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https://api.github.com/repos/huggingface/datasets/issues/6891 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6891/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6891/comments | https://api.github.com/repos/huggingface/datasets/issues/6891/events | https://github.com/huggingface/datasets/issues/6891 | 2,291,118,869 | I_kwDODunzps6Ij7MV | 6,891 | Unable to load JSON saved using `to_json` | {
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"Hi @DarshanDeshpande,\r\n\r\nPlease note that the default format of the method `Dataset.to_json` is [JSON-Lines](https://jsonlines.org/): it passes `orient=\"records\", lines=True` to `pandas.DataFrame.to_json`. This format is specially useful for large datasets, since unlike regular JSON files, it does not require loading all the data into memory at once, but can be done iteratively by batches.\r\n\r\nIn order to read this file using the `json` library, you should parse line by line:\r\n```python\r\nwith open(\"full_dataset.json\", \"r\") as f:\r\n data = [json.loads(line) for line in f]\r\nlen(data)\r\n```\r\nMaybe we should explain this better in our docs.",
"Now we explain this better in out docs:\r\n- #6895"
] | 1970-01-01T00:00:00.000001 | 1,715 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
Datasets stored in the JSON format cannot be loaded using `json.load()`
### Steps to reproduce the bug
```
import json
from datasets import load_dataset
dataset = load_dataset("squad")
train_dataset, test_dataset = dataset["train"], dataset["validation"]
test_dataset.to_json("full_dataset.json")
# This works
loaded_test = load_dataset("json", data_files="full_dataset.json")
# This fails
loaded_test = json.load(open("full_dataset.json", "r"))
```
### Expected behavior
The JSON should be correctly formatted when writing so that it can be loaded using `json.load()`.
### Environment info
Colab: https://colab.research.google.com/drive/1st1iStFUVgu9ZPvnzSzL4vDeYWDwYpUm?usp=sharing | {
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] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,715 | null | NONE | null | ### Feature request
when working with a really large dataset it would save us a lot of time (and compute resources) to use either with_transform or the set_transform from the Dataset class instead of waiting for the entire dataset to map
### Motivation
don't want to wait for a really long dataset to map, this would give IterableDataset an extra advantage over the Dataset class.
reducing time and resources
### Your contribution
I am a little busy with my job search lately, but would post about this feature in my social media.
Apologies again (dad going to kick me out soon), if I ever have some free time I will contribute to making this a reality, but that's going to be hard
/ (┬┬﹏┬┬)\ | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6887 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6887/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6887/comments | https://api.github.com/repos/huggingface/datasets/issues/6887/events | https://github.com/huggingface/datasets/issues/6887 | 2,286,786,396 | I_kwDODunzps6ITZdc | 6,887 | FAISS load to None | {
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"Hello,\r\n\r\nI'm not sure I understand. \r\nThe return value of `ds.load_faiss_index` is None as expected.\r\n\r\nI see that loading an Index on a dataset that doesn't have an `embedding` column doesn't raise an Issue. Is that the issue?\r\n\r\nSo `ds` doesn't have an `embedding` column, but we load an index that looks for it. But this will raise an issue only when calling `ds.search`."
] | 1970-01-01T00:00:00.000001 | 1,715 | null | NONE | null | ### Describe the bug
I've use FAISS with Datasets and save to FAISS.
Then load to save FAISS then no error, then ds to None
```python
ds.load_faiss_index('embeddings', 'my_index.faiss')
```
### Steps to reproduce the bug
# 1.
```python
ds_with_embeddings = ds.map(lambda example: {'embeddings': model(transforms(example['image']).unsqueeze(0)).squeeze()}, batch_size=64)
ds_with_embeddings.add_faiss_index(column='embeddings')
ds_with_embeddings.save_faiss_index('embeddings', 'index.faiss')
```
# 2.
```python
ds.load_faiss_index('embeddings', 'my_index.faiss')
```
### Expected behavior
Add column in Datasets.
### Environment info
Google Colab, SageMaker Notebook | null | {
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with python 3.11 I execute:
```py
from transformers import Wav2Vec2Processor, Data2VecAudioModel
import torch
from torch import nn
from datasets import load_dataset, concatenate_datasets
# load demo audio and set processor
dataset_clean = load_dataset("librispeech_asr", "clean", split="validation", data_dir="data", cache_dir="cache")
```
This fails in the last line with
```log
Found cached dataset librispeech_asr (file:///Users/as/Documents/Project/git/audio2vec/cache/librispeech_asr/clean-data_dir=data/2.1.0/cff5df6e7955c80a67f80e27e7e655de71c689e2d2364bece785b972acb37fe7)
Traceback (most recent call last):
File "/Users/as/Documents/Project/git/audio2vec/src/music2vec-v1.py", line 7, in <module>
dataset_clean = load_dataset("librispeech_asr", "clean", split="validation", data_dir="data", cache_dir="cache")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/as/anaconda3/lib/python3.11/site-packages/datasets/load.py", line 1810, in load_dataset
ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/as/anaconda3/lib/python3.11/site-packages/datasets/builder.py", line 1113, in as_dataset
raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")
NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.
```
### Steps to reproduce the bug
I setup an venv with requirements.txt
```txt
transformers==4.40.2
torch==2.2.2
datasets==2.16.0
fsspec==2023.9.2
```
pip freeze is:
```
aiohttp==3.9.5
aiosignal==1.3.1
attrs==23.2.0
certifi==2024.2.2
charset-normalizer==3.3.2
datasets==2.16.0
dill==0.3.7
filelock==3.14.0
frozenlist==1.4.1
fsspec==2023.9.2
huggingface-hub==0.23.0
idna==3.7
Jinja2==3.1.4
MarkupSafe==2.1.5
mpmath==1.3.0
multidict==6.0.5
multiprocess==0.70.15
networkx==3.3
numpy==1.26.4
packaging==24.0
pandas==2.2.2
pyarrow==16.0.0
pyarrow-hotfix==0.6
python-dateutil==2.9.0.post0
pytz==2024.1
PyYAML==6.0.1
regex==2024.4.28
requests==2.31.0
safetensors==0.4.3
six==1.16.0
sympy==1.12
tokenizers==0.19.1
torch==2.2.2
tqdm==4.66.4
transformers==4.40.2
typing_extensions==4.11.0
tzdata==2024.1
urllib3==2.2.1
xxhash==3.4.1
yarl==1.9.4
```
I execute this on a M1 Mac.
### Expected behavior
I don't understand the error message. Why is "local" caching not supported. Would it possible to give some additional hint with the error message how to solve this issue?
### Environment info
source ....
python -u example.py | null | {
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] | null | [] | 1970-01-01T00:00:00.000001 | 1,715 | 1970-01-01T00:00:00.000001 | MEMBER | null | After jax-0.4.27 release (https://github.com/google/jax/releases/tag/jax-v0.4.27), our CI is broken with the error:
```Python traceback
AttributeError: 'jaxlib.xla_extension.DeviceList' object has no attribute 'device'. Did you mean: 'devices'?
```
See: https://github.com/huggingface/datasets/actions/runs/8997488610/job/24715736153
```Python traceback
___________________ FormatterTest.test_jax_formatter_device ____________________
[gw1] linux -- Python 3.10.14 /opt/hostedtoolcache/Python/3.10.14/x64/bin/python
self = <tests.test_formatting.FormatterTest testMethod=test_jax_formatter_device>
@require_jax
def test_jax_formatter_device(self):
import jax
from datasets.formatting import JaxFormatter
pa_table = self._create_dummy_table()
device = jax.devices()[0]
formatter = JaxFormatter(device=str(device))
row = formatter.format_row(pa_table)
> assert row["a"].device() == device
E AttributeError: 'jaxlib.xla_extension.DeviceList' object has no attribute 'device'. Did you mean: 'devices'?
tests/test_formatting.py:630: AttributeError
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6882 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6882/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6882/comments | https://api.github.com/repos/huggingface/datasets/issues/6882/events | https://github.com/huggingface/datasets/issues/6882 | 2,284,803,158 | I_kwDODunzps6IL1RW | 6,882 | Connection Error When Using By-pass Proxies | {
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"Changing the supplier of the proxy will solve this problem, or you can visit and follow the instructions in https://hf-mirror.com "
] | 1970-01-01T00:00:00.000001 | 1,715 | null | NONE | null | ### Describe the bug
I'm currently using Clash for Windows as my proxy tunnel, after exporting HTTP_PROXY and HTTPS_PROXY to the port that clash provides🤔, it runs into a connection error saying "Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f969d391870>: Failed to establish a new connection: [Errno 111] Connection refused'))")))"
I have already read the documentation provided on the hugginface, but I think I didn't see the detailed instruction on how to set up proxies for this library.
### Steps to reproduce the bug
1. Turn on any proxy software like Clash / ShadosocksR etc.
2. export system varibles to the port provided by your proxy software in wsl (It's ok for other applications to use proxy expect dataset-library)
3. load any dataset from hugginface online
### Expected behavior
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
Cell In[33], [line 3](vscode-notebook-cell:?execution_count=33&line=3)
[1](vscode-notebook-cell:?execution_count=33&line=1) from datasets import load_metric
----> [3](vscode-notebook-cell:?execution_count=33&line=3) metric = load_metric("seqeval")
File ~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:46, in deprecated.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
[44](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:44) warnings.warn(warning_msg, category=FutureWarning, stacklevel=2)
[45](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:45) _emitted_deprecation_warnings.add(func_hash)
---> [46](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:46) return deprecated_function(*args, **kwargs)
File ~/.local/lib/python3.10/site-packages/datasets/load.py:2104, in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, revision, trust_remote_code, **metric_init_kwargs)
[2101](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2101) warnings.filterwarnings("ignore", message=".*https://huggingface.co/docs/evaluate$", category=FutureWarning)
[2103](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2103) download_mode = DownloadMode(download_mode or DownloadMode.REUSE_DATASET_IF_EXISTS)
-> [2104](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2104) metric_module = metric_module_factory(
[2105](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2105) path,
[2106](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2106) revision=revision,
[2107](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2107) download_config=download_config,
[2108](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2108) download_mode=download_mode,
[2109](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2109) trust_remote_code=trust_remote_code,
[2110](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2110) ).module_path
[2111](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2111) metric_cls = import_main_class(metric_module, dataset=False)
[2112](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2112) metric = metric_cls(
[2113](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2113) config_name=config_name,
[2114](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2114) process_id=process_id,
...
--> [633](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:633) raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
[634](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:634) elif response is not None:
[635](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:635) raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))")))
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.2.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6881 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6881/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6881/comments | https://api.github.com/repos/huggingface/datasets/issues/6881/events | https://github.com/huggingface/datasets/issues/6881 | 2,284,794,009 | I_kwDODunzps6ILzCZ | 6,881 | AttributeError: module 'PIL.Image' has no attribute 'ExifTags' | {
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"@albertvillanova @lhoestq just ran into it and requiring newer pillow isn't a solution as it breaks Pillow-SIMD which is behind Pillow quite a few versions but necessary for training with reasonable throughput. \r\n\r\nA couple things here... \r\n\r\n1. This can be done with a method that isn't an issue for any somewhat recent Pillow\r\n`image = ImageOps.exif_transpose(image)`\r\n\r\n2. I'd rather this not be done for me automatically. Sometimes exif data is correct, sometimes it's not. Sometimes I might want to correct the orientation, sometimes I might not. \r\n\r\nIn any case if I've preprocessed the images properly myself I don't want to incur overhead, possible further fp seeks, parsing, to load the exif that's not loaded and parsed when you just open and decode the image.",
"Hi @rwightman, thanks for your feedback.\r\n\r\nFirst, as a side note comment, please note that you are depending on Pillow-SIMD and that library seems no longer maintained:\r\n- it has not been updated for more than a year: last commit to main was on June 20, 2023: https://github.com/uploadcare/pillow-simd/commit/faae977a00472275690664fe27e21df4e4e8ce07\r\n- in PyPI, the last release was more than 2 years ago, on January 4, 2022: https://pypi.org/project/Pillow-SIMD/#history\r\n\r\nIn relation with your suggestions for the `datasets` library, the changes were introduced by this PR:\r\n- #6739\r\n\r\nI agree maybe we should have given the option whether to perform this operation or not.",
"@albertvillanova \r\n\r\nHuh, thought I'd just installed the current datasets when I ran into this, maybe it was behind...\r\n\r\nI'm aware the support for SIMD is a problem, but it's up to 8x faster than non SIMD Pillow and really necessary in many training situations or you have lots of idle GPUs. The current situation is unfortunate but most changes since 9.0 aren't all that important for 'decoding jpegs and resizing'"
] | 1970-01-01T00:00:00.000001 | 1,721 | 1970-01-01T00:00:00.000001 | MEMBER | null | When trying to load an image dataset in an old Python environment (with Pillow-8.4.0), an error is raised:
```Python traceback
AttributeError: module 'PIL.Image' has no attribute 'ExifTags'
```
The error traceback:
```Python traceback
~/huggingface/datasets/src/datasets/iterable_dataset.py in __iter__(self)
1391 # `IterableDataset` automatically fills missing columns with None.
1392 # This is done with `_apply_feature_types_on_example`.
-> 1393 example = _apply_feature_types_on_example(
1394 example, self.features, token_per_repo_id=self._token_per_repo_id
1395 )
~/huggingface/datasets/src/datasets/iterable_dataset.py in _apply_feature_types_on_example(example, features, token_per_repo_id)
1080 encoded_example = features.encode_example(example)
1081 # Decode example for Audio feature, e.g.
-> 1082 decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
1083 return decoded_example
1084
~/huggingface/datasets/src/datasets/features/features.py in decode_example(self, example, token_per_repo_id)
1974
-> 1975 return {
1976 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1977 if self._column_requires_decoding[column_name]
~/huggingface/datasets/src/datasets/features/features.py in <dictcomp>(.0)
1974
1975 return {
-> 1976 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1977 if self._column_requires_decoding[column_name]
1978 else value
~/huggingface/datasets/src/datasets/features/features.py in decode_nested_example(schema, obj, token_per_repo_id)
1339 # we pass the token to read and decode files from private repositories in streaming mode
1340 if obj is not None and schema.decode:
-> 1341 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
1342 return obj
1343
~/huggingface/datasets/src/datasets/features/image.py in decode_example(self, value, token_per_repo_id)
187 image = PIL.Image.open(BytesIO(bytes_))
188 image.load() # to avoid "Too many open files" errors
--> 189 if image.getexif().get(PIL.Image.ExifTags.Base.Orientation) is not None:
190 image = PIL.ImageOps.exif_transpose(image)
191 if self.mode and self.mode != image.mode:
~/huggingface/datasets/venv/lib/python3.9/site-packages/PIL/Image.py in __getattr__(name)
75 )
76 return categories[name]
---> 77 raise AttributeError(f"module '{__name__}' has no attribute '{name}'")
78
79
AttributeError: module 'PIL.Image' has no attribute 'ExifTags'
```
### Environment info
Since datasets 2.19.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6880 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6880/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6880/comments | https://api.github.com/repos/huggingface/datasets/issues/6880/events | https://github.com/huggingface/datasets/issues/6880 | 2,283,278,337 | I_kwDODunzps6IGBAB | 6,880 | Webdataset: KeyError: 'png' on some datasets when streaming | {
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"The error is caused by malformed basenames of the files within the TARs:\r\n- `15_Cohen_1-s2.0-S0929664620300449-gr3_lrg-b.png` becomes `15_Cohen_1-s2` as the grouping `__key__`, and `0-S0929664620300449-gr3_lrg-b.png` as the additional key to be added to the example\r\n- whereas the intended behavior was to use `15_Cohen_1-s2.0-S0929664620300449-gr3_lrg-b` as the grouping `__key__`, and `png` as the additional key to be added to the example\r\n\r\nTo get the expected behavior, the basenames of the files within the TARs should be fixed so that they only contain a single dot, the one separating the file extension.",
"I reopen it because I think we should try to give a clearer error message with a specific error code.\r\n\r\nFor now, it's hard for the user to understand where the error comes from (not everybody knows the subtleties of the webdataset filename structure).\r\n\r\n(we can transfer it to https://github.com/huggingface/dataset-viewer if it fits better there)",
"same with .jpg -> https://huggingface.co/datasets/ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions\r\n\r\n```\r\nError code: DatasetGenerationError\r\nException: DatasetGenerationError\r\nMessage: An error occurred while generating the dataset\r\nTraceback: Traceback (most recent call last):\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1748, in _prepare_split_single\r\n for key, record in generator:\r\n File \"/src/services/worker/src/worker/job_runners/config/parquet_and_info.py\", line 818, in wrapped\r\n for item in generator(*args, **kwargs):\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py\", line 109, in _generate_examples\r\n example[field_name] = {\"path\": example[\"__key__\"] + \".\" + field_name, \"bytes\": example[field_name]}\r\n KeyError: 'jpg'\r\n \r\n The above exception was the direct cause of the following exception:\r\n \r\n Traceback (most recent call last):\r\n File \"/src/services/worker/src/worker/job_runners/config/parquet_and_info.py\", line 1316, in compute_config_parquet_and_info_response\r\n parquet_operations, partial = stream_convert_to_parquet(\r\n File \"/src/services/worker/src/worker/job_runners/config/parquet_and_info.py\", line 909, in stream_convert_to_parquet\r\n builder._prepare_split(\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1627, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1784, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\n datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n",
"More details in the spec (https://docs.google.com/document/d/18OdLjruFNX74ILmgrdiCI9J1fQZuhzzRBCHV9URWto0/edit#heading=h.hkptaq2kct2s)\r\n\r\n> The prefix of a file is all directory components of the file plus the file name component up to the first “.” in the file name.\r\n> The last extension (i.e., the portion after the last “.”) in a file name determines the file type.\r\n\r\n> Example:\r\n\timages17/image194.left.jpg\r\n\timages17/image194.right.jpg\r\n\timages17/image194.json\r\n\timages17/image12.left.jpg\r\n\timages17/image12.json\r\n\timages17/image12.right.jpg\r\n\timages3/image1459.left.jpg\r\n> \t…\r\n> When reading this with a WebDataset library, you would get the following two dictionaries back in sequence:\r\n\r\n { “__key__”: “images17/image194”, “left.jpg”: b”...”, “right.jpg”: b”...”, “json”: b”...”}\r\n { “__key__”: “images17/image12”, “left.jpg”: b”...”, “right.jpg”: b”...”, “json”: b”...”}\r\n",
"OK, the issue is different in the latter case: some files are suffixed as `.jpeg`, and others as `.jpg` :)\r\n\r\nIs it a limitation of the webdataset format, or of the datasets library @lhoestq? And could we be able to give a clearer error?"
] | 1970-01-01T00:00:00.000001 | 1,715 | null | MEMBER | null | reported at https://huggingface.co/datasets/tbone5563/tar_images/discussions/1
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("tbone5563/tar_images")
Downloading data: 100%
1.41G/1.41G [00:48<00:00, 17.2MB/s]
Downloading data: 100%
619M/619M [00:11<00:00, 57.4MB/s]
Generating train split:
970/0 [00:02<00:00, 534.94 examples/s]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)
1747 _time = time.time()
-> 1748 for key, record in generator:
1749 if max_shard_size is not None and writer._num_bytes > max_shard_size:
7 frames
[/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/webdataset/webdataset.py](https://localhost:8080/#) in _generate_examples(self, tar_paths, tar_iterators)
108 for field_name in image_field_names + audio_field_names:
--> 109 example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]}
110 yield f"{tar_idx}_{example_idx}", example
KeyError: 'png'
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
[<ipython-input-2-8e0fbb7badc9>](https://localhost:8080/#) in <cell line: 3>()
1 from datasets import load_dataset
2
----> 3 ds = load_dataset("tbone5563/tar_images")
[/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, trust_remote_code, **config_kwargs)
2607
2608 # Download and prepare data
-> 2609 builder_instance.download_and_prepare(
2610 download_config=download_config,
2611 download_mode=download_mode,
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in 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)
1025 if num_proc is not None:
1026 prepare_split_kwargs["num_proc"] = num_proc
-> 1027 self._download_and_prepare(
1028 dl_manager=dl_manager,
1029 verification_mode=verification_mode,
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs)
1787
1788 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):
-> 1789 super()._download_and_prepare(
1790 dl_manager,
1791 verification_mode,
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
1120 try:
1121 # Prepare split will record examples associated to the split
-> 1122 self._prepare_split(split_generator, **prepare_split_kwargs)
1123 except OSError as e:
1124 raise OSError(
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1625 job_id = 0
1626 with pbar:
-> 1627 for job_id, done, content in self._prepare_split_single(
1628 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1629 ):
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)
1782 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1783 e = e.__context__
-> 1784 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1785
1786 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
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https://api.github.com/repos/huggingface/datasets/issues/6879 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6879/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6879/comments | https://api.github.com/repos/huggingface/datasets/issues/6879/events | https://github.com/huggingface/datasets/issues/6879 | 2,282,968,259 | I_kwDODunzps6IE1TD | 6,879 | Batched mapping does not raise an error if values for an existing column are empty | {
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} | [] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,715 | null | NONE | null | ### Describe the bug
Using `Dataset.map(fn, batched=True)` allows resizing the dataset by returning a dict of lists, all of which must be the same size. If they are not the same size, an error like `pyarrow.lib.ArrowInvalid: Column 1 named x expected length 1 but got length 0` is raised.
This is not the case if the function returns an empty list for an existing column in the dataset. In that case, the dataset is silently resized to 0 rows.
### Steps to reproduce the bug
MWE:
```
import datasets
data = datasets.Dataset.from_dict({"test": [1]})
def mapping_fn(examples):
return {"test": [], "y": [1]}
data = data.map(mapping_fn, batched=True)
print(len(data))
```
Note that when returning `"x": []`, the error is raised correctly, also when returning `"test": [1,2]`.
### Expected behavior
Expected an exception: `pyarrow.lib.ArrowInvalid: Column 1 named test expected length 1 but got length 0` or `pyarrow.lib.ArrowInvalid: Column 2 named y expected length 0 but got length 1`.
Any exception would be acceptable.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-5.4.0-153-generic-x86_64-with-glibc2.31
- Python version: 3.11.8
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6877 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6877/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6877/comments | https://api.github.com/repos/huggingface/datasets/issues/6877/events | https://github.com/huggingface/datasets/issues/6877 | 2,282,068,337 | I_kwDODunzps6IBZlx | 6,877 | OSError: [Errno 24] Too many open files | {
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"ulimit -n 8192 can solve this problem",
"> ulimit -n 8192 can solve this problem\r\n\r\nWould there be a systematic way to do this ? The data loading is part of the [MTEB](https://github.com/embeddings-benchmark/mteb) library",
"> > ulimit -n 8192 can solve this problem\r\n> \r\n> Would there be a systematic way to do this ? The data loading is part of the [MTEB](https://github.com/embeddings-benchmark/mteb) library\r\n\r\n I think we could modify the _prepare_split_single function",
"I fixed it with https://github.com/huggingface/datasets/pull/6893, feel free to re-open if you're still having the issue :)",
"> I fixed it with #6893, feel free to re-open if you're still having the issue :)\r\n\r\nThanks a lot!"
] | 1970-01-01T00:00:00.000001 | 1,717 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
I am trying to load the 'default' subset of the following dataset which contains lots of files (828 per split): [https://huggingface.co/datasets/mteb/biblenlp-corpus-mmteb](https://huggingface.co/datasets/mteb/biblenlp-corpus-mmteb)
When trying to load it using the `load_dataset` function I get the following error
```python
>>> from datasets import load_dataset
>>> d = load_dataset('mteb/biblenlp-corpus-mmteb')
Downloading readme: 100%|████████████████████████████████████████████████████████████████████████| 201k/201k [00:00<00:00, 1.07MB/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 1069.15it/s]
Resolving data files: 100%|███████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 436182.33it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 2228.75it/s]
Resolving data files: 100%|███████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 646478.73it/s]
Resolving data files: 100%|███████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 831032.24it/s]
Resolving data files: 100%|███████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 517645.51it/s]
Downloading data: 100%|████████████████████████████████████████████████████████████████████████| 828/828 [00:33<00:00, 24.87files/s]
Downloading data: 100%|████████████████████████████████████████████████████████████████████████| 828/828 [00:30<00:00, 27.48files/s]
Downloading data: 100%|████████████████████████████████████████████████████████████████████████| 828/828 [00:30<00:00, 26.94files/s]
Generating train split: 1571592 examples [00:03, 461438.97 examples/s]
Generating test split: 11163 examples [00:00, 118190.72 examples/s]
Traceback (most recent call last):
File ".env/lib/python3.12/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
for _, table in generator:
File ".env/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 99, in _generate_tables
with open(file, "rb") as f:
^^^^^^^^^^^^^^^^
File ".env/lib/python3.12/site-packages/datasets/streaming.py", line 75, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".env/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1224, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".env/lib/python3.12/site-packages/fsspec/core.py", line 135, in open
return self.__enter__()
^^^^^^^^^^^^^^^^
File ".env/lib/python3.12/site-packages/fsspec/core.py", line 103, in __enter__
f = self.fs.open(self.path, mode=mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".env/lib/python3.12/site-packages/fsspec/spec.py", line 1293, in open
f = self._open(
^^^^^^^^^^^
File ".env/lib/python3.12/site-packages/datasets/filesystems/compression.py", line 81, in _open
return self.file.open()
^^^^^^^^^^^^^^^^
File ".env/lib/python3.12/site-packages/fsspec/core.py", line 135, in open
return self.__enter__()
^^^^^^^^^^^^^^^^
File ".env/lib/python3.12/site-packages/fsspec/core.py", line 103, in __enter__
f = self.fs.open(self.path, mode=mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".env/lib/python3.12/site-packages/fsspec/spec.py", line 1293, in open
f = self._open(
^^^^^^^^^^^
File ".env/lib/python3.12/site-packages/fsspec/implementations/local.py", line 197, in _open
return LocalFileOpener(path, mode, fs=self, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".env/lib/python3.12/site-packages/fsspec/implementations/local.py", line 322, in __init__
self._open()
File ".env/lib/python3.12/site-packages/fsspec/implementations/local.py", line 327, in _open
self.f = open(self.path, mode=self.mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
OSError: [Errno 24] Too many open files: '.cache/huggingface/datasets/downloads/3a347186abfc0f9c924dde0221d246db758c7232c0101523f04a87c17d696618'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File ".env/lib/python3.12/site-packages/datasets/builder.py", line 981, in incomplete_dir
yield tmp_dir
File ".env/lib/python3.12/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File ".env/lib/python3.12/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File ".env/lib/python3.12/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File ".env/lib/python3.12/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".env/lib/python3.12/site-packages/datasets/load.py", line 2609, in load_dataset
builder_instance.download_and_prepare(
File ".env/lib/python3.12/site-packages/datasets/builder.py", line 1007, in download_and_prepare
with incomplete_dir(self._output_dir) as tmp_output_dir:
File "/usr/lib/python3.12/contextlib.py", line 158, in __exit__
self.gen.throw(value)
File ".env/lib/python3.12/site-packages/datasets/builder.py", line 988, in incomplete_dir
shutil.rmtree(tmp_dir)
File "/usr/lib/python3.12/shutil.py", line 785, in rmtree
_rmtree_safe_fd(fd, path, onexc)
File "/usr/lib/python3.12/shutil.py", line 661, in _rmtree_safe_fd
onexc(os.scandir, path, err)
File "/usr/lib/python3.12/shutil.py", line 657, in _rmtree_safe_fd
with os.scandir(topfd) as scandir_it:
^^^^^^^^^^^^^^^^^
OSError: [Errno 24] Too many open files: '.cache/huggingface/datasets/mteb___biblenlp-corpus-mmteb/default/0.0.0/3912ed967b0834547f35b2da9470c4976b357c9a.incomplete'
```
I looked for the maximum number of open files on my machine (Ubuntu 24.04) and it seems to be 1024, but even when I try to load a single split (`load_dataset('mteb/biblenlp-corpus-mmteb', split='train')`) I get the same error
### Steps to reproduce the bug
```python
from datasets import load_dataset
d = load_dataset('mteb/biblenlp-corpus-mmteb')
```
### Expected behavior
Load the dataset without error
### Environment info
- `datasets` version: 2.19.0
- Platform: Linux-6.8.0-31-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
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https://api.github.com/repos/huggingface/datasets/issues/6869 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6869/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6869/comments | https://api.github.com/repos/huggingface/datasets/issues/6869/events | https://github.com/huggingface/datasets/issues/6869 | 2,280,048,297 | I_kwDODunzps6H5sap | 6,869 | Download is broken for dict of dicts: FileNotFoundError | {
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] | null | [] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | MEMBER | null | It seems there is a bug when downloading a dict of dicts of URLs introduced by:
- #6794
## Steps to reproduce the bug:
```python
from datasets import DownloadManager
dl_manager = DownloadManager()
paths = dl_manager.download({"train": {"frr": "hf://datasets/wikimedia/wikipedia/20231101.frr/train-00000-of-00001.parquet"}})
```
Stack trace:
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-7-0e0d76d25b09> in <module>
----> 1 paths = dl_manager.download({"train": {"frr": "hf://datasets/wikimedia/wikipedia/20231101.frr/train-00000-of-00001.parquet"}})
.../huggingface/datasets/src/datasets/download/download_manager.py in download(self, url_or_urls)
255 start_time = datetime.now()
256 with stack_multiprocessing_download_progress_bars():
--> 257 downloaded_path_or_paths = map_nested(
258 download_func,
259 url_or_urls,
.../huggingface/datasets/src/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc)
506 batch_size = max(len(iterable) // num_proc + int(len(iterable) % num_proc > 0), 1)
507 iterable = list(iter_batched(iterable, batch_size))
--> 508 mapped = [
509 _single_map_nested((function, obj, batched, batch_size, types, None, True, None))
510 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc)
.../huggingface/datasets/src/datasets/utils/py_utils.py in <listcomp>(.0)
507 iterable = list(iter_batched(iterable, batch_size))
508 mapped = [
--> 509 _single_map_nested((function, obj, batched, batch_size, types, None, True, None))
510 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc)
511 ]
.../huggingface/datasets/src/datasets/utils/py_utils.py in _single_map_nested(args)
375 and all(not isinstance(v, types) for v in data_struct)
376 ):
--> 377 return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
378
379 # Reduce logging to keep things readable in multiprocessing with tqdm
.../huggingface/datasets/src/datasets/utils/py_utils.py in <listcomp>(.0)
375 and all(not isinstance(v, types) for v in data_struct)
376 ):
--> 377 return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
378
379 # Reduce logging to keep things readable in multiprocessing with tqdm
.../huggingface/datasets/src/datasets/download/download_manager.py in _download_batched(self, url_or_filenames, download_config)
311 )
312 else:
--> 313 return [
314 self._download_single(url_or_filename, download_config=download_config)
315 for url_or_filename in url_or_filenames
.../huggingface/datasets/src/datasets/download/download_manager.py in <listcomp>(.0)
312 else:
313 return [
--> 314 self._download_single(url_or_filename, download_config=download_config)
315 for url_or_filename in url_or_filenames
316 ]
.../huggingface/datasets/src/datasets/download/download_manager.py in _download_single(self, url_or_filename, download_config)
321 # append the relative path to the base_path
322 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 323 out = cached_path(url_or_filename, download_config=download_config)
324 out = tracked_str(out)
325 out.set_origin(url_or_filename)
.../huggingface/datasets/src/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
220 elif is_local_path(url_or_filename):
221 # File, but it doesn't exist.
--> 222 raise FileNotFoundError(f"Local file {url_or_filename} doesn't exist")
223 else:
224 # Something unknown
FileNotFoundError: Local file .../huggingface/datasets/{'frr': 'hf:/datasets/wikimedia/wikipedia/20231101.frr/train-00000-of-00001.parquet'} doesn't exist
```
Related to:
- #6850
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https://api.github.com/repos/huggingface/datasets/issues/6868 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6868/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6868/comments | https://api.github.com/repos/huggingface/datasets/issues/6868/events | https://github.com/huggingface/datasets/issues/6868 | 2,279,385,159 | I_kwDODunzps6H3KhH | 6,868 | datasets.BuilderConfig does not work. | {
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} | [] | closed | false | null | [] | null | [
"I guess the issue is caused by the customization of BuilderConfig that you use from the repo [https://github.com/BeyonderXX/InstructUIE](https://github.com/BeyonderXX/InstructUIE/blob/master/src/uie_dataset.py). You should report to them.\r\n\r\nI see you already opened an issue in their repo:\r\n- https://github.com/BeyonderXX/InstructUIE/issues/40"
] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
I custom a BuilderConfig and GeneratorBasedBuilder.
Here is the code for BuilderConfig
```
class UIEConfig(datasets.BuilderConfig):
def __init__(
self,
*args,
data_dir=None,
instruction_file=None,
instruction_strategy=None,
task_config_dir=None,
num_examples=None,
max_num_instances_per_task=None,
max_num_instances_per_eval_task=None,
over_sampling=None,
**kwargs
):
super().__init__(*args, **kwargs)
self.data_dir = data_dir
self.num_examples = num_examples
self.over_sampling = over_sampling
self.instructions = self._parse_instruction(instruction_file)
self.task_configs = self._parse_task_config(task_config_dir)
self.instruction_strategy = instruction_strategy
self.max_num_instances_per_task = max_num_instances_per_task
self.max_num_instances_per_eval_task = max_num_instances_per_eval_task
```
Besides, here is the code for GeneratorBasedBuilder.
```
class UIEInstructions(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("2.0.0")
BUILDER_CONFIG_CLASS = UIEConfig
BUILDER_CONFIGS = [
UIEConfig(name="default", description="Default config for NaturalInstructions")
]
DEFAULT_CONFIG_NAME = "default"
```
Here is the load_dataset
```
raw_datasets = load_dataset(
os.path.join(CURRENT_DIR, "uie_dataset.py"),
data_dir=data_args.data_dir,
task_config_dir=data_args.task_config_dir,
instruction_file=data_args.instruction_file,
instruction_strategy=data_args.instruction_strategy,
cache_dir=data_cache_dir, # for debug, change dataset size, otherwise open it
max_num_instances_per_task=data_args.max_num_instances_per_task,
max_num_instances_per_eval_task=data_args.max_num_instances_per_eval_task,
num_examples=data_args.num_examples,
over_sampling=data_args.over_sampling
)
```
Finally, I met the error.
```
BuilderConfig UIEConfig(name='default', version=0.0.0, data_dir=None, data_files=None, description='Default config for NaturalInstructions') doesn't have a 'task_config_dir' key.
```
I debugged the code, but I find the parameters added by me may not work.
### Steps to reproduce the bug
https://github.com/BeyonderXX/InstructUIE/blob/master/src/uie_dataset.py
### Expected behavior
```
BuilderConfig UIEConfig(name='default', version=0.0.0, data_dir=None, data_files=None, description='Default config for NaturalInstructions') doesn't have a 'task_config_dir' key.
