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PR_kwDODunzps5sz9eC
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Remove `os.path.relpath` in `resolve_patterns`
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... to save a few seconds when resolving repos with many data files.
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PR_kwDODunzps5sx-9V
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Add Dataset.take and Dataset.skip
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...to be aligned with IterableDataset.take and IterableDataset.skip
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PR_kwDODunzps5svgoq
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Run CI
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PR_kwDODunzps5srOtR
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add allow_primitive_to_str and allow_decimal_to_str instead of allow_number_to_str
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Fix #6805
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I_kwDODunzps6FsPyp
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Allow deleting a subset/config from a no-script dataset
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MEMBER
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As proposed by @BramVanroy, it would be neat to have this functionality through the API.
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Make convert_to_parquet CLI command create script branch
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Make convert_to_parquet CLI command create a "script" branch and keep the script file on it.
This PR proposes the simplest UX approach: whenever `--revision` is not explicitly passed (i.e., when the script is in the main branch), try to create a "script" branch from the "main" branch; if the "script" branch exists already, then do nothing.
Follow-up of:
- #6795
Close #6808.
CC: @severo
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As proposed by @severo, maybe we should add this functionality as well to the CLI command to convert a script-dataset to Parquet. See: https://github.com/huggingface/datasets/pull/6795#discussion_r1562819168
> 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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Fix hf-internal-testing/dataset_with_script commit SHA in CI test
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Fix test using latest commit SHA in hf-internal-testing/dataset_with_script dataset: https://huggingface.co/datasets/hf-internal-testing/dataset_with_script/commits/refs%2Fconvert%2Fparquet
Fix #6796.
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Batched mapping of existing string column casts boolean to string
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NONE
| null | null | 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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PR_kwDODunzps5sXyct
| 6,803 |
#6791 Improve type checking around FAISS
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Fixes #6791
Small PR to raise a better error when a dataset is not embedded properly.
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PR_kwDODunzps5sV0m8
| 6,802 |
Fix typo in docs (upload CLI)
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Related to https://huggingface.slack.com/archives/C04RG8YRVB8/p1712643948574129 (interal)
Positional args must be placed before optional args.
Feel free to merge whenever it's ready.
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I_kwDODunzps6FVI_E
| 6,801 |
got fileNotFound
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[] | null | 2 | 2024-04-11T04:57:41 | 2024-04-12T16:47:43 | 2024-04-12T16:47:43 |
NONE
| null | null | 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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PR_kwDODunzps5sRk_r
| 6,799 |
fix `DatasetBuilder._split_generators` incomplete type annotation
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solve #6798:
add missing `StreamingDownloadManager` type annotation to the `dl_manager` argument of the `DatasetBuilder._split_generators` function
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I_kwDODunzps6FQyA7
| 6,798 |
`DatasetBuilder._split_generators` incomplete type annotation
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[] | null | 3 | 2024-04-10T14:38:50 | 2024-04-11T15:34:59 | 2024-04-11T15:34:59 |
CONTRIBUTOR
| null | null | 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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Fix CI test_load_dataset_distributed_with_script
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Fix #6796.
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I_kwDODunzps6FNa3C
| 6,796 |
CI is broken due to hf-internal-testing/dataset_with_script
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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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PR_kwDODunzps5sJAC8
| 6,795 |
Add CLI function to convert script-dataset to Parquet
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Close #6690.
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| 2,233,202,088 |
PR_kwDODunzps5sHkJF
| 6,794 |
Multithreaded downloads
|
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...for faster dataset download when there are many many small files (e.g. imagefolder, audiofolder)
### Behcnmark
for example on [lhoestq/tmp-images-writer_batch_size](https://hf.co/datasets/lhoestq/tmp-images-writer_batch_size) (128 images)
| | duration of the download step in `load_dataset()` |
|--| ----------------------------------------------------------------------|
| Before | 58s |
| Now | 3s |
This should fix issues with the Dataset Viewer taking too much time to show up for imagefolder/audiofolder datasets.
### Implementation details
The main change is in the `DownloadManager`:
```diff
- download_func = partial(self._download, download_config=download_config)
+ download_func = partial(self._download_batched, download_config=download_config)
downloaded_path_or_paths = map_nested(
download_func,
url_or_urls,
map_tuple=True,
num_proc=download_config.num_proc,
desc="Downloading data files",
+ batched=True,
+ batch_size=-1,
)
```
and `_download_batched` is a multithreaded function.
I only enable multithreading if there are more than 16 files and files are small though, otherwise the progress bar that counts the number of downloaded files is not fluid (updating when a big batch of big files are done downloading). To do so I simply check if the first file is smaller than 20MB.
I also had to tweak `map_nested` to support batching. In particular it slices the data correctly if the user also enables multiprocessing.
