Datasets:

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Fancellu

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[ "Hi, this dataset has been disabled by the authors, so unfortunately it's no longer possible to download it." ]

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[ "@mariosasko, @lhoestq, @albertvillanova\r\nhey guys! can anyone help? or can you guys suggest who can help with this?", "Hi ! \r\n\r\n1. When the dataset is running of of examples, the last batches received by the GPU can be incomplete or empty/missing. We haven't implemented yet a way to ignore the last batch. It might require the datasets to provide the number of examples per shard though, so that we can know when to stop.\r\n2. Samplers are not compatible with IterableDatasets in pytorch\r\n3. if `dataset.n_shards % world_size != 0` then all the nodes will read/stream the full dataset in order (possibly reading/streaming the same data multiple times), BUT will only yield one example out of `world_size` so that each example goes to one exactly one GPU.\r\n4. no, sharding should be down up-front and can take some time depending on the dataset size and format", "> if dataset.n_shards % world_size != 0 then all the nodes will read/stream the full dataset in order (possibly reading/streaming the same data multiple times), BUT will only yield one example out of world_size so that each example goes to one exactly one GPU.\r\n\r\nconsidering there's just 1 shard and 2 worker nodes, do you mean each worker node will load the whole dataset but still receive half of that shard while streaming?", "Yes both nodes will stream from the 1 shard, but each node will skip half of the examples. This way in total each example is seen once and exactly once during you distributed training.\r\n\r\nThough it terms of I/O, the dataset is effectively read/streamed twice.", "what if the number of samples in that shard % num_nodes != 0? it will break/get stuck? or is the data repeated in that case for gradient sync?", "In the case one at least one of the noes will get an empty/incomplete batch. The data is not repeated in that case. If the training loop doesn't take this into account it can lead to unexpected behaviors indeed.\r\n\r\nIn the future we'd like to add a feature that would allow the nodes to ignore the last batch, this way all the nodes would only have full batches.", "> In the case one at least one of the noes will get an empty/incomplete batch. The data is not repeated in that case. If the training loop doesn't take this into account it can lead to unexpected behaviors indeed.\r\n> \r\n> In the future we'd like to add a feature that would allow the nodes to ignore the last batch, this way all the nodes would only have full batches.\r\n\r\nIs there any method to modify one dataset's n_shard? modify the number of files is ok? one file == one shard?", "> modify the number of files is ok? one file == one shard?\r\n\r\nYep, one file == one shard :)" ]

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[ "This should now be fixed by https://github.com/huggingface/datasets/pull/6550 and updated with https://github.com/huggingface/datasets/pull/6646\r\n\r\nFeel free to re-open if you're still having issues :)" ]

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[ "Thanks for reporting, @kiehls90.\r\n\r\nAs this seems an issue with the specific \"wiki_dpr\" dataset, I am transferring the issue to the corresponding dataset page: https://huggingface.co/datasets/wiki_dpr/discussions/13" ]

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[ "Hi! Can you please share the error's stack trace so we can see where it comes from?", "We cannot reproduce the issue and we do not have enough information: environment info (need to run `datasets-cli env`), stack trace,...\r\n\r\nI am closing the issue. Feel free to reopen it (with additional information) if the problem persists.", "Yeah 👍\r\n\r\nOn Tue, 6 Feb 2024 at 2:56 PM, Albert Villanova del Moral <\r\n***@***.***> wrote:\r\n\r\n> We cannot reproduce the issue and we do not have enough information:\r\n> environment info (need to run datasets-cli env), stack trace,...\r\n>\r\n> I am closing the issue. Feel free to reopen it (with additional\r\n> information) if the problem persists.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6618#issuecomment-1929102334>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ASS4PJ3XOIIWISPY3VX3QRTYSHZK5AVCNFSM6AAAAABCL3BT4SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMRZGEYDEMZTGQ>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n" ]

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[ "Sorry I posted in the wrong repo, please delete.. thanks!" ]

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[ "Hi ! We recently updated `cnn_dailymail` and now `datasets>=2.14` is needed to load it.\r\n\r\nYou can update `datasets` with\r\n\r\n```\r\npip install -U datasets\r\n```" ]

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[ "Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n```python\r\nais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n```", "> Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n> \r\n> ```python\r\n> ais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n> ```\r\n\r\nthanks" ]