```
### Environment info
torch 2.3.0+cu118
transformers 4.40.1
python 3.8 | {
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https://api.github.com/repos/huggingface/datasets/issues/6867 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6867/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6867/comments | https://api.github.com/repos/huggingface/datasets/issues/6867/events | https://github.com/huggingface/datasets/issues/6867 | 2,279,059,787 | I_kwDODunzps6H17FL | 6,867 | Improve performance of JSON loader | {
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"Thanks! Feel free to ping me for examples. May not respond immediately because we're all busy but would like to help.",
"Hi @natolambert, could you please give some examples of JSON files to benchmark?\r\n\r\nPlease note that this JSON file (https://huggingface.co/datasets/allenai/reward-bench-results/blob/main/eval-set-scores/Ray2333/reward-model-Mistral-7B-instruct-Unified-Feedback.json) is not in \"records\" orient; instead it has the following structure:\r\n```json\r\n{\r\n \"chat_template\": \"tulu\",\r\n \"id\": [30, 34, 35,...],\r\n \"model\": \"Ray2333/reward-model-Mistral-7B-instruct-Unified-Feedback\",\r\n \"model_type\": \"Seq. Classifier\",\r\n \"results\": [1, 1, 1, ...],\r\n \"scores_chosen\": [4.421875, 1.8916015625, 3.8515625,...],\r\n \"scores_rejected\": [-2.416015625, -1.47265625, -0.9912109375,...],\r\n \"subset\": [\"alpacaeval-easy\", \"alpacaeval-easy\", \"alpacaeval-easy\",...]\r\n \"text_chosen\": [\"<s>[INST] How do I detail a...\",...],\r\n \"text_rejected\": [\"<s>[INST] How do I detail a...\",...]\r\n}\r\n```\r\n\r\nNote that \"records\" orient should be a list (not a dict) with each row as one item of the list:\r\n```json\r\n[\r\n {\"chat_template\": \"tulu\", \"id\": 30,... },\r\n {\"chat_template\": \"tulu\", \"id\": 34,... },\r\n ...\r\n]\r\n```",
"We use a mix (which is a mess), here's an example with the records orient\r\nhttps://huggingface.co/datasets/allenai/reward-bench-results/blob/main/best-of-n/alpaca_eval/tulu-13b/OpenAssistant/oasst-rm-2.1-pythia-1.4b-epoch-2.5.json\r\n\r\nThere are more in that folder, ~40mb maybe?",
"@albertvillanova here's a snippet so you don't need to click\r\n```\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 0\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 3.076171875\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 1\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 3.87890625\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 2\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 3.287109375\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 3\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 1.6337890625\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 4\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 5.27734375\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 5\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 3.0625\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 6\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 2.29296875\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 7\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 6.77734375\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 8\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 3.853515625\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 9\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 4.86328125\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 10\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 2.890625\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 11\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 4.70703125\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 12\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 4.45703125\r\n}\r\n```",
"Thanks again for your feedback, @natolambert.\r\n\r\nHowever, strictly speaking, the last file is not in JSON format but in kind of JSON-Lines like format (although not properly either because there are multiple newline characters within each object). Not even pandas can read that file format.\r\n\r\nAnyway, for JSON-Lines, I would expect that `datasets` and `pandas` have the same performance for JSON Lines files, as both use `pyarrow` under the hood...\r\n\r\nA proper JSON file in records orient should be a list (a JSON array): the first character should be `[`.\r\n\r\nAnyway, I am generating a JSON file from your JSON-Lines file to test performance."
] | 1970-01-01T00:00:00.000001 | 1,715 | 1970-01-01T00:00:00.000001 | MEMBER | null | As reported by @natolambert, loading regular JSON files with `datasets` shows poor performance.
The cause is that we use the `json` Python standard library instead of other faster libraries. See my old comment: https://github.com/huggingface/datasets/pull/2638#pullrequestreview-706983714
> There are benchmarks that compare different JSON packages, with the Standard Library one among the worst performant:
> - https://github.com/ultrajson/ultrajson#benchmarks
> - https://github.com/ijl/orjson#performance
I remember having a discussion about this and it was decided that it was better not to include an additional dependency on a 3rd-party library.
However:
- We already depend on `pandas` and `pandas` depends on `ujson`: so we have an indirect dependency on `ujson`
- Even if the above were not the case, we always could include `ujson` as an optional extra dependency, and check at runtime if it is installed to decide which library to use, either json or ujson | {
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https://api.github.com/repos/huggingface/datasets/issues/6866 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6866/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6866/comments | https://api.github.com/repos/huggingface/datasets/issues/6866/events | https://github.com/huggingface/datasets/issues/6866 | 2,278,736,221 | I_kwDODunzps6H0sFd | 6,866 | DataFilesNotFoundError for datasets in the open-llm-leaderboard | {
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"Potentially related:\r\n* #6864\r\n* #6850\r\n* #6848\r\n* #6819",
"Hi @jerome-white, thnaks for reporting.\r\n\r\nHowever, I cannot reproduce your issue:\r\n```python\r\n>>> from datasets import get_dataset_config_names\r\n\r\n>>> get_dataset_config_names(\"open-llm-leaderboard/details_davidkim205__Rhea-72b-v0.5\")\r\n['harness_arc_challenge_25',\r\n 'harness_gsm8k_5',\r\n 'harness_hellaswag_10',\r\n 'harness_hendrycksTest_5',\r\n 'harness_hendrycksTest_abstract_algebra_5',\r\n 'harness_hendrycksTest_anatomy_5',\r\n 'harness_hendrycksTest_astronomy_5',\r\n 'harness_hendrycksTest_business_ethics_5',\r\n 'harness_hendrycksTest_clinical_knowledge_5',\r\n 'harness_hendrycksTest_college_biology_5',\r\n 'harness_hendrycksTest_college_chemistry_5',\r\n 'harness_hendrycksTest_college_computer_science_5',\r\n 'harness_hendrycksTest_college_mathematics_5',\r\n 'harness_hendrycksTest_college_medicine_5',\r\n 'harness_hendrycksTest_college_physics_5',\r\n 'harness_hendrycksTest_computer_security_5',\r\n 'harness_hendrycksTest_conceptual_physics_5',\r\n 'harness_hendrycksTest_econometrics_5',\r\n 'harness_hendrycksTest_electrical_engineering_5',\r\n 'harness_hendrycksTest_elementary_mathematics_5',\r\n 'harness_hendrycksTest_formal_logic_5',\r\n 'harness_hendrycksTest_global_facts_5',\r\n 'harness_hendrycksTest_high_school_biology_5',\r\n 'harness_hendrycksTest_high_school_chemistry_5',\r\n 'harness_hendrycksTest_high_school_computer_science_5',\r\n 'harness_hendrycksTest_high_school_european_history_5',\r\n 'harness_hendrycksTest_high_school_geography_5',\r\n 'harness_hendrycksTest_high_school_government_and_politics_5',\r\n 'harness_hendrycksTest_high_school_macroeconomics_5',\r\n 'harness_hendrycksTest_high_school_mathematics_5',\r\n 'harness_hendrycksTest_high_school_microeconomics_5',\r\n 'harness_hendrycksTest_high_school_physics_5',\r\n 'harness_hendrycksTest_high_school_psychology_5',\r\n 'harness_hendrycksTest_high_school_statistics_5',\r\n 'harness_hendrycksTest_high_school_us_history_5',\r\n 'harness_hendrycksTest_high_school_world_history_5',\r\n 'harness_hendrycksTest_human_aging_5',\r\n 'harness_hendrycksTest_human_sexuality_5',\r\n 'harness_hendrycksTest_international_law_5',\r\n 'harness_hendrycksTest_jurisprudence_5',\r\n 'harness_hendrycksTest_logical_fallacies_5',\r\n 'harness_hendrycksTest_machine_learning_5',\r\n 'harness_hendrycksTest_management_5',\r\n 'harness_hendrycksTest_marketing_5',\r\n 'harness_hendrycksTest_medical_genetics_5',\r\n 'harness_hendrycksTest_miscellaneous_5',\r\n 'harness_hendrycksTest_moral_disputes_5',\r\n 'harness_hendrycksTest_moral_scenarios_5',\r\n 'harness_hendrycksTest_nutrition_5',\r\n 'harness_hendrycksTest_philosophy_5',\r\n 'harness_hendrycksTest_prehistory_5',\r\n 'harness_hendrycksTest_professional_accounting_5',\r\n 'harness_hendrycksTest_professional_law_5',\r\n 'harness_hendrycksTest_professional_medicine_5',\r\n 'harness_hendrycksTest_professional_psychology_5',\r\n 'harness_hendrycksTest_public_relations_5',\r\n 'harness_hendrycksTest_security_studies_5',\r\n 'harness_hendrycksTest_sociology_5',\r\n 'harness_hendrycksTest_us_foreign_policy_5',\r\n 'harness_hendrycksTest_virology_5',\r\n 'harness_hendrycksTest_world_religions_5',\r\n 'harness_truthfulqa_mc_0',\r\n 'harness_winogrande_5',\r\n 'results']\r\n```\r\n\r\nMaybe it was just a temporary issue...",
"> Maybe it was just a temporary issue...\r\n\r\nPerhaps. I've changed my workflow to use the hub's `HfFileSystem`, so for now this is no longer a blocker for me. I'll reopen the issue if that changes."
] | 1970-01-01T00:00:00.000001 | 1,715 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
When trying to get config names or load any dataset within the open-llm-leaderboard ecosystem (`open-llm-leaderboard/details_`) I receive the DataFilesNotFoundError. For the last month or so I've been loading datasets from the leaderboard almost everyday; yesterday was the first time I started seeing this.
### Steps to reproduce the bug
This snippet has three cells:
1. Loads the modules
2. Tries to get config names
3. Tries to load the dataset
I've chosen "davidkim205"'s Rhea-72b-v0.5 model because it is one of the best performers on the leaderboard should likely have no dataset issues:
```python
In [1]: from datasets import load_dataset, get_dataset_config_names
In [2]: get_dataset_config_names("open-llm-leaderboard/details_davidkim205__Rhea
...: -72b-v0.5")
---------------------------------------------------------------------------
DataFilesNotFoundError Traceback (most recent call last)
Cell In[2], line 1
----> 1 get_dataset_config_names("open-llm-leaderboard/details_davidkim205__Rhea-72b-v0.5")
File ~/open-llm-bda/venv/lib/python3.11/site-packages/datasets/inspect.py:347, in get_dataset_config_names(path, revision, download_config, download_mode, dynamic_modules_path, data_files, **download_kwargs)
291 def get_dataset_config_names(
292 path: str,
293 revision: Optional[Union[str, Version]] = None,
(...)
298 **download_kwargs,
299 ):
300 """Get the list of available config names for a particular dataset.
301
302 Args:
(...)
345 ```
346 """
--> 347 dataset_module = dataset_module_factory(
348 path,
349 revision=revision,
350 download_config=download_config,
351 download_mode=download_mode,
352 dynamic_modules_path=dynamic_modules_path,
353 data_files=data_files,
354 **download_kwargs,
355 )
356 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=os.path.basename(path))
357 return list(builder_cls.builder_configs.keys()) or [
358 dataset_module.builder_kwargs.get("config_name", builder_cls.DEFAULT_CONFIG_NAME or "default")
359 ]
File ~/open-llm-bda/venv/lib/python3.11/site-packages/datasets/load.py:1821, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
1812 return LocalDatasetModuleFactoryWithScript(
1813 combined_path,
1814 download_mode=download_mode,
1815 dynamic_modules_path=dynamic_modules_path,
1816 trust_remote_code=trust_remote_code,
1817 ).get_module()
1818 elif os.path.isdir(path):
1819 return LocalDatasetModuleFactoryWithoutScript(
1820 path, data_dir=data_dir, data_files=data_files, download_mode=download_mode
-> 1821 ).get_module()
1822 # Try remotely
1823 elif is_relative_path(path) and path.count("/") <= 1:
File ~/open-llm-bda/venv/lib/python3.11/site-packages/datasets/load.py:1039, in LocalDatasetModuleFactoryWithoutScript.get_module(self)
1033 patterns = get_data_patterns(base_path)
1034 data_files = DataFilesDict.from_patterns(
1035 patterns,
1036 base_path=base_path,
1037 allowed_extensions=ALL_ALLOWED_EXTENSIONS,
1038 )
-> 1039 module_name, default_builder_kwargs = infer_module_for_data_files(
1040 data_files=data_files,
1041 path=self.path,
1042 )
1043 data_files = data_files.filter_extensions(_MODULE_TO_EXTENSIONS[module_name])
1044 # Collect metadata files if the module supports them
File ~/open-llm-bda/venv/lib/python3.11/site-packages/datasets/load.py:597, in infer_module_for_data_files(data_files, path, download_config)
595 raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}")
596 if not module_name:
--> 597 raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
598 return module_name, default_builder_kwargs
DataFilesNotFoundError: No (supported) data files found in open-llm-leaderboard/details_davidkim205__Rhea-72b-v0.5
In [3]: data = load_dataset("open-llm-leaderboard/details_davidkim205__Rhea-72b-
...: v0.5", "harness_winogrande_5")
---------------------------------------------------------------------------
DataFilesNotFoundError Traceback (most recent call last)
Cell In[3], line 1
----> 1 data = load_dataset("open-llm-leaderboard/details_davidkim205__Rhea-72b-v0.5", "harness_winogrande_5")
File ~/open-llm-bda/venv/lib/python3.11/site-packages/datasets/load.py:2587, 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, trust_remote_code, **config_kwargs)
2582 verification_mode = VerificationMode(
2583 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2584 )
2586 # Create a dataset builder
-> 2587 builder_instance = load_dataset_builder(
2588 path=path,
2589 name=name,
2590 data_dir=data_dir,
2591 data_files=data_files,
2592 cache_dir=cache_dir,
2593 features=features,
2594 download_config=download_config,
2595 download_mode=download_mode,
2596 revision=revision,
2597 token=token,
2598 storage_options=storage_options,
2599 trust_remote_code=trust_remote_code,
2600 _require_default_config_name=name is None,
2601 **config_kwargs,
2602 )
2604 # Return iterable dataset in case of streaming
2605 if streaming:
File ~/open-llm-bda/venv/lib/python3.11/site-packages/datasets/load.py:2259, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
2257 download_config = download_config.copy() if download_config else DownloadConfig()
2258 download_config.storage_options.update(storage_options)
-> 2259 dataset_module = dataset_module_factory(
2260 path,
2261 revision=revision,
2262 download_config=download_config,
2263 download_mode=download_mode,
2264 data_dir=data_dir,
2265 data_files=data_files,
2266 cache_dir=cache_dir,
2267 trust_remote_code=trust_remote_code,
2268 _require_default_config_name=_require_default_config_name,
2269 _require_custom_configs=bool(config_kwargs),
2270 )
2271 # Get dataset builder class from the processing script
2272 builder_kwargs = dataset_module.builder_kwargs
File ~/open-llm-bda/venv/lib/python3.11/site-packages/datasets/load.py:1821, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
1812 return LocalDatasetModuleFactoryWithScript(
1813 combined_path,
1814 download_mode=download_mode,
1815 dynamic_modules_path=dynamic_modules_path,
1816 trust_remote_code=trust_remote_code,
1817 ).get_module()
1818 elif os.path.isdir(path):
1819 return LocalDatasetModuleFactoryWithoutScript(
1820 path, data_dir=data_dir, data_files=data_files, download_mode=download_mode
-> 1821 ).get_module()
1822 # Try remotely
1823 elif is_relative_path(path) and path.count("/") <= 1:
File ~/open-llm-bda/venv/lib/python3.11/site-packages/datasets/load.py:1039, in LocalDatasetModuleFactoryWithoutScript.get_module(self)
1033 patterns = get_data_patterns(base_path)
1034 data_files = DataFilesDict.from_patterns(
1035 patterns,
1036 base_path=base_path,
1037 allowed_extensions=ALL_ALLOWED_EXTENSIONS,
1038 )
-> 1039 module_name, default_builder_kwargs = infer_module_for_data_files(
1040 data_files=data_files,
1041 path=self.path,
1042 )
1043 data_files = data_files.filter_extensions(_MODULE_TO_EXTENSIONS[module_name])
1044 # Collect metadata files if the module supports them
File ~/open-llm-bda/venv/lib/python3.11/site-packages/datasets/load.py:597, in infer_module_for_data_files(data_files, path, download_config)
595 raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}")
596 if not module_name:
--> 597 raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
598 return module_name, default_builder_kwargs
DataFilesNotFoundError: No (supported) data files found in open-llm-leaderboard/details_davidkim205__Rhea-72b-v0.5
```
### Expected behavior
No exceptions from `get_dataset_config_names` or `load_dataset`
### Environment info
- `datasets` version: 2.19.0
- Platform: Linux-6.5.0-1018-aws-aarch64-with-glibc2.35
- Python version: 3.11.8
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/6865 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6865/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6865/comments | https://api.github.com/repos/huggingface/datasets/issues/6865/events | https://github.com/huggingface/datasets/issues/6865 | 2,277,304,832 | I_kwDODunzps6HvOoA | 6,865 | Example on Semantic segmentation contains bug | {
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} | [] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,714 | null | NONE | null | ### Describe the bug
https://huggingface.co/docs/datasets/en/semantic_segmentation shows wrong example with torchvision transforms.
Specifically, as one can see in screenshot below, the object boundaries have weird colors.
<img width="689" alt="image" src="https://github.com/huggingface/datasets/assets/4803565/59aa0e2c-2e3e-415b-9d42-2314044c5aee">
Original example with `albumentations` is correct
<img width="705" alt="image" src="https://github.com/huggingface/datasets/assets/4803565/27dbd725-cea5-4e48-ba59-7050c3ce17b3">
That is because `torch vision.transforms.Resize` interpolates with bilinear everything which is wrong when used for segmentation labels - you just cannot mix them. Overall, `torchvision.transforms` is designed for classification only and cannot be used to images and masks together, unless you write two separate branches of augmentations.
The correct way would be to use `v2` version of transforms and convert the segmentation labels to https://pytorch.org/vision/main/generated/torchvision.tv_tensors.Mask.html#torchvision.tv_tensors.Mask object
### Steps to reproduce the bug
Go to the website.
<img width="689" alt="image" src="https://github.com/huggingface/datasets/assets/4803565/ea1276d0-d69a-48cf-b9c2-cd61217815ef">
https://huggingface.co/docs/datasets/en/semantic_segmentation
### Expected behavior
Results, similar to `albumentation`. Or remove the torch vision part altogether. Or use `kornia` instead.
### Environment info
Irrelevant | null | {
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"Hi @vinodrajendran001, thanks for reporting.\r\n\r\nIndeed the dataset no longer exists on the Hub. The URL https://huggingface.co/datasets/rewardsignal/reddit_writing_prompts gives 404 Not Found error."
] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
The dataset `rewardsignal/reddit_writing_prompts` is missing in Huggingface Hub.
### Steps to reproduce the bug
```
from datasets import load_dataset
prompt_response_dataset = load_dataset("rewardsignal/reddit_writing_prompts", data_files="prompt_responses_full.csv", split='train[:80%]')
```
### Expected behavior
DatasetNotFoundError: Dataset 'rewardsignal/reddit_writing_prompts' doesn't exist on the Hub or cannot be accessed
### Environment info
Nothing to do with versions | {
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https://api.github.com/repos/huggingface/datasets/issues/6863 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6863/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6863/comments | https://api.github.com/repos/huggingface/datasets/issues/6863/events | https://github.com/huggingface/datasets/issues/6863 | 2,276,977,534 | I_kwDODunzps6Ht-t- | 6,863 | Revert temporary pin huggingface-hub < 0.23.0 | {
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] | null | [] | 1970-01-01T00:00:00.000001 | 1,716 | 1970-01-01T00:00:00.000001 | MEMBER | null | Revert temporary pin huggingface-hub < 0.23.0 introduced by
- #6861
once the following issue is fixed and released:
- huggingface/transformers#30618 | {
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https://api.github.com/repos/huggingface/datasets/issues/6860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6860/comments | https://api.github.com/repos/huggingface/datasets/issues/6860/events | https://github.com/huggingface/datasets/issues/6860 | 2,275,537,137 | I_kwDODunzps6HofDx | 6,860 | CI fails after huggingface_hub-0.23.0 release: FutureWarning: "resume_download" | {
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"I think this needs to be fixed on transformers.\r\n\r\nCC: @Wauplin ",
"See:\r\n- https://github.com/huggingface/transformers/issues/30618",
"Opened https://github.com/huggingface/transformers/pull/30620"
] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | MEMBER | null | CI fails after latest huggingface_hub-0.23.0 release: https://github.com/huggingface/huggingface_hub/releases/tag/v0.23.0
```
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_bertscore - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_perplexity - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_tokenizer - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_tokenizer_with_cache - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
FAILED tests/test_arrow_dataset.py::MiscellaneousDatasetTest::test_set_format_encode - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
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https://api.github.com/repos/huggingface/datasets/issues/6858 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6858/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6858/comments | https://api.github.com/repos/huggingface/datasets/issues/6858/events | https://github.com/huggingface/datasets/issues/6858 | 2,274,917,185 | I_kwDODunzps6HmHtB | 6,858 | Segmentation fault | {
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"I downloaded the jsonl file and extract it manually. \r\nThe issue seems to be related to pyarrow.json \r\n\r\n\r\n\r\npython3 -q -X faulthandler -c \"from datasets import load_dataset; load_dataset('json', data_files='/Users/scampion/Downloads/1998-09.jsonl')\"\r\nGenerating train split: 0 examples [00:00, ? examples/s]Fatal Python error: Segmentation fault\r\n\r\nThread 0x00007000000c1000 (most recent call first):\r\n <no Python frame>\r\n\r\nThread 0x00007000024df000 (most recent call first):\r\n File \"/usr/local/Cellar/[email protected]/3.11.7/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py\", line 331 in wait\r\n File \"/usr/local/Cellar/[email protected]/3.11.7/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py\", line 629 in wait\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/tqdm/_monitor.py\", line 60 in run\r\n File \"/usr/local/Cellar/[email protected]/3.11.7/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py\", line 1045 in _bootstrap_inner\r\n File \"/usr/local/Cellar/[email protected]/3.11.7/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py\", line 1002 in _bootstrap\r\n\r\nThread 0x00007ff845c66640 (most recent call first):\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/packaged_modules/json/json.py\", line 122 in _generate_tables\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/builder.py\", line 1995 in _prepare_split_single\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/builder.py\", line 1882 in _prepare_split\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/builder.py\", line 1122 in _download_and_prepare\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/builder.py\", line 1027 in download_and_prepare\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/load.py\", line 2609 in load_dataset\r\n File \"<string>\", line 1 in <module>\r\n\r\nExtension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, pyarrow.lib, pyarrow._hdfsio, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, charset_normalizer.md, yaml._yaml, pyarrow._parquet, pyarrow._fs, pyarrow._hdfs, pyarrow._gcsfs, pyarrow._s3fs, multidict._multidict, yarl._quoting_c, aiohttp._helpers, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket, frozenlist._frozenlist, xxhash._xxhash, pyarrow._json (total: 72)\r\n[1] 56678 segmentation fault python3 -q -X faulthandler -c\r\n/usr/local/Cellar/[email protected]/3.11.7/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown\r\n warnings.warn('resource_tracker: There appear to be %d '\r\n(venv_test)",
"The error comes from data where one line contains \"null\""
] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
Using various version for datasets, I'm no more longer able to load that dataset without a segmentation fault.
Several others files are also concerned.
### Steps to reproduce the bug
# Create a new venv
python3 -m venv venv_test
source venv_test/bin/activate
# Install the latest version
pip install datasets
# Load that dataset
python3 -q -X faulthandler -c "from datasets import load_dataset; load_dataset('EuropeanParliament/Eurovoc', '1998-09')"
### Expected behavior
Data must be loaded
### Environment info
datasets==2.19.0
Python 3.11.7
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https://api.github.com/repos/huggingface/datasets/issues/6856 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6856/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6856/comments | https://api.github.com/repos/huggingface/datasets/issues/6856/events | https://github.com/huggingface/datasets/issues/6856 | 2,274,828,933 | I_kwDODunzps6HlyKF | 6,856 | CI fails on Windows for test_delete_from_hub and test_xgetsize_private due to new-line character | {
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"After investigation, I have found that when a local file is uploaded to the Hub, the new line character is no longer transformed to \"\\n\": on Windows machine now it is kept as \"\\r\\n\".\r\n\r\nAny idea why this changed?\r\nCC: @lhoestq "
] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | MEMBER | null | CI fails on Windows for test_delete_from_hub after the merge of:
- #6820
This is weird because the CI was green in the PR branch before merging to main.
```
FAILED tests/test_hub.py::test_delete_from_hub - AssertionError: assert [CommitOperat...\r\n---\r\n')] == [CommitOperat...in/*\n---\n')]
At index 1 diff: CommitOperationAdd(path_in_repo='README.md', path_or_fileobj=b'---\r\nconfigs:\r\n- config_name: cats\r\n data_files:\r\n - split: train\r\n path: cats/train/*\r\n---\r\n') != CommitOperationAdd(path_in_repo='README.md', path_or_fileobj=b'---\nconfigs:\n- config_name: cats\n data_files:\n - split: train\n path: cats/train/*\n---\n')
Full diff:
[
CommitOperationDelete(
path_in_repo='dogs/train/0000.csv',
is_folder=False,
),
CommitOperationAdd(
path_in_repo='README.md',
- path_or_fileobj=b'---\nconfigs:\n- config_name: cats\n data_files:\n '
? --------
+ path_or_fileobj=b'---\r\nconfigs:\r\n- config_name: cats\r\n data_f'
? ++ ++ ++
- b' - split: train\n path: cats/train/*\n---\n',
? ^^^^^^ -
+ b'iles:\r\n - split: train\r\n path: cats/train/*\r'
? ++++++++++ ++ ^
+ b'\n---\r\n',
),
]
``` | {
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] | null | [] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | MEMBER | null | As reported by @Wauplin, when loading a community dataset with script, there is a bug in the example of usage of the error message if the dataset has multiple configs (and no default config) and the user does not pass any config. For example:
```python
>>> ds = load_dataset("google/fleurs")
ValueError: Config name is missing.
Please pick one among the available configs: ['af_za', 'am_et', 'ar_eg', 'as_in', 'ast_es', 'az_az', 'be_by', 'bg_bg', 'bn_in', 'bs_ba', 'ca_es', 'ceb_ph', 'ckb_iq', 'cmn_hans_cn', 'cs_cz', 'cy_gb', 'da_dk', 'de_de', 'el_gr', 'en_us', 'es_419', 'et_ee', 'fa_ir', 'ff_sn', 'fi_fi', 'fil_ph', 'fr_fr', 'ga_ie', 'gl_es', 'gu_in', 'ha_ng', 'he_il', 'hi_in', 'hr_hr', 'hu_hu', 'hy_am', 'id_id', 'ig_ng', 'is_is', 'it_it', 'ja_jp', 'jv_id', 'ka_ge', 'kam_ke', 'kea_cv', 'kk_kz', 'km_kh', 'kn_in', 'ko_kr', 'ky_kg', 'lb_lu', 'lg_ug', 'ln_cd', 'lo_la', 'lt_lt', 'luo_ke', 'lv_lv', 'mi_nz', 'mk_mk', 'ml_in', 'mn_mn', 'mr_in', 'ms_my', 'mt_mt', 'my_mm', 'nb_no', 'ne_np', 'nl_nl', 'nso_za', 'ny_mw', 'oc_fr', 'om_et', 'or_in', 'pa_in', 'pl_pl', 'ps_af', 'pt_br', 'ro_ro', 'ru_ru', 'sd_in', 'sk_sk', 'sl_si', 'sn_zw', 'so_so', 'sr_rs', 'sv_se', 'sw_ke', 'ta_in', 'te_in', 'tg_tj', 'th_th', 'tr_tr', 'uk_ua', 'umb_ao', 'ur_pk', 'uz_uz', 'vi_vn', 'wo_sn', 'xh_za', 'yo_ng', 'yue_hant_hk', 'zu_za', 'all']
Example of usage:
`load_dataset('fleurs', 'af_za')`
```
Note the example of usage in the error message suggests loading "fleurs" instead of "google/fleurs". | {
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https://api.github.com/repos/huggingface/datasets/issues/6853 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6853/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6853/comments | https://api.github.com/repos/huggingface/datasets/issues/6853/events | https://github.com/huggingface/datasets/issues/6853 | 2,272,570,000 | I_kwDODunzps6HdKqQ | 6,853 | Support soft links for load_datasets imagefolder | {
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] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,714 | null | NONE | null | ### Feature request
Load_dataset from a folder of images doesn't seem to support soft links. It would be nice if it did, especially during methods development where image folders are being curated.
### Motivation
Images are coming from a complex variety of sources and we'd like to be able to soft link directly from the originating folders as opposed to copying. Having a copy of the file ensures that there may be issues with image versioning as well as having double the amount of required disk space.
### Your contribution
N/A | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6852 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6852/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6852/comments | https://api.github.com/repos/huggingface/datasets/issues/6852/events | https://github.com/huggingface/datasets/issues/6852 | 2,272,465,011 | I_kwDODunzps6HcxBz | 6,852 | Write token isn't working while pushing to datasets | {
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} | [] | closed | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
<img width="1001" alt="Screenshot 2024-05-01 at 3 37 06 AM" src="https://github.com/huggingface/datasets/assets/130903099/00fcf12c-fcc1-4749-8592-d263d4efcbcc">
As you can see I logged in to my account and the write token is valid.
But I can't upload on my main account and I am getting that error. It was okay on my test account at first try.
(I refreshed the token, tried a new token but still doesn't work)
### Steps to reproduce the bug
1. I loaded a dataset.
2. I logged in using both cli and huggingface_hub
3. I pushed to my down dataset
(It went well without any issues on my test account)
### Expected behavior
It should have gone smoothly and this is not even my first time uploading to huggingface datasets
### Environment info
colab, dataset (tried multiple versions) | {
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"I met the same problem, here is my code:\r\n```\r\nfrom datasets import load_dataset\r\n\r\nds_name = \"togethercomputer/RedPajama-Data-1T\"\r\nds = load_dataset(ds_name, download_mode=DownloadMode.FORCE_REDOWNLOAD)\r\n```\r\nAnd output error is:\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/yatorho/doc/projs/TransformerEngine/local/download_redpajama.py\", line 10, in <module>\r\n ds = load_dataset(ds_name, download_mode=DownloadMode.FORCE_REDOWNLOAD)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 2606, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 2277, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 1923, in dataset_module_factory\r\n raise e1 from None\r\n File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 1875, in dataset_module_factory\r\n can_load_config_from_parquet_export = \"DEFAULT_CONFIG_NAME\" not in f.read()\r\n ^^^^^^^^\r\n File \"<frozen codecs>\", line 322, in decode\r\nUnicodeDecodeError: 'utf-8' codec can't decode byte 0xb5 in position 1: invalid start byte\r\n```\r\nMy `datasets` version is 2.21.0. Any help here would be appreciated!\r\n",
"> I met the same problem, here is my code:\r\n> \r\n> ```\r\n> from datasets import load_dataset\r\n> \r\n> ds_name = \"togethercomputer/RedPajama-Data-1T\"\r\n> ds = load_dataset(ds_name, download_mode=DownloadMode.FORCE_REDOWNLOAD)\r\n> ```\r\n> \r\n> And output error is:\r\n> \r\n> ```\r\n> Traceback (most recent call last):\r\n> File \"/home/yatorho/doc/projs/TransformerEngine/local/download_redpajama.py\", line 10, in <module>\r\n> ds = load_dataset(ds_name, download_mode=DownloadMode.FORCE_REDOWNLOAD)\r\n> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n> File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 2606, in load_dataset\r\n> builder_instance = load_dataset_builder(\r\n> ^^^^^^^^^^^^^^^^^^^^^\r\n> File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 2277, in load_dataset_builder\r\n> dataset_module = dataset_module_factory(\r\n> ^^^^^^^^^^^^^^^^^^^^^^^\r\n> File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 1923, in dataset_module_factory\r\n> raise e1 from None\r\n> File \"/home/yatorho/anaconda3/envs/t24/lib/python3.12/site-packages/datasets/load.py\", line 1875, in dataset_module_factory\r\n> can_load_config_from_parquet_export = \"DEFAULT_CONFIG_NAME\" not in f.read()\r\n> ^^^^^^^^\r\n> File \"<frozen codecs>\", line 322, in decode\r\n> UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb5 in position 1: invalid start byte\r\n> ```\r\n> \r\n> My `datasets` version is 2.21.0. Any help here would be appreciated!\r\n\r\nI passed encoding=\"utf-16\" to the `load_dataset` call and now it works for me.\r\n```\r\nds = load_dataset(ds_name, download_mode=DownloadMode.FORCE_REDOWNLOAD, encoding=\"utf-16\")\r\n```"
] | 1970-01-01T00:00:00.000001 | 1,725 | null | NONE | null | ### Describe the bug
**emotions = load_dataset('emotion')**
_UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte_
### Steps to reproduce the bug
load_dataset('emotion')
### Expected behavior
succese
### Environment info
py3.10
transformers 4.41.0.dev0
datasets 2.19.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6850 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6850/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6850/comments | https://api.github.com/repos/huggingface/datasets/issues/6850/events | https://github.com/huggingface/datasets/issues/6850 | 2,269,500,624 | I_kwDODunzps6HRdTQ | 6,850 | Problem loading voxpopuli dataset | {
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"Version 2.18 works without problem.",
"@Namangarg110 @mohsen-goodarzi The bug appears because the number of urls is less than 16 and the algorithm is meant to work on the previously created mode for a single url as stated on line 314: https://github.com/huggingface/datasets/blob/1bf8a46cc7b096d5c547ea3794f6a4b6c31ea762/src/datasets/download/download_manager.py#L314\r\n\r\nIn addition, previously `map_nested` function was supported without batching and it is meant to be the default performance. \r\n\r\nOne of the shortest walk-arounds would be changing the part of the manager with the current setting:\r\n```\r\n if len(url_or_urls) >= 16:\r\n download_func = partial(self._download_batched, download_config=download_config)\r\n else:\r\n download_func = partial(self._download_single, download_config=download_config)\r\n\r\n start_time = datetime.now()\r\n with stack_multiprocessing_download_progress_bars():\r\n downloaded_path_or_paths = map_nested(\r\n download_func,\r\n url_or_urls,\r\n map_tuple=True,\r\n num_proc=download_config.num_proc,\r\n desc=\"Downloading data files\",\r\n batched=True if len(url_or_urls) >= 16 else False,\r\n batch_size=-1,\r\n )\r\n```\r\n\r\nI would suggest to consider other datasets for similar issues and make a pull-request. ",
"Thanks for reporting @Namangarg110 and thanks for the investigation @MilanaShhanukova.\r\n\r\nApparently, there is an issue with the download functionality.\r\nI am proposing a fix."
] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
```
Exception has occurred: FileNotFoundError
Couldn't find file at https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/{'en': 'data/en/asr_train.tsv'}
```
Error in logic for link url creation. The link should be https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/en/asr_train.tsv
Basically there should be links directly under ```metadata["train"]```, not under ```metadata["train"][self.config.languages[0]]```
same for audio urls
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("facebook/voxpopuli","en")
```
### Expected behavior
Dataset should be loaded successfully.
### Environment info
- `datasets` version: 2.19.0
- Platform: Linux-5.15.0-1041-aws-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.22.2
- PyArrow version: 16.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.12.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/6848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6848/comments | https://api.github.com/repos/huggingface/datasets/issues/6848/events | https://github.com/huggingface/datasets/issues/6848 | 2,268,622,609 | I_kwDODunzps6HOG8R | 6,848 | Cant Downlaod Common Voice 17.0 hy-AM | {
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"Same issue here."
