project/huggingface_cache/test-mlm ``` it successfully runs and at my cache folder has `cache-1982fea76aa54a13_00001_of_00020.arrow`..... `cache-1982fea76aa54a13_00020_of_00020.arrow ` as tokenization cache of `map` method. And the cache works fine every time I run the command above. However, when I switched to jupyter notebook (since I do not want to load datasets every time when I changed other parameters not related to the dataloading). It is not recognizing the cache files and starts to re-run the entire tokenization process. I changed my code to ```python tokenized_datasets = raw_datasets["train"].map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[text_column_name], load_from_cache_file=True, desc="Running tokenizer on dataset line_by_line", # cache_file_names= {"train": "cache-1982fea76aa54a13.arrow"} cache_file_name="cache-1982fea76aa54a13.arrow", new_fingerprint="1982fea76aa54a13" ) ``` it still does not recognize the previously cached files and trying to re-run the tokenization process. ### Steps to reproduce the bug use jupyter notebook for dataset map function. ### Expected behavior the map function accepts the given cache_file_name and new_fingerprint then load the previously cached files. ### Environment info - `datasets` version: 2.14.4.dev0 - Platform: Linux-3.10.0-1160.59.1.el7.x86_64-x86_64-with-glibc2.10 - Python version: 3.8.8 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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I_kwDODunzps5vnZTP
6,194
Support custom fingerprinting with `Dataset.from_generator`
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[ "The `fingerprint` parameter serves a slightly different purpose - we use it to inject a new fingerprint after transforming a `Dataset` (computed from the previous fingerprint + transform + transform args), e.g., to be able to compute the cache file for a transform. There is no concept of `fingerprint` before a `Dataset` is fully initialized, but we still need to hash the args (e.g., generator func) of the \"dataset creation methods\" (`from_generator`, `from_csv`, etc.) to compute the cache directory (to store the initial version and transformed dataset versions)\r\n\r\nI agree it should be easier to bypass the hashing mechanism in this instance, too. However, we should probably first address https://github.com/huggingface/datasets/issues/5080 before solving this (e.g., maybe exposing `hash` in `load_dataset`/`load_dataset_builder`.", "Adding +1 here:\r\n\r\nIf the generator needs to access some external resources or state, then it's not always straightforward to make it pickle-able. So I'd like to be able to override how the default cache key derivation needs to pickle the generator (and of course, I'd accept responsibility for that part of cache consistency).\r\n\r\nAppears to be a recurrent roadbump: #6118 #5963 #5819 #5750 #4983 ", "Silly hack incoming:\r\n\r\n```python\r\nimport uuid\r\n\r\nclass _DatasetGeneratorPickleHack:\r\n def __init__(self, generator, generator_id=None):\r\n self.generator = generator\r\n self.generator_id = (\r\n generator_id if generator_id is not None else str(uuid.uuid4())\r\n )\r\n\r\n def __call__(self, *args, **kwargs):\r\n return self.generator(*kwargs, **kwargs)\r\n\r\n def __reduce__(self):\r\n return (_DatasetGeneratorPickleHack_raise, (self.generator_id,))\r\n\r\n\r\ndef _DatasetGeneratorPickleHack_raise(*args, **kwargs):\r\n raise AssertionError(\"cannot actually unpickle _DatasetGeneratorPickleHack!\")\r\n```\r\n\r\nNow `Dataset.from_generator(_DatasetGeneratorPickleHack(gen))` works even if `gen` is unpicklable, because Dataset just pickles the shim object that avoids actually traversing `gen`. Then, one can work out how to set `generator_id` meaningfully to allow cache reuse.", "I'd like some way to do this too. I find that sometimes the hash doesn't cover enough, and that the dataset is not regenerated even when underlying data has changed, and by supplying a custom fingerprint I could do a better job of controlling when my dataset is regenerated.", "This is what I did and it works: \r\n\r\nhttps://github.com/stevemadere/s3-datasets/blob/e475a566a16d3051656a66f8ff4d3baa4c55a66c/src/tokengenerators/text_ds_2_tokens_generator.py#L200\r\n" ]
2023-08-29T22:43:13
2024-02-29T03:46:54
null
NONE
null
null
null
### Feature request When using `Dataset.from_generator`, the generator is hashed when building the fingerprint. Similar to `.map`, it would be interesting to let the user bypass this hashing by accepting a `fingerprint` argument to `.from_generator`. ### Motivation Using the `.from_generator` constructor with a non-picklable generator fails. By accepting a `fingerprint` argument to `.from_generator`, the user would have the opportunity to manually fingerprint the dataset and thus bypass the crash. ### Your contribution If validated, I can try to submit a PR for this.
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6,193
Dataset loading script method does not work with .pyc file
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[ "Before dynamically loading `.py` scripts with `importlib.import_module`, we also parse their contents to check imports, which is tricky to implement for binary `.pyc` files (requires parsing bytecode), so I don't think this is something we want to support (unless more users request it ofc) as this use case is a bit too specific.\r\n\r\n@lhoestq What's your opinion on this?", "> Before dynamically loading .py scripts with importlib.import_module, we also parse their contents to check imports, which is tricky to implement for binary .pyc files (requires parsing bytecode), so I don't think this is something we want to support (unless more users request it ofc) as this use case is a bit too specific.\r\n\r\nYes indeed. Though you can use a .py that imports a package that contains your .pyc code and that you previously installed", "Hi @lhoestq ,\r\nCould you share some example code related to the approach that you are suggesting? " ]
2023-08-29T19:35:06
2023-08-31T19:47:29
null
NONE
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### Describe the bug The huggingface dataset library specifically looks for ‘.py’ file while loading the dataset using loading script approach and it does not work with ‘.pyc’ file. While deploying in production, it becomes an issue when we are restricted to use only .pyc files. Is there any work around for this ? ### Steps to reproduce the bug 1. Create a dataset loading script to read the custom data. 2. compile the code to make sure that .pyc file is created 3. Delete the loading script and re-run the code. Usually, python should make use of complied .pyc files. However, in this case, the dataset library errors out with the message that it's unable to find the data loader loading script. ### Expected behavior The code should make use of .pyc file and run without any error. ### Environment info NA
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6,190
`Invalid user token` even when correct user token is passed!
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[ "This is because `download_config.use_auth_token` is deprecated - you should use `download_config.token` instead", "Works! Thanks for the quick fix! <3" ]
2023-08-29T12:37:03
2023-08-29T13:01:10
2023-08-29T13:01:09
MEMBER
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### Describe the bug I'm working on a dataset which comprises other datasets on the hub. URL: https://huggingface.co/datasets/open-asr-leaderboard/datasets-test-only Note: Some of the sub-datasets in this metadataset require explicit access. All the other datasets work fine, except, `common_voice`. ### Steps to reproduce the bug https://github.com/Vaibhavs10/scratchpad/blob/main/cv_datasets_bug_repro.ipynb ### Expected behavior It should work if the provided access token is valid (as it does for all the other datasets) ### Environment info datasets version -> 2.14.4
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I_kwDODunzps5vhQF4
6,188
[Feature Request] Check the length of batch before writing so that empty batch is allowed
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[ "I think this error means you filter all examples within an (input) batch by deleting its columns. In that case, to avoid the error, you can set the column value to an empty list (`input_batch[\"col\"] = []`) instead." ]
2023-08-29T06:37:34
2023-09-19T21:55:38
2023-09-19T21:55:37
NONE
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### Use Case I use `dataset.map(process_fn, batched=True)` to process the dataset, with data **augmentations or filtering**. However, when all examples within a batch is filtered out, i.e. **an empty batch is returned**, the following error will be thrown: ``` ValueError: Schema and number of arrays unequal ``` This is because the empty batch does not comply with the schema of other batches. I think an empty batch should be allowed to facilitate coding (one does not need to assign an empty list manually for all keys.) A simple fix is to check the length of `batch` before writing: ``` if len(batch): writer.write_batch(batch) ``` instead of https://github.com/huggingface/datasets/blob/74d60213dcbd7c99484c62ce1d3dfd90a1df0770/src/datasets/arrow_dataset.py#L3493
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6,187
Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory
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[ "Hi! You can load this dataset with:\r\n```python\r\ndata_files = {\r\n \"train\": \"/content/PUBHEALTH/train.tsv\",\r\n \"validation\": \"/content/PUBHEALTH/dev.tsv\",\r\n \"test\": \"/content/PUBHEALTH/test.tsv\",\r\n}\r\n\r\ntsv_datasets_reloaded = load_dataset(\"csv\", data_files=data_files, sep=\"\\t\")\r\n```\r\n\r\nTo support your `load_dataset` call, defining aliases for the packaged builders, as suggested in https://github.com/huggingface/datasets/issues/5625, must be implemented. We can consider adding this feature if more people request it.\r\n \r\n(Also answered on the Discord [here](https://discord.com/channels/879548962464493619/1145956791134470224/1146071491260186744))" ]
2023-08-29T05:49:56
2023-08-29T16:21:45
null
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### Describe the bug ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) [<ipython-input-48-6a7b3e847019>](https://localhost:8080/#) in <cell line: 7>() 5 } 6 ----> 7 csv_datasets_reloaded = load_dataset("tsv", data_files=data_files) 8 csv_datasets_reloaded 2 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1489 raise e1 from None 1490 if isinstance(e1, FileNotFoundError): -> 1491 raise FileNotFoundError( 1492 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1493 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Dataset 'tsv' doesn't exist on the Hub ``` ### Steps to reproduce the bug ``` data_files = { "train": "/content/PUBHEALTH/train.tsv", "validation": "/content/PUBHEALTH/dev.tsv", "test": "/content/PUBHEALTH/test.tsv", } tsv_datasets_reloaded = load_dataset("tsv", data_files=data_files) tsv_datasets_reloaded ``` ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) <ipython-input-48-6a7b3e847019> in <cell line: 7>() 5 } 6 ----> 7 csv_datasets_reloaded = load_dataset("tsv", data_files=data_files) 8 csv_datasets_reloaded 2 frames /usr/local/lib/python3.10/dist-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1489 raise e1 from None 1490 if isinstance(e1, FileNotFoundError): -> 1491 raise FileNotFoundError( 1492 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1493 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Dataset 'tsv' doesn't exist on the Hub ``` ### Expected behavior load the data, push to hub ### Environment info jupyter notebook RTX 3090
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6,186
Feature request: add code example of multi-GPU processing
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[ "That'd be a great idea! @mariosasko or @lhoestq, would it be possible to fix the code snippet or do you have another suggested way for doing this?", "Indeed `if __name__ == \"__main__\"` is important in this case.\r\n\r\nNot sure about the imbalanced GPU usage though, but maybe you can try using the `torch.cuda.device` context manager ?\r\n\r\n> also, should I do it like this or use nn.DataParallel?\r\n\r\nIn this case you wouldn't need a multiprocessed map no ? Since nn.DataParallel would take care of parallelism", "Adding this Tweet for reference: https://twitter.com/jxmnop/status/1716834517909119019.", "I think the issue is that we set `CUDA_VISIBLE_DEVICES` after pytorch is imported ?\r\n\r\nWe should use `torch.cuda.set_device(...)` instead", "@lhoestq \r\n> In this case you wouldn't need a multiprocessed map no ?\r\n\r\nYes. But how to load a model to 2 GPU simultaneously without something like accelerate?", "> @lhoestq\r\n> \r\n> > In this case you wouldn't need a multiprocessed map no ?\r\n> \r\n> Yes. But how to load a model to 2 GPU simultaneously without something like accelerate?\r\n\r\nTake a look at this fix #6550 . Basically, you move the model to each GPU inside of the function to be mapped. \r\n\r\n", "In case someone also runs into this issue, I wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete working example by compiling information from several PRs and issues here. Hope it can help. This issue cost me a few hours. I hope my blog post can save you time before the official document gets fixed. ", "Thanks ! I updated the docs in https://github.com/huggingface/datasets/pull/6550", "hey @forrestbao , i was too struggling with the same issue for weeks hence i checked out your blog. great work on the blog. \r\nhowever i wanted to ask you could we scale up the process by reinitializing the same model on the same GPU multiple times for even more speedups ? \r\n\r\ni mean to say given that on a multi GPU setup where GPU vram is above 40GB each, after intializing the translation model which is barely 1-2GB in VRAM size, the rest of VRAM sits idle, how could i keep creating multiple instances of the same model on the same GPU for all GPUs to maxmize flops ? ", "You can use one single instance on your GPU and increase the batch size until you fill the VRAM", "@lhoestq i tried that, but i noticed that after a certain number of batch_size, using a larger batch_size makes the overall process really slow than using a lower batch_size." ]
2023-08-28T10:00:59
2024-02-22T08:51:08
2023-11-22T15:42:20
CONTRIBUTOR
null
null
null
### Feature request Would be great to add a code example of how to do multi-GPU processing with 🤗 Datasets in the documentation. cc @stevhliu Currently the docs has a small [section](https://huggingface.co/docs/datasets/v2.3.2/en/process#map) on this saying "your big GPU call goes here", however it didn't work for me out-of-the-box. Let's say you have a PyTorch model that can do translation, and you have multiple GPUs. In that case, you'd like to duplicate the model on each GPU, each processing (translating) a chunk of the data in parallel. Here's how I tried to do that: ``` from datasets import load_dataset from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from multiprocess import set_start_method import torch import os dataset = load_dataset("mlfoundations/datacomp_small") tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") # put model on each available GPU # also, should I do it like this or use nn.DataParallel? model.to("cuda:0") model.to("cuda:1") set_start_method("spawn") def translate_captions(batch, rank): os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count()) texts = batch["text"] inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(model.device) translated_tokens = model.generate( **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["eng_Latn"], max_length=30 ) translated_texts = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True) batch["translated_text"] = translated_texts return batch updated_dataset = dataset.map(translate_captions, with_rank=True, num_proc=2, batched=True, batch_size=256) ``` I've personally tried running this script on a machine with 2 A100 GPUs. ## Error 1 Running the code snippet above from the terminal (python script.py) resulted in the following error: ``` Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 125, in _main prepare(preparation_data) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 236, in prepare _fixup_main_from_path(data['init_main_from_path']) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 289, in run_path return _run_module_code(code, init_globals, run_name, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 96, in _run_module_code _run_code(code, mod_globals, init_globals, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/home/niels/python_projects/datacomp/datasets_multi_gpu.py", line 16, in <module> set_start_method("spawn") File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 247, in set_start_method raise RuntimeError('context has already been set') RuntimeError: context has already been set ``` ## Error 2 Then, based on [this Stackoverflow answer](https://stackoverflow.com/a/71616344/7762882), I put the `set_start_method("spawn")` section in a try: catch block. This resulted in the following error: ``` File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/dataset_dict.py", line 817, in <dictcomp> k: dataset.map( File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2926, in map with Pool(nb_of_missing_shards, initargs=initargs, initializer=initializer) as pool: File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 119, in Pool return Pool(processes, initializer, initargs, maxtasksperchild, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 215, in __init__ self._repopulate_pool() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 306, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 329, in _repopulate_pool_static w.start() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/process.py", line 121, in start self._popen = self._Popen(self) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 288, in _Popen return Popen(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 32, in __init__ super().__init__(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19, in __init__ self._launch(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 42, in _launch prep_data = spawn.get_preparation_data(process_obj._name) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 154, in get_preparation_data _check_not_importing_main() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 134, in _check_not_importing_main raise RuntimeError(''' RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. ``` So then I put the last line under a `if __name__ == '__main__':` block. Then the code snippet seemed to work, but it seemed that it's only leveraging a single GPU (based on monitoring `nvidia-smi`): ``` Mon Aug 28 12:19:24 2023 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 NVIDIA A100-SXM... On | 00000000:01:00.0 Off | 0 | | N/A 55C P0 76W / 275W | 8747MiB / 81920MiB | 0% Default | | | | Disabled | +-------------------------------+----------------------+----------------------+ | 1 NVIDIA A100-SXM... On | 00000000:47:00.0 Off | 0 | | N/A 67C P0 274W / 275W | 59835MiB / 81920MiB | 100% Default | | | | Disabled | ``` Both GPUs should have equal GPU usage, but I've always noticed that the last GPU has way more usage than the other ones. This made me think that `os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count())` might not work inside a Python script, especially if done after importing PyTorch? ### Motivation Would be great to clarify how to do multi-GPU data processing. ### Your contribution If my code snippet can be fixed, I can contribute it to the docs :)
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6,185
Error in saving the PIL image into *.arrow files using datasets.arrow_writer
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[ "You can cast the `input_image` column to the `Image` type to fix the issue:\r\n```python\r\nds.cast_column(\"input_image\", datasets.Image())\r\n```" ]
2023-08-26T12:15:57
2023-08-29T14:49:58
null
NONE
null
null
null
### Describe the bug I am using the ArrowWriter from datasets.arrow_writer to save a json-style file as arrow files. Within the dictionary, it contains a feature called "image" which is a list of PIL.Image objects. I am saving the json using the following script: ``` def save_to_arrow(path,temp): with ArrowWriter(path=path,writer_batch_size=20) as writer: writer.write_batch(temp) writer.finalize() ``` However, when I attempt to restore the dataset and use the ```Dataset.from_file(path)``` function to load the arrow file, there seems to be an issue with the PIL.Image object in the dataset. The list of PIL.Images appears as follows rather than a normal PIL.Image object: ![1693051705440](https://github.com/huggingface/datasets/assets/14247682/03b204c2-d0fa-4d19-beff-6f4d7b83c848) ### Steps to reproduce the bug 1. Storing the data json into arrow files: ``` def save_to_arrow(path,temp): with ArrowWriter(path=path,writer_batch_size=20) as writer: writer.write_batch(temp) writer.finalize() save_to_arrow( path, json_file ) ``` 2. try to load the arrow file into the Dataset object using the ```Dataset.from_file(path)``` ### Expected behavior Except to saving the contained "image" feature as a list PIL.Image objects as the arrow file. And I can restore the dataset from the file. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.4.0-150-generic-x86_64-with-glibc2.17 - Python version: 3.8.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.4.4
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1,867,766,143
I_kwDODunzps5vU9l_
6,184
Map cache does not detect function changes in another module
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[ "This issue is a duplicate of https://github.com/huggingface/datasets/issues/3297. This is a limitation of `dill`, a package we use for caching (non-`__main__` module objects are serialized by reference). You can find more info about it here: https://github.com/uqfoundation/dill/issues/424.\r\n\r\nIn your case, moving \r\n```\r\ndata = datasets.load_dataset('json', data_files=['/tmp/test.json'], split='train')\r\ndata = data.map(transform)\r\n``` \r\nto `test.py` and setting `transform.__module__ = None` at the end of `dataset.py` should fix the issue.", "I understand this may be a limitation of an upstream tool, but for a user for datasets this is very annoying, as when you have dozens of different datasets with different preprocessing functions you can't really move them all into the same file. It may be worth seeing if there is a way to specialize the dependency (eg. subclass it) and enforce behaviors that makes sense for your product.\r\n\r\nI was able to work around this for now by setting `__module__ = None`. If such workarounds are required for now it may be better to document it somewhere than a single obscure issue from a long time ago.\r\n\r\nAs this is a duplicate issue I'm closing it.\r\n\r\nI have another issue with the cache https://github.com/huggingface/datasets/issues/6179 can you take a look?" ]
2023-08-25T22:59:14
2023-08-29T20:57:07
2023-08-29T20:56:49
NONE
null
null
null
```python # dataset.py import os import datasets if not os.path.exists('/tmp/test.json'): with open('/tmp/test.json', 'w') as file: file.write('[{"text": "hello"}]') def transform(example): text = example['text'] # text += ' world' return {'text': text} data = datasets.load_dataset('json', data_files=['/tmp/test.json'], split='train') data = data.map(transform) ``` ```python # test.py import dataset print(next(iter(dataset.data))) ``` Initialize cache ``` python3 test.py # {'text': 'hello'} ``` Edit dataset.py and uncomment the commented line, run again ``` python3 test.py # {'text': 'hello'} # expected: {'text': 'hello world'} ``` Clear cache and run again ``` rm -rf ~/.cache/huggingface/datasets/* python3 test.py # {'text': 'hello world'} ``` If instead the two files are combined, then changes to the function are detected correctly. But it's expected when working on any realistic codebase that things will be modularized into separate files.