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[ "+1", "same error in 2.16.1", "@kongjiellx any luck with the issue?", "I opened https://github.com/huggingface/datasets/pull/6632 to fix this issue. Once it's merged we'll do a new release of `datasets`", "Thanks @lhoestq !" ]

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[ "Addressed in https://github.com/huggingface/transformers/pull/28715." ]

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[ "I've opened a PR with a fix.", "I don't think the PR fixes the root cause, since it still relies on the `random` library which will often have its seed fixed. I think the builtin `uuid.uuid4()` is a better choice: https://docs.python.org/3/library/uuid.html" ]

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[ "Unfortunately, I'm unable to reproduce this error. Can you share the reproducer?", "```\r\nds = datasets.Dataset.from_dict(dict(a=[i for i in range(100)]))\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-fn\") # this worked\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-folder/filename\") # this failed\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-folder/\") # this failed\r\n\r\n\r\nFileNotFoundError: [Errno 2] No such file or directory: '/tmp/whatever-folder/tmp1_izxvoo'\r\n```\r\n\r\nIt will fail if the filename parents do not exists. If we have `os.makedirs(\"/tmp/whatever-folder\")`, then it worked.\r\n\r\nMaybe add the `mkdir -p` into the map function?" ]

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[ "Hi! Parquet is the only format that supports complex/nested features such as `Translation`. So, this should work:\r\n```python\r\ntest_dataset = load_dataset(\"opus100\", name=\"en-fr\", split=\"test\")\r\n\r\n# Save with .to_parquet()\r\ntest_parquet_path = \"try_testset_save.parquet\"\r\ntest_dataset.to_parquet(test_parquet_path)\r\n\r\n# Load dataset from the Parquet\r\nloaded_dataset = load_dataset(\"parquet\", data_files=test_parquet_path)\r\nprint(test_dataset_fromfile[0][\"translation\"])\r\nprint(test_dataset_fromfile[0][\"translation\"][\"en\"])\r\n```", "Indeed this works great, thank you !" ]

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[ "Hi! Non-generic data processing is out of this library's scope, so it's downstream libraries/users' responsibility to implement such logic.", "That's fair. Thanks" ]

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[ "I am facing similar issue while reading a csv file from s3. Wondering if somebody has found a workaround. ", "same thing happened to other formats like parquet", "I am facing similar issue while reading a parquet file from s3.\r\ni try with every version between 2.14 to 2.16.1 but it dosen't work ", "Re-define the DownloadConfig might work:\r\n\r\n```\r\nclass ReviseDownloadConfig(DownloadConfig):\r\n def __post_init__(self, use_auth_token):\r\n if use_auth_token != \"deprecated\":\r\n warnings.warn(\r\n \"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\\n\"\r\n f\"You can remove this warning by passing 'token={use_auth_token}' instead.\",\r\n FutureWarning,\r\n )\r\n self.token = use_auth_token\r\n\r\n def copy(self):\r\n return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})\r\n\r\ndownloadconfig = ReviseDownloadConfig()\r\n```\r\n", "> Re-define the DownloadConfig might work:\r\n> \r\n> ```\r\n> class ReviseDownloadConfig(DownloadConfig):\r\n> def __post_init__(self, use_auth_token):\r\n> if use_auth_token != \"deprecated\":\r\n> warnings.warn(\r\n> \"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\\n\"\r\n> f\"You can remove this warning by passing 'token={use_auth_token}' instead.\",\r\n> FutureWarning,\r\n> )\r\n> self.token = use_auth_token\r\n> ```\r\nThis seemed to work for me.\r\n" ]