] | 1970-01-01T00:00:00.000001 | 1,715 | null | NONE | null | ### Describe the bug
I want to download Common Voice 17.0 hy-AM but it returns an error.
```
The version_base parameter is not specified.
Please specify a compatability version level, or None.
Will assume defaults for version 1.1
@hydra.main(config_name='hfds_config', config_path=None)
/usr/local/lib/python3.10/dist-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.
See https://hydra.cc/docs/1.2/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.
ret = run_job(
/usr/local/lib/python3.10/dist-packages/datasets/load.py:1429: FutureWarning: The repository for mozilla-foundation/common_voice_17_0 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/mozilla-foundation/common_voice_17_0
You can avoid this message in future by passing the argument `trust_remote_code=True`.
Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
warnings.warn(
Reading metadata...: 6180it [00:00, 133224.37it/s]les/s]
Generating train split: 0 examples [00:00, ? examples/s]
HuggingFace datasets failed due to some reason (stack trace below).
For certain datasets (eg: MCV), it may be necessary to login to the huggingface-cli (via `huggingface-cli login`).
Once logged in, you need to set `use_auth_token=True` when calling this script.
Traceback error for reference :
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1743, in _prepare_split_single
example = self.info.features.encode_example(record) if self.info.features is not None else record
File "/usr/local/lib/python3.10/dist-packages/datasets/features/features.py", line 1878, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.10/dist-packages/datasets/features/features.py", line 1243, in encode_nested_example
{
File "/usr/local/lib/python3.10/dist-packages/datasets/features/features.py", line 1243, in <dictcomp>
{
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 326, in zip_dict
yield key, tuple(d[key] for d in dicts)
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 326, in <genexpr>
yield key, tuple(d[key] for d in dicts)
KeyError: 'sentence_id'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/workspace/nemo/scripts/speech_recognition/convert_hf_dataset_to_nemo.py", line 358, in main
dataset = load_dataset(
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2549, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1005, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1767, in _download_and_prepare
super()._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1100, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1605, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1762, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
```
from datasets import load_dataset
cv_17 = load_dataset("mozilla-foundation/common_voice_17_0", "hy-AM")
```
### Expected behavior
It works fine with common_voice_16_1
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.15.0-1042-nvidia-x86_64-with-glibc2.35
- Python version: 3.11.6
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.2
- `fsspec` version: 2024.2.0 | null | {
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"This should help fixing this issue: https://github.com/huggingface/datasets/pull/6832",
"I'm having a similar issue when using splices:\r\n<img width=\"947\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/28941213/2153faac-e1fe-4b6d-a79b-30b2699407e8\">\r\n<img width=\"823\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/28941213/80919eca-eb6c-407d-8070-52642fdcee54\">\r\n<img width=\"914\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/28941213/5219c201-e22e-4536-acc3-a922677785ff\">\r\n\r\n\r\nIt seems to be downloading, loading, and generating splits using the entire dataset."
] | 1970-01-01T00:00:00.000001 | 1,715 | null | MEMBER | null | e.g. [thangvip](https://huggingface.co/thangvip)/[cosmopedia_vi_math](https://huggingface.co/datasets/thangvip/cosmopedia_vi_math) has 300 splits and it takes a very long time to load only one split.
This is due to `load_dataset()` resolving the files of all the splits even if only one is needed.
In `dataset-viewer` the splits are loaded in different jobs so it results in 300 jobs that resolve 300 splits -> 90k calls to `/paths-info` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6846 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6846/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6846/comments | https://api.github.com/repos/huggingface/datasets/issues/6846/events | https://github.com/huggingface/datasets/issues/6846 | 2,267,352,120 | I_kwDODunzps6HJQw4 | 6,846 | Unimaginable super slow iteration | {
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"In every iteration you load the full \"random_input\" column in memory, only then to access it's i-th element.\r\n\r\nYou can try using this instead\r\n\r\na,b=dataset[i]['random_input'],dataset[i]['random_output']"
] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
Assuming there is a dataset with 52000 sentences, each with a length of 500, it takes 20 seconds to extract a sentence from the dataset……？Is there something wrong with my iteration?
### Steps to reproduce the bug
```python
import datasets
import time
import random
num_rows = 52000
num_cols = 500
random_input = [[random.randint(1, 100) for _ in range(num_cols)] for _ in range(num_rows)]
random_output = [[random.randint(1, 100) for _ in range(num_cols)] for _ in range(num_rows)]
s=time.time()
d={'random_input':random_input,'random_output':random_output}
dataset=datasets.Dataset.from_dict(d)
print('from dict',time.time()-s)
print(dataset)
for i in range(len(dataset)):
aa=time.time()
a,b=dataset['random_input'][i],dataset['random_output'][i]
print(time.time()-aa)
```
corresponding output
```bash
from dict 9.215498685836792
Dataset({
features: ['random_input', 'random_output'],
num_rows: 52000
})
19.129778146743774
19.329464197158813
19.27668261528015
19.28557538986206
19.247620582580566
19.624247074127197
19.28673791885376
19.301053047180176
19.290496110916138
19.291821718215942
19.357765197753906
```
### Expected behavior
Under normal circumstances, iteration should be very rapid as it does not involve the main tasks other than getting items
### Environment info
- `datasets` version: 2.19.0
- Platform: Linux-3.10.0-1160.71.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.13
- `huggingface_hub` version: 0.21.4
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6845 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6845/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6845/comments | https://api.github.com/repos/huggingface/datasets/issues/6845/events | https://github.com/huggingface/datasets/issues/6845 | 2,265,876,551 | I_kwDODunzps6HDohH | 6,845 | load_dataset doesn't support list column | {
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} | [] | open | false | null | [] | null | [
"I encountered this same issue when loading a customized dataset for ORPO training, in which there were three columns and two of them were lists. \r\nI debugged and found that it might be caused by the type-infer mechanism and because in some chunks one of the columns is always an empty list ([]), it was regarded as ```list<item: null>```, however in some other chunk it was ```list<item: string>```. This triggered a TypeError running the function ```table_cast()```.\r\n\r\nI temporarily fixed this by re-dumping the file into a regular JSON format instead of lines of JSON dict. I didn't dig deeper for the lack of knowledge and programming ability but I do hope some developer of this repo will find and fix it."
] | 1970-01-01T00:00:00.000001 | 1,715 | null | NONE | null | ### Describe the bug
dataset = load_dataset("Doraemon-AI/text-to-neo4j-cypher-chinese")
got exception:
Generating train split: 1834 examples [00:00, 5227.98 examples/s]
Traceback (most recent call last):
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 2295, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 2254, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 2254, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 1802, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 1802, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 2018, in cast_array_to_feature
casted_array_values = _c(array.values, feature[0])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 2115, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
struct<m.name: string, x.name: string, p.name: string, n.name: string, h.name: string, name: string, c: int64, collect(r.name): list<item: string>, q.name: string, rel.name: string, count(p): int64, 1: int64, p.location: string, max(n.name): null, mn.name: string, p.time: int64, min(q.name): string>
to
{'q.name': Value(dtype='string', id=None), 'mn.name': Value(dtype='string', id=None), 'x.name': Value(dtype='string', id=None), 'p.name': Value(dtype='string', id=None), 'n.name': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None), 'm.name': Value(dtype='string', id=None), 'h.name': Value(dtype='string', id=None), 'count(p)': Value(dtype='int64', id=None), 'rel.name': Value(dtype='string', id=None), 'c': Value(dtype='int64', id=None), 'collect(r.name)': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '1': Value(dtype='int64', id=None), 'p.location': Value(dtype='string', id=None), 'substring(h.name,0,5)': Value(dtype='string', id=None), 'p.time': Value(dtype='int64', id=None)}
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/ubuntu/llm/train-2.py", line 150, in <module>
dataset = load_dataset("Doraemon-AI/text-to-neo4j-cypher-chinese")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/load.py", line 2609, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
### Steps to reproduce the bug
dataset = load_dataset("Doraemon-AI/text-to-neo4j-cypher-chinese")
### Expected behavior
no exception
### Environment info
python 3.11
datasets 2.19.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6843 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6843/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6843/comments | https://api.github.com/repos/huggingface/datasets/issues/6843/events | https://github.com/huggingface/datasets/issues/6843 | 2,265,432,897 | I_kwDODunzps6HB8NB | 6,843 | IterableDataset raises exception instead of retrying | {
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} | [] | open | false | null | [] | null | [
"Thanks for reporting! I've opened a PR with a fix.",
"Thanks, @mariosasko! Related question (although I guess this is a feature request): could we have some kind of exponential back-off for these retries? Here's my reasoning:\r\n- If a one-time accidental error happens, you should retry immediately and will succeed immediately.\r\n- If the Hub has a small outage on the order of minutes, you don't want to retry on the order of hours. \r\n- If the Hub has a prologned outage of several hours, we don't want to keep retrying on the order of minutes.\r\n\r\nThere actually already exists an implementation for (clipped) exponential backoff in the HuggingFace suite ([here](https://github.com/huggingface/huggingface_hub/blob/61b156a4f2e5fe1a492ed8712b26803e2122bde0/src/huggingface_hub/utils/_http.py#L306)), but I don't think it is used here.\r\n\r\nThe requirements are basically that you have an initial minimum waiting time and a maximum waiting time, and with each retry, the waiting time is doubled. We don't want to overload your servers with needless retries, especially when they're down :sweat_smile:",
"Oh, I've just remembered that we added retries to the `HfFileSystem` in `huggingface_hub` 0.21.0 (see [this](https://github.com/huggingface/huggingface_hub/blob/61b156a4f2e5fe1a492ed8712b26803e2122bde0/src/huggingface_hub/hf_file_system.py#L703)), so I'll close the linked PR as we don't want to retry the retries :).\r\n\r\nI agree with the exponential backoff suggestion, so I'll open another PR.",
"@mariosasko The call you linked indeed points to the implementation I linked in my previous comment, yes, but it has no configurability. Arguably, you want to have this hidden backoff under the hood that catches small network disturbances on the time scale of seconds -- perhaps even with hardcoded limits as is the case currently -- but you also still want to have a separate backoff on top of that with the configurability as suggested by @lhoestq in [the comment I linked](https://github.com/huggingface/datasets/issues/6172#issuecomment-1794876229).\r\n\r\nMy particular use-case is that I'm streaming a dataset while training on a university cluster with a very long scheduling queue. This means that when the backoff runs out of retries (which happens in under 30 seconds with the call you linked), I lose my spot on the cluster and have to queue for a whole day or more. Ideally, I should be able to specify that I want to retry for 2 to 3 hours but with more and more time between requests, so that I can smooth over hours-long outages without a setback of days.",
"I also have my runs crash a surprising amount due to the dataloader crashing because of the hub, some way to address this would be nice.",
"@mariosasko The implementation for retries is still broken and there is still no exponential back-off.\r\n\r\nHuggingFace has a two-tiered back-off:\r\n- `huggingface_hub.utils` provides the low-level `http_backoff` function which is used for all HTTP requests. It retries first with 1 second delay, then 2, then 4, then 8, then 8, and then it crashes. This is not even half a minute of exponential backoff in total.\r\n- `datasets.utils.file_utils` provides a function `_add_retries_to_file_obj_read_method` that monkey-patches the `read` method of an `HfFileSystemFile` to have constant-time backoff on certain exceptions. The amount of retries and seconds between retries is customisable as explained by @lhoestq [here](https://github.com/huggingface/datasets/issues/6172#issuecomment-1794876229). The implementation looks like this:\r\n\r\nhttps://github.com/huggingface/datasets/blob/65f6eb54aa0e8bb44cea35deea28e0e8fecc25b9/src/datasets/utils/file_utils.py#L822-L841\r\n\r\nThis **still does not catch the correct exceptions** and hence no backoff happens **at all** which means that as soon as the hub is out for more than half a minute, processes will already start failing. Here is a stack trace of an uncaught exception:\r\n\r\n```\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 268, in __iter__\r\n for key, pa_table in self.generate_tables_fn(**gen_kwags):\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/packaged_modules/json/json.py\", line 123, in _generate_tables\r\n batch = f.read(self.config.chunksize)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py\", line 830, in read_with_retries\r\n out = read(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 757, in read\r\n return super().read(length)\r\n ^^^^^^^^^^^^^^^^^^^^\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/spec.py\", line 1856, in read\r\n out = self.cache._fetch(self.loc, self.loc + length)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/caching.py\", line 189, in _fetch\r\n self.cache = self.fetcher(start, end) # new block replaces old\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 713, in _fetch_range\r\n r = http_backoff(\r\n ^^^^^^^^^^^^^\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/utils/_http.py\", line 326, in http_backoff\r\n raise err\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/utils/_http.py\", line 307, in http_backoff\r\n response = session.request(method=method, url=url, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/requests/sessions.py\", line 589, in request\r\n resp = self.send(prep, **send_kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/requests/sessions.py\", line 703, in send\r\n r = adapter.send(request, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/utils/_http.py\", line 93, in send\r\n return super().send(request, *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/requests/adapters.py\", line 713, in send\r\n raise ReadTimeout(e, request=request)\r\nrequests.exceptions.ReadTimeout: (ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 3d145d98-e4fa-442f-bead-6be060e60d59)')\r\n```\r\n**requests.exceptions.ReadTimeout** is not caught and hence the code fails after **0 retries**.",
"I merged a fix for this, thanks for reporting ! It will now retry on any `requests` Timeout error, including ReadTimeoutError: https://github.com/huggingface/datasets/pull/7256"
] | 1970-01-01T00:00:00.000001 | 1,730 | null | NONE | null | ### Describe the bug
In light of the recent server outages, I decided to look into whether I could somehow wrap my IterableDataset streams to retry rather than error out immediately. To my surprise, `datasets` [already supports retries](https://github.com/huggingface/datasets/issues/6172#issuecomment-1794876229). Since a commit by @lhoestq [last week](https://github.com/huggingface/datasets/commit/a188022dc43a76a119d90c03832d51d6e4a94d91), that code lives here:
https://github.com/huggingface/datasets/blob/fe2bea6a4b09b180bd23b88fe96dfd1a11191a4f/src/datasets/utils/file_utils.py#L1097C1-L1111C19
If GitHub code snippets still aren't working, here's a copy:
```python
def read_with_retries(*args, **kwargs):
disconnect_err = None
for retry in range(1, max_retries + 1):
try:
out = read(*args, **kwargs)
break
except (ClientError, TimeoutError) as err:
disconnect_err = err
logger.warning(
f"Got disconnected from remote data host. Retrying in {config.STREAMING_READ_RETRY_INTERVAL}sec [{retry}/{max_retries}]"
)
time.sleep(config.STREAMING_READ_RETRY_INTERVAL)
else:
raise ConnectionError("Server Disconnected") from disconnect_err
return out
```
With the latest outage, the end of my stack trace looked like this:
```
...
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 342, in read_with_retries
out = read(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/miniconda3/envs/draft/lib/python3.11/gzip.py", line 301, in read
return self._buffer.read(size)
^^^^^^^^^^^^^^^^^^^^^^^
File "/miniconda3/envs/draft/lib/python3.11/_compression.py", line 68, in readinto
data = self.read(len(byte_view))
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/miniconda3/envs/draft/lib/python3.11/gzip.py", line 505, in read
buf = self._fp.read(io.DEFAULT_BUFFER_SIZE)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/miniconda3/envs/draft/lib/python3.11/gzip.py", line 88, in read
return self.file.read(size)
^^^^^^^^^^^^^^^^^^^^
File "/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/spec.py", line 1856, in read
out = self.cache._fetch(self.loc, self.loc + length)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/caching.py", line 189, in _fetch
self.cache = self.fetcher(start, end) # new block replaces old
^^^^^^^^^^^^^^^^^^^^^^^^
File "/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py", line 626, in _fetch_range
hf_raise_for_status(r)
File "/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/utils/_errors.py", line 333, in hf_raise_for_status
raise HfHubHTTPError(str(e), response=response) from e
huggingface_hub.utils._errors.HfHubHTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/datasets/allenai/c4/resolve/1588ec454efa1a09f29cd18ddd04fe05fc8653a2/en/c4-train.00346-of-01024.json.gz
```
Indeed, the code for retries only catches `ClientError`s and `TimeoutError`s, and all other exceptions, *including HuggingFace's own custom HTTP error class*, **are not caught. Nothing is retried,** and instead the exception is propagated upwards immediately.
### Steps to reproduce the bug
Not sure how you reproduce this. Maybe unplug your Ethernet cable while streaming a dataset; the issue is pretty clear from the stack trace.
### Expected behavior
All HTTP errors while iterating a streamable dataset should cause retries.
### Environment info
Output from `datasets-cli env`:
- `datasets` version: 2.18.0
- Platform: Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28
- Python version: 3.11.7
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6842 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6842/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6842/comments | https://api.github.com/repos/huggingface/datasets/issues/6842/events | https://github.com/huggingface/datasets/issues/6842 | 2,264,692,159 | I_kwDODunzps6G_HW_ | 6,842 | Datasets with files with colon : in filenames cannot be used on Windows | {
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} | [] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,714 | null | NONE | null | ### Describe the bug
Datasets (such as https://huggingface.co/datasets/MLCommons/peoples_speech) cannot be used on Windows due to the fact that windows does not allow colons ":" in filenames. These should be converted into alternative strings.
### Steps to reproduce the bug
1. Attempt to run load_dataset on MLCommons/peoples_speech
### Expected behavior
Does not crash during extraction
### Environment info
Windows 11, NTFS filesystem, Python 3.12
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https://api.github.com/repos/huggingface/datasets/issues/6841 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6841/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6841/comments | https://api.github.com/repos/huggingface/datasets/issues/6841/events | https://github.com/huggingface/datasets/issues/6841 | 2,264,687,683 | I_kwDODunzps6G_GRD | 6,841 | Unable to load wiki_auto_asset_turk from GEM | {
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"Hi! I've opened a [PR](https://huggingface.co/datasets/GEM/wiki_auto_asset_turk/discussions/5) with a fix. While waiting for it to be merged, you can load the dataset from the PR branch with `datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\")`",
"Thanks Mario. Still getting the same issue though with the suggested fix\r\n\r\n#cat gem_sari.py\r\nimport datasets\r\nprint (datasets.__version__)\r\ndataset =datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\")\r\n\r\nEnd up with \r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/load.py\", line 2582, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1005, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1767, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1100, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1565, in _prepare_split\r\n split_info = self.info.splits[split_generator.name]\r\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/splits.py\", line 532, in __getitem__\r\n instructions = make_file_instructions(\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_reader.py\", line 121, in make_file_instructions\r\n info.name: filenames_for_dataset_split(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/naming.py\", line 72, in filenames_for_dataset_split\r\n prefix = os.path.join(path, prefix)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"<frozen posixpath>\", line 76, in join\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType",
"Hmm, that's weird. Maybe try deleting the cache with `!rm -rf ~/.cache/huggingface/datasets` and then re-download.",
"Tried that a couple of time. It does download the data fresh but end up with same error. Is there a way to see if its using the right version ?",
"You can check the version with `python -c \"import datasets; print(datasets.__version__)\"`",
"the datasets version is 2.18. \r\n\r\nI wanted to see if the command datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\") is using the right revision (refs/pr/5). \r\n\r\n\r\n\r\n\r\n\r\n ",
"Still have this problem",
"The issue is fixed once the fixing PR has been merged and the dataset has been converted to Parquet.\r\n\r\nIf the problem persists on your side, you should update your `datasets` library:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd if you have already the latest version of `datasets`, then you need to delete the old version of this dataset in your cache:\r\n```shell\r\nrm -fr ~/.cache/huggingface/datasets/GEM___wiki_auto_asset_turk\r\nrm -fr ~/.cache/huggingface/modules/datasets_modules/datasets/GEM--wiki_auto_asset_turk\r\n```"
] | 1970-01-01T00:00:00.000001 | 1,716 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
I am unable to load the wiki_auto_asset_turk dataset. I get a fatal error while trying to access wiki_auto_asset_turk and load it with datasets.load_dataset. The error (TypeError: expected str, bytes or os.PathLike object, not NoneType) is from filenames_for_dataset_split in a os.path.join call
>>import datasets
>>print (datasets.__version__)
>>dataset = datasets.load_dataset("GEM/wiki_auto_asset_turk")
System output:
Generating train split: 100%|█| 483801/483801 [00:03<00:00, 127164.26 examples/s
Generating validation split: 100%|█| 20000/20000 [00:00<00:00, 116052.94 example
Generating test_asset split: 100%|██| 359/359 [00:00<00:00, 76155.93 examples/s]
Generating test_turk split: 100%|███| 359/359 [00:00<00:00, 87691.76 examples/s]
Traceback (most recent call last):
File "/Users/abhinav.sethy/Code/openai_evals/evals/evals/grammarly_tasks/gem_sari.py", line 3, in <module>
dataset = datasets.load_dataset("GEM/wiki_auto_asset_turk")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/load.py", line 2582, in load_dataset
builder_instance.download_and_prepare(
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py", line 1005, in download_and_prepare
self._download_and_prepare(
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py", line 1767, in _download_and_prepare
super()._download_and_prepare(
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py", line 1100, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py", line 1565, in _prepare_split
split_info = self.info.splits[split_generator.name]
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/splits.py", line 532, in __getitem__
instructions = make_file_instructions(
^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_reader.py", line 121, in make_file_instructions
info.name: filenames_for_dataset_split(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/naming.py", line 72, in filenames_for_dataset_split
prefix = os.path.join(path, prefix)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen posixpath>", line 76, in join
TypeError: expected str, bytes or os.PathLike object, not NoneType
### Steps to reproduce the bug
import datasets
print (datasets.__version__)
dataset = datasets.load_dataset("GEM/wiki_auto_asset_turk")
### Expected behavior
Should be able to load the dataset without any issues
### Environment info
datasets version 2.18.0 (was able to reproduce bug with older versions 2.16 and 2.14 also)
Python 3.12.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6840 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6840/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6840/comments | https://api.github.com/repos/huggingface/datasets/issues/6840/events | https://github.com/huggingface/datasets/issues/6840 | 2,264,604,766 | I_kwDODunzps6G-yBe | 6,840 | Delete uploaded files from the UI | {
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] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,714 | null | NONE | null | ### Feature request
Once a file is uploaded and the commit is made, I am unable to delete individual files without completely deleting the whole dataset via the website UI.
### Motivation
Would be a useful addition
### Your contribution
Would love to help out with some guidance | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6838 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6838/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6838/comments | https://api.github.com/repos/huggingface/datasets/issues/6838/events | https://github.com/huggingface/datasets/issues/6838 | 2,263,674,843 | I_kwDODunzps6G7O_b | 6,838 | Remove token arg from CLI examples | {
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] | null | [] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | MEMBER | null | As suggested by @Wauplin, see: https://github.com/huggingface/datasets/pull/6831#discussion_r1579492603
> I would not advertise the --token arg in the example as this shouldn't be the recommended way (best to login with env variable or huggingface-cli login) | {
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https://api.github.com/repos/huggingface/datasets/issues/6837 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6837/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6837/comments | https://api.github.com/repos/huggingface/datasets/issues/6837/events | https://github.com/huggingface/datasets/issues/6837 | 2,263,273,983 | I_kwDODunzps6G5tH_ | 6,837 | Cannot use cached dataset without Internet connection (or when servers are down) | {
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"There are 2 workarounds, tho:\r\n1. Download datasets from web and just load them locally\r\n2. Use metadata directly (temporal solution, since metadata can change)\r\n```\r\nimport datasets\r\nfrom datasets.data_files import DataFilesDict, DataFilesList\r\n\r\ndata_files_list = DataFilesList(\r\n [\r\n \"hf://datasets/allenai/c4@1588ec454efa1a09f29cd18ddd04fe05fc8653a2/en/c4-train.00000-of-01024.json.gz\"\r\n ],\r\n [(\"allenai/c4\", \"1588ec454efa1a09f29cd18ddd04fe05fc8653a2\")],\r\n)\r\ndata_files = DataFilesDict({\"train\": data_files_list})\r\nc4_dataset = datasets.load_dataset(\r\n path=\"allenai/c4\",\r\n data_files=data_files,\r\n split=\"train\",\r\n cache_dir=\"/datesets/cache\",\r\n download_mode=\"reuse_cache_if_exists\",\r\n token=False,\r\n)\r\n```\r\nSecond solution also shows where to find the bug. I suggest that the hashing functions should always use only original parameter `data_files`, and not the one they get after connecting to the server and creating `DataFilesDict`",
"Hi! You need to set the `HF_DATASETS_OFFLINE` env variable to `1` to load cached datasets offline, as explained in the docs [here](https://huggingface.co/docs/datasets/v2.19.0/en/loading#offline).",
"Just tested. It doesn't work, because of the exact problem I described above: hash of dataset config is different.\r\nThe only error difference is the reason why it cannot connect to HuggingFace (now it's 'offline mode is enabled')\r\n\r\n",
"Met a pretty similar issue here, as I manually load the dataset into ~/.cache and try to let `load_dataset` detect it automatically, but it will always try reach hub even I set `HF_DATASETS_OFFLINE` to 1. Have you solved it? ",
"same here!"
] | 1970-01-01T00:00:00.000001 | 1,729 | null | NONE | null | ### Describe the bug
I want to be able to use cached dataset from HuggingFace even when I have no Internet connection (or when HuggingFace servers are down, or my company has network issues).
The problem why I can't use it:
`data_files` argument from `datasets.load_dataset()` function get it updates from the server before calculating hash for caching. As a result, when I run the same code with and without Internet I get different dataset configuration directory name.
### Steps to reproduce the bug
```
import datasets
c4_dataset = datasets.load_dataset(
path="allenai/c4",
data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
split="train",
cache_dir="/datesets/cache",
download_mode="reuse_cache_if_exists",
token=False,
)
```
1. Run this code with the Internet.
2. Run the same code without the Internet.
### Expected behavior
When running without the Internet connection, the loader should be able to get dataset from cache
### Environment info
- `datasets` version: 2.19.0
- Platform: Windows-10-10.0.19044-SP0
- Python version: 3.10.13
- `huggingface_hub` version: 0.22.2
- PyArrow version: 16.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/6836 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6836/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6836/comments | https://api.github.com/repos/huggingface/datasets/issues/6836/events | https://github.com/huggingface/datasets/issues/6836 | 2,262,249,919 | I_kwDODunzps6G1zG_ | 6,836 | ExpectedMoreSplits error on load_dataset when upgrading to 2.19.0 | {
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"Get same error on same datasets too.",
"+1",
"same error"
] | 1970-01-01T00:00:00.000001 | 1,715 | null | NONE | null | ### Describe the bug
Hi there, thanks for the great library! We have been using it a lot in torchtune and it's been a huge help for us.
Regarding the bug: the same call to `load_dataset` errors with `ExpectedMoreSplits` in 2.19.0 after working fine in 2.18.0. Full details given in the repro below.
### Steps to reproduce the bug
On 2.18.0, things work fine:
```
# First clear the locally cached dataset
rm -r ~/.cache/huggingface/datasets/lvwerra___stack-exchange-paired
pip install "datasets==2.18.0"
python3
>>> from datasets import load_dataset
>>> dataset = load_dataset('lvwerra/stack-exchange-paired', split='train', data_dir='data/rl')
```
On 2.19.0, they do not:
```
# First clear the locally cached dataset
rm -r ~/.cache/huggingface/datasets/lvwerra___stack-exchange-paired
pip install "datasets==2.19.0"
python3
>>> from datasets import load_dataset
>>> dataset = load_dataset('lvwerra/stack-exchange-paired', split='train', data_dir='data/rl')
```
The stack trace I see from the 2.19.0 version of load_dataset can be seen [here](https://gist.github.com/ebsmothers/f9b1f1949bee7030a8d7bb8a491550d2).
(Maybe unsurprising but) notably if I do not delete the cache first I am able to load the dataset successfully. So based on this I suspect the cause is somewhere in the download logic.
### Expected behavior
Download the dataset successfully :)
### Environment info
- `datasets` version: 2.19.0
- Platform: Linux-5.12.0-0_fbk16_zion_7661_geb00762ce6d2-x86_64-with-glibc2.34
- Python version: 3.11.9
- `huggingface_hub` version: 0.22.2
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- Pandas version: 2.2.2
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https://api.github.com/repos/huggingface/datasets/issues/6834 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6834/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6834/comments | https://api.github.com/repos/huggingface/datasets/issues/6834/events | https://github.com/huggingface/datasets/issues/6834 | 2,261,078,104 | I_kwDODunzps6GxVBY | 6,834 | largelisttype not supported (.from_polars()) | {
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] | null | [] | 1970-01-01T00:00:00.000001 | 1,723 | 1970-01-01T00:00:00.000001 | CONTRIBUTOR | null | ### Describe the bug
The following code fails because LargeListType is not supported.
This is especially a problem for .from_polars since polars uses LargeListType.
### Steps to reproduce the bug
```python
import datasets
import polars as pl
df = pl.DataFrame({"list": [[]]})
datasets.Dataset.from_polars(df)
```
### Expected behavior
Convert LargeListType to list.
### Environment info
- `datasets` version: 2.19.1.dev0
- Platform: Linux-6.8.7-200.fc39.x86_64-x86_64-with-glibc2.38
- Python version: 3.12.2
- `huggingface_hub` version: 0.22.2
- PyArrow version: 16.0.0
- Pandas version: 2.1.4
- `fsspec` version: 2024.3.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/6833 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6833/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6833/comments | https://api.github.com/repos/huggingface/datasets/issues/6833/events | https://github.com/huggingface/datasets/issues/6833 | 2,259,731,274 | I_kwDODunzps6GsMNK | 6,833 | Super slow iteration with trivial custom transform | {
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"Similar issue in text process \r\n\r\n```python\r\n\r\ntokenizer=AutoTokenizer.from_pretrained(model_dir[args.model])\r\ntrain_dataset=datasets.load_from_disk(dataset_dir[args.dataset],keep_in_memory=True)['train']\r\ntrain_dataset=train_dataset.map(partial(dname2func[args.dataset],tokenizer=tokenizer),batched=True,num_proc =50,remove_columns=train_dataset.features.keys(),desc='tokenize',keep_in_memory=True)\r\n\r\n```\r\nAfter this train_dataset will be like\r\n```python\r\nDataset({\r\n features: ['input_ids', 'labels'],\r\n num_rows: 51760\r\n})\r\n```\r\nIn which input_ids and labels are both List[int]\r\nHowever, per iter on dataset cost 7.412479639053345s ……？\r\n```python\r\nfor j in tqdm(range(len(train_dataset)),desc='first stage'):\r\n input_id,label=train_dataset['input_ids'][j],train_dataset['labels'][j]\r\n\r\n``` ",
"The transform currently replaces the numpy formatting.\r\n\r\nSo you're back to copying data to long python lists which is super slow.\r\n\r\nIt would be cool for the transform to not remove the formatting in this case, but this requires a few changes in the lib",
"This also (somewhat surprisingly) affects iterable datasets, making map very challenging to use for data with large arrays, unless there is some workaround?",
"For iterable datasets you should be able to do this without slow downs\r\n```python\r\nds = ds.with_format(\"arrow\").map(...)\r\n```\r\n\r\nI haven't tried with \"numpy\" though, maybe there is a step that does Arrow -> List -> NumPy instead of Arrow -> NumPy directly. If it's the case it would be cool to avoid that",
"Thanks! this works for me\r\n\r\nHowever, it raises an error if batched=False and map batch_size isn't explicitly set to 1 due to map's default batch_size affecting the batch size of the RebatchedArrowExamplesIterable - is this a bug?",
"Thanks for the fix @alex-hh !",
"opened a new issue for the numpy slowdown #7206 "
] | 1970-01-01T00:00:00.000001 | 1,728 | null | NONE | null | ### Describe the bug
Dataset is 10X slower when applying trivial transforms:
```
import time
import numpy as np
from datasets import Dataset, Features, Array2D
a = np.zeros((800, 800))
a = np.stack([a] * 1000)
features = Features({"a": Array2D(shape=(800, 800), dtype="uint8")})
ds1 = Dataset.from_dict({"a": a}, features=features).with_format('numpy')
def transform(batch):
return batch
ds2 = ds1.with_transform(transform)
%time sum(1 for _ in ds1)
%time sum(1 for _ in ds2)
```
```
CPU times: user 472 ms, sys: 319 ms, total: 791 ms
Wall time: 794 ms
CPU times: user 9.32 s, sys: 443 ms, total: 9.76 s
Wall time: 9.78 s
```
In my real code I'm using set_transform to apply some post-processing on-the-fly for the 2d array, but it significantly slows down the dataset even if the transform itself is trivial.
Related issue: https://github.com/huggingface/datasets/issues/5841
### Steps to reproduce the bug
Use code in the description to reproduce.
### Expected behavior
Trivial custom transform in the example should not slowdown the dataset iteration.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.15.0-79-generic-x86_64-with-glibc2.35
- Python version: 3.11.4
- `huggingface_hub` version: 0.20.2
- PyArrow version: 15.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/6830 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6830/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6830/comments | https://api.github.com/repos/huggingface/datasets/issues/6830/events | https://github.com/huggingface/datasets/issues/6830 | 2,258,433,178 | I_kwDODunzps6GnPSa | 6,830 | Add a doc page for the convert_to_parquet CLI | {
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https://api.github.com/repos/huggingface/datasets/issues/6829 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6829/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6829/comments | https://api.github.com/repos/huggingface/datasets/issues/6829/events | https://github.com/huggingface/datasets/issues/6829 | 2,258,424,577 | I_kwDODunzps6GnNMB | 6,829 | Load and save from/to disk no longer accept pathlib.Path | {
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> This change is breaking in
> https://github.com/huggingface/datasets/blob/f96e74d5c633cd5435dd526adb4a74631eb05c43/src/datasets/arrow_dataset.py#L1515
> when the input is `pathlib.Path`. The issue is that `url_to_fs` expects a `str` and cannot deal with `Path`. `get_fs_token_paths` converts to `str` so it is not a problem
This change was introduced in:
- #6704 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6827 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6827/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6827/comments | https://api.github.com/repos/huggingface/datasets/issues/6827/events | https://github.com/huggingface/datasets/issues/6827 | 2,254,011,833 | I_kwDODunzps6GWX25 | 6,827 | Loading a remote dataset fails in the last release (v2.19.0) | {
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} | [] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,713 | null | NONE | null | While loading a dataset with multiple splits I get an error saying `Couldn't find file at <URL>`
I am loading the dataset like so, nothing out of the ordinary.
This dataset needs a token to access it.
```
token="hf_myhftoken-sdhbdsjgkhbd"
load_dataset("speechcolab/gigaspeech", "test", cache_dir=f"gigaspeech/test", token=token)
```
I get the following error
Now you can see that the URL that it is trying to reach has the JSON object of the dataset split appended to the base URL. I think this may be due to a newly introduced issue.
I did not have this issue with the previous version of the datasets. Everything was fine for me yesterday and after the release 12 hours ago, this seems to have broken. Also, the dataset in question runs custom code and I checked and there have been no commits to the dataset on Huggingface in 6 months.
### Steps to reproduce the bug
Since this happened with one particular dataset for me, I am listing steps to use that dataset.
1. Open https://huggingface.co/datasets/speechcolab/gigaspeech and fill the form to get access.
2. Create a token on your huggingface account with read access.
3. Run the following line, substituing `<your_token_here>` with your token.
```
load_dataset("speechcolab/gigaspeech", "test", cache_dir=f"gigaspeech/test", token="<your_token_here>")
```
### Expected behavior
Be able to load the dataset in question.