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1,867,743,276
I_kwDODunzps5vU4As
6,183
Load dataset with non-existent file
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[ "Same problem", "This was fixed in https://github.com/huggingface/datasets/pull/6155, which will be included in the next release (or you can install `datasets` from source to use it immediately)." ]
2023-08-25T22:21:22
2023-08-29T13:26:22
2023-08-29T13:26:22
NONE
null
null
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### Describe the bug When load a dataset from datasets and pass a wrong path to json with the data, error message does not contain something abount "wrong path" or "file do not exist" - ```SchemaInferenceError: Please pass `features` or at least one example when writing data``` ### Steps to reproduce the bug ```python from datasets import load_dataset load_dataset('json', data_files='/home/alexey/unreal_file.json') ``` ### Expected behavior Raise os FileNotFound error or custom error with informative message ### Environment info ``` # packages in environment at /home/alexey/.conda/envs/alex_LoRA: # # Name Version Build Channel _libgcc_mutex 0.1 main _openmp_mutex 5.1 1_gnu accelerate 0.21.0 pypi_0 pypi aiohttp 3.8.5 pypi_0 pypi aiosignal 1.3.1 pypi_0 pypi antlr4-python3-runtime 4.9.3 pypi_0 pypi appdirs 1.4.4 pypi_0 pypi asttokens 2.0.5 pyhd3eb1b0_0 async-timeout 4.0.3 pypi_0 pypi attrs 23.1.0 pypi_0 pypi backcall 0.2.0 pyhd3eb1b0_0 bitsandbytes 0.41.1 pypi_0 pypi bzip2 1.0.8 h7b6447c_0 ca-certificates 2023.05.30 h06a4308_0 certifi 2023.7.22 pypi_0 pypi charset-normalizer 3.2.0 pypi_0 pypi click 8.1.6 pypi_0 pypi cmake 3.27.2 pypi_0 pypi comm 0.1.2 py310h06a4308_0 contourpy 1.1.0 pypi_0 pypi cycler 0.11.0 pypi_0 pypi datasets 2.14.4 pypi_0 pypi debugpy 1.6.7 py310h6a678d5_0 decorator 5.1.1 pyhd3eb1b0_0 dill 0.3.7 pypi_0 pypi docker-pycreds 0.4.0 pypi_0 pypi executing 0.8.3 pyhd3eb1b0_0 filelock 3.12.2 pypi_0 pypi fire 0.5.0 pypi_0 pypi fonttools 4.42.0 pypi_0 pypi frozenlist 1.4.0 pypi_0 pypi fsspec 2023.6.0 pypi_0 pypi gitdb 4.0.10 pypi_0 pypi gitpython 3.1.32 pypi_0 pypi huggingface-hub 0.16.4 pypi_0 pypi idna 3.4 pypi_0 pypi ipykernel 6.25.0 py310h2f386ee_0 ipython 8.12.2 py310h06a4308_0 ipython-genutils 0.2.0 pypi_0 pypi ipywidgets 8.0.4 py310h06a4308_0 jedi 0.18.1 py310h06a4308_1 jinja2 3.1.2 pypi_0 pypi jsonschema 4.19.0 pypi_0 pypi jsonschema-specifications 2023.7.1 pypi_0 pypi jupyter_client 8.1.0 py310h06a4308_0 jupyter_core 5.3.0 py310h06a4308_0 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nvidia-cusparse-cu11 11.7.4.91 pypi_0 pypi nvidia-nccl-cu11 2.14.3 pypi_0 pypi nvidia-nvtx-cu11 11.7.91 pypi_0 pypi omegaconf 2.3.0 pypi_0 pypi openssl 1.1.1v h7f8727e_0 packaging 23.0 py310h06a4308_0 pandas 2.0.3 pypi_0 pypi parso 0.8.3 pyhd3eb1b0_0 pathtools 0.1.2 pypi_0 pypi peft 0.4.0 pypi_0 pypi pexpect 4.8.0 pyhd3eb1b0_3 pickleshare 0.7.5 pyhd3eb1b0_1003 pillow 10.0.0 pypi_0 pypi pip 23.2.1 py310h06a4308_0 platformdirs 2.5.2 py310h06a4308_0 plotly 5.16.1 pypi_0 pypi prompt-toolkit 3.0.36 py310h06a4308_0 protobuf 4.24.0 pypi_0 pypi psutil 5.9.0 py310h5eee18b_0 ptyprocess 0.7.0 pyhd3eb1b0_2 pure_eval 0.2.2 pyhd3eb1b0_0 pyarrow 12.0.1 pypi_0 pypi pygments 2.15.1 py310h06a4308_1 pyparsing 3.0.9 pypi_0 pypi python 3.10.0 h12debd9_5 python-dateutil 2.8.2 pyhd3eb1b0_0 pytorch-lightning 2.0.6 pypi_0 pypi pytz 2023.3 pypi_0 pypi pyyaml 6.0.1 pypi_0 pypi pyzmq 25.1.0 py310h6a678d5_0 readline 8.2 h5eee18b_0 referencing 0.30.2 pypi_0 pypi regex 2023.8.8 pypi_0 pypi requests 2.31.0 pypi_0 pypi rpds-py 0.9.2 pypi_0 pypi safetensors 0.3.2 pypi_0 pypi scipy 1.11.1 pypi_0 pypi sentencepiece 0.1.99 pypi_0 pypi sentry-sdk 1.29.2 pypi_0 pypi setproctitle 1.3.2 pypi_0 pypi setuptools 68.0.0 py310h06a4308_0 six 1.16.0 pyhd3eb1b0_1 smmap 5.0.0 pypi_0 pypi sqlite 3.41.2 h5eee18b_0 stack_data 0.2.0 pyhd3eb1b0_0 sympy 1.12 pypi_0 pypi tenacity 8.2.3 pypi_0 pypi termcolor 2.3.0 pypi_0 pypi tk 8.6.12 h1ccaba5_0 tokenizers 0.13.3 pypi_0 pypi torch 2.0.1 pypi_0 pypi torchmetrics 1.0.3 pypi_0 pypi tornado 6.3.2 py310h5eee18b_0 tqdm 4.66.1 pypi_0 pypi traitlets 5.7.1 py310h06a4308_0 transformers 4.31.0 pypi_0 pypi triton 2.0.0 pypi_0 pypi typing-extensions 4.7.1 pypi_0 pypi tzdata 2023.3 pypi_0 pypi urllib3 2.0.4 pypi_0 pypi wandb 0.15.8 pypi_0 pypi wcwidth 0.2.5 pyhd3eb1b0_0 wheel 0.38.4 py310h06a4308_0 widgetsnbextension 4.0.5 py310h06a4308_0 xxhash 3.3.0 pypi_0 pypi xz 5.4.2 h5eee18b_0 yarl 1.9.2 pypi_0 pypi zeromq 4.3.4 h2531618_0 zlib 1.2.13 h5eee18b_0 active environment : None user config file : /home/alexey/.condarc populated config files : conda version : 23.1.0 conda-build version : 3.22.0 python version : 3.9.13.final.0 virtual packages : __archspec=1=x86_64 __cuda=12.0=0 __glibc=2.35=0 __linux=5.19.0=0 __unix=0=0 base environment : /opt/anaconda/anaconda3 (read only) conda av data dir : /opt/anaconda/anaconda3/etc/conda conda av metadata url : None channel URLs : https://repo.anaconda.com/pkgs/main/linux-64 https://repo.anaconda.com/pkgs/main/noarch https://repo.anaconda.com/pkgs/r/linux-64 https://repo.anaconda.com/pkgs/r/noarch package cache : /opt/anaconda/anaconda3/pkgs /home/alexey/.conda/pkgs envs directories : /home/alexey/.conda/envs /opt/anaconda/anaconda3/envs platform : linux-64 user-agent : conda/23.1.0 requests/2.31.0 CPython/3.9.13 Linux/5.19.0-46-generic ubuntu/22.04.2 glibc/2.35 UID:GID : 1009:1009 netrc file : /home/alexey/.netrc offline mode : False ```
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1,867,203,131
I_kwDODunzps5vS0I7
6,182
Loading Meteor metric in HF evaluate module crashes due to datasets import issue
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[ "Our minimal Python version requirement is 3.8, so we dropped `importlib_metadata`. \r\n\r\nFeel free to open a PR in the `evaluate` repo to replace the problematic import with\r\n```python\r\nif PY_VERSION < version.parse(\"3.8\"):\r\n import importlib_metadata\r\nelse:\r\n import importlib.metadata as importlib_metadata\r\n```", "Any idea when you guys will release the next version which deals with this problem?\r\nI'm still having the same issue with py 3.10 when I install the lib with pip.\r\nI'm assuming that it has not yet been updated since the merge was 3 days ago.", "Yes, this requires a new `evaluate` release (cc @lvwerra for this). \r\n\r\nIn the meantime, you can get the fixed version by installing `evaluate` from `main`: `pip install git+https://github.com/huggingface/evaluate.git`", "I'll aim for a release this week!" ]
2023-08-25T14:54:06
2023-09-04T16:41:11
2023-08-31T14:38:23
NONE
null
null
null
### Describe the bug When using python3.9 and ```evaluate``` module loading Meteor metric crashes at a non-existent import from ```datasets.config``` in ```datasets v2.14``` ### Steps to reproduce the bug ``` from evaluate import load meteor = load("meteor") ``` produces the following error: ``` from datasets.config import importlib_metadata, version ImportError: cannot import name 'importlib_metadata' from 'datasets.config' (<path_to_project>/venv/lib/python3.9/site-packages/datasets/config.py) ``` ### Expected behavior ```datasets``` of v2.10 has the following workaround in ```config.py```: ``` if PY_VERSION < version.parse("3.8"): import importlib_metadata else: import importlib.metadata as importlib_metadata ``` However, it's absent in v2.14 which might be the cause of the issue. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.5-arm64-arm-64bit - Python version: 3.9.6 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - Evaluate version: 0.4.0
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6,179
Map cache with tokenizer
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[ "https://github.com/huggingface/datasets/issues/5147 may be a solution, by passing in the tokenizer in a fn_kwargs and ignoring it in the fingerprint calculations", "I have a similar issue. I was using a Jupyter Notebook and every time I call the map function it performs tokenization from scratch again although the cache files of last run still exists. \r\n\r\nI ran with 20 processes and now in the cache folder there are two groups of cached results of tokenized dataset:\r\n\r\n```\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:46 2023 cache-1982fea76aa54a13_00001_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 13:02:08 2023 cache-1982fea76aa54a13_00004_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:40 2023 cache-1982fea76aa54a13_00005_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:50:59 2023 cache-1982fea76aa54a13_00006_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:37 2023 cache-1982fea76aa54a13_00007_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:31 2023 cache-1982fea76aa54a13_00008_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:59:47 2023 cache-1982fea76aa54a13_00010_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:59:44 2023 cache-1982fea76aa54a13_00011_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:55:24 2023 cache-1982fea76aa54a13_00012_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:56:21 2023 cache-1982fea76aa54a13_00013_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:24 2023 cache-1982fea76aa54a13_00014_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 13:00:48 2023 cache-1982fea76aa54a13_00015_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:56 2023 cache-1982fea76aa54a13_00017_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:54 2023 cache-1982fea76aa54a13_00018_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:27 2023 cache-1982fea76aa54a13_00019_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:15:40 2023 cache-454431f643cdc5e8_00000_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:46 2023 cache-454431f643cdc5e8_00001_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:53 2023 cache-454431f643cdc5e8_00002_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:13:10 2023 cache-454431f643cdc5e8_00003_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:13:04 2023 cache-454431f643cdc5e8_00004_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:42 2023 cache-454431f643cdc5e8_00005_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:01:29 2023 cache-454431f643cdc5e8_00006_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:41 2023 cache-454431f643cdc5e8_00007_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:04 2023 cache-454431f643cdc5e8_00008_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:41 2023 cache-454431f643cdc5e8_00009_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:06 2023 cache-454431f643cdc5e8_00010_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:17:16 2023 cache-454431f643cdc5e8_00011_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:15:13 2023 cache-454431f643cdc5e8_00012_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:16:01 2023 cache-454431f643cdc5e8_00013_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:35 2023 cache-454431f643cdc5e8_00014_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:20 2023 cache-454431f643cdc5e8_00015_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:48 2023 cache-454431f643cdc5e8_00016_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 18:59:32 2023 cache-454431f643cdc5e8_00017_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:58 2023 cache-454431f643cdc5e8_00018_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:15:25 2023 cache-454431f643cdc5e8_00019_of_00020.arrow\r\n```\r\n\r\ncan we specify the cache file for map so that it won't redo everything again?", "@Luosuu [map](https://huggingface.co/docs/datasets/v2.14.4/en/package_reference/main_classes#datasets.Dataset.map) has cache_file_name parameter\r\n\r\nIn my case, I do want the cache to detect when the map function changes, so I can't pass a constant cache file name.", "Implementing a proper hashing function for the (fast) tokenizers is currently impossible for the reasons mentioned in the referenced issues. So the only alternative to the `cache_file_name` (or `new_fingerprint`) parameter is a custom serializer (e.g., that deserializes the tokenizer from a local save path) defined using `copyreg` or a class that wraps the tokenizer object and has `__reduce__`(`__setstate__`/`__getstate__`) " ]
2023-08-25T12:55:18
2023-08-31T15:17:24
null
NONE
null
null
null
Similar issue to https://github.com/huggingface/datasets/issues/5985, but across different sessions rather than two calls in the same session. Unlike that issue, explicitly calling tokenizer(my_args) before the map() doesn't help, because the tokenizer was created with a different hash to begin with... setup ``` from transformers import AutoTokenizer AutoTokenizer.from_pretrained('bert-base-uncased').save_pretrained("tok") ``` this prints different value each time ``` from transformers import AutoTokenizer from datasets.utils.py_utils import dumps # Huggingface datasets print(hash(dumps(AutoTokenizer.from_pretrained("tok")))) ```
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1,866,610,102
I_kwDODunzps5vQjW2
6,178
'import datasets' throws "invalid syntax error"
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[ "This seems to be related to your environment and not the `datasets` code (e.g., this could happen when exposing the Python 3.9 site packages to a lower Python version (interpreter))" ]
2023-08-25T08:35:14
2023-09-27T17:33:39
2023-09-27T17:33:39
NONE
null
null
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### Describe the bug Hi, I have been trying to import the datasets library but I keep gtting this error. `Traceback (most recent call last): File /opt/local/jupyterhub/lib64/python3.9/site-packages/IPython/core/interactiveshell.py:3508 in run_code exec(code_obj, self.user_global_ns, self.user_ns) Cell In[2], line 1 import datasets File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/__init__.py:22 from .arrow_dataset import Dataset File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_dataset.py:67 from .arrow_writer import ArrowWriter, OptimizedTypedSequence File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_writer.py:27 from .features import Features, Image, Value File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/__init__.py:17 from .audio import Audio File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/audio.py:11 from ..download.streaming_download_manager import xopen, xsplitext File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/__init__.py:10 from .streaming_download_manager import StreamingDownloadManager File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/streaming_download_manager.py:18 from aiohttp.client_exceptions import ClientError File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/__init__.py:7 from .connector import * # noqa File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/connector.py:12 from .client import ClientRequest File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/client.py:144 yield from asyncio.async(resp.release(), loop=loop) ^ SyntaxError: invalid syntax` I have simply used these commands: `import datasets` and `from datasets import load_dataset` ### Environment info The library has been installed a virtual machine on JupyterHub. Although I have used this library multiple times (on the same VM) before, to train/test an ASR or other ML models, I had never encountered this error.
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6,176
how to limit the size of memory mapped file?
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[ "Hi! Can you share the error this reproducer throws in your environment? `streaming=True` streams the dataset as it's iterated over without creating a memory-map file.", "The trace of the error. Streaming works but is slower.\r\n```\r\nRoot Cause (first observed failure):\r\n[0]:\r\n time : 2023-08-24_06:06:01\r\n host : compute-126.cm.cluster\r\n rank : 0 (local_rank: 0)\r\n exitcode : 1 (pid: 48442)\r\n error_file: /tmp/torchelastic_4fqzcuuz/none_rx2470jl/attempt_0/0/error.json\r\n traceback : Traceback (most recent call last):\r\n File \"/users/yli7/.conda/envs/pytorch2.0/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py\", line 346, in wrapper\r\n return f(*args, **kwargs)\r\n File \"Pretrain.py\", line 214, in main\r\n pair_dataset, c4_dataset = create_dataset('pretrain', config)\r\n File \"/dcs05/qiao/data/william/project/DaVinci/dataset/__init__.py\", line 109, in create_dataset\r\n c4_dataset = load_dataset(\"c4\", \"en\", split=\"train\").to_iterable_dataset(num_shards=1024).map(pre_caption_huggingface)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/load.py\", line 1810, in load_dataset\r\n ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1145, in as_dataset\r\n datasets = map_nested(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 436, in map_nested\r\n return function(data_struct)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1175, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1246, in _as_dataset\r\n dataset_kwargs = ArrowReader(cache_dir, self.info).read(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 244, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 265, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 200, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 336, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 357, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 1059, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 65, in _memory_mapped_arrow_table_from_file\r\n opened_stream = _memory_mapped_record_batch_reader_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 50, in _memory_mapped_record_batch_reader_from_file\r\n memory_mapped_stream = pa.memory_map(filename)\r\n File \"pyarrow/io.pxi\", line 1009, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 956, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\n OSError: Memory mapping file failed: Cannot allocate memory\r\n```", "This issue has previously been reported here: https://github.com/huggingface/datasets/issues/5710. Reporting it in the Arrow repo makes more sense as they have control over memory mapping.\r\n\r\nPS: this is the API to reduce the size of the generated Arrow file:\r\n```python\r\nfrom datasets import load_dataset\r\nbuilder = load_dataset_builder(\"c4\", \"en\")\r\nbuilder.download_and_prepare(max_shard_size=\"5GB\")\r\ndataset = builder.as_dataset()\r\n```\r\n\r\nIf this resolves the issue, we can consider exposing `max_shard_size` in `load_dataset`.", "Thanks for the response. The problem seems not resolved. The memory I allocated to the environment is 64G and the following error still occurs\r\n`Python 3.8.16 (default, Jun 12 2023, 18:09:05) \r\n[GCC 11.2.0] :: Anaconda, Inc. on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset_builder\r\n>>> builder = load_dataset_builder(\"c4\", \"en\")\r\n>>> builder.download_and_prepare(max_shard_size=\"5GB\")\r\nFound cached dataset c4 (/users/yli7/.cache/huggingface/datasets/c4/en/0.0.0/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01)\r\n>>> dataset = builder.as_dataset()\r\n 0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1145, in as_dataset\r\n datasets = map_nested(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 444, in map_nested\r\n mapped = [\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 445, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True, None))\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 347, in _single_map_nested\r\n return function(data_struct)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1175, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1246, in _as_dataset\r\n dataset_kwargs = ArrowReader(cache_dir, self.info).read(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 244, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 265, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 200, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 336, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 357, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 1059, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 65, in _memory_mapped_arrow_table_from_file\r\n opened_stream = _memory_mapped_record_batch_reader_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 50, in _memory_mapped_record_batch_reader_from_file\r\n memory_mapped_stream = pa.memory_map(filename)\r\n File \"pyarrow/io.pxi\", line 1009, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 956, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory`", "Have you solved the problem?", "Nope. Streaming works but is slower." ]
2023-08-24T05:33:45
2023-10-11T06:00:10
null
NONE
null
null
null
### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine)
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1,863,422,065
I_kwDODunzps5vEZBx
6,173
Fix CI for pyarrow 13.0.0
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2023-08-23T14:11:20
2023-08-25T13:06:53
2023-08-25T13:06:53
MEMBER
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pyarrow 13.0.0 just came out ``` FAILED tests/test_formatting.py::ArrowExtractorTest::test_pandas_extractor - AssertionError: Attributes of Series are different Attribute "dtype" are different [left]: datetime64[us, UTC] [right]: datetime64[ns, UTC] ``` ``` FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type fixed_size_list<item: int32>[3] to Sequence(feature=Value(dtype='int64', id=None), length=3, id=None) ``` e.g. in https://github.com/huggingface/datasets/actions/runs/5952253963/job/16143847230 first error may be related to https://github.com/apache/arrow/issues/33321 second one maybe because `feature.length * len(array) == len(array_values)` is not satisfied anymore somehow ?
