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} | null | null | null | ### Describe the bug
If we add a new column to our iterable dataset using the hack described in #5752, when we then interleave datasets the new column is pinned to one value.
### Steps to reproduce the bug
What we're going to do here is:
1. Load an iterable dataset in streaming mode (`original_dataset`)
2. Add a new column to this dataset using the hack in #5752 (`modified_dataset_1`)
3. Create another new dataset by adding a column with the same key but different values (`modified_dataset_2`)
4. Interleave our new datasets (`modified_dataset_1` + `modified_dataset_2`)
5. Check the value of our newly added column (`new_column`)
```python
from datasets import load_dataset
# load an iterable dataset
original_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# now add a new column to our streaming dataset using our hack from 5752
name = "new_column"
column = [f"new dataset 1, row {i}" for i in range(50)]
new_features = original_dataset.features.copy()
new_features[name] = new_features["file"] # I know that "file" has the right column type to match our new feature
def add_column_fn(example, idx):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: column[idx]}
modified_dataset_1 = original_dataset.map(add_column_fn, with_indices=True, features=new_features)
# now create a second modified dataset using the same trick
column = [f"new dataset 2, row {i}" for i in range(50)]
def add_column_fn(example, idx):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: column[idx]}
modified_dataset_2 = original_dataset.map(add_column_fn, with_indices=True, features=new_features)
# interleave these datasets
interleaved_dataset = interleave_datasets([modified_dataset_1, modified_dataset_2])
# now check what the value of the added column is
for i, sample in enumerate(interleaved_dataset):
print(sample["new_column"])
if i == 10:
break
```
**Print Output:**
```
new dataset 2, row 0
new dataset 2, row 0
new dataset 2, row 1
new dataset 2, row 1
new dataset 2, row 2
new dataset 2, row 2
new dataset 2, row 3
new dataset 2, row 3
new dataset 2, row 4
new dataset 2, row 4
new dataset 2, row 5
```
We see that we only get outputs from our second dataset.
### Expected behavior
We should interleave between dataset 1 and 2 and increase in row value:
```
new dataset 1, row 0
new dataset 2, row 0
new dataset 1, row 1
new dataset 2, row 1
new dataset 1, row 2
new dataset 2, row 2
...
```
### Environment info
- datasets version: 2.10.2.dev0
- Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.16
- Huggingface_hub version: 0.13.3
- PyArrow version: 10.0.1
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} | null | null | null | ### Describe the bug
After appending a new column to a streaming dataset using `.add_column`, we can no longer access the list of dataset features using the `.feature` method.
### Steps to reproduce the bug
```python
from datasets import load_dataset
original_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
print(original_dataset.features.keys())
# now add a new column to our streaming dataset
modified_dataset = original_dataset.add_column("new_column", ["some random text" for _ in range(50)])
print(modified_dataset.features.keys())
```
**Print Output:**
```
dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[1], line 8
6 # now add a new column to our streaming dataset
7 modified_dataset = original_dataset.add_column("new_column", ["some random text" for _ in range(50)])
----> 8 print(modified_dataset.features.keys())
AttributeError: 'NoneType' object has no attribute 'keys'
```
We see that we get the features for the original dataset, but not the modified one with the added column.
### Expected behavior
Features should be persevered after adding a new column, i.e. calling:
```python
print(modified_dataset.features.keys())
```
Should return:
```
dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id', 'new_column'])
```
### Environment info
- `datasets` version: 2.10.2.dev0
- Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.16
- Huggingface_hub version: 0.13.3
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | null | {
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} | Return a list of lists instead of a list of NumPy arrays when converting the variable-shaped `ArrayXD` to Python. Additionally, improve the NumPy conversion by returning a numeric NumPy array when the offsets are equal or a NumPy object array when they aren't, and allow converting the variable-shaped `ArrayXD` to Pandas.
(Reported in https://github.com/huggingface/datasets/issues/5719#issuecomment-1507579671) | {
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Creating a dataset from a generator using `Dataset.from_generator()` fails if the generator is the [Google Big Query Python client](https://cloud.google.com/python/docs/reference/bigquery/latest). The problem is that the Big Query client is not pickable. And the function `create_config_id` tries to get a hash of the generator by pickling it. So the following error is generated:
```
_pickle.PicklingError: Pickling client objects is explicitly not supported.
Clients have non-trivial state that is local and unpickleable.
```
### Steps to reproduce the bug
1. Install the big query client and datasets `pip install google-cloud-bigquery datasets`
2. Run the following code:
```py
from datasets import Dataset
from google.cloud import bigquery
client = bigquery.Client()
# Perform a query.
QUERY = (
'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '
'WHERE state = "TX" '
'LIMIT 100')
query_job = client.query(QUERY) # API request
rows = query_job.result() # Waits for query to finish
ds = Dataset.from_generator(rows)
for r in ds:
print(r)
```
### Expected behavior
Two options:
1. Ignore the pickle errors when computing the hash
2. Provide a scape hutch so that we can avoid calculating the hash for the generator. For example, allowing to provide a hash from the user.
### Environment info
python 3.9
google-cloud-bigquery 3.9.0
datasets 2.11.0
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} | null | null | null | ### Describe the bug
When I run
from datasets import load_dataset
data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
AttributeError: 'Version' object has no attribute 'match'
### Steps to reproduce the bug
from datasets import load_dataset
data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
### Expected behavior
This is error trace:
Downloading and preparing dataset visual_genome/region_descriptions_v1.2.0 to C:/Users/Acer/.cache/huggingface/datasets/visual_genome/region_descriptions_v1.2.0/1.2.0/136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3...
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
File ~\.conda\envs\aai\Lib\site-packages\datasets\load.py:1791, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
1788 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1790 # Download and prepare data
-> 1791 builder_instance.download_and_prepare(
1792 download_config=download_config,
1793 download_mode=download_mode,
1794 verification_mode=verification_mode,
1795 try_from_hf_gcs=try_from_hf_gcs,
1796 num_proc=num_proc,
1797 storage_options=storage_options,
1798 )
1800 # Build dataset for splits
1801 keep_in_memory = (
1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1803 )
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:891, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
889 if num_proc is not None:
890 prepare_split_kwargs["num_proc"] = num_proc
--> 891 self._download_and_prepare(
892 dl_manager=dl_manager,
893 verification_mode=verification_mode,
894 **prepare_split_kwargs,
895 **download_and_prepare_kwargs,
896 )
897 # Sync info
898 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:1651, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs)
1650 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):
-> 1651 super()._download_and_prepare(
1652 dl_manager,
1653 verification_mode,
1654 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS
1655 or verification_mode == VerificationMode.ALL_CHECKS,
1656 **prepare_splits_kwargs,
1657 )
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:964, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
962 split_dict = SplitDict(dataset_name=self.name)
963 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 964 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
966 # Checksums verification
967 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:
File ~\.cache\huggingface\modules\datasets_modules\datasets\visual_genome\136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3\visual_genome.py:377, in VisualGenome._split_generators(self, dl_manager)
375 def _split_generators(self, dl_manager):
376 # Download image meta datas.
--> 377 image_metadatas_dir = dl_manager.download_and_extract(self.config.image_metadata_url)
378 image_metadatas_file = os.path.join(
379 image_metadatas_dir, _get_decompressed_filename_from_url(self.config.image_metadata_url)
380 )
382 # Download annotations
File ~\.cache\huggingface\modules\datasets_modules\datasets\visual_genome\136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3\visual_genome.py:328, in VisualGenomeConfig.image_metadata_url(self)
326 @property
327 def image_metadata_url(self):
--> 328 if not self.version.match(_LATEST_VERSIONS["image_metadata"]):
329 logger.warning(
330 f"Latest image metadata version is {_LATEST_VERSIONS['image_metadata']}. Trying to generate a dataset of version: {self.version}. Please double check that image data are unchanged between the two versions."
331 )
332 return f"{_BASE_ANNOTATION_URL}/image_data.json.zip"
### Environment info
datasets 2.11.0
python 3.11.3 | {
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} | null | null | null | The `load_dataset` function with Pandas `1.5.3` has no issue (just a FutureWarning) but crashes with Pandas `2.0.0`.
For your convenience, I opened a draft Pull Request to fix it quickly: https://github.com/huggingface/datasets/pull/5745
---
* The FutureWarning mentioned above:
```
FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
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} | null | null | null | ### Describe the bug
"e:\Krish_naik\FSDSRegression\venv\Lib\dataclasses.py" is overriding the stdlib module "dataclasses"
### Steps to reproduce the bug
module issue
### Expected behavior
overriding the stdlib module "dataclasses"
### Environment info
VS code | {
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} | This PR fixes the fixtures of our CI mock filesystems.
Before, we had to pass `clobber=True` to `fsspec.register_implementation` to overwrite the still present previously added "mock" filesystem. That meant that the mock filesystem fixture was not working properly, because the previously added "mock" filesystem, should have been deleted by the fixture.
This PR fixes the mock filesystem fixtures, so that the "mock" filesystem is properly deleted from the inner `fsspec` registry.
Tests were added to check the correct behavior of the mock filesystem fixtures.
Related to:
- #5733 | {
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} | null | null | null | ### Describe the bug
The regex defined here https://github.com/huggingface/datasets/blob/f2607935c4e45c70c44fcb698db0363ca7ba83d4/src/datasets/utils/py_utils.py#L158
will cause a weird result during dataset split when data path starts with `/data`
### Steps to reproduce the bug
1. clone dataset into local path
```
cd /data/train/raw/
git lfs clone https://huggingface.co/datasets/deepmind/code_contests.git
ls /data/train/raw/code_contests
# README.md data dataset_infos.json
ls /data/train/raw/code_contests/data
# test-00000-of-00001-9c49eeff30aacaa8.parquet
# train-[0-9]+-of-[0-9]+-xx.parquet
# valid-00000-of-00001-5e672c5751f060d3.parquet
```
2. loading data from local
```
from datasets import load_dataset
dataset = load_dataset('/data/train/raw/code_contests')
FileNotFoundError: Unable to resolve any data file that matches '['data/train/raw/code_contests/data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*']' at /data/train/raw/code_contests with any supported extension
```
weird path `data/train/raw/code_contests/data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*`
While dive deep into `LocalDatasetModuleFactoryWithoutScript` defined in [load.py](https://github.com/huggingface/datasets/blob/f2607935c4e45c70c44fcb698db0363ca7ba83d4/src/datasets/load.py#L627) and _get_data_files_patterns https://github.com/huggingface/datasets/blob/f2607935c4e45c70c44fcb698db0363ca7ba83d4/src/datasets/data_files.py#L228. I found the weird behavior caused by `string_to_dict`
3. check `string_to_dict`
```
p = '/data/train/raw/code_contests/data/test-00000-of-00001-9c49eeff30aacaa8.parquet'
split_pattern = 'data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*'
string_to_dict(p, split_pattern)
# {'split': 'train/raw/code_contests/data/test'}
p = '/data2/train/raw/code_contests/data/test-00000-of-00001-9c49eeff30aacaa8.parquet'
string_to_dict(p, split_pattern)
{'split': 'test'}
```
go deep into string_to_dict https://github.com/huggingface/datasets/blob/f2607935c4e45c70c44fcb698db0363ca7ba83d4/src/datasets/utils/py_utils.py#L158.
4. test the regex:
<img width="680" alt="image" src="https://user-images.githubusercontent.com/1772912/231351129-75179f01-fb9f-4f12-8fa9-0dfcc3d5f3bd.png">
<img width="679" alt="image" src="https://user-images.githubusercontent.com/1772912/231351025-009f3d83-2cf3-4e15-9ed4-6b9663dcb2ee.png">
### Expected behavior
statement in `steps to reproduce the bug`
3. check `string_to_dict`
```
p = '/data/train/raw/code_contests/data/test-00000-of-00001-9c49eeff30aacaa8.parquet'
split_pattern = 'data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*'
string_to_dict(p, split_pattern)
# {'split': 'train/raw/code_contests/data/test'}
p = '/data2/train/raw/code_contests/data/test-00000-of-00001-9c49eeff30aacaa8.parquet'
string_to_dict(p, split_pattern)
{'split': 'test'}
```
### Environment info
- linux(debian)
- python 3.7
- datasets 2.8.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5738 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5738/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5738/comments | https://api.github.com/repos/huggingface/datasets/issues/5738/events | https://github.com/huggingface/datasets/issues/5738 | 1,663,477,690 | I_kwDODunzps5jJqe6 | 5,738 | load_dataset("text","dataset.txt") loads the wrong dataset! | {
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} | [] | closed | false | null | [] | null | 1 | 2023-04-12T01:07:46 | 2023-04-19T12:08:27 | 2023-04-19T12:08:27 | NONE | null | {
"total": 0,
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} | null | null | null | ### Describe the bug
I am trying to load my own custom text dataset using the load_dataset function. My dataset is a bunch of ordered text, think along the lines of shakespeare plays. However, after I load the dataset and I inspect it, the dataset is a table with a bunch of latitude and longitude values! What in the world??
### Steps to reproduce the bug
my_dataset = load_dataset("text","TextFile.txt")
my_dataset
### Expected behavior
I expected the dataset to contain the actual data from the text document that I used.