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[ "It is caused by these code lines: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1688-L1694", "Also note the information in the docstring: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1582-L1585\r\n\r\n> Also accepts `<dataset_name>`, which will default to the namespace of the logged-in user.\r\n\r\nThis behavior was \"reverted\" by the PR: \r\n- #6519\r\n\r\nWe have therefore contradictory requirements. We should decide:\r\n- whether to support passing dataset_namespace without user/org that defaults to the logged-in user (and not support canonical datasets)\r\n- or vice-versa, to support canonical datasets and not support passing only dataset_name\r\n\r\nAs canonical datasets are \"deprecated\" (and will eventually disappear), I would choose the first option. However, if so, the Space to convert datasets to Parquet will not work for canonical datasets: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet", "IIUC, this could also be \"fixed\" by `create_repo(\"dataset_name\")` not defaulting to `create_repo(\"user/dataset_name\")` (when the user's token is available), which would be consistent with the rest of the `HfApi` ops used in the `push_to_hub` implementation. This is a (small) breaking change for `huggingface_hub`, but justified to make the API more consistent.", "I tag @Wauplin to have his opinion as well.", "Hmm, creating repo with implicit namespace (e.g. `create_repo(\"dataset_name\")`) is a convenient feature used in a lot of integrations. It is not consistent with other HfApi methods specifically because it is the method to create repos. Once the repo is created, the return value provides the explicit repo_id (`namespace/repo_name`) that has to be passed to every `HfApi` method. Otherwise, libraries/scripts would often need to do a `whoami` call to get the namespace before creating a repo.\r\n\r\n Another solution for https://github.com/huggingface/datasets/issues/6597#issuecomment-1893746690 could be that implicit namespace is allowed (same as today) except if the `repo_id` is in a hard-coded list of canonical datasets. This list can be maintained automatically and should be slowly decreasing. **Caveat:** as a normal user I wouldn't be able to implicitly push to `imagenet-1k` if I wanted to push to `Wauplin/imagenet-1k`. Shouldn't be too problematic, no? Worse case, would need to add a `whoami` call and allow implicit-canonical-name for non-HF users for instance (a bit too over-engineered IMO but doable). ", "As canonical datasets are going to disappear in the following couple of months, I would not make any effort on their support.\r\n\r\nI propose reverting #6519, so that the behavior of `push_to_hub` is aligned with the one described in its dosctring: \"Also accepts `<dataset_name>`, which will default to the namespace of the logged-in user.\"\r\n\r\nI'm opening a PR." ]

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[ "Hi ! I think the issue comes from the \"float16\" features that are not supported yet in Parquet\r\n\r\nFeel free to open an issue in `pyarrow` about this. In the meantime, I'd encourage you to use \"float32\" for your \"pooled_prompt_embeds\" and \"prompt_embeds\" features.\r\n\r\nYou can cast them to \"float32\" using\r\n\r\n```python\r\nfrom datasets import Value\r\n\r\nds = ds.cast_column(\"pooled_prompt_embeds\", Value(\"float32\"))\r\nds = ds.cast_column(\"prompt_embeds\", Value(\"float32\"))\r\n```", "@lhoestq hm. Thank you very much.\r\n\r\nDo you think it won't have any impact on the training? That it won't break it or the quality won't degrade because of this?\r\n\r\nI need to use it for [SDXL training](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py)", "Increasing the precision should not degrade training (it only increases the precision), but make sure that it doesn't break your pytorch code (e.g. if it expects a float16 instead of a float32 somewhere)", "@lhoestq just fyi pyarrow 15.0.0 (just released) supports float16 as the underlying parquetcpp does as well now :)", "Oh that's amazing ! (and great timing ^^)\r\n\r\n@kopyl can you try to update `pyarrow` and try again ?\r\n\r\nBtw @assignUser there seems to be some casting implementations missing with float16 in 15.0.0, e.g.\r\n\r\n```\r\nArrowNotImplementedError: Unsupported cast from int64 to halffloat using function cast_half_float\r\n```\r\n\r\n```\r\nArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float\r\n```", "Ah you are right casting is not implemented yet, it's even mentioned in the docs. This pr references the relevant issues if you'd like to track them\nhttps://github.com/apache/arrow/pull/38494", "Cool thank you :)", "@lhoestq i just recently found out that it's supported in 15.0.0, but wanted to try it first before telling you...\r\n\r\nTrying this right now and it seemingly works (although i need to wait till the end to make sure there is nothing wrong). Will update you when it's finished.\r\n\r\n<img width=\"918\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/4821e215-e782-4736-8c76-d06187078175\">\r\n\r\nA couple of questions though:\r\n\r\n1. What does that missing casting implementation mean for my specific case and what does it mean in general?\r\n2. Do you know how to `push_to_hub` with multiple processes?", "@lhoestq also it's strange that there was no error for a dataset with the same features, same data type, but smaller (much smaller).\r\n\r\nAltho i'm not sure about this, but chances are the dataset was loaded directly, not `load_from_disk`.... Maybe because of this.", "> What does that missing casting implementation mean for my specific case and what does it mean in general?\r\n\r\nNothing for you, just that casting to float16 using `.cast_column(\"my_column_name\", Value(\"float16\"))` raises an error\r\n\r\n> Do you know how to push_to_hub with multiple processes?\r\n\r\nIt's not possible (yet ?). Mostly because we haven't implemented yet how to do parallel uploads to the Hub from `datasets`.\r\nThough if you want faster uploads you can already enable `hf_transfer` \r\n\r\n```\r\npip install hf_transfer\r\n```\r\n\r\nand setting `HF_HUB_ENABLE_HF_TRANSFER=1` as an environment variable\r\n\r\nsee https://huggingface.co/docs/huggingface_hub/guides/upload#tips-and-tricks-for-large-uploads", "@lhoestq thank you very much.\r\n\r\nThat would be amazing, I need to create a feature request for this :)\r\n\r\nBy the way, in short, how does hf_transfer improves the upload speed under the hood?", "@lhoestq i was just able to successfully upload without the dataset with the new pyarrow update and without increasing the precision :)", "Awesome !\r\n\r\nRegarding hf_transfer: it's been optimized in rust ;)", "@lhoestq wow, cool :)" ]