### Environment info
datasets == 2.19.0
python == 3.10
kernel == Linux 6.1.58+ | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6824 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6824/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6824/comments | https://api.github.com/repos/huggingface/datasets/issues/6824/events | https://github.com/huggingface/datasets/issues/6824 | 2,251,076,197 | I_kwDODunzps6GLLJl | 6,824 | Winogrande does not seem to be compatible with datasets version of 1.18.0 | {
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"Hi ! Do you mean 2.18 ? Can you try to update `fsspec` and `huggingface_hub` ?\r\n\r\n```\r\npip install -U fsspec huggingface_hub\r\n```",
"Yes I meant 2.18, and it works after updating `fsspec` and `huggingface_hub`. Thanks!"
] | 1970-01-01T00:00:00.000001 | 1,713 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
I get the following error when simply running `load_dataset('winogrande','winogrande_xl')`.
I do not have such an issue in the 1.17.0 version.
```Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2265, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 371, in __init__
self.config, self.config_id = self._create_builder_config(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 620, in _create_builder_config
builder_config._resolve_data_files(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 211, in _resolve_data_files
self.data_files = self.data_files.resolve(base_path, download_config)
File "/usr/local/lib/python3.10/dist-packages/datasets/data_files.py", line 799, in resolve
out[key] = data_files_patterns_list.resolve(base_path, download_config)
File "/usr/local/lib/python3.10/dist-packages/datasets/data_files.py", line 752, in resolve
resolve_pattern(
File "/usr/local/lib/python3.10/dist-packages/datasets/data_files.py", line 393, in resolve_pattern
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to find 'hf://datasets/winogrande@ebf71e3c7b5880d019ecf6099c0b09311b1084f5/winogrande_xl/train/0000.parquet' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.geoparquet', '.gpq', '.arrow', '.txt', '.tar', '.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', '.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', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']```
### Steps to reproduce the bug
from datasets import load_dataset
datasets = load_dataset('winogrande','winogrande_xl')
### Expected behavior
```Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.06M/2.06M [00:00<00:00, 5.16MB/s]
Downloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 118k/118k [00:00<00:00, 360kB/s]
Downloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 85.9k/85.9k [00:00<00:00, 242kB/s]
Generating train split: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 40398/40398 [00:00<00:00, 845491.12 examples/s]
Generating test split: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 1767/1767 [00:00<00:00, 362501.11 examples/s]
Generating validation split: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 1267/1267 [00:00<00:00, 318768.11 examples/s]```
### Environment info
datasets version: 1.18.0
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https://api.github.com/repos/huggingface/datasets/issues/6823 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6823/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6823/comments | https://api.github.com/repos/huggingface/datasets/issues/6823/events | https://github.com/huggingface/datasets/issues/6823 | 2,250,775,569 | I_kwDODunzps6GKBwR | 6,823 | Loading problems of Datasets with a single shard | {
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"Has there been a PR to resolve this already?",
"The problem rises from using a wrong api.\r\nWhen loading a save_to_disk dataset, **load_from_disk** (instead of load_dataset) is what should be used.\r\n\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndst.save_to_disk(\"cache\")\r\ndst = load_from_disk(\"cache\")\r\n```"
] | 1970-01-01T00:00:00.000001 | 1,732 | null | NONE | null | ### Describe the bug
When saving a dataset on disk and it has a single shard it is not loaded as when it is saved in multiple shards. I installed the latest version of datasets via pip.
### Steps to reproduce the bug
The code below reproduces the behavior. All works well when the range of the loop is 10000 but it fails when it is 1000.
```
from PIL import Image
import numpy as np
from datasets import Dataset, DatasetDict, load_dataset
def load_image():
# Generate random noise image
noise = np.random.randint(0, 256, (256, 256, 3), dtype=np.uint8)
return Image.fromarray(noise)
def create_dataset():
input_images = []
output_images = []
text_prompts = []
for _ in range(10000): # this is the problematic parameter
input_images.append(load_image())
output_images.append(load_image())
text_prompts.append('test prompt')
data = {'input_image': input_images, 'output_image': output_images, 'text_prompt': text_prompts}
dataset = Dataset.from_dict(data)
return DatasetDict({'train': dataset})
dataset = create_dataset()
print('dataset before saving')
print(dataset)
print(dataset['train'].column_names)
dataset.save_to_disk('test_ds')
print('dataset after loading')
dataset_loaded = load_dataset('test_ds')
print(dataset_loaded)
print(dataset_loaded['train'].column_names)
```
The output for 1000 iterations is:
```
dataset before saving
DatasetDict({
train: Dataset({
features: ['input_image', 'output_image', 'text_prompt'],
num_rows: 1000
})
})
['input_image', 'output_image', 'text_prompt']
Saving the dataset (1/1 shards): 100%|█| 1000/1000 [00:00<00:00, 5156.00 example
dataset after loading
Generating train split: 1 examples [00:00, 230.52 examples/s]
DatasetDict({
train: Dataset({
features: ['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split'],
num_rows: 1
})
})
['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split']
```
For 10000 iteration (8 shards) it is correct:
```
dataset before saving
DatasetDict({
train: Dataset({
features: ['input_image', 'output_image', 'text_prompt'],
num_rows: 10000
})
})
['input_image', 'output_image', 'text_prompt']
Saving the dataset (8/8 shards): 100%|█| 10000/10000 [00:01<00:00, 6237.68 examp
dataset after loading
Generating train split: 10000 examples [00:00, 10773.16 examples/s]
DatasetDict({
train: Dataset({
features: ['input_image', 'output_image', 'text_prompt'],
num_rows: 10000
})
})
['input_image', 'output_image', 'text_prompt']
```
### Expected behavior
The procedure should work for a dataset with one shrad the same as for one with multiple shards
### Environment info
- `datasets` version: 2.18.0
- Platform: macOS-14.1-arm64-arm-64bit
- Python version: 3.11.8
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.2
- `fsspec` version: 2024.2.0
Edit: I looked in the source code of load.py in datasets. I should have used "load_from_disk" and it indeed works that way. But ideally load_dataset would have raisen an error the same way as if I call a path:
```
if Path(path, config.DATASET_STATE_JSON_FILENAME).exists():
raise ValueError(
"You are trying to load a dataset that was saved using `save_to_disk`. "
"Please use `load_from_disk` instead."
)
```
nevertheless I find it interesting that it works just well and without a warning if there are multiple shards. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6819 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6819/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6819/comments | https://api.github.com/repos/huggingface/datasets/issues/6819/events | https://github.com/huggingface/datasets/issues/6819 | 2,248,043,797 | I_kwDODunzps6F_m0V | 6,819 | Give more details in `DataFilesNotFoundError` when getting the config names | {
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] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,713 | null | COLLABORATOR | null | ### Feature request
After https://huggingface.co/datasets/cis-lmu/Glot500/commit/39060e01272ff228cc0ce1d31ae53789cacae8c3, the dataset viewer gives the following error:
```
{
"error": "Cannot get the config names for the dataset.",
"cause_exception": "DataFilesNotFoundError",
"cause_message": "No (supported) data files found in cis-lmu/Glot500",
"cause_traceback": [
"Traceback (most recent call last):\n",
" File \"/src/services/worker/src/worker/job_runners/dataset/config_names.py\", line 73, in compute_config_names_response\n config_names = get_dataset_config_names(\n",
" File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 347, in get_dataset_config_names\n dataset_module = dataset_module_factory(\n",
" File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1873, in dataset_module_factory\n raise e1 from None\n",
" File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1854, in dataset_module_factory\n return HubDatasetModuleFactoryWithoutScript(\n",
" File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1245, in get_module\n module_name, default_builder_kwargs = infer_module_for_data_files(\n",
" File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 595, in infer_module_for_data_files\n raise DataFilesNotFoundError(\"No (supported) data files found\" + (f\" in {path}\" if path else \"\"))\n",
"datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in cis-lmu/Glot500\n"
]
}
```
because the deleted files were still listed in the README, see https://huggingface.co/datasets/cis-lmu/Glot500/discussions/4
Ideally, the error message would include the name of the first configuration with missing files, to help the user understand how to fix it. Here, it would tell that configuration `aze_Ethi` has no supported data files, instead of telling that the `cis-lmu/Glot500` *dataset* has no supported data files (which is not true).
### Motivation
Giving more detail in the error would help the Datasets Hub users to debug why the dataset viewer does not work.
### Your contribution
Not sure how to best fix this, as there are a lot of loops on the dataset configs in the traceback methods. "maybe" it would be easier to handle if the code was completely isolating each config. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6814 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6814/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6814/comments | https://api.github.com/repos/huggingface/datasets/issues/6814/events | https://github.com/huggingface/datasets/issues/6814 | 2,245,857,902 | I_kwDODunzps6F3RJu | 6,814 | `map` with `num_proc` > 1 leads to OOM | {
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"Hi ! You can try to reduce `writer_batch_size`. It corresponds to the number of samples that stay in RAM before being flushed to disk"
] | 1970-01-01T00:00:00.000001 | 1,713 | null | CONTRIBUTOR | null | ### Describe the bug
When running `map` on parquet dataset loaded from local machine, the RAM usage increases linearly eventually leading to OOM. I was wondering if I should I save the `cache_file` after every n steps in order to prevent this?
### Steps to reproduce the bug
```
ds = load_dataset("parquet", data_files=dataset_path, split="train")
ds = ds.shard(num_shards=4, index=0)
ds = ds.cast_column("audio", datasets.features.Audio(sampling_rate=16_000))
ds = ds.map(prepare_dataset,
num_proc=32,
writer_batch_size=1000,
keep_in_memory=False,
desc="preprocess dataset")
```
```
def prepare_dataset(batch):
# load audio
sample = batch["audio"]
inputs = feature_extractor(sample["array"], sampling_rate=16000)
batch["input_values"] = inputs.input_values[0]
batch["input_length"] = len(sample["array"].squeeze())
return batch
```
### Expected behavior
It shouldn't run into OOM problem.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.17
- Python version: 3.8.19
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.0.3
- `fsspec` version: 2024.2.0 | null | {
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"Probably best to implement this as a CLI command?",
"Thanks for your comment, @mariosasko. Or maybe both (in Python and as CLI command)? The Python command would be just the reverse of `push_to_hub`...\r\n\r\nI am working on a draft implementation, so we can discuss about the API and UX."
] | 1970-01-01T00:00:00.000001 | 1,714 | 1970-01-01T00:00:00.000001 | MEMBER | null | As proposed by @BramVanroy, it would be neat to have this functionality through the API. | {
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> When providing support, we sometimes suggest that users store their script in a script branch. What do you think of this alternative to deleting the files? | {
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https://api.github.com/repos/huggingface/datasets/issues/6805 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6805/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6805/comments | https://api.github.com/repos/huggingface/datasets/issues/6805/events | https://github.com/huggingface/datasets/issues/6805 | 2,239,034,951 | I_kwDODunzps6FdPZH | 6,805 | Batched mapping of existing string column casts boolean to string | {
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"This seems to be hardcoded behavior in table.py `array_cast`.\r\n```python\r\nif (\r\n not allow_number_to_str\r\n and pa.types.is_string(pa_type)\r\n and (pa.types.is_floating(array.type) or pa.types.is_integer(array.type))\r\n ):\r\n raise TypeError(\r\n f\"Couldn't cast array of type {array.type} to {pa_type} since allow_number_to_str is set to {allow_number_to_str}\"\r\n )\r\n if pa.types.is_null(pa_type) and not pa.types.is_null(array.type):\r\n raise TypeError(f\"Couldn't cast array of type {array.type} to {pa_type}\")\r\n return array.cast(pa_type)\r\n```\r\nwhere floats and integers are not cast to string but booleans are.\r\nMaybe this should be extended to booleans?",
"Thanks for reporting! @Modexus Do you want to open a PR with the suggested fix?",
"I'll gladly create a PR but not sure what the behavior should be.\r\n\r\nShould a value returned from map be cast to the current feature?\r\nAt the moment this seems very inconsistent since `datetime `is also cast (this would only fix `boolean`) but nested structures are not.\r\n\r\n```python\r\ndset = Dataset.from_dict({\"a\": [\"Hello world!\"]})\r\ndset = dset.map(lambda x: {\"a\": date(2021, 1, 1)})\r\n# dset[0][\"a\"] == '2021-01-01'\r\n```\r\n```python\r\ndset = Dataset.from_dict({\"a\": [\"Hello world!\"]})\r\ndset = dset.map(lambda x: {\"a\": [True]})\r\n# dset[0][\"a\"] == [True]\r\n```\r\n\r\nIs there are reason to cast the value if the user doesn't specify it explicitly?\r\nSeems tricky that some things are cast and some are not.",
"Indeed, it also makes sense to raise a `TypeError` for temporal and decimal types.\r\n\r\n> Is there are reason to cast the value if the user doesn't specify it explicitly?\r\n\r\nThis is how PyArrow's built-in `cast` behaves - it allows casting from primitive types to strings. Hence, we need `allow_number_to_str` to disallow such casts (e.g., in the [scenario](https://github.com/huggingface/datasets/blob/a3bc89d8bfd47c2a175c3ce16d92b7307cdeafd6/src/datasets/arrow_writer.py#L208) when we are \"trying a type\" to preserve the original type if there is a column in the output dataset with the same name as in the input one).\r\n\r\nPS: In the PR, we can introduce `allow_numeric_to_str` (for floats, integers, decimals, booleans) and `allow_temporal_to_str` (for dates, timestamps, ...) and deprecate `allow_number_to_str` to make it clear what each parameter does.",
"Would just `allow_primitive_to_str` work?\r\nThis should include all `numeric`, `boolean `and `temporal`formats.\r\n\r\nNote that at least in the [ C++ implementation](https://arrow.apache.org/docs/cpp/api/utilities.html#_CPPv410is_numericRK8DataType) `numeric `seems to exclude `boolean`.\r\n[](https://arrow.apache.org/docs/cpp/api/utilities.html#_CPPv410is_numericRK8DataType)",
"Indeed, `allow_primitive_to_str` sounds better.\r\n\r\nPS: PyArrow's `pa.types.is_primitive` returns `False` for decimal types, but I think is okay for us to treat decimals as primitive types (or we can have `allow_decimal_to_str` to be fully consistent with PyArrow)",
"Fixed by:\r\n- #6811"
] | 1970-01-01T00:00:00.000001 | 1,720 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
Let the dataset contain a column named 'a', which is of the string type.
If 'a' is converted to a boolean using batched mapping, the mapper automatically casts the boolean to a string (e.g., True -> 'true').
It only happens when the original column and the mapped column name are identical.
Thank you!
### Steps to reproduce the bug
```python
from datasets import Dataset
dset = Dataset.from_dict({'a': ['11', '22']})
dset = dset.map(lambda x: {'a': [True for _ in x['a']]}, batched=True)
print(dset['a'])
```
```
> ['true', 'true']
```
### Expected behavior
[True, True]
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.4.0-148-generic-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.21.4
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2023.12.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/6801 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6801/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6801/comments | https://api.github.com/repos/huggingface/datasets/issues/6801/events | https://github.com/huggingface/datasets/issues/6801 | 2,236,911,556 | I_kwDODunzps6FVI_E | 6,801 | got fileNotFound | {
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"Hi! I'll open a PR on the Hub to fix this, but please use the Hub's [Community tab](https://huggingface.co/datasets/nyanko7/danbooru2023/discussions) to report such issues in the future.",
"I've opened a [PR](https://huggingface.co/datasets/nyanko7/danbooru2023/discussions/8) in the repo, so let's continue the discussion there"
] | 1970-01-01T00:00:00.000001 | 1,712 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
When I use load_dataset to load the nyanko7/danbooru2023 data set, the cache is read in the form of a symlink. There may be a problem with the arrow_dataset initialization process and I get FileNotFoundError: [Errno 2] No such file or directory: '2945000.jpg'
### Steps to reproduce the bug
#code show as below
from datasets import load_dataset
data = load_dataset("nyanko7/danbooru2023",cache_dir=<symlink>)
data["train"][0]
### Expected behavior
I should get this result:
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=365x256 at 0x7FB730CB4070>, 'label': 0}
### Environment info
datasets==2.12.0
python==3.10.14
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https://api.github.com/repos/huggingface/datasets/issues/6800 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6800/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6800/comments | https://api.github.com/repos/huggingface/datasets/issues/6800/events | https://github.com/huggingface/datasets/issues/6800 | 2,236,431,288 | I_kwDODunzps6FTTu4 | 6,800 | High overhead when loading lots of subsets from the same dataset | {
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"Hi !\r\n\r\nIt's possible to multiple files at once:\r\n\r\n```python\r\ndata_files = \"data/*.jsonl\"\r\n# Or pass a list of files\r\nlangs = ['ka-ml', 'br-sr', 'ka-pt', 'id-ko', ..., 'fi-ze_zh', 'he-kk', 'ka-tr']\r\ndata_files = [f\"data/{lang}.jsonl\" for lang in langs]\r\nds = load_dataset(\"loicmagne/open-subtitles-250-bitext-mining\", data_files=data_files, split=\"train\")\r\n```\r\n\r\nAlso maybe you can add a subset called \"all\" for people that want to load all the data without having to list all the languages ?\r\n\r\n```yaml\r\n - config_name: all\r\n data_files: data/*.jsonl\r\n```\r\n",
"Thanks for your reply, it is indeed much faster, however the result is a dataset where all the subsets are \"merged\" together, the language pair is lost:\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence1', 'sentence2'],\r\n num_rows: 247809\r\n })\r\n})\r\n```\r\nI guess I could add a 'lang' feature for each row in the dataset, is there a better way to do it ?",
"Hi @lhoestq over at https://github.com/embeddings-benchmark/mteb/issues/530 we have started examining these issues and would love to make a PR for datasets if we believe there is a way to improve the speed. As I assume you have a better overview than me @lhoestq, would you be interested in a PR, and might you have an idea about where we would start working on it?\r\n\r\nWe see a speed comparison of \r\n1. 15 minutes (for ~20% of the languages) when loaded using a for loop\r\n2. 17 minutes using the your suggestion\r\n3. ~30 seconds when using @loicmagne \"merged\" method.\r\n\r\nWorth mentioning is that solution 2 looses the language information.",
"Can you retry using `datasets` 2.19 ? We improved a lot the speed of downloading datasets with tons of small files.\r\n\r\n```\r\npip install -U datasets\r\n```\r\n\r\nNow this takes 17sec on my side instead of the 17min minutes @loicmagne mentioned :)\r\n\r\n```python\r\n>>> %time ds = load_dataset(\"loicmagne/open-subtitles-250-bitext-mining\", data_files=\"data/*.jsonl\")\r\nDownloading readme: 100%|█████████████████████████████████| 13.7k/13.7k [00:00<00:00, 5.47MB/s]\r\nResolving data files: 100%|█████████████████████████████████| 250/250 [00:00<00:00, 612.51it/s]\r\nDownloading data: 100%|██████████████████████████████████| 250/250 [00:12<00:00, 19.68files/s]\r\nGenerating train split: 247809 examples [00:00, 1057071.08 examples/s]\r\nCPU times: user 4.95 s, sys: 3.1 s, total: 8.05 s\r\nWall time: 17.4 s\r\n```",
"> Can you retry using `datasets` 2.19 ? We improved a lot the speed of downloading datasets with tons of small files.\r\n> \r\n> ```\r\n> pip install -U datasets\r\n> ```\r\n> \r\n> Now this takes 17sec on my side instead of the 17min minutes @loicmagne mentioned :)\r\n> \r\n> ```python\r\n> >>> %time ds = load_dataset(\"loicmagne/open-subtitles-250-bitext-mining\", data_files=\"data/*.jsonl\")\r\n> Downloading readme: 100%|█████████████████████████████████| 13.7k/13.7k [00:00<00:00, 5.47MB/s]\r\n> Resolving data files: 100%|█████████████████████████████████| 250/250 [00:00<00:00, 612.51it/s]\r\n> Downloading data: 100%|██████████████████████████████████| 250/250 [00:12<00:00, 19.68files/s]\r\n> Generating train split: 247809 examples [00:00, 1057071.08 examples/s]\r\n> CPU times: user 4.95 s, sys: 3.1 s, total: 8.05 s\r\n> Wall time: 17.4 s\r\n> ```\r\n\r\nI was actually just noticing that, I bumped from 2.18 to 2.19 and got a massive speedup, amazing!\r\n\r\nAbout the fact that subset names are lost when loading all files at once, currently my solution is to add a 'lang' feature to each rows, convert to polars and use:\r\n\r\n```python\r\nds_split = ds.to_polars().group_by('lang')\r\n```\r\n\r\nIt's fast so I think it's an acceptable solution, but is there a better way to do it ?",
"It's the fastest way I think :)\r\n\r\nAlternatively you can download the dataset repository locally using [huggingface_hub](https://huggingface.co/docs/huggingface_hub/guides/download) (either via CLI or in python) and load the subsets one by one locally using a for loop as you were doing before (just pass the directory path to load_dataset instead of the dataset_id). "
] | 1970-01-01T00:00:00.000001 | 1,713 | null | NONE | null | ### Describe the bug
I have a multilingual dataset that contains a lot of subsets. Each subset corresponds to a pair of languages, you can see here an example with 250 subsets: [https://hf.co/datasets/loicmagne/open-subtitles-250-bitext-mining](). As part of the MTEB benchmark, we may need to load all the subsets of the dataset. The dataset is relatively small and contains only ~45MB of data, but when I try to load every subset, it takes 15 minutes from the HF hub and 13 minutes from the cache
This issue https://github.com/huggingface/datasets/issues/5499 also referenced this overhead, but I'm wondering if there is anything I can do to speedup loading different subsets of the same dataset, both when loading from disk and from the HF hub? Currently each subset is stored in a jsonl file
### Steps to reproduce the bug
```
from datasets import load_dataset
for subset in ['ka-ml', 'br-sr', 'bg-br', 'kk-lv', 'br-sk', 'br-fi', 'eu-ze_zh', 'kk-nl', 'kk-vi', 'ja-kk', 'br-sv', 'kk-zh_cn', 'kk-ms', 'br-et', 'br-hu', 'eo-kk', 'br-tr', 'ko-tl', 'te-zh_tw', 'br-hr', 'br-nl', 'ka-si', 'br-cs', 'br-is', 'br-ro', 'br-de', 'et-kk', 'fr-hy', 'br-no', 'is-ko', 'br-da', 'br-en', 'eo-lt', 'is-ze_zh', 'eu-ko', 'br-it', 'br-id', 'eu-zh_cn', 'is-ja', 'br-sl', 'br-gl', 'br-pt_br', 'br-es', 'br-pt', 'is-th', 'fa-is', 'br-ca', 'eu-ka', 'is-zh_cn', 'eu-ur', 'id-kk', 'br-sq', 'eu-ja', 'uk-ur', 'is-zh_tw', 'ka-ko', 'eu-zh_tw', 'eu-th', 'eu-is', 'is-tl', 'br-eo', 'eo-ze_zh', 'eu-te', 'ar-kk', 'eo-lv', 'ko-ze_zh', 'ml-ze_zh', 'is-lt', 'br-fr', 'ko-te', 'kk-sl', 'eu-fa', 'eo-ko', 'ka-ze_en', 'eo-eu', 'ta-zh_tw', 'eu-lv', 'ko-lv', 'lt-tl', 'eu-si', 'hy-ru', 'ar-is', 'eu-lt', 'eu-tl', 'eu-uk', 'ka-ze_zh', 'si-ze_zh', 'el-is', 'bn-is', 'ko-ze_en', 'eo-si', 'cs-kk', 'is-uk', 'eu-ze_en', 'ta-ze_zh', 'is-pl', 'is-mk', 'eu-ta', 'ko-lt', 'is-lv', 'fa-ko', 'bn-ko', 'hi-is', 'bn-ze_zh', 'bn-eu', 'bn-ja', 'is-ml', 'eu-ru', 'ko-ta', 'is-vi', 'ja-tl', 'eu-mk', 'eu-he', 'ka-zh_tw', 'ka-zh_cn', 'si-tl', 'is-kk', 'eu-fi', 'fi-ko', 'is-ur', 'ka-th', 'ko-ur', 'eo-ja', 'he-is', 'is-tr', 'ka-ur', 'et-ko', 'eu-vi', 'is-sk', 'gl-is', 'fr-is', 'is-sq', 'hu-is', 'fr-kk', 'eu-sq', 'is-ru', 'ja-ka', 'fi-tl', 'ka-lv', 'fi-is', 'is-si', 'ar-ko', 'ko-sl', 'ar-eu', 'ko-si', 'bg-is', 'eu-hu', 'ko-sv', 'bn-hu', 'kk-ro', 'eu-hi', 'ka-ms', 'ko-th', 'ko-sr', 'ko-mk', 'fi-kk', 'ka-vi', 'eu-ml', 'ko-ml', 'de-ko', 'fa-ze_zh', 'eu-sk', 'is-sl', 'et-is', 'eo-is', 'is-sr', 'is-ze_en', 'kk-pt_br', 'hr-hy', 'kk-pl', 'ja-ta', 'is-ms', 'hi-ze_en', 'is-ro', 'ko-zh_cn', 'el-eu', 'ka-pl', 'ka-sq', 'eu-sl', 'fa-ka', 'ko-no', 'si-ze_en', 'ko-uk', 'ja-ze_zh', 'hu-ko', 'kk-no', 'eu-pl', 'is-pt_br', 'bn-lv', 'tl-zh_cn', 'is-nl', 'he-ko', 'ko-sq', 'ta-th', 'lt-ta', 'da-ko', 'ca-is', 'is-ta', 'bn-fi', 'ja-ml', 'lv-si', 'eu-sv', 'ja-te', 'bn-ur', 'bn-ca', 'bs-ko', 'bs-is', 'eu-sr', 'ko-vi', 'ko-zh_tw', 'et-tl', 'kk-tr', 'eo-vi', 'is-it', 'ja-ko', 'eo-et', 'id-is', 'bn-et', 'bs-eu', 'bn-lt', 'tl-uk', 'bn-zh_tw', 'da-eu', 'el-ko', 'no-tl', 'ko-sk', 'is-pt', 'hu-kk', 'si-zh_tw', 'si-te', 'ka-ru', 'lt-ml', 'af-ja', 'bg-eu', 'eo-th', 'cs-is', 'pl-ze_zh', 'el-kk', 'kk-sv', 'ka-nl', 'ko-pl', 'bg-ko', 'ka-pt_br', 'et-eu', 'tl-zh_tw', 'ka-pt', 'id-ko', 'fi-ze_zh', 'he-kk', 'ka-tr']:
load_dataset('loicmagne/open-subtitles-250-bitext-mining', subset)
```
### Expected behavior
Faster loading?
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-27-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.2
- `fsspec` version: 2023.5.0
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https://api.github.com/repos/huggingface/datasets/issues/6798 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6798/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6798/comments | https://api.github.com/repos/huggingface/datasets/issues/6798/events | https://github.com/huggingface/datasets/issues/6798 | 2,235,768,891 | I_kwDODunzps6FQyA7 | 6,798 | `DatasetBuilder._split_generators` incomplete type annotation | {
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"Good catch! Feel free to open a PR with the suggested fix :).",
"There is also the [`MockDownloadManager`](https://github.com/JonasLoos/datasets/blob/main/src/datasets/download/mock_download_manager.py#L33), which seems like it might get passed here too. However, to me, it doesn't really seem relevant to the users of the datasets library, so I would just ignore it. What do you think, @mariosasko?",
"The API (`dummy_data` CLI command ) that uses the `MockDownloadManager` has been deprecated, so ignoring it sounds good!"
] | 1970-01-01T00:00:00.000001 | 1,712 | 1970-01-01T00:00:00.000001 | CONTRIBUTOR | null | ### Describe the bug
The [`DatasetBuilder._split_generators`](https://github.com/huggingface/datasets/blob/0f27d7b77c73412cfc50b24354bfd7a3e838202f/src/datasets/builder.py#L1449) function has currently the following signature:
```python
class DatasetBuilder:
def _split_generators(self, dl_manager: DownloadManager):
...
```
However, the `dl_manager` argument can also be of type [`StreamingDownloadManager`](https://github.com/huggingface/datasets/blob/0f27d7b77c73412cfc50b24354bfd7a3e838202f/src/datasets/download/streaming_download_manager.py#L962), which has different functionality. For example, the `download` function doesn't download, but rather just returns the given url(s).
I suggest changing the function signature to:
```python
class DatasetBuilder:
def _split_generators(self, dl_manager: Union[DownloadManager, StreamingDownloadManager]):
...
```
and also adjust the docstring accordingly.
I would like to create a Pull Request to fix this, and have the following questions:
* Are there also other options than `DownloadManager`, and `StreamingDownloadManager`?
* Should this also be changed in other functions?
### Steps to reproduce the bug
Minimal example to print the different class names:
```python
import tempfile
from datasets import load_dataset
example = b'''
from datasets import GeneratorBasedBuilder, DatasetInfo, Features, Value, SplitGenerator
class Test(GeneratorBasedBuilder):
def _info(self):
return DatasetInfo(features=Features({"x": Value("int64")}))
def _split_generators(self, dl_manager):
print(type(dl_manager))
return [SplitGenerator('test')]
def _generate_examples(self):
yield 0, {'x': 42}
'''
with tempfile.NamedTemporaryFile(suffix='.py') as f:
f.write(example)
f.flush()
load_dataset(f.name, streaming=False)
load_dataset(f.name, streaming=True)
```
### Expected behavior
complete type annotations
### Environment info
/ | {
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"Finally:\r\n- the initial issue seems it was temporary\r\n- there is a different issue now: https://github.com/huggingface/datasets/actions/runs/8627153993/job/23646584590?pr=6797\r\n```\r\nFAILED tests/test_load.py::ModuleFactoryTest::test_HubDatasetModuleFactoryWithParquetExport - datasets.utils._dataset_viewer.DatasetViewerError: No exported Parquet files available.\r\nFAILED tests/test_load.py::ModuleFactoryTest::test_HubDatasetModuleFactoryWithParquetExport_errors_on_wrong_sha - datasets.utils._dataset_viewer.DatasetViewerError: No exported Parquet files available.\r\nFAILED tests/test_load.py::test_load_dataset_builder_for_community_dataset_with_script - AssertionError: assert 'dataset_with_script' == 'parquet'\r\n \r\n - parquet\r\n + dataset_with_script\r\n```\r\n\r\nMaybe related to `hf-internal-testing/dataset_with_script` dataset: https://huggingface.co/datasets/hf-internal-testing/dataset_with_script",
"This URL: https://datasets-server.huggingface.co/parquet?dataset=hf-internal-testing/dataset_with_script\r\nraises:\r\n> {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}\r\n\r\nWas there a recent change on the Hub enforcing this behavior?",
"OK, I just saw this PR:\r\n- https://github.com/huggingface/dataset-viewer/pull/2689\r\n\r\nOnce merged and deployed, it should fix the issue.",
"Once the script-dataset has been allowed in the dataset-viewer, we should fix our test to make the CI pass.\r\n\r\nI am addressing this."
] | 1970-01-01T00:00:00.000001 | 1,712 | 1970-01-01T00:00:00.000001 | MEMBER | null | CI is broken for test_load_dataset_distributed_with_script. See: https://github.com/huggingface/datasets/actions/runs/8614926216/job/23609378127
```
FAILED tests/test_load.py::test_load_dataset_distributed_with_script[None] - assert False
+ where False = all(<generator object test_load_dataset_distributed_with_script.<locals>.<genexpr> at 0x7f0c741de3b0>)
FAILED tests/test_load.py::test_load_dataset_distributed_with_script[force_redownload] - assert False
+ where False = all(<generator object test_load_dataset_distributed_with_script.<locals>.<genexpr> at 0x7f0be45f6ea0>)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6793 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6793/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6793/comments | https://api.github.com/repos/huggingface/datasets/issues/6793/events | https://github.com/huggingface/datasets/issues/6793 | 2,231,400,200 | I_kwDODunzps6FAHcI | 6,793 | Loading just one particular split is not possible for imagenet-1k | {
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"+1"
] | 1970-01-01T00:00:00.000001 | 1,726 | null | NONE | null | ### Describe the bug
I'd expect the following code to download just the validation split but instead I get all data on my disk (train, test and validation splits)
`
from datasets import load_dataset
dataset = load_dataset("imagenet-1k", split="validation", trust_remote_code=True)
`
Is it expected to work like that?
### Steps to reproduce the bug
1. Install the required libraries (python, datasets, huggingface_hub)
2. Login using huggingface cli
2. Run the code in the description
### Expected behavior
Just a single (validation) split should be downloaded.
### Environment info
python: 3.12.2
datasets: 2.18.0
huggingface_hub: 0.22.2 | null | {
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"I realized I was passing a string column to this instead of float. Is it possible to add a warning or error to prevent users from falsely believing there's a bug?",
"Hello!\r\n\r\nI agree that we could add some safeguards around the type of `ds[column]`. At least for FAISS, we need the column to be made of embeddings as FAISS doesn't perform the embeddings itself.\r\n\r\nI can propose a PR sometime this week.",
"@Dref360 thanks for the initiative!"
] | 1970-01-01T00:00:00.000001 | 1,712 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
Calling `add_faiss_index` on a `Dataset` with a column argument raises a ValueError. The following is the trace
```python
214 def replacement_add(self, x):
215 """Adds vectors to the index.
216 The index must be trained before vectors can be added to it.
217 The vectors are implicitly numbered in sequence. When `n` vectors are
(...)
224 `dtype` must be float32.
225 """
--> 227 n, d = x.shape
228 assert d == self.d
229 x = np.ascontiguousarray(x, dtype='float32')
ValueError: not enough values to unpack (expected 2, got 1)
```
### Steps to reproduce the bug
1. Load any dataset like `ds = datasets.load_dataset("wikimedia/wikipedia", "20231101.en")["train"]`
2. Add an FAISS index on any column `ds.add_faiss_index('title')`
### Expected behavior
The index should be created
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-26-generic-x86_64-with-glibc2.35
- Python version: 3.9.19
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0
- `faiss-cpu` version: 1.8.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6790 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6790/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6790/comments | https://api.github.com/repos/huggingface/datasets/issues/6790/events | https://github.com/huggingface/datasets/issues/6790 | 2,229,915,236 | I_kwDODunzps6E6c5k | 6,790 | PyArrow 'Memory mapping file failed: Cannot allocate memory' bug | {
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"Thanks for a very clean explanation. This happened to me too, and I don't have sudo access to update the value. I wonder if there might be another workaround.",
"One option is to just have more data in each file - /proc/sys/vm/max_map_count limits the maximum number of concurrently open files, but I don't know if the size of a single file is restricted in any way. E.g. 5000 files with 1GB each is 5TB of data. https://huggingface.co/docs/datasets/v2.18.0/en/package_reference/main_classes#datasets.concatenate_datasets can come in handy."
] | 1970-01-01T00:00:00.000001 | 1,725 | null | NONE | null | ### Describe the bug
Hello,
I've been struggling with a problem using Huggingface datasets caused by PyArrow memory allocation. I finally managed to solve it, and thought to document it since similar issues have been raised here before (https://github.com/huggingface/datasets/issues/5710, https://github.com/huggingface/datasets/issues/6176).