### Environment info
Google Colab | {
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https://api.github.com/repos/huggingface/datasets/issues/5737 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5737/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5737/comments | https://api.github.com/repos/huggingface/datasets/issues/5737/events | https://github.com/huggingface/datasets/issues/5737 | 1,662,919,811 | I_kwDODunzps5jHiSD | 5,737 | ClassLabel Error | {
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} | [] | closed | false | null | [] | null | 2 | 2023-04-11T17:14:13 | 2023-04-13T16:49:57 | 2023-04-13T16:49:57 | NONE | null | {
"total": 0,
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"percent_completed": 0
} | null | null | null | ### Describe the bug
I still getting the error "call() takes 1 positional argument but 2 were given" even after ensuring that the value being passed to the label object is a single value and that the ClassLabel object has been created with the correct number of label classes
### Steps to reproduce the bug
from datasets import ClassLabel, Dataset
1. Create the ClassLabel object with 3 label values and their corresponding names
label_test = ClassLabel(num_classes=3, names=["label_1", "label_2", "label_3"])
2. Define a dictionary with text and label fields
data = {
'text': ['text_1', 'text_2', 'text_3'],
'label': [1, 2, 3],
}
3. Create a Hugging Face dataset from the dictionary
dataset = Dataset.from_dict(data)
print(dataset.features)
4. Map the label values to their corresponding label names using the label object
dataset = dataset.map(lambda example: {'text': example['text'], 'label': label_test(example['label'])})
5. Print the resulting dataset
print(dataset)
### Expected behavior
I hope my label type is class label instead int.
### Environment info
python 3.9
google colab | {
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https://api.github.com/repos/huggingface/datasets/issues/5736 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5736/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5736/comments | https://api.github.com/repos/huggingface/datasets/issues/5736/events | https://github.com/huggingface/datasets/issues/5736 | 1,662,286,061 | I_kwDODunzps5jFHjt | 5,736 | FORCE_REDOWNLOAD raises "Directory not empty" exception on second run | {
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} | [] | open | false | null | [] | null | 3 | 2023-04-11T11:29:15 | 2023-11-30T07:16:58 | null | NONE | null | {
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} | null | null | null | ### Describe the bug
Running `load_dataset(..., download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD)` twice raises a `Directory not empty` exception on the second run.
### Steps to reproduce the bug
I cannot test this on datasets v2.11.0 due to #5711, but this happens in v2.10.1.
1. Set up a script `my_dataset.py` to generate and load an offline dataset.
2. Load it with
```python
ds = datasets.load_dataset(path=/path/to/my_dataset.py,
name='toy',
data_dir=/path/to/my_dataset.py,
cache_dir=cache_dir,
download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD,
)
```
It loads fine
```
Dataset my_dataset downloaded and prepared to /path/to/cache/toy-..e05e/1.0.0/...5b4c. Subsequent calls will reuse this data.
```
3. Try to load it again with the same snippet and the splits are generated, but at the end of the loading process it raises the error
```
2023-04-11 12:10:19,965: DEBUG: open file: /path/to/cache/toy-..e05e/1.0.0/...5b4c.incomplete/dataset_info.json
Traceback (most recent call last):
File "<string>", line 2, in <module>
File "/path/to/conda/environment/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset
builder_instance.download_and_prepare(
File "/path/to/conda/environment/lib/python3.10/site-packages/datasets/builder.py", line 852, in download_and_prepare
with incomplete_dir(self._output_dir) as tmp_output_dir:
File "/path/to/conda/environment/lib/python3.10/contextlib.py", line 142, in __exit__
next(self.gen)
File "/path/to/conda/environment/lib/python3.10/site-packages/datasets/builder.py", line 826, in incomplete_dir
shutil.rmtree(dirname)
File "/path/to/conda/environment/lib/python3.10/shutil.py", line 730, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/path/to/conda/environment/lib/python3.10/shutil.py", line 728, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/path/to/cache/toy-..e05e/1.0.0/...5b4c'
```
### Expected behavior
Regenerate the dataset from scratch and reload it.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-4.18.0-483.el8.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.2
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/5735 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5735/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5735/comments | https://api.github.com/repos/huggingface/datasets/issues/5735/events | https://github.com/huggingface/datasets/pull/5735 | 1,662,150,903 | PR_kwDODunzps5OAY3A | 5,735 | Implement sharding on merged iterable datasets | {
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} | [] | closed | false | null | [] | null | 11 | 2023-04-11T10:02:25 | 2023-04-27T16:39:04 | 2023-04-27T16:32:09 | CONTRIBUTOR | null | {
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"merged_at": "2023-04-27T16:32:09"
} | This PR allows sharding of merged iterable datasets.
Merged iterable datasets with for instance the `interleave_datasets` command are comprised of multiple sub-iterable, one for each dataset that has been merged.
With this PR, sharding a merged iterable will result in multiple merged datasets each comprised of sharded sub-iterable, ensuring that there is no duplication of data.
As a result it is now possible to set any amount of workers in the dataloader as long as it is lower or equal to the lowest amount of shards amongst the datasets. Before it had to be set to 0.
I previously talked about this issue on the forum [here](https://discuss.huggingface.co/t/interleaving-iterable-dataset-with-num-workers-0/35801) | {
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https://api.github.com/repos/huggingface/datasets/issues/5734 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5734/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5734/comments | https://api.github.com/repos/huggingface/datasets/issues/5734/events | https://github.com/huggingface/datasets/issues/5734 | 1,662,058,028 | I_kwDODunzps5jEP4s | 5,734 | Remove temporary pin of fsspec | {
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- #5731 | {
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} | In `fsspec--2023.4.0` default value for clobber when registering an implementation was changed from True to False. See:
- https://github.com/fsspec/filesystem_spec/pull/1237
This PR recovers previous behavior by passing clobber True when registering mock implementations.
This PR also removes the temporary pin introduced by:
- #5731
Fix #5734. | {
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} | null | null | null | ### Feature request
The HuggingFace Datasets library currently supports two BuilderConfigs for Enwik8. One config yields individual lines as examples, while the other config yields the entire dataset as a single example. Both support only a monolithic split: it is all grouped as "train".
The HuggingFace Datasets library should include a BuilderConfig for Enwik8 with train, validation, and test sets derived from the first 90 million bytes, next 5 million bytes, and last 5 million bytes, respectively. This Enwik8 split is standard practice in LM papers, as elaborated and motivated below.
### Motivation
Enwik8 is commonly split into 90M, 5M, 5M consecutive bytes. This is done in the Transformer-XL [codebase](https://github.com/kimiyoung/transformer-xl/blob/44781ed21dbaec88b280f74d9ae2877f52b492a5/getdata.sh#L34), and is additionally mentioned in the Sparse Transformers [paper](https://arxiv.org/abs/1904.10509) and the Compressive Transformers [paper](https://arxiv.org/abs/1911.05507). This split is pretty much universal among language modeling papers.
One may obtain the splits by manual wrangling, using the data yielded by the ```enwik8-raw``` BuilderConfig. However, this undermines the seamless functionality of the library: one must slice the single raw example, extract it into three tensors, and wrap each in a separate dataset.
This becomes even more of a nuisance if using the current Enwik8 HuggingFace dataset as a TfdsDataSource with [SeqIO](https://github.com/google/seqio), where a pipeline of preprocessors is typically included in a SeqIO Task definition, to be applied immediately after loading the data with TFDS.
### Your contribution
Supporting this functionality in HuggingFace Datasets will only require an additional BuilderConfig for Enwik8 and a few additional lines of code. I will submit a PR. | {
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See: https://github.com/huggingface/datasets/actions/runs/4665326892/jobs/8258580948
```
=========================== short test summary info ============================
ERROR tests/test_builder.py::test_builder_with_filesystem_download_and_prepare - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_builder.py::test_builder_with_filesystem_download_and_prepare_reload - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_dataset_dict.py::test_dummy_datasetdict_serialize_fs - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_file_utils.py::test_get_from_cache_fsspec - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_filesystem.py::test_is_remote_filesystem - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xexists[tmp_path/file.txt-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xexists[tmp_path/file_that_doesnt_exist.txt-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xexists[mock://top_level/second_level/date=2019-10-01/a.parquet-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xexists[mock://top_level/second_level/date=2019-10-01/file_that_doesnt_exist.parquet-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xlistdir[tmp_path-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xlistdir[mock://-expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xlistdir[mock://top_level-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xlistdir[mock://top_level/second_level/date=2019-10-01-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisdir[tmp_path-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisdir[tmp_path/file.txt-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisdir[mock://-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisdir[mock://top_level-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisdir[mock://dir_that_doesnt_exist-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisfile[tmp_path/file.txt-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisfile[tmp_path/file_that_doesnt_exist.txt-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisfile[mock://-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisfile[mock://top_level/second_level/date=2019-10-01/a.parquet-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xgetsize[tmp_path/file.txt-100] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xgetsize[mock://-0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xgetsize[mock://top_level/second_level/date=2019-10-01/a.parquet-100] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xglob[tmp_path/*.txt-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xglob[mock://*-expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xglob[mock://top_*-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xglob[mock://top_level/second_level/date=2019-10-0[1-4]-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xglob[mock://top_level/second_level/date=2019-10-0[1-4]/*-expected_paths4] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xwalk[tmp_path-expected_outputs0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xwalk[mock://top_level/second_level-expected_outputs1] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[tmp_path/file.txt-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[tmp_path/file_that_doesnt_exist.txt-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[mock://top_level/second_level/date=2019-10-01/a.parquet-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[mock://top_level/second_level/date=2019-10-01/file_that_doesnt_exist.parquet-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[tmp_path-*.txt-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://-*-expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://-top_*-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://top_level/second_level-date=2019-10-0[1-4]-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://top_level/second_level-date=2019-10-0[1-4]/*-expected_paths4] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[tmp_path-*.txt-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://-date=2019-10-0[1-4]-expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://-date=2019-10-0[1-4]/*-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]/*-expected_paths4] - ValueError: Name (mock) already in the registry and clobber is False
===== 2105 passed, 18 skipped, 38 warnings, 46 errors in 236.22s (0:03:56) =====
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Fix #5728. | {
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https://api.github.com/repos/huggingface/datasets/issues/5728 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5728/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5728/comments | https://api.github.com/repos/huggingface/datasets/issues/5728/events | https://github.com/huggingface/datasets/issues/5728 | 1,661,925,932 | I_kwDODunzps5jDvos | 5,728 | The order of data split names is nondeterministic | {
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```
FAILED tests/test_data_files.py::test_get_data_files_patterns[data_file_per_split4] - AssertionError: assert ['random', 'train'] == ['train', 'random']
At index 0 diff: 'random' != 'train'
Full diff:
- ['train', 'random']
+ ['random', 'train']
```
I have checked locally and found out that the data split order is nondeterministic.
This is caused by the use of `set` for sharded splits. | {
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https://api.github.com/repos/huggingface/datasets/issues/5727 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5727/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5727/comments | https://api.github.com/repos/huggingface/datasets/issues/5727/events | https://github.com/huggingface/datasets/issues/5727 | 1,661,536,363 | I_kwDODunzps5jCQhr | 5,727 | load_dataset fails with FileNotFound error on Windows | {
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} | null | null | null | ### Describe the bug
Although I can import and run the datasets library in a Colab environment, I cannot successfully load any data on my own machine (Windows 10) despite following the install steps:
(1) create conda environment
(2) activate environment
(3) install with: ``conda` install -c huggingface -c conda-forge datasets`
Then
```
from datasets import load_dataset
# this or any other example from the website fails with the FileNotFoundError
glue = load_dataset("glue", "ax")
```
**Below I have pasted the error omitting the full path**:
```
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at C:\Users\...\glue\glue.py or any data file in the same directory. Couldn't find 'glue' on the Hugging Face Hub either: FileNotFoundError: [WinError 3] The system cannot find the path specified:
'C:\\Users\\...\\.cache\\huggingface'
```
### Steps to reproduce the bug
On Windows 10
1) create a minimal conda environment (with just Python)
(2) activate environment
(3) install datasets with: ``conda` install -c huggingface -c conda-forge datasets`
(4) import load_dataset and follow example usage from any dataset card.
### Expected behavior
The expected behavior is to load the file into the Python session running on my machine without error.