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[ "Hi! `tqdm` doesn't work well in non-interactive environments, so there isn't much we can do about this. It's best to [disable it](https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/utilities#datasets.disable_progress_bars) in such environments and instead use logging to track progress." ]

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[ "Hi! Indeed, Dropbox is not a reliable host. I've just merged https://huggingface.co/datasets/PolyAI/minds14/discussions/24 to fix this by hosting the data files inside the repo." ]

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[ "We'll do a new release of `datasets` in the coming days with a fix !", "@lhoestq Thank you very much!" ]

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[ "Hi ! This issue comes from the fact that `map()` with `num_proc>1` shards the dataset in multiple chunks to be processed (one per process) and merges them. The DatasetInfos of each chunk are then merged together, but for some fields like `dataset_name` it's not been implemented and default to None.\r\n\r\nThe DatasetInfo merge is defined here, in case you'd like to contribute an improvement: \r\n\r\nhttps://github.com/huggingface/datasets/blob/d2e0034122a788015c0834a72e6c6279e7ecbac5/src/datasets/info.py#L269-L270", "#self-assign" ]

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[ "@lhoestq\r\nCan you provide me with some ideas？", "Hi ! What's the error ?", "@lhoestq \r\n```\r\nTraceback (most recent call last):\r\n File \"/home/dongzf/miniconda3/envs/dataset_ai/lib/python3.11/runpy.py\", line 198, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/dongzf/miniconda3/envs/dataset_ai/lib/python3.11/runpy.py\", line 88, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/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 \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/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 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/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 \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/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 \"/mnt/sda/code/dataset_ai/dataset_ai/example/test.py\", line 83, in <module>\r\n data = xnumpy_fromfile(current_dir, download_config=config,dtype=numpy.float32,)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/mnt/sda/code/dataset_ai/dataset_ai/src/datasets/download/streaming_download_manager.py\", line 765, in xnumpy_fromfile\r\n return np.fromfile(xopen(filepath_or_buffer, \"rb\", download_config=download_config).read(), *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: embedded null byte\r\n```", " not add read() \r\nthe error is \r\n\r\nreturn np.fromfile(xopen(filepath_or_buffer, \"rb\", download_config=download_config), *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nio.UnsupportedOperation: fileno", "xopen return obj do not have fileno function\r\nI don't know why?", "I used this method to read point cloud data in the script\r\n\r\n\r\n```python\r\nwith open(velodyne_filepath,\"rb\") as obj:\r\n velodyne_data = numpy.frombuffer(obj.read(), dtype=numpy.float32).reshape([-1, 4])\r\n```" ]

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[ "Hi @haok1402, I have created an issue in the Discussion tab of the corresponding dataset: https://huggingface.co/datasets/eli5/discussions/7\r\nLet's continue the discussion there!" ]