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https://github.com/huggingface/datasets/issues/6172
1,863,318,027
I_kwDODunzps5vD_oL
6,172
Make Dataset streaming queries retryable
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[ "Hi! The streaming mode also retries requests - `datasets.config.STREAMING_READ_MAX_RETRIES` (20 sec by default) controls the number of retries and `datasets.config.STREAMING_READ_RETRY_INTERVAL` (5 sec) the sleep time between retries.\r\n\r\n> At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch dataloader\r\n\r\nA minor Hub outage that we experienced yesterday could be the cause.", "I wanted something similar. I have a huge dataset I want to process (laion-2b), but after processing several batches, it sometimes fails with this error: `HTTP 502 Bad Gateway for url`. I had the following code to handle it but this way I believe it restarts processing the data from the first batch? How can I set the attribute values you mention above?\r\n\r\n```\r\niterable_dataset = load_dataset(\"laion/laion2B-multi\", streaming=True, split='train')\r\ndataloader = DataLoader(iterable_dataset, batch_size=131072, collate_fn=custom_collate_fn, num_workers=8)\r\n\r\nMAX_RETRIES = 5\r\nRETRY_WAIT = 10 # wait 10 seconds before retry\r\n\r\n for retry in range(MAX_RETRIES):\r\n try:\r\n for j, batch in enumerate(dataloader):\r\n < process batch>\r\n\r\n except HfHubHTTPError as e:\r\n if \"502\" in str(e) and retry < MAX_RETRIES - 1:\r\n logging.warning(f\"Encountered a 502 error on batch {j}. Waiting for {RETRY_WAIT} seconds before retrying.\")\r\n time.sleep(RETRY_WAIT)\r\n continue\r\n else:\r\n raise", "Hey all! Wondering if there's a way of making Datasets streaming mode somewhat robust to Hub outages? Over the weekend, I got two quite cryptic errors, which I reckon were probably from Hub issues:\r\n\r\n<details>\r\n<summary> Stack Trace 1 </summary>\r\n\r\n```\r\n File \"/home/sanchitgandhi/small-12-4-tpu-timestamped-prob-0.2/run_distillation.py\", line 2119, in <module>\r\n main()\r\n File \"/home/sanchitgandhi/small-12-4-tpu-timestamped-prob-0.2/run_distillation.py\", line 1954, in main\r\n for batch in train_loader:\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 630, in __next__\r\n data = self._next_data()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1325, in _next_data\r\n return self._process_data(data)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1371, in _process_data\r\n data.reraise()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/_utils.py\", line 694, in reraise\r\n raise exception\r\nConnectionError: Caught ConnectionError in DataLoader worker process 8.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 1155, in _create_direct_connection\r\n hosts = await asyncio.shield(host_resolved)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 874, in _resolve_host\r\n addrs = await self._resolver.resolve(host, port, family=self._family)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/resolver.py\", line 33, in resolve\r\n infos = await self._loop.getaddrinfo(\r\n File \"/usr/lib/python3.10/asyncio/base_events.py\", line 863, in getaddrinfo\r\n return await self.run_in_executor(\r\n File \"/usr/lib/python3.10/concurrent/futures/thread.py\", line 58, in run\r\n result = self.fn(*self.args, **self.kwargs)\r\n File \"/usr/lib/python3.10/socket.py\", line 955, in getaddrinfo\r\n for res in _socket.getaddrinfo(host, port, family, type, proto, flags):\r\nsocket.gaierror: [Errno -3] Temporary failure in name resolution\r\nThe above exception was the direct cause of the following exception:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 333, in read_with_retries\r\n out = read(*args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 612, in read\r\n return super().read(length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/spec.py\", line 1856, in read\r\n out = self.cache._fetch(self.loc, self.loc + length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/caching.py\", line 439, in _fetch\r\n new = self.fetcher(self.end, bend)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 118, in wrapper\r\n return sync(self.loop, func, *args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 103, in sync\r\n raise return_result\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 56, in _runner\r\n result[0] = await coro\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 660, in async_fetch_range\r\n r = await self.session.get(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/client.py\", line 562, in _request\r\n conn = await self._connector.connect(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 540, in connect\r\n proto = await self._create_connection(req, traces, timeout)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 901, in _create_connection\r\n _, proto = await self._create_direct_connection(req, traces, timeout)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 1169, in _create_direct_connection\r\n raise ClientConnectorError(req.connection_key, exc) from exc\r\naiohttp.client_exceptions.ClientConnectorError: Cannot connect to host huggingface.co:443 ssl:default [Temporary failure in name resolution]\r\nThe above exception was the direct cause of the following exception:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 32, in fetch\r\n data.append(next(self.dataset_iter))\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1358, in __iter__\r\n yield from self._iter_pytorch()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1293, in _iter_pytorch\r\n for key, example in ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 982, in __iter__\r\n for x in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 429, in __iter__\r\n if not iterators[i].hasnext():\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 106, in hasnext\r\n self._thenext = next(self.it)\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 281, in __iter__\r\n for key, pa_table in self.generate_tables_fn(**self.kwargs):\r\n File \"/home/sanchitgandhi/.cache/huggingface/modules/datasets_modules/datasets/distil-whisper--switchboard-data/9472ee64cca0e1a7e11909c7033c2354511fa62805f81a2e07616980c765abfe/switchboard-data.py\", line 247, in _generate_tables\r\n for record_batch in pf.iter_batches():\r\n File \"pyarrow/_parquet.pyx\", line 1327, in iter_batches\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 342, in read_with_retries\r\n raise ConnectionError(\"Server Disconnected\") from disconnect_err\r\nConnectionError: Server Disconnected\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n<summary> Stack Trace 2 </summary>\r\n\r\n```\r\n File \"/home/sanchitgandhi/small-12-2-tpu-v3-timestamped-prob-0.2-bs-512/run_distillation.py\", line 2119, in <module>\r\n main()\r\n File \"/home/sanchitgandhi/small-12-2-tpu-v3-timestamped-prob-0.2-bs-512/run_distillation.py\", line 1954, in main\r\n for batch in train_loader:\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 630, in __next__\r\n data = self._next_data()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1325, in _next_data\r\n return self._process_data(data)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1371, in _process_data\r\n data.reraise()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/_utils.py\", line 694, in reraise\r\n raise exception\r\nrequests.exceptions.ConnectionError: Caught ConnectionError in DataLoader worker process 13.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 791, in urlopen\r\n response = self._make_request(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 537, in _make_request\r\n response = conn.getresponse()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connection.py\", line 461, in getresponse\r\n httplib_response = super().getresponse()\r\n File \"/usr/lib/python3.10/http/client.py\", line 1375, in getresponse\r\n response.begin()\r\n File \"/usr/lib/python3.10/http/client.py\", line 318, in begin\r\n version, status, reason = self._read_status()\r\n File \"/usr/lib/python3.10/http/client.py\", line 287, in _read_status\r\n raise RemoteDisconnected(\"Remote end closed connection without\"\r\nhttp.client.RemoteDisconnected: Remote end closed connection without response\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/adapters.py\", line 486, in send\r\n resp = conn.urlopen(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 845, in urlopen\r\n retries = retries.increment(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/util/retry.py\", line 470, in increment\r\n raise reraise(type(error), error, _stacktrace)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/util/util.py\", line 38, in reraise\r\n raise value.with_traceback(tb)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 791, in urlopen\r\n response = self._make_request(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 537, in _make_request\r\n response = conn.getresponse()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connection.py\", line 461, in getresponse\r\n httplib_response = super().getresponse()\r\n File \"/usr/lib/python3.10/http/client.py\", line 1375, in getresponse\r\n response.begin()\r\n File \"/usr/lib/python3.10/http/client.py\", line 318, in begin\r\n version, status, reason = self._read_status()\r\n File \"/usr/lib/python3.10/http/client.py\", line 287, in _read_status\r\n raise RemoteDisconnected(\"Remote end closed connection without\"\r\nurllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 32, in fetch\r\n data.append(next(self.dataset_iter))\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1358, in __iter__\r\n yield from self._iter_pytorch()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1293, in _iter_pytorch\r\n for key, example in ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 982, in __iter__\r\n for x in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 429, in __iter__\r\n if not iterators[i].hasnext():\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 106, in hasnext\r\n self._thenext = next(self.it)\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 281, in __iter__\r\n for key, pa_table in self.generate_tables_fn(**self.kwargs):\r\n File \"/home/sanchitgandhi/datasets/src/datasets/packaged_modules/parquet/parquet.py\", line 87, in _generate_tables\r\n for batch_idx, record_batch in enumerate(\r\n File \"pyarrow/_parquet.pyx\", line 1327, in iter_batches\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 333, in read_with_retries\r\n out = read(*args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/spec.py\", line 1856, in read\r\n out = self.cache._fetch(self.loc, self.loc + length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/caching.py\", line 189, in _fetch\r\n self.cache = self.fetcher(start, end) # new block replaces old\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py\", line 410, in _fetch_range\r\n r = http_backoff(\"GET\", url, headers=headers)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/utils/_http.py\", line 258, in http_backoff\r\n response = session.request(method=method, url=url, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/sessions.py\", line 589, in request\r\n resp = self.send(prep, **send_kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/sessions.py\", line 703, in send\r\n r = adapter.send(request, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/utils/_http.py\", line 63, in send\r\n return super().send(request, *args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/adapters.py\", line 501, in send\r\n raise ConnectionError(err, request=request)\r\nrequests.exceptions.ConnectionError: (ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 5fce9fc2-e22f-41c2-91af-529f13f1d611)')\r\n```\r\n\r\n</details>\r\n\r\nHaving streaming mode fail when the Hub goes down makes using it problematic for long training runs where large amounts of data is involved. However, this is the precise situation for which streaming mode is so appealing!\r\n\r\nWondering if there were a 'common' set of Hub errors that we could catch in `iterable_datasets` and prevent from crashing the script?\r\n\r\ncc @lhoestq @mariosasko ", "Errors are already caught and requests are already retried.\r\n\r\nWhat you can do is increase the number of retries before an error is raised.\r\n\r\n```python\r\nimport datasets\r\n\r\ndatasets.config.STREAMING_READ_MAX_RETRIES = 20 # default\r\ndatasets.config.STREAMING_READ_RETRY_INTERVAL = 5 # default\r\n```" ]
2023-08-23T13:15:38
2023-11-06T13:54:16
null
NONE
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### Feature request Streaming datasets, as intended, do not load the entire dataset in memory or disk. However, while querying the next data chunk from the remote, sometimes it is possible that the service is down or there might be other issues that may cause the query to fail. In such a scenario, it would be nice to make these queries retryable (perhaps with a backoff strategy). ### Motivation I was working on a model and the model checkpoints after every 1000 steps. At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch `dataloader`. Given the size of my model and data, it took around 2 hours to reach 1800 steps and now it will take about an hour to recover the lost 800. It would be better to get a retryable querying strategy. ### Your contribution It would be better if someone having experience in this area takes this up as this would require some testing.
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6,169
Configurations in yaml not working
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[ "Unfortunately, I cannot reproduce this behavior on my machine or Colab - the reproducer returns `['main_data', 'additional_data']` as expected.", "Thank you for looking into this, Mario. Is this on [my repository](https://huggingface.co/datasets/tsor13/test), or on another one that you have reproduced? Would you mind pointing me to it if so?", "Whoa, in colab I received the correct behavior using my dataset. It must have something to do with my local copy of `datasets` (which again just failed).\r\n\r\nI've tried uninstalling/reinstnalling to no avail", "hi @tsor13 , I haven't been able to reproduce your issue on `tsor13/test` dataset locally either. reinstalling doesn't help?" ]
2023-08-23T00:13:22
2023-08-23T15:35:31
null
NONE
null
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null
### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1
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I_kwDODunzps5uuftB
6,163
Error type: ArrowInvalid Details: Failed to parse string: '[254,254]' as a scalar of type int32
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[ "Answered on the forum [here](https://discuss.huggingface.co/t/error-type-arrowinvalid-details-failed-to-parse-string-254-254-as-a-scalar-of-type-int32/51323)." ]
2023-08-19T11:34:40
2023-08-21T13:28:16
null
NONE
null
null
null
### Describe the bug I am getting the following error while I am trying to upload the CSV sheet to train a model. My CSV sheet content is exactly same as shown in the example CSV file in the Auto Train page. Attaching screenshot of error for reference. I have also tried converting the index of the answer that are integer into string by placing inverted commas and also without inverted commas. Can anyone please help me out? FYI : I am using Chrome browser. Error type: ArrowInvalid Details: Failed to parse string: '[254,254]' as a scalar of type int32 ![Screenshot 2023-08-19 165827](https://github.com/huggingface/datasets/assets/90616801/95fad96e-7dce-4bb5-9f83-9f1659a32891) ### Steps to reproduce the bug Kindly let me know how to fix this? ### Expected behavior Kindly let me know how to fix this? ### Environment info Kindly let me know how to fix this?
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6,162
load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields
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[ "Hi ! Feel free to open a discussion at https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T/discussions to ask the file to be fixed (or directly open a PR with the fixed file)\r\n\r\n`datasets` expects all the examples to have the same fields", "@lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570). Maybe setting `streaming=True` can workaround this problem. Would you agree with my statement? ", "> @lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570).\r\n\r\nCorrect. Therefore any example that doesn't follow the inferred schema will make the code fail.\r\n\r\n> Maybe setting streaming=True can workaround this problem. Would you agree with my statement?\r\n\r\nYou'll meet the same problem but later - when streaming and arriving at the problematic example", "@lhoestq I just run below test with streaming=True and is not failing at the problematic example\r\n```python\r\nds = load_dataset('json', data_files='/path_to_local_RedPajamaData/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl', streaming=True)\r\ncount = 0\r\nfor i in ds['train']:\r\n count += 1\r\n print(count)\r\n```\r\n\r\nand completes the 262241 samples successfully. It does error our when streaming is not used " ]
2023-08-18T07:19:39
2023-08-18T17:00:35
null
NONE
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### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>** to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the **symlink_target: string>** do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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6,159
Add `BoundingBox` feature
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2023-08-17T20:49:51
2023-08-17T20:49:51
null
CONTRIBUTOR
null
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... to make working with object detection datasets easier. Currently, `Sequence(int_or_float, length=4)` can be used to represent this feature optimally (in the storage backend), so I only see this feature being useful if we make it work with the viewer. Also, bounding boxes usually come in 4 different formats (explained [here](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/)), so we need to decide which one to support (or maybe all of them). cc @NielsRogge @severo
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6,157
DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'
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[ "Thanks for reporting, but we can only fix this issue if you can provide a reproducer that consistently reproduces it.", "@mariosasko Ok. What exactly does it mean to provide a reproducer", "To provide a code that reproduces the issue :)", "@mariosasko I complete the above code, is it enough?", "@mariosasko That's all the code, I'm using locally stored data", "Does this error occur even if you change the cache directory (the `cache_dir` parameter in `load_dataset`)?", "@mariosasko I didn't add any parameters for catch. Nor did any cache configuration change.", "@mariosasko And I changed the data file, but executing load_dataset is always the previous result. I had to change something in images.py to use the new results. Using 'cleanup_cache_files' is invalid! Help me.", "@mariosasko I added a comprehensive error message. Check that _column_requires_decoding is being passed where it shouldn't be. DatasetInfo.__init__() Whether this parameter is required", "I can see the issue now... \r\n\r\nYou can fix it by returning a `DatasetInfo` object in the `_info` method as follows:\r\n```python\r\n def _info(self):\r\n if self.config.name == \"similar_pairs\":\r\n features = datasets.Features(\r\n {\r\n \"image1\": datasets.features.Image(),\r\n \"prompt1\": datasets.Value(\"string\"),\r\n \"image2\": datasets.features.Image(),\r\n \"prompt2\": datasets.Value(\"string\"),\r\n \"similarity\": datasets.Value(\"float32\"),\r\n }\r\n )\r\n elif self.config.name == \"image_prompt_pairs\":\r\n features = datasets.Features(\r\n {\"image\": datasets.features.Image(), \"prompt\": datasets.Value(\"string\")}\r\n )\r\n return datasets.DatasetInfo(features=features)\r\n```", "@mariosasko Oh, that's the problem. Thank you very much. Returned the wrong object and it actually works? I've been training with it for a long time", "@mariosasko The original code can still see progress. emmm, I can't see how many examples is generated so far, so I don't know if we should wait", "The original issue has been addressed, so I'm closing it. \r\n\r\nPlease open a new issue if you encounter more errors." ]
2023-08-17T15:48:11
2023-09-27T17:36:14
2023-09-27T17:36:14
NONE
null
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### Describe the bug When I was in load_dataset, it said "DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'". The second time I ran it, there was no error and the dataset object worked ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[3], line 1 ----> 1 dataset = load_dataset( 2 "/home/aihao/workspace/DeepLearningContent/datasets/manga", 3 data_dir="/home/aihao/workspace/DeepLearningContent/datasets/manga", 4 split="train", 5 ) File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2142 # Build dataset for splits 2143 keep_in_memory = ( 2144 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2145 ) -> 2146 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) 2147 # Rename and cast features to match task schema 2148 if task is not None: 2149 # To avoid issuing the same warning twice File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190), in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory) 1187 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS) 1189 # Create a dataset for each of the given splits -> 1190 datasets = map_nested( 1191 partial( 1192 self._build_single_dataset, ... File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379), in DatasetInfo.copy(self) 378 def copy(self) -> "DatasetInfo": --> 379 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) TypeError: DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding' ``` ### Steps to reproduce the bug /home/aihao/workspace/DeepLearningContent/datasets/images/images.py ```python from logging import config import datasets import os from PIL import Image import csv import json class ImagesConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(ImagesConfig, self).__init__(**kwargs) class Images(datasets.GeneratorBasedBuilder): def _split_generators(self, dl_manager: datasets.DownloadManager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"split": datasets.Split.TRAIN}, ) ] BUILDER_CONFIGS = [ ImagesConfig( name="similar_pairs", description="simliar pair dataset,item is a pair of similar images", ), ImagesConfig( name="image_prompt_pairs", description="image prompt pairs", ), ] def _info(self): if self.config.name == "similar_pairs": return datasets.Features( { "image1": datasets.features.Image(), "image2": datasets.features.Image(), "similarity": datasets.Value("float32"), } ) elif self.config.name == "image_prompt_pairs": return datasets.Features( {"image": datasets.features.Image(), "prompt": datasets.Value("string")} ) def _generate_examples(self, split): data_path = os.path.join(self.config.data_dir, "data") if self.config.name == "similar_pairs": prompts = {} with open(os.path.join(data_path ,"prompts.json"), "r") as f: prompts = json.load(f) with open(os.path.join(data_path, "similar_pairs.csv"), "r") as f: reader = csv.reader(f) for row in reader: image1_path, image2_path, similarity = row yield image1_path + ":" + image2_path + ":", { "image1": Image.open(image1_path), "prompt1": prompts[image1_path], "image2": Image.open(image2_path), "prompt2": prompts[image2_path], "similarity": float(similarity), } ``` Code that indicates an error: ```python from datasets import load_dataset import json import csv import ast import torch data_dir = "/home/aihao/workspace/DeepLearningContent/datasets/images" dataset = load_dataset(data_dir, data_dir=data_dir, name="similar_pairs") ``` ### Expected behavior The first execution gives an error, but it works fine ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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I_kwDODunzps5ujYXq
6,156
Why not use self._epoch as seed to shuffle in distributed training with IterableDataset
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[ "@lhoestq ", "`_effective_generator` returns a RNG that takes into account `self._epoch` and the current dataset's base shuffling RNG (which can be set by specifying `seed=` in `.shuffle() for example`).\r\n\r\nTo fix your error you can pass `seed=` to `.shuffle()`. And the shuffling will depend on both this seed and `self._epoch`", "Thanks for the reply" ]
2023-08-17T10:58:20
2023-08-17T14:33:15
2023-08-17T14:33:14
CONTRIBUTOR
null
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### Describe the bug Currently, distributed training with `IterableDataset` needs to pass fixed seed to shuffle to keep each node use the same seed to avoid overlapping. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1174-L1177 My question is why not directly use `self._epoch` which is set by `set_epoch` as seed? It's almost the same across nodes. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1790-L1801 If not using `self._epoch` as shuffling seed, what does this method do to prepare an epoch seeded generator? https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1206 ### Steps to reproduce the bug As mentioned above. ### Expected behavior As mentioned above. ### Environment info Not related
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I_kwDODunzps5uatM2
6,152
FolderBase Dataset automatically resolves under current directory when data_dir is not specified
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[ "@lhoestq ", "Makes sense, I guess this can be fixed in the load_dataset_builder method.\r\nIt concerns every packaged builder I think (see values in `_PACKAGED_DATASETS_MODULES`)", "I think the behavior is related to these lines, which short circuited the error handling.\r\nhttps://github.com/huggingface/datasets/blob/664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63/src/datasets/load.py#L946-L952\r\n\r\nSo should data_dir be checked here or still delegating to actual `DatasetModule`? In that case, how to properly set `data_files` here.", "This is location in PackagedDatasetModuleFactory.get_module seems the be the right place to check if at least data_dir or data_files are passed", "@mariosasko can you please assign this issue to me,I want to work on this", "#self-assign", "@mariosasko is this issue still open? i would love to kickstart my journey to open source with this issue!\r\nRegards\r\nzutarich", "@zutarich It is unless @debrupf2946 is working on it.", "#self-assign", "I am working and will open a pull request soon @Etelis \r\n" ]
2023-08-16T04:38:09
2024-01-22T15:04:51
null
CONTRIBUTOR
null
null
null
### Describe the bug FolderBase Dataset automatically resolves under current directory when data_dir is not specified. For example: ``` load_dataset("audiofolder") ``` takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59 ### Steps to reproduce the bug ``` load_dataset("audiofolder") ``` ### Expected behavior Error report ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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1,851,497,818
I_kwDODunzps5uW51a
6,151
Faster sorting for single key items
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[ "`Dataset.sort` essentially does the same thing except it uses `pyarrow.compute.sort_indices` which doesn't involve copying the data into python objects (saving memory)\r\n\r\n```python\r\nsort_keys = [(col, \"ascending\") for col in column_names]\r\nindices = pc.sort_indices(self.data, sort_keys=sort_keys)\r\nreturn self.select(indices)\r\n```", "Ok interesting, I'll continue debugging to see what is going wrong on my end." ]
2023-08-15T14:02:31
2023-08-21T14:38:26
2023-08-21T14:38:25
NONE
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### Feature request A faster way to sort a dataset which contains a large number of rows. ### Motivation The current sorting implementations took significantly longer than expected when I was running on a dataset trying to sort by timestamps. **Code snippet:** ```python ds = datasets.load_dataset( "json", **{"data_files": {"train": "path-to-jsonlines"}, "split": "train"}, num_proc=os.cpu_count(), keep_in_memory=True) sorted_ds = ds.sort("pubDate", keep_in_memory=True) ``` However, once I switched to a different method which 1. unpacked to a list of tuples 2. sorted tuples by key 3. run `.select` with the sorted list of indices It was significantly faster (orders of magnitude, especially with M's of rows) ### Your contribution I'd be happy to implement a crude single key sorting algorithm so that other users can benefit from this trick. Broadly, this would take a `Dataset` and perform; ```python # ds is a Dataset object # key_name is the sorting key class Dataset: ... def _sort(key_name: str) -> Dataset: index_keys = [(i,x) for i,x in enumerate(self[key_name])] sorted_rows = sorted(row_pubdate, key=lambda x: x[1]) sorted_indicies = [x[0] for x in sorted_rows] return self.select(sorted_indicies) ```
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6,150
Allow dataset implement .take
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[ "```\r\n dataset = IterableDataset(dataset) if type(dataset) != IterableDataset else dataset # to force dataset.take(batch_size) to work in non-streaming mode\r\n ```\r\n", "hf discuss: https://discuss.huggingface.co/t/how-does-one-make-dataset-take-512-work-with-streaming-false-with-hugging-face-data-set/50770", "so: https://stackoverflow.com/questions/76902824/how-does-one-make-dataset-take512-work-with-streaming-false-with-hugging-fac", "Feel free to work on this. In addition, `IterableDataset` supports `skip`, so we should also add this method to `Dataset`." ]
2023-08-15T00:17:51