In my case, I was trying to load ~70k dataset files from disk using `datasets.load_from_disk(data_path)` (meaning 70k repeated calls to load_from_disk). This triggered an (uninformative) exception around 64k loaded files:
```
File "pyarrow/io.pxi", line 1053, in pyarrow.lib.memory_map
File "pyarrow/io.pxi", line 1000, in pyarrow.lib.MemoryMappedFile._open
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
OSError: Memory mapping file failed: Cannot allocate memory
```
Despite system RAM usage being very low. After a lot of digging around, I discovered that my Ubuntu machine had a limit on the maximum number of memory mapped files in `/proc/sys/vm/max_map_count` set to 65530, which was causing my data loader to crash. Increasing the limit in the file (`echo <new_mmap_size> | sudo tee /proc/sys/vm/max_map_count`) made the issue go away.
While this isn't a bug as such in either Datasets or PyArrow, this behavior can be very confusing to users. Maybe this should be mentioned in documentation? I suspect the other issues raised here about memory mapping OOM errors could actually be consequence of system configuration.
Br,
Lauri
### Steps to reproduce the bug
```
import numpy as np
import pyarrow as pa
import tqdm
# Write some data to disk
arr = pa.array(np.arange(100))
schema = pa.schema([
pa.field('nums', arr.type)
])
with pa.OSFile('arraydata.arrow', 'wb') as sink:
with pa.ipc.new_file(sink, schema=schema) as writer:
batch = pa.record_batch([arr], schema=schema)
writer.write(batch)
# Number of times to open the memory map
nums = 70000
# Read the data back
arrays = [pa.memory_map('arraydata.arrow', 'r') for _ in tqdm.tqdm(range(nums))]
```
### Expected behavior
No errors.
### Environment info
datasets: 2.18.0
pyarrow: 15.0.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6789 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6789/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6789/comments | https://api.github.com/repos/huggingface/datasets/issues/6789/events | https://github.com/huggingface/datasets/issues/6789 | 2,229,527,001 | I_kwDODunzps6E4-HZ | 6,789 | Issue with map | {
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"Default `writer_batch_size `is set to 1000 (see [map](https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/main_classes#datasets.Dataset.map)).\r\nThe \"tmp1335llua\" is probably the temp file it creates while writing to disk.\r\nMaybe try lowering the `writer_batch_size`.\r\n\r\nFor multi-processing you should probably pass the `processor `as an argument (with e.g. partial) to the function or create it inside so that the sub-processes have access to it and maybe add `if __name__ == \"__main__\"` (not sure that's necessary?).\r\n",
"Hi @Modexus,\r\n\r\nThank you very much for the help! Yep after playing around with map, I managed to get the parallel processing to work by implementing it like you suggested.\r\n\r\nRegarding the temp files, it seems like the temp files just keep growing in size as the map continues. Eventually, once map finishes, the temp files are deleted, but they are instead saved as cache .arrow files. These cache files are absolutely gigantic (~ 30-50x the size of the initial dataset!).\r\n\r\nAfter playing around with the `prepare_dataset()` function above, it seems this issue is caused by the following line in the function, where the log-Mel spectrogram of the audio is calculated:\r\n\r\n`# compute log-Mel input features from input audio array\r\n batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], \r\n sampling_rate=audio[\"sampling_rate\"]).input_features[0]\r\n`\r\n\r\nWhen I remove this line, the final cache files are approximately the same size as the initial dataset.\r\n\r\nCan I check whether this is expected behavior with the whisper feature extractor? I cant imagine the spectrograms are that large!\r\n\r\nThank you so much for the help!",
"I'm having a similar issue with the spectrographs taking up an incredibly large amount of space. (i.e. 100GB for 3GB of audio). Is this really normal behavior?",
"Upon taking a look at the hex contents of the mapped dataset files I found that the overwhelming majority of the data contained within them was duplicated junk similar to this. I'm not very familiar with the inner workings of AI but I have to assume this is an inefficient way of storing data at best and a bug at worst.\r\n\r\n",
"Same problem, dataset.map takes long time to process 12GB raw audio data and create 200GB cache file. Is there any method can run process(map) during train, instead current run \r\nonce and save cache file ? ",
"Same issue here. Just trying to normalise image data for a 300MB dataset, ends up with an 11GB cache. The initial .map() call takes 80s over the 15000 images, but then simply iterating over the dataset takes almost 2 minutes. It should be doing no processing here! Something seems wrong.\r\nkeep_in_memory=True also offers no speedup.\r\nEDIT: Running the normalisation with set_transform (i.e. on the fly) iterates through the dataset in 18s. With no normalisation it takes around 14s. No reason for .map() to take 5 mins!",
"@eufrizz How you handle this using set_transform?\r\nI have a really big dataset of size 1.2TB and i am going to use it for fine-tunning whisper model. if i use map for dataset_preparing function it will take over 20 days!!!",
"> @eufrizz How you handle this using set_transform?\n> I have a really big dataset of size 1.2TB and i am going to use it for fine-tunning whisper model. if i use map for dataset_preparing function it will take over 20 days!!!\n\nJust give the preprocessing function you were using for map to set_transform. Just look at the set_transform documentation. If you're going to do lots of epochs you might be better off just saving the preprocessed data into a new dataset. "
] | 1970-01-01T00:00:00.000001 | 1,721 | null | NONE | null | ### Describe the bug
Map has been taking extremely long to preprocess my data.
It seems to process 1000 examples (which it does really fast in about 10 seconds), then it hangs for a good 1-2 minutes, before it moves on to the next batch of 1000 examples.
It also keeps eating up my hard drive space for some reason by creating a file named tmp1335llua that is over 300GB.
Trying to set num_proc to be >1 also gives me the following error: NameError: name 'processor' is not defined
Please advise on how I could optimise this?
### Steps to reproduce the bug
In general, I have been using map as per normal. Here is a snippet of my code:
````
########################### DATASET LOADING AND PREP #########################
def load_custom_dataset(split):
ds = []
if split == 'train':
for dset in args.train_datasets:
ds.append(load_from_disk(dset))
if split == 'test':
for dset in args.test_datasets:
ds.append(load_from_disk(dset))
ds_to_return = concatenate_datasets(ds)
ds_to_return = ds_to_return.shuffle(seed=22)
return ds_to_return
def prepare_dataset(batch):
# load and (possibly) resample audio data to 16kHz
audio = batch["audio"]
# compute log-Mel input features from input audio array
batch["input_features"] = processor.feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]
# compute input length of audio sample in seconds
batch["input_length"] = len(audio["array"]) / audio["sampling_rate"]
# optional pre-processing steps
transcription = batch["sentence"]
if do_lower_case:
transcription = transcription.lower()
if do_remove_punctuation:
transcription = normalizer(transcription).strip()
# encode target text to label ids
batch["labels"] = processor.tokenizer(transcription).input_ids
return batch
print('DATASET PREPARATION IN PROGRESS...')
# case 3: combine_and_shuffle is true, only train provided
# load train datasets
train_set = load_custom_dataset('train')
# split dataset
raw_dataset = DatasetDict()
raw_dataset = train_set.train_test_split(test_size = args.test_size, shuffle=True, seed=42)
raw_dataset = raw_dataset.cast_column("audio", Audio(sampling_rate=args.sampling_rate))
print("Before Map:")
print(raw_dataset)
raw_dataset = raw_dataset.map(prepare_dataset, num_proc=1)
print("After Map:")
print(raw_dataset)
````
### Expected behavior
Based on the speed at which map is processing examples, I would expect a 5-6 hours completion for all mapping
However, because it hangs every 1000 examples, I instead roughly estimate it would take about 40 hours!
Moreover, i cant even finish the map because it keeps exponentially eating up my hard drive space
### Environment info
- `datasets` version: 2.18.0
- Platform: Windows-10-10.0.22631-SP0
- Python version: 3.10.14
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
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"All data is saved in the arrow format on disk.\r\nIf you return a tensor it gets converted to arrow before saving to disk when using map.\r\n\r\nTo get a tensor when you access data elements you can use `dataset.set_format(\"pt\")`.\r\nNote that this just changes how the data is loaded, not how it is stored.",
"> All data is saved in the arrow format on disk. If you return a tensor it gets converted to arrow before saving to disk when using map.\r\n> \r\n> To get a tensor when you access data elements you can use `dataset.set_format(\"pt\")`. Note that this just changes how the data is loaded, not how it is stored.\r\n\r\nThank you very much for your explanation, I understand what you mean now. So you're saying that when streaming=True, there's no need to convert it to the arrow format and save it to disk. But if we directly load all formats and then convert them into the arrow format after passing through the map function, it will convert torch.Tensor into a List. I see."
] | 1970-01-01T00:00:00.000001 | 1,712 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
Hello,
I have a question regarding the map function in the Hugging Face datasets.
The situation is as follows: when I load a jsonl file using load_dataset(..., streaming=False), and then utilize the map function to process it, I specify that the returned example should be of type Torch.tensor. However, I noticed that after applying the map function, the datatype automatically changes to List, which leads to errors in my program.
I attempted to use load_dataset(..., streaming=True), and this issue no longer occurs. I'm not entirely clear on why this happens. Could you please provide some insights into this?
### Steps to reproduce the bug
1.dataset = load_dataset(xxx, streaming = False)
2. dataset.map(function), function will return torch.Tensor.
3. you will find the format of data in dataset is List.
### Expected behavior
I expected to receieve the format of data is torch.Tensor.
### Environment info
2.18.0 | {
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"From my current understanding, this timeout is only used when we need to get the results.\r\n\r\nOne of:\r\n1. All tasks are done\r\n2. One worker died\r\n\r\nYour function should work fine and it's definitely a bug if it doesn't.",
"When one of the `map`'s worker processes crashes, the linked code re-raises an error from the crash and returns it to the caller.\r\n\r\nIf your question is how to limit the time of long-running tasks/worker processes, such functionality doesn't exist in `datasets` (yet), which means you need to implement it yourself.\r\n\r\nE.g., you can implement it using the built-in `signal` module like this:\r\n```python\r\nimport time\r\nimport signal\r\nfrom contextlib import contextmanager\r\n\r\nfrom datasets import Dataset\r\n\r\n\r\n@contextmanager\r\ndef max_exec_time(t):\r\n def raise_timeout_handler(signum, frame):\r\n raise TimeoutError\r\n \r\n orig_handler = signal.getsignal(signal.SIGALRM)\r\n signal.signal(signal.SIGALRM, raise_timeout_handler)\r\n try:\r\n signal.alarm(t)\r\n yield\r\n finally:\r\n signal.alarm(0)\r\n signal.signal(signal.SIGALRM, orig_handler)\r\n\r\n\r\ndef worker(example, rank):\r\n try:\r\n with max_exec_time(20): # 20 sec execution limit\r\n if rank % 2 == 0:\r\n time.sleep(50) # simulate a long-running task\r\n example[\"a\"] = 100\r\n except TimeoutError:\r\n example[\"a\"] = None # Or return empty batches here in the \"batched\" mode\r\n return example\r\n\r\ndata = Dataset.from_list([{\"a\": 1}, {\"a\": 2}])\r\ndata = data.map(worker, num_proc=2, with_rank=True)\r\nprint(data[0])\r\n```",
"> From my current understanding, this timeout is only used when we need to get the results.\r\n> \r\n> One of:\r\n> \r\n> 1. All tasks are done\r\n> 2. One worker died\r\n> \r\n> Your function should work fine and it's definitely a bug if it doesn't.\r\n\r\nthanks for responding! can you reproduce the stuck with the above example code?",
"> When one of the `map`'s worker processes crashes, the linked code re-raises an error from the crash and returns it to the caller.\r\n> \r\n> If your question is how to limit the time of long-running tasks/worker processes, such functionality doesn't exist in `datasets` (yet), which means you need to implement it yourself.\r\n> \r\n> E.g., you can implement it using the built-in `signal` module like this:\r\n> \r\n> ```python\r\n> import time\r\n> import signal\r\n> from contextlib import contextmanager\r\n> \r\n> from datasets import Dataset\r\n> \r\n> \r\n> @contextmanager\r\n> def max_exec_time(t):\r\n> def raise_timeout_handler(signum, frame):\r\n> raise TimeoutError\r\n> \r\n> orig_handler = signal.getsignal(signal.SIGALRM)\r\n> signal.signal(signal.SIGALRM, raise_timeout_handler)\r\n> try:\r\n> signal.alarm(t)\r\n> yield\r\n> finally:\r\n> signal.alarm(0)\r\n> signal.signal(signal.SIGALRM, orig_handler)\r\n> \r\n> \r\n> def worker(example, rank):\r\n> try:\r\n> with max_exec_time(20): # 20 sec execution limit\r\n> if rank % 2 == 0:\r\n> time.sleep(50) # simulate a long-running task\r\n> example[\"a\"] = 100\r\n> except TimeoutError:\r\n> example[\"a\"] = None # Or return empty batches here in the \"batched\" mode\r\n> return example\r\n> \r\n> data = Dataset.from_list([{\"a\": 1}, {\"a\": 2}])\r\n> data = data.map(worker, num_proc=2, with_rank=True)\r\n> print(data[0])\r\n> ```\r\n\r\nthanks for responding! However, I don't think we should use `signal` in the context of multiprocessing since sometimes it will crash one process and raise the following error\r\nhttps://github.com/huggingface/datasets/blob/c3ddb1ef00334a6f973679a51e783905fbc9ef0b/src/datasets/utils/py_utils.py#L664",
"> thanks for responding! However, I don't think we should use signal in the context of multiprocessing since sometimes it will crash one process and raise the following error\r\n\r\nThe above code has `try/except` to catch the error from the handler. Or do you get an error other than `TimeoutError`?",
"> > thanks for responding! However, I don't think we should use signal in the context of multiprocessing since sometimes it will crash one process and raise the following error\r\n> \r\n> The above code has `try/except` to catch the error from the handler. Or do you get an error other than `TimeoutError`?\r\n\r\nyup, it will raise the RuntimeError: https://github.com/huggingface/datasets/blob/c3ddb1ef00334a6f973679a51e783905fbc9ef0b/src/datasets/utils/py_utils.py#L667C19-L670C22\r\n\r\n```\r\n raise RuntimeError(\r\n \"One of the subprocesses has abruptly died during map operation.\"\r\n \"To debug the error, disable multiprocessing.\"\r\n )\r\n```",
"What @mariosasko proposed it's very useful for debugging. Thank you!"
] | 1970-01-01T00:00:00.000001 | 1,723 | null | CONTRIBUTOR | null | ### Describe the bug
```python
from datasets import Dataset
def worker(example):
while True:
continue
example['a'] = 100
return example
data = Dataset.from_list([{"a": 1}, {"a": 2}])
data = data.map(worker)
print(data[0])
```
I'm implementing a worker function whose runtime will depend on specific examples (e.g., while most examples take 0.01s in worker, several examples may take 50s).
Therefore, I would like to know how the current implementation will handle those subprocesses that require a long (e.g., >= 5min) or even infinite time.
I notice that the current implementation set a timeout of 0.05 second
https://github.com/huggingface/datasets/blob/c3ddb1ef00334a6f973679a51e783905fbc9ef0b/src/datasets/utils/py_utils.py#L674
However, this example code still gets stuck.
### Steps to reproduce the bug
run the example above
### Expected behavior
I want to set a default worker to handle these timeout cases, instead of getting stuck
### Environment info
main branch version | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6783 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6783/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6783/comments | https://api.github.com/repos/huggingface/datasets/issues/6783/events | https://github.com/huggingface/datasets/issues/6783 | 2,228,179,466 | I_kwDODunzps6Ez1IK | 6,783 | AttributeError: module 'numpy' has no attribute 'object'. in Kaggle Notebook | {
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"Hi! You can fix this by updating the `datasets` package with `pip install -U datasets` and restarting the notebook.\r\n",
"Kaggle removed the problematic `datasets==2.1.0` pin last week, so I'm closing this issue (now it pre-installs the latest version)."
] | 1970-01-01T00:00:00.000001 | 1,712 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
# problem
I can't resample audio dataset in Kaggle Notebook. It looks like some code in `datasets` library use aliases that were deprecated in NumPy 1.20.
## code for resampling
```
from datasets import load_dataset, Audio
from transformers import AutoFeatureExtractor
from transformers import AutoModelForAudioClassification, TrainingArguments, Trainer
minds = load_dataset("PolyAI/minds14", name="en-US", split="train")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
def preprocess_function(examples):
audio_arrays = [x["array"] for x in examples["audio"]]
inputs = feature_extractor(
audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=16000, truncation=True
)
return inputs
dataset = dataset.map(preprocess_function, remove_columns="audio", batched=True, batch_size=100)
```
## the error I got
<details>
<summary>Click to expand</summary>
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[20], line 1
----> 1 dataset = dataset.map(preprocess_function, remove_columns="audio", batched=True, batch_size=100)
2 dataset
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1955, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1952 disable_tqdm = not logging.is_progress_bar_enabled()
1954 if num_proc is None or num_proc == 1:
-> 1955 return self._map_single(
1956 function=function,
1957 with_indices=with_indices,
1958 with_rank=with_rank,
1959 input_columns=input_columns,
1960 batched=batched,
1961 batch_size=batch_size,
1962 drop_last_batch=drop_last_batch,
1963 remove_columns=remove_columns,
1964 keep_in_memory=keep_in_memory,
1965 load_from_cache_file=load_from_cache_file,
1966 cache_file_name=cache_file_name,
1967 writer_batch_size=writer_batch_size,
1968 features=features,
1969 disable_nullable=disable_nullable,
1970 fn_kwargs=fn_kwargs,
1971 new_fingerprint=new_fingerprint,
1972 disable_tqdm=disable_tqdm,
1973 desc=desc,
1974 )
1975 else:
1977 def format_cache_file_name(cache_file_name, rank):
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:520, in transmit_tasks.<locals>.wrapper(*args, **kwargs)
518 self: "Dataset" = kwargs.pop("self")
519 # apply actual function
--> 520 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
521 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
522 for dataset in datasets:
523 # Remove task templates if a column mapping of the template is no longer valid
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:487, in transmit_format.<locals>.wrapper(*args, **kwargs)
480 self_format = {
481 "type": self._format_type,
482 "format_kwargs": self._format_kwargs,
483 "columns": self._format_columns,
484 "output_all_columns": self._output_all_columns,
485 }
486 # apply actual function
--> 487 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
488 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
489 # re-apply format to the output
File /opt/conda/lib/python3.10/site-packages/datasets/fingerprint.py:458, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
452 kwargs[fingerprint_name] = update_fingerprint(
453 self._fingerprint, transform, kwargs_for_fingerprint
454 )
456 # Call actual function
--> 458 out = func(self, *args, **kwargs)
460 # Update fingerprint of in-place transforms + update in-place history of transforms
462 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:2356, in Dataset._map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2354 writer.write_table(batch)
2355 else:
-> 2356 writer.write_batch(batch)
2357 if update_data and writer is not None:
2358 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_writer.py:507, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)
505 col_try_type = try_features[col] if try_features is not None and col in try_features else None
506 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 507 arrays.append(pa.array(typed_sequence))
508 inferred_features[col] = typed_sequence.get_inferred_type()
509 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
File /opt/conda/lib/python3.10/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()
File /opt/conda/lib/python3.10/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_writer.py:184, in TypedSequence.__arrow_array__(self, type)
182 out = numpy_to_pyarrow_listarray(data)
183 elif isinstance(data, list) and data and isinstance(first_non_null_value(data)[1], np.ndarray):
--> 184 out = list_of_np_array_to_pyarrow_listarray(data)
185 else:
186 trying_cast_to_python_objects = True
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1174, in list_of_np_array_to_pyarrow_listarray(l_arr, type)
1172 """Build a PyArrow ListArray from a possibly nested list of NumPy arrays"""
1173 if len(l_arr) > 0:
-> 1174 return list_of_pa_arrays_to_pyarrow_listarray(
1175 [numpy_to_pyarrow_listarray(arr, type=type) if arr is not None else None for arr in l_arr]
1176 )
1177 else:
1178 return pa.array([], type=type)
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1163, in list_of_pa_arrays_to_pyarrow_listarray(l_arr)
1160 null_indices = [i for i, arr in enumerate(l_arr) if arr is None]
1161 l_arr = [arr for arr in l_arr if arr is not None]
1162 offsets = np.cumsum(
-> 1163 [0] + [len(arr) for arr in l_arr], dtype=np.object
1164 ) # convert to dtype object to allow None insertion
1165 offsets = np.insert(offsets, null_indices, None)
1166 offsets = pa.array(offsets, type=pa.int32())
File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr)
319 warnings.warn(
320 f"In the future `np.{attr}` will be defined as the "
321 "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
323 if attr in __former_attrs__:
--> 324 raise AttributeError(__former_attrs__[attr])
326 if attr == 'testing':
327 import numpy.testing as testing
AttributeError: module 'numpy' has no attribute 'object'.
`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
```
</details>
### Steps to reproduce the bug
Run above code in Kaggle Notebook.
### Expected behavior
I can resample audio data without fail.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.133+-x86_64-with-glibc2.31
- Python version: 3.10.13
- PyArrow version: 11.0.0
- Pandas version: 2.2.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/6782 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6782/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6782/comments | https://api.github.com/repos/huggingface/datasets/issues/6782/events | https://github.com/huggingface/datasets/issues/6782 | 2,228,081,955 | I_kwDODunzps6EzdUj | 6,782 | Image cast_storage very slow for arrays (e.g. numpy, tensors) | {
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"This may be a solution that only changes `cast_storage` of `Image`.\r\nHowever, I'm not totally sure that the assumptions hold that are made about the `ListArray`.\r\n\r\n```python\r\nelif pa.types.is_list(storage.type):\r\n from .features import Array3DExtensionType\r\n\r\n def get_shapes(arr):\r\n shape = ()\r\n while isinstance(arr, pa.ListArray):\r\n len_curr = len(arr)\r\n arr = arr.flatten()\r\n len_new = len(arr)\r\n shape = shape + (len_new // len_curr,)\r\n return shape\r\n\r\n def get_dtypes(arr):\r\n dtype = storage.type\r\n while hasattr(dtype, \"value_type\"):\r\n dtype = dtype.value_type\r\n return dtype\r\n\r\n arrays = []\r\n for i, is_null in enumerate(storage.is_null()):\r\n if not is_null.as_py():\r\n storage_part = storage.take([i])\r\n shape = get_shapes(storage_part)\r\n dtype = get_dtypes(storage_part)\r\n\r\n extension_type = Array3DExtensionType(shape=shape, dtype=str(dtype))\r\n array = pa.ExtensionArray.from_storage(extension_type, storage_part)\r\n arrays.append(array.to_numpy().squeeze(0))\r\n else:\r\n arrays.append(None)\r\n\r\n bytes_array = pa.array(\r\n [encode_np_array(arr)[\"bytes\"] if arr is not None else None for arr in arrays],\r\n type=pa.binary(),\r\n )\r\n path_array = pa.array([None] * len(storage), type=pa.string())\r\n storage = pa.StructArray.from_arrays(\r\n [bytes_array, path_array], [\"bytes\", \"path\"], mask=bytes_array.is_null()\r\n )\r\n```\r\n(Edited): to handle nulls\r\n\r\nNotably this doesn't change anything about the passing through of data or other things, just in the `Image` class.\r\nSeems quite fast:\r\n```bash\r\nFri Apr 5 17:55:51 2024 restats\r\n\r\n 63818 function calls (61995 primitive calls) in 0.812 seconds\r\n\r\n Ordered by: cumulative time\r\n List reduced from 1051 to 20 due to restriction <20>\r\n\r\n ncalls tottime percall cumtime percall filename:lineno(function)\r\n 47/1 0.000 0.000 0.810 0.810 {built-in method builtins.exec}\r\n 2/1 0.000 0.000 0.810 0.810 <string>:1(<module>)\r\n 2/1 0.000 0.000 0.809 0.809 arrow_dataset.py:594(wrapper)\r\n 2/1 0.000 0.000 0.809 0.809 arrow_dataset.py:551(wrapper)\r\n 2/1 0.000 0.000 0.809 0.809 arrow_dataset.py:2916(map)\r\n 3 0.000 0.000 0.807 0.269 arrow_dataset.py:3277(_map_single)\r\n 1 0.000 0.000 0.760 0.760 arrow_writer.py:589(finalize)\r\n 1 0.000 0.000 0.760 0.760 arrow_writer.py:423(write_examples_on_file)\r\n 1 0.000 0.000 0.759 0.759 arrow_writer.py:527(write_batch)\r\n 1 0.001 0.001 0.754 0.754 arrow_writer.py:161(__arrow_array__)\r\n 2/1 0.000 0.000 0.719 0.719 table.py:1800(wrapper)\r\n 1 0.000 0.000 0.719 0.719 table.py:1950(cast_array_to_feature)\r\n 1 0.006 0.006 0.718 0.718 image.py:209(cast_storage)\r\n 1 0.000 0.000 0.451 0.451 image.py:361(encode_np_array)\r\n 1 0.000 0.000 0.444 0.444 image.py:343(image_to_bytes)\r\n 1 0.000 0.000 0.413 0.413 Image.py:2376(save)\r\n 1 0.000 0.000 0.413 0.413 PngImagePlugin.py:1233(_save)\r\n 1 0.000 0.000 0.413 0.413 ImageFile.py:517(_save)\r\n 1 0.000 0.000 0.413 0.413 ImageFile.py:545(_encode_tile)\r\n 397 0.409 0.001 0.409 0.001 {method 'encode' of 'ImagingEncoder' objects}\r\n```",
"Also encounter this problem. Has been strugging with it for a long time...",
"This actually applies to all arrays (numpy or tensors like in torch), not only from external files.\r\n```python\r\nimport numpy as np\r\nimport datasets\r\n\r\nds = datasets.Dataset.from_dict(\r\n {\"image\": [np.random.randint(0, 255, (2048, 2048, 3), dtype=np.uint8)]},\r\n features=datasets.Features({\"image\": datasets.Image(decode=True)}),\r\n)\r\nds.set_format(\"numpy\")\r\n\r\nds = ds.map(load_from_cache_file=False)\r\n```"
] | 1970-01-01T00:00:00.000001 | 1,712 | null | CONTRIBUTOR | null | Update: see comments below
### Describe the bug
Operations that save an image from a path are very slow.
I believe the reason for this is that the image data (`numpy`) is converted into `pyarrow` format but then back to python using `.pylist()` before being converted to a numpy array again.
`pylist` is already slow but used on a multi-dimensional numpy array such as an image it takes a very long time.
From the trace below we can see that `__arrow_array__` takes a long time.
It is currently also called in `get_inferred_type`, this should be removable #6781 but doesn't change the underyling issue.
The conversion to `pyarrow` and back also leads to the `numpy` array having type `int64` which causes a warning message because the image type excepts `uint8`.
However, originally the `numpy` image array was in `uint8`.
### Steps to reproduce the bug
```python
from PIL import Image
import numpy as np
import datasets
import cProfile
image = Image.fromarray(np.random.randint(0, 255, (2048, 2048, 3), dtype=np.uint8))
image.save("test_image.jpg")
ds = datasets.Dataset.from_dict(
{"image": ["test_image.jpg"]},
features=datasets.Features({"image": datasets.Image(decode=True)}),
)
# load as numpy array, e.g. for further processing with map
# same result as map returning numpy arrays
ds.set_format("numpy")
cProfile.run("ds.map(writer_batch_size=1, load_from_cache_file=False)", "restats")
```
```bash
Fri Apr 5 14:56:17 2024 restats
66817 function calls (64992 primitive calls) in 33.382 seconds
Ordered by: cumulative time
List reduced from 1073 to 20 due to restriction <20>
ncalls tottime percall cumtime percall filename:lineno(function)
46/1 0.000 0.000 33.382 33.382 {built-in method builtins.exec}
1 0.000 0.000 33.382 33.382 <string>:1(<module>)
1 0.000 0.000 33.382 33.382 arrow_dataset.py:594(wrapper)
1 0.000 0.000 33.382 33.382 arrow_dataset.py:551(wrapper)
1 0.000 0.000 33.379 33.379 arrow_dataset.py:2916(map)
4 0.000 0.000 33.327 8.332 arrow_dataset.py:3277(_map_single)
1 0.000 0.000 33.311 33.311 arrow_writer.py:465(write)
2 0.000 0.000 33.311 16.656 arrow_writer.py:423(write_examples_on_file)
1 0.000 0.000 33.311 33.311 arrow_writer.py:527(write_batch)
2 14.484 7.242 33.260 16.630 arrow_writer.py:161(__arrow_array__)
1 0.001 0.001 16.438 16.438 arrow_writer.py:121(get_inferred_type)
1 0.000 0.000 14.398 14.398 threading.py:637(wait)
1 0.000 0.000 14.398 14.398 threading.py:323(wait)
8 14.398 1.800 14.398 1.800 {method 'acquire' of '_thread.lock' objects}
4/2 0.000 0.000 4.337 2.169 table.py:1800(wrapper)
2 0.000 0.000 4.337 2.169 table.py:1950(cast_array_to_feature)
2 0.475 0.238 4.337 2.169 image.py:209(cast_storage)
9 2.583 0.287 2.583 0.287 {built-in method numpy.array}
2 0.000 0.000 1.284 0.642 image.py:319(encode_np_array)
2 0.000 0.000 1.246 0.623 image.py:301(image_to_bytes)
```
### Expected behavior
The `numpy` image data should be passed through as it will be directly consumed by `pillow` to convert it to bytes.
As an example one can replace `list_of_np_array_to_pyarrow_listarray(data)` in `__arrow_array__` with just `out = data` as a test.
We have to change `cast_storage` of the `Image` feature so it handles the passed through data (& if to handle type before)
```python
bytes_array = pa.array(
[encode_np_array(arr)["bytes"] if arr is not None else None for arr in storage],
type=pa.binary(),
)
```
Leading to the following:
```bash
Fri Apr 5 15:44:27 2024 restats
66419 function calls (64595 primitive calls) in 0.937 seconds
Ordered by: cumulative time
List reduced from 1023 to 20 due to restriction <20>
ncalls tottime percall cumtime percall filename:lineno(function)
47/1 0.000 0.000 0.935 0.935 {built-in method builtins.exec}
2/1 0.000 0.000 0.935 0.935 <string>:1(<module>)
2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:594(wrapper)
2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:551(wrapper)
2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:2916(map)
4 0.000 0.000 0.933 0.233 arrow_dataset.py:3277(_map_single)
1 0.000 0.000 0.883 0.883 arrow_writer.py:466(write)
2 0.000 0.000 0.883 0.441 arrow_writer.py:424(write_examples_on_file)
1 0.000 0.000 0.882 0.882 arrow_writer.py:528(write_batch)
2 0.000 0.000 0.877 0.439 arrow_writer.py:161(__arrow_array__)
4/2 0.000 0.000 0.877 0.439 table.py:1800(wrapper)
2 0.000 0.000 0.877 0.439 table.py:1950(cast_array_to_feature)
2 0.009 0.005 0.877 0.439 image.py:209(cast_storage)
2 0.000 0.000 0.868 0.434 image.py:335(encode_np_array)
2 0.000 0.000 0.856 0.428 image.py:317(image_to_bytes)
2 0.000 0.000 0.822 0.411 Image.py:2376(save)
2 0.000 0.000 0.822 0.411 PngImagePlugin.py:1233(_save)
2 0.000 0.000 0.822 0.411 ImageFile.py:517(_save)
2 0.000 0.000 0.821 0.411 ImageFile.py:545(_encode_tile)
589 0.803 0.001 0.803 0.001 {method 'encode' of 'ImagingEncoder' objects}
```
This is of course only a test as it passes through all `numpy` arrays irrespective of if they should be an image.
Also I guess `cast_storage` is meant for casting `pyarrow` storage exclusively.
Converting to `pyarrow` array seems like a good solution as it also handles `pytorch` tensors etc., maybe there is a more efficient way to create a PIL image from a `pyarrow` array?
Not sure how this should be handled but I would be happy to help if there is a good solution.
### Environment info
- `datasets` version: 2.18.1.dev0
- Platform: Linux-6.7.11-200.fc39.x86_64-x86_64-with-glibc2.38
- Python version: 3.12.2
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
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"Hello!\r\n\r\nThis is due to how pandas write numpy arrays to csv. [Source](https://stackoverflow.com/questions/54753179/to-csv-saves-np-array-as-string-instead-of-as-a-list)\r\nTo fix this, you can convert them to list yourselves.\r\n\r\n```python\r\ndf = ds.to_pandas()\r\ndf['int'] = df['int'].apply(lambda arr: list(arr))\r\ndf.to_csv(index=False, '../output/temp.csv')\r\n```\r\n\r\nI think it would be good if `datasets` would do the conversion itself, but it's a breaking change and I would wait for the greenlight from someone from HF."
] | 1970-01-01T00:00:00.000001 | 1,712 | null | NONE | null | ### Describe the bug
The `to_csv()` method does not output commas in lists. So when the Dataset is loaded back in the data structure of the column with a list is not correct.
Here's an example:
Obviously, it's not as trivial as inserting commas in the list, since its a comma-separated file. But hopefully there's a way to export the list in a way that it'll be imported by `load_dataset()` correctly.
### Steps to reproduce the bug
Here's some code to reproduce the bug:
```python
from datasets import Dataset
ds = Dataset.from_dict(
{
"pokemon": ["bulbasaur", "squirtle"],
"type": ["grass", "water"]
}
)
def ascii_to_hex(text):
return [ord(c) for c in text]
ds = ds.map(lambda x: {"int": ascii_to_hex(x['pokemon'])})
ds.to_csv('../output/temp.csv')
```
temp.csv then contains:
```
### Expected behavior
ACTUAL OUTPUT:
```
pokemon,type,int
bulbasaur,grass,[ 98 117 108 98 97 115 97 117 114]
squirtle,water,[115 113 117 105 114 116 108 101]
```
EXPECTED OUTPUT:
```
pokemon,type,int
bulbasaur,grass,[98, 117, 108, 98, 97, 115, 97, 117, 114]
squirtle,water,[115, 113, 117, 105, 114, 116, 108, 101]
```
or probably something more like this since it's a CSV file:
```
pokemon,type,int
bulbasaur,grass,"[98, 117, 108, 98, 97, 115, 97, 117, 114]"
squirtle,water,"[115, 113, 117, 105, 114, 116, 108, 101]"
```
### Environment info
### Package Version
Name: datasets
Version: 2.16.1
### Python
version: 3.10.12
### OS Info
PRETTY_NAME="Ubuntu 22.04.4 LTS"
NAME="Ubuntu"
VERSION_ID="22.04"
VERSION="22.04.4 LTS (Jammy Jellyfish)"
VERSION_CODENAME=jammy
ID=ubuntu
ID_LIKE=debian
...