### Environment info
```
# Name Version Build Channel
aiohttp 3.8.4 py311ha68e1ae_0 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
arrow-cpp 11.0.0 h57928b3_13_cpu conda-forge
async-timeout 4.0.2 pyhd8ed1ab_0 conda-forge
attrs 22.2.0 pyh71513ae_0 conda-forge
aws-c-auth 0.6.26 h1262f0c_1 conda-forge
aws-c-cal 0.5.21 h7cda486_2 conda-forge
aws-c-common 0.8.14 hcfcfb64_0 conda-forge
aws-c-compression 0.2.16 h8a79959_5 conda-forge
aws-c-event-stream 0.2.20 h5f78564_4 conda-forge
aws-c-http 0.7.6 h2545be9_0 conda-forge
aws-c-io 0.13.19 h0d2781e_3 conda-forge
aws-c-mqtt 0.8.6 hd211e0c_12 conda-forge
aws-c-s3 0.2.7 h8113e7b_1 conda-forge
aws-c-sdkutils 0.1.8 h8a79959_0 conda-forge
aws-checksums 0.1.14 h8a79959_5 conda-forge
aws-crt-cpp 0.19.8 he6d3b81_12 conda-forge
aws-sdk-cpp 1.10.57 h64004b3_8 conda-forge
brotlipy 0.7.0 py311ha68e1ae_1005 conda-forge
bzip2 1.0.8 h8ffe710_4 conda-forge
c-ares 1.19.0 h2bbff1b_0
ca-certificates 2023.01.10 haa95532_0
certifi 2022.12.7 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 py311h7d9ee11_3 conda-forge
charset-normalizer 2.1.1 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
cryptography 40.0.1 py311h28e9c30_0 conda-forge
dataclasses 0.8 pyhc8e2a94_3 conda-forge
datasets 2.11.0 py_0 huggingface
dill 0.3.6 pyhd8ed1ab_1 conda-forge
filelock 3.11.0 pyhd8ed1ab_0 conda-forge
frozenlist 1.3.3 py311ha68e1ae_0 conda-forge
fsspec 2023.4.0 pyh1a96a4e_0 conda-forge
gflags 2.2.2 ha925a31_1004 conda-forge
glog 0.6.0 h4797de2_0 conda-forge
huggingface_hub 0.13.4 py_0 huggingface
idna 3.4 pyhd8ed1ab_0 conda-forge
importlib-metadata 6.3.0 pyha770c72_0 conda-forge
importlib_metadata 6.3.0 hd8ed1ab_0 conda-forge
intel-openmp 2023.0.0 h57928b3_25922 conda-forge
krb5 1.20.1 heb0366b_0 conda-forge
libabseil 20230125.0 cxx17_h63175ca_1 conda-forge
libarrow 11.0.0 h04c43f8_13_cpu conda-forge
libblas 3.9.0 16_win64_mkl conda-forge
libbrotlicommon 1.0.9 hcfcfb64_8 conda-forge
libbrotlidec 1.0.9 hcfcfb64_8 conda-forge
libbrotlienc 1.0.9 hcfcfb64_8 conda-forge
libcblas 3.9.0 16_win64_mkl conda-forge
libcrc32c 1.1.2 h0e60522_0 conda-forge
libcurl 7.88.1 h68f0423_1 conda-forge
libexpat 2.5.0 h63175ca_1 conda-forge
libffi 3.4.2 h8ffe710_5 conda-forge
libgoogle-cloud 2.8.0 hf2ff781_1 conda-forge
libgrpc 1.52.1 h32da247_1 conda-forge
libhwloc 2.9.0 h51c2c0f_0 conda-forge
libiconv 1.17 h8ffe710_0 conda-forge
liblapack 3.9.0 16_win64_mkl conda-forge
libprotobuf 3.21.12 h12be248_0 conda-forge
libsqlite 3.40.0 hcfcfb64_0 conda-forge
libssh2 1.10.0 h9a1e1f7_3 conda-forge
libthrift 0.18.1 h9ce19ad_0 conda-forge
libutf8proc 2.8.0 h82a8f57_0 conda-forge
libxml2 2.10.3 hc3477c8_6 conda-forge
libzlib 1.2.13 hcfcfb64_4 conda-forge
lz4-c 1.9.4 hcfcfb64_0 conda-forge
mkl 2022.1.0 h6a75c08_874 conda-forge
multidict 6.0.4 py311ha68e1ae_0 conda-forge
multiprocess 0.70.14 py311ha68e1ae_3 conda-forge
numpy 1.24.2 py311h0b4df5a_0 conda-forge
openssl 3.1.0 hcfcfb64_0 conda-forge
orc 1.8.3 hada7b9e_0 conda-forge
packaging 23.0 pyhd8ed1ab_0 conda-forge
pandas 2.0.0 py311hf63dbb6_0 conda-forge
parquet-cpp 1.5.1 2 conda-forge
pip 23.0.1 pyhd8ed1ab_0 conda-forge
pthreads-win32 2.9.1 hfa6e2cd_3 conda-forge
pyarrow 11.0.0 py311h6a6099b_13_cpu conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pyopenssl 23.1.1 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 pyh0701188_6 conda-forge
python 3.11.3 h2628c8c_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-tzdata 2023.3 pyhd8ed1ab_0 conda-forge
python-xxhash 3.2.0 py311ha68e1ae_0 conda-forge
python_abi 3.11 3_cp311 conda-forge
pytz 2023.3 pyhd8ed1ab_0 conda-forge
pyyaml 6.0 py311ha68e1ae_5 conda-forge
re2 2023.02.02 h63175ca_0 conda-forge
requests 2.28.2 pyhd8ed1ab_1 conda-forge
setuptools 67.6.1 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
snappy 1.1.10 hfb803bf_0 conda-forge
tbb 2021.8.0 h91493d7_0 conda-forge
tk 8.6.12 h8ffe710_0 conda-forge
tqdm 4.65.0 pyhd8ed1ab_1 conda-forge
typing-extensions 4.5.0 hd8ed1ab_0 conda-forge
typing_extensions 4.5.0 pyha770c72_0 conda-forge
tzdata 2023c h71feb2d_0 conda-forge
ucrt 10.0.22621.0 h57928b3_0 conda-forge
urllib3 1.26.15 pyhd8ed1ab_0 conda-forge
vc 14.3 hb6edc58_10 conda-forge
vs2015_runtime 14.34.31931 h4c5c07a_10 conda-forge
wheel 0.40.0 pyhd8ed1ab_0 conda-forge
win_inet_pton 1.1.0 pyhd8ed1ab_6 conda-forge
xxhash 0.8.1 hcfcfb64_0 conda-forge
xz 5.2.10 h8cc25b3_1
yaml 0.2.5 h8ffe710_2 conda-forge
yarl 1.8.2 py311ha68e1ae_0 conda-forge
zipp 3.15.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 hcfcfb64_4 conda-forge
zstd 1.5.4 hd43e919_0
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5726 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5726/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5726/comments | https://api.github.com/repos/huggingface/datasets/issues/5726/events | https://github.com/huggingface/datasets/issues/5726 | 1,660,944,807 | I_kwDODunzps5jAAGn | 5,726 | Fallback JSON Dataset loading does not load all values when features specified manually | {
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] | null | 1 | 2023-04-10T15:22:14 | 2023-04-21T06:35:28 | 2023-04-21T06:35:28 | NONE | null | {
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} | null | null | null | ### Describe the bug
The fallback JSON dataset loader located here:
https://github.com/huggingface/datasets/blob/1c4ec00511868bd881e84a6f7e0333648d833b8e/src/datasets/packaged_modules/json/json.py#L130-L153
does not load the values of features correctly when features are specified manually and not all features have a value in the first entry of the dataset. I'm pretty sure this is not supposed to be expected bahavior?
To fix this you'd have to change this line:
https://github.com/huggingface/datasets/blob/1c4ec00511868bd881e84a6f7e0333648d833b8e/src/datasets/packaged_modules/json/json.py#L140
To pass a schema to pyarrow which has the same structure as the features argument passed to the load_dataset() method.
### Steps to reproduce the bug
Consider a dataset JSON like this:
```
[
{
"instruction": "Do stuff",
"output": "Answer stuff"
},
{
"instruction": "Do stuff2",
"input": "Additional Input2",
"output": "Answer stuff2"
}
]
```
Using this code to load the dataset:
```
from datasets import load_dataset, Features, Value
features = {
"instruction": Value("string"),
"input": Value("string"),
"output": Value("string")
}
features = Features(features)
ds = load_dataset("json", data_files="./ds.json", features=features)
for row in ds["train"]:
print(row)
```
we get a dataset that looks like this:
| **Instruction** | **Input** | **Output** |
|-----------------|--------------------|-----------------|
| "Do stuff" | None | "Answer Stuff" |
| "Do stuff2" | None | "Answer Stuff2" |
### Expected behavior
The input column should contain values other than None for dataset entries that have the "input" attribute set:
| **Instruction** | **Input** | **Output** |
|-----------------|--------------------|-----------------|
| "Do stuff" | None | "Answer Stuff" |
| "Do stuff2" | "Additional Input2" | "Answer Stuff2" |
### Environment info
Python 3.10.10
Datasets 2.11.0
Windows 10 | {
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https://api.github.com/repos/huggingface/datasets/issues/5725 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5725/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5725/comments | https://api.github.com/repos/huggingface/datasets/issues/5725/events | https://github.com/huggingface/datasets/issues/5725 | 1,660,455,202 | I_kwDODunzps5i-Iki | 5,725 | How to limit the number of examples in dataset, for testing? | {
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} | [] | closed | false | null | [] | null | 3 | 2023-04-10T08:41:43 | 2023-04-21T06:16:24 | 2023-04-21T06:16:24 | NONE | null | {
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} | null | null | null | ### Describe the bug
I am using this command:
`data = load_dataset("json", data_files=data_path)`
However, I want to add a parameter, to limit the number of loaded examples to be 10, for development purposes, but can't find this simple parameter.
### Steps to reproduce the bug
In the description.
### Expected behavior
To be able to limit the number of examples
### Environment info
Nothing special | {
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https://api.github.com/repos/huggingface/datasets/issues/5724 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5724/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5724/comments | https://api.github.com/repos/huggingface/datasets/issues/5724/events | https://github.com/huggingface/datasets/issues/5724 | 1,659,938,135 | I_kwDODunzps5i8KVX | 5,724 | Error after shuffling streaming IterableDatasets with downloaded dataset | {
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} | [] | closed | false | null | [] | null | 1 | 2023-04-09T16:58:44 | 2023-04-20T20:37:30 | 2023-04-20T20:37:30 | NONE | null | {
"total": 0,
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} | null | null | null | ### Describe the bug
I downloaded the C4 dataset, and used streaming IterableDatasets to read it. Everything went normal until I used `dataset = dataset.shuffle(seed=42, buffer_size=10_000)` to shuffle the dataset. Shuffled dataset will throw the following error when it is used by `next(iter(dataset))`:
```
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 937, in __iter__
for key, example in ex_iterable:
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 627, in __iter__
for x in self.ex_iterable:
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 138, in __iter__
yield from self.generate_examples_fn(**kwargs_with_shuffled_shards)
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 763, in wrapper
for key, table in generate_tables_fn(**kwargs):
File "/data/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 101, in _generate_tables
batch = f.read(self.config.chunksize)
File "/data/miniconda3/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 372, in read_with_retries
out = read(*args, **kwargs)
File "/data/miniconda3/lib/python3.9/gzip.py", line 300, in read
return self._buffer.read(size)
File "/data/miniconda3/lib/python3.9/_compression.py", line 68, in readinto
data = self.read(len(byte_view))
File "/data/miniconda3/lib/python3.9/gzip.py", line 487, in read
if not self._read_gzip_header():
File "/data/miniconda3/lib/python3.9/gzip.py", line 435, in _read_gzip_header
raise BadGzipFile('Not a gzipped file (%r)' % magic)
gzip.BadGzipFile: Not a gzipped file (b've')
```
I found that there is no problem to use the dataset in this way without shuffling. Also, use `dataset = datasets.load_dataset('c4', 'en', split='train', streaming=True)`, which will download the dataset on-the-fly instead of loading from the local file, will also not have problems even after shuffle.
### Steps to reproduce the bug
1. Download C4 dataset from https://huggingface.co/datasets/allenai/c4
2.
```
import datasets
dataset = datasets.load_dataset('/path/to/your/data/dir', 'en', streaming=True, split='train')
dataset = dataset.shuffle(buffer_size=10_000, seed=42)
next(iter(dataset))
```
### Expected behavior
`next(iter(dataset))` should give me a sample from the dataset
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.4.32-1-tlinux4-0001-x86_64-with-glibc2.28
- Python version: 3.9.16
- Huggingface_hub version: 0.13.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5722 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5722/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5722/comments | https://api.github.com/repos/huggingface/datasets/issues/5722/events | https://github.com/huggingface/datasets/issues/5722 | 1,659,837,510 | I_kwDODunzps5i7xxG | 5,722 | Distributed Training Error on Customized Dataset | {
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} | [] | closed | false | null | [] | null | 1 | 2023-04-09T11:04:59 | 2023-07-24T14:50:46 | 2023-07-24T14:50:46 | NONE | null | {
"total": 0,
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} | null | null | null | Hi guys, recently I tried to use `datasets` to train a dual encoder.
I finish my own datasets according to the nice [tutorial](https://huggingface.co/docs/datasets/v2.11.0/en/dataset_script)
Here are my code:
```python
class RetrivalDataset(datasets.GeneratorBasedBuilder):
"""CrossEncoder dataset."""
BUILDER_CONFIGS = [RetrivalConfig(name="DuReader")]
# DEFAULT_CONFIG_NAME = "DuReader"
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"documents": Sequence(datasets.Value("string")),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
train_file = self.config.data_dir + self.config.train_file
valid_file = self.config.data_dir + self.config.valid_file
logger.info(f"Training on {self.config.train_file}")
logger.info(f"Evaluating on {self.config.valid_file}")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"file_path": train_file}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"file_path": valid_file}
),
]
def _generate_examples(self, file_path):
with jsonlines.open(file_path, "r") as f:
for record in f:
label = record["label"]
question = record["question"]
# dual encoder
all_documents = record["all_documents"]
positive_paragraph = all_documents.pop(label)
all_documents = [positive_paragraph] + all_documents
u_id = "{}_#_{}".format(
md5_hash(question + "".join(all_documents)),
"".join(random.sample(string.ascii_letters + string.digits, 7)),
)
item = {
"question": question,
"documents": all_documents,
"id": u_id,
}
yield u_id, item
```
It works well on single GPU, but got errors as follows when used DDP:
```python
Detected mismatch between collectives on ranks. Rank 1 is running collective: CollectiveFingerPrint(OpType=BARRIER), but Rank 0 is running collective: CollectiveFingerPrint(OpType=ALLGATHER_COALESCED)
```
Here are my train script on a two A100 mechine:
```bash
export TORCH_DISTRIBUTED_DEBUG=DETAIL
export TORCH_SHOW_CPP_STACKTRACES=1
export NCCL_DEBUG=INFO
export NCCL_DEBUG_SUBSYS=INIT,COLL,ENV
nohup torchrun --nproc_per_node 2 train.py experiments/de-big.json >logs/de-big.log 2>&1&
```
I am not sure if this error below related to my dataset code when use DDP. And I notice the PR(#5369 ), but I don't know when and where should I used the function(`split_dataset_by_node`) .
@lhoestq hope you could help me?