2023-08-17T13:49:37
null
NONE
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### Feature request I want to do: ``` dataset.take(512) ``` but it only works with streaming = True ### Motivation uniform interface to data sets. Really surprising the above only works with streaming = True. ### Your contribution Should be trivial to copy paste the IterableDataset .take to use the local path in the data (when streaming = False)
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Dataset.from_parquet cannot load subset of columns
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null
[ "Looks like this regression was introduced in `datasets==2.13.0` (`2.12.0` could load a subset of columns)\r\n\r\nThis does not appear to be fixed by https://github.com/huggingface/datasets/pull/6045 (bug still exists on `main`)" ]
2023-08-14T23:28:22
2023-08-17T22:36:05
2023-08-17T22:36:05
CONTRIBUTOR
null
null
null
### Describe the bug When using `Dataset.from_parquet(path_or_paths, columns=[...])` and a subset of columns, loading fails with a variant of the following ``` ValueError: Couldn't cast a: int64 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 273 to {'a': Value(dtype='int64', id=None), 'b': Value(dtype='int64', id=None)} because column names don't match The above exception was the direct cause of the following exception: ``` Looks to be triggered by https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/table.py#L2285-L2286 ### Steps to reproduce the bug ``` import pandas as pd from datasets import Dataset pd.DataFrame([{"a": 1, "b": 2}]).to_parquet("test.pq") Dataset.from_parquet("test.pq", columns=["a"]) ``` ### Expected behavior A subset of columns should be loaded without error ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.10.0-23-cloud-amd64-x86_64-with-glibc2.2.5 - Python version: 3.8.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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6,147
ValueError when running BeamBasedBuilder with GCS path in cache_dir
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[ "The cause of the error seems to be that `datasets` adds \"gcs://\" as a schema, while `beam` checks only \"gs://\".\r\n\r\ndatasets: https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/builder.py#L822\r\n\r\nbeam: [link](https://github.com/apache/beam/blob/25e1a64641b1c8a3c0a6c75c6e86031b87307f22/sdks/python/apache_beam/io/filesystems.py#L98-L101)\r\n```\r\n systems = [\r\n fs for fs in FileSystem.get_all_subclasses()\r\n if fs.scheme() == path_scheme\r\n ]\r\n```" ]
2023-08-14T03:11:34
2023-08-14T03:19:43
null
NONE
null
null
null
### Describe the bug When running the BeamBasedBuilder with a GCS path specified in the cache_dir, the following ValueError occurs: ``` ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://my-bucket/huggingface_datasets/my_beam_dataset/default/0.0.0/my_beam_dataset-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite'] ``` Same error occurs after running `pip install apache-beam[gcp]` as instructed. ### Steps to reproduce the bug Put `my_beam_dataset.py`: ```python import datasets class MyBeamDataset(datasets.BeamBasedBuilder): def _info(self): features = datasets.Features({"value": datasets.Value("int64")}) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager, pipeline): return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={})] def _build_pcollection(self, pipeline): import apache_beam as beam return pipeline | beam.Create([{"value": i} for i in range(10)]) ``` Run: ```bash datasets-cli run_beam my_beam_dataset.py --cache_dir=gs://my-bucket/huggingface_datasets/ --beam_pipeline_options="runner=DirectRunner" ``` ### Expected behavior Running the BeamBasedBuilder with a GCS cache path without any errors. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 2.0.3
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6,146
DatasetGenerationError when load glue benchmark datasets from `load_dataset`
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[ "I've tried clear the .cache file, doesn't work.", "This issue happens on AWS sagemaker", "This issue can happen if there is a directory named \"glue\" relative to the Python script with the `load_dataset` call (similar issue to this one: https://github.com/huggingface/datasets/issues/5228). Is this the case?", "> This issue can happen if there is a directory named \"glue\" relative to the Python script with the `load_dataset` call (similar issue to this one: #5228). Is this the case?\r\n\r\nThats correct!\r\nSorry for my late response." ]
2023-08-13T05:17:56
2023-08-26T22:09:09
2023-08-26T22:09:09
NONE
null
null
null
### Describe the bug Package version: datasets-2.14.4 When I run the codes: ``` from datasets import load_dataset dataset = load_dataset("glue", "ax") ``` I got the following errors: --------------------------------------------------------------------------- SchemaInferenceError Traceback (most recent call last) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1949, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1948 num_shards = shard_id + 1 -> 1949 num_examples, num_bytes = writer.finalize() 1950 writer.close() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/arrow_writer.py:598, in ArrowWriter.finalize(self, close_stream) 597 self.stream.close() --> 598 raise SchemaInferenceError("Please pass `features` or at least one example when writing data") 599 logger.debug( 600 f"Done writing {self._num_examples} {self.unit} in {self._num_bytes} bytes {self._path if self._path else ''}." 601 ) SchemaInferenceError: Please pass `features` or at least one example when writing data The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[5], line 3 1 from datasets import load_dataset ----> 3 dataset = load_dataset("glue", "ax") File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2136, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2133 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2135 # Download and prepare data -> 2136 builder_instance.download_and_prepare( 2137 download_config=download_config, 2138 download_mode=download_mode, 2139 verification_mode=verification_mode, 2140 try_from_hf_gcs=try_from_hf_gcs, 2141 num_proc=num_proc, 2142 storage_options=storage_options, 2143 ) 2145 # Build dataset for splits 2146 keep_in_memory = ( 2147 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2148 ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 **prepare_split_kwargs, 958 **download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1045 split_dict.add(split_generator.split_info) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, **prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( 1052 "Cannot find data file. " 1053 + (self.manual_download_instructions or "") 1054 + "\nOriginal error:\n" 1055 + str(e) 1056 ) from None File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): 1816 if done: 1817 result = content File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("glue", "ax") ### Expected behavior When generating the train split: Generating train split: 0/0 [00:00<?, ? examples/s] It raise the error: DatasetGenerationError: An error occurred while generating the dataset ### Environment info datasets-2.14.4. Python 3.10
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6,153
custom load dataset to hub
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[ "This is an issue for the [Datasets repo](https://github.com/huggingface/datasets).", "> This is an issue for the [Datasets repo](https://github.com/huggingface/datasets).\r\n\r\nThanks @sgugger , I guess I will wait for them to address the issue . Looking forward to hearing from them ", "You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.", "> You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.\r\n\r\nhow about subset? like `.load_dataset(\"<username>/<repo>\", \"<subset>\")`, how can I upload multi-dataset in one repo? thanks a lot ! ", "> > You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.\r\n> \r\n> how about subset? like `.load_dataset(\"<username>/<repo>\", \"<subset>\")`, how can I upload multi-dataset in one repo? thanks a lot !\r\n\r\nI solved it by upgrading `Datasets` version to 2.15.0" ]
2023-08-13T04:42:22
2023-11-21T11:50:28
2023-10-08T17:04:16
NONE
null
null
null
### System Info kaggle notebook i transformed dataset: ``` dataset = load_dataset("Dahoas/first-instruct-human-assistant-prompt") ``` to formatted_dataset: ``` Dataset({ features: ['message_tree_id', 'message_tree_text'], num_rows: 33143 }) ``` but would like to know how to upload to hub ### Who can help? @ArthurZucker @younesbelkada ### Information - [ ] The official example scripts - [ ] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction shared above ### Expected behavior load dataset to hub
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NIH exporter file not found
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[ "related: https://github.com/huggingface/datasets/issues/3504", "another file not found:\r\n```\r\nTraceback (most recent call last):\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 417, in _info\r\n await _file_info(\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 837, in _file_info\r\n r.raise_for_status()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/aiohttp/client_reqrep.py\", line 1005, in raise_for_status\r\n raise ClientResponseError(\r\naiohttp.client_exceptions.ClientResponseError: 404, message='Not Found', url=URL('https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar')\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py\", line 86, in _run_code\r\n exec(code, run_globals)\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 284, in run_file\r\n runpy.run_path(target, run_name=\"__main__\")\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 321, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 135, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py\", line 526, in <module>\r\n experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights()\r\n File \"/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py\", line 475, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights\r\n column_names = next(iter(dataset)).keys()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 1353, in __iter__\r\n for key, example in ex_iterable:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 207, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py\", line 257, in _generate_examples\r\n for path, file in files[subset]:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 840, in __iter__\r\n yield from self.generator(*self.args, **self.kwargs)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 891, in _iter_from_urlpath\r\n with xopen(urlpath, \"rb\", download_config=download_config) as f:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 496, in xopen\r\n file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 134, in open\r\n return self.__enter__()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 102, in __enter__\r\n f = self.fs.open(self.path, mode=mode)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py\", line 1241, in open\r\n f = self._open(\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 356, in _open\r\n size = size or self.info(path, **kwargs)[\"size\"]\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 121, in wrapper\r\n return sync(self.loop, func, *args, **kwargs)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 106, in sync\r\n raise return_result\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 61, in _runner\r\n result[0] = await coro\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 430, in _info\r\n raise FileNotFoundError(url) from exc\r\nFileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar\r\n```", "```\r\nFileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar\r\n```\r\nmost relevant line I think.", "link to tweet: https://twitter.com/BrandoHablando/status/1690081313519489024?s=20 about issue", "so: https://stackoverflow.com/questions/76891189/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-th", "this seems to work but it's rather annoying.\r\n\r\nSummary of how to make it work:\r\n1. get urls to parquet files into a list\r\n2. load list to load_dataset via `load_dataset('parquet', data_files=urls)` (note api names to hf are really confusing sometimes)\r\n3. then it should work, print a batch of text.\r\n\r\npresudo code\r\n```python\r\nurls_hacker_news = [\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00000-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00001-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00002-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00003-of-00004.parquet\"\r\n]\r\n\r\n...\r\n\r\n\r\n # streaming = False\r\n from diversity.pile_subset_urls import urls_hacker_news\r\n path, name, data_files = 'parquet', 'hacker_news', urls_hacker_news\r\n # not changing\r\n batch_size = 512\r\n today = datetime.datetime.now().strftime('%Y-m%m-d%d-t%Hh_%Mm_%Ss')\r\n run_name = f'{path} div_coeff_{num_batches=} ({today=} ({name=}) {data_mixture_name=} {probabilities=})'\r\n print(f'{run_name=}')\r\n\r\n # - Init wandb\r\n debug: bool = mode == 'dryrun'\r\n run = wandb.init(mode=mode, project=\"beyond-scale\", name=run_name, save_code=True)\r\n wandb.config.update({\"num_batches\": num_batches, \"path\": path, \"name\": name, \"today\": today, 'probabilities': probabilities, 'batch_size': batch_size, 'debug': debug, 'data_mixture_name': data_mixture_name, 'streaming': streaming, 'data_files': data_files})\r\n # run.notify_on_failure() # https://community.wandb.ai/t/how-do-i-set-the-wandb-alert-programatically-for-my-current-run/4891\r\n print(f'{debug=}')\r\n print(f'{wandb.config=}')\r\n\r\n # -- Get probe network\r\n from datasets import load_dataset\r\n import torch\r\n from transformers import GPT2Tokenizer, GPT2LMHeadModel\r\n\r\n tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\n if tokenizer.pad_token_id is None:\r\n tokenizer.pad_token = tokenizer.eos_token\r\n probe_network = GPT2LMHeadModel.from_pretrained(\"gpt2\")\r\n device = torch.device(f\"cuda:{0}\" if torch.cuda.is_available() else \"cpu\")\r\n probe_network = probe_network.to(device)\r\n\r\n # -- Get data set\r\n def my_load_dataset(path, name):\r\n print(f'{path=} {name=} {streaming=}')\r\n if path == 'json' or path == 'bin' or path == 'csv':\r\n print(f'{data_files_prefix+name=}')\r\n return load_dataset(path, data_files=data_files_prefix+name, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n elif path == 'parquet':\r\n print(f'{data_files=}')\r\n return load_dataset(path, data_files=data_files, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n else:\r\n return load_dataset(path, name, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n # - get data set for real now\r\n if isinstance(path, str):\r\n dataset = my_load_dataset(path, name)\r\n else:\r\n print('-- interleaving datasets')\r\n datasets = [my_load_dataset(path, name).with_format(\"torch\") for path, name in zip(path, name)]\r\n [print(f'{dataset.description=}') for dataset in datasets]\r\n dataset = interleave_datasets(datasets, probabilities)\r\n print(f'{dataset=}')\r\n batch = dataset.take(batch_size)\r\n print(f'{next(iter(batch))=}')\r\n column_names = next(iter(batch)).keys()\r\n print(f'{column_names=}')\r\n\r\n # - Prepare functions to tokenize batch\r\n def preprocess(examples):\r\n return tokenizer(examples[\"text\"], padding=\"max_length\", max_length=128, truncation=True, return_tensors=\"pt\")\r\n remove_columns = column_names # remove all keys that are not tensors to avoid bugs in collate function in task2vec's pytorch data loader\r\n def map(batch):\r\n return batch.map(preprocess, batched=True, remove_columns=remove_columns)\r\n tokenized_batch = map(batch)\r\n print(f'{next(iter(tokenized_batch))=}')\r\n```\r\n\r\nhttps://stackoverflow.com/questions/76891189/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-th/76902681#76902681\r\n\r\nhttps://discuss.huggingface.co/t/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-the-files-are-not-available/50555/5?u=severo" ]
2023-08-11T19:05:25
2023-08-14T23:28:38
null
NONE
null
null
null
### Describe the bug can't use or download the nih exporter pile data. ``` 15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights() 16 File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights 17 column_names = next(iter(dataset)).keys() 18 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__ 19 for key, example in ex_iterable: 20 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__ 21 yield from self.generate_examples_fn(**self.kwargs) 22 File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 236, in _generate_examples 23 with zstd.open(open(files[subset], "rb"), "rt", encoding="utf-8") as f: 24 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py", line 74, in wrapper 25 return function(*args, download_config=download_config, **kwargs) 26 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen 27 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() 28 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open 29 return self.__enter__() 30 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__ 31 f = self.fs.open(self.path, mode=mode) 32 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open 33 f = self._open( 34 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open 35 size = size or self.info(path, **kwargs)["size"] 36 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper 37 return sync(self.loop, func, *args, **kwargs) 38 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync 39 raise return_result 40 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner 41 result[0] = await coro 42 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info 43 raise FileNotFoundError(url) from exc 44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst ``` ### Steps to reproduce the bug run this: ``` from datasets import load_dataset path, name = 'EleutherAI/pile', 'nih_exporter' # -- Get data set dataset = load_dataset(path, name, streaming=True, split="train").with_format("torch") batch = dataset.take(512) print(f'{batch=}') ``` ### Expected behavior print the batch ### Environment info ``` (beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.4 - Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ```
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https://api.github.com/repos/huggingface/datasets/issues/6144/timeline
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https://api.github.com/repos/huggingface/datasets/issues/6142
https://api.github.com/repos/huggingface/datasets
https://api.github.com/repos/huggingface/datasets/issues/6142/labels{/name}
https://api.github.com/repos/huggingface/datasets/issues/6142/comments
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https://github.com/huggingface/datasets/issues/6142
1,846,205,216
I_kwDODunzps5uCtsg
6,142
the-stack-dedup fails to generate
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[]
closed
false
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[ "@severo ", "It seems that some parquet files have additional columns.\r\n\r\nI ran a scan and found that two files have the additional `__id__` column:\r\n\r\n1. `hf://datasets/bigcode/the-stack-dedup/data/numpy/data-00000-of-00001.parquet`\r\n2. `hf://datasets/bigcode/the-stack-dedup/data/omgrofl/data-00000-of-00001.parquet`\r\n\r\nWe should open a PR to fix those two files", "I opened https://huggingface.co/datasets/bigcode/the-stack-dedup/discussions/31", "The files have been fixed ! I'm closing this one but feel free to re-open if you still have the issue" ]
2023-08-11T05:10:49
2023-08-17T09:26:13
2023-08-17T09:26:13
NONE
null
null
null
### Describe the bug I'm getting an error generating the-stack-dedup with datasets 2.13.1, and with 2.14.4 nothing happens. ### Steps to reproduce the bug My code: ``` import os import datasets as ds MY_CACHE_DIR = "/home/ubuntu/the-stack-dedup-local" MY_TOKEN="my-token" the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="train", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, use_auth_token=MY_TOKEN, num_proc=64) ``` The exception: ``` Generating train split: 233248251 examples [54:31, 57280.00 examples/s] multiprocess.pool.RemoteTraceback: """ Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1879, in _prepare_split_single for _, table in generator: File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 82, in _generate_tables yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 61, in _cast_table pa_table = table_cast(pa_table, self.info.features.arrow_schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2324, in table_cast return cast_table_to_schema(table, schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2282, in cast_table_to_schema raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nb ecause column names don't match") ValueError: Couldn't cast hexsha: string size: int64 ext: string lang: string max_stars_repo_path: string max_stars_repo_name: string max_stars_repo_head_hexsha: string max_stars_repo_licenses: list<item: string> child 0, item: string max_stars_count: int64 max_stars_repo_stars_event_min_datetime: string max_stars_repo_stars_event_max_datetime: string max_issues_repo_path: string max_issues_repo_name: string max_issues_repo_head_hexsha: string max_issues_repo_licenses: list<item: string> child 0, item: string max_issues_count: int64 max_issues_repo_issues_event_min_datetime: string max_issues_repo_issues_event_max_datetime: string max_forks_repo_path: string max_forks_repo_name: string max_forks_repo_head_hexsha: string max_forks_repo_licenses: list<item: string> child 0, item: string max_forks_count: int64 max_forks_repo_forks_event_min_datetime: string max_forks_repo_forks_event_max_datetime: string content: string avg_line_length: double max_line_length: int64 alphanum_fraction: double __id__: int64 -- schema metadata -- huggingface: '{"info": {"features": {"hexsha": {"dtype": "string", "_type' + 1979 to {'hexsha': Value(dtype='string', id=None), 'size': Value(dtype='int64', id=None), 'ext': Value(dtype='string', id=None), 'lang': Value(dtype='string', id=None), 'max_stars_repo_path': Value(dtype='string', id=None), 'max_stars_repo_name': Value(dtype='string', id=None), 'max_stars_repo_head_hexsha': Value(dtype='string', id=None), 'max_stars_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_stars_count': Value(dtype='int64', id=None), 'max_stars_repo_stars_event_min_datetime': Value(dtype='string', id=None), 'max_stars_repo_stars_event_max_datetime': Value(dtype='string', id=None), 'max_issues_repo_path': Value(dtype='string', id=None), 'max_issues_repo_name': Value(dtype='string', id=None), 'max_issues_repo_head_hexsha': Value(dtype='string', id=None), 'max_issues_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_issues_count': Value(dtype='int64', id=None), 'max_issues_repo_issues_event_min_datetime': Value(dtype='string', id=None), 'max_issues_repo_issues_event_max_datetime': Value(dtype='string', id=None), 'max_forks_repo_path': Value(dtype='string', id=None), 'max_forks_repo_name': Value(dtype='string', id=None), 'max_forks_repo_head_hexsha': Value(dtype='string', id=None), 'max_forks_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_forks_count': Value(dtype='int64', id=None), 'max_forks_repo_forks_event_min_datetime': Value(dtype='string', id=None), 'max_forks_repo_forks_event_max_datetime': Value(dtype='string', id=None), 'content': Value(dtype='string', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'alphanum_fraction': Value(dtype='float64', id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1328, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating th e dataset") from e datasets.builder.DatasetGenerationError: An error occurred while genera ting the dataset """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/download_the_stack.py", line 7, in <module> the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="tr ain", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, us e_auth_token=MY_TOKEN, num_proc=64) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/load. py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1796, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 774, in get raise self._value datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior The dataset downloads properly. @lhoestq @loub ### Environment info Datasets 2.13.1, large VM with 2TB RAM, Ubuntu 20.04
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6,141
TypeError: ClientSession._request() got an unexpected keyword argument 'https'
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[ "Hi! I cannot reproduce this error on my machine or in Colab. Which version of `fsspec` do you have installed?" ]
2023-08-11T02:40:32
2023-08-30T13:51:33
2023-08-30T13:51:33
NONE
null
null
null
### Describe the bug Hello, when I ran the [code snippet](https://huggingface.co/docs/datasets/v2.14.4/en/loading#json) on the document, I encountered the following problem: ``` Python 3.10.9 (main, Mar 1 2023, 18:23:06) [GCC 11.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" >>> dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 2112, in load_dataset builder_instance = load_dataset_builder( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1798, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1413, in dataset_module_factory ).get_module() File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 949, in get_module data_files = DataFilesDict.from_patterns( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 672, in from_patterns DataFilesList.from_patterns( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 578, in from_patterns resolve_pattern( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 340, in resolve_pattern for filepath, info in fs.glob(pattern, detail=True).items() File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 113, in wrapper return sync(self.loop, func, *args, **kwargs) File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 98, in sync raise return_result File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 53, in _runner result[0] = await coro File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 449, in _glob elif await self._exists(path): File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 306, in _exists r = await session.get(self.encode_url(path), **kw) File "/home/liushuai/anaconda3/lib/python3.10/site-packages/aiohttp/client.py", line 922, in get self._request(hdrs.METH_GET, url, allow_redirects=allow_redirects, **kwargs) TypeError: ClientSession._request() got an unexpected keyword argument 'https' ``` ### Steps to reproduce the bug ``` from datasets import load_dataset base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data") ``` ### Expected behavior able to load normally ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.4.54-2-x86_64-with-glibc2.27 - Python version: 3.10.9 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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I_kwDODunzps5t_lYI
6,140
Misalignment between file format specified in configs metadata YAML and the inferred builder
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2023-08-10T15:07:34
2023-08-17T20:37:20
2023-08-17T20:37:20
MEMBER
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There is a misalignment between the format of the `data_files` specified in the configs metadata YAML (CSV): ```yaml configs: - config_name: default data_files: - split: train path: data.csv ``` and the inferred builder (JSON). Note there are multiple JSON files in the repo, but they do not appear in the configs metadata YAML. See: https://huggingface.co/datasets/freddyaboulton/chatinterface_with_image_csv/discussions/1 CC: @freddyaboulton @polinaeterna
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Offline dataset viewer
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[ "Hi, thanks for the suggestion. It's not possible at the moment. The viewer is part of the Hub codebase and only works on public datasets. Also, it relies on [Datasets Server](https://github.com/huggingface/datasets-server/), which prepares the data and provides an API to access the rows, size, etc.\r\n\r\nIf you're interested in hosting your data as a private dataset on the Hub, you might want to look at https://github.com/huggingface/datasets-server/issues/39.", "Hi, we are building an offline dataset viewer: https://github.com/Renumics/spotlight\r\nIt supports many HF datasets, but currently you have to use it via Pandas:\r\ndf=ds.to_pandas()\r\nspotlight.show(df)\r\n\r\nWould love to hear from you if that works for your use case. If not, feel free to open an issue on the repo: https://github.com/Renumics/spotlight/issues", "@ssuwelack thank you! I will definitely try it out.", "Related issues:\r\n- https://github.com/huggingface/datasets-server/issues/213\r\n- https://github.com/huggingface/datasets-server/issues/441\r\n- https://github.com/huggingface/datasets/issues/6014", "Closing for now, as developing and maintaining an offline viewer is not planned.", "@yuvalkirstain the dataset viewer is now available on private datasets for [PRO users](https://huggingface.co/pricing#pro) and [Enterprise Hub orgs](https://huggingface.co/enterprise). Would it fit your needs?" ]
2023-08-10T11:30:00
2024-02-13T12:22:55
2023-09-29T13:10:22
NONE
null
null
null
### Feature request The dataset viewer feature is very nice. It enables to the user to easily view the dataset. However, when working for private companies we cannot always upload the dataset to the hub. Is there a way to create dataset viewer offline? I.e. to run a code that will open some kind of html or something that makes it easy to view the dataset. ### Motivation I want to easily view my dataset even when it is hosted locally. ### Your contribution N.A.