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Fix cache conflict in `_check_legacy_cache2`
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It was reloading from the wrong cache dir because of a bug in `_check_legacy_cache2`. This function should not trigger if there are config_kwars like `sample_by=`
fix https://github.com/huggingface/datasets/issues/6758
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I_kwDODunzps6E7Kk8
| 6,791 |
`add_faiss_index` raises ValueError: not enough values to unpack (expected 2, got 1)
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[] | null | 3 | 2024-04-08T01:57:03 | 2024-04-11T15:38:05 | 2024-04-11T15:38:05 |
NONE
| null | null | 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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I_kwDODunzps6E3wHh
| 6,788 |
A Question About the Map Function
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[] | null | 2 | 2024-04-06T11:45:23 | 2024-04-11T05:29:35 | 2024-04-11T05:29:35 |
NONE
| null | null | 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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PR_kwDODunzps5r3dCw
| 6,785 |
rename datasets-server to dataset-viewer
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See https://github.com/huggingface/dataset-viewer/issues/2650
Tell me if it's OK, or if it's a breaking change that must be handled differently.
Also note that the docs page is still https://huggingface.co/docs/datasets-server/, so I didn't change it.
And the API URL is still https://datasets-server.huggingface.co/ (and [might always be](https://github.com/huggingface/dataset-viewer/issues/2666)), so I let it too.
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PR_kwDODunzps5r3UTj
| 6,784 |
Extract data on the fly in packaged builders
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Instead of waiting for data files to be extracted in the packaged builders, we can prepend the compression prefix and extract them as they are being read (using `fsspec`). This saves disk space (deleting extracted archives is not set by default) and slightly speeds up dataset generation (less disk reads)
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I_kwDODunzps6Ez1IK
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AttributeError: module 'numpy' has no attribute 'object'. in Kaggle Notebook
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NONE
| null | null | 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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| 2,228,026,497 |
PR_kwDODunzps5r2DMe
| 6,781 |
Remove get_inferred_type from ArrowWriter write_batch
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[] | null | 2 | 2024-04-05T13:21:05 | 2024-04-09T07:49:11 | 2024-04-09T07:49:11 |
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Inferring the type seems to be unnecessary given that the pyarrow array has already been created.
Because pyarrow array creation is sometimes extremely slow this doubles the time write_batch takes.
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PR_kwDODunzps5rvkyj
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Fix CI
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Updates the `wmt_t2t` test to pin the `revision` to the version with a loading script (cc @albertvillanova).
Additionally, it replaces the occurrences of the `lhoestq/test` repo id with `hf-internal-testing/dataset_with_script` and re-enables logging checks in the `Dataset.from_sql` tests.
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Install dependencies with `uv` in CI
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`diffusers` (https://github.com/huggingface/diffusers/pull/7116) and `huggingface_hub` (https://github.com/huggingface/huggingface_hub/pull/2072) also use `uv` to install their dependencies, so we can do the same here.
It seems to make the "Install dependencies" step in the `ubuntu` jobs 5-8x faster and 1.5-2x in the `windows` one.
Besides introducing `uv` in CI, this PR bumps the `tensorflow` minimal version requirement to align with Transformers and simplifies the SpaCy hashing tests (use blank language models instead of the pre-trained ones)
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I_kwDODunzps6EYoUh
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Dataset on Hub re-downloads every time?
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NONE
| null | null | 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
- `fsspec` version: 2023.10.0
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PR_kwDODunzps5rdKZ2
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`remove_columns`/`rename_columns` doc fixes
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Use more consistent wording in `remove_columns` to explain why it's faster than `map` and update `remove_columns`/`rename_columns` docstrings to fix in-place calls.
Reported in https://github.com/huggingface/datasets/issues/6700
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Datasets FileNotFoundError when trying to generate examples.
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[] | null | 2 | 2024-04-02T10:24:57 | 2024-04-04T14:22:03 | 2024-04-04T14:22:03 |
NONE
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### 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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[Bug Report] `datasets==2.18.0` is not compatible with `fsspec==2023.12.2`
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[] | null | 1 | 2024-04-01T20:17:48 | 2024-04-11T17:31:44 | 2024-04-11T17:31:44 |
NONE
| null | null | 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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PR_kwDODunzps5rQO9J
| 6,767 |
fixing the issue 6755(small typo)
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Fixed the issue #6755 on the typo mistake
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I_kwDODunzps6EFHZL
| 6,765 |
Compatibility issue between s3fs, fsspec, and datasets
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NONE
| null | null | 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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Allow polars as valid output type
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I was trying out polars as an output for a map function and found that it wasn't a valid return type in `validate_function_output`. Thought that we should accommodate this by creating and adding it to the `allowed_processed_input_types` variable.
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PR_kwDODunzps5rCAu8
| 6,761 |
Remove deprecated code
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What does this PR do?
1. remove `list_files_info` in favor of `list_repo_tree`. As of `0.23`, `list_files_info` will be removed for good. `datasets` had a utility to support both pre-0.20 and post-0.20 versions. Since `hfh` version is already pinned to `>=0.21.2`, I removed the legacy part.