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https://api.github.com/repos/huggingface/datasets/issues/5721 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5721/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5721/comments | https://api.github.com/repos/huggingface/datasets/issues/5721/events | https://github.com/huggingface/datasets/issues/5721 | 1,659,680,682 | I_kwDODunzps5i7Leq | 5,721 | Calling datasets.load_dataset("text" ...) results in a wrong split. | {
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} | [] | open | false | null | [] | null | 0 | 2023-04-08T23:55:12 | 2023-04-08T23:55:12 | null | NONE | null | {
"total": 0,
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} | null | null | null | ### Describe the bug
When creating a text dataset, the training split should have the bulk of the examples by default. Currently, testing does.
### Steps to reproduce the bug
I have a folder with 18K text files in it. Each text file essentially consists in a document or article scraped from online. Calling the following codeL
```
folder_path = "/home/cyril/Downloads/llama_dataset"
data = datasets.load_dataset("text", data_dir=folder_path)
data.save_to_disk("/home/cyril/Downloads/data.hf")
data = datasets.load_from_disk("/home/cyril/Downloads/data.hf")
print(data)
```
Results in the following split:
```
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 2114
})
test: Dataset({
features: ['text'],
num_rows: 200882
})
validation: Dataset({
features: ['text'],
num_rows: 152
})
})
```
It seems to me like the train/test/validation splits are in the wrong order since test split >>>> train_split
### Expected behavior
Train split should have the bulk of the training examples.
### Environment info
datasets 2.11.0, python 3.10.6 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5720 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5720/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5720/comments | https://api.github.com/repos/huggingface/datasets/issues/5720/events | https://github.com/huggingface/datasets/issues/5720 | 1,659,610,705 | I_kwDODunzps5i66ZR | 5,720 | Streaming IterableDatasets do not work with torch DataLoaders | {
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} | [] | open | false | null | [] | null | 10 | 2023-04-08T18:45:48 | 2025-03-19T14:06:47 | null | NONE | null | {
"total": 0,
"completed": 0,
"percent_completed": 0
} | null | null | null | ### Describe the bug
When using streaming datasets set up with train/val split using `.skip()` and `.take()`, the following error occurs when iterating over a torch dataloader:
```
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 363, in __iter__
self._iterator = self._get_iterator()
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 314, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 927, in __init__
w.start()
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 47, in _launch
reduction.dump(process_obj, fp)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object '_generate_examples_from_tables_wrapper.<locals>.wrapper'
```
To reproduce, run the code
```
from datasets import load_dataset
data = load_dataset(args.dataset_name, split="train", streaming=True)
train_len = 5000
val_len = 100
train, val = data.take(train_len), data.skip(train_len).take(val_len)
traindata = IterableClipDataset(data, context_length=args.max_len, tokenizer=tokenizer, image_key="url", text_key="text")
traindata = DataLoader(traindata, batch_size=args.batch_size, num_workers=args.num_workers, persistent_workers=True)
```
Where the class IterableClipDataset is a simple wrapper to cast the dataset to a torch iterabledataset, defined via
```
from torch.utils.data import Dataset, IterableDataset
from torchvision.transforms import Compose, Resize, ToTensor
from transformers import AutoTokenizer
import requests
from PIL import Image
class IterableClipDataset(IterableDataset):
def __init__(self, dataset, context_length: int, image_transform=None, tokenizer=None, image_key="image", text_key="text"):
self.dataset = dataset
self.context_length = context_length
self.image_transform = Compose([Resize((224, 224)), ToTensor()]) if image_transform is None else image_transform
self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") if tokenizer is None else tokenizer
self.image_key = image_key
self.text_key = text_key
def read_image(self, url: str):
try: # Try to read the image
image = Image.open(requests.get(url, stream=True).raw)
except:
image = Image.new("RGB", (224, 224), (0, 0, 0))
return image
def process_sample(self, image, text):
if isinstance(image, str):
image = self.read_image(image)
if self.image_transform is not None:
image = self.image_transform(image)
text = self.tokenizer.encode(
text, add_special_tokens=True, max_length=self.context_length, truncation=True, padding="max_length"
)
text = torch.tensor(text, dtype=torch.long)
return image, text
def __iter__(self):
for sample in self.dataset:
image, text = sample[self.image_key], sample[self.text_key]
yield self.process_sample(image, text)
```
### Steps to reproduce the bug
Steps to reproduce
1. Install `datasets`, `torch`, and `PIL` (if you want to reproduce exactly)
2. Run the code above
### Expected behavior
Batched data is produced from the dataloader
### Environment info
```
datasets == 2.9.0
python == 3.9.12
torch == 1.11.0
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5719 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5719/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5719/comments | https://api.github.com/repos/huggingface/datasets/issues/5719/events | https://github.com/huggingface/datasets/issues/5719 | 1,659,203,222 | I_kwDODunzps5i5W6W | 5,719 | Array2D feature creates a list of list instead of a numpy array | {
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"type": "User",
"user_view_type": "public",
"site_admin": false
} | [] | closed | false | null | [] | null | 4 | 2023-04-07T21:04:08 | 2023-04-20T15:34:41 | 2023-04-20T15:34:41 | NONE | null | {
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} | null | null | null | ### Describe the bug
I'm not sure if this is expected behavior or not. When I create a 2D array using `Array2D`, the data has list type instead of numpy array. I think it should not be the expected behavior especially when I feed a numpy array as input to the data creation function. Why is it converting my array into a list?
Also if I change the first dimension of the `Array2D` shape to None, it's returning array correctly.
### Steps to reproduce the bug
Run this code:
```py
from datasets import Dataset, Features, Array2D
import numpy as np
# you have to change the first dimension of the shape to None to make it return an array
features = Features(dict(seq=Array2D((2,2), 'float32')))
ds = Dataset.from_dict(dict(seq=[np.random.rand(2,2)]), features=features)
a = ds[0]['seq']
print(a)
print(type(a))
```
The following will be printed in stdout:
```
[[0.8127174377441406, 0.3760348856449127], [0.7510159611701965, 0.4322739541530609]]
<class 'list'>
```
### Expected behavior
Each indexed item should be a list or numpy array. Currently, `Array((2,2))` yields a list but `Array((None,2))` yields an array.
### Environment info
- `datasets` version: 2.11.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.9.13
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 1.4.4
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The default order becomes: [train, validation, test] instead of [train, test, validation]. | {
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https://api.github.com/repos/huggingface/datasets/issues/5717 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5717/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5717/comments | https://api.github.com/repos/huggingface/datasets/issues/5717/events | https://github.com/huggingface/datasets/issues/5717 | 1,658,729,866 | I_kwDODunzps5i3jWK | 5,717 | Errror when saving to disk a dataset of images | {
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} | null | null | null | ### Describe the bug
Hello!
I have an issue when I try to save on disk my dataset of images. The error I get is:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1442, in save_to_disk
for job_id, done, content in Dataset._save_to_disk_single(**kwargs):
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1473, in _save_to_disk_single
writer.write_table(pa_table)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_writer.py", line 570, in write_table
pa_table = embed_table_storage(pa_table)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2268, in embed_table_storage
arrays = [
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2269, in <listcomp>
embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 1817, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 1817, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2142, in embed_array_storage
return feature.embed_storage(array)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/features/image.py", line 269, in embed_storage
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
File "pyarrow/array.pxi", line 2766, in pyarrow.lib.StructArray.from_arrays
File "pyarrow/array.pxi", line 2961, in pyarrow.lib.c_mask_inverted_from_obj
TypeError: Mask must be a pyarrow.Array of type boolean
```
My dataset is around 50K images, is this error might be due to a bad image?
Thanks for the help.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="/path/to/dataset")
dataset["train"].save_to_disk("./myds", num_shards=40)
```
### Expected behavior
Having my dataset properly saved to disk.
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.10
- Huggingface_hub version: 0.13.3
- PyArrow version: 11.0.0
- Pandas version: 2.0.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5716 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5716/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5716/comments | https://api.github.com/repos/huggingface/datasets/issues/5716/events | https://github.com/huggingface/datasets/issues/5716 | 1,658,613,092 | I_kwDODunzps5i3G1k | 5,716 | Handle empty audio | {
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} | null | null | null | Some audio paths exist, but they are empty, and an error will be reported when reading the audio path.How to use the filter function to avoid the empty audio path?
when a audio is empty, when do resample , it will break:
`array, sampling_rate = sf.read(f) array = librosa.resample(array, orig_sr=sampling_rate, target_sr=self.sampling_rate)` | {
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} | null | null | null | ### Feature request
There are old known issues, but they can be easily forgettable problems in multiprocessing with pytorch-dataloader:
Too high usage of RAM or shared-memory in pytorch when we set num workers > 1 and returning type of dataset or dataloader is "List" or "Dict".
https://github.com/pytorch/pytorch/issues/13246
With huggingface datasets, unfortunately, the default return type is the list, so the problem is raised too often if we do not set anything for the issue.
However, this issue can be released when the returning output is fixed in length.
Therefore, I request the mode, returning outputs with fixed length (e.g. numpy array) rather than list.
The design would be good when we load datasets as
```python
load_dataset(..., with_return_as_fixed_tensor=True)
```
### Motivation
The general solution for this issue is already in the comments: https://github.com/pytorch/pytorch/issues/13246#issuecomment-905703662
: Numpy or Pandas seems not to have problems, while both have the string type.
(I'm not sure that the sequence of huggingface datasets can solve this problem as well)
### Your contribution
I'll read it ! thanks | {
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} | [] | closed | false | null | [] | null | 2 | 2023-04-06T13:01:45 | 2023-04-07T09:23:54 | 2023-04-07T09:16:57 | MEMBER | null | {
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/5714",
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"diff_url": "https://github.com/huggingface/datasets/pull/5714.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5714.patch",
"merged_at": "2023-04-07T09:16:57"
} | PR:
- #5626
implemented support for streaming `.npy` files by using `numpy.load`.
However, it introduced a bug when used with `.npz` files, within a context manager:
```
ValueError: seek of closed file
```
or in streaming mode:
```
ValueError: I/O operation on closed file.
```
This PR fixes the bug and tests for both `.npy` and `.npz` files.
Fix #5711. | {
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https://api.github.com/repos/huggingface/datasets/issues/5713 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5713/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5713/comments | https://api.github.com/repos/huggingface/datasets/issues/5713/events | https://github.com/huggingface/datasets/issues/5713 | 1,657,141,251 | I_kwDODunzps5ixfgD | 5,713 | ArrowNotImplementedError when loading dataset from the hub | {
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} | [] | closed | false | null | [] | null | 2 | 2023-04-06T10:27:22 | 2023-04-06T13:06:22 | 2023-04-06T13:06:21 | CONTRIBUTOR | null | {
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} | null | null | null | ### Describe the bug
Hello,
I have created a dataset by using the image loader. Once the dataset is created I try to download it and I get the error:
```
Traceback (most recent call last):
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/builder.py", line 1860, in _prepare_split_single
for _, table in generator:
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 69, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1323, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/load.py", line 1791, in load_dataset
builder_instance.download_and_prepare(
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/builder.py", line 891, in download_and_prepare
self._download_and_prepare(
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/builder.py", line 986, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/builder.py", line 1748, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/builder.py", line 1893, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
Create the dataset and push it to the hub:
```python
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="/path/to/dataset")
dataset.push_to_hub("org/dataset-name", private=True, max_shard_size="1GB")
```
Then use it:
```python
from datasets import load_dataset
dataset = load_dataset("org/dataset-name")
```
### Expected behavior
To properly download and use the pushed dataset.
Something else to note is that I specified to have shards of 1GB max, but at the end, for the train set, it is an almost 7GB single file that is pushed.
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.10
- Huggingface_hub version: 0.13.3
- PyArrow version: 11.0.0
- Pandas version: 2.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5712 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5712/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5712/comments | https://api.github.com/repos/huggingface/datasets/issues/5712/events | https://github.com/huggingface/datasets/issues/5712 | 1,655,972,106 | I_kwDODunzps5itCEK | 5,712 | load_dataset in v2.11.0 raises "ValueError: seek of closed file" in np.load() | {
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} | [] | closed | false | null | [] | null | 2 | 2023-04-05T16:47:10 | 2023-04-06T08:32:37 | 2023-04-05T17:17:44 | NONE | null | {
"total": 0,
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} | null | null | null | ### Describe the bug
Hi,
I have some `dataset_load()` code of a custom offline dataset that works with datasets v2.10.1.
```python
ds = datasets.load_dataset(path=dataset_dir,
name=configuration,
data_dir=dataset_dir,
cache_dir=cache_dir,
aux_dir=aux_dir,
# download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD,
num_proc=18)
```
When upgrading datasets to 2.11.0, it fails with error
```
Traceback (most recent call last):
File "<string>", line 2, in <module>
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/site-packages/datasets/load.py", line 1791, in load_dataset
builder_instance.download_and_prepare(
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/site-packages/datasets/builder.py", line 891, in download_and_prepare
self._download_and_prepare(
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/site-packages/datasets/builder.py", line 1651, in _download_and_prepare
super()._download_and_prepare(
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/site-packages/datasets/builder.py", line 964, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/ramon.casero/.cache/huggingface/modules/datasets_modules/datasets/71f67f69e6e00e139903a121f96b71f39b65a6b6aaeb0862e6a5da3a3f565b4c/mydataset.py", line 682, in _split_generators
self.some_function()
File "/home/ramon.casero/.cache/huggingface/modules/datasets_modules/datasets/71f67f69e6e00e139903a121f96b71f39b65a6b6aaeb0862e6a5da3a3f565b4c/mydataset.py", line 1314, in some_function()
x_df = pd.DataFrame({'cell_type_descriptor': fp['x'].tolist()})
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/site-packages/numpy/lib/npyio.py", line 248, in __getitem__
bytes = self.zip.open(key)
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/zipfile.py", line 1530, in open
fheader = zef_file.read(sizeFileHeader)
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/zipfile.py", line 744, in read
self._file.seek(self._pos)
ValueError: seek of closed file
```
### Steps to reproduce the bug
Sorry, I cannot share the data or code because they are not mine to share, but the point of failure is a call in `some_function()`
```python
with np.load(filename) as fp:
x_df = pd.DataFrame({'feature': fp['x'].tolist()})
```
I'll try to generate a short snippet that reproduces the error.