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"Hi! `metadata.jsonl` (or `metadata.csv`) is the only allowed name for the `imagefolder`'s metadata files.",
"@mariosasko hey i tried with metadata.jsonl also and it still doesn't get the right columns",
"@mariosasko it says metadata.csv not found\r\n<img width=\"1150\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/81643693/3754980c-6185-4413-88fa-b499bcdd4195\">\r\n\r\ndataset = load_dataset('/dataset',metadata.csv) \r\n\r\n| workspace\r\n|| source code\r\n| dataset\r\n| |-- images\r\n| |-- metadata.csv\r\n| |-- metadata.jsonl\r\n| |-- padded_images\r\n\r\nExample of metadata.jsonl file\r\n{\"caption\": \"a drawing depicts a full shot of a black t-shirt with a triangular pattern on the front there is a white label on the left side of the triangle\", \"image\": \"images/212734.png\", \"gaussian_padded_image\": \"padded_images/p_212734.png\"}\r\n{\"caption\": \"an eye-level full shot of a large elephant and a baby elephant standing in a watering hole on the left side is a small elephant with its head turned to the right of dry land, trees, and bushes\", \"image\": \"images/212735.png\", \"gaussian_padded_image\": \"padded_images/p_212735.png\"}\r\n",
"Loading more than one image per row with `imagefolder` is not supported currently. You can subscribe to https://github.com/huggingface/datasets/issues/5760 to see when it will be.\r\n\r\nInstead, you can load the dataset with `Dataset.from_generator`:\r\n```python\r\nimport json\r\nfrom datasets import Dataset, Value, Image, Features\r\n\r\ndef gen():\r\n with open(\"./dataset/metadata.jsonl\") as f:\r\n for line in f:\r\n line = json.loads(line)\r\n yield {\"caption\": line[\"caption\"], \"image\": os.path.join(\"./dataset\", line[\"image\"], \"gaussian_padded_image\": os.path.join(\"./dataset\", line[\"gaussian_padded_image\"]))}\r\n\r\nfeatures = Features({\"caption\": Value(\"string\"), \"image\": Image(), \"gaussian_padded_image\": Image()})\r\ndataset = Dataset.from_generator(gen, features=features)\r\n```\r\n(E.g., if you want to share this dataset on the Hub, you can call `dataset.push_to_hub(...)` afterward)",
"hi Thanks for sharing this, Actually I was trying with a webdataset format of the data as well and it did'nt work. Could you share how i can create Dataset object from webdataset format of this data?"
] | 1970-01-01T00:00:00.000001 | 1,712 | null | NONE | null | ### Describe the bug
Hi I have the following directory structure:
|--dataset
| |-- images
| |-- metadata1000.csv
| |-- metadata1000.jsonl
| |-- padded_images
Example of metadata1000.jsonl file
{"caption": "a drawing depicts a full shot of a black t-shirt with a triangular pattern on the front there is a white label on the left side of the triangle", "image": "images/212734.png", "gaussian_padded_image": "padded_images/p_212734.png"}
{"caption": "an eye-level full shot of a large elephant and a baby elephant standing in a watering hole on the left side is a small elephant with its head turned to the right of dry land, trees, and bushes", "image": "images/212735.png", "gaussian_padded_image": "padded_images/p_212735.png"}
.
.
.
I'm trying to use dataset = load_dataset("imagefolder", data_dir='/dataset/', split='train') to load the the dataset, however it is not able to load according to the fields in the metadata1000.jsonl .
please assist to load the data properly
also getting
```
File "/workspace/train_trans_vae.py", line 1089, in <module>
print(get_metadata_patterns('/dataset/'))
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 499, in get_metadata_patterns
raise FileNotFoundError(f"The directory at {base_path} doesn't contain any metadata file") from None
FileNotFoundError: The directory at /dataset/ doesn't contain any metadata file
```
when trying
```
from datasets.data_files import get_metadata_patterns
print(get_metadata_patterns('/dataset/'))
```
### Steps to reproduce the bug
dataset Version: 2.18.0
make a similar jsonl and similar directory format
### Expected behavior
creates a dataset object with the column names, caption,image,gaussian_padded_image
### Environment info
dataset Version: 2.18.0 | null | {
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"Same problem.",
"Hi! You should be able to fix this by passing `remove_unused_columns=False` to the `transformers` `TrainingArguments` as explained in https://github.com/huggingface/peft/issues/1299.\r\n\r\n(I'm not familiar with Vertex AI, but I'd assume `remove_unused_columns` can be passed as a flag to the docker container) ",
"I had the same problem, but I spent a whole day trying different combination with my own dataset with the example data set and found the reason: the example data is multi-turn conversation between human and assistant, so # Humman or # Assistant appear at least twice. If your own custom data only has single turn conversation, it might end up with the same error. What you can do is repeat your single turn conversation twice in your training data (keep the key 'text' the same) and maybe it works. I guess the reason is the specific way processing the data requires and counts multi-turn only (single turn will be discarded so it ends up with no training data), but since I am using Google Vertex AI, I don't have direct access to the underlying code so that was just my guess. ",
"> Hi! You should be able to fix this by passing `remove_unused_columns=False` to the `transformers` `TrainingArguments` as explained in [huggingface/peft#1299](https://github.com/huggingface/peft/issues/1299).\r\n> \r\n> (I'm not familiar with Vertex AI, but I'd assume `remove_unused_columns` can be passed as a flag to the docker container)\r\n\r\n@mariosasko Thanks for the response and suggestion. \r\nWhen I set `remove_unused_columns` as `False` , I end up getting different error (will post the error soon). \r\nEither the Vertex-AI does not support `remove_unused_columns` or my dataset is completely wrong. \r\n\r\nThank you, \r\nKK",
"> I had the same problem, but I spent a whole day trying different combination with my own dataset with the example data set and found the reason: the example data is multi-turn conversation between human and assistant, so # Humman or # Assistant appear at least twice. If your own custom data only has single turn conversation, it might end up with the same error. What you can do is repeat your single turn conversation twice in your training data (keep the key 'text' the same) and maybe it works. I guess the reason is the specific way processing the data requires and counts multi-turn only (single turn will be discarded so it ends up with no training data), but since I am using Google Vertex AI, I don't have direct access to the underlying code so that was just my guess.\r\n\r\n@cyberyu Thanks for your suggestions. \r\nI have tried the approach you suggested, copied the same conversation in each jsonl element so every jsonl item has 2 `HUMAN` and `ASSISTANT`. \r\nHowever in my case, the issue persists. I am gonna give few more tries, and post the results here. \r\nYou can find my dataset [here](https://huggingface.co/datasets/kk2491/test/tree/main) \r\n\r\nThank you, \r\nKK ",
"> > I had the same problem, but I spent a whole day trying different combination with my own dataset with the example data set and found the reason: the example data is multi-turn conversation between human and assistant, so # Humman or # Assistant appear at least twice. If your own custom data only has single turn conversation, it might end up with the same error. What you can do is repeat your single turn conversation twice in your training data (keep the key 'text' the same) and maybe it works. I guess the reason is the specific way processing the data requires and counts multi-turn only (single turn will be discarded so it ends up with no training data), but since I am using Google Vertex AI, I don't have direct access to the underlying code so that was just my guess.\r\n> \r\n> @cyberyu Thanks for your suggestions. I have tried the approach you suggested, copied the same conversation in each jsonl element so every jsonl item has 2 `HUMAN` and `ASSISTANT`. However in my case, the issue persists. I am gonna give few more tries, and post the results here. You can find my dataset [here](https://huggingface.co/datasets/kk2491/test/tree/main)\r\n> \r\n> Thank you, KK\r\n\r\nI think another reason is your training sample length is too short. I saw a relevant report (https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/16) stating that the processing code might have a bug discarding sequence length short than max_seq_length, which is 512. Not sure the Vertex AI backend code has fixed that bug or not. So I tried to add some garbage content in your data, and extended the length longer than 512 for a single turn, and repeated twice. You can copy the following line as 5 repeated lines as your training data jsonl file of five samples (no eval or test needed, for speed up, set evaluation step to 5 and training step to 10,), and it will pass.\r\n\r\n{\"text\":\"### Human: You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You will handle customers queries and provide effective help message. Please provide response to 'Can Interplai software optimize routes for minimizing package handling and transfer times in distribution centers'? ### Assistant: Yes, Interplai software can optimize routes for distribution centers by streamlining package handling processes, minimizing transfer times between loading docks and storage areas, and optimizing warehouse layouts for efficient order fulfillment. ### Human: You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You will handle customers queries and provide effective help message. Please provide response to 'Can Interplai software optimize routes for minimizing package handling and transfer times in distribution centers'? ### Assistant: Yes, Interplai software can optimize routes for distribution centers by streamlining package handling processes, minimizing transfer times between loading docks and storage areas, and optimizing warehouse layouts for efficient order fulfillment.\"}\r\n",
"@cyberyu **Thank you so much, You saved my day (+ so many days)**. \r\nI tried the example you provided above, and the training is successfully completed in Vertex-AI (through GUI). \r\nI never thought there would be constraints on the length of the samples and also on the number of turns. \r\nI will update my complete dataset and see update here once the training is completed. \r\n\r\nThank you, \r\nKK "
] | 1970-01-01T00:00:00.000001 | 1,712 | null | NONE | null | ### Describe the bug
I am trying to fine-tune llama2-7b model in GCP. The notebook I am using for this can be found [here](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_llama2_peft_finetuning.ipynb).
When I use the dataset given in the example, the training gets successfully completed (example dataset can be found [here](https://huggingface.co/datasets/timdettmers/openassistant-guanaco)).
However when I use my own dataset which is in the same format as the example dataset, I get the below error (my dataset can be found [here](https://huggingface.co/datasets/kk2491/finetune_dataset_002)).
I see the files are being read correctly from the logs:
### Steps to reproduce the bug
1. Clone the [vertex-ai-samples](https://github.com/GoogleCloudPlatform/vertex-ai-samples) repository.
2. Run the [llama2-7b peft fine-tuning](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_llama2_peft_finetuning.ipynb).
3. Change the dataset `kk2491/finetune_dataset_002`
### Expected behavior
The training should complete successfully, and model gets deployed to an endpoint.
### Environment info
Python version : Python 3.10.12
Dataset : https://huggingface.co/datasets/kk2491/finetune_dataset_002
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https://api.github.com/repos/huggingface/datasets/issues/6774 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6774/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6774/comments | https://api.github.com/repos/huggingface/datasets/issues/6774/events | https://github.com/huggingface/datasets/issues/6774 | 2,222,164,316 | I_kwDODunzps6Ec4lc | 6,774 | Generating split is very slow when Image format is PNG | {
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"I think this is due to the speed of reading a `png` image using pillow compared to a `jpg` image.\r\nNotably the same is true with `tiff`, it is even faster than `jpg` in my case."
] | 1970-01-01T00:00:00.000001 | 1,712 | null | NONE | null | ### Describe the bug
When I create a dataset, it gets stuck while generating cached data.
The image format is PNG, and it will not get stuck when the image format is jpeg.
After debugging, I know that it is because of the `pa.array` operation in [arrow_writer](https://github.com/huggingface/datasets/blob/2.13.0/src/datasets/arrow_writer.py#L553), but i don't why.
### Steps to reproduce the bug
```
from datasets import Dataset
def generator(lines):
for line in lines:
img = Image.open(open(line["url"], "rb"))
# print(img.format) # "PNG"
yield {
"image": img,
}
lines = open(dataset_path, "r")
dataset = Dataset.from_generator(
generator,
gen_kwargs={"lines": lines}
)
```
### Expected behavior
Generating split done.
### Environment info
datasets 2.13.0 | null | {
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"The caching works as expected when I try to reproduce this locally or on Colab...",
"hi @mariosasko , Thank you for checking. I also tried running this again just now, and it seems like the `load_dataset()` caches properly (though I'll double check later).\r\n\r\nI think the issue might be in the caching of the function output for `territories.map(lambda row: {'Claimants': row['Claimants'].split(';')})`. My current run re-ran this, even though I have run this many times before, and as demonstrated by loading from cache, the loaded dataset is the same.\r\n\r\nI wonder if the issue stems from using CSV output. Do you recommend changing to Parquet, and if so, is there an easy way to take the already uploaded data on the Hub and reformat?",
"This issue seems similar to https://github.com/huggingface/datasets/issues/6184 (`dill` serializes objects defined outside the `__main__` module by reference). You should be able to work around this limitation by defining the lambdas outside of `load_borderlines_hf` (as module variables) and then setting their `__module__` attribute's value to `None` to force serializing them by value, e.g., like this: \r\n```python\r\nsplit_Claimants_row = lambda row: {'Claimants': row['Claimants'].split(';')}\r\nsplit_Claimants_row.__module__ = None\r\n```",
"Thank you, I'll give this a try. Your fix makes sense to me, so this issue can be closed for now.\r\n\r\nUnrelated comment -- for \"Downloads last month\" on the hub page, I'm assuming for this project that each downloaded CSV is 1 download? The dataset consists of 51 CSVs, so I'm trying to see why it's incrementing so quickly (1125 2 days ago, 1246 right now).",
"This doc explains how we count \"Downloads last month\": https://huggingface.co/docs/hub/datasets-download-stats"
] | 1970-01-01T00:00:00.000001 | 1,712 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
Hi, I have a dataset on the hub [here](https://huggingface.co/datasets/manestay/borderlines). It has 1k+ downloads, which I sure is mostly just me and my colleagues working with it. It should have far fewer, since I'm using the same machine with a properly set up HF_HOME variable. However, whenever I run the below function `load_borderlines_hf`, it downloads the entire dataset from the hub and then does the other logic:
https://github.com/manestay/borderlines/blob/4e161f444661e2ebfe643f3fe149d9258d63a57d/run_gpt/lib.py#L80
Let me know what I'm doing wrong here, or if it's a bug with the `datasets` library itself. On the hub I have my data stored in CSVs, but several columns are lists, so that's why I have the code to map splitting on `;`. I looked into dataset loading scripts, but it seemed difficult to set up. I have verified that other `datasets` and `models` on my system are using the cache properly (e.g. I have a 13B parameter model and large datasets, but those are cached and don't redownload).
__EDIT: __ as pointed out in the discussion below, it may be the `map()` calls that aren't being cached properly. Supposing the `load_dataset()` retrieve from the cache, then it should be the case that the `map()` calls also retrieve from the cached output. But the `map()` commands re-execute sometimes.
### Steps to reproduce the bug
1. Copy and paste the function from [here](https://github.com/manestay/borderlines/blob/4e161f444661e2ebfe643f3fe149d9258d63a57d/run_gpt/lib.py#L80) (lines 80-100)
2. Run it in Python `load_borderlines_hf(None)`
3. It completes successfully, downloading from HF hub, then doing the mapping logic etc.
4. If you run it again after some time, it will re-download, ignoring the cache
### Expected behavior
Re-running the code, which calls `datasets.load_dataset('manestay/borderlines', 'territories')`, should use the cached version
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.14.21-150500.55.7-default-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 1.5.3
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"Hi! I've opened a PR in the repo to fix this issue: https://huggingface.co/datasets/RitchieP/VerbaLex_voice/discussions/6",
"@mariosasko Thanks for the PR and help! Guess I could close the issue for now. Appreciate the help!"
] | 1970-01-01T00:00:00.000001 | 1,712 | 1970-01-01T00:00:00.000001 | NONE | null | ### Discussed in https://github.com/huggingface/datasets/discussions/6768
<div type='discussions-op-text'>
<sup>Originally posted by **RitchieP** April 1, 2024</sup>
Currently, I have a dataset hosted on Huggingface with a custom script [here](https://huggingface.co/datasets/RitchieP/VerbaLex_voice).
I'm loading my dataset as below.
```py
from datasets import load_dataset, IterableDatasetDict
dataset = IterableDatasetDict()
dataset["train"] = load_dataset("RitchieP/VerbaLex_voice", "ar", split="train", use_auth_token=True, streaming=True)
dataset["test"] = load_dataset("RitchieP/VerbaLex_voice", "ar", split="test", use_auth_token=True, streaming=True)
```
And when I try to see the data I have loaded with
```py
list(dataset["train"].take(1))
```
And it gives me this stack trace
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[2], line 1
----> 1 list(dataset["train"].take(1))
File /opt/conda/lib/python3.10/site-packages/datasets/iterable_dataset.py:1388, in IterableDataset.__iter__(self)
1385 yield formatter.format_row(pa_table)
1386 return
-> 1388 for key, example in ex_iterable:
1389 if self.features:
1390 # `IterableDataset` automatically fills missing columns with None.
1391 # This is done with `_apply_feature_types_on_example`.
1392 example = _apply_feature_types_on_example(
1393 example, self.features, token_per_repo_id=self._token_per_repo_id
1394 )
File /opt/conda/lib/python3.10/site-packages/datasets/iterable_dataset.py:1044, in TakeExamplesIterable.__iter__(self)
1043 def __iter__(self):
-> 1044 yield from islice(self.ex_iterable, self.n)
File /opt/conda/lib/python3.10/site-packages/datasets/iterable_dataset.py:234, in ExamplesIterable.__iter__(self)
233 def __iter__(self):
--> 234 yield from self.generate_examples_fn(**self.kwargs)
File ~/.cache/huggingface/modules/datasets_modules/datasets/RitchieP--VerbaLex_voice/9465eaee58383cf9d7c3e14111d7abaea56398185a641b646897d6df4e4732f7/VerbaLex_voice.py:127, in VerbaLexVoiceDataset._generate_examples(self, local_extracted_archive_paths, archives, meta_path)
125 for i, audio_archive in enumerate(archives):
126 print(audio_archive)
--> 127 for path, file in audio_archive:
128 _, filename = os.path.split(path)
129 if filename in metadata:
File /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:869, in _IterableFromGenerator.__iter__(self)
868 def __iter__(self):
--> 869 yield from self.generator(*self.args, **self.kwargs)
File /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:919, in ArchiveIterable._iter_from_urlpath(cls, urlpath, download_config)
915 @classmethod
916 def _iter_from_urlpath(
917 cls, urlpath: str, download_config: Optional[DownloadConfig] = None
918 ) -> Generator[Tuple, None, None]:
--> 919 compression = _get_extraction_protocol(urlpath, download_config=download_config)
920 # Set block_size=0 to get faster streaming
921 # (e.g. for hf:// and https:// it uses streaming Requests file-like instances)
922 with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
File /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:400, in _get_extraction_protocol(urlpath, download_config)
398 urlpath, storage_options = _prepare_path_and_storage_options(urlpath, download_config=download_config)
399 try:
--> 400 with fsspec.open(urlpath, **(storage_options or {})) as f:
401 return _get_extraction_protocol_with_magic_number(f)
402 except FileNotFoundError:
File /opt/conda/lib/python3.10/site-packages/fsspec/core.py:100, in OpenFile.__enter__(self)
97 def __enter__(self):
98 mode = self.mode.replace("t", "").replace("b", "") + "b"
--> 100 f = self.fs.open(self.path, mode=mode)
102 self.fobjects = [f]
104 if self.compression is not None:
File /opt/conda/lib/python3.10/site-packages/fsspec/spec.py:1307, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)
1305 else:
1306 ac = kwargs.pop("autocommit", not self._intrans)
-> 1307 f = self._open(
1308 path,
1309 mode=mode,
1310 block_size=block_size,
1311 autocommit=ac,
1312 cache_options=cache_options,
1313 **kwargs,
1314 )
1315 if compression is not None:
1316 from fsspec.compression import compr
File /opt/conda/lib/python3.10/site-packages/fsspec/implementations/local.py:180, in LocalFileSystem._open(self, path, mode, block_size, **kwargs)
178 if self.auto_mkdir and "w" in mode:
179 self.makedirs(self._parent(path), exist_ok=True)
--> 180 return LocalFileOpener(path, mode, fs=self, **kwargs)
File /opt/conda/lib/python3.10/site-packages/fsspec/implementations/local.py:302, in LocalFileOpener.__init__(self, path, mode, autocommit, fs, compression, **kwargs)
300 self.compression = get_compression(path, compression)
301 self.blocksize = io.DEFAULT_BUFFER_SIZE
--> 302 self._open()
File /opt/conda/lib/python3.10/site-packages/fsspec/implementations/local.py:307, in LocalFileOpener._open(self)
305 if self.f is None or self.f.closed:
306 if self.autocommit or "w" not in self.mode:
--> 307 self.f = open(self.path, mode=self.mode)
308 if self.compression:
309 compress = compr[self.compression]
FileNotFoundError: [Errno 2] No such file or directory: '/kaggle/working/h'
```
After looking into the stack trace, and referring to the source codes, it looks like its trying to access a directory in the notebook's environment and I don't understand why.
Not sure if its a bug in Datasets library, so I'm opening a discussions first. Feel free to ask for more information if needed. Appreciate any help in advance!</div>
Hi, referring to the discussion title above, after further digging, I think it's an issue within the datasets library. But not quite sure where it is.
If you require any more info or actions from me, please let me know. Appreciate any help in advance! | {
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"You should be able to fix this by updating `huggingface_hub` with `pip install -U huggingface_hub`. We use this package under the hood to resolve the Hub's files."
] | 1970-01-01T00:00:00.000001 | 1,712 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
`Datasets==2.18.0` is not compatible with `fsspec==2023.12.2`.
I have to downgrade fsspec to `fsspec==2023.10.0` to make `Datasets==2.18.0` work properly.
### Steps to reproduce the bug
To reproduce the bug:
1. Make sure that `Datasets==2.18.0` and `fsspec==2023.12.2`.
2. Run the following code:
```
from datasets import load_dataset
dataset = load_dataset("trec")
```
3. Then one will get the following error message:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2265, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 371, in __init__
self.config, self.config_id = self._create_builder_config(
File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 620, in _create_builder_config
builder_config._resolve_data_files(
File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 211, in _resolve_data_files
self.data_files = self.data_files.resolve(base_path, download_config)
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 799, in resolve
out[key] = data_files_patterns_list.resolve(base_path, download_config)
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 752, in resolve
resolve_pattern(
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 393, in resolve_pattern
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to find 'hf://datasets/trec@65752bf53af25bc935a0dce92fb5b6c930728450/default/train/0000.parquet' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.geoparquet', '.gpq', '.arrow', '.txt', '.tar', '.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', '.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', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']
```
4. Similar issue also found for the following code:
```
dataset = load_dataset("sst", "default")
```
### Expected behavior
If the dataset is loaded correctly, one will have:
```
>>> print(dataset)
DatasetDict({
train: Dataset({
features: ['text', 'coarse_label', 'fine_label'],
num_rows: 5452
})
test: Dataset({
features: ['text', 'coarse_label', 'fine_label'],
num_rows: 500
})
})
>>>
```
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-6.2.0-35-generic-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.1
- Pandas version: 2.2.1
- `fsspec` version: 2023.12.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/6769 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6769/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6769/comments | https://api.github.com/repos/huggingface/datasets/issues/6769/events | https://github.com/huggingface/datasets/issues/6769 | 2,218,242,015 | I_kwDODunzps6EN6_f | 6,769 | (Willing to PR) Datasets with custom python objects | {
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] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,711 | null | CONTRIBUTOR | null | ### Feature request
Hi thanks for the library! I would like to have a huggingface Dataset, and one of its column is custom (non-serializable) Python objects. For example, a minimal code:
```
class MyClass:
pass
dataset = datasets.Dataset.from_list([
dict(a=MyClass(), b='hello'),
])
```
It gives error:
```
ArrowInvalid: Could not convert <__main__.MyClass object at 0x7a852830d050> with type MyClass: did not recognize Python value type when inferring an Arrow data type
```
I guess it is because Dataset forces to convert everything into arrow format. However, is there any ways to make the scenario work? Thanks!
### Motivation
(see above)
### Your contribution
Yes, I am happy to PR!
Cross-posted: https://discuss.huggingface.co/t/datasets-with-custom-python-objects/79050?u=fzyzcjy
EDIT: possibly related https://github.com/huggingface/datasets/issues/5766 | null | {
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"Hi! Instead of running `pip install` separately for each package, you should pass all the packages to a single `pip install` call (in this case, `pip install datasets s3fs`) to let `pip` properly resolve their versions.",
"> Hi! Instead of running `pip install` separately for each package, you should pass all the packages to a single `pip install` call (in this case, `pip install datasets s3fs`) to let `pip` properly resolve their versions.\r\n\r\nThanks so much! My inexperience with pip is showing 😆 🙈 ",
"> Hi! Instead of running `pip install` separately for each package, you should pass all the packages to a single `pip install` call (in this case, `pip install datasets s3fs`) to let `pip` properly resolve their versions.\r\n\r\nyou are awesome bro",
"Hey, the suggestion by @mariosasko unfortunately only address this issue via pip. The original message was about poetry and I am still facing a dependency conflict with that.\r\n\r\nThe following command complains first about `fsspec` (`... no versions of fsspec match ...`) and then I get an error.\r\n\r\nCommand:\r\n`poetry add datasets s3fs` \r\n\r\nError: \r\n` ... your project ... depends on both datasets (^3.1.0) and s3fs (^2024.10.0), version solving failed`\r\n\r\nInstalling first `s3fs` and then the rest of the huggingface libraries, like `datasets`, also did not help."
] | 1970-01-01T00:00:00.000001 | 1,731 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
Here is the full error stack when installing:
```
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
datasets 2.18.0 requires fsspec[http]<=2024.2.0,>=2023.1.0, but you have fsspec 2024.3.1 which is incompatible.
Successfully installed aiobotocore-2.12.1 aioitertools-0.11.0 botocore-1.34.51 fsspec-2024.3.1 jmespath-1.0.1 s3fs-2024.3.1 urllib3-2.0.7 wrapt-1.16.0
```
When I install with pip, pip allows this error to exist while still installing s3fs, but this error breaks poetry, since poetry will refuse to install s3fs because of the dependency conflict.
Maybe I'm missing something so maybe it's not a bug but some mistake on my end? Any input would be helpful. Thanks!
### Steps to reproduce the bug
1. conda create -n tmp python=3.10 -y
2. conda activate tmp
3. pip install datasets
4. pip install s3fs
### Expected behavior
I would expect there to be no error.
### Environment info
MacOS (ARM), Python3.10, conda 23.11.0. | {
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] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,711 | null | NONE | null | ### Feature request
Enable the `load_dataset` function to load local datasets with symbolic links.
E.g, this dataset can be loaded:
├── example_dataset/
│ ├── data/
│ │ ├── train/
│ │ │ ├── file0
│ │ │ ├── file1
│ │ ├── dev/
│ │ │ ├── file2
│ │ │ ├── file3
│ ├── metadata.csv
while this dataset can't:
├── example_dataset_symlink/
│ ├── data/
│ │ ├── train/
│ │ │ ├── sym0 -> file0
│ │ │ ├── sym1 -> file1
│ │ ├── dev/
│ │ │ ├── sym2 -> file2
│ │ │ ├── sym3 -> file3
│ ├── metadata.csv
I have created an example dataset in order to reproduce the problem:
1. Unzip `example_dataset.zip`.
2. Run `no_symlink.sh`. Training should start without issues.
3. Run `symlink.sh`. You will see that all four examples will be in train split, instead of having two examples in train and two examples in dev. The script won't load the correct audio files.
[example_dataset.zip](https://github.com/huggingface/datasets/files/14807053/example_dataset.zip)
### Motivation
I have a very large dataset locally. Instead of initiating training on the entire dataset, I need to start training on smaller subsets of the data. Due to the purpose of the experiments I am running, I will need to create many smaller datasets with overlapping data. Instead of copying the all the files for each subset, I would prefer copying symbolic links of the data. This way, the memory usage would not significantly increase beyond the initial dataset size.
Advantages of this approach:
- It would leave a smaller memory footprint on the hard drive
- Creating smaller datasets would be much faster
### Your contribution
I would gladly contribute, if this is something useful to the community. It seems like a simple change of code, something like `file_path = os.path.realpath(file_path)` should be added before loading the files. If anyone has insights on how to incorporate this functionality, I would greatly appreciate your knowledge and input. | null | {
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"The same error with mteb datasets.",
"Unfortunately, I'm unable to reproduce this error locally or on Colab.",
"Here is the requirements.txt from a clean virtual environment (managed by conda) where I only install `datasets` by \r\n`pip install datasets`. \r\nThe pip list:\r\n```\r\naiohttp==3.9.3\r\naiosignal==1.3.1\r\nattrs==23.2.0\r\ncertifi==2024.2.2\r\ncharset-normalizer==3.3.2\r\ndatasets==2.18.0\r\ndill==0.3.8\r\nfilelock==3.13.3\r\nfrozenlist==1.4.1\r\nfsspec==2024.2.0\r\nhuggingface-hub==0.22.2\r\nidna==3.6\r\nmultidict==6.0.5\r\nmultiprocess==0.70.16\r\nnumpy==1.26.4\r\npackaging==24.0\r\npandas==2.2.1\r\npyarrow==15.0.2\r\npyarrow-hotfix==0.6\r\npython-dateutil==2.9.0.post0\r\npytz==2024.1\r\nPyYAML==6.0.1\r\nrequests==2.31.0\r\nsix==1.16.0\r\ntqdm==4.66.2\r\ntyping_extensions==4.11.0\r\ntzdata==2024.1\r\nurllib3==2.2.1\r\nxxhash==3.4.1\r\nyarl==1.9.4\r\n```\r\nAnd the error can be reproduced.\r\n\r\nDowngrading to datasets==2.14.6 changes some packages' versions:\r\n\r\n```\r\nSuccessfully installed datasets-2.14.6 dill-0.3.7 fsspec-2023.10.0 multiprocess-0.70.15\r\n```\r\nand the dataset can be downloaded and loaded. \r\n\r\nThen I upgrade the version to 2.18.0 again; now the dataset can be loaded with such a line:\r\n```Using the latest cached version of the module from /home/xxx/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--apps/04ac807715d07d6e5cc580f59cdc8213cd7dc4529d0bb819cca72c9f8e8c1aa5 (last modified on Sun Apr 7 09:06:43 2024) since it couldn't be found locally at codeparrot/apps, or remotely on the Hugging Face Hub. ```\r\n\r\nSo the latest version works wrong when requesting the dataset info. \r\n\r\n**But if you cannot reproduce this, I may ignore some detailed information: I use `HF_ENDPOINT=https://hf-mirror.com` for some reason (if not use this I cannot connect to huggingface resources) and the error occurs when requesting the dataset's info card.** \r\nMaybe the error is caused by this environment variable.\r\nI'll open an issue in the author's repo now.",
"> Here is the requirements.txt from a clean virtual environment (managed by conda) where I only install `datasets` by `pip install datasets`. The pip list:\r\n> \r\n> ```\r\n> aiohttp==3.9.3\r\n> aiosignal==1.3.1\r\n> attrs==23.2.0\r\n> certifi==2024.2.2\r\n> charset-normalizer==3.3.2\r\n> datasets==2.18.0\r\n> dill==0.3.8\r\n> filelock==3.13.3\r\n> frozenlist==1.4.1\r\n> fsspec==2024.2.0\r\n> huggingface-hub==0.22.2\r\n> idna==3.6\r\n> multidict==6.0.5\r\n> multiprocess==0.70.16\r\n> numpy==1.26.4\r\n> packaging==24.0\r\n> pandas==2.2.1\r\n> pyarrow==15.0.2\r\n> pyarrow-hotfix==0.6\r\n> python-dateutil==2.9.0.post0\r\n> pytz==2024.1\r\n> PyYAML==6.0.1\r\n> requests==2.31.0\r\n> six==1.16.0\r\n> tqdm==4.66.2\r\n> typing_extensions==4.11.0\r\n> tzdata==2024.1\r\n> urllib3==2.2.1\r\n> xxhash==3.4.1\r\n> yarl==1.9.4\r\n> ```\r\n> \r\n> And the error can be reproduced.\r\n> \r\n> Downgrading to datasets==2.14.6 changes some packages' versions:\r\n> \r\n> ```\r\n> Successfully installed datasets-2.14.6 dill-0.3.7 fsspec-2023.10.0 multiprocess-0.70.15\r\n> ```\r\n> \r\n> and the dataset can be downloaded and loaded.\r\n> \r\n> Then I upgrade the version to 2.18.0 again; now the dataset can be loaded with such a line: `Using the latest cached version of the module from /home/xxx/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--apps/04ac807715d07d6e5cc580f59cdc8213cd7dc4529d0bb819cca72c9f8e8c1aa5 (last modified on Sun Apr 7 09:06:43 2024) since it couldn't be found locally at codeparrot/apps, or remotely on the Hugging Face Hub. `\r\n> \r\n> So the latest version works wrong when requesting the dataset info.\r\n> \r\n> **But if you cannot reproduce this, I may ignore some detailed information: I use `HF_ENDPOINT=https://hf-mirror.com` for some reason (if not use this I cannot connect to huggingface resources) and the error occurs when requesting the dataset's info card.** Maybe the error is caused by this environment variable. I'll open an issue in the author's repo now.\r\n\r\nThis is useful and my same error is settled!!!"
] | 1970-01-01T00:00:00.000001 | 1,718 | null | NONE | null | ### Describe the bug
This happens with datasets-2.18.0; I downgraded the version to 2.14.6 fixing this temporarily.
```
Traceback (most recent call last):
File "/home/xxx/miniconda3/envs/py310/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "/home/xxx/miniconda3/envs/py310/lib/python3.10/site-packages/datasets/load.py", line 2228, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/xxx/miniconda3/envs/py310/lib/python3.10/site-packages/datasets/load.py", line 1879, in dataset_module_factory
raise e1 from None
File "/home/xxx/miniconda3/envs/py310/lib/python3.10/site-packages/datasets/load.py", line 1831, in dataset_module_factory
can_load_config_from_parquet_export = "DEFAULT_CONFIG_NAME" not in f.read()
File "/home/xxx/miniconda3/envs/py310/lib/python3.10/codecs.py", line 322, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte
```
### Steps to reproduce the bug
1. Using Python3.10/3.11
2. Install datasets-2.18.0
3. test with
```
from datasets import load_dataset
dataset = load_dataset("codeparrot/apps")
```
### Expected behavior
Normally it should manage to download and load the dataset without such error.
### Environment info
Ubuntu, Python3.10/3.11 | null | {
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Running .map and filter functions with `num_procs` consecutively instantiates several multiprocessing pools iteratively.
As instantiating a Pool is very resource intensive it can be a bottleneck to performing iteratively filtering.
My ideas:
1. There should be an option to declare `persistent_workers` similar to pytorch DataLoader. Downside would be that would be complex to determine the correct resource allocation and deallocation of the pool. i.e. the dataset can outlive the utility of the pool.
2. Provide a pool as an argument. Downside would be the expertise required by the user. Upside, is that there is better resource management.
### Motivation
Is really slow to iteratively perform map and filter operations on a dataset.
### Your contribution
If approved I could integrate it. I would need to know what method would be most suitable to implement from the two options above. | null | {
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"Thanks for reporting! We are working on a fix."
] | 1970-01-01T00:00:00.000001 | 1,712 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
I have a dataset that consists of a bunch of text files, each representing an example. There is an undocumented `sample_by` argument for the `TextConfig` class that is used by `Text` to decide whether to split files into lines, paragraphs or take them whole. Passing `sample_by=“document”` to `load_dataset` results in files getting split into lines regardless. I have edited `src/datasets/packaged_modules/text/text.py` for myself to switch the default and it works fine.
As a side note, the `if-else` for `sample_by` will silently load an empty dataset if someone makes a typo in the argument, which is not ideal.