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[ "cc @mariosasko @lhoestq ", "Hi! We should be able to avoid this error by retrying to read the data when it happens. I'll open a PR in `huggingface_hub` to address this.", "Thanks for the fix @mariosasko! Just wondering whether \"500 error\" should also be excluded? I got these errors overnight:\r\n\r\n```\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/da\r\ntasets/sanchit-gandhi/concatenated-train-set-label-length-256/resolve/91e6a0cd0356605b021384ded813cfcf356a221c/train/tra\r\nin-02618-of-04012.parquet (Request ID: Root=1-65b18b81-627f2c2943bbb8ab68d19ee2;129537bd-1934-4257-a4d8-1cb774f8e1f8) \r\n \r\nInternal Error - We're working hard to fix this as soon as possible! \r\n```", "Gently pining @mariosasko and @Wauplin - when trying to stream this large dataset from the HF Hub, I'm running into `500 Internal Server Errors` as described above. I'd love to be able to use the Hub exclusively to stream data when training, but this error pops up a few times a week, terminating training runs and causing me to have to rewind to the last saved checkpoint. Do we reckon there's a way we can protect Datasets' streaming against these errors? The same reproducer as the [original comment](https://github.com/huggingface/datasets/issues/6577#issue-2074790848) can be used, but it's somewhat random whether we hit a 500 error. Leaving the full traceback below: \r\n\r\n```\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_loo\r\np \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 1367, in __iter__ \r\n yield from self._iter_pytorch() \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1302, in _iter_pytorch \r\n for key, example in ex_iterable: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 987, in __iter__ \r\n for x in self.ex_iterable: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 867, in __iter__ \r\n yield from self._iter() \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 904, in _iter \r\n for key, example in iterator: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 679, in __iter__ \r\n yield from self._iter() \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 741, in _iter [235/1892]\r\n for key, example in iterator: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1119, in __iter__ \r\n for key, example in self.ex_iterable: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 282, 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 1587, in iter_batches \r\n File \"pyarrow/types.pxi\", line 88, in pyarrow.lib._datatype_to_pep3118 \r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 342, in read_with_retrie\r\ns \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 629, in _fetch_rang\r\ne \r\n hf_raise_for_status(r) \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py\", line 362, in hf_raise_for\r\n_status \r\n raise HfHubHTTPError(str(e), response=response) from e \r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/da\r\ntasets/sanchit-gandhi/concatenated-train-set-label-length-256-conditioned/resolve/3c3c0cce51df9f9d2e75968bb2a1851894f504\r\n0d/train/train-03515-of-04010.parquet (Request ID: Root=1-65c7c4c4-153fe71401558c8c2d272c8a;fec3ec68-4a0a-4bfd-95ba-b0a0\r\n5684d612) \r\n \r\nInternal Error - We're working hard to fix this as soon as possible! ", "@sanchit-gandhi thanks for the feedback. I've opened https://github.com/huggingface/huggingface_hub/pull/2026 to make the download process more robust. I believe that you've witness this problem on Saturday due to the Hub outage. Hope the PR will make your life easier though :)", "Awesome, thanks @Wauplin! Makes sense re the Hub outage" ]

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[ "Thanks for reporting! I've opened a PR with a fix." ]

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[ "Though the `build / build_main_documentation` CI job ran for 2.16.0: https://github.com/huggingface/datasets/actions/runs/7300836845/job/19896275099 🤔 ", "Yes, I saw it. Maybe @mishig25 can give us some hint...", "fixed https://huggingface.co/docs/datasets/v2.16.0/en/index", "Still missing 2.16.1.", "> Still missing 2.16.1.\r\n\r\nre-running the doc-buld job for the missing ones should fix\r\n\r\n", "Re-running the job for the 2.16.1 release: https://github.com/huggingface/datasets/actions/runs/7365231552/job/20310278583", "Fixed for 2.16.1: https://huggingface.co/docs/datasets/v2.16.1/en/index" ]

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[ "Seems like I just used the old code which did not have `keep_in_memory=True` argument, sorry.\r\n\r\nAlthough i encountered a different problem – at 97% my python process just hung for around 11 minutes with no logs (when running dataset.map without `keep_in_memory=True` over around 3 million of dataset samples)...", "Can you open a new issue and provide a bit more details ? What kind of map operations did you run ?", "Hey. I will try to find some free time to describe it.\r\n\r\n(can't do it now, cause i need to reproduce it myself to be sure about everything, which requires spinning a new Azuree VM, copying a huge dataset to drive from network disk for a long time etc...)", "@lhoestq loading dataset like this does not spawn 50 python processes:\r\n\r\n```\r\ndatasets.load_dataset(\"/preprocessed_2256k/train\", num_proc=50)\r\n```\r\n\r\nI have 64 vCPU so i hoped it could speed up the dataset loading...\r\n\r\nMy dataset onlly has images and metadata.csv with text column alongside image file path column", "now noticed\r\n```\r\n'Setting num_proc from 50 back to 1 for the train split to disable multiprocessing as it only contains one shard\r\n```\r\n\r\nAny way to work around this?", "@lhoestq thanks, [this helped](https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/arrow_dataset.py#L1053)\r\n\r\n" ]