### Expected behavior
I would expect that `load_dataset` works on the custom datasets generation script for v2.11.0 the same way it works for 2.10.1, without making `np.load()` give a `ValueError: seek of closed file` error.
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-4.18.0-483.el8.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.8
- Huggingface_hub version: 0.12.0
- PyArrow version: 11.0.0
- Pandas version: 1.5.2
- numpy: 1.24.2
- This is an offline dataset that uses `datasets.config.HF_DATASETS_OFFLINE = True` in the generation script.
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] | null | 2 | 2023-04-05T16:46:49 | 2023-04-07T09:16:59 | 2023-04-07T09:16:59 | NONE | null | {
"total": 0,
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} | null | null | null | ### Describe the bug
Hi,
I have some `dataset_load()` code of a custom offline dataset that works with datasets v2.10.1.
```python
ds = datasets.load_dataset(path=dataset_dir,
name=configuration,
data_dir=dataset_dir,
cache_dir=cache_dir,
aux_dir=aux_dir,
# download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD,
num_proc=18)
```
When upgrading datasets to 2.11.0, it fails with error
```
Traceback (most recent call last):
File "<string>", line 2, in <module>
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/site-packages/datasets/load.py", line 1791, in load_dataset
builder_instance.download_and_prepare(
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/site-packages/datasets/builder.py", line 891, in download_and_prepare
self._download_and_prepare(
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/site-packages/datasets/builder.py", line 1651, in _download_and_prepare
super()._download_and_prepare(
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/site-packages/datasets/builder.py", line 964, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/ramon.casero/.cache/huggingface/modules/datasets_modules/datasets/71f67f69e6e00e139903a121f96b71f39b65a6b6aaeb0862e6a5da3a3f565b4c/mydataset.py", line 682, in _split_generators
self.some_function()
File "/home/ramon.casero/.cache/huggingface/modules/datasets_modules/datasets/71f67f69e6e00e139903a121f96b71f39b65a6b6aaeb0862e6a5da3a3f565b4c/mydataset.py", line 1314, in some_function()
x_df = pd.DataFrame({'cell_type_descriptor': fp['x'].tolist()})
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/site-packages/numpy/lib/npyio.py", line 248, in __getitem__
bytes = self.zip.open(key)
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/zipfile.py", line 1530, in open
fheader = zef_file.read(sizeFileHeader)
File "/home/ramon.casero/opt/miniconda3/envs/myenv/lib/python3.10/zipfile.py", line 744, in read
self._file.seek(self._pos)
ValueError: seek of closed file
```
### Steps to reproduce the bug
Sorry, I cannot share the data or code because they are not mine to share, but the point of failure is a call in `some_function()`
```python
with np.load(embedding_filename) as fp:
x_df = pd.DataFrame({'feature': fp['x'].tolist()})
```
I'll try to generate a short snippet that reproduces the error.
### Expected behavior
I would expect that `load_dataset` works on the custom datasets generation script for v2.11.0 the same way it works for 2.10.1, without making `np.load()` give a `ValueError: seek of closed file` error.
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-4.18.0-483.el8.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.8
- Huggingface_hub version: 0.12.0
- PyArrow version: 11.0.0
- Pandas version: 1.5.2
- numpy: 1.24.2
- This is an offline dataset that uses `datasets.config.HF_DATASETS_OFFLINE = True` in the generation script.
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https://api.github.com/repos/huggingface/datasets/issues/5710 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5710/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5710/comments | https://api.github.com/repos/huggingface/datasets/issues/5710/events | https://github.com/huggingface/datasets/issues/5710 | 1,655,703,534 | I_kwDODunzps5isAfu | 5,710 | OSError: Memory mapping file failed: Cannot allocate memory | {
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} | null | null | null | ### Describe the bug
Hello, I have a series of datasets each of 5 GB, 600 datasets in total. So together this makes 3TB.
When I trying to load all the 600 datasets into memory, I get the above error message.
Is this normal because I'm hitting the max size of memory mapping of the OS?
Thank you
```terminal
0_21/cache-e9c42499f65b1881.arrow
load_hf_datasets_from_disk: 82%|████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 494/600 [07:26<01:35, 1.11it/s]
Traceback (most recent call last):
File "example_load_genkalm_dataset.py", line 35, in <module>
multi_ds.post_process(max_node_num=args.max_node_num,max_seq_length=args.max_seq_length,delay=args.delay)
File "/home/geng/GenKaLM/src/dataloader/dataset.py", line 142, in post_process
genkalm_dataset = GenKaLM_Dataset.from_hf_dataset(path_or_name=ds_path, max_seq_length=self.max_seq_length,
File "/home/geng/GenKaLM/src/dataloader/dataset.py", line 47, in from_hf_dataset
hf_ds = load_from_disk(path_or_name)
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/load.py", line 1848, in load_from_disk
return Dataset.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1549, in load_from_disk
arrow_table = concat_tables(
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/table.py", line 1805, in concat_tables
tables = list(tables)
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1550, in <genexpr>
table_cls.from_file(Path(dataset_path, data_file["filename"]).as_posix())
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/table.py", line 1065, in from_file
table = _memory_mapped_arrow_table_from_file(filename)
File "/home/geng/.conda/envs/genkalm/lib/python3.8/site-packages/datasets/table.py", line 50, in _memory_mapped_arrow_table_from_file
memory_mapped_stream = pa.memory_map(filename)
File "pyarrow/io.pxi", line 950, in pyarrow.lib.memory_map
File "pyarrow/io.pxi", line 911, in pyarrow.lib.MemoryMappedFile._open
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: Memory mapping file failed: Cannot allocate memory
```
### Steps to reproduce the bug
Sorry I can not provide a reproducible code as the data is stored on my server and it's too large to share.
### Expected behavior
I expect the 3TB of data can be fully mapped to memory
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-4.15.0-204-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.6
- PyArrow version: 11.0.0
- Pandas version: 1.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5709 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5709/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5709/comments | https://api.github.com/repos/huggingface/datasets/issues/5709/events | https://github.com/huggingface/datasets/issues/5709 | 1,655,423,503 | I_kwDODunzps5iq8IP | 5,709 | Manually dataset info made not taken into account | {
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} | [] | closed | false | null | [] | null | 2 | 2023-04-05T11:15:17 | 2023-04-06T08:52:20 | 2023-04-06T08:52:19 | CONTRIBUTOR | null | {
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} | null | null | null | ### Describe the bug
Hello,
I'm manually building an image dataset with the `from_dict` approach. I also build the features with the `cast_features` methods. Once the dataset is created I push it on the hub, and a default `dataset_infos.json` file seems to have been automatically added to the repo in same time. Hence I update it manually with all the missing info, but when I download the dataset the info are never updated.
Former `dataset_infos.json` file:
```
{"default": {
"description": "",
"citation": "",
"homepage": "",
"license": "",
"features": {
"image": {
"_type": "Image"
},
"labels": {
"names": [
"Fake",
"Real"
],
"_type": "ClassLabel"
}
},
"splits": {
"validation": {
"name": "validation",
"num_bytes": 901010094.0,
"num_examples": 3200,
"dataset_name": null
},
"train": {
"name": "train",
"num_bytes": 901010094.0,
"num_examples": 3200,
"dataset_name": null
}
},
"download_size": 1802008414,
"dataset_size": 1802020188.0,
"size_in_bytes": 3604028602.0
}}
```
After I update it manually it looks like:
```
{
"bstrai--deepfake-detection":{
"description":"",
"citation":"",
"homepage":"",
"license":"",
"features":{
"image":{
"decode":true,
"id":null,
"_type":"Image"
},
"labels":{
"num_classes":2,
"names":[
"Fake",
"Real"
],
"id":null,
"_type":"ClassLabel"
}
},
"supervised_keys":{
"input":"image",
"output":"labels"
},
"task_templates":[
{
"task":"image-classification",
"image_column":"image",
"label_column":"labels"
}
],
"config_name":null,
"splits":{
"validation":{
"name":"validation",
"num_bytes":36627822,
"num_examples":123,
"dataset_name":"deepfake-detection"
},
"train":{
"name":"train",
"num_bytes":901023694,
"num_examples":3200,
"dataset_name":"deepfake-detection"
}
},
"download_checksums":null,
"download_size":937562209,
"dataset_size":937651516,
"size_in_bytes":1875213725
}
}
```
Anything I should do to have the new infos in the `dataset_infos.json` to be taken into account? Or it is not possible yet?
Thanks!
### Steps to reproduce the bug
-
### Expected behavior
-
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.10
- Huggingface_hub version: 0.13.3
- PyArrow version: 11.0.0
- Pandas version: 2.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5708 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5708/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5708/comments | https://api.github.com/repos/huggingface/datasets/issues/5708/events | https://github.com/huggingface/datasets/issues/5708 | 1,655,023,642 | I_kwDODunzps5ipaga | 5,708 | Dataset sizes are in MiB instead of MB in dataset cards | {
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] | null | 12 | 2023-04-05T06:36:03 | 2023-12-21T10:20:28 | 2023-12-21T10:20:27 | MEMBER | null | {
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} | null | null | null | As @severo reported in an internal discussion (https://github.com/huggingface/moon-landing/issues/5929):
Now we show the dataset size:
- from the dataset card (in the side column)
- from the datasets-server (in the viewer)
But, even if the size is the same, we see a mismatch because the viewer shows MB, while the info from the README generally shows MiB (even if it's written MB -> https://huggingface.co/datasets/blimp/blob/main/README.md?code=true#L1932)
<img width="664" alt="Capture d’écran 2023-04-04 à 10 16 01" src="https://user-images.githubusercontent.com/1676121/229730887-0bd8fa6e-9462-46c6-bd4e-4d2c5784cabb.png">
TODO: Values to be fixed in: `Size of downloaded dataset files:`, `Size of the generated dataset:` and `Total amount of disk used:`
- [x] Bulk edit on the Hub to fix this in all canonical datasets
- [x] Bulk PR on the Hub to fix ancient canonical datasets that were moved to organizations | {
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https://api.github.com/repos/huggingface/datasets/issues/5706 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5706/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5706/comments | https://api.github.com/repos/huggingface/datasets/issues/5706/events | https://github.com/huggingface/datasets/issues/5706 | 1,653,545,835 | I_kwDODunzps5ijxtr | 5,706 | Support categorical data types for Parquet | {
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} | null | null | null | ### Feature request
Huggingface datasets does not seem to support categorical / dictionary data types for Parquet as of now. There seems to be a `TODO` in the code for this feature but no implementation yet. Below you can find sample code to reproduce the error that is currently thrown when attempting to read a Parquet file with categorical columns:
```python
import pandas as pd
import pyarrow.parquet as pq
from datasets import load_dataset
# Create categorical sample DataFrame
df = pd.DataFrame({'type': ['foo', 'bar']}).astype('category')
df.to_parquet('data.parquet')
# Read back as pyarrow table
table = pq.read_table('data.parquet')
print(table.schema)
# type: dictionary<values=string, indices=int32, ordered=0>
# Load with huggingface datasets
load_dataset('parquet', data_files='data.parquet')
```
Error:
```
Traceback (most recent call last):
File ".venv/lib/python3.10/site-packages/datasets/builder.py", line 1875, in _prepare_split_single
writer.write_table(table)
File ".venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 566, in write_table
self._build_writer(inferred_schema=pa_table.schema)
File ".venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 379, in _build_writer
inferred_features = Features.from_arrow_schema(inferred_schema)
File ".venv/lib/python3.10/site-packages/datasets/features/features.py", line 1622, in from_arrow_schema
obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema}
File ".venv/lib/python3.10/site-packages/datasets/features/features.py", line 1622, in <dictcomp>
obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema}
File ".venv/lib/python3.10/site-packages/datasets/features/features.py", line 1361, in generate_from_arrow_type
raise NotImplementedError # TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table
NotImplementedError
```
### Motivation
Categorical data types, as offered by Pandas and implemented with the `DictionaryType` dtype in `pyarrow` can significantly reduce dataset size and are a handy way to turn textual features into numerical representations and back. Lack of support in Huggingface datasets greatly reduces compatibility with a common Pandas / Parquet feature.
### Your contribution
I could provide a PR. However, it would be nice to have an initial complexity estimate from one of the core developers first. | {
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https://api.github.com/repos/huggingface/datasets/issues/5705 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5705/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5705/comments | https://api.github.com/repos/huggingface/datasets/issues/5705/events | https://github.com/huggingface/datasets/issues/5705 | 1,653,500,383 | I_kwDODunzps5ijmnf | 5,705 | Getting next item from IterableDataset took forever. | {
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I have a large dataset, about 500GB. The format of the dataset is parquet.
I then load the dataset and try to get the first item
```python
def get_one_item():
dataset = load_dataset("path/to/datafiles", split="train", cache_dir=".", streaming=True)
dataset = dataset.filter(lambda example: example['text'].startswith('Ar'))
print(next(iter(dataset)))
```
However, this function never finish. I waited ~10mins, the function was still running so I killed the process. I'm now using `line_profiler` to profile how long it would take to return one item. I'll be patient and wait for as long as it needs.
I suspect the filter operation is the reason why it took so long. Can I get some possible reasons behind this?
### Steps to reproduce the bug
Unfortunately without my data files, there is no way to reproduce this bug.