2. `preupload_lfs_files` had also a different behavior between `<0.20` and `>=0.20`. I remove it since huggingface_hub is now pinned to `>=0.21.2`
3. `hf_hub_url` is overwritten to default to the dataset repo_type. I do think it is misleading to keep the same method naming for it. I renamed it to `get_dataset_url` for clarity. Let me know if you prefer to see this change reverted.
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I_kwDODunzps6DovLe
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Passing `sample_by` to `load_dataset` when loading text data does not work
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NONE
| null | null | 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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I_kwDODunzps6DdiPd
| 6,756 |
Support SQLite files?
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[] | null | 3 | 2024-03-25T11:48:05 | 2024-03-26T16:09:32 | 2024-03-26T16:09:32 |
COLLABORATOR
| null | null | 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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I_kwDODunzps6DZx5p
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Small typo on the documentation
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NONE
| null | null | 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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PR_kwDODunzps5qk-nr
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Fix cache path to snakecase for `CachedDatasetModuleFactory` and `Cache`
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CONTRIBUTOR
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Fix https://github.com/huggingface/datasets/issues/6750#issuecomment-2016678729
I didn't find a guideline on how to run the tests, so i just run the following steps to make sure that this bug is fixed.
1. `python test.py`,
2. then `HF_DATASETS_OFFLINE=1 python test.py`
The `test.py` is
```
import datasets
datasets.utils.logging.set_verbosity_info()
ds = datasets.load_dataset('izhx/STS17-debug')
print(ds)
ds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4')
print(ds)
```
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I_kwDODunzps6DYLzT
| 6,753 |
Type error when importing datasets on Kaggle
|
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[] | null | 8 | 2024-03-24T03:01:30 | 2024-10-02T11:49:35 | 2024-03-30T00:23:49 |
NONE
| null | null | 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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PR_kwDODunzps5qkKLH
| 6,751 |
Use 'with' operator for some download functions
|
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Some functions in `streaming_download_manager.py` are not closing the file they open which lead to `Unclosed file` warnings in our code. This fixes a few of them.
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| 6,750 |
`load_dataset` requires a network connection for local download?
|
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[] | null | 3 | 2024-03-23T01:06:32 | 2024-04-15T15:38:52 | 2024-04-15T15:38:52 |
NONE
| null | null | 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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PR_kwDODunzps5qeoSk
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Fix fsspec tqdm callback
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Following changes at https://github.com/fsspec/filesystem_spec/pull/1497 for `fsspec>=2024.2.0`
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PR_kwDODunzps5qa5L-
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chore(deps): bump fsspec
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There were a few fixes released recently, some DVC ecosystem packages require newer version of `fsspec`.
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| 2,198,993,949 |
I_kwDODunzps6DEfwd
| 6,746 |
ExpectedMoreSplits error when loading C4 dataset
|
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[] | null | 8 | 2024-03-21T02:53:04 | 2024-09-18T19:57:14 | 2024-07-29T07:21:08 |
NONE
| null | null | 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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I_kwDODunzps6DCxWk
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Scraping the whole of github including private repos is bad; kindly stop
|
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[] | null | 1 | 2024-03-20T20:54:06 | 2024-03-21T12:28:04 | 2024-03-21T10:24:56 |
NONE
| null | null | 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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| 2,195,481,697 |
PR_kwDODunzps5qHeMZ
| 6,743 |
Allow null values in dict columns
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Fix #6738
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| 2,195,134,854 |
PR_kwDODunzps5qGSfG
| 6,742 |
Fix missing download_config in get_data_patterns
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Reported in https://github.com/huggingface/datasets-server/issues/2607
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PR_kwDODunzps5qEiu3
| 6,741 |
Fix offline mode with single config
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Reported in https://github.com/huggingface/datasets/issues/4760
The cache was not able to reload a dataset with a single config form the cache if the config name is not specificed
For example
```python
from datasets import load_dataset, config
config.HF_DATASETS_OFFLINE = True
load_dataset("openai_humaneval")
```
This was due to a regression in https://github.com/huggingface/datasets/pull/6632
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| 2,193,172,074 |
I_kwDODunzps6CuSZq
| 6,740 |
Support for loading geotiff files as a part of the ImageFolder
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NONE
| null | null | 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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PR_kwDODunzps5p-Bwe
| 6,739 |
Transpose images with EXIF Orientation tag
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Closes https://github.com/huggingface/datasets/issues/6252
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I_kwDODunzps6CrSno
| 6,738 |
Dict feature is non-nullable while nested dict feature is
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[] | null | 3 | 2024-03-18T14:31:47 | 2024-03-20T10:24:15 | 2024-03-19T20:05:20 |
CONTRIBUTOR
| null | null | 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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Invalid pattern: '**' can only be an entire path component
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[] | null | 7 | 2024-03-16T19:28:46 | 2024-07-23T14:23:28 | 2024-05-13T11:32:57 |
NONE
| null | null | 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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PR_kwDODunzps5px84g
| 6,735 |
Add `mode` parameter to `Image` feature
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Fix https://github.com/huggingface/datasets/issues/6675
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Unexpected behavior when using load_dataset with streaming=True in a for loop
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[] | null | 2 | 2024-03-12T23:26:43 | 2024-04-16T00:00:00 | 2024-04-16T00:00:00 |
NONE
| null | null | 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
Ubuntu 20.04
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| 2,181,881,499 |
PR_kwDODunzps5pZDsB
| 6,730 |
Deprecate Pandas builder
|
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[] | null | 2 | 2024-03-12T15:12:13 | 2024-03-12T17:42:33 | 2024-03-12T17:36:24 |
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| null | false |
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|
The Pandas packaged builder is undocumented and relies on `pickle` to read the data, making it **unsafe**. Moreover, I haven't seen a single instance of this builder being used (not even using the GH/Hub search), so we should deprecate it.