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[ "I think you are reporting an issue with the `transformers` library. Note this is the repository of the `datasets` library. I recommend that you open an issue in their repository: https://github.com/huggingface/transformers/issues\r\n\r\nEDIT: I have not the rights to transfer the issue\r\n~~I am transferring your issue to their repository.~~", "Thanks, I hope someone from transformers library addresses this issue.\r\n\r\nOn Mon, Jan 8, 2024 at 15:29 Albert Villanova del Moral <\r\n***@***.***> wrote:\r\n\r\n> I think you are reporting an issue with the transformers library. Note\r\n> this is the repository of the datasets library. I am transferring your\r\n> issue to their repository.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6567#issuecomment-1880688586>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AE4LJNOYMD6WJMXFKPMH6DLYNO7PJAVCNFSM6AAAAABBQ63HWOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQOBQGY4DQNJYGY>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n", "@andysingal, I recommend that you open an issue in their repository: https://github.com/huggingface/transformers/issues\r\nI don't have the rights to transfer this issue to their repo." ]

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[ "I also see the same error and get passed it by casting that line to float. \r\n\r\nso `for x in obj.detach().cpu().numpy()` becomes `for x in obj.detach().to(torch.float).cpu().numpy()`\r\n\r\nI got the idea from [this ](https://github.com/kohya-ss/sd-webui-additional-networks/pull/128/files) PR where someone was facing a similar issue (in a different repository). I guess numpy doesn't support bfloat16.\r\n\r\n" ]

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[ "My current workaround this issue is to return `None` in the second element and then filter out samples which have `None` in them.\r\n\r\n```python\r\ndef merge_samples(batch):\r\n if len(batch['a']) == 1:\r\n batch['c'] = [batch['a'][0]]\r\n batch['d'] = [None]\r\n else:\r\n batch['c'] = [batch['a'][0]]\r\n batch['d'] = [batch['a'][1]]\r\n return batch\r\n \r\ndef filter_fn(x):\r\n return x['d'] is not None\r\n\r\n# other code...\r\nmapped = mapped.filter(filter_fn)\r\n```" ]

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[ "Thanks for reporting! I've opened a PR with a fix", "@mariosasko thank you very much :)" ]

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[ "@Wauplin Do you happen to know what's up?", "<del>Installing `datasets` from `main` did the trick so I guess it will be fixed in the next release.\r\n\r\nNVM https://github.com/huggingface/datasets/blob/d26abadce0b884db32382b92422d8a6aa997d40a/src/datasets/utils/info_utils.py#L5", "@wasertech upgrading `huggingface_hub` to a newer version should fix your issue. Latest version is 0.20.2. ", "Ha yes I had pinned `tokenizers` to an old version so it downgraded `huggingface_hub`. Note to myself keep HuggingFace modules relatively close together chronologically release wise.", "Glad to know your problem's solved! ", "@Wauplin Thanks for your insight 👍", "pip install --upgrade huggingface-hub" ]

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[ "In particular, I would like to have an example of how to replace the following configuration (from https://huggingface.co/docs/hub/datasets-manual-configuration#splits)\r\n\r\n```\r\n---\r\nconfigs:\r\n- config_name: default\r\n data_files:\r\n - split: train\r\n path: \"data/*.csv\"\r\n - split: test\r\n path: \"holdout/*.csv\"\r\n---\r\n```\r\n\r\nwith the `data_dir` field." ]