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I_kwDODunzps5t97z4
6,137
(`from_spark()`) Unable to connect HDFS in pyspark YARN setting
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2023-08-10T11:03:08
2023-08-10T11:03:08
null
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### Describe the bug related issue: https://github.com/apache/arrow/issues/37057#issue-1841013613 --- Hello. I'm trying to interact with HDFS storage from a driver and workers of pyspark YARN cluster. Precisely I'm using **huggingface's `datasets`** ([link](https://github.com/huggingface/datasets)) library that relies on pyarrow to communicate with HDFS. The `from_spark()` ([link](https://huggingface.co/docs/datasets/use_with_spark#load-from-spark)) is what I'm invoking in my script. Below is the error I'm encountering. Note that I've masked sensitive paths. My code is sent to worker containers (docker) from driver container then executed. I confirmed that in both driver and worker images I can connect to HDFS using pyarrow since the envs and required jars are properly set, but strangely that becomes impossible when the same image runs as remote worker process. These are some peculiarities in my environment that might caused this issue. * **Cluster requires kerberos authentication** * But I think the error message implies that's not the problem in this case * **The user that runs the worker process is different from that built the docker image** * To avoid permission-related issues I made all directories that are accessed from the script accessible to everyone * **Pyspark-part of my code has no problem interacting with HDFS.** * Even pyarrow doesn't experience problem when I run the code in interactive session of the same docker images (driver, worker) * The problem occurs only when it runs as cluster's worker runtime Hope I could get some help. Thanks. ```bash 2023-08-08 18:51:19,638 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 2023-08-08 18:51:20,280 WARN shortcircuit.DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded. 23/08/08 18:51:22 WARN TaskSetManager: Lost task 0.0 in stage 142.0 (TID 9732) (ac3bax2062.bdp.bdata.ai executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 830, in main process() File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 820, in process out_iter = func(split_index, iterator) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func File "/root/spark/python/pyspark/rdd.py", line 828, in func File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe open(probe_file, "a") File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper return function(*args, download_config=download_config, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open out = open_files( ^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files fs, fs_token, paths = get_fs_token_paths( ^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths fs = filesystem(protocol, **inkwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem return cls(**storage_options) ^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__ obj = super().__call__(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__ fs = HadoopFileSystem( ^^^^^^^^^^^^^^^^^ File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__ File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: HDFS connection failed at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:366) at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358) at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345) at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92) at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161) at org.apache.spark.scheduler.Task.run(Task.scala:139) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) 23/08/08 18:51:24 WARN TaskSetManager: Lost task 0.1 in stage 142.0 (TID 9733) (ac3iax2079.bdp.bdata.ai executor 2): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 830, in main process() File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 820, in process out_iter = func(split_index, iterator) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func File "/root/spark/python/pyspark/rdd.py", line 828, in func File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe open(probe_file, "a") File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper return function(*args, download_config=download_config, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open out = open_files( ^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files fs, fs_token, paths = get_fs_token_paths( ^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths fs = filesystem(protocol, **inkwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem return cls(**storage_options) ^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__ obj = super().__call__(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__ fs = HadoopFileSystem( ^^^^^^^^^^^^^^^^^ File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__ File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: HDFS connection failed at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:366) at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358) at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345) at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92) at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161) at org.apache.spark.scheduler.Task.run(Task.scala:139) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) 23/08/08 18:51:38 WARN TaskSetManager: Lost task 0.2 in stage 142.0 (TID 9734) (<MASKED> executor 4): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 830, in main process() File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 820, in process out_iter = func(split_index, iterator) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func File "/root/spark/python/pyspark/rdd.py", line 828, in func File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe open(probe_file, "a") File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper return function(*args, download_config=download_config, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open out = open_files( ^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files fs, fs_token, paths = get_fs_token_paths( ^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths fs = filesystem(protocol, **inkwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem return cls(**storage_options) ^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__ obj = super().__call__(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__ fs = HadoopFileSystem( ^^^^^^^^^^^^^^^^^ File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__ File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: HDFS connection failed at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:366) at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358) at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345) at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92) at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161) at org.apache.spark.scheduler.Task.run(Task.scala:139) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) ``` ### Steps to reproduce the bug Use `from_spark()` function in pyspark YARN setting. I set `cache_dir` to HDFS path. ### Expected behavior Work as described in document ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.17 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 10.0.1 - Pandas version: 1.5.3
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1,844,887,866
I_kwDODunzps5t9sE6
6,136
CI check_code_quality error: E721 Do not compare types, use `isinstance()`
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2023-08-10T10:19:50
2023-08-10T11:22:58
2023-08-10T11:22:58
MEMBER
null
null
null
After latest release of `ruff` (https://pypi.org/project/ruff/0.0.284/), we get the following CI error: ``` src/datasets/utils/py_utils.py:689:12: E721 Do not compare types, use `isinstance()` ```
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6,134
`datasets` cannot be installed alongside `apache-beam`
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[ "I noticed that this is actually covered by issue #5613, which for some reason I didn't see when I searched the issues in this repo the first time." ]
2023-08-10T06:54:32
2023-09-01T03:19:49
2023-08-10T15:22:10
NONE
null
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### Describe the bug If one installs `apache-beam` alongside `datasets` (which is required for the [wikipedia](https://huggingface.co/datasets/wikipedia#dataset-summary) dataset) in certain environments (such as a Google Colab notebook), they appear to install successfully, however, actually trying to do something such as importing the `load_dataset` method from `datasets` results in a crashing error. I think the problem is that `apache-beam` version 2.49.0 requires `dill>=0.3.1.1,<0.3.2`, but the latest version of `multiprocess` (0.70.15) (on which `datasets` depends) requires `dill>=0.3.7,`, so this is causing the dependency resolver to use an older version of `multiprocess` which leads to the `datasets` crashing since it doesn't actually appear to be compatible with older versions. ### Steps to reproduce the bug See this [Google Colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing) to easily reproduce the bug. In some environments, I have been able to reproduce the bug by running the following in Bash: ```bash $ pip install datasets apache-beam ``` then the following in a Python shell: ```python from datasets import load_dataset ``` Here is my stacktrace from running on Google Colab: <details> <summary>stacktrace</summary> ``` [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.14.4" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 64 65 from . import config ---> 66 from .arrow_reader import ArrowReader 67 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 68 from .data_files import sanitize_patterns [/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module> 28 import pyarrow.parquet as pq 29 ---> 30 from .download.download_config import DownloadConfig 31 from .naming import _split_re, filenames_for_dataset_split 32 from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables [/usr/local/lib/python3.10/dist-packages/datasets/download/__init__.py](https://localhost:8080/#) in <module> 7 8 from .download_config import DownloadConfig ----> 9 from .download_manager import DownloadManager, DownloadMode 10 from .streaming_download_manager import StreamingDownloadManager [/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py](https://localhost:8080/#) in <module> 33 from ..utils.info_utils import get_size_checksum_dict 34 from ..utils.logging import get_logger, is_progress_bar_enabled, tqdm ---> 35 from ..utils.py_utils import NestedDataStructure, map_nested, size_str 36 from .download_config import DownloadConfig 37 [/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <module> 38 import dill 39 import multiprocess ---> 40 import multiprocess.pool 41 import numpy as np 42 from packaging import version [/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in <module> 607 # 608 --> 609 class ThreadPool(Pool): 610 611 from .dummy import Process [/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in ThreadPool() 609 class ThreadPool(Pool): 610 --> 611 from .dummy import Process 612 613 def __init__(self, processes=None, initializer=None, initargs=()): [/usr/local/lib/python3.10/dist-packages/multiprocess/dummy/__init__.py](https://localhost:8080/#) in <module> 85 # 86 ---> 87 class Condition(threading._Condition): 88 # XXX 89 if sys.version_info < (3, 0): AttributeError: module 'threading' has no attribute '_Condition' ``` </details> I've also found that attempting to install these `datasets` and `apache-beam` in certain environments (e.g. via pip inside a conda env) simply causes pip to hang indefinitely. ### Expected behavior I would expect to be able to import methods from `datasets` without crashing. I have tested that this is possible as long as I do not attempt to install `apache-beam`. ### Environment info Google Colab
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6,133
Dataset is slower after calling `to_iterable_dataset`
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[ "@lhoestq ", "It's roughly the same code between the two so we can expected roughly the same speed, could you share a benchmark ?" ]
2023-08-10T06:36:23
2023-08-16T09:18:54
null
CONTRIBUTOR
null
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### Describe the bug Can anyone explain why looping over a dataset becomes slower after calling `to_iterable_dataset` to convert to `IterableDataset` ### Steps to reproduce the bug Any dataset after converting to `IterableDataset` ### Expected behavior Maybe it should be faster on big dataset? I only test on small dataset ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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to_iterable_dataset is missing in document
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[ "Fixed with PR" ]
2023-08-09T15:15:03
2023-08-16T04:43:36
2023-08-16T04:43:29
CONTRIBUTOR
null
null
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### Describe the bug to_iterable_dataset is missing in document ### Steps to reproduce the bug to_iterable_dataset is missing in document ### Expected behavior document enhancement ### Environment info unrelated
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default config name doesn't work when config kwargs are specified.
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[ "@lhoestq ", "What should be the behavior in this case ? Should it override the default config with the added parameter ?", "I know why it should be treated as a new config if overriding parameters are passed. But in some case, I just pass in some common fields like `data_dir`.\r\n\r\nFor example, I want to extend the FolderBasedBuilder as a multi-config version, the `data_dir` or `data_files` are always passed by user and should not be considered as overriding the default config. In current state, I cannot leverage the feature of default config since passing `data_dir` will disable the default config.", "Thinking more about it I think the current behavior is the right one.\r\n\r\nProvided parameters should be passed to instantiate a new BuilderConfig.\r\n\r\nWhat's the error you're getting ?", "For example, this works to use default config with name '_all_':\r\n```python\r\ndatasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", split=\"train\")\r\n```\r\nwhile this failed to use default config\r\n```python\r\ndatasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", split=\"train\", data_dir='.')\r\n```\r\nAfter manually specifying it, it works again.\r\n```python\r\ndatasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", \"_all_\", split=\"train\", data_dir='.')\r\n```", "@lhoestq ", "It should work if you explicitly ask for the config you want to override\r\n\r\n```python\r\nload_dataset('/dataset/with/multiple/config', 'name_of_the_default_config', some_field_in_config='some')\r\n```\r\n\r\nAlternatively you can have a BuilderConfig class that when instantiated returns a config with the right default values. In this case this code would instantiate this config with the default values except for the parameter to override:\r\n\r\n```python\r\nload_dataset('/dataset/with/multiple/config', some_field_in_config='some')\r\n```", "@lhoestq Yes. But it doesn't work for me.\r\n\r\nHere's my dataset for example.\r\n```\r\nlass MyDatasetConfig(datasets.BuilderConfig):\r\n def __init__(self, name: str, version: str, **kwargs):\r\n self.option1 = kwargs.pop(\"option1\", False)\r\n self.option2 = kwargs.pop(\"option2\", 5)\r\n\r\n super().__init__(\r\n name=name,\r\n version=datasets.Version(version),\r\n **kwargs)\r\n\r\n\r\nclass MyDataset(datasets.GeneratorBasedBuilder):\r\n DEFAULT_CONFIG_NAME = \"v1\"\r\n\r\n BUILDER_CONFIGS = [\r\n UnifiedTtsDatasetConfig(\r\n name=\"v1\",\r\n version=\"1.0.0\",\r\n description=\"Initial version of the dataset\"\r\n ),\r\n ]\r\n\r\n def _info(self) -> DatasetInfo:\r\n _ = self.option1\r\n ....\r\n```\r\n\r\nHere it's okay to use `load_dataset('my_dataset.py')` for loading the default config `v1`.\r\n\r\nBut if I want to override the default values in config with `load_dataset('my_dataset.py', option2=3)`, it failed to find my default config `v1.\r\n\r\nUnless I use `load_dataset('my_dataset.py', 'v1', option2=3)`\r\n\r\nSo according to your advice, how can I modify my dataset to be able to override default config without manually specifying it.", "What's the error ? It should try to instantiate `MyDatasetConfig` with `option2=3`", "@lhoestq The error is\r\n```\r\ndef _info(self) -> DatasetInfo:\r\n _ = self.option1 <-\r\n ....\r\nAttributeError: 'BuilderConfig' object has no attribute 'option1'\r\n```\r\nwhich seems to find another unknown config.\r\n\r\nYou can try this line `datasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", split=\"train\", data_dir='.')`, it's a multi-config dataset on HF hub and the error is the same.\r\n\r\nMy insights:\r\nhttps://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518\r\nif `config_kwargs` is provided here, the if branch is skipped.", "I see, you just have to set this class attribute to your builder class :)\r\n\r\n```python\r\nBUILDER_CONFIG_CLASS = MyDatasetConfig\r\n```", "So what does this attribute do? In most cases it's not used and the [documents for multi-config dataset](https://huggingface.co/docs/datasets/main/en/image_dataset#multiple-configurations) never mentioned that.", "It tells which builder config class to instantiate if additional config parameters are passed to load_dataset", "@lhoestq maybe we can enhance the document to say something about the common attributes of `DatasetBuilder`", "Ah indeed it's missing in the docs, thanks for reporting. I'm opening a PR" ]
2023-08-09T12:43:15
2023-11-22T11:50:49
2023-11-22T11:50:48
CONTRIBUTOR
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### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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[ "Hi @TomasAndersonFang,\r\n\r\nHave you tried instead to use `torch_compile` in `transformers.TrainingArguments`? https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.torch_compile", "> \r\n\r\nI tried this and got the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 324, in _compile\r\n out_code = transform_code_object(code, transform)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py\", line 445, in transform_code_object\r\n transformations(instructions, code_options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 311, in transform\r\n tracer.run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1726, in run\r\n super().run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 576, in run\r\n and self.step()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 540, in step\r\n getattr(self, inst.opname)(inst)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1030, in LOAD_ATTR\r\n result = BuiltinVariable(getattr).call_function(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 566, in call_function\r\n result = handler(tx, *args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 931, in call_getattr\r\n return obj.var_getattr(tx, name).add_options(options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py\", line 124, in var_getattr\r\n subobj = inspect.getattr_static(base, name)\r\n File \"/apps/Arch/software/Python/3.10.8-GCCcore-12.2.0/lib/python3.10/inspect.py\", line 1777, in getattr_static\r\n raise AttributeError(attr)\r\nAttributeError: config\r\n\r\nfrom user code:\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/peft/peft_model.py\", line 909, in forward\r\n if self.base_model.config.model_type == \"mpt\":\r\n\r\nSet torch._dynamo.config.verbose=True for more information\r\n\r\n\r\nYou can suppress this exception and fall back to eager by setting:\r\n torch._dynamo.config.suppress_errors = True\r\n\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 228, in <module>\r\n main()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 221, in main\r\n trainer.train()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1539, in train\r\n return inner_training_loop(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1809, in _inner_training_loop\r\n tr_loss_step = self.training_step(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2654, in training_step\r\n loss = self.compute_loss(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2679, in compute_loss\r\n outputs = model(**inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\r\n return forward_call(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 82, in forward\r\n return self.dynamo_ctx(self._orig_mod.forward)(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 209, in _fn\r\n return fn(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 581, in forward\r\n return model_forward(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 569, in __call__\r\n return convert_to_fp32(self.model_forward(*args, **kwargs))\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/amp/autocast_mode.py\", line 14, in decorate_autocast\r\n return func(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 337, in catch_errors\r\n return callback(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 404, in _convert_frame\r\n result = inner_convert(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 104, in _fn\r\n return fn(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 262, in _convert_frame_assert\r\n return _compile(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/utils.py\", line 163, in time_wrapper\r\n r = func(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 394, in _compile\r\n raise InternalTorchDynamoError() from e\r\ntorch._dynamo.exc.InternalTorchDynamoError\r\n```", "Hi @TomasAndersonFang,\r\n\r\nI guess in this case it may be an issue with `transformers` (or `PyTorch`). I would recommend you open an issue on their repo.", "@albertvillanova Thanks for your recommendation. I'll do it", "@TomasAndersonFang were you able to find a solution to this issue? I would highly appreciate any help. \r\n\r\nThanks!" ]
2023-08-08T15:32:08
2023-12-26T07:51:57
2023-08-11T13:35:09
NONE
null
null
null
### Describe the bug This bug generates when I use torch.compile(model) in my code, which seems to raise an error in datasets lib. ### Steps to reproduce the bug I use the following code to fine-tune Falcon on my private dataset. ```python import transformers from transformers import ( AutoModelForCausalLM, AutoTokenizer, AutoConfig, DataCollatorForSeq2Seq, Trainer, Seq2SeqTrainer, HfArgumentParser, Seq2SeqTrainingArguments, BitsAndBytesConfig, ) from peft import ( LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, set_peft_model_state_dict, ) import torch import os import evaluate import functools from datasets import load_dataset import bitsandbytes as bnb import logging import json import copy from typing import Dict, Optional, Sequence from dataclasses import dataclass, field # Lora settings LORA_R = 8 LORA_ALPHA = 16 LORA_DROPOUT= 0.05 LORA_TARGET_MODULES = ["query_key_value"] @dataclass class ModelArguments: model_name_or_path: Optional[str] = field(default="Salesforce/codegen2-7B") @dataclass class DataArguments: data_path: str = field(default=None, metadata={"help": "Path to the training data."}) train_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) eval_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) cache_path: str = field(default=None, metadata={"help": "Path to the cache directory."}) num_proc: int = field(default=4, metadata={"help": "Number of processes to use for data preprocessing."}) @dataclass class TrainingArguments(transformers.TrainingArguments): # cache_dir: Optional[str] = field(default=None) optim: str = field(default="adamw_torch") model_max_length: int = field( default=512, metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, ) is_lora: bool = field(default=True, metadata={"help": "Whether to use LORA."}) def tokenize(text, tokenizer, max_seq_len=512, add_eos_token=True): result = tokenizer( text, truncation=True, max_length=max_seq_len, padding=False, return_tensors=None, ) if ( result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < max_seq_len and add_eos_token ): result["input_ids"].append(tokenizer.eos_token_id) result["attention_mask"].append(1) if add_eos_token and len(result["input_ids"]) >= max_seq_len: result["input_ids"][max_seq_len - 1] = tokenizer.eos_token_id result["attention_mask"][max_seq_len - 1] = 1 result["labels"] = result["input_ids"].copy() return result def main(): parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() config = AutoConfig.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, trust_remote_code=True, ) if training_args.is_lora: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, torch_dtype=torch.float16, trust_remote_code=True, load_in_8bit=True, quantization_config=BitsAndBytesConfig( load_in_8bit=True, llm_int8_threshold=6.0 ), ) model = prepare_model_for_int8_training(model) config = LoraConfig( r=LORA_R, lora_alpha=LORA_ALPHA, target_modules=LORA_TARGET_MODULES, lora_dropout=LORA_DROPOUT, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) else: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, torch_dtype=torch.float16, cache_dir=data_args.cache_path, trust_remote_code=True, ) model.config.use_cache = False def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" ) print_trainable_parameters(model) tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, model_max_length=training_args.model_max_length, padding_side="left", use_fast=True, trust_remote_code=True, ) tokenizer.pad_token = tokenizer.eos_token # Load dataset def generate_and_tokenize_prompt(sample): input_text = sample["input"] target_text = sample["output"] + tokenizer.eos_token full_text = input_text + target_text tokenized_full_text = tokenize(full_text, tokenizer, max_seq_len=512) tokenized_input_text = tokenize(input_text, tokenizer, max_seq_len=512) input_len = len(tokenized_input_text["input_ids"]) - 1 # -1 for eos token tokenized_full_text["labels"] = [-100] * input_len + tokenized_full_text["labels"][input_len:] return tokenized_full_text data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.eval_file is not None: data_files["eval"] = data_args.eval_file dataset = load_dataset(data_args.data_path, data_files=data_files) train_dataset = dataset["train"] eval_dataset = dataset["eval"] train_dataset = train_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) eval_dataset = eval_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) data_collator = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True) # Evaluation metrics def compute_metrics(eval_preds, tokenizer): metric = evaluate.load('exact_match') preds, labels = eval_preds # In case the model returns more than the prediction logits if isinstance(preds, tuple): preds = preds[0] decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Replace -100s in the labels as we can't decode them labels[labels == -100] = tokenizer.pad_token_id decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Some simple post-processing decoded_preds = [pred.strip() for pred in decoded_preds] decoded_labels = [label.strip() for label in decoded_labels] result = metric.compute(predictions=decoded_preds, references=decoded_labels) return {'exact_match': result['exact_match']} compute_metrics_fn = functools.partial(compute_metrics, tokenizer=tokenizer) model = torch.compile(model) # Training trainer = Trainer( model=model, train_dataset=train_dataset, eval_dataset=eval_dataset, args=training_args, data_collator=data_collator, compute_metrics=compute_metrics_fn, ) trainer.train() trainer.save_state() trainer.save_model(output_dir=training_args.output_dir) tokenizer.save_pretrained(save_directory=training_args.output_dir) if __name__ == "__main__": main() ``` When I didn't use `torch.cpmpile(model)`, my code worked well. But when I added this line to my code, It produced the following error: ``` Traceback (most recent call last): File "falcon_sft.py", line 230, in <module> main() File "falcon_sft.py", line 223, in main trainer.train() File "python3.10/site-packages/transformers/trainer.py", line 1539, in train return inner_training_loop( File "python3.10/site-packages/transformers/trainer.py", line 1787, in _inner_training_loop for step, inputs in enumerate(epoch_iterator): File "python3.10/site-packages/accelerate/data_loader.py", line 384, in __iter__ current_batch = next(dataloader_iter) File "python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__ data = self._next_data() File "python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = self.dataset.__getitems__(possibly_batched_index) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__ batch = self.__getitem__(keys) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table _check_valid_index_key(key, size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key _check_valid_index_key(int(max(key)), size=size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 88 is out of bounds for size 0 ``` So I'm confused about why this error was generated, and how to fix it. Is this error produced by datasets or `torch.compile`? ### Expected behavior I want to use `torch.compile` in my code. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.8 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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6,126
Private datasets do not load when passing token