|
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I_kwDODunzps6B88dn
| 6,729 |
Support zipfiles that span multiple disks?
|
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[] | null | 6 | 2024-03-11T21:07:41 | 2024-06-26T05:08:59 | 2024-06-26T05:05:28 |
COLLABORATOR
| null | null | 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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| 2,178,607,012 |
I_kwDODunzps6B2uek
| 6,728 |
Issue Downloading Certain Datasets After Setting Custom `HF_ENDPOINT`
|
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[] | null | 3 | 2024-03-11T09:06:38 | 2024-03-15T14:52:07 | 2024-03-15T14:52:07 |
NONE
| null | null | 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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| 2,177,826,110 |
PR_kwDODunzps5pLJyE
| 6,727 |
Using a registry instead of calling globals for fetching feature types
|
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[] | null | 6 | 2024-03-10T17:47:51 | 2024-03-13T12:08:49 | 2024-03-13T10:46:02 |
CONTRIBUTOR
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Hello,
When working with bio-data, each feature often has metadata associated with it (e.g. species, lineage, snp position, etc). To store this, I like to use the feature classes with the added `metadata` attribute. However, when saving or loading with custom features, you get an error since that class doesn't exist in the global namespace in `datasets.features.features`. Take for example,
```python
from dataclasses import dataclass, field
from datasets import Dataset
from datasets.features.features import Value, Features
@dataclass
class FeatureA(Value):
metadata: dict = field(default=dict)
_type: str = field(default="FeatureA", init=False, repr=False)
@dataclass
class FeatureB(Value):
metadata: dict = field(default_factory=dict)
_type: str = field(default="FeatureB", init=False, repr=False)
test_data = {
"a": [1, 2, 3],
"b": [4, 5, 6],
}
test_data = Dataset.from_dict(
test_data,
features=Features({
"a": FeatureA("int32", metadata={"species": "lactobacillus acetotolerans"}),
"b": FeatureB("int32", metadata={"species": "lactobacillus iners"}),
})
)
# returns an error since FeatureA and FeatureB are not in the global namespace
test_data.save_to_disk('./test_data')
```
Saving the dataset (0/1 shards): 0%| | 0/3 [00:00<?, ? examples/s]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[2], line 28
19 test_data = Dataset.from_dict(
20 test_data,
21 features=Features({
(...)
24 })
25 )
27 # returns an error since FeatureA and FeatureB are not in the global namespace
---> 28 test_data.save_to_disk('./test_data')
...
File ~\Documents\datasets\src\datasets\features\features.py:1361, in generate_from_dict(obj)
1359 return {key: generate_from_dict(value) for key, value in obj.items()}
1360 obj = dict(obj)
-> 1361 class_type = globals()[obj.pop("_type")]
1363 if class_type == Sequence:
1364 return Sequence(feature=generate_from_dict(obj["feature"]), length=obj.get("length", -1))
KeyError: 'FeatureA'
We can avoid this by having a registry (like formatters) and doing
```python
from datasets.features.features import register_feature
register_feature(FeatureA, "FeatureA")
register_feature(FeatureB, "FeatureB")
test_data.save_to_disk('./test_data')
```
Saving the dataset (1/1 shards): 100%|------| 3/3 [00:00<00:00, 211.13 examples/s]
and loading from disk returns with all metadata information
```python
from datasets import load_from_disk
test_data = load_from_disk('./test_data')
test_data.features
```
{'a': FeatureA(dtype='int32', id=None, metadata={'species': 'lactobacillus acetotolerans'}),
'b': FeatureB(dtype='int32', id=None, metadata={'species': 'lactobacillus iners'})}
|
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PR_kwDODunzps5o_fPU
| 6,723 |
get_dataset_default_config_name docstring
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MEMBER
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|
fix https://github.com/huggingface/datasets/pull/6722
|
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| 2,174,332,127 |
PR_kwDODunzps5o_ch0
| 6,722 |
Add details in docstring
|
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[] | null | 1 | 2024-03-07T17:02:07 | 2024-03-07T17:21:10 | 2024-03-07T17:21:08 |
COLLABORATOR
| null | false |
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|
see https://github.com/huggingface/datasets-server/pull/2554#discussion_r1516516867
|
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I_kwDODunzps6Bjo6D
| 6,720 |
TypeError: 'str' object is not callable
|
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[] | null | 2 | 2024-03-07T11:07:09 | 2024-03-08T07:34:53 | 2024-03-07T15:13:58 |
CONTRIBUTOR
| null | null | 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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PR_kwDODunzps5ouwwE
| 6,718 |
Fix concurrent script loading with force_redownload
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I added `lock_importable_file` in `get_dataset_builder_class` and `extend_dataset_builder_for_streaming` to fix the issue, and I also added a test
cc @clefourrier
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I_kwDODunzps6BQ9X-
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Non-deterministic `Dataset.builder_name` value
|
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NONE
| null | null | 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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| 2,167,747,095 |
PR_kwDODunzps5oo36i
| 6,715 |
Fix sliced ConcatenationTable pickling with mixed schemas vertically
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A sliced + pickled ConcatenationTable could end up with a different schema than the original schema, if the slice only contains blocks with only a subset of the columns.