### Steps to reproduce the bug
1. Prepare data as a bunch of files in a directory.
2. Load that data via `load_dataset(“text”, data_files=<data_dir>/<files_glob>, …, sample_by=“document”)`.
3. Inspect the resultant dataset — every item should have the form of `{“text”: <a line from a file>}`.
### Expected behavior
`load_dataset(“text”, data_files=<data_dir>/<files_glob>, …, sample_by=“document”)` should result in a dataset with items of the form `{“text”: <one document>}`.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.15.0-1046-nvidia-x86_64-with-glibc2.35
- Python version: 3.11.8
- `huggingface_hub` version: 0.21.4
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0 | {
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"You can use `Dataset.from_sql(path_to_sql_file)` already. Though we haven't added the Sql dataset builder to the `_PACKAGED_DATASETS_MODULES` list or in `_EXTENSION_TO_MODULE` to map `.sqlite` to the Sql dataset builder\r\n\r\nThis would allow to load a dataset repository with a `.sqlite` file using `load_dataset` and enable the Dataset Viewer",
"Considering `Dataset.from_sql`'s (extremely) low usage, I don't think many users are interested in using this format for their datasets. Also, SQLite files are hard/impossible to stream efficiently and require custom logic to define splits/subsets, so IMO we shouldn't encourage people to use SQLite on the Hub.\r\n\r\n@severo Do you have some real-world examples of datasets published in this format?",
"No. Indeed, it seems better to explicitly not support sqlite"
] | 1970-01-01T00:00:00.000001 | 1,711 | 1970-01-01T00:00:00.000001 | COLLABORATOR | null | ### Feature request
Support loading a dataset from a SQLite file
https://huggingface.co/datasets/severo/test_iris_sqlite/tree/main
### Motivation
SQLite is a popular file format.
### Your contribution
See discussion on slack: https://huggingface.slack.com/archives/C04L6P8KNQ5/p1702481859117909 (internal)
In particular: a SQLite file can contain multiple tables, which might be matched to multiple configs. Maybe the detail of splits and configs should be defined in the README YAML, or use the same format as for ZIP files: `Iris.sqlite::Iris`.
See dataset here: https://huggingface.co/datasets/severo/test_iris_sqlite
Note: should we also support DuckDB files? | {
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https://api.github.com/repos/huggingface/datasets/issues/6755 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6755/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6755/comments | https://api.github.com/repos/huggingface/datasets/issues/6755/events | https://github.com/huggingface/datasets/issues/6755 | 2,204,573,289 | I_kwDODunzps6DZx5p | 6,755 | Small typo on the documentation | {
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"Thanks for reporting @fostiropoulos! I've edited your comment to fix the link to the problematic line.\r\n",
"@mariosasko can i take this up?",
"#self-assign"
] | 1970-01-01T00:00:00.000001 | 1,712 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
There is a small typo on https://github.com/huggingface/datasets/blob/d5468836fe94e8be1ae093397dd43d4a2503b926/src/datasets/dataset_dict.py#L938
It should be `caching is enabled`.
### Steps to reproduce the bug
Please visit
https://github.com/huggingface/datasets/blob/d5468836fe94e8be1ae093397dd43d4a2503b926/src/datasets/dataset_dict.py#L938
### Expected behavior
`caching is enabled`
### Environment info
- `datasets` version: 2.17.1
- Platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35
- Python version: 3.11.7
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
- `fsspec` version: 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6753 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6753/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6753/comments | https://api.github.com/repos/huggingface/datasets/issues/6753/events | https://github.com/huggingface/datasets/issues/6753 | 2,204,155,091 | I_kwDODunzps6DYLzT | 6,753 | Type error when importing datasets on Kaggle | {
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"I have the same problem \r\nIt seems that it only appears when you are using GPU \r\nIt seems to work fine with the 2.17 version though",
"Same here.",
"> I have the same problem\r\n> It seems that it only appears when you are using GPU\r\n> It seems to work fine with the 2.17 version though\r\n\r\nI downgraded from 2.18 to 2.17, and it works with CPU/GPU .. except now pyarrow complains\r\n\r\n```\r\n...\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/array.pxi:830, in pyarrow.lib._PandasConvertible.to_pandas()\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/table.pxi:3989, in pyarrow.lib.Table._to_pandas()\r\n\r\nImportError: cannot import name table_to_blockmanager\r\n```\r\n\r\nsee also https://www.kaggle.com/competitions/pii-detection-removal-from-educational-data/discussion/487474#2722594",
"Solved for me by downgrading `!pip install -U datasets==2.16.0` Works with gpu aswell",
"I think you should remain open this issue. It works at the previous version but not the latter versions. It is possible as a bug that the maintainer could take note for.",
"> Solved for me by downgrading `!pip install -U datasets==2.16.0` Works with gpu as well\r\n\r\nVerified it's working w/ GPU if I make these 3 updates.\r\n\r\n```\r\ndatasets==2.16.0\r\nfsspec==2023.10.0\r\ngcsfs==2023.10.0\r\n```\r\n\r\nbut the issue shouldn't be closed, this is just a workaround until they get the issue with 2.18.0 resolved.\r\n\r\nSee also: https://www.kaggle.com/competitions/pii-detection-removal-from-educational-data/discussion/487474",
"> > Solved for me by downgrading `!pip install -U datasets==2.16.0` Works with gpu as well\r\n> \r\n> Verified it's working w/ GPU if I make these 3 updates.\r\n> \r\n> ```\r\n> datasets==2.16.0\r\n> fsspec==2023.10.0\r\n> gcsfs==2023.10.0\r\n> ```\r\n> \r\n> but the issue shouldn't be closed, this is just a workaround until they get the issue with 2.18.0 resolved.\r\n> \r\n> See also: https://www.kaggle.com/competitions/pii-detection-removal-from-educational-data/discussion/487474\r\n\r\nThis also works for me, thanks",
"I am seeing similar error but with pandas while using kaggle kernel. \r\n`---> 38 PANDAS_VERSION = version.parse(importlib.metadata.version(\"pandas\"))\r\nTypeError: expected string or bytes-like object\r\n\r\n`"
] | 1970-01-01T00:00:00.000001 | 1,727 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
When trying to run
```
import datasets
print(datasets.__version__)
```
It generates the following error
```
TypeError: expected string or bytes-like object
```
It looks like It cannot find the valid versions of `fsspec`
though fsspec version is fine when I checked Via command
```
import fsspec
print(fsspec.__version__)
# output: 2024.3.1
```
Detailed crash report
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[1], line 1
----> 1 import datasets
2 print(datasets.__version__)
File /opt/conda/lib/python3.10/site-packages/datasets/__init__.py:18
1 # ruff: noqa
2 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
3 #
(...)
13 # See the License for the specific language governing permissions and
14 # limitations under the License.
16 __version__ = "2.18.0"
---> 18 from .arrow_dataset import Dataset
19 from .arrow_reader import ReadInstruction
20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:66
63 from multiprocess import Pool
64 from tqdm.contrib.concurrent import thread_map
---> 66 from . import config
67 from .arrow_reader import ArrowReader
68 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File /opt/conda/lib/python3.10/site-packages/datasets/config.py:41
39 # Imports
40 DILL_VERSION = version.parse(importlib.metadata.version("dill"))
---> 41 FSSPEC_VERSION = version.parse(importlib.metadata.version("fsspec"))
42 PANDAS_VERSION = version.parse(importlib.metadata.version("pandas"))
43 PYARROW_VERSION = version.parse(importlib.metadata.version("pyarrow"))
File /opt/conda/lib/python3.10/site-packages/packaging/version.py:49, in parse(version)
43 """
44 Parse the given version string and return either a :class:`Version` object
45 or a :class:`LegacyVersion` object depending on if the given version is
46 a valid PEP 440 version or a legacy version.
47 """
48 try:
---> 49 return Version(version)
50 except InvalidVersion:
51 return LegacyVersion(version)
File /opt/conda/lib/python3.10/site-packages/packaging/version.py:264, in Version.__init__(self, version)
261 def __init__(self, version: str) -> None:
262
263 # Validate the version and parse it into pieces
--> 264 match = self._regex.search(version)
265 if not match:
266 raise InvalidVersion(f"Invalid version: '{version}'")
TypeError: expected string or bytes-like object
```
### Steps to reproduce the bug
1. run `!pip install -U datasets` on kaggle
2. check datasets is installed via
```
import datasets
print(datasets.__version__)
```
### Expected behavior
Expected to print datasets version, like `2.18.0`
### Environment info
Running on Kaggle, latest enviornment , here is the notebook https://www.kaggle.com/code/jtv199/mistrial-7b-part2 | {
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https://api.github.com/repos/huggingface/datasets/issues/6752 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6752/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6752/comments | https://api.github.com/repos/huggingface/datasets/issues/6752/events | https://github.com/huggingface/datasets/issues/6752 | 2,204,043,839 | I_kwDODunzps6DXwo_ | 6,752 | Precision being changed from float16 to float32 unexpectedly | {
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"This is because of the formatter (`torch` in this case).\r\nIt defaults to `float32`.\r\n\r\nYou can load it in `float16` using `dataset.set_format(\"torch\", dtype=torch.float16)`."
] | 1970-01-01T00:00:00.000001 | 1,712 | null | NONE | null | ### Describe the bug
I'm loading a HuggingFace Dataset for images.
I'm running a preprocessing (map operation) step that runs a few operations, one of them being conversion to float16. The Dataset features also say that the 'img' is of type float16. Whenever I take an image from that HuggingFace Dataset instance, the type turns out to be float32.
### Steps to reproduce the bug
```python
import torchvision.transforms.v2 as transforms
from datasets import load_dataset
dataset = load_dataset('cifar10', split='test')
dataset = dataset.with_format("torch")
data_transform = transforms.Compose([transforms.Resize((32, 32)),
transforms.ToDtype(torch.float16, scale=True),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])
def _preprocess(examples):
# Permutes from (BS x H x W x C) to (BS x C x H x W)
images = torch.permute(examples['img'], (0, 3, 2, 1))
examples['img'] = data_transform(images)
return examples
dataset = dataset.map(_preprocess, batched=True, batch_size=8)
```
Now at this point the dataset.features are showing float16 which is great because that's what I want.
```python
print(data_loader.features['img'])
Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float16', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None)
```
But when I try to sample an image from this dataloader; I'm getting a float32 image, when I'm expecting float16:
```python
print(next(iter(data_loader))['img'].dtype)
torch.float32
```
### Expected behavior
I'm expecting the images loaded after the transformation to stay in float16.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.9
- `huggingface_hub` version: 0.21.4
- PyArrow version: 14.0.2
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6750 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6750/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6750/comments | https://api.github.com/repos/huggingface/datasets/issues/6750/events | https://github.com/huggingface/datasets/issues/6750 | 2,203,590,658 | I_kwDODunzps6DWCAC | 6,750 | `load_dataset` requires a network connection for local download? | {
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"Are you using `HF_DATASETS_OFFLINE=1` ?",
"> Are you using `HF_DATASETS_OFFLINE=1` ?\r\n\r\nThis doesn't work for me. `datasets=2.18.0`\r\n\r\n`test.py`:\r\n```\r\nimport datasets\r\n\r\ndatasets.utils.logging.set_verbosity_info()\r\n\r\nds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4')\r\n\r\nprint(ds)\r\n```\r\n\r\nrun `python test.py`\r\n```\r\nGenerating dataset afqmc (/home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4)\r\nDownloading and preparing dataset afqmc/default to /home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nhf://datasets/C-MTEB/AFQMC@b44c3b011063adb25877c13823db83bb193913c4/data/validation-00000-of-00001-b8fc393b5ddedac7.parquet not found in cache or force_download set to True, downloading to /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b.incomplete\r\nDownloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 240k/240k [00:01<00:00, 178kB/s]\r\nstoring hf://datasets/C-MTEB/AFQMC@b44c3b011063adb25877c13823db83bb193913c4/data/validation-00000-of-00001-b8fc393b5ddedac7.parquet in cache at /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b\r\ncreating metadata file for /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nGenerating test split\r\nGenerating test split: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3861/3861 [00:00<00:00, 3972.00 examples/s]\r\nGenerating train split\r\nGenerating train split: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 34334/34334 [00:00<00:00, 34355.50 examples/s]\r\nGenerating validation split\r\nGenerating validation split: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4316/4316 [00:00<00:00, 4477.00 examples/s]\r\nAll the splits matched successfully.\r\nDataset afqmc downloaded and prepared to /home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4. Subsequent calls will reuse this data.\r\nDatasetDict({\r\n test: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 3861\r\n })\r\n train: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 34334\r\n })\r\n validation: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 4316\r\n })\r\n})\r\n```\r\n\r\nThen run `HF_DATASETS_OFFLINE=1 python test.py`\r\n```\r\nTraceback (most recent call last):\r\n File \"test.py\", line 9, in <module>\r\n ds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4')\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 2556, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 2228, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 1871, in dataset_module_factory\r\n raise ConnectionError(f\"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}\") from None\r\nConnectionError: Couldn't reach the Hugging Face Hub for dataset 'C-MTEB/AFQMC': Offline mode is enabled.\r\n```\r\n\r\n",
"I was having similar inexplicable issues.\r\n\r\nDoing this I *think* helped, but, `datasets` still *clearly* does not want to respect the cache:\r\n\r\n```python\r\npip install --upgrade datasets # now it is 2.18.0\r\nHF_DATASETS_OFFLINE=\"1\" python blah.py\r\n```\r\n\r\nOr similarly, I must spacify that env var to resuse the cache, IE, no arg to `load_dataset` helps it reuse the cache:\r\n\r\n```python\r\n\r\nimport os\r\nos.environ[\"HF_DATASETS_OFFLINE\"] = \"1\"\r\n\r\nimport logging\r\nlogging.basicConfig(level=logging.DEBUG)\r\n\r\nimport datasets\r\n# >>> datasets.__version__\r\n# '2.18.0'\r\n\r\ndatasets.utils.logging.set_verbosity_info()\r\ndata = datasets.load_dataset(\"c-s-ale/dolly-15k-instruction-alpaca-format\")\r\n```"
] | 1970-01-01T00:00:00.000001 | 1,713 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
Hi all - I see that in the past a network dependency has been mistakenly introduced into `load_dataset` even for local loads. Is it possible this has happened again?
### Steps to reproduce the bug
```
>>> import datasets
>>> datasets.load_dataset("hh-rlhf")
Repo card metadata block was not found. Setting CardData to empty.
*hangs bc i'm firewalled*
````
stack trace from ctrl-c:
```
^CTraceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/load.py", line 2582, in load_dataset
builder_instance.download_and_prepare(
output_path = get_from_cache( [0/122]
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 532, in get_from_cache
response = http_head(
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 419, in http_head
response = _request_with_retry(
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 304, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/adapters.py", line 487, in send
resp = conn.urlopen(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 363, in connect
self.sock = conn = self._new_conn()
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
```
### Expected behavior
loads the dataset
### Environment info
```
> pip show datasets
Name: datasets
Version: 2.18.0
```
Python 3.10.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/6748 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6748/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6748/comments | https://api.github.com/repos/huggingface/datasets/issues/6748/events | https://github.com/huggingface/datasets/issues/6748 | 2,201,517,348 | I_kwDODunzps6DOH0k | 6,748 | Strange slicing behavior | {
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} | [] | open | false | null | [] | null | [
"As explained in the [docs](https://huggingface.co/docs/datasets/v2.18.0/en/access#slicing), slicing a `Dataset` returns a dictionary that maps its column names to their values. So, `len(dataset[:300])=2` is expected, assuming your dataset has 2 columns (the returned dict has 2 keys, but each value in the dict has 300 items).\r\n` "
] | 1970-01-01T00:00:00.000001 | 1,711 | null | NONE | null | ### Describe the bug
I have loaded a dataset, and then slice first 300 samples using `:` ops, however, the resulting dataset is not expected, as the output below:
```bash
len(dataset)=1050324
len(dataset[:300])=2
len(dataset[0:300])=2
len(dataset.select(range(300)))=300
```
### Steps to reproduce the bug
load a dataset then:
```bash
dataset = load_from_disk(args.train_data_dir)
print(f"{len(dataset)=}", flush=True)
print(f"{len(dataset[:300])=}", flush=True)
print(f"{len(dataset[0:300])=}", flush=True)
print(f"{len(dataset.select(range(300)))=}", flush=True)
```
### Expected behavior
```bash
len(dataset)=1050324
len(dataset[:300])=300
len(dataset[0:300])=300
len(dataset.select(range(300)))=300
```
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.35
- Python version: 3.10.11
- `huggingface_hub` version: 0.20.2
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
- `fsspec` version: 2023.10.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6746 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6746/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6746/comments | https://api.github.com/repos/huggingface/datasets/issues/6746/events | https://github.com/huggingface/datasets/issues/6746 | 2,198,993,949 | I_kwDODunzps6DEfwd | 6,746 | ExpectedMoreSplits error when loading C4 dataset | {
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} | [] | closed | false | null | [] | null | [
"Hi ! We updated the `allenai/c4` repository to allow people to specify which language to load easily (the the [c4 dataset page](https://huggingface.co/datasets/allenai/c4))\r\n\r\nTo fix this issue **you can update** `datasets` and remove the mention of the legacy configuration name \"allenai--c4\":\r\n\r\n```python\r\ntraindata = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train')\r\nvaldata = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation')\r\n```",
"Did you solve this problem？I have the same bug.It is no use to delete \"allenai--c4\".",
"Did you solve it? I met this problem too.",
"But after I romove allenai--c4,it still fails",
"For me it works this way. I'm using datasets version 2.17.0",
"First, pip install --upgrade datasets.\r\nSecond, Update the following two lines of code in data.py (in lib)\r\ntraindata = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train')\r\nvaldata = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation')",
"The error is in the Wanda repository: https://github.com/locuslab/wanda\r\n- https://github.com/locuslab/wanda/issues/57\r\n\r\nConcretely, in these code lines:\r\nhttps://github.com/locuslab/wanda/blob/8e8fc87b4a2f9955baa7e76e64d5fce7fa8724a6/lib/data.py#L43-L44\r\n\r\nPlease report there and/or make the fix in their code.",
"> traindata = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train')\r\n> valdata = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation')\r\n\r\nSolved for me ! Thanks!"
] | 1970-01-01T00:00:00.000001 | 1,726 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
I encounter bug when running the example command line
```python
python main.py \
--model decapoda-research/llama-7b-hf \
--prune_method wanda \
--sparsity_ratio 0.5 \
--sparsity_type unstructured \
--save out/llama_7b/unstructured/wanda/
```
The bug occurred at these lines of code (when loading c4 dataset)
```python
traindata = load_dataset('allenai/c4', 'allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train')
valdata = load_dataset('allenai/c4', 'allenai--c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation')
```
The error message states:
```
raise ExpectedMoreSplits(str(set(expected_splits) - set(recorded_splits)))
datasets.utils.info_utils.ExpectedMoreSplits: {'validation'}
```
### Steps to reproduce the bug
1. I encounter bug when running the example command line
### Expected behavior
The error message states:
```
raise ExpectedMoreSplits(str(set(expected_splits) - set(recorded_splits)))
datasets.utils.info_utils.ExpectedMoreSplits: {'validation'}
```
### Environment info
I'm using cuda 12.4, so I use ```pip install pytorch``` instead of conda provided in install.md
Also, I've tried another environment using the same commands in install.md, but the same bug occured | {
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"It's not twitter here"
] | 1970-01-01T00:00:00.000001 | 1,711 | 1970-01-01T00:00:00.000001 | NONE | null | ### Feature request
https://github.com/bigcode-project/opt-out-v2 - opt out is not consent. kindly quit this ridiculous nonsense.
### Motivation
[EDITED: insults not tolerated]
### Your contribution
[EDITED: insults not tolerated] | {
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https://api.github.com/repos/huggingface/datasets/issues/6744 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6744/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6744/comments | https://api.github.com/repos/huggingface/datasets/issues/6744/events | https://github.com/huggingface/datasets/issues/6744 | 2,197,910,168 | I_kwDODunzps6DAXKY | 6,744 | Option to disable file locking | {
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] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,710 | null | NONE | null | ### Feature request
Commands such as `load_dataset` creates file locks with `filelock.FileLock`. It would be good if there was a way to disable this.
### Motivation
File locking doesn't work on all file-systems (in my case NFS mounted Weka). If the `cache_dir` only had small files then it would be possible to point to local disk and the problem would be solved. However, as cache_dir is both where the small info files are written and the processed datasets are put this isn't a feasible solution.
Considering https://github.com/huggingface/datasets/issues/6395 I still do think this is something that belongs in HuggingFace. The possibility to control packages separately is valuable. It might be that a user has their dataset on a file-system that doesn't support file-locking while they are using file locking on local disk to control some other type of access.
### Your contribution
My suggested solution:
```
diff --git a/src/datasets/utils/_filelock.py b/src/datasets/utils/_filelock.py
index 19620e6e..58f41a02 100644
--- a/src/datasets/utils/_filelock.py
+++ b/src/datasets/utils/_filelock.py
@@ -18,11 +18,15 @@
import os
from filelock import FileLock as FileLock_
-from filelock import UnixFileLock
+from filelock import SoftFileLock, UnixFileLock
from filelock import __version__ as _filelock_version
from packaging import version
+if os.getenv('HF_USE_SOFTFILELOCK', 'false').lower() in ('true', '1'):
+ FileLock_ = SoftFileLock
+
+
class FileLock(FileLock_):
"""
A `filelock.FileLock` initializer that handles long paths.
```
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https://api.github.com/repos/huggingface/datasets/issues/6740 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6740/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6740/comments | https://api.github.com/repos/huggingface/datasets/issues/6740/events | https://github.com/huggingface/datasets/issues/6740 | 2,193,172,074 | I_kwDODunzps6CuSZq | 6,740 | Support for loading geotiff files as a part of the ImageFolder | {
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] | closed | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,711 | 1970-01-01T00:00:00.000001 | NONE | null | ### Feature request
Request for adding rasterio support to load geotiff as a part of ImageFolder, instead of using PIL
### Motivation
As of now, there are many datasets in HuggingFace Hub which are predominantly focussed towards RemoteSensing or are from RemoteSensing. The current ImageFolder (if I have understood correctly) uses PIL. This is not really optimized because mostly these datasets have images with many channels and additional metadata. Using PIL makes one loose it unless we provide a custom script. Hence, maybe an API could be added to have this in common?
### Your contribution
If the issue is accepted - i can contribute the code, because I would like to have it automated and generalised. | {
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https://api.github.com/repos/huggingface/datasets/issues/6738 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6738/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6738/comments | https://api.github.com/repos/huggingface/datasets/issues/6738/events | https://github.com/huggingface/datasets/issues/6738 | 2,192,386,536 | I_kwDODunzps6CrSno | 6,738 | Dict feature is non-nullable while nested dict feature is | {
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"It looks like a bug, by default every feature should be nullable.",
"I've linked a PR with a fix :)",
"@mariosasko awesome thank you!"
] | 1970-01-01T00:00:00.000001 | 1,710 | 1970-01-01T00:00:00.000001 | CONTRIBUTOR | null | When i try to create a `Dataset` object with None values inside a dict column, like this:
```python
from datasets import Dataset, Features, Value
Dataset.from_dict(
{
"dict": [{"a": 0, "b": 0}, None],
}, features=Features(
{"dict": {"a": Value("int16"), "b": Value("int16")}}
)
)
```
i get `ValueError: Got None but expected a dictionary instead`.
At the same time, having None in _nested_ dict feature works, for example, this doesn't throw any errors:
```python
from datasets import Dataset, Features, Value, Sequence
dataset = Dataset.from_dict(
{
"list_dict": [[{"a": 0, "b": 0}], None],
"sequence_dict": [[{"a": 0, "b": 0}], None],
}, features=Features({
"list_dict": [{"a": Value("int16"), "b": Value("int16")}],
"sequence_dict": Sequence({"a": Value("int16"), "b": Value("int16")}),
})
)
```
Other types of features also seem to be nullable (but I haven't checked all of them).
Version of `datasets` is the latest atm (2.18.0)
Is this an expected behavior or a bug? | {
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https://api.github.com/repos/huggingface/datasets/issues/6737 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6737/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6737/comments | https://api.github.com/repos/huggingface/datasets/issues/6737/events | https://github.com/huggingface/datasets/issues/6737 | 2,190,198,425 | I_kwDODunzps6Ci8aZ | 6,737 | Invalid pattern: '**' can only be an entire path component | {
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"I couldn't reproduce the issue on my side on MacOS, I guess the issue comes from the recent `fsspec` on Windows.\r\n\r\nCan you try downgrading to `fsspec==2023.9.2` for now ? It would also be great to investigate this and see if we need a fix in `datasets` or `fsspec`",
"I had the same issue! \r\nDowngrading to fsspec from 2023.10.0 to 2023.9.2 solved it for me.\r\n\r\n(env: python 3.11.7, datasets version: 2.15.0, Windows 10 22H2, Build 19045.4170)\r\n\r\nThanks a lot!",
"Ubuntu 20.04 had the same issue\r\npython 3.9 \r\n\r\nFile \"/home/delight-gpu/Workspace2/azuryl/FLAP/main.py\", line 112, in <module>\r\n main()\r\n File \"/home/delight-gpu/Workspace2/azuryl/FLAP/main.py\", line 85, in main\r\n prune_flap(args, model, tokenizer, device)\r\n File \"/home/delight-gpu/Workspace2/azuryl/FLAP/lib/prune.py\", line 294, in prune_flap\r\n dataloader, _ = get_loaders(\"wikitext2\", nsamples=args.nsamples,seed=args.seed,seqlen=model.seqlen,tokenizer=tokenizer)\r\n File \"/home/delight-gpu/Workspace2/azuryl/FLAP/lib/data.py\", line 159, in get_loaders\r\n return get_wikitext2(nsamples, seed, seqlen, tokenizer)\r\n File \"/home/delight-gpu/Workspace2/azuryl/FLAP/lib/data.py\", line 79, in get_wikitext2\r\n traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')\r\n File \"/home/azuryl/anaconda3/envs/flap/lib/python3.9/site-packages/datasets/load.py\", line 1767, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/azuryl/anaconda3/envs/flap/lib/python3.9/site-packages/datasets/load.py\", line 1498, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/home/azuryl/anaconda3/envs/flap/lib/python3.9/site-packages/datasets/load.py\", line 1215, in dataset_module_factory\r\n raise e1 from None\r\n File \"/home/azuryl/anaconda3/envs/flap/lib/python3.9/site-packages/datasets/load.py\", line 1192, in dataset_module_factory\r\n return HubDatasetModuleFactoryWithoutScript(\r\n File \"/home/azuryl/anaconda3/envs/flap/lib/python3.9/site-packages/datasets/load.py\", line 765, in get_module\r\n else get_data_patterns_in_dataset_repository(hfh_dataset_info, self.data_dir)\r\n File \"/home/azuryl/anaconda3/envs/flap/lib/python3.9/site-packages/datasets/data_files.py\", line 675, in get_data_patterns_in_dataset_repository\r\n return _get_data_files_patterns(resolver)\r\n File \"/home/azuryl/anaconda3/envs/flap/lib/python3.9/site-packages/datasets/data_files.py\", line 236, in _get_data_files_patterns\r\n data_files = pattern_resolver(pattern)\r\n File \"/home/azuryl/anaconda3/envs/flap/lib/python3.9/site-packages/datasets/data_files.py\", line 486, in _resolve_single_pattern_in_dataset_repository\r\n glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)]\r\n File \"/home/azuryl/anaconda3/envs/flap/lib/python3.9/site-packages/fsspec/spec.py\", line 606, in glob\r\n pattern = glob_translate(path + (\"/\" if ends_with_sep else \"\"))\r\n File \"/home/azuryl/anaconda3/envs/flap/lib/python3.9/site-packages/fsspec/utils.py\", line 734, in glob_translate\r\n raise ValueError(\r\nValueError: Invalid pattern: '**' can only be an entire path component",
"on ubuntu you just need to have the latest `datasets` and `fsspec`\r\n\r\n```\r\npip install -U datasets fsspec\r\n```",
"The issue was caused by an incompatibility between the versions of `datasets`, `huggingface-hub` and `fsspec`.\r\n\r\nThe issue was fixed in:\r\n- huggingface-hub-0.21.2: https://github.com/huggingface/huggingface_hub/pull/2056\r\n- and datasets-2.18.0: https://github.com/huggingface/datasets/pull/6687\r\n - datasets-2.19.1 fixed the minimum requirement huggingface-hub >= 0.21.2: https://github.com/huggingface/datasets/pull/6713",
"@albertvillanova, thank you for this solution. I encountered the same issue and had to use:\r\n```\r\nconda install -c conda-forge huggingface_hub=0.21.2 datasets=2.19.1\r\n```\r\n\r\nCheers",
"Cheers！"
] | 1970-01-01T00:00:00.000001 | 1,721 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
ValueError: Invalid pattern: '**' can only be an entire path component
when loading any dataset
### Steps to reproduce the bug
import datasets
ds = datasets.load_dataset("TokenBender/code_instructions_122k_alpaca_style")
### Expected behavior
loading the dataset successfully
### Environment info
- `datasets` version: 2.18.0
- Platform: Windows-10-10.0.22631-SP0
- Python version: 3.11.7
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
- `fsspec` version: 2023.12.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/6736 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6736/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6736/comments | https://api.github.com/repos/huggingface/datasets/issues/6736/events | https://github.com/huggingface/datasets/issues/6736 | 2,190,181,422 | I_kwDODunzps6Ci4Qu | 6,736 | Mosaic Streaming (MDS) Support | {
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"Hi ! that would be great :) Though note that `datasets` doesn't implement format-specific resuming when streaming, so in general I think it's better if users can use the mosaic-streaming library to read their MDS datasets. I wonder if they support `hf://` paths though...\r\n\r\nAnyway for those interested, the code for WebDataset is a single file here: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/webdataset/webdataset.py.\r\n\r\nIt implements `_split_generators` that downloads files and returns the lists of splits (train/validation/test) and `_split_generators` to generate examples (dicts) from the downloaded files. Streaming is automatically supported by making download steps lazy and by extending `open()` to work with remote URLs."
] | 1970-01-01T00:00:00.000001 | 1,710 | null | NONE | null | ### Feature request
I'm a huge fan of the current HF Datasets `webdataset` integration (especially the built-in streaming support). However, I'd love to upload some robotics and multimodal datasets I've processed for use with [Mosaic Streaming](https://docs.mosaicml.com/projects/streaming/en/stable/), specifically their [MDS Format](https://docs.mosaicml.com/projects/streaming/en/stable/fundamentals/dataset_format.html#mds).
Because the shard files have similar semantics to WebDataset, I'm hoping that adding such support won't be too much trouble?
### Motivation
One of the downsides with WebDataset is a lack of out-of-the-box determinism (especially for large-scale training and reproducibility), easy job resumption, and the ability to quickly debug / visualize individual examples.
Mosaic Streaming provides a [great interface for this out of the box](https://docs.mosaicml.com/projects/streaming/en/stable/#key-features), so I'd love to see it supported in HF Datasets.
### Your contribution
Happy to help test things / provide example data. Can potentially submit a PR if maintainers could point me to the necessary WebDataset logic / steps for adding a new streaming format! | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6734 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6734/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6734/comments | https://api.github.com/repos/huggingface/datasets/issues/6734/events | https://github.com/huggingface/datasets/issues/6734 | 2,187,646,694 | I_kwDODunzps6CZNbm | 6,734 | Tokenization slows towards end of dataset | {
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"Hi ! First note that if the dataset is not heterogeneous / shuffled, there might be places in the data with shorter texts that are faster to tokenize.\r\n\r\nMoreover, the way `num_proc` works is by slicing the dataset and passing each slice to a process to run the `map()` function. So at the very end of `map()`, some processes might have finished transforming their slice of data while others are still running, causing the throughput to become lower.",
"I did see some comments about how num_proc=None could help and outputting numpy arrays can also help in the docs, but this seems quite odd now dropping down to 1it/s\r\n\r\n```bash\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46048888/46390354 [12:33:30<4:20:32, 21.84 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46049888/46390354 [12:36:11<8:37:59, 10.95 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46050888/46390354 [12:46:35<24:56:56, 3.78 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46051888/46390354 [12:56:43<35:08:10, 2.68 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46052888/46390354 [13:06:58<42:05:41, 2.23 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46053888/46390354 [13:16:01<44:40:18, 2.09 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46054888/46390354 [13:25:11<46:35:28, 2.00 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46055888/46390354 [13:34:23<47:55:34, 1.94 examples/s]\r\n```\r\n\r\n",
"@ethansmith2000 Hi, did you solve this problem? I'm strugging with the same problem now."
] | 1970-01-01T00:00:00.000001 | 1,712 | null | NONE | null | ### Describe the bug
Mapped tokenization slows down substantially towards end of dataset.
train set started off very slow, caught up to 20k then tapered off til the end.
what's particularly strange is that the tokenization crashed a few times before due to errors with invalid tokens somewhere or corrupted downloads, and the speed ups/downs consistently happened the same times
```bash
Running tokenizer on dataset (num_proc=48): 0%| | 847000/881416735 [12:18<252:45:45, 967.72 examples/s]
Running tokenizer on dataset (num_proc=48): 0%| | 848000/881416735 [12:19<224:16:10, 1090.66 examples/s]
Running tokenizer on dataset (num_proc=48): 10%|▉ | 84964000/881416735 [3:48:00<11:21:34, 19476.01 examples/s]
Running tokenizer on dataset (num_proc=48): 10%|▉ | 84967000/881416735 [3:48:00<12:04:01, 18333.79 examples/s]
Running tokenizer on dataset (num_proc=48): 61%|██████ | 538631977/881416735 [13:46:40<27:50:04, 3420.84 examples/s]
Running tokenizer on dataset (num_proc=48): 61%|██████ | 538632977/881416735 [13:46:40<23:48:20, 3999.77 examples/s]
Running tokenizer on dataset (num_proc=48): 100%|█████████▉| 881365886/881416735 [38:30:19<04:34, 185.10 examples/s]
Running tokenizer on dataset (num_proc=48): 100%|█████████▉| 881366886/881416735 [38:30:25<04:36, 180.57 examples/s]
```
and validation set as well
```bash
Running tokenizer on dataset (num_proc=48): 90%|████████▉ | 41544000/46390354 [28:44<02:37, 30798.76 examples/s]
Running tokenizer on dataset (num_proc=48): 90%|████████▉ | 41550000/46390354 [28:44<02:08, 37698.08 examples/s]
Running tokenizer on dataset (num_proc=48): 96%|█████████▋| 44747422/46390354 [2:15:48<12:22:44, 36.87 examples/s]
Running tokenizer on dataset (num_proc=48): 96%|█████████▋| 44747422/46390354 [2:16:00<12:22:44, 36.87 examples/s]
```
### Steps to reproduce the bug
using the following kwargs
```python
with accelerator.main_process_first():
lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=48
load_from_cache_file=True,
desc=f"Grouping texts in chunks of {block_size}",
)
```
running through slurm script
```bash
#SBATCH --partition=gpu-nvidia-a100
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --gpus-per-task=8
#SBATCH --cpus-per-task=96
```
using this dataset https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T
### Expected behavior
Constant speed throughout
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-5.15.0-1049-aws-x86_64-with-glibc2.10
- Python version: 3.8.18
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6733 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6733/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6733/comments | https://api.github.com/repos/huggingface/datasets/issues/6733/events | https://github.com/huggingface/datasets/issues/6733 | 2,186,811,724 | I_kwDODunzps6CWBlM | 6,733 | EmptyDatasetError when loading dataset downloaded with HuggingFace cli | {
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"Hi! `datasets` is not compatible with `huggingface_hub`'s cache structure, hence the error.\r\n\r\nYou can track https://github.com/huggingface/datasets/issues/5080 to get notified when this is implemented."