### Expected behavior
With `IteralbeDataset`, I expect the first item to be returned instantly.
### Environment info
- datasets version: 2.11.0
- python: 3.7.12 | {
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https://api.github.com/repos/huggingface/datasets/issues/5704 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5704/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5704/comments | https://api.github.com/repos/huggingface/datasets/issues/5704/events | https://github.com/huggingface/datasets/pull/5704 | 1,653,471,356 | PR_kwDODunzps5NkEvJ | 5,704 | 5537 speedup load | {
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} | I reimplemented fsspec.spec.glob() in `hffilesystem.py` as `_glob`, used it in `_resolve_single_pattern_in_dataset_repository` only, and saw a 20% speedup in times to load the config, on average.
That's not much when usually this step takes only 2-3 seconds for most datasets, but in this particular case, `bigcode/the-stack-dedup` , the loading time to get the config (not download the entire 6tb dataset, of course), went from ~170 secs to ~20 secs.
What makes this work is this code in `_glob`:
```
if self.dir_cache is not None:
allpaths = self.dir_cache
else:
allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs)
```
I also had to `import glob.has_magic( )` for `_glob()` (confusing, I know).
I hope there is no issue with copying most of the code from `fsspec.spec.glob`, as it is a BSD 3-Clause License,
and I left a comment about this in the docstring of` _glob()`, that we may want to delete.
As mentioned, I evaluated the speedup across a random selection of about 1000 datasets (not all 27k+), and verified that old_config.eq(new_method_config) with the build in method, but deleted this test and related code changes on the subsequent commit. It's in the commit history if anyone wants to see it. (Note this does not include the outlier of `bigcode/the-stack-dedup`
| | old_time | new _time | diff | pct_diff |
| -- | -- | -- | -- | -- |
| mean | 3.340 | 2.642 | 0.698 | 18.404 |
| min | 2.024 | 1.976 | -0.840 | -37.634 |
| max | 66.582 | 41.517 | 30.927 | 85.538 | | null | {
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} | null | null | null | ### Feature request
Hello! Apologies if my question sounds naive:
I was wondering if it’s possible, or how one would go about defining a 'datasets.Sequence' element in datasets.Features that could potentially be either a dict, a str, or None?
Specifically, I’d like to define a feature for a list that contains 18 elements, each of which has been pre-defined as either a `dict or None` or `str or None` - as demonstrated in the slightly misaligned data provided below:
```json
[
[
{"text":"老妇人","idxes":[0,1,2]},null,{"text":"跪","idxes":[3]},null,null,null,null,{"text":"在那坑里","idxes":[4,5,6,7]},null,null,null,null,null,null,null,null,null,null],
[
{"text":"那些水","idxes":[13,14,15]},null,{"text":"舀","idxes":[11]},null,null,null,null,null,{"text":"在那坑里","idxes":[4,5,6,7]},null,{"text":"出","idxes":[12]},null,null,null,null,null,null,null],
[
{"text":"水","idxes":[38]},
null,
{"text":"舀","idxes":[40]},
"假", // note this is just a standalone string
null,null,null,{"text":"坑里","idxes":[35,36]},null,null,null,null,null,null,null,null,null,null]]
```
### Motivation
I'm currently working with a dataset of the following structure and I couldn't find a solution in the [documentation](https://huggingface.co/docs/datasets/v2.11.0/en/package_reference/main_classes#datasets.Features).
```json
{"qid":"3-train-1058","context":"桑桑害怕了。从玉米地里走到田埂上，他遥望着他家那幢草房子里的灯光，知道母亲没有让他回家的意思，很伤感，有点想哭。但没哭，转身朝阿恕家走去。","corefs":[[{"text":"桑桑","idxes":[0,1]},{"text":"他","idxes":[17]}]],"non_corefs":[],"outputs":[[{"text":"他","idxes":[17]},null,{"text":"走","idxes":[11]},null,null,null,null,null,{"text":"从玉米地里","idxes":[6,7,8,9,10]},{"text":"到田埂上","idxes":[12,13,14,15]},null,null,null,null,null,null,null,null],[{"text":"他","idxes":[17]},null,{"text":"走","idxes":[66]},null,null,null,null,null,null,null,{"text":"转身朝阿恕家去","idxes":[60,61,62,63,64,65,67]},null,null,null,null,null,null,null],[{"text":"灯光","idxes":[30,31]},null,null,null,null,null,null,{"text":"草房子里","idxes":[25,26,27,28]},null,null,null,null,null,null,null,null,null,null],[{"text":"他","idxes":[17]},{"text":"他家那幢草房子","idxes":[21,22,23,24,25,26,27]},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"远"],[{"text":"他","idxes":[17]},{"text":"阿恕家","idxes":[63,64,65]},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"变近"]]}
```
### Your contribution
I'm going to provide the dataset at https://huggingface.co/datasets/2030NLP/SpaCE2022 . | {
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https://api.github.com/repos/huggingface/datasets/issues/5701 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5701/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5701/comments | https://api.github.com/repos/huggingface/datasets/issues/5701/events | https://github.com/huggingface/datasets/pull/5701 | 1,652,931,399 | PR_kwDODunzps5NiSCy | 5,701 | Add Dataset.from_spark | {
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} | [] | closed | false | null | [] | null | 21 | 2023-04-03T23:51:29 | 2023-06-16T16:39:32 | 2023-04-26T15:43:39 | CONTRIBUTOR | null | {
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} | Adds static method Dataset.from_spark to create datasets from Spark DataFrames.
This approach alleviates users of the need to materialize their dataframe---a common use case is that the user loads their dataset into a dataframe, uses Spark to apply some transformation to some of the columns, and then wants to train on the dataset.
Related issue: https://github.com/huggingface/datasets/issues/5678 | {
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https://api.github.com/repos/huggingface/datasets/issues/5700 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5700/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5700/comments | https://api.github.com/repos/huggingface/datasets/issues/5700/events | https://github.com/huggingface/datasets/pull/5700 | 1,652,527,530 | PR_kwDODunzps5Ng6g_ | 5,700 | fix: fix wrong modification of the 'cache_file_name' -related paramet… | {
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} | …ers values in 'train_test_split' + fix bad interaction between 'keep_in_memory' and 'cache_file_name' -related parameters (#5699) | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5699 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5699/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5699/comments | https://api.github.com/repos/huggingface/datasets/issues/5699/events | https://github.com/huggingface/datasets/issues/5699 | 1,652,437,419 | I_kwDODunzps5ifjGr | 5,699 | Issue when wanting to split in memory a cached dataset | {
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} | null | null | null | ### Describe the bug
**In the 'train_test_split' method of the Dataset class** (defined datasets/arrow_dataset.py), **if 'self.cache_files' is not empty**, then, **regarding the input parameters 'train_indices_cache_file_name' and 'test_indices_cache_file_name', if they are None**, we modify them to make them not None, to see if we can just provide back / work from cached data. But if we can't provide cached data, we move on with the call to the method, except those two values are not None anymore, which will conflict with the use of the 'keep_in_memory' parameter down the line.
Indeed, at some point we end up calling the 'select' method, **and if 'keep_in_memory' is True**, since the value of this method's parameter 'indices_cache_file_name' is now not None anymore, **an exception is raised, whose message is "Please use either 'keep_in_memory' or 'indices_cache_file_name' but not both.".**
Because of that, it's impossible to perform a train / test split of a cached dataset while requesting that the result not be cached. Which is inconvenient when one is just performing experiments, with no intention of caching the result.
Aside from this being inconvenient, **the code which lead up to that situation seems simply wrong** to me: the input variable should not be modified so as to change the user's intention just to perform a test, if that test can fail and respecting the user's intention is necessary to proceed in that case.
To fix this, I suggest to use other variables / other variable names, in order to host the value(s) needed to perform the test, so as not to change the originally input values needed by the rest of the method's code.
Also, **I don't see why an exception should be raised when the 'select' method is called with both 'keep_in_memory'=True and 'indices_cache_file_name'!=None**: should the use of 'keep_in_memory' not prevail anyway, specifying that the user does not want to perform caching, and so making irrelevant the value of 'indices_cache_file_name'? This is indeed what happens when we look further in the code, in the '\_select_with_indices_mapping' method: when 'keep_in_memory' is True, then the value of indices_cache_file_name does not matter, the data will be written to a stream buffer anyway.
Hence I suggest to remove the raising of exception in those circumstances. Notably, to remove the raising of it in the 'select', '\_select_with_indices_mapping', 'shuffle' and 'map' methods.
### Steps to reproduce the bug
```python
import datasets
def generate_examples():
for i in range(10):
yield {"id": i}
dataset_ = datasets.Dataset.from_generator(
generate_examples,
keep_in_memory=False,
)
dataset_.train_test_split(
test_size=3,
shuffle=False,
keep_in_memory=True,
train_indices_cache_file_name=None,
test_indices_cache_file_name=None,
)
```
### Expected behavior
The result of the above code should be a DatasetDict instance.
Instead, we get the following exception stack:
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[3], line 1
----> 1 dataset_.train_test_split(
2 test_size=3,
3 shuffle=False,
4 keep_in_memory=True,
5 train_indices_cache_file_name=None,
6 test_indices_cache_file_name=None,
7 )
File ~/Work/Developments/datasets/src/datasets/arrow_dataset.py:528, in transmit_format.<locals>.wrapper(*args, **kwargs)
521 self_format = {
522 "type": self._format_type,
523 "format_kwargs": self._format_kwargs,
524 "columns": self._format_columns,
525 "output_all_columns": self._output_all_columns,
526 }
527 # apply actual function
--> 528 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
529 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
530 # re-apply format to the output
File ~/Work/Developments/datasets/src/datasets/fingerprint.py:511, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
507 validate_fingerprint(kwargs[fingerprint_name])
509 # Call actual function
--> 511 out = func(dataset, *args, **kwargs)
513 # Update fingerprint of in-place transforms + update in-place history of transforms
515 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File ~/Work/Developments/datasets/src/datasets/arrow_dataset.py:4428, in Dataset.train_test_split(self, test_size, train_size, shuffle, stratify_by_column, seed, generator, keep_in_memory, load_from_cache_file, train_indices_cache_file_name, test_indices_cache_file_name, writer_batch_size, train_new_fingerprint, test_new_fingerprint)
4425 test_indices = permutation[:n_test]
4426 train_indices = permutation[n_test : (n_test + n_train)]
-> 4428 train_split = self.select(
4429 indices=train_indices,
4430 keep_in_memory=keep_in_memory,
4431 indices_cache_file_name=train_indices_cache_file_name,
4432 writer_batch_size=writer_batch_size,
4433 new_fingerprint=train_new_fingerprint,
4434 )
4435 test_split = self.select(
4436 indices=test_indices,
4437 keep_in_memory=keep_in_memory,
(...)
4440 new_fingerprint=test_new_fingerprint,
4441 )
4443 return DatasetDict({"train": train_split, "test": test_split})
File ~/Work/Developments/datasets/src/datasets/arrow_dataset.py:528, in transmit_format.<locals>.wrapper(*args, **kwargs)
521 self_format = {
522 "type": self._format_type,
523 "format_kwargs": self._format_kwargs,
524 "columns": self._format_columns,
525 "output_all_columns": self._output_all_columns,
526 }
527 # apply actual function
--> 528 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
529 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
530 # re-apply format to the output
File ~/Work/Developments/datasets/src/datasets/fingerprint.py:511, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
507 validate_fingerprint(kwargs[fingerprint_name])
509 # Call actual function
--> 511 out = func(dataset, *args, **kwargs)
513 # Update fingerprint of in-place transforms + update in-place history of transforms
515 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File ~/Work/Developments/datasets/src/datasets/arrow_dataset.py:3679, in Dataset.select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
3645 """Create a new dataset with rows selected following the list/array of indices.
3646
3647 Args:
(...)
3676 ```
3677 """
3678 if keep_in_memory and indices_cache_file_name is not None:
-> 3679 raise ValueError("Please use either `keep_in_memory` or `indices_cache_file_name` but not both.")
3681 if len(self.list_indexes()) > 0:
3682 raise DatasetTransformationNotAllowedError(
3683 "Using `.select` on a dataset with attached indexes is not allowed. You can first run `.drop_index() to remove your index and then re-add it."
3684 )
ValueError: Please use either `keep_in_memory` or `indices_cache_file_name` but not both.
```
### Environment info
- `datasets` version: 2.11.1.dev0
- Platform: Linux-5.4.236-1-MANJARO-x86_64-with-glibc2.2.5
- Python version: 3.8.12
- Huggingface_hub version: 0.13.3
- PyArrow version: 11.0.0
- Pandas version: 2.0.0
***
***
EDIT:
Now with a pull request to fix this [here](https://github.com/huggingface/datasets/pull/5700) | null | {
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] | open | false | null | [] | null | 1 | 2023-04-03T14:25:19 | 2023-04-11T10:28:40 | null | CONTRIBUTOR | null | {
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} | null | null | null | ### Feature request
I'd suggest adding Qdrant (https://qdrant.tech) as another search index available, so users can directly build an index from a dataset. Currently, FAISS and ElasticSearch are only supported: https://huggingface.co/docs/datasets/faiss_es
### Motivation
ElasticSearch is a keyword-based search system, while FAISS is a vector search library. Vector database, such as Qdrant, is a different tool based on similarity (like FAISS) but is not limited to a single machine. It makes the vector database well-suited for bigger datasets and collaboration if several people want to access a particular dataset.