This can lead to issues when saving datasets from a concatenation of datasets with mixed schemas
Reported in https://discuss.huggingface.co/t/datasetdict-save-to-disk-with-num-proc-1-seems-to-hang-with-error/75595
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| 2,167,569,080 |
PR_kwDODunzps5ooQd2
| 6,714 |
Expand no-code dataset info with datasets-server info
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E.g., to have info about a dataset's number of examples for more informative TQDM bars.
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| 2,166,797,560 |
PR_kwDODunzps5olmqh
| 6,713 |
Bump huggingface-hub lower version to 0.21.2
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This should fix the version compatibility issue when using `huggingface_hub` < 0.21.2 and latest fsspec (>=2023.12.0).
See my comment: https://github.com/huggingface/datasets/pull/6687#issuecomment-1976493336
>> EDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main`
>
>Please note that people using `huggingface_hub` < 0.21.2 and latest `fsspec` will have issues when using `datasets`:
>- https://github.com/huggingface/lighteval/actions/runs/8139147047/job/22241658122?pr=86
>- https://github.com/huggingface/lighteval/pull/84
CC: @clefourrier
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| 2,166,588,373 |
PR_kwDODunzps5ok4VF
| 6,712 |
fix CastError pickling
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reported in https://discuss.huggingface.co/t/datasetdict-save-to-disk-with-num-proc-1-seems-to-hang-with-error/75595
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| 2,164,781,564 |
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| 6,710 |
Persist IterableDataset epoch in workers
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Use shared memory for the IterableDataset epoch.
This way calling `ds.set_epoch()` in the main process will update the epoch in the DataLoader workers as well.
This is useful especially because the epoch is used to compute the `effective_seed` used for shuffling.
I used torch's shared memory in case users want to send dataset copies without shared memory using pickle. I also find it easier to use than using `multiprocessing.shared_memory` than requires unlinking only in the main process, or `mp.Value` that is not picklable.
close https://github.com/huggingface/datasets/issues/6673
cc @rwightman
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set dev version
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| 6,706 |
Update ruff
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| 6,705 |
Fix data_files when passing data_dir
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This code should not return empty data files
```python
from datasets import load_dataset_builder
revision = "3d406e70bc21c3ca92a9a229b4c6fc3ed88279fd"
b = load_dataset_builder("bigcode/the-stack-v2-dedup", data_dir="data/Dockerfile", revision=revision)
print(b.config.data_files)
```
Previously it would return no data files because it would apply the YAML `data_files: data/**/train-*` pattern to this directory
cc @anton-l
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PR_kwDODunzps5obZyj
| 6,704 |
Improve default patterns resolution
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Separate the default patterns that match directories from the ones matching files and ensure directories are checked first (reverts the change from https://github.com/huggingface/datasets/pull/6244, which merged these patterns). Also, ensure that the glob patterns do not overlap to avoid duplicates in the result.
Additionally, replace `get_fs_token_paths` with `url_to_fs` to avoid [unnecessary glob calls](https://github.com/fsspec/filesystem_spec/blob/14dce8ca78f7aa509a20edb263bff83a7760c24d/fsspec/core.py#L655-L656).
fix https://github.com/huggingface/datasets/issues/6259
fix https://github.com/huggingface/datasets/issues/6272
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I_kwDODunzps6A8JWe
| 6,703 |
Unable to load dataset that was saved with `save_to_disk`
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NONE
| null | null | 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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I_kwDODunzps6A3JA0
| 6,702 |
Push samples to dataset on hub without having the dataset locally
|
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NONE
| null | null | null |
### Feature request
Say I have the following code:
```
from datasets import Dataset
import pandas as pd
new_data = {
"column_1": ["value1", "value2"],
"column_2": ["value3", "value4"],
}
df_new = pd.DataFrame(new_data)
dataset_new = Dataset.from_pandas(df_new)
# add these samples to a remote dataset
```
It would be great to have a way to push dataset_new to a remote dataset that respects the same schema. This way one would not have to do the following:
```
from datasets import load_dataset
dataset = load_dataset('username/dataset_name', use_auth_token='your_hf_token_here')
updated_dataset = dataset['train'].concatenate(dataset_new)
updated_dataset.push_to_hub('username/dataset_name', use_auth_token='your_hf_token_here')
```
### Motivation
No need to download the dataset.