] | 1970-01-01T00:00:00.000001 | 1,710 | null | NONE | null | ### Describe the bug
I am using a cluster that does not have access to the internet when given a job. I tried downloading the dataset using the huggingface-cli command and then loading it with load_dataset but I get an error:
```raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None```
The dataset I'm using is "lmsys/chatbot_arena_conversations". The folder structure is
- README.md
- data
- train-00000-of-00001-cced8514c7ed782a.parquet
### Steps to reproduce the bug
1. Download dataset using HuggingFace CLI: ```huggingface-cli download lmsys/chatbot_arena_conversations --local-dir ./lmsys/chatbot_arena_conversations```
2. In Python
```
from datasets import load_dataset
load_dataset("lmsys/chatbot_arena_conversations")
```
### Expected behavior
Should return a Dataset Dict in the form of
```
DatasetDict({
train: Dataset({
features: [...],
num_rows: 33,000
})
})
```
### Environment info
Python 3.11.5
Datasets 2.18.0
Transformers 4.38.2
Pytorch 2.2.0
Pyarrow 15.0.1
Rocky Linux release 8.9 (Green Obsidian)
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https://api.github.com/repos/huggingface/datasets/issues/6731 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6731/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6731/comments | https://api.github.com/repos/huggingface/datasets/issues/6731/events | https://github.com/huggingface/datasets/issues/6731 | 2,182,844,673 | I_kwDODunzps6CG5EB | 6,731 | Unexpected behavior when using load_dataset with streaming=True in a for loop | {
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"This is normal behavior in python when using `lambda`: the `i` defined in your `lambda` refers to the global variable `i` in your loop, and `i` equals to `1` when you run your `for e in res[0]` line.\r\n\r\nYou should pass `fn_kwargs` that will be passed to your `lambda` instead of using the global variable:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nres=[]\r\nfor i in [0,1]:\r\n di = load_dataset(\r\n \"json\", \r\n data_files='path_to.json', \r\n split='train',\r\n streaming=True, \r\n ).map(lambda x, source: {\"source\": source}, fn_kwargs={\"source\": i})\r\n\r\n res.append(di)\r\n\r\nfor e in res[0]:\r\n print(e)\r\n```\r\n\r\nThis doesn't happen in non-streaming since in that case `map` is executed while the variable `i` has the right value. In streaming mode, `map` is executed on-the-fly when you iterate on the dataset.",
"Thank you very much for your answer. I think this issue can be closed now."
] | 1970-01-01T00:00:00.000001 | 1,713 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
### My Code
```
from datasets import load_dataset
res=[]
for i in [0,1]:
di=load_dataset(
"json",
data_files='path_to.json',
split='train',
streaming=True,
).map(lambda x: {"source": i})
res.append(di)
for e in res[0]:
print(e)
```
### Unexpected Behavior
Data in `res[0]` has `source=1`. However the expected value is 0.
### FYI
I further switch `streaming` to `False`. And the output value is as expected (0). So there may exist bugs in setting `streaming=True` in a for loop.
### Environment
Python 3.8.0
datasets==2.18.0
transformers==4.28.1
### Steps to reproduce the bug
1. Create a Json file with any content.
2. Run the provided code.
3. Switch `streaming` to `False` and run again to see the expected behavior.
### Expected behavior
The expected behavior is the data are mapped with its corresponding value in the for loop.
### Environment info
Python 3.8.0
datasets==2.18.0
transformers==4.28.1
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https://api.github.com/repos/huggingface/datasets/issues/6729 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6729/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6729/comments | https://api.github.com/repos/huggingface/datasets/issues/6729/events | https://github.com/huggingface/datasets/issues/6729 | 2,180,237,159 | I_kwDODunzps6B88dn | 6,729 | Support zipfiles that span multiple disks? | {
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"@severo were you able to solve it?",
"No. cc @albertvillanova @lhoestq @polinaeterna for an evaluation of what it would take to support this feature.",
"The underlying issue issue is that the dataset repository has used split ZIP archive files: https://huggingface.co/datasets/PhilEO-community/PhilEO-downstream/tree/main/data\r\n```\r\ndownstream_dataset_patches_npzip.z01\r\ndownstream_dataset_patches_npzip.z02\r\n...\r\ndownstream_dataset_patches_npzip.zip\r\n```\r\nand these are not supported by the Python standard library package `zipfile`.",
"It's a pretty bad way to share a dataset since one needs to download the full dataset to use it.\r\n\r\nWe likely won't support this format.",
"I agree it is a format we maybe should not support: streaming is not possible.",
"I opened a PR in the reported repo to disable the viewer: https://huggingface.co/datasets/PhilEO-community/PhilEO-downstream/discussions/1"
] | 1970-01-01T00:00:00.000001 | 1,719 | 1970-01-01T00:00:00.000001 | COLLABORATOR | null | See https://huggingface.co/datasets/PhilEO-community/PhilEO-downstream
The dataset viewer gives the following error:
```
Error code: ConfigNamesError
Exception: BadZipFile
Message: zipfiles that span multiple disks are not supported
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1871, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1846, in dataset_module_factory
return HubDatasetModuleFactoryWithoutScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1240, in get_module
module_name, default_builder_kwargs = infer_module_for_data_files(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 584, in infer_module_for_data_files
split_modules = {
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 585, in <dictcomp>
split: infer_module_for_data_files_list(data_files_list, download_config=download_config)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 526, in infer_module_for_data_files_list
return infer_module_for_data_files_list_in_archives(data_files_list, download_config=download_config)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 554, in infer_module_for_data_files_list_in_archives
for f in xglob(extracted, recursive=True, download_config=download_config)[
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 576, in xglob
fs, *_ = fsspec.get_fs_token_paths(urlpath, storage_options=storage_options)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 622, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 290, in filesystem
return cls(**storage_options)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 57, in __init__
self.zip = zipfile.ZipFile(
File "/usr/local/lib/python3.9/zipfile.py", line 1266, in __init__
self._RealGetContents()
File "/usr/local/lib/python3.9/zipfile.py", line 1329, in _RealGetContents
endrec = _EndRecData(fp)
File "/usr/local/lib/python3.9/zipfile.py", line 286, in _EndRecData
return _EndRecData64(fpin, -sizeEndCentDir, endrec)
File "/usr/local/lib/python3.9/zipfile.py", line 232, in _EndRecData64
raise BadZipFile("zipfiles that span multiple disks are not supported")
zipfile.BadZipFile: zipfiles that span multiple disks are not supported
```
The files (https://huggingface.co/datasets/PhilEO-community/PhilEO-downstream/tree/main/data) are:
<img width="629" alt="Capture d’écran 2024-03-11 à 22 07 30" src="https://github.com/huggingface/datasets/assets/1676121/0bb15a51-d54f-4d73-8572-e427ea644b36">
| {
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} | https://api.github.com/repos/huggingface/datasets/issues/6729/timeline | null | not_planned | null | null | false | 0 |
https://api.github.com/repos/huggingface/datasets/issues/6728 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6728/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6728/comments | https://api.github.com/repos/huggingface/datasets/issues/6728/events | https://github.com/huggingface/datasets/issues/6728 | 2,178,607,012 | I_kwDODunzps6B2uek | 6,728 | Issue Downloading Certain Datasets After Setting Custom `HF_ENDPOINT` | {
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} | [] | closed | false | null | [] | null | [
"Through debugging, I found a potential solution is to modify the code in the error handling module of `huggingface_hub`: https://github.com/huggingface/huggingface_hub/commit/56d6c798c44e83d2a3167e74c022737d8fcbe822 ",
"@Wauplin ",
"Thanks for investigating and reporting the bug @padeoe! I've opened a PR in `huggingface_hub` with your suggested fix! :) https://github.com/huggingface/huggingface_hub/pull/2119"
] | 1970-01-01T00:00:00.000001 | 1,710 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
This bug is triggered under the following conditions:
- datasets repo ids without organization names trigger errors, such as `bookcorpus`, `gsm8k`, `wikipedia`, rather than in the form of `A/B`.
- If `HF_ENDPOINT` is set and the hostname is not in the form of `(hub-ci.)?huggingface.co`.
- This issue occurs with `datasets>2.15.0` or `huggingface-hub>0.19.4`. For example, using the latest versions: `datasets==2.18.0` and `huggingface-hub==0.21.4`,
### Steps to reproduce the bug
the issue can be reproduced with the following code:
1. install specific datasets and huggingface_hub.
```bash
pip install datasets==2.18.0
pip install huggingface_hub==0.21.4
```
2. execute python code.
```Python
import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
from datasets import load_dataset
bookcorpus = load_dataset('bookcorpus', split='train')
```
console output:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 2228, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 1879, in dataset_module_factory
raise e1 from None
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 1830, in dataset_module_factory
with fs.open(f"datasets/{path}/{filename}", "r", encoding="utf-8") as f:
File "/home/padeoe/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1295, in open
self.open(
File "/home/padeoe/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1307, in open
f = self._open(
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 228, in _open
return HfFileSystemFile(self, path, mode=mode, revision=revision, block_size=block_size, **kwargs)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 615, in __init__
self.resolved_path = fs.resolve_path(path, revision=revision)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 180, in resolve_path
repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 117, in _repo_and_revision_exist
self._api.repo_info(repo_id, revision=revision, repo_type=repo_type)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2413, in repo_info
return method(
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2286, in dataset_info
hf_raise_for_status(r)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 362, in hf_raise_for_status
raise HfHubHTTPError(str(e), response=response) from e
huggingface_hub.utils._errors.HfHubHTTPError: 401 Client Error: Unauthorized for url: https://hf-mirror.com/api/datasets/bookcorpus/bookcorpus.py (Request ID: Root=1-65ee8659-5ab10eec5960c63e71f2bb58;b00bdbea-fd6e-4a74-8fe0-bc4682ae090e)
```
### Expected behavior
The dataset was downloaded correctly without any errors.
### Environment info
datasets==2.18.0
huggingface-hub==0.21.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/6726 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6726/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6726/comments | https://api.github.com/repos/huggingface/datasets/issues/6726/events | https://github.com/huggingface/datasets/issues/6726 | 2,177,097,232 | I_kwDODunzps6Bw94Q | 6,726 | Profiling for HF Filesystem shows there are easy performance gains to be made | {
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} | [] | open | false | null | [] | null | [
"FWIW I debugged this while waiting for it to go",
"Oh I forgot to mention you can also cache resolve_pattern, and that seemed to also substantially improves things, if you want to load a dataset twice for whatever reason."
] | 1970-01-01T00:00:00.000001 | 1,709 | null | NONE | null | ### Describe the bug
# Let's make it faster
First, an evidence...
Figure 1: CProfile for loading 3 files from cerebras/SlimPajama-627B train split, and 3 files from test split using streaming=True. X axis is 1106 seconds long.
See? It's pretty slow.
What is resolve pattern doing?
```
resolve_pattern called with **/train/** and hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543
resolve_pattern took 20.815081119537354 seconds
```
Makes sense. How to improve it?
## Bigger project, biggest payoff
Databricks (and consequently, spark) store a compressed manifest file of the files contained in the remote filesystem.
Then, you download one tiny file, decompress it, and all the operations are local instead of this shenanigans.
It seems pretty straightforward to make dataset uploads compute a manifest and upload it alongside their data.
This would make resolution time so fast that nobody would ever think about it again.
It also means you either need to have the uploader compute it _every time_, or have a hook that computes it.
## Smaller project, immediate payoff: Be diligent in avoiding deepcopy
Revise the _ls_tree method to avoid deepcopy:
```
def _ls_tree(
self,
path: str,
recursive: bool = False,
refresh: bool = False,
revision: Optional[str] = None,
expand_info: bool = True,
):
..... omitted .....
for path_info in tree:
if isinstance(path_info, RepoFile):
cache_path_info = {
"name": root_path + "/" + path_info.path,
"size": path_info.size,
"type": "file",
"blob_id": path_info.blob_id,
"lfs": path_info.lfs,
"last_commit": path_info.last_commit,
"security": path_info.security,
}
else:
cache_path_info = {
"name": root_path + "/" + path_info.path,
"size": 0,
"type": "directory",
"tree_id": path_info.tree_id,
"last_commit": path_info.last_commit,
}
parent_path = self._parent(cache_path_info["name"])
self.dircache.setdefault(parent_path, []).append(cache_path_info)
out.append(cache_path_info)
return copy.deepcopy(out) # copy to not let users modify the dircache
```
Observe this deepcopy at the end. It is making a copy of a very simple data structure. We do not need to copy. We can simply generate the data structure twice instead. It will be much faster.
```
def _ls_tree(
self,
path: str,
recursive: bool = False,
refresh: bool = False,
revision: Optional[str] = None,
expand_info: bool = True,
):
..... omitted .....
def make_cache_path_info(path_info):
if isinstance(path_info, RepoFile):
return {
"name": root_path + "/" + path_info.path,
"size": path_info.size,
"type": "file",
"blob_id": path_info.blob_id,
"lfs": path_info.lfs,
"last_commit": path_info.last_commit,
"security": path_info.security,
}
else:
return {
"name": root_path + "/" + path_info.path,
"size": 0,
"type": "directory",
"tree_id": path_info.tree_id,
"last_commit": path_info.last_commit,
}
for path_info in tree:
cache_path_info = make_cache_path_info(path_info)
out_cache_path_info = make_cache_path_info(path_info) # copy to not let users modify the dircache
parent_path = self._parent(cache_path_info["name"])
self.dircache.setdefault(parent_path, []).append(cache_path_info)
out.append(out_cache_path_info)
return out
```
Note there is no longer a deepcopy in this method. We have replaced it with generating the output twice. This is substantially faster. For me, the entire resolution went from 1100s to 360s.
## Medium project, medium payoff
After the above change, we have this profile:
Figure 2: x-axis is 355 seconds. Note that globbing and _ls_tree deep copy is gone. No surprise there. It's much faster now, but we still spend ~187seconds in get_fs_token_paths.
Well get_fs_token_paths is part of fsspec. We don't need to fix that because we can trust their developers to write high performance code. Probably the caller has misconfigured something. Let's take a look at the storage_options being provided to the filesystem that is constructed during this call.
Ah yes, streaming_download_manager::_prepare_single_hop_path_and_storage_options. We know streaming download manager is not compatible with async right now, but we really need this specific part of the code to be async. We're spending so much time checking isDir on the remote filesystem, it's a huge waste.
We can make the call easily 20-30x faster by using async, removing this performance bottleneck almost entirely (and reducing the total time of this part of the code to <30s. There is no reason to block async isDir calls for streaming.
I'm not going to mess w/ this one myself; I didn't write the streaming impl, and I don't know how it works, but I know the isDir check can be async.
### Steps to reproduce the bug
```
with cProfile.Profile() as pr:
pr.enable()
# Begin Data
if not os.path.exists(data_cache_dir):
os.makedirs(data_cache_dir, exist_ok=True)
training_dataset = load_dataset(training_dataset_name, split=training_split, cache_dir=data_cache_dir, streaming=True).take(training_slice)
eval_dataset = load_dataset(eval_dataset_name, split=eval_split, cache_dir=data_cache_dir, streaming=True).take(eval_slice)
# End Data
pr.disable()
pr.create_stats()
if not os.path.exists(profiling_path):
os.makedirs(profiling_path, exist_ok=True)
pr.dump_stats(os.path.join(profiling_path, "cprofile.prof"))
```
run this code for "cerebras/SlimPajama-627B" and whatever other params
### Expected behavior
Something better.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.13
- `huggingface_hub` version: 0.21.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6725 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6725/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6725/comments | https://api.github.com/repos/huggingface/datasets/issues/6725/events | https://github.com/huggingface/datasets/issues/6725 | 2,175,527,530 | I_kwDODunzps6Bq-pq | 6,725 | Request for a comparison of huggingface datasets compared with other data format especially webdataset | {
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"color": "a2eeef",
"default": true,
"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
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] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,709 | null | NONE | null | ### Feature request
Request for a comparison of huggingface datasets compared with other data format especially webdataset
### Motivation
I see huggingface datasets uses Apache Arrow as its backend, it seems to be great, but I'm curious about how it is good compared with other dataset format, like webdataset, what's the pros/cons of them.
### Your contribution
More information | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6724 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6724/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6724/comments | https://api.github.com/repos/huggingface/datasets/issues/6724/events | https://github.com/huggingface/datasets/issues/6724 | 2,174,398,227 | I_kwDODunzps6Bmq8T | 6,724 | Dataset with loading script does not work in renamed repos | {
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} | [] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,709 | null | CONTRIBUTOR | null | ### Describe the bug
My data repository was first called `BramVanroy/hplt-mono-v1-2` but I then renamed to use underscores instead of dashes. However, it seems that `datasets` retrieves the old repo name when it checks whether the repo contains data loading scripts in this line.
https://github.com/huggingface/datasets/blob/6fb6c834f008996c994b0a86c3808d0a33d44525/src/datasets/load.py#L1845
When I print `filename` it returns `hplt-mono-v1-2.py` but the files in the repo are of course `['.gitattributes', 'README.md', 'hplt_mono_v1_2.py']`. So the `filename` is the original reponame instead of the renamed one.
I am not sure if this is a caching issue or not or how I can resolve it.
### Steps to reproduce the bug
```
from datasets import load_dataset
ds = load_dataset(
"BramVanroy/hplt-mono-v1-2",
"ky",
trust_remote_code=True
)
```
### Expected behavior
That the most recent repo name is used when `filename` is generated.
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.14.0-284.25.1.el9_2.x86_64-x86_64-with-glibc2.34
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.2
- PyArrow version: 14.0.1
- Pandas version: 2.1.3
- `fsspec` version: 2023.10.0
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https://api.github.com/repos/huggingface/datasets/issues/6721 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6721/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6721/comments | https://api.github.com/repos/huggingface/datasets/issues/6721/events | https://github.com/huggingface/datasets/issues/6721 | 2,173,931,714 | I_kwDODunzps6Bk5DC | 6,721 | Hi,do you know how to load the dataset from local file now? | {
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} | [] | open | false | null | [] | null | [
"\r\n@Gera001\r\n# Loading Dataset from Local Files Using 🤗Hugging Face.\r\n\r\nTo load a dataset from local files using the Hugging Face datasets library, you can use the `load_dataset` function.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files={'train': 'path/to/train.csv',\r\n 'test': 'path/to/test.csv'})\r\n```\r\n\r\nReference to [HF Datasets docs for loading from local](https://huggingface.co/docs/datasets/en/loading#csv). \r\n\r\n@albertvillanova\r\nthis issue can be closed here.",
"like this: from datasets import load_from_disk\r\ndataset = load_from_disk(data_path)\r\n",
"@ge00009 \r\n> like this: from datasets import load_from_disk dataset = load_from_disk(data_path)\r\n\r\nLoads a dataset that was previously saved using `save_to_disk()`.\r\n\r\nReference link:\r\nhttps://huggingface.co/docs/datasets/en/package_reference/loading_methods#datasets.load_from_disk.example"
] | 1970-01-01T00:00:00.000001 | 1,711 | null | NONE | null | Hi, if I want to load the dataset from local file, then how to specify the configuration name?
_Originally posted by @WHU-gentle in https://github.com/huggingface/datasets/issues/2976#issuecomment-1333455222_
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https://api.github.com/repos/huggingface/datasets/issues/6720 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6720/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6720/comments | https://api.github.com/repos/huggingface/datasets/issues/6720/events | https://github.com/huggingface/datasets/issues/6720 | 2,173,603,459 | I_kwDODunzps6Bjo6D | 6,720 | TypeError: 'str' object is not callable | {
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"Hi ! I opened a PR to fix an issue in the Features defined in your code\r\n\r\nBasically changing\r\n```python\r\nSequence(\"float32\")\r\n```\r\n\r\nto\r\n```python\r\nSequence(Value(\"float32\"))\r\n```\r\n\r\n\r\nhttps://huggingface.co/datasets/BramVanroy/hplt_mono_v1_2/discussions/1",
"D'oh! Was wondering why the `str() is not callable` was in there. Glad the error is my end though, and not related to zstandard (which I had not used in the past).\r\n\r\nThanks a lot!"
] | 1970-01-01T00:00:00.000001 | 1,709 | 1970-01-01T00:00:00.000001 | CONTRIBUTOR | null | ### Describe the bug
I am trying to get the HPLT datasets on the hub. Downloading/re-uploading would be too time- and resource consuming so I wrote [a dataset loader script](https://huggingface.co/datasets/BramVanroy/hplt_mono_v1_2/blob/main/hplt_mono_v1_2.py). I think I am very close but for some reason I always get the error below. It happens during the clean-up phase where the directory cannot be removed because it is not empty.
My only guess would be that this may have to do with zstandard
```
Traceback (most recent call last):
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1744, in _prepare_split_single
writer.write(example, key)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write
self.write_examples_on_file()
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 434, in write_examples_on_file
if self.schema
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 409, in schema
else (pa.schema(self._features.type) if self._features is not None else None)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1643, in type
return get_nested_type(self)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in get_nested_type
{key: get_nested_type(schema[key]) for key in schema}
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in <dictcomp>
{key: get_nested_type(schema[key]) for key in schema}
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1221, in get_nested_type
value_type = get_nested_type(schema.feature)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1228, in get_nested_type
return schema()
TypeError: 'str' object is not callable
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1753, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 588, in finalize
self.write_examples_on_file()
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 434, in write_examples_on_file
if self.schema
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 409, in schema
else (pa.schema(self._features.type) if self._features is not None else None)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1643, in type
return get_nested_type(self)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in get_nested_type
{key: get_nested_type(schema[key]) for key in schema}
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in <dictcomp>
{key: get_nested_type(schema[key]) for key in schema}
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1221, in get_nested_type
value_type = get_nested_type(schema.feature)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1228, in get_nested_type
return schema()
TypeError: 'str' object is not callable
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 959, in incomplete_dir
yield tmp_dir
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1005, in download_and_prepare
self._download_and_prepare(
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1767, in _download_and_prepare
super()._download_and_prepare(
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1100, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1605, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1762, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/pricie/vanroy/.config/JetBrains/PyCharm2023.3/scratches/scratch_5.py", line 4, in <module>
ds = load_dataset(
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/load.py", line 2549, in load_dataset
builder_instance.download_and_prepare(
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 985, in download_and_prepare
with incomplete_dir(self._output_dir) as tmp_output_dir:
File "/home/pricie/vanroy/.pyenv/versions/3.10.13/lib/python3.10/contextlib.py", line 153, in __exit__
self.gen.throw(typ, value, traceback)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 966, in incomplete_dir
shutil.rmtree(tmp_dir)
File "/home/pricie/vanroy/.pyenv/versions/3.10.13/lib/python3.10/shutil.py", line 731, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/home/pricie/vanroy/.pyenv/versions/3.10.13/lib/python3.10/shutil.py", line 729, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/home/pricie/vanroy/.cache/huggingface/datasets/BramVanroy___hplt_mono_v1_2/ky/1.2.0/7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47.incomplete'
```
Interestingly, though, this directory _does_ appear to be empty:
```shell
> cd /home/pricie/vanroy/.cache/huggingface/datasets/BramVanroy___hplt_mono_v1_2/ky/1.2.0/7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47.incomplete
> ls -lah
total 0
drwxr-xr-x. 1 vanroy vanroy 0 Mar 7 12:01 .
drwxr-xr-x. 1 vanroy vanroy 304 Mar 7 11:52 ..
> cd ..
> ls
7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47_builder.lock 7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47.incomplete
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset(
"BramVanroy/hplt_mono_v1_2",
"ky",
trust_remote_code=True
)
```
### Expected behavior
No error.
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.14.0-284.25.1.el9_2.x86_64-x86_64-with-glibc2.34
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.2
- PyArrow version: 14.0.1
- Pandas version: 2.1.3
- `fsspec` version: 2023.10.0
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https://api.github.com/repos/huggingface/datasets/issues/6719 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6719/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6719/comments | https://api.github.com/repos/huggingface/datasets/issues/6719/events | https://github.com/huggingface/datasets/issues/6719 | 2,169,585,727 | I_kwDODunzps6BUUA_ | 6,719 | Is there any way to solve hanging of IterableDataset using split by node + filtering during inference | {
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} | [] | open | false | null | [] | null | [] | 1970-01-01T00:00:00.000001 | 1,709 | null | NONE | null | ### Describe the bug
I am using an iterable dataset in a multi-node setup, trying to do training/inference while filtering the data on the fly. I usually do not use `split_dataset_by_node` but it is very slow using the IterableDatasetShard in `accelerate` and `transformers`. When I filter after applying `split_dataset_by_node`, it results in shards that are not equal sizes due to unequal samples filtered from each one.
The distributed process hangs when trying to accomplish this. Is there any way to resolve this or is it impossible to implement?
### Steps to reproduce the bug
Here is a toy example of what I am trying to do that reproduces the behavior
```
# torchrun --nproc-per-node 2 file.py
import os
import pandas as pd
import torch
from accelerate import Accelerator
from datasets import Features, Value, load_dataset
from datasets.distributed import split_dataset_by_node
from torch.utils.data import DataLoader
accelerator = Accelerator(device_placement=True, dispatch_batches=False)
if accelerator.is_main_process:
if not os.path.exists("scratch_data"):
os.mkdir("scratch_data")
n_shards = 4
for i in range(n_shards):
df = pd.DataFrame({"id": list(range(10 * i, 10 * (i + 1)))})
df.to_parquet(f"scratch_data/shard_{i}.parquet")
world_size = accelerator.num_processes
local_rank = accelerator.process_index
def collate_fn(examples):
input_ids = []
for example in examples:
input_ids.append(example["id"])
return torch.LongTensor(input_ids)
dataset = load_dataset(
"parquet", data_dir="scratch_data", split="train", streaming=True
)
dataset = (
split_dataset_by_node(dataset, rank=local_rank, world_size=world_size)
.filter(lambda x: x["id"] < 35)
.shuffle(seed=42, buffer_size=100)
)
batch_size = 2
train_dataloader = DataLoader(
dataset,
batch_size=batch_size,
collate_fn=collate_fn,
num_workers=2
)
for x in train_dataloader:
x = x.to(accelerator.device)
print({"rank": local_rank, "id": x})
y = accelerator.gather_for_metrics(x)
if accelerator.is_main_process:
print("gathered", y)
```
### Expected behavior
Is there any way to continue training/inference on the GPUs that have remaining data left without waiting for the others? Is it impossible to filter when
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.10.209-198.812.amzn2.x86_64-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.21.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
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https://api.github.com/repos/huggingface/datasets/issues/6717 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6717/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6717/comments | https://api.github.com/repos/huggingface/datasets/issues/6717/events | https://github.com/huggingface/datasets/issues/6717 | 2,168,726,432 | I_kwDODunzps6BRCOg | 6,717 | `remove_columns` method used with a streaming enable dataset mode produces a LibsndfileError on multichannel audio | {
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"And it also works well with `dataset = dataset.select_columns([\"audio\"])`",
"Same issue here, disable stream=True fix the problem"
] | 1970-01-01T00:00:00.000001 | 1,723 | null | NONE | null | ### Describe the bug
When loading a HF dataset in streaming mode and removing some columns, it is impossible to load a sample if the audio contains more than one channel. I have the impression that the time axis and channels are swapped or concatenated.
### Steps to reproduce the bug
Minimal error code:
```python
from datasets import load_dataset
dataset_name = "zinc75/Vibravox_dummy"
config_name = "BWE_Larynx_microphone"
# if we use "ASR_Larynx_microphone" subset which is a monochannel audio, no error is thrown.
dataset = load_dataset(
path=dataset_name, name=config_name, split="train", streaming=True
)
dataset = dataset.remove_columns(["sensor_id"])
# dataset = dataset.map(lambda x:x, remove_columns=["sensor_id"])
# The commented version does not produce an error, but loses the dataset features.
sample = next(iter(dataset))
```
Error:
```
Traceback (most recent call last):
File "/home/julien/Bureau/github/vibravox/tmp.py", line 15, in <module>
sample = next(iter(dataset))
^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1392, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1080, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1889, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1244, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1244, in <dictcomp>
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1300, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/audio.py", line 98, in encode_example
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 343, in write
with SoundFile(file, 'w', samplerate, channels,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 658, in __init__
self._file = self._open(file, mode_int, closefd)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 1216, in _open
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7fd795d24680>: Format not recognised.
Process finished with exit code 1
```
### Expected behavior
I would expect this code to run without error.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-21-generic-x86_64-with-glibc2.35
- Python version: 3.11.0
- `huggingface_hub` version: 0.21.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
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https://api.github.com/repos/huggingface/datasets/issues/6716 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6716/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6716/comments | https://api.github.com/repos/huggingface/datasets/issues/6716/events | https://github.com/huggingface/datasets/issues/6716 | 2,168,706,558 | I_kwDODunzps6BQ9X- | 6,716 | Non-deterministic `Dataset.builder_name` value | {
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"When `rotten_tomatoes` is printed out, the following warning message is also printed out:\r\n\r\n```\r\nYou can avoid this message in future by passing the argument `trust_remote_code=True`.\r\nPassing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\r\n```",
"Hi ! This behavior happens because the dataset was originakky created using a dataset script [rotten_tomatoes.py](https://huggingface.co/datasets/rotten_tomatoes/blob/26f40d324d7b281d8b3fb1c47f30f8b9957f206b/rotten_tomatoes.py) and because we added features recently allowing to download the dataset directly from Parquet files (parquet builder) without running the dataset script (rotten_tomatoes). The flakiness must come from the availability of the Parquet files (we automatically export them in the refs/convert/parquet branch and we recently had to move some files).\r\n\r\nAnyway the easy fix on our side is to remove the dataset script completely, let me open a PR at https://huggingface.co/datasets/rotten_tomatoes\r\n\r\nEDIT: opened https://huggingface.co/datasets/rotten_tomatoes/discussions/6, feel free to comment there if you're ok with that change",
"@lhoestq Thanks for the comment, explanation, and patch!",
"> we automatically export them in the refs/convert/parquet branch\r\n\r\nWhen this operation is in progress, the parquet files become temporarily unavailable?",
"> When this operation is in progress, the parquet files become temporarily unavailable?\r\n\r\nYes correct. I just merged the patch btw :)",
"@lhoestq Thanks for merging the PR! I think this issue can be closed."
] | 1970-01-01T00:00:00.000001 | 1,710 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
I'm not sure if this is a bug, but `print(ds.builder_name)` in the following code sometimes prints out `rotten_tomatoes` instead of `parquet`:
```python
import datasets
for _ in range(100):
ds = datasets.load_dataset("rotten_tomatoes", split="train")
print(ds.builder_name) # prints out "rotten_tomatoes" sometimes instead of "parquet"
```
Output:
```
...
parquet
parquet
parquet
rotten_tomatoes
parquet
parquet
parquet
...
```
Here's a reproduction using GitHub Actions:
https://github.com/mlflow/mlflow/actions/runs/8153247984/job/22284263613?pr=11329#step:12:241
One of our tests is flaky because `builder_name` is not deterministic.
### Steps to reproduce the bug
1. Run the code above.
### Expected behavior
Always prints out `parquet`?
### Environment info
```
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-1015-azure-x86_64-with-glibc2.34
- Python version: 3.8.18
- `huggingface_hub` version: 0.21.3
- PyArrow version: 15.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2024.2.0
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https://api.github.com/repos/huggingface/datasets/issues/6703 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6703/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6703/comments | https://api.github.com/repos/huggingface/datasets/issues/6703/events | https://github.com/huggingface/datasets/issues/6703 | 2,163,250,590 | I_kwDODunzps6A8JWe | 6,703 | Unable to load dataset that was saved with `save_to_disk` | {
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"`save_to_disk` uses a special serialization that can only be read using `load_from_disk`.\r\n\r\nContrary to `load_dataset`, `load_from_disk` directly loads Arrow files and uses the dataset directory as cache.\r\n\r\nOn the other hand `load_dataset` does a conversion step to get Arrow files from the raw data files (could be in JSON, CSV, Parquet etc.) and caches them in the `datasets` cache directory (default is `~/.cache/huggingface/datasets`). We haven't implemented any logic in `load_dataset` to support datasets saved with `save_to_disk` because they don't use the same cache.\r\n\r\nEDIT: note that you can save your dataset in Parquet format locally using `.to.parquet()` (make sure to shard in multiple files your dataset if it's multiple GBs - you can use `.shard()` + `.to_parquet()` to do that) and you'll be able to reload it using `load_dataset`",
"@lhoestq, so is it correctly understood that if I run `to_parquet()` and then `save_to_disk()`, I can load it with `load_dataset`? If yes, then it would resolve this issue (and should probably be documented somewhere 😄)",
"Here is an example:\r\n```python\r\nds.to_parquet(\"my/local/dir/data.parquet\")\r\n\r\n# later\r\nds = load_dataset(\"my/local/dir\")\r\n```\r\n\r\nand for bigger datasets:\r\n```python\r\nnum_shards = 1024 # set number of files to save (e.g. try to have files smaller than 5GB)\r\nfor shard_idx in num_shards:\r\n shard = ds.shard(index=shard_idx, num_shards=num_shards)\r\n shard.to_parquet(f\"my/local/dir/{shard_idx:05d}.parquet\") # 00000.parquet to 01023.parquet\r\n\r\n# later\r\nds = load_dataset(\"my/local/dir\")\r\n```\r\n\r\n\r\nI hope this helps :)",
"Thanks for helping out! Does this approach work with `s3fs`? e.g. something like this:\r\n\r\n```python\r\nimport s3fs\r\ns3 = s3fs.S3FileSystem(anon=True)\r\nwith s3.open('mybucket/new-file.parquet', 'w') as f:\r\n ds.to_parquet(f)\r\n```\r\n\r\nThis is instead of `save_to_disk` to save to an S3 bucket.\r\n\r\nOtherwise, I am not sure how to make this work when saving the dataset to an S3 bucket. Would `dataset.set_format(\"arrow\")` work as a replacement?",
"`load_dataset` does't support S3 buckets unfortunately :/",
"> `load_dataset` does't support S3 buckets unfortunately :/\r\n\r\nI am aware but I have some code that downloads it to disk before using that method. The most important part is to store it in a format that load_dataset is compatible with. ",
"Feel free to use Parquet then :)",
"I ended up with this. Not ideal to save to local disk, but it works and loads via `load_datasets` after downloading from S3 with another method.\r\n\r\n```python\r\nwith tempfile.TemporaryDirectory() as dir:\r\n dataset_nbytes = ds._estimate_nbytes()\r\n max_shard_size_local = convert_file_size_to_int(max_shard_size)\r\n num_shards = int(dataset_nbytes / max_shard_size_local) + 1\r\n\r\n for shard_idx in range(num_shards):\r\n shard = ds.shard(index=shard_idx, num_shards=num_shards)\r\n shard.to_parquet(f\"{dir}/{shard_idx:05d}.parquet\")\r\n \r\n fs.upload(\r\n lpath=dir,\r\n rpath=s3_path,\r\n recursive=True,\r\n )\r\n```"
] | 1970-01-01T00:00:00.000001 | 1,709 | 1970-01-01T00:00:00.000001 | NONE | null | ### Describe the bug
I get the following error message: You are trying to load a dataset that was saved using `save_to_disk`. Please use `load_from_disk` instead.
### Steps to reproduce the bug
1. Save a dataset with `save_to_disk`
2. Try to load it with `load_datasets`
### Expected behavior
I am able to load the dataset again with `load_datasets` which most packages uses over `load_from_disk`. I want to have a workaround that allows me to create the same indexing that `push_to_hub` creates for you before using `save_to_disk` - how can that be achieved?
### Environment info
datasets 2.17.1, python 3.10 | {
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