### Your contribution
I can provide a PR implementing that functionality on my own. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5696 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5696/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5696/comments | https://api.github.com/repos/huggingface/datasets/issues/5696/events | https://github.com/huggingface/datasets/issues/5696 | 1,651,707,008 | I_kwDODunzps5icwyA | 5,696 | Shuffle a sharded iterable dataset without seed can lead to duplicate data | {
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} | null | null | null | As reported in https://github.com/huggingface/datasets/issues/5360
If `seed=None` in `.shuffle()`, shuffled datasets don't use the same shuffling seed across nodes.
Because of that, the lists of shards is not shuffled the same way across nodes, and therefore some shards may be assigned to multiple nodes instead of exactly one.
This can happen only when you have a number of shards that is a factor of the number of nodes.
The current workaround is to always set a `seed` in `.shuffle()` | {
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https://api.github.com/repos/huggingface/datasets/issues/5695 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5695/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5695/comments | https://api.github.com/repos/huggingface/datasets/issues/5695/events | https://github.com/huggingface/datasets/issues/5695 | 1,650,974,156 | I_kwDODunzps5iZ93M | 5,695 | Loading big dataset raises pyarrow.lib.ArrowNotImplementedError | {
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} | null | null | null | ### Describe the bug
Calling `datasets.load_dataset` to load the (publicly available) dataset `theodor1289/wit` fails with `pyarrow.lib.ArrowNotImplementedError`.
### Steps to reproduce the bug
Steps to reproduce this behavior:
1. `!pip install datasets`
2. `!huggingface-cli login`
3. This step will throw the error (it might take a while as the dataset has ~170GB):
```python
from datasets import load_dataset
dataset = load_dataset("theodor1289/wit", "train", use_auth_token=True)
```
Stack trace:
```
(torch-multimodal) bash-4.2$ python test.py
Downloading and preparing dataset None/None to /cluster/work/cotterell/tamariucai/HuggingfaceDatasets/theodor1289___parquet/theodor1289--wit-7a3e984414a86a0f/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...
Downloading data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 491.68it/s]
Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 16.93it/s]
Traceback (most recent call last):
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py", line 1860, in _prepare_split_single
for _, table in generator:
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 69, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1323, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/cluster/work/cotterell/tamariucai/multimodal-mirror/examples/test.py", line 2, in <module>
dataset = load_dataset("theodor1289/wit", "train", use_auth_token=True)
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/load.py", line 1791, in load_dataset
builder_instance.download_and_prepare(
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py", line 891, in download_and_prepare
self._download_and_prepare(
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py", line 986, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py", line 1748, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py", line 1893, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
The dataset is loaded in variable `dataset`.
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.4
- Huggingface_hub version: 0.13.3
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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We could have something like `config.json` to define the configuration of a dataset.
```json
{
"data_dir": "data"
"data_files": {
"train": "train-[0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*"
}
}
```
we could also support a list for several configs with a 'config_name' field.
The alternative was to use YAML in the README.md.
I think it could also support a `dataset_type` field to specify which dataset builder class to use, and the other parameters would be the builder's parameters. Some parameters exist for all builders like `data_files` and `data_dir`, but some parameters are builder specific like `sep` for csv.
This format would be used in `push_to_hub` to be able to push multiple configs.
cc @huggingface/datasets
EDIT: actually we're going for the YAML approach in README.md | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5692 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5692/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5692/comments | https://api.github.com/repos/huggingface/datasets/issues/5692/events | https://github.com/huggingface/datasets/issues/5692 | 1,649,818,644 | I_kwDODunzps5iVjwU | 5,692 | pyarrow.lib.ArrowInvalid: Unable to merge: Field <field> has incompatible types | {
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} | null | null | null | ### Describe the bug
When loading the dataset [wikianc-en](https://huggingface.co/datasets/cyanic-selkie/wikianc-en) which I created using [this](https://github.com/cyanic-selkie/wikianc) code, I get the following error:
```
Traceback (most recent call last):
File "/home/sven/code/rector/answer-detection/train.py", line 106, in <module>
(dataset, weights) = get_dataset(args.dataset, tokenizer, labels, args.padding)
File "/home/sven/code/rector/answer-detection/dataset.py", line 106, in get_dataset
dataset = load_dataset("cyanic-selkie/wikianc-en")
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/load.py", line 1794, in load_dataset
ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/builder.py", line 1106, in as_dataset
datasets = map_nested(
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 443, in map_nested
mapped = [
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 444, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 346, in _single_map_nested
return function(data_struct)
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/builder.py", line 1136, in _build_single_dataset
ds = self._as_dataset(
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/builder.py", line 1207, in _as_dataset
dataset_kwargs = ArrowReader(cache_dir, self.info).read(
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 239, in read
return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 260, in read_files
pa_table = self._read_files(files, in_memory=in_memory)
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 203, in _read_files
pa_table = concat_tables(pa_tables) if len(pa_tables) != 1 else pa_tables[0]
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1808, in concat_tables
return ConcatenationTable.from_tables(tables, axis=axis)
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1514, in from_tables
return cls.from_blocks(blocks)
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1427, in from_blocks
table = cls._concat_blocks(blocks, axis=0)
File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1373, in _concat_blocks
return pa.concat_tables(pa_tables, promote=True)
File "pyarrow/table.pxi", line 5224, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Unable to merge: Field paragraph_anchors has incompatible types: list<: struct<start: uint32 not null, end: uint32 not null, qid: uint32, pageid: uint32, title: string not null> not null> vs list<item: struct<start: uint32, end: uint32, qid: uint32, pageid: uint32, title: string>>
```
This only happens when I load the `train` split, indicating that the size of the dataset is the deciding factor.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("cyanic-selkie/wikianc-en", split="train")
```
### Expected behavior
The dataset should load normally without any errors.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-6.2.8-arch1-1-x86_64-with-glibc2.37
- Python version: 3.10.10
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | null | {
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} | This PR addresses the comments in #5687 about compressing text file extensions before uploading to the Hub. Also clarified what "too large" means based on the GitLFS [docs](https://docs.github.com/en/repositories/working-with-files/managing-large-files/about-git-large-file-storage). | {
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https://api.github.com/repos/huggingface/datasets/issues/5689 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5689/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5689/comments | https://api.github.com/repos/huggingface/datasets/issues/5689/events | https://github.com/huggingface/datasets/pull/5689 | 1,648,956,349 | PR_kwDODunzps5NVMuI | 5,689 | Support streaming Beam datasets from HF GCS preprocessed data | {
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} | This PR implements streaming Apache Beam datasets that are already preprocessed by us and stored in the HF Google Cloud Storage:
- natural_questions
- wiki40b
- wikipedia
This is done by streaming from the prepared Arrow files in HF Google Cloud Storage.
This will fix their corresponding dataset viewers. Related to:
- https://github.com/huggingface/datasets-server/pull/988#discussion_r1150767138
Related to:
- https://huggingface.co/datasets/natural_questions/discussions/4
- https://huggingface.co/datasets/wiki40b/discussions/2
- https://huggingface.co/datasets/wikipedia/discussions/9
CC: @severo | {
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} | null | null | null | ### Describe the bug
rta.sh
Traceback (most recent call last):
File "run.py", line 7, in <module>
import datasets
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/builder.py", line 44, in <module>
from .data_files import DataFilesDict, _sanitize_patterns
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/data_files.py", line 120, in <module>
dataset_info: huggingface_hub.hf_api.DatasetInfo,
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/huggingface_hub/__init__.py", line 290, in __getattr__
raise AttributeError(f"No {package_name} attribute {name}")
AttributeError: No huggingface_hub attribute hf_api
### Reproduction
_No response_
### Logs
```shell
Traceback (most recent call last):
File "run.py", line 7, in <module>
import datasets
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/builder.py", line 44, in <module>
from .data_files import DataFilesDict, _sanitize_patterns
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/data_files.py", line 120, in <module>
dataset_info: huggingface_hub.hf_api.DatasetInfo,
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/huggingface_hub/__init__.py", line 290, in __getattr__
raise AttributeError(f"No {package_name} attribute {name}")
AttributeError: No huggingface_hub attribute hf_api
```
### System info
```shell
- huggingface_hub version: 0.13.2
- Platform: Linux-5.4.0-144-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /home/appuser/.cache/huggingface/token
- Has saved token ?: False
- Configured git credential helpers:
- FastAI: N/A
- Tensorflow: N/A
- Torch: 1.7.1
- Jinja2: N/A
- Graphviz: N/A
- Pydot: N/A
- Pillow: 9.3.0
- hf_transfer: N/A
- ENDPOINT: https://huggingface.co
- HUGGINGFACE_HUB_CACHE: /home/appuser/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /home/appuser/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/appuser/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
```
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https://api.github.com/repos/huggingface/datasets/issues/5688 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5688/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5688/comments | https://api.github.com/repos/huggingface/datasets/issues/5688/events | https://github.com/huggingface/datasets/issues/5688 | 1,648,463,504 | I_kwDODunzps5iQY6Q | 5,688 | Wikipedia download_and_prepare for GCS | {
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} | null | null | null | ### Describe the bug
I am unable to download the wikipedia dataset onto GCS.
When I run the script provided the memory firstly gets eaten up, then it crashes.
I tried running this on a VM with 128GB RAM and all I got was a two empty files: _data_builder.lock_, _data.incomplete/beam-temp-wikipedia-train-1ab2039acf3611ed87a9893475de0093_
I have troubleshot this for two straight days now, but I am just unable to get the dataset into storage.
### Steps to reproduce the bug
Run this and insert a path:
```
import datasets
builder = datasets.load_dataset_builder(
"wikipedia", language="en", date="20230320", beam_runner="DirectRunner")
builder.download_and_prepare({path}, file_format="parquet")
```
This is where the problem of it eating RAM occurs.
I have also tried several versions of this, based on the docs:
```
import gcsfs
import datasets
storage_options = {"project": "tdt4310", "token": "cloud"}
fs = gcsfs.GCSFileSystem(**storage_options)
output_dir = "gcs://wikipediadata/"
builder = datasets.load_dataset_builder(
"wikipedia", date="20230320", language="en", beam_runner="DirectRunner")
builder.download_and_prepare(
output_dir, storage_options=storage_options, file_format="parquet")
```
The error message that is received here is:
> ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://wikipediadata/wikipedia-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite']
I have ran `pip install apache-beam[gcp]`
### Expected behavior
The wikipedia data loaded into GCS
Everything worked when testing with a smaller demo dataset found somewhere in the docs
### Environment info
Newest published version of datasets. Python 3.9. Also tested with Python 3.7. 128GB RAM Google Cloud VM instance. | {
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https://api.github.com/repos/huggingface/datasets/issues/5687 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5687/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5687/comments | https://api.github.com/repos/huggingface/datasets/issues/5687/events | https://github.com/huggingface/datasets/issues/5687 | 1,647,009,018 | I_kwDODunzps5iK1z6 | 5,687 | Document to compress data files before uploading | {
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} | null | null | null | In our docs to [Share a dataset to the Hub](https://huggingface.co/docs/datasets/upload_dataset), we tell users to upload directly their data files, like CSV, JSON, JSON-Lines, text,... However, these extensions are not tracked by Git LFS by default, as they are not in the `.giattributes` file. Therefore, if they are too large, Git will fail to commit/upload them.
I think for those file extensions (.csv, .json, .jsonl, .txt), we should better recommend to **compress** their data files (using ZIP for example) before uploading them to the Hub.
- Compressed files are tracked by Git LFS in our default `.gitattributes` file
What do you think?
CC: @stevhliu
See related issue:
- https://huggingface.co/datasets/tcor0005/langchain-docs-400-chunksize/discussions/1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5686 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5686/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5686/comments | https://api.github.com/repos/huggingface/datasets/issues/5686/events | https://github.com/huggingface/datasets/pull/5686 | 1,646,308,228 | PR_kwDODunzps5NMXdu | 5,686 | set dev version | {
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https://api.github.com/repos/huggingface/datasets/issues/5685 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5685/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5685/comments | https://api.github.com/repos/huggingface/datasets/issues/5685/events | https://github.com/huggingface/datasets/issues/5685 | 1,646,048,667 | I_kwDODunzps5iHLWb | 5,685 | Broken Image render on the hub website | {
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} | null | null | null | ### Describe the bug
Hi :wave:
Not sure if this is the right place to ask, but I am trying to load a huge amount of datasets on the hub (:partying_face: ) but I am facing a little issue with the `image` type
See this [dataset](https://huggingface.co/datasets/Francesco/cell-towers), basically for some reason the first image has numerical bytes inside, not sure if that is okay, but the image render feature **doesn't work**
So the dataset is stored in the following way
```python
builder.download_and_prepare(output_dir=str(output_dir))
ds = builder.as_dataset(split="train")
# [NOTE] no idea how to push it from the builder folder
ds.push_to_hub(repo_id=repo_id)
builder.as_dataset(split="validation").push_to_hub(repo_id=repo_id)
ds = builder.as_dataset(split="test")
ds.push_to_hub(repo_id=repo_id)
```
The build is this class
```python
class COCOLikeDatasetBuilder(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"image_id": datasets.Value("int64"),
"image": datasets.Image(),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
"objects": datasets.Sequence(
{
"id": datasets.Value("int64"),
"area": datasets.Value("int64"),
"bbox": datasets.Sequence(
datasets.Value("float32"), length=4
),
"category": datasets.ClassLabel(names=categories),
}
),
}
)
return datasets.DatasetInfo(
description=description,
features=features,
homepage=homepage,
license=license,
citation=citation,
)
def _split_generators(self, dl_manager):
archive = dl_manager.download(url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"annotation_file_path": "train/_annotations.coco.json",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"annotation_file_path": "test/_annotations.coco.json",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"annotation_file_path": "valid/_annotations.coco.json",
"files": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, annotation_file_path, files):
def process_annot(annot, category_id_to_category):
return {
"id": annot["id"],
"area": annot["area"],
"bbox": annot["bbox"],
"category": category_id_to_category[annot["category_id"]],
}
image_id_to_image = {}
idx = 0
# This loop relies on the ordering of the files in the archive:
# Annotation files come first, then the images.
for path, f in files:
file_name = os.path.basename(path)
if annotation_file_path in path:
annotations = json.load(f)
category_id_to_category = {
category["id"]: category["name"]
for category in annotations["categories"]
}
print(category_id_to_category)
image_id_to_annotations = collections.defaultdict(list)
for annot in annotations["annotations"]:
image_id_to_annotations[annot["image_id"]].append(annot)
image_id_to_image = {
annot["file_name"]: annot for annot in annotations["images"]
}
elif file_name in image_id_to_image:
image = image_id_to_image[file_name]
objects = [
process_annot(annot, category_id_to_category)
for annot in image_id_to_annotations[image["id"]]
]
print(file_name)
yield idx, {
"image_id": image["id"],
"image": {"path": path, "bytes": f.read()},
"width": image["width"],
"height": image["height"],
"objects": objects,
}
idx += 1
```
Basically, I want to add to the hub every dataset I come across on coco format
Thanks
Fra
### Steps to reproduce the bug
In this case, you can just navigate on the [dataset](https://huggingface.co/datasets/Francesco/cell-towers)
### Expected behavior
I was expecting the image rendering feature to work
### Environment info
Not a lot to share, I am using `datasets` from a fresh venv | {
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} | This PR fixes the values assigned to `verification_mode` when passing `ignore_verifications` to `load_dataset`.