### Your contribution
Maybe this feature already exists, didnt see it though. I do not have the expertise to do this.
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PR_kwDODunzps5oTfO_
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Base parquet batch_size on parquet row group size
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[] | null | 2 | 2024-02-29T14:53:01 | 2024-02-29T15:15:18 | 2024-02-29T15:08:55 |
MEMBER
| null | false |
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This allows to stream datasets like [Major-TOM/Core-S2L2A](https://huggingface.co/datasets/Major-TOM/Core-S2L2A) which have row groups with few rows (one row is ~10MB). Previously the cold start would take a lot of time and OOM because it would download many row groups before yielding the first example.
I tried on OpenOrca and imagenet-hard and it does't affect overall throughput.
Even if the overall throughput doesn't change for datasets like imagenet-hard with big rows, note that it does create shorter and more frequent pauses to download the next row group. Though I find it fine because previously the pauses were less frequent but very long (downloading multiple row groups at a time)
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I_kwDODunzps6ArcH-
| 6,700 |
remove_columns is not in-place but the doc shows it is in-place
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[] | null | 3 | 2024-02-28T12:36:22 | 2024-04-02T17:15:28 | 2024-04-02T17:15:28 |
NONE
| null | null | null |
### Describe the bug
The doc of `datasets` v2.17.0/v2.17.1 shows that `remove_columns` is in-place. [link](https://huggingface.co/docs/datasets/v2.17.1/en/package_reference/main_classes#datasets.DatasetDict.remove_columns)
In the text classification example of transformers v4.38.1, the columns are not removed.
https://github.com/huggingface/transformers/blob/a0857740c0e6127485c11476650314df3accc2b6/examples/pytorch/text-classification/run_classification.py#L421
### Steps to reproduce the bug
https://github.com/huggingface/transformers/blob/a0857740c0e6127485c11476650314df3accc2b6/examples/pytorch/text-classification/run_classification.py#L421
### Expected behavior
Actually remove the columns.
### Environment info
1. datasets v2.17.0
2. transformers v4.38.1
|
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| 2,157,752,392 |
PR_kwDODunzps5oG6Xt
| 6,698 |
Faster `xlistdir`
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Pass `detail=False` to the `fsspec` `listdir` to avoid unnecessarily fetching expensive metadata about the paths.
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I_kwDODunzps6Alh_w
| 6,697 |
Unable to Load Dataset in Kaggle
|
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[] | null | 4 | 2024-02-27T18:19:34 | 2024-02-29T17:32:42 | 2024-02-29T17:32:41 |
NONE
| null | null | null |
### Describe the bug
Having installed the latest versions of transformers==4.38.1 and datasets==2.17.1 Unable to load the dataset in a kaggle notebook.
Get this Error:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[8], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("llm-blender/mix-instruct")
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1664, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1661 ignore_verifications = ignore_verifications or save_infos
1663 # Create a dataset builder
-> 1664 builder_instance = load_dataset_builder(
1665 path=path,
1666 name=name,
1667 data_dir=data_dir,
1668 data_files=data_files,
1669 cache_dir=cache_dir,
1670 features=features,
1671 download_config=download_config,
1672 download_mode=download_mode,
1673 revision=revision,
1674 use_auth_token=use_auth_token,
1675 **config_kwargs,
1676 )
1678 # Return iterable dataset in case of streaming
1679 if streaming:
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1490, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1488 download_config = download_config.copy() if download_config else DownloadConfig()
1489 download_config.use_auth_token = use_auth_token
-> 1490 dataset_module = dataset_module_factory(
1491 path,
1492 revision=revision,
1493 download_config=download_config,
1494 download_mode=download_mode,
1495 data_dir=data_dir,
1496 data_files=data_files,
1497 )
1499 # Get dataset builder class from the processing script
1500 builder_cls = import_main_class(dataset_module.module_path)
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1242, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1237 if isinstance(e1, FileNotFoundError):
1238 raise FileNotFoundError(
1239 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
1240 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1241 ) from None
-> 1242 raise e1 from None
1243 else:
1244 raise FileNotFoundError(
1245 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory."
1246 )
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1230, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1215 return HubDatasetModuleFactoryWithScript(
1216 path,
1217 revision=revision,
(...)
1220 dynamic_modules_path=dynamic_modules_path,
1221 ).get_module()
1222 else:
1223 return HubDatasetModuleFactoryWithoutScript(
1224 path,
1225 revision=revision,
1226 data_dir=data_dir,
1227 data_files=data_files,
1228 download_config=download_config,
1229 download_mode=download_mode,
-> 1230 ).get_module()
1231 except Exception as e1: # noqa: all the attempts failed, before raising the error we should check if the module is already cached.