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[ "Our CI did not catch this issue because with current implementation, stored token in `HfFolder` (which always exists) is used by default.", "I can confirm this and have the same problem (and just went almost crazy because I couldn't figure out the source of this problem because on another computer everything worked well even with `DownloadMode.FORCE_REDOWNLOAD`).", "We are planning to do a patch release today, after the merge of the fix:\r\n- #6127\r\n\r\nIn the meantime, the problem can be circumvented by passing `download_config` instead:\r\n```python\r\nfrom datasets import DownloadConfig, load_dataset\r\n\r\nload_dataset(\"<DATASET-NAME>\", split=\"train\", download_config=DownloadConfig(token=\"<TOKEN>\"))\r\n``` ", "> We are planning to do a patch release today, after the merge of the fix:\r\n> \r\n> * [Fix authentication issues #6127](https://github.com/huggingface/datasets/pull/6127)\r\n> \r\n> \r\n> In the meantime, the problem can be circumvented by passing `download_config` instead:\r\n> \r\n> ```python\r\n> from datasets import DownloadConfig, load_dataset\r\n> \r\n> load_dataset(\"<DATASET-NAME>\", split=\"train\", download_config=DownloadConfig(token=\"<TOKEN>\"))\r\n> ```\r\n\r\nThis did not work for me (there was some other error with the split being an unexpected size 0). Downgrading to 2.13 fixed it...." ]
2023-08-07T15:06:47
2023-08-08T15:16:23
2023-08-08T15:16:23
MEMBER
null
null
null
### Describe the bug Since the release of `datasets` 2.14, private/gated datasets do not load when passing `token`: they raise `EmptyDatasetError`. This is a non-planned backward incompatible breaking change. Note that private datasets do load if instead `download_config` is passed: ```python from datasets import DownloadConfig, load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", download_config=DownloadConfig(token="<MY-TOKEN>")) ds ``` gives ``` Dataset({ features: ['text'], num_rows: 4 }) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") ``` gives ``` --------------------------------------------------------------------------- EmptyDatasetError Traceback (most recent call last) [<ipython-input-2-25b48732107a>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2107 2108 # Create a dataset builder -> 2109 builder_instance = load_dataset_builder( 2110 path=path, 2111 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs) 1793 download_config = download_config.copy() if download_config else DownloadConfig() 1794 download_config.storage_options.update(storage_options) -> 1795 dataset_module = dataset_module_factory( 1796 path, 1797 revision=revision, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1484 raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1485 if isinstance(e1, EmptyDatasetError): -> 1486 raise e1 from None 1487 if isinstance(e1, FileNotFoundError): 1488 raise FileNotFoundError( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1474 download_config=download_config, 1475 download_mode=download_mode, -> 1476 ).get_module() 1477 except ( 1478 Exception [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 1030 sanitize_patterns(self.data_files) 1031 if self.data_files is not None -> 1032 else get_data_patterns(base_path, download_config=self.download_config) 1033 ) 1034 data_files = DataFilesDict.from_patterns( [/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in get_data_patterns(base_path, download_config) 457 return _get_data_files_patterns(resolver) 458 except FileNotFoundError: --> 459 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None 460 461 EmptyDatasetError: The directory at hf://datasets/albertvillanova/tmp-private@79b9e4fe79670a9a050d6ebc385464891915a71d doesn't contain any data files ``` ### Expected behavior The dataset should load. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3
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6,125
Reinforcement Learning and Robotics are not task categories in HF datasets metadata
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2023-08-05T23:59:42
2023-08-18T12:28:42
2023-08-18T12:28:42
NONE
null
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### Describe the bug In https://huggingface.co/models there are task categories for RL and robotics but none in https://huggingface.co/datasets Our lab is currently moving our datasets over to hugging face and would like to be able to add those 2 tags Moreover we see some older datasets that do have that tag, but we can't seem to add it ourselves. ### Steps to reproduce the bug 1. Create a new dataset on Hugging face 2. Try to type reinforcemement-learning or robotics into the tasks categories, it does not allow you to commit ### Expected behavior Expected to be able to add RL and robotics as task categories as some previous datasets have these tags ### Environment info N/A
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1,837,868,112
I_kwDODunzps5ti6RQ
6,124
Datasets crashing runs due to KeyError
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[ "i once had the same error and I could fix that by pushing a fake or a dummy commit on my hugging face dataset repo", "Hi! We need a reproducer to fix this. Can you provide a link to the dataset (if it's public)?", "> Hi! We need a reproducer to fix this. Can you provide a link to the dataset (if it's public)?\r\n\r\nHi Mario,\r\n\r\nUnfortunately, the dataset in question is currently private until the model is trained and released.\r\n\r\nThis is not happening with one dataset but numerous hosted private datasets.\r\n\r\nI am only loading the dataset and doing nothing else currently. It seems to happen completely sporadically.\r\n\r\nThank you,\r\n\r\nEnrico", "Hi,\r\n\r\nI have the same error in the dataset viewer with my dataset\r\nhttps://huggingface.co/datasets/elsaEU/ELSA10M_track1\r\n\r\nHas anyone solved this issue?\r\n\r\nEdit: After a dummy commit the error changed in ConfigNamesError", "@rs9000 The problem seems to be the (large) number of commits, as explained in https://huggingface.co/docs/hub/repositories-recommendations. This can be fixed by running:\r\n```python\r\nimport huggingface_hub\r\nhuggingface_hub.super_squash_history(repo_id=\"elsaEU/ELSA10M_track1\")\r\n``` \r\n\r\nThe issue stems from `push_to_hub` creating one commit per shard - https://github.com/huggingface/datasets/pull/6269 should fix this issue (will create one commit per 50 uploaded shards by default). The linked PR will be included in the next `datasets` release.\r\n\r\n\r\ncc @lhoestq @severo for visibility", "Thank you @mariosasko it works.", "#6269 has been merged, so I'm closing this issue" ]
2023-08-05T17:48:56
2023-11-30T16:28:57
2023-11-30T16:28:57
NONE
null
null
null
### Describe the bug Hi all, I have been running into a pretty persistent issue recently when trying to load datasets. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` I receive a KeyError which crashes the runs. ``` Traceback (most recent call last): main() train_dataset = load_dataset( ^^^^^^^^^^^^^ builder_instance = load_dataset_builder( ^^^^^^^^^^^^^^^^^^^^^ dataset_module = dataset_module_factory( ^^^^^^^^^^^^^^^^^^^^^^^ raise e1 from None ).get_module() ^^^^^^^^^^^^ else get_data_patterns(base_path, download_config=self.download_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ return _get_data_files_patterns(resolver) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ data_files = pattern_resolver(pattern) ^^^^^^^^^^^^^^^^^^^^^^^^^ fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ paths = [f for f in sorted(fs.glob(paths)) if not fs.isdir(f)] ^^^^^^^^^^^^^^ allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ for _, dirs, files in self.walk(path, maxdepth, detail=True, **kwargs): listing = self.ls(path, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ "last_modified": parse_datetime(tree_item["lastCommit"]["date"]), ~~~~~~~~~^^^^^^^^^^^^^^ KeyError: 'lastCommit' ``` Any help would be greatly appreciated. Thank you, Enrico ### Steps to reproduce the bug Load the dataset from the Huggingface hub. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` ### Expected behavior Loads the dataset. ### Environment info datasets-2.14.3 CUDA 11.8 Python 3.11
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1,837,789,294
I_kwDODunzps5tinBu
6,123
Inaccurate Bounding Boxes in "wildreceipt" Dataset
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[ "Hi! Thanks for the investigation, but we are not the authors of these datasets, so please report this on the Hub instead so that the actual authors can fix it." ]
2023-08-05T14:34:13
2023-08-17T14:25:27
2023-08-17T14:25:26
NONE
null
null
null
### Describe the bug I would like to bring to your attention an issue related to the accuracy of bounding boxes within the "wildreceipt" dataset, which is made available through the Hugging Face API. Specifically, I have identified a discrepancy between the bounding boxes generated by the dataset loading commands, namely `load_dataset("Theivaprakasham/wildreceipt")` and `load_dataset("jinhybr/WildReceipt")`, and the actual labels and corresponding bounding boxes present in the dataset. To illustrate this divergence, I've provided two examples in the form of screenshots. These screenshots highlight the contrasting outcomes between my personal implementation of the dataloader and the implementation offered by Hugging Face: **Example 1:** ![image](https://github.com/huggingface/datasets/assets/50714796/7a6604d2-899d-4102-a008-1a28c90698f1) ![image](https://github.com/huggingface/datasets/assets/50714796/eba458c7-d3af-4868-a520-8b683aa96f66) ![image](https://github.com/huggingface/datasets/assets/50714796/9f394891-5f5b-46f7-8e52-071b724aedab) **Example 2:** ![image](https://github.com/huggingface/datasets/assets/50714796/a2b2a8d3-124e-4990-b64a-5133cf4be2fe) ![image](https://github.com/huggingface/datasets/assets/50714796/6ee25642-35aa-40ad-ac1e-899d33be90df) ![image](https://github.com/huggingface/datasets/assets/50714796/5e42ff91-9fc4-4520-8803-0e225656f96c) It's important to note that my dataloader implementation is based on the same dataset files as utilized in the Hugging Face implementation. For your reference, you can access the dataset files through this link: [wildreceipt dataset files](https://download.openmmlab.com/mmocr/data/wildreceipt.tar). This inconsistency in bounding box accuracy warrants investigation and rectification for maintaining the integrity of the "wildreceipt" dataset. Your attention and assistance in addressing this matter would be greatly appreciated. ### Steps to reproduce the bug ```python import matplotlib.pyplot as plt from datasets import load_dataset # Define functions to convert bounding box formats def convert_format1(box): x, y, w, h = box x2, y2 = x + w, y + h return [x, y, x2, y2] def convert_format2(box): x1, y1, x2, y2 = box return [x1, y1, x2, y2] def plot_cropped_image(image, box, title): cropped_image = image.crop(box) plt.imshow(cropped_image) plt.title(title) plt.axis('off') plt.savefig(title+'.png') plt.show() doc_index = 1 word_index = 3 dataset = load_dataset("Theivaprakasham/wildreceipt")['train'] bbox_hugging_face = dataset[doc_index]['bboxes'][word_index] text_unit_face = dataset[doc_index]['words'][word_index] common_box_hugface_1 = convert_format1(bbox_hugging_face) common_box_hugface_2 = convert_format2(bbox_hugging_face) plot_cropped_image(image_hugging, common_box_hugface_1, f'Hugging Face Bouding boxes (x,y,w,h format) \n its associated text unit: {text_unit_face}') plot_cropped_image(image_hugging, common_box_hugface_2, f'Hugging Face Bouding boxes (x1,y1,x2, y2 format) \n its associated text unit: {text_unit_face}') ``` ### Expected behavior The bounding boxes generated by the "wildreceipt" dataset in HuggingFace implementation loading commands should accurately match the actual labels and bounding boxes of the dataset. ### Environment info - Python version: 3.8 - Hugging Face datasets version: 2.14.2 - Dataset file taken from this link: https://download.openmmlab.com/mmocr/data/wildreceipt.tar
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I_kwDODunzps5tg4Sp
6,122
Upload README via `push_to_hub`
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[ "You can use `huggingface_hub`'s [Card API](https://huggingface.co/docs/huggingface_hub/package_reference/cards) to programmatically push a dataset card to the Hub." ]
2023-08-04T21:00:27
2023-08-21T18:18:54
2023-08-21T18:18:54
NONE
null
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### Feature request `push_to_hub` now allows users to upload datasets programmatically. However, based on the latest doc, we still need to open the dataset page to add readme file manually. However, I do discover snippets to intialize a README for every `push_to_hub`: ``` dataset_card = ( DatasetCard( "---\n" + str(dataset_card_data) + "\n---\n" + f'# Dataset Card for "{repo_id.split("/")[-1]}"\n\n[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)' ) if dataset_card is None else dataset_card ) HfApi(endpoint=config.HF_ENDPOINT).upload_file( path_or_fileobj=str(dataset_card).encode(), path_in_repo="README.md", repo_id=repo_id, token=token, repo_type="dataset", revision=branch, ) ``` So, if we can enable `push_to_hub` to upload a readme file by ourselves instead of using the auto generated ones, it can save ton of time, and will definitely alleviate the current "lack-of-dataset-card" situation. ### Motivation as elabrated above. ### Your contribution I might be able to make a pr.
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6,120
Lookahead streaming support?
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[ "In which format is your dataset? We could expose the `pre_buffer` flag for Parquet to use PyArrow's background thread pool to speed up loading. " ]
2023-08-04T04:01:52
2023-08-17T17:48:42
null
NONE
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### Feature request From what I understand, streaming dataset currently pulls the data, and process the data as it is requested. This can introduce significant latency delays when data is loaded into the training process, needing to wait for each segment. While the delays might be dataset specific (or even mapping instruction/tokenizer specific) Is it possible to introduce a `streaming_lookahead` parameter, which is used for predictable workloads (even shuffled dataset with fixed seed). As we can predict in advance what the next few datasamples will be. And fetch them while the current set is being trained. With enough CPU & bandwidth to keep up with the training process, and a sufficiently large lookahead, this will reduce the various latency involved while waiting for the dataset to be ready between batches. ### Motivation Faster streaming performance, while training over extra large TB sized datasets ### Your contribution I currently use HF dataset, with pytorch lightning trainer for RWKV project, and would be able to help test this feature if supported.
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6,118
IterableDataset.from_generator() fails with pickle error when provided a generator or iterator
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[ "Hi! `IterableDataset.from_generator` expects a generator function, not the object (to be consistent with `Dataset.from_generator`).\r\n\r\nYou can fix the above snippet as follows:\r\n```python\r\ntrain_dataset = IterableDataset.from_generator(line_generator, fn_kwargs={\"files\": model_training_files})\r\n```", "to anyone reaching this issue, the argument is `gen_kwargs`:\r\n```py\r\ntrain_dataset = IterableDataset.from_generator(line_generator, gen_kwargs={\"files\": model_training_files})\r\n```" ]
2023-08-04T01:45:04
2023-12-04T09:28:50
null
NONE
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### Describe the bug **Description** Providing a generator in an instantiation of IterableDataset.from_generator() fails with `TypeError: cannot pickle 'generator' object` when the generator argument is supplied with a generator. **Code example** ``` def line_generator(files: List[Path]): if isinstance(files, str): files = [Path(files)] for file in files: if isinstance(file, str): file = Path(file) yield from open(file,'r').readlines() ... model_training_files = ['file1.txt', 'file2.txt', 'file3.txt'] train_dataset = IterableDataset.from_generator(generator=line_generator(model_training_files)) ``` **Traceback** Traceback (most recent call last): File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/contextlib.py", line 135, in __exit__ self.gen.throw(type, value, traceback) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 691, in _no_cache_fields yield File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 701, in dumps dump(obj, file) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 676, in dump Pickler(file, recurse=True).dump(obj) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 394, in dump StockPickler.dump(self, obj) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 487, in dump self.save(obj) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save StockPickler.save(self, obj, save_persistent_id) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 1186, in save_module_dict StockPickler.save_dict(pickler, obj) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 971, in save_dict self._batch_setitems(obj.items()) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 997, in _batch_setitems save(v) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save StockPickler.save(self, obj, save_persistent_id) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 578, in save rv = reduce(self.proto) TypeError: cannot pickle 'generator' object ### Steps to reproduce the bug 1. Create a set of text files to iterate over. 2. Create a generator that returns the lines in each file until all files are exhausted. 3. Instantiate the dataset over the generator by instantiating an IterableDataset.from_generator(). 4. Wait for the explosion. ### Expected behavior I would expect that since the function claims to accept a generator that there would be no crash. Instead, I would expect the dataset to return all the lines in the files as queued up in the `line_generator()` function. ### Environment info datasets.__version__ == '2.13.1' Python 3.9.6 Platform: Darwin WE35261 22.5.0 Darwin Kernel Version 22.5.0: Thu Jun 8 22:22:22 PDT 2023; root:xnu-8796.121.3~7/RELEASE_X86_64 x86_64
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1,835,098,484
I_kwDODunzps5tYWF0
6,116
[Docs] The "Process" how-to guide lacks description of `select_columns` function
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[ "Great idea, feel free to open a PR! :)" ]
2023-08-03T13:45:10
2023-08-16T10:02:53
2023-08-16T10:02:53
CONTRIBUTOR
null
null
null
### Feature request The [how to process dataset guide](https://huggingface.co/docs/datasets/main/en/process) currently does not mention the [`select_columns`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.select_columns) function. It would be nice to include it in the guide. ### Motivation This function is a commonly requested feature (see this [forum thread](https://discuss.huggingface.co/t/how-to-create-a-new-dataset-from-another-dataset-and-select-specific-columns-and-the-data-along-with-the-column/15120) and #5468 #5474). However, it has not been included in the guide since its implementation by PR #5480. Mentioning it in the guide would help future users discover this added feature. ### Your contribution I could submit a PR to add a brief description of the function to said guide.
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I_kwDODunzps5tUNtg
6,114
Cache not being used when loading commonvoice 8.0.0
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[ "You can avoid this by using the `revision` parameter in `load_dataset` to always force downloading a specific commit (if not specified it defaults to HEAD, hence the redownload).", "Thanks @mariosasko this works well, looks like I should have read the documentation a bit more carefully. \r\n\r\nIt is still a bit confusing which hash I should provide: passing `revision = c8fd66e85f086e3abb11eeee55b1737a3d1e8487` from https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/commits/main caused the cached version at `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a` to be loaded, so I had to know that it was the previous commit unless I've missed something else." ]
2023-08-02T23:18:11
2023-08-18T23:59:00
2023-08-18T23:59:00
NONE
null
null
null
### Describe the bug I have commonvoice 8.0.0 downloaded in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. The folder contains all the arrow files etc, and was used as the cached version last time I touched the ec2 instance I'm working on. Now, with the same command that downloaded it initially: ``` dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>") ``` it tries to redownload the dataset to `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/05bdc7940b0a336ceeaeef13470c89522c29a8e4494cbeece64fb472a87acb32` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` 2. dataset is updated by maintainers 3. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` ### Expected behavior I expect that it uses the already downloaded data in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. Not sure what's happening in 2. but if, say it's an issue with the dataset referenced by "mozilla-foundation/common_voice_8_0" being modified by the maintainers, how would I force datasets to point to the original version I downloaded? EDIT: It was indeed that the maintainers had updated the dataset (v 8.0.0). However I still cant load the dataset from disk instead of redownloading, with for example: ``` load_dataset(".cache/huggingface/datasets/downloads/extracted/<hash>/cv-corpus-8.0-2022-01-19/en/", "en") > ... > File [~/miniconda3/envs/aa_torch2/lib/python3.10/site-packages/datasets/table.py:1938](.../ python3.10/site-packages/datasets/table.py:1938), in cast_array_to_feature(array, feature, allow_number_to_str) 1937 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 1938 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ... 1794 e = e.__context__ -> 1795 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1797 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info datasets==2.7.0 python==3.10.8 OS: AWS Linux
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6,113
load_dataset() fails with streamlit caching inside docker
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[ "Hi! This should be fixed in the latest (patch) release (run `pip install -U datasets` to install it). This behavior was due to a bug in our authentication logic." ]
2023-08-02T20:20:26
2023-08-21T18:18:27
2023-08-21T18:18:27
NONE
null
null
null
### Describe the bug When calling `load_dataset` in a streamlit application running within a docker container, get a failure with the error message: EmptyDatasetError: The directory at hf://datasets/fetch-rewards/inc-rings-2000@bea27cf60842b3641eae418f38864a2ec4cde684 doesn't contain any data files Traceback: File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script exec(code, module.__dict__) File "/home/user/app/app.py", line 62, in <module> dashboard() File "/home/user/app/app.py", line 47, in dashboard feat_dict, path_gml = load_data(hf_repo, model_gml_dict[selected_model], hf_token) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 211, in wrapper return cached_func(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 240, in __call__ return self._get_or_create_cached_value(args, kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 266, in _get_or_create_cached_value return self._handle_cache_miss(cache, value_key, func_args, func_kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 320, in _handle_cache_miss computed_value = self._info.func(*func_args, **func_kwargs) File "/home/user/app/hf_interface.py", line 16, in load_data hf_dataset = load_dataset(repo_id, use_auth_token=hf_token) File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2109, in load_dataset builder_instance = load_dataset_builder( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1795, in load_dataset_builder dataset_module = dataset_module_factory( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1486, in dataset_module_factory raise e1 from None File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1476, in dataset_module_factory ).get_module() File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1032, in get_module else get_data_patterns(base_path, download_config=self.download_config) File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 458, in get_data_patterns raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None ### Steps to reproduce the bug ```python @st.cache_resource def load_data(repo_id: str, hf_token=None): """Load data from HuggingFace Hub """ hf_dataset = load_dataset(repo_id, use_auth_token=hf_token) hf_dataset = hf_dataset.map(lambda x: json.loads(x["ground_truth"]), remove_columns=["ground_truth"]) return hf_dataset ``` ### Expected behavior Expect to load. Note: works fine with datasets==2.13.1 ### Environment info datasets==2.14.2, Ubuntu bionic-based Docker container.