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[ "Hi ! The \"allenai--c4\" config doesn't exist (this naming schema comes from old versions of `datasets`)\r\n\r\nYou can load it this way instead:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ncache_dir = 'path/to/your/cache/directory'\r\ndataset = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', cache_dir=cache_dir)\r\n```", "> Hi ! The \"allenai--c4\" config doesn't exist (this naming schema comes from old versions of `datasets`)\r\n> \r\n> You can load it this way instead:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> cache_dir = 'path/to/your/cache/directory'\r\n> dataset = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', cache_dir=cache_dir)\r\n> ```\r\n\r\nthanks, the command run successfully in the latest version\r\n" ]

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[ "You can add \r\n\r\n```python\r\nfrom PIL import ImageFile\r\nImageFile.LOAD_TRUNCATED_IMAGES = True\r\n```\r\n\r\nafter the imports to be able to read truncated images." ]

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[ "I don't think this bug is a thing ? Do you have some code that leads to this issue ?" ]

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[ "I don't know My conpany conputer cannot work. but in my computer, it work?", "Do you have a folder in your working directory called datasets?" ]

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[ "This bug comes from the `huggingface_hub` library, see: https://github.com/huggingface/huggingface_hub/issues/1952\r\n\r\nA fix is provided at https://github.com/huggingface/huggingface_hub/pull/1953. Feel free to install `huggingface_hub` from this PR, or wait for it to be merged and the new version of `huggingface_hub` to be released", "Thanks!" ]

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[ "Maybe we can add a helper message like `Maybe try again using \"hf://path/without/resolve\"` if the path contains `/resolve/` ?\r\n\r\ne.g.\r\n\r\n```\r\nFileNotFoundError: Unable to find 'hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json'\r\nIt looks like you used parts of the URL of the file from the Hugging Face website, but you should remove the \"/resolve/<revision>\" part to have a valid `hf://` path.\r\nPlease try again using this path instead:\r\n hf://datasets/HuggingFaceTB/eval_data/eval_data_context_and_answers.json\r\n```\r\n\r\nand suggest `f\"hf://datasets/HuggingFaceTB/eval_data@{revision}/eval_data_context_and_answers.json\"` if revision != \"main\"\r\n\r\nEDIT: I think this message should also be raised from the `huggingface_hub`'s `HfFileSystem` implementation" ]

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[ "It looks like a transient DNS issue. It should work fine now if you try again.\r\n\r\nThere is no parameter in load_dataset to skip failed downloads. In your case it would have skipped every single subsequent download until the DNS issue was resolved anyway." ]

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[ "Hi ! We now recommend using the `wikimedia/wikipedia` dataset, can you try loading this one instead ?\r\n\r\n```python\r\nwiki_dataset = load_dataset(\"wikimedia/wikipedia\", \"20231101.en\")\r\n```", "This bug has been fixed in `2.16.1` thanks to https://github.com/huggingface/datasets/pull/6544, feel free to update `datasets` and re-run your code :)\r\n\r\n```\r\npip install -U datasets\r\n```" ]

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[ "This is a problem with your environment. You should be able to fix it by upgrading `numpy` based on [this](https://github.com/numpy/numpy/issues/23570) issue.", "Bro I already update numpy package.", "Then, this shouldn't throw an error on your machine:\r\n```python\r\nimport numpy\r\nnumpy._no_nep50_warning\r\n```\r\n\r\nIf it does, run `python -m pip install numpy` to ensure the correct `pip` is used for the package installation.", "Your suggestion to run `python -m pip install numpy` proved to be successful, and my issue has been resolved. I am grateful for your assistance, @mariosasko" ]

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[ "Concatenating datasets doesn't create any indices mapping - so flattening indices is not needed (unless you shuffle the dataset).\r\nCan you share the snippet of code you are using to merge your datasets and save them to disk ?" ]