1232 try:
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:846, in HubDatasetModuleFactoryWithoutScript.get_module(self)
836 token = self.download_config.use_auth_token
837 hfh_dataset_info = HfApi(config.HF_ENDPOINT).dataset_info(
838 self.name,
839 revision=self.revision,
840 token=token,
841 timeout=100.0,
842 )
843 patterns = (
844 sanitize_patterns(self.data_files)
845 if self.data_files is not None
--> 846 else get_patterns_in_dataset_repository(hfh_dataset_info)
847 )
848 data_files = DataFilesDict.from_hf_repo(
849 patterns,
850 dataset_info=hfh_dataset_info,
851 allowed_extensions=ALL_ALLOWED_EXTENSIONS,
852 )
853 infered_module_names = {
854 key: infer_module_for_data_files(data_files_list, use_auth_token=self.download_config.use_auth_token)
855 for key, data_files_list in data_files.items()
856 }
File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:471, in get_patterns_in_dataset_repository(dataset_info)
469 resolver = partial(_resolve_single_pattern_in_dataset_repository, dataset_info)
470 try:
--> 471 return _get_data_files_patterns(resolver)
472 except FileNotFoundError:
473 raise FileNotFoundError(
474 f"The dataset repository at '{dataset_info.id}' doesn't contain any data file."
475 ) from None
File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:99, in _get_data_files_patterns(pattern_resolver)
97 try:
98 for pattern in patterns:
---> 99 data_files = pattern_resolver(pattern)
100 if len(data_files) > 0:
101 non_empty_splits.append(split)
File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:303, in _resolve_single_pattern_in_dataset_repository(dataset_info, pattern, allowed_extensions)
301 data_files_ignore = FILES_TO_IGNORE
302 fs = HfFileSystem(repo_info=dataset_info)
--> 303 glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)]
304 matched_paths = [
305 filepath
306 for filepath in glob_iter
307 if filepath.name not in data_files_ignore and not filepath.name.startswith(".")
308 ]
309 if allowed_extensions is not None:
File /opt/conda/lib/python3.10/site-packages/fsspec/spec.py:606, in AbstractFileSystem.glob(self, path, maxdepth, **kwargs)
602 depth = None
604 allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs)
--> 606 pattern = glob_translate(path + ("/" if ends_with_sep else ""))
607 pattern = re.compile(pattern)
609 out = {
610 p: info
611 for p, info in sorted(allpaths.items())
(...)
618 )
619 }
File /opt/conda/lib/python3.10/site-packages/fsspec/utils.py:734, in glob_translate(pat)
732 continue
733 elif "**" in part:
--> 734 raise ValueError(
735 "Invalid pattern: '**' can only be an entire path component"
736 )
737 if part:
738 results.extend(_translate(part, f"{not_sep}*", not_sep))
ValueError: Invalid pattern: '**' can only be an entire path component
```
```
After loading this dataset
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("llm-blender/mix-instruct")
```
### Expected behavior
The dataset should load with desired split.
### Environment info
- `datasets` version: 2.17.1
- Platform: Linux-5.15.133+-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0
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PR_kwDODunzps5n6ipH
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Make JSON builder support an array of strings
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Support JSON file with an array of strings.
Fix #6695.
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Support loading a dataset from a JSON file with an array of strings.
See: https://huggingface.co/datasets/CausalLM/Refined-Anime-Text/discussions/1
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Update the print message for chunked_dataset in process.mdx
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Update documentation to align with `Dataset.__repr__` change after #423
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Enhancement: Enable loading TSV files in load_dataset()
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Fix #6691
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load_dataset() does not support tsv
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NONE
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### Feature request
the load_dataset() for local functions support file types like csv, json etc but not of type tsv (tab separated values).
### Motivation
cant easily load files of type tsv, have to convert them to another type like csv then load
### Your contribution
Can try by raising a PR with a little help, currently went through the code but didn't fully understand
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Add function to convert a script-dataset to Parquet
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MEMBER
| null | null | null |
Add function to convert a script-dataset to Parquet and push it to the Hub, analogously to the Space: "Convert a Hugging Face dataset to Parquet"
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I_kwDODunzps6AIAFb
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.load_dataset() method defaults to zstandard
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NONE
| null | null | null |
### Describe the bug
Regardless of what method I use, datasets defaults to zstandard for unpacking my datasets.
This is poor behavior, because not only is zstandard not a dependency in the huggingface package (and therefore, your dataset loading will be interrupted while it asks you to install the package), but it happens on datasets that are uploaded in json format too, meaning the dataset loader will attempt to convert the data to a zstandard compatible format, and THEN try to unpackage it.
My 4tb drive runs out of room when using zstandard on slimpajama. It loads fine on 1.5tb when using json, however I lack the understanding of the "magic numbers" system used to select the unpackaging algorithm, so I can't push a change myself.