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I_kwDODunzps5tS_Bz
6,112
yaml error using push_to_hub with generated README.md
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null
[ "Thanks for reporting! This is a bug in converting the `ArrayXD` types to YAML. It will be fixed soon." ]
2023-08-02T18:21:21
2023-12-12T15:00:44
2023-12-12T15:00:44
NONE
null
null
null
### Describe the bug When I construct a dataset with the following features: ``` features = Features( { "pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)), "input_ids": Sequence(feature=Value(dtype="int64")), "attention_mask": Sequence(Value(dtype="int64")), "tokens": Sequence(Value(dtype="string")), "bbox": Array2D(dtype="int64", shape=(512, 4)), } ) ``` and run `push_to_hub`, the individual `*.parquet` files are pushed, but when trying to upload the auto-generated README, I run into the following error: ``` Traceback (most recent call last): File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 261, in hf_raise_for_status response.raise_for_status() File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/requests/models.py", line 1021, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/looppayments/multitask_document_classification_dataset/commit/main The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 297, in <module> build_dataset() File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 290, in build_dataset push_to_hub(dataset, "multitask_document_classification_dataset") File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 135, in push_to_hub dataset.push_to_hub(f"looppayments/{dataset_name}", private=True) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5577, in push_to_hub HfApi(endpoint=config.HF_ENDPOINT).upload_file( File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, *args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file commit_info = self.create_commit( File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, *args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2728, in create_commit hf_raise_for_status(commit_resp, endpoint_name="commit") File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 299, in hf_raise_for_status raise BadRequestError(message, response=response) from e huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-64ca9c3d-2d2bbef354e102482a9a168e;bc00371c-8549-4859-9f41-43ff140ad36e) Bad request for commit endpoint: Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple> (10:9) 7 | - 3 8 | - 224 9 | - 224 10 | dtype: float64 --------------^ 11 | - name: input_ids 12 | sequence: int64 ``` My guess is that the auto-generated yaml is unable to be parsed for some reason. ### Steps to reproduce the bug The description contains most of what's needed to reproduce the issue, but I've added a shortened code snippet: ``` from datasets import Array2D, Array3D, ClassLabel, Dataset, Features, Sequence, Value from PIL import Image from transformers import AutoProcessor features = Features( { "pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)), "input_ids": Sequence(feature=Value(dtype="int64")), "attention_mask": Sequence(Value(dtype="int64")), "tokens": Sequence(Value(dtype="string")), "bbox": Array2D(dtype="int64", shape=(512, 4)), } ) processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) def preprocess_dataset(rows): # Get images images = [ Image.open(png_filename).convert("RGB") for png_filename in rows["png_filename"] ] encoding = processor( images, rows["tokens"], boxes=rows["bbox"], truncation=True, padding="max_length", ) encoding["tokens"] = rows["tokens"] return encoding dataset = dataset.map( preprocess_dataset, batched=True, batch_size=5, features=features, ) ``` ### Expected behavior Using datasets==2.11.0, I'm able to succesfully push_to_hub, no issues, but with datasets==2.14.2, I run into the above error. ### Environment info - `datasets` version: 2.14.2 - Platform: macOS-12.5-arm64-arm-64bit - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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raise FileNotFoundError("Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." )
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[ "any idea?", "This should work: `load_dataset(\"path/to/downloaded_repo\")`\r\n\r\n`load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`", "> This should work: `load_dataset(\"path/to/downloaded_repo\")`\r\n> \r\n> `load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`\r\n\r\nThanks for your help. This works." ]
2023-08-02T09:17:29
2023-08-29T02:00:28
2023-08-29T02:00:28
NONE
null
null
null
### Describe the bug For researchers in some countries or regions, it is usually the case that the download ability of `load_dataset` is disabled due to the complex network environment. People in these regions often prefer to use git clone or other programming tricks to manually download the files to the disk (for example, [How to elegantly download hf models, zhihu zhuanlan](https://zhuanlan.zhihu.com/p/475260268) proposed a crawlder based solution, and [Is there any mirror for hf_hub, zhihu answer](https://www.zhihu.com/question/371644077) provided some cloud based solutions, and [How to avoid pitfalls on Hugging face downloading, zhihu zhuanlan] gave some useful suggestions), and then use `load_from_disk` to get the dataset object. However, when one finally has the local files on the disk, it is still buggy when trying to load the files into objects. ### Steps to reproduce the bug Steps to reproduce the bug: 1. Found CIFAR dataset in hugging face: https://huggingface.co/datasets/cifar100/tree/main 2. Click ":" button to show "Clone repository" option, and then follow the prompts on the box: ```bash cd my_directory_absolute git lfs install git clone https://huggingface.co/datasets/cifar100 ls my_directory_absolute/cifar100 # confirm that the directory exists and it is OK. ``` 3. Write A python file to try to load the dataset ```python from datasets import load_dataset, load_from_disk dataset = load_from_disk("my_directory_absolute/cifar100") ``` Notice that according to issue #3700 , it is wrong to use load_dataset("my_directory_absolute/cifar100"), so we must use load_from_disk instead. 4. Then you will see the error reported: ```log --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[5], line 9 1 from datasets import load_dataset, load_from_disk ----> 9 dataset = load_from_disk("my_directory_absolute/cifar100") File [~/miniconda3/envs/ai/lib/python3.10/site-packages/datasets/load.py:2232), in load_from_disk(dataset_path, fs, keep_in_memory, storage_options) 2230 return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) 2231 else: -> 2232 raise FileNotFoundError( 2233 f"Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." 2234 ) FileNotFoundError: Directory my_directory_absolute/cifar100 is neither a `Dataset` directory nor a `DatasetDict` directory. ``` ### Expected behavior The dataset should be load successfully. ### Environment info ```bash datasets-cli env ``` -> results: ```txt Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.2 - Platform: Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ```
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[BUG] Dataset initialized from in-memory data does not create cache.
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[ "This is expected behavior. You must provide `cache_file_name` when performing `.map` on an in-memory dataset for the result to be cached." ]
2023-08-01T11:58:58
2023-08-17T14:03:01
2023-08-17T14:03:00
NONE
null
null
null
### Describe the bug `Dataset` initialized from in-memory data (dictionary in my case, haven't tested with other types) does not create cache when processed with the `map` method, unlike `Dataset` initialized by other methods such as `load_dataset`. ### Steps to reproduce the bug ```python # below code was run the second time so the map function can be loaded from cache if exists from datasets import load_dataset, Dataset dataset = load_dataset("tatsu-lab/alpaca")['train'] dataset = dataset.map(lambda x: {'input': x['input'] + 'hi'}) # some random map print(len(dataset.cache_files)) # 1 # copy the exact same data but initialize from a dictionary memory_dataset = Dataset.from_dict({ 'instruction': dataset['instruction'], 'input': dataset['input'], 'output': dataset['output'], 'text': dataset['text']}) memory_dataset = memory_dataset.map(lambda x: {'input': x['input'] + 'hi'}) # exact same map print(len(memory_dataset.cache_files)) # Map: 100%|██████████| 52002[/52002] # 0 ``` ### Expected behavior The `map` function should create cache regardless of the method the `Dataset` was created. ### Environment info - `datasets` version: 2.14.2 - Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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Problems in downloading Amazon reviews from HF
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[ "Thanks for reporting, @610v4nn1.\r\n\r\nIndeed, the source data files are no longer available. We have contacted the authors of the dataset and they report that Amazon has decided to stop distributing the multilingual reviews dataset.\r\n\r\nWe are adding a notification about this issue to the dataset card.\r\n\r\nSee: https://huggingface.co/datasets/amazon_reviews_multi/discussions/4#64c3898db63057f1fd3ce1a0 " ]
2023-08-01T08:38:29
2023-08-02T07:12:07
2023-08-02T07:12:07
NONE
null
null
null
### Describe the bug I have a script downloading `amazon_reviews_multi`. When the download starts, I get ``` Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 1.43MB/s] Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.54s/it] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 842.40it/s] Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 928kB/s] Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.42s/it] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 832.70it/s] Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 1.81MB/s] Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.40s/it] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 1294.14it/s] Generating train split: 0%| | 0/200000 [00:00<?, ? examples/s] ``` the file is clearly too small to contain the requested dataset, in fact it contains en error message: ``` <?xml version="1.0" encoding="UTF-8"?> <Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>AGJWSY3ZADT2QVWE</RequestId><HostId>Gx1O2KXnxtQFqvzDLxyVSTq3+TTJuTnuVFnJL3SP89Yp8UzvYLPTVwd1PpniE4EvQzT3tCaqEJw=</HostId></Error> ``` obviously the script fails: ``` > raise DatasetGenerationError("An error occurred while generating the dataset") from e E datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug 1. load_dataset("amazon_reviews_multi", name="en", split="train", cache_dir="ADDYOURPATHHERE") ### Expected behavior I would expect the dataset to be downloaded and processed ### Environment info * The problem is present with both datasets 2.12.0 and 2.14.2 * python version 3.10.12
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Loading local datasets got strangely stuck
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[ "Yesterday I waited for more than 12 hours to make sure it was really **stuck** instead of proceeding too slow.", "I've had similar weird issues with `load_dataset` as well. Not multiple files, but dataset is quite big, about 50G.", "We use a generic multiprocessing code, so there is little we can do about this - unfortunately, turning off multiprocessing seems to be the only solution. Multithreading would make our code easier to maintain and (most likely) avoid issues such as this one, but we cannot use it until the GIL is dropped (no-GIL Python should be released in 2024, so we can start exploring this then)", "The problem seems to be the `Generating train split`. Is it possible to avoid that? I have a dataset saved, just want to load it but somehow running into issues with that again.", "Hey guys, recently I ran into this problem again and I spent one whole day trying to locate the problem. Finally I found the problem seems to be with `pyarrow`'s json parser, and it seems a long-existing problem. Similar issue can be found in #2181. Anyway, my solution is to adjust the `load_dataset`'s parameter `chunksize`. You can inspect the parameter set in `datasets/packaged_modules/json/json.py`, now the actual chunksize should be very small, and you can increase the value. For me, `chunksize=10<<23` could solve the stuck problem. But I also find that too big `chunksize`, like `10 << 30`, would also cause a stuck, which is rather weird. I think I may explore this when I am free. And hope this can help those who also encounter the same problem. ", "Experiencing the same issue with the `kaist-ai/Feedback-Collection` dataset, which is comparatively small i.e. 100k rows.\r\nCode to reproduce\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"kaist-ai/Feedback-Collection\")\r\n```\r\n\r\nI have tried setting `num_proc=1` as well as `chunksize=1024, 64` but problem persists. Any pointers?" ]
2023-08-01T02:28:06
2024-02-05T08:55:16
null
NONE
null
null
null
### Describe the bug I try to use `load_dataset()` to load several local `.jsonl` files as a dataset. Every line of these files is a json structure only containing one key `text` (yeah it is a dataset for NLP model). The code snippet is as: ```python ds = load_dataset("json", data_files=LIST_OF_FILE_PATHS, num_proc=16)['train'] ``` However, I found that the loading process can get stuck -- the progress bar `Generating train split` no more proceed. When I was trying to find the cause and solution, I found a really strange behavior. If I load the dataset in this way: ```python dlist = list() for _ in LIST_OF_FILE_PATHS: dlist.append(load_dataset("json", data_files=_)['train']) ds = concatenate_datasets(dlist) ``` I can actually successfully load all the files despite its slow speed. But if I load them in batch like above, things go wrong. I did try to use Control-C to trace the stuck point but the program cannot be terminated in this way when `num_proc` is set to `None`. The only thing I can do is use Control-Z to hang it up then kill it. If I use more than 2 cpus, a Control-C would simply cause the following error: ```bash ^C Process ForkPoolWorker-1: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 314, in _bootstrap self.run() File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 114, in worker task = get() File "/usr/local/lib/python3.10/dist-packages/multiprocess/queues.py", line 368, in get res = self._reader.recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 224, in recv_bytes buf = self._recv_bytes(maxlength) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt Generating train split: 92431 examples [01:23, 1104.25 examples/s] Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1373, in iflatmap_unordered yield queue.get(timeout=0.05) File "<string>", line 2, in get File "/usr/local/lib/python3.10/dist-packages/multiprocess/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 258, in recv buf = self._recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/data/liyongyuan/source/batch_load.py", line 11, in <module> a = load_dataset( File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1842, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 770, in get raise TimeoutError multiprocess.context.TimeoutError ``` I have validated the basic correctness of these `.jsonl` files. They are correctly formatted (or they cannot be loaded singly by `load_dataset`) though some of the json may contain too long text (more than 1e7 characters). I do not know if this could be the problem. And there should not be any bottleneck in system's resource. The whole dataset is ~300GB, and I am using a cloud server with plenty of storage and 1TB ram. Thanks for your efforts and patience! Any suggestion or help would be appreciated. ### Steps to reproduce the bug 1. use load_dataset() with `data_files = LIST_OF_FILES` ### Expected behavior All the files should be smoothly loaded. ### Environment info - Datasets: A private dataset. ~2500 `.jsonl` files. ~300GB in total. Each json structure only contains one key: `text`. Format checked. - `datasets` version: 2.14.2 - Platform: Linux-4.19.91-014.kangaroo.alios7.x86_64-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 10.0.1.dev0+ga6eabc2b.d20230609 - Pandas version: 1.5.2
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1,829,131,223
I_kwDODunzps5tBlPX
6,106
load local json_file as dataset
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[ "Hi! We use PyArrow to read JSON files, and PyArrow doesn't allow different value types in the same column. #5776 should address this.\r\n\r\nIn the meantime, you can combine `Dataset.from_generator` with the above code to cast the values to the same type. ", "Thanks for your help!" ]
2023-07-31T12:53:49
2023-08-18T01:46:35
2023-08-18T01:46:35
NONE
null
null
null
### Describe the bug I tried to load local json file as dataset but failed to parsing json file because some columns are 'float' type. ### Steps to reproduce the bug 1. load json file with certain columns are 'float' type. For example `data = load_data("json", data_files=JSON_PATH)` 2. Then, the error will be triggered like `ArrowInvalid: Could not convert '-0.2253' with type str: tried to convert to double ### Expected behavior Should allow some columns are 'float' type, at least it should convert those columns to str type. I tried to avoid the error by naively convert the float item to str: ```python # if col type is not str, we need to convert it to str mapping = {} for col in keys: if isinstance(dataset[0][col], str): mapping[col] = [row.get(col) for row in dataset] else: mapping[col] = [str(row.get(col)) for row in dataset] ``` ### Environment info - `datasets` version: 2.14.2 - Platform: Linux-5.4.0-52-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.0 - Pandas version: 2.0.1
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1,828,959,107
I_kwDODunzps5tA7OD
6,104
HF Datasets data access is extremely slow even when in memory
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[ "Possibly related:\r\n- https://github.com/pytorch/pytorch/issues/22462" ]
2023-07-31T11:12:19
2023-08-01T11:22:43
null
CONTRIBUTOR
null
null
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### Describe the bug Doing a simple `some_dataset[:10]` can take more than a minute. Profiling it: <img width="1280" alt="image" src="https://github.com/huggingface/datasets/assets/36224762/e641fb95-ff02-4072-9016-5416a65f75ab"> `some_dataset` is completely in memory with no disk cache. This is proving fatal to my usage of HF Datasets. Is there a way I can forgo the arrow format and store the dataset as PyTorch tensors so that `_tensorize` is not needed? And is `_consolidate` supposed to take this long? It's faster to produce the dataset from scratch than to access it from HF Datasets! ### Steps to reproduce the bug I have uploaded the dataset that causes this problem [here](https://huggingface.co/datasets/NightMachinery/hf_datasets_bug1). ```python #!/usr/bin/env python3 import sys import time import torch from datasets import load_dataset def main(dataset_name): # Start the timer start_time = time.time() # Load the dataset from Hugging Face Hub dataset = load_dataset(dataset_name) # Set the dataset format as torch dataset.set_format(type="torch") # Perform an identity map dataset = dataset.map(lambda example: example, batched=True, batch_size=20) # End the timer end_time = time.time() # Print the time taken print(f"Time taken: {end_time - start_time:.2f} seconds") if __name__ == "__main__": dataset_name = "NightMachinery/hf_datasets_bug1" print(f"dataset_name: {dataset_name}") main(dataset_name) ``` ### Expected behavior _ ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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1,828,118,930
I_kwDODunzps5s9uGS
6,100
TypeError when loading from GCP bucket
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null
[ "Thanks for reporting, @bilelomrani1.\r\n\r\nWe are fixing it. ", "We have fixed it. We are planning to do a patch release today." ]
2023-07-30T23:03:00
2023-08-03T10:00:48
2023-08-01T10:38:55
NONE
null
null
null
### Describe the bug Loading a dataset from a GCP bucket raises a type error. This bug was introduced recently (either in 2.14 or 2.14.1), and appeared during a migration from 2.13.1. ### Steps to reproduce the bug Load any file from a GCP bucket: ```python import datasets datasets.load_dataset("json", data_files=["gs://..."]) ``` The following exception is raised: ```python Traceback (most recent call last): ... packages/datasets/data_files.py", line 335, in resolve_pattern protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else "" TypeError: can only concatenate tuple (not "str") to tuple ``` With a `GoogleFileSystem`, the attribute `fs.protocol` is a tuple `('gs', 'gcs')` and hence cannot be concatenated with a string. ### Expected behavior The file should be loaded without exception. ### Environment info - `datasets` version: 2.14.1 - Platform: macOS-13.2.1-x86_64-i386-64bit - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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1,827,893,576
I_kwDODunzps5s83FI
6,099
How do i get "amazon_us_reviews
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[ "Seems like the problem isn't with the library, but the dataset itself hosted on AWS S3.\r\n\r\nIts [homepage](https://s3.amazonaws.com/amazon-reviews-pds/readme.html) returns an `AccessDenied` XML response, which is the same thing you get if you try to log the `record` that triggers the exception\r\n\r\n```python\r\ntry:\r\n example = self.info.features.encode_example(record) if self.info.features is not None else record\r\nexcept Exception as e:\r\n print(record)\r\n```\r\n\r\n⬇️\r\n\r\n```\r\n{'<?xml version=\"1.0\" encoding=\"UTF-8\"?>': '<Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>N2HFJ82ZV8SZW9BV</RequestId><HostId>Zw2DQ0V2GdRmvH5qWEpumK4uj5+W8YPcilQbN9fLBr3VqQOcKPHOhUZLG3LcM9X5fkOetxp48Os=</HostId></Error>'}\r\n```", "I'm getting same errors when loading this dataset", "I have figured it out. there was an option of **parquet formated files** i downloaded some from there. ", "this dataset is unfortunately no longer public", "Thanks for reporting, @IqraBaluch.\r\n\r\nWe contacted the authors and unfortunately they reported that Amazon has decided to stop distributing this dataset.", "If anyone still needs this dataset, you could find it on kaggle here : https://www.kaggle.com/datasets/cynthiarempel/amazon-us-customer-reviews-dataset", "Thanks @Maryam-Mostafa ", "@albertvillanova don't tell 'em, we have figured it out. XD", "I noticed that some book data is missing, we can only get Books_v1_02 data. \r\nIs there any way we can get the Books_v1_00 and Books_v1_01? \r\nReally appreciate !!!", "@albertvillanova will this dataset be retired given the data are no longer hosted on S3? What is done in cases such as these?" ]
2023-07-30T11:02:17
2023-08-21T05:08:08
2023-08-10T05:02:35
NONE
null
null
null
### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data
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6,097
Dataset.get_nearest_examples does not return all feature values for the k most similar datapoints - side effect of Dataset.set_format
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[ "Actually, my bad -- specifying\r\n```python\r\nfoo.set_format('numpy', ['vectors'], output_all_columns=True)\r\n```\r\nfixes it." ]
2023-07-28T20:31:59
2023-07-28T20:49:58
2023-07-28T20:49:58
NONE
null
null
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### Describe the bug Hi team! I observe that there seems to be a side effect of `Dataset.set_format`: after setting a format and creating a FAISS index, the method `get_nearest_examples` from the `Dataset` class, fails to retrieve anything else but the embeddings themselves - not super useful. This is not the case if not using the `set_format` method: you can also retrieve any other feature value, such as an index/id/etc. Are you able to reproduce what I observe? ### Steps to reproduce the bug ```python from datasets import Dataset import numpy as np foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]} foo = Dataset.from_dict(foo) foo.set_format('numpy', ['vectors']) foo.add_faiss_index('vectors') new_vector = np.random.random(1024) scores, res = foo.get_nearest_examples('vectors', new_vector, k=3) ``` This will return, for the resulting most similar vectors to `new_vector` - in particular it will not return the `ids` feature: ``` {'vectors': array([[random values ...]])} ``` ### Expected behavior The expected behavior happens when the `set_format` method is not called: ```python from datasets import Dataset import numpy as np foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]} foo = Dataset.from_dict(foo) # foo.set_format('numpy', ['vectors']) foo.add_faiss_index('vectors') new_vector = np.random.random(1024) scores, res = foo.get_nearest_examples('vectors', new_vector, k=3) ``` This *will* return the `ids` of the similar vectors - with unfortunately a list of lists in lieu of the array I think for caching reasons - read it elsewhere ``` {'vectors': [[random values on multiple lines...]], 'ids': ['x', 'y', 'z']} ``` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.4.0-155-generic-x86_64-with-glibc2.31 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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I_kwDODunzps5s1H1T
6,090
FilesIterable skips all the files after a hidden file
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[ "Thanks for reporting. We've merged a PR with a fix." ]
2023-07-28T07:25:57
2023-07-28T10:51:14
2023-07-28T10:50:11
NONE
null
null
null
### Describe the bug When initializing `FilesIterable` with a list of file paths using `FilesIterable.from_paths`, it will discard all the files after a hidden file. The problem is in [this line](https://github.com/huggingface/datasets/blob/88896a7b28610ace95e444b94f9a4bc332cc1ee3/src/datasets/download/download_manager.py#L233C26-L233C26) where `return` should be replaced by `continue`. ### Steps to reproduce the bug https://colab.research.google.com/drive/1SQlxs4y_LSo1Q89KnFoYDSyyKEISun_J#scrollTo=93K4_blkW-8- ### Expected behavior The script should print all the files except the hidden one. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3
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6,089
AssertionError: daemonic processes are not allowed to have children
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[ "We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).", "> We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).\r\n\r\nGreat! Download takes more time than extract, multiple threads can download in parallel, which can speed up a lot." ]
2023-07-28T06:04:00
2023-07-31T02:34:02
null
NONE
null
null
null
### Describe the bug When I load_dataset with num_proc > 0 in a deamon process, I got an error: ```python File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 564, in download_and_extract return self.extract(self.download(url_or_urls)) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 427, in download downloaded_path_or_paths = map_nested( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 468, in map_nested mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/experimental.py", line 40, in _inner_fn return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 34, in parallel_map return _map_with_multiprocessing_pool( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 64, in _map_with_multiprocessing_pool with Pool(num_proc, initargs=initargs, initializer=initializer) as pool: ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/context.py", line 119, in Pool return Pool(processes, initializer, initargs, maxtasksperchild, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 215, in __init__ self._repopulate_pool() ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 306, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 329, in _repopulate_pool_static w.start() File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/process.py", line 118, in start assert not _current_process._config.get('daemon'), ^^^^^^^^^^^^^^^^^ AssertionError: daemonic processes are not allowed to have children ``` The download is io-intensive computing, may be datasets can replece the multi processing pool by a multi threading pool if in a deamon process. ### Steps to reproduce the bug 1. start a deamon process 2. run load_dataset with num_proc > 0 ### Expected behavior No error. ### Environment info Python 3.11.4 datasets latest master
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1,825,665,235
I_kwDODunzps5s0XDT
6,088
Loading local data files initiates web requests
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2023-07-28T04:06:26
2023-07-28T05:02:22
2023-07-28T05:02:22
NONE
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As documented in the [official docs](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/loading_methods#datasets.load_dataset.example-2), I tried to load datasets from local files by ```python # Load a JSON file from datasets import load_dataset ds = load_dataset('json', data_files='path/to/local/my_dataset.json') ``` But this failed on a web request because I'm executing the script on a machine without Internet access. Stacktrace shows ``` in PackagedDatasetModuleFactory.__init__(self, name, data_dir, data_files, download_config, download_mode) 940 self.download_config = download_config 941 self.download_mode = download_mode --> 942 increase_load_count(name, resource_type="dataset") ``` I've read from the source code that this can be fixed by setting environment variable to run in offline mode. I'm just wondering that is this an expected behaviour that even loading a LOCAL JSON file requires Internet access by default? And what's the point of requesting to `increase_load_count` on some server when loading just LOCAL data files?