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[ "Hi ! Are you sure you have `datasets` 2.16 ? I just checked and on 2.16 I can run `from datasets.arrow_writer import SchemaInferenceError` without error", "I have the same issue - using with datasets version 2.16.1. Also this is on a kaggle notebook - other people with the same issue also seem to be having it on kaggle?", "I have the same issue now and didn't have this problem around 2 weeks ago.", "> Hi ! Are you sure you have `datasets` 2.16 ? I just checked and on 2.16 I can run `from datasets.arrow_writer import SchemaInferenceError` without error\r\n\r\nYes, I am sure\r\n\r\n```\r\n!pip show datasets\r\nName: datasets\r\nVersion: 2.16.1\r\nSummary: HuggingFace community-driven open-source library of datasets\r\nHome-page: https://github.com/huggingface/datasets\r\nAuthor: HuggingFace Inc.\r\nAuthor-email: thomas@huggingface.co\r\nLicense: Apache 2.0\r\nLocation: /opt/conda/lib/python3.10/site-packages\r\nRequires: aiohttp, dill, filelock, fsspec, huggingface-hub, multiprocess, numpy, packaging, pandas, pyarrow, pyarrow-hotfix, pyyaml, requests, tqdm, xxhash\r\nRequired-by: trl\r\n```", "> I have the same issue - using with datasets version 2.16.1. Also this is on a kaggle notebook - other people with the same issue also seem to be having it on kaggle?\r\n\r\nDon't know about other people. But I am having this issue whose solution I can't find anywhere. And this issue still persists. ", "> I have the same issue now and didn't have this problem around 2 weeks ago.\r\n\r\nSame here", "I was having the same issue but the datasets version was 2.6.1, after I updated it to latest(2.16), error is gone while importing.\r\n", "> I was having the same issue but the datasets version was 2.6.1, after I updated it to latest(2.16), error is gone while importing.\r\n\r\nI also have datasets version 2.16, but the error is still there.", "Can you try re-installing `datasets` ?", "> Can you try re-installing `datasets` ?\r\n\r\nI tried re-installing. Still getting the same error. \r\n", "> > Can you try re-installing `datasets` ?\r\n> \r\n> I tried re-installing. Still getting the same error.\r\n\r\nIn kaggle I used:\r\n- `%pip install -U datasets`\r\nand then restarted runtime and then everything works fine.", "> > > Can you try re-installing `datasets` ?\r\n> > \r\n> > \r\n> > I tried re-installing. Still getting the same error.\r\n> \r\n> In kaggle I used:\r\n> \r\n> * `%pip install -U datasets`\r\n> and then restarted runtime and then everything works fine.\r\n\r\nYes, this is working. When I restart the runtime after installing packages, it's working perfectly. Thank you so much. But why do we need to restart runtime every time after installing packages?", "> > > > Can you try re-installing `datasets` ?\r\n> > > \r\n> > > \r\n> > > I tried re-installing. Still getting the same error.\r\n> > \r\n> > \r\n> > In kaggle I used:\r\n> > \r\n> > * `%pip install -U datasets`\r\n> > and then restarted runtime and then everything works fine.\r\n> \r\n> Yes, this is working. When I restart the runtime after installing packages, it's working perfectly. Thank you so much. But why do we need to restart runtime every time after installing packages?\r\nFor some packages it is required.\r\nhttps://stackoverflow.com/questions/57831187/need-to-restart-runtime-before-import-an-installed-package-in-colab\r\n", "> > > > > Can you try re-installing `datasets` ?\r\n> > > > \r\n> > > > \r\n> > > > I tried re-installing. Still getting the same error.\r\n> > > \r\n> > > \r\n> > > In kaggle I used:\r\n> > > \r\n> > > * `%pip install -U datasets`\r\n> > > and then restarted runtime and then everything works fine.\r\n> > \r\n> > \r\n> > Yes, this is working. When I restart the runtime after installing packages, it's working perfectly. Thank you so much. But why do we need to restart runtime every time after installing packages?\r\n> > For some packages it is required.\r\n> > https://stackoverflow.com/questions/57831187/need-to-restart-runtime-before-import-an-installed-package-in-colab\r\n\r\nThank you for your assistance. I dedicated the past 2-3 weeks to resolving this issue. Interestingly, it runs flawlessly in Colab without requiring a runtime restart. However, the problem persisted exclusively in Kaggle. I appreciate your help once again. Thank you.", "Closing this issue as it is not related to the datasets library; rather, it's linked to platform-related issues." ]

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[ "Related to #3113 ", "Conceptually, we can use xarray to load the netCDF file, then xarray -> pandas -> pyarrow.", "I'd still need to verify that such a conversion would be lossless, especially for multi-dimensional data." ]

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[ "Hi ! Thanks for reporting\r\n\r\nThis is a bug in 2.16.0 for some datasets when `cache_dir` is a relative path. I opened https://github.com/huggingface/datasets/pull/6543 to fix this", "We just released 2.16.1 with a fix:\r\n\r\n```\r\npip install -U datasets\r\n```" ]