Commenting out this line, in "/datasets/utils/extract.py" fixes the issue, and causes SlimPajama to properly extract using rational amounts of storage, however it completely disables zstandard, which is probably undesirable behavior. Someone with an understanding of the "magic numbers" system should probably take a pass over this issue.
```
class Extractor:
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
extractors: Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
#"zstd": ZstdExtractor, # This line needs to go, in order for datasets to work w/o non-dependent packages
"rar": RarExtractor,
"bz2": Bzip2Extractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": Lz4Extractor, # <Added version="2.4.0"/>
}
```
### Steps to reproduce the bug
'''
from datasaets import load_dataset
load_dataset(path="/cerebras/SlimPajama-627B")
'''
This alone should trigger the error on any system that does not have zstandard pip installed.
### Expected behavior
This repository (which is encoded in json format, not zstandard) should check whether zstandard is installed before defaulting to it. Additionally, using zstandard should not use more than 3x the required space that other extraction mechanisms use.
### Environment info
- `datasets` version: 2.17.1
- Platform: Linux-6.5.0-18-generic-x86_64-with-glibc2.35
- Python version: 3.12.0
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0
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fsspec: support fsspec>=2023.12.0 glob changes
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CONTRIBUTOR
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- adds support for the `fs.glob` changes introduced in `fsspec==2023.12.0` and unpins the current upper bound
Should close #6644
Should close #6645
The `test_data_files` glob/pattern tests pass for me in:
- `fsspec==2023.10.0` (the pinned max version in datasets `main`)
- `fsspec==2023.12.0` (#6644)
- `fsspec==2024.2.0` (#6645)
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Updated Quickstart Notebook link
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Fixed Quickstart Notebook Link in the [Overview notebook](https://github.com/huggingface/datasets/blob/main/notebooks/Overview.ipynb)
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| 6,684 |
Improve error message for gated datasets on load
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Internal Slack discussion: https://huggingface.slack.com/archives/C02V51Q3800/p1708424971135029
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| 2,142,751,955 |
PR_kwDODunzps5nTxGu
| 6,683 |
Fix imagefolder dataset url
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| 2,142,000,800 |
PR_kwDODunzps5nRME6
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Update GitHub Actions to Node 20
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Update GitHub Actions to Node 20.
Fix #6679.
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PR_kwDODunzps5nRItQ
| 6,681 |
Update release instructions
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Update release instructions.
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https://api.github.com/repos/huggingface/datasets/issues/6680
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PR_kwDODunzps5nRHcz
| 6,680 |
Set dev version
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| 2,141,953,981 |
I_kwDODunzps5_q5-9
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Node.js 16 GitHub Actions are deprecated
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MEMBER
| null | null | null |
`Node.js` 16 GitHub Actions are deprecated. See: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/
We should update them to Node 20.
See warnings in our CI, e.g.: https://github.com/huggingface/datasets/actions/runs/7957295009?pr=6678
> Node.js 16 actions are deprecated. Please update the following actions to use Node.js 20: actions/checkout@v3, actions/setup-python@v4. For more information see: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/.
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PR_kwDODunzps5nQ2ZO
| 6,678 |
Release: 2.17.1
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MEMBER
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PR_kwDODunzps5nOmo_
| 6,677 |
Pass through information about location of cache directory.
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CONTRIBUTOR
| null | false |
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If cache directory is set, information is not passed through.
Pass download config in as an arg too.
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I_kwDODunzps5_iFI9
| 6,675 |
Allow image model (color conversion) to be specified as part of datasets Image() decode
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[] | null | 1 | 2024-02-16T23:43:20 | 2024-03-18T15:41:34 | 2024-03-18T15:41:34 |
NONE
| null | null | null |
### Feature request
Typical torchvision / torch Datasets in image applications apply color conversion in the Dataset portion of the code as part of image decode, separately from the image transform stack. This is true for PIL.Image where convert is usually called in dataset, for native torchvision https://pytorch.org/vision/main/generated/torchvision.io.decode_jpeg.html, and similarly in tensorflow.data pipelines decode_jpeg or https://www.tensorflow.org/api_docs/python/tf/io/decode_and_crop_jpeg have a channels arg that allows controlling the image mode in the decode step.
datasets currently requires this pattern (from [examples](https://huggingface.co/docs/datasets/main/en/image_process)):
```
from torchvision.transforms import Compose, ColorJitter, ToTensor
jitter = Compose(
[
ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.7),
ToTensor(),
]
)
def transforms(examples):
examples["pixel_values"] = [jitter(image.convert("RGB")) for image in examples["image"]]
return examples
```
### Motivation
It would be nice to be able to handle `image.convert("RGB")` (or other modes) in the decode step, before applying torchvision transforms, this would reduce differences in code when handling pipelines that can handle torchvision, webdatset, or hf datasets with fewer code differences and without needing to handle image mode argument passing in two different stages of the pipelines...
### Your contribution
Can do a PR with guidance on how mode should be passed / set on the dataset.
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