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6,087
fsspec dependency is set too low
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[ "Thanks for reporting! A PR with a fix has just been merged." ]
2023-07-27T20:08:22
2023-07-28T10:07:56
2023-07-28T10:07:03
NONE
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### Describe the bug fsspec.callbacks.TqdmCallback (used in https://github.com/huggingface/datasets/blob/73bed12ecda17d1573fd3bf73ed5db24d3622f86/src/datasets/utils/file_utils.py#L338) was first released in fsspec [2022.3.0](https://github.com/fsspec/filesystem_spec/releases/tag/2022.3.0, commit where it was added: https://github.com/fsspec/filesystem_spec/commit/9577c8a482eb0a69092913b81580942a68d66a76#diff-906155c7e926a9ff58b9f23369bb513b09b445f5b0f41fa2a84015d0b471c68cR180), however the dependency is set to 2021.11.1 https://github.com/huggingface/datasets/blob/main/setup.py#L129 ### Steps to reproduce the bug 1. Install fsspec==2021.11.1 2. Install latest datasets==2.14.1 3. Import datasets, import fails due to lack of `fsspec.callbacks.TqdmCallback` ### Expected behavior No import issue ### Environment info N/A
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I_kwDODunzps5sx250
6,086
Support `fsspec` in `Dataset.to_<format>` methods
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[ "Hi @mariosasko unless someone's already working on it, I guess I can tackle it!", "Hi! Sure, feel free to tackle this.", "#self-assign", "I'm assuming this should just cover `to_csv`, `to_parquet`, and `to_json`, right? As `to_list` and `to_dict` just return Python objects, `to_pandas` returns a `pandas.DataFrame` and `to_sql` just inserts into a SQL DB, is that right?", "Fixed by #6096. " ]
2023-07-27T19:08:37
2024-03-07T07:22:43
2024-03-07T07:22:42
CONTRIBUTOR
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Supporting this should be fairly easy. Requested on the forum [here](https://discuss.huggingface.co/t/how-can-i-convert-a-loaded-dataset-in-to-a-parquet-file-and-save-it-to-the-s3/48353).
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1,824,896,761
I_kwDODunzps5sxbb5
6,084
Changing pixel values of images in the Winoground dataset
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2023-07-27T17:55:35
2023-07-27T17:55:35
null
NONE
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Hi, as I followed the instructions, with lasted "datasets" version: " from datasets import load_dataset examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>) " I got slightly different datasets in colab and in my hpc environment. Specifically, the pixel values of images are slightly different. I thought it was due to the package version difference, but today's morning I found out that my winoground dataset in colab became the same with the one in my hpc environment. The dataset in colab can produce the correct result but now it is gone as well. Can you help me with this? What causes the datasets to have the wrong pixel values?
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1,822,597,471
I_kwDODunzps5soqFf
6,079
Iterating over DataLoader based on HF datasets is stuck forever
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[ "When the process starts to hang, can you interrupt it with CTRL + C and paste the error stack trace here? ", "Thanks @mariosasko for your prompt response, here's the stack trace:\r\n\r\n```\r\nKeyboardInterrupt Traceback (most recent call last)\r\nCell In[12], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self)\r\n 1350 yield formatter.format_row(pa_table)\r\n 1351 return\r\n-> 1353 for key, example in ex_iterable:\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:956, in BufferShuffledExamplesIterable.__iter__(self)\r\n 954 # this is the shuffle buffer that we keep in memory\r\n 955 mem_buffer = []\r\n--> 956 for x in self.ex_iterable:\r\n 957 if len(mem_buffer) == buffer_size: # if the buffer is full, pick and example from it\r\n 958 i = next(indices_iterator)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:296, in ShuffledDataSourcesArrowExamplesIterable.__iter__(self)\r\n 294 for key, pa_table in self.generate_tables_fn(**kwargs_with_shuffled_shards):\r\n 295 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER):\r\n--> 296 formatted_batch = formatter.format_batch(pa_subtable)\r\n 297 for example in _batch_to_examples(formatted_batch):\r\n 298 yield key, example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:448, in PythonFormatter.format_batch(self, pa_table)\r\n 446 if self.lazy:\r\n 447 return LazyBatch(pa_table, self)\r\n--> 448 batch = self.python_arrow_extractor().extract_batch(pa_table)\r\n 449 batch = self.python_features_decoder.decode_batch(batch)\r\n 450 return batch\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:150, in PythonArrowExtractor.extract_batch(self, pa_table)\r\n 149 def extract_batch(self, pa_table: pa.Table) -> dict:\r\n--> 150 return pa_table.to_pydict()\r\n\r\nKeyboardInterrupt: \r\n```\r\n", "Update: If i let it run, it eventually fails with:\r\n\r\n```\r\nRuntimeError Traceback (most recent call last)\r\nCell In[16], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1360, in IterableDataset.__iter__(self)\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n-> 1360 yield format_dict(example) if format_dict else example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:85, in TorchFormatter.recursive_tensorize(self, data_struct)\r\n 84 def recursive_tensorize(self, data_struct: dict):\r\n---> 85 return map_nested(self._recursive_tensorize, data_struct, map_list=False)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:463, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n--> 463 mapped = [\r\n 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:464, in <listcomp>(.0)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n 463 mapped = [\r\n--> 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:366, in _single_map_nested(args)\r\n 364 # Singleton first to spare some computation\r\n 365 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 366 return function(data_struct)\r\n 368 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n 369 if rank is not None and logging.get_verbosity() < logging.WARNING:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:82, in TorchFormatter._recursive_tensorize(self, data_struct)\r\n 80 elif isinstance(data_struct, (list, tuple)):\r\n 81 return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])\r\n---> 82 return self._tensorize(data_struct)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:68, in TorchFormatter._tensorize(self, value)\r\n 66 if isinstance(value, PIL.Image.Image):\r\n 67 value = np.asarray(value)\r\n---> 68 return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})\r\n\r\nRuntimeError: Could not infer dtype of decimal.Decimal\r\n```", "PyTorch tensors cannot store `Decimal` objects. Casting the column with decimals to `float` should fix the issue.", "I already have cast in collate_fn, in which I perform .astype(float) for each numerical field.\r\nOn the same instance, I installed a conda env with python 3.6, and this works well.\r\n\r\nSample:\r\n\r\n```\r\ndef streaming_data_collate_fn(batch):\r\n df = pd.DataFrame.from_dict(batch)\r\n feat_vals = torch.FloatTensor(np.nan_to_num(np.array(df[feats].astype(float))))\r\n\r\n```", "`collate_fn` is applied after the `torch` formatting step, so I think the only option when working with an `IterableDataset` is to remove the `with_format` call and perform the conversion from Python values to PyTorch tensors in `collate_fn`. The standard `Dataset` supports `with_format(\"numpy\")`, which should make this conversion faster.", "Thanks! \r\nPython 3.10 conda-env: After replacing with_format(\"torch\") with with_format(\"numpy\"), the error went away. However, it was still taking over 2 minutes to load a very small batch of 64 samples with num_workers set to 32. Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nPython 3.6 based conda-env: When I switch the kernel , neither of the above work, and with_format(\"torch\") is the only thing that works, and executes in 1.6 seconds.\r\n\r\nI feel something else is also amiss here.", "Can you share the `datasets` and `torch` versions installed in these conda envs?\r\n\r\n> Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nHmm, that's surprising. What are your dataset's `.features`?", "Python 3.6: \r\ndatasets.__version__ 2.4.0\r\ntorch.__version__ 1.10.1+cu102\r\n\r\nPython 3.10:\r\ndatasets.__version__ 2.14.0\r\ntorch.__version__ 2.0.0\r\n\r\nAnonymized features are of the form (subset shown here):\r\n{\r\n'string_feature_i': Value(dtype='string', id=None),\r\n'numerical_feature_i': Value(dtype='decimal128(38, 0)', id=None),\r\n'numerical_feature_series_i': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None),\r\n}\r\n\r\n\r\nThere is no output from .features in python 3.6 kernel BTW.", "One more thing, in python 3.10 based kernel, interestingly increasing num_workers seem to be increasing the runtime of iterating I was trying out. In python 3.10 kernel execution, I do not even see multiple CPU cores spiking unlike in 3.6.\r\n\r\n512 batch size on 32 workers executes in 2.4 seconds on python 3.6 kernel, while it takes ~118 seconds on 3.10!", "**Update**: It seems the latency part is more of a multiprocessing issue with torch and some host specific issue, and I had to scourge through relevant pytorch issues, when I stumbled across these threads:\r\n1. https://github.com/pytorch/pytorch/issues/102494\r\n2. https://github.com/pytorch/pytorch/issues/102269\r\n3. https://github.com/pytorch/pytorch/issues/99625\r\n\r\nOut of the suggested solutions, the one that worked in my case was:\r\n```\r\nos.environ['KMP_AFFINITY'] = \"disabled\"\r\n```\r\nIt is working for now, though I have no clue why, just I hope it does not get stuck when I do actual model training, will update by tomorrow.\r\n\r\n\r\n", "I'm facing a similar situation in the local VS Code. \r\n\r\nDatasets version 2.14.4\r\nTorch 2.0.1+cu118\r\n\r\nSame code runs without issues in Colab\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"Supermaxman/esa-hubble\", streaming=True)\r\nsample = next(iter(dataset[\"train\"]))\r\n```\r\n\r\nis stuck for minutes. If I interrupt, I get\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nKeyboardInterrupt Traceback (most recent call last)\r\nCell In[5], line 5\r\n 1 from datasets import load_dataset\r\n 3 dataset = load_dataset(\"Supermaxman/esa-hubble\", streaming=True)\r\n----> 5 sample = next(iter(dataset[\"train\"]))\r\n 6 print(sample[\"text\"])\r\n 7 sample[\"image\"]\r\n\r\nFile [~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353](https://file+.vscode-resource.vscode-cdn.net/home/osanseviero/Desktop/workspace/genai/nbs/~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353), in IterableDataset.__iter__(self)\r\n 1350 yield formatter.format_row(pa_table)\r\n 1351 return\r\n-> 1353 for key, example in ex_iterable:\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n\r\nFile [~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:255](https://file+.vscode-resource.vscode-cdn.net/home/osanseviero/Desktop/workspace/genai/nbs/~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:255), in ArrowExamplesIterable.__iter__(self)\r\n 253 def __iter__(self):\r\n 254 formatter = PythonFormatter()\r\n--> 255 for key, pa_table in self.generate_tables_fn(**self.kwargs):\r\n 256 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER):\r\n...\r\n-> 1130 return self._sslobj.read(len, buffer)\r\n 1131 else:\r\n 1132 return self._sslobj.read(len)\r\n```", "@osanseviero I assume the `self._sslobj.read(len, buffer)` line comes from the built-in `ssl` module, so this probably has something to do with your network. Please open a new issue with the full stack trace in case you haven't resolved this yet.", "Thank you reporting this and sharing the solution, I ran into this as well!", "Ran into same issue after upgrading to pytorch-2.0. Disabling KMP_AFFINITY as mentioned above worked for me. Thanks!\r\n" ]
2023-07-26T14:52:37
2024-02-07T17:46:52
2023-07-30T14:09:06
NONE
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### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
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1,822,501,472
I_kwDODunzps5soSpg
6,078
resume_download with streaming=True
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[ "Currently, it's not possible to efficiently resume streaming after an error. Eventually, we plan to support this for Parquet (see https://github.com/huggingface/datasets/issues/5380). ", "Ok thank you for your answer", "I'm closing this as a duplicate of #5380" ]
2023-07-26T14:08:22
2023-07-28T11:05:03
2023-07-28T11:05:03
NONE
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### Describe the bug I used: ``` dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, split="train" ) ``` Unfortunately, the server had a problem during the training process. I saved the step my training stopped at. But how can I resume download from step 1_000_´000 without re-streaming all the first 1 million docs of the dataset? `download_config=DownloadConfig(resume_download=True)` seems to not work with streaming=True. ### Steps to reproduce the bug ``` from datasets import load_dataset, DownloadConfig dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, # optional split="train", download_config=DownloadConfig(resume_download=True) ) # interupt the run and try to relaunch it => this restart from scratch ``` ### Expected behavior I would expect a parameter to start streaming from a given index in the dataset. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.0
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1,822,486,810
I_kwDODunzps5soPEa
6,077
Mapping gets stuck at 99%
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[ "The `MAX_MAP_BATCH_SIZE = 1_000_000_000` hack is bad as it loads the entire dataset into RAM when performing `.map`. Instead, it's best to use `.iter(batch_size)` to iterate over the data batches and compute `mean` for each column. (`stddev` can be computed in another pass).\r\n\r\nAlso, these arrays are big, so it makes sense to reduce `batch_size`/`writer_batch_size` to avoid RAM issues and slow IO.", "Hi @mariosasko !\r\n\r\nI agree, it's an ugly hack, but it was convenient since the resulting `mean_std` could be cached by the library. For my large dataset (which doesn't fit in RAM), I'm actually using something similar to what you suggested. I got rid of the first mapping in the above scripts and replaced it with an iterator, but the issue with the second mapping still persists.", "Have you tried to reduce `batch_size`/`writer_batch_size` in the 2nd `.map`? Also, can you interrupt the process when it gets stuck and share the error stack trace?", "I think `batch_size/writer_batch_size` is already at its lowest in the 2nd `.map` since `batched=False` implies `batch_size=1` and `len(ds) = 1000 = writer_batch_size`.\r\n\r\nHere is also a bunch of stack traces when I interrupted the process:\r\n\r\n<details>\r\n <summary>stack trace 1</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 967/1000 [00:01<00:00, 534.87 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 263, in _cast_to_python_objects\r\n def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]:\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(**dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in <listcomp>\r\n [\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n <summary>stack trace 2</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 988/1000 [00:20<00:00, 526.19 examples/s]Applying mean/std: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [00:21<00:00, 9.66 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 263, in _cast_to_python_objects\r\n def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]:\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(**dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 291, in _cast_to_python_objects\r\n if config.JAX_AVAILABLE and \"jax\" in sys.modules:\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n <summary>stack trace 3</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 989/1000 [00:01<00:00, 504.80 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(**dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 298, in _cast_to_python_objects\r\n if obj.ndim == 0:\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\r\n" ]
2023-07-26T14:00:40
2023-07-28T09:21:07
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CONTRIBUTOR
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### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.randn(30000, 3), "normals": torch.randn(30000, 3), "features": torch.randn(30000, 6), "scalars": torch.randn(20), } ``` ```python # main.py import datasets import torch def apply_mean_std( dataset: datasets.Dataset, means: dict[str, torch.Tensor], stds: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature. std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in means.keys(): # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = means[key] # type: ignore std = stds[key] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean (along last axis) means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} for batch in ds.iter(batch_size=8): for key in ds.column_names: data = batch[key] batch_size = data.shape[0] data = data.reshape(-1, data.shape[-1]) means[key] += data.mean(dim=0) / len(ds) * batch_size means_sq[key] += (data**2).mean(dim=0) / len(ds) * batch_size # compute std (along last axis) stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names} # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "means": means, "stds": stds, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2
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https://api.github.com/repos/huggingface/datasets/issues/6075
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https://github.com/huggingface/datasets/issues/6075
1,822,341,398
I_kwDODunzps5snrkW
6,075
Error loading music files using `load_dataset`
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[ "This code behaves as expected on my local machine or in Colab. Which version of `soundfile` do you have installed? MP3 requires `soundfile>=0.12.1`.", "I upgraded the `soundfile` and it's working now! \r\nThanks @mariosasko for the help!" ]
2023-07-26T12:44:05
2023-07-26T13:08:08
2023-07-26T13:08:08
NONE
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### Describe the bug I tried to load a music file using `datasets.load_dataset()` from the repository - https://huggingface.co/datasets/susnato/pop2piano_real_music_test I got the following error - ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2788, in _getitem formatted_output = format_table( File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 629, in format_table return formatter(pa_table, query_type=query_type) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 398, in __call__ return self.format_column(pa_table) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 442, in format_column column = self.python_features_decoder.decode_column(column, pa_table.column_names[0]) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 218, in decode_column return self.features.decode_column(column, column_name) if self.features else column File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in decode_column [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in <listcomp> [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1325, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/audio.py", line 184, in decode_example array, sampling_rate = sf.read(f) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 372, in read with SoundFile(file, 'r', samplerate, channels, File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 740, in __init__ self._file = self._open(file, mode_int, closefd) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1264, in _open _error_check(_snd.sf_error(file_ptr), File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1455, in _error_check raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace')) RuntimeError: Error opening <_io.BufferedReader name='/home/susnato/.cache/huggingface/datasets/downloads/d2b09cb974b967b13f91553297c40c0f02f3c0d4c8356350743598ff48d6f29e'>: Format not recognised. ``` ### Steps to reproduce the bug Code to reproduce the error - ```python from datasets import load_dataset ds = load_dataset("susnato/pop2piano_real_music_test", split="test") print(ds[0]) ``` ### Expected behavior I should be able to read the music file without any error. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-50-generic-x86_64-with-glibc2.35 - Python version: 3.9.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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