Related to:
- #5303
Fix #5682. | {
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} | null | null | null | When passing `ignore_verifications=True` to `load_dataset`, we get a ValueError:
```
ValueError: 'none' is not a valid VerificationMode
``` | {
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} | null | null | null | Following [this](https://github.com/huggingface/datasets/issues/5650) issue I think we should add a note about the order of patterns that is used to find splits, see [my comment](https://github.com/huggingface/datasets/issues/5650#issuecomment-1488412527). Also we should reference this page in pages about packaged loaders.
I have a déjà vu that it had already been discussed as some point but I don't remember.... | {
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https://api.github.com/repos/huggingface/datasets/issues/5680 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5680/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5680/comments | https://api.github.com/repos/huggingface/datasets/issues/5680/events | https://github.com/huggingface/datasets/pull/5680 | 1,645,430,103 | PR_kwDODunzps5NJYNz | 5,680 | Fix a description error for interleave_datasets. | {
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} | There is a description mistake in the annotation of interleave_dataset with "all_exhausted" stopping_strategy.
``` python
d1 = Dataset.from_dict({"a": [0, 1, 2]})
d2 = Dataset.from_dict({"a": [10, 11, 12, 13]})
d3 = Dataset.from_dict({"a": [20, 21, 22, 23, 24]})
dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted")
```
According to the interleave way, the correct output of `dataset["a"]` is `[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 10, 24]`, not `[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 0, 24]` | {
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https://api.github.com/repos/huggingface/datasets/issues/5679 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5679/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5679/comments | https://api.github.com/repos/huggingface/datasets/issues/5679/events | https://github.com/huggingface/datasets/issues/5679 | 1,645,184,622 | I_kwDODunzps5iD4Zu | 5,679 | Allow load_dataset to take a working dir for intermediate data | {
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As a user, I can set a working dir for intermediate data creation. The processed files will be moved to the cache dir, like
```
load_dataset(…, working_dir=”/temp/dir”, cache_dir=”/cloud_dir”).
```
### Motivation
This will help the use case for using datasets with cloud storage as cache. It will help boost the performance.
### Your contribution
I can provide a PR to fix this if the proposal seems reasonable. | null | {
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} | null | null | null | ### Feature request
Add a new API `Dataset.from_spark` to create a Dataset from Spark DataFrame.
### Motivation
Spark is a distributed computing framework that can handle large datasets. By supporting loading Spark DataFrames directly into Hugging Face Datasets, we enable take the advantages of spark to processing the data in parallel.
By providing a seamless integration between these two frameworks, we make it easier for data scientists and developers to work with both Spark and Hugging Face in the same workflow.
### Your contribution
We can discuss about the ideas and I can help preparing a PR for this feature. | {
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`Dataset.map()` crashes any time any column contains more than `writer_batch_size` (default 1000) empty dictionaries, regardless of whether the column is being operated on. The error does not occur if the dictionaries are non-empty.
### Steps to reproduce the bug
Example:
```
import datasets
def add_one(example):
example["col2"] += 1
return example
n = 1001 # crashes
# n = 999 # works
ds = datasets.Dataset.from_dict({"col1": [{}] * n, "col2": [1] * n})
ds = ds.map(add_one, writer_batch_size=1000)
```
### Expected behavior
Above code should not crash
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.10
- Python version: 3.8.15
- PyArrow version: 9.0.0
- Pandas version: 1.5.3 | {
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However, some of the datasets tagged by ISO language code are not accessible by this search form.
For example, [myv_ru_2022](https://huggingface.co/datasets/slone/myv_ru_2022) is has `myv` language tag but it is not included in Languages search form.
I've also noticed the same problem with `mhr` (see https://huggingface.co/datasets/AigizK/mari-russian-parallel-corpora) | {
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https://api.github.com/repos/huggingface/datasets/issues/5673 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5673/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5673/comments | https://api.github.com/repos/huggingface/datasets/issues/5673/events | https://github.com/huggingface/datasets/pull/5673 | 1,641,066,352 | PR_kwDODunzps5M6wc3 | 5,673 | Pass down storage options | {
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} | Remove implementation-specific kwargs from `file_utils.fsspec_get` and `file_utils.fsspec_head`, instead allowing them to be passed down via `storage_options`. This fixes an issue where s3fs did not recognize a timeout arg as well as fixes an issue mentioned in https://github.com/huggingface/datasets/issues/5281 by allowing users to pass down `storage_options` all the way from `datasets.load_dataset` to support implementation-specific credentials
Supports something like the following to provide credentials explicitly instead of relying on boto's methods of locating them
```
load_dataset(..., data_files=["s3://..."], storage_options={"profile": "..."})
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5672 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5672/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5672/comments | https://api.github.com/repos/huggingface/datasets/issues/5672/events | https://github.com/huggingface/datasets/issues/5672 | 1,641,005,322 | I_kwDODunzps5hz8EK | 5,672 | Pushing dataset to hub crash | {
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} | null | null | null | ### Describe the bug
Uploading a dataset with `push_to_hub()` fails without error description.
### Steps to reproduce the bug
Hey there,
I've built a image dataset of 100k images + text pair as described here https://huggingface.co/docs/datasets/image_dataset#imagefolder
Now I'm trying to push it to the hub but I'm running into issues. First, I tried doing it via git directly, I added all the files in git lfs and pushed but I got hit with an error saying huggingface only accept up to 10k files in a folder.
So I'm now trying with the `push_to_hub()` func as follow:
```python
from datasets import load_dataset
import os
dataset = load_dataset("imagefolder", data_dir="./data", split="train")
dataset.push_to_hub("tzvc/organization-logos", token=os.environ.get('HF_TOKEN'))
```
But again, this produces an error:
```
Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 100212/100212 [00:00<00:00, 439108.61it/s]
Downloading and preparing dataset imagefolder/default to /home/contact_theochampion/.cache/huggingface/datasets/imagefolder/default-20567ffc703aa314/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f...
Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 100211/100211 [00:00<00:00, 149323.73it/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15947.92it/s]
Extracting data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2245.34it/s]
Dataset imagefolder downloaded and prepared to /home/contact_theochampion/.cache/huggingface/datasets/imagefolder/default-20567ffc703aa314/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f. Subsequent calls will reuse this data.
Resuming upload of the dataset shards.
Pushing dataset shards to the dataset hub: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 14/14 [00:31<00:00, 2.24s/it]
Downloading metadata: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 118/118 [00:00<00:00, 225kB/s]
Traceback (most recent call last):
File "/home/contact_theochampion/organization-logos/push_to_hub.py", line 5, in <module>
dataset.push_to_hub("tzvc/organization-logos", token=os.environ.get('HF_TOKEN'))
File "/home/contact_theochampion/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 5245, in push_to_hub
repo_info = dataset_infos[next(iter(dataset_infos))]
StopIteration
```
What could be happening here ?
### Expected behavior
The dataset is pushed to the hub
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.10.0-21-cloud-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5671 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5671/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5671/comments | https://api.github.com/repos/huggingface/datasets/issues/5671/events | https://github.com/huggingface/datasets/issues/5671 | 1,640,840,012 | I_kwDODunzps5hzTtM | 5,671 | How to use `load_dataset('glue', 'cola')` | {
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} | [] | closed | false | null | [] | null | 2 | 2023-03-26T09:40:34 | 2023-03-28T07:43:44 | 2023-03-28T07:43:43 | NONE | null | {
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} | null | null | null | ### Describe the bug
I'm new to use HuggingFace datasets but I cannot use `load_dataset('glue', 'cola')`.
- I was stacked by the following problem:
```python
from datasets import load_dataset
cola_dataset = load_dataset('glue', 'cola')
---------------------------------------------------------------------------
InvalidVersion Traceback (most recent call last)
File <timed exec>:1
(Omit because of long error message)
File /usr/local/lib/python3.8/site-packages/packaging/version.py:197, in Version.__init__(self, version)
195 match = self._regex.search(version)
196 if not match:
--> 197 raise InvalidVersion(f"Invalid version: '{version}'")
199 # Store the parsed out pieces of the version
200 self._version = _Version(
201 epoch=int(match.group("epoch")) if match.group("epoch") else 0,
202 release=tuple(int(i) for i in match.group("release").split(".")),
(...)
208 local=_parse_local_version(match.group("local")),
209 )
InvalidVersion: Invalid version: '0.10.1,<0.11'
```
- You can check this full error message in my repository: [MLOps-Basics/week_0_project_setup/experimental_notebooks/data_exploration.ipynb](https://github.com/makinzm/MLOps-Basics/blob/eabab4b837880607d9968d3fa687c70177b2affd/week_0_project_setup/experimental_notebooks/data_exploration.ipynb)
### Steps to reproduce the bug
- This is my repository to reproduce: [MLOps-Basics/week_0_project_setup](https://github.com/makinzm/MLOps-Basics/tree/eabab4b837880607d9968d3fa687c70177b2affd/week_0_project_setup)
1. cd `/DockerImage` and command `docker build . -t week0`
2. cd `/` and command `docker-compose up`
3. Run `experimental_notebooks/data_exploration.ipynb`
----
Just to be sure, I wrote down Dockerfile and requirements.txt
- Dockerfile
```Dockerfile
FROM python:3.8
WORKDIR /root/working
RUN apt-get update && \
apt-get install -y python3-dev python3-pip python3-venv && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
COPY requirements.txt .
RUN pip3 install --no-cache-dir jupyter notebook && pip install --no-cache-dir -r requirements.txt
CMD ["bash"]
```
- requirements.txt
```txt
pytorch-lightning==1.2.10
datasets==1.6.2
transformers==4.5.1
scikit-learn==0.24.2
```
### Expected behavior
There is no bug to implement `load_dataset('glue', 'cola')`
### Environment info
I already wrote it. | {
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https://api.github.com/repos/huggingface/datasets/issues/5670 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5670/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5670/comments | https://api.github.com/repos/huggingface/datasets/issues/5670/events | https://github.com/huggingface/datasets/issues/5670 | 1,640,607,045 | I_kwDODunzps5hya1F | 5,670 | Unable to load multi class classification datasets | {
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} | null | null | null | ### Describe the bug
I've been playing around with huggingface library, mostly with `datasets` and wanted to download the multi class classification datasets to fine tune BERT on this task. ([link](https://huggingface.co/docs/transformers/training#train-with-pytorch-trainer)).
While loading the dataset, I'm getting the following error snippet.
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[44], line 3
1 from datasets import load_dataset
----> 3 imdb_dataset = load_dataset("yelp_review_full")
4 imdb_dataset
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/load.py:1719, 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)
1716 ignore_verifications = ignore_verifications or save_infos
1718 # Create a dataset builder
-> 1719 builder_instance = load_dataset_builder(
1720 path=path,
1721 name=name,
1722 data_dir=data_dir,
1723 data_files=data_files,
1724 cache_dir=cache_dir,
1725 features=features,
1726 download_config=download_config,
1727 download_mode=download_mode,
1728 revision=revision,
1729 use_auth_token=use_auth_token,
1730 **config_kwargs,
1731 )
1733 # Return iterable dataset in case of streaming
1734 if streaming:
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/load.py:1523, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1520 raise ValueError(error_msg)
1522 # Instantiate the dataset builder
-> 1523 builder_instance: DatasetBuilder = builder_cls(
1524 cache_dir=cache_dir,
1525 config_name=config_name,
1526 data_dir=data_dir,
1527 data_files=data_files,
1528 hash=hash,
1529 features=features,
1530 use_auth_token=use_auth_token,
1531 **builder_kwargs,
1532 **config_kwargs,
1533 )
1535 return builder_instance
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/builder.py:1292, in GeneratorBasedBuilder.__init__(self, writer_batch_size, *args, **kwargs)
1291 def __init__(self, *args, writer_batch_size=None, **kwargs):
-> 1292 super().__init__(*args, **kwargs)
1293 # Batch size used by the ArrowWriter
1294 # It defines the number of samples that are kept in memory before writing them
1295 # and also the length of the arrow chunks
1296 # None means that the ArrowWriter will use its default value
1297 self._writer_batch_size = writer_batch_size or self.DEFAULT_WRITER_BATCH_SIZE
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/builder.py:312, in DatasetBuilder.__init__(self, cache_dir, config_name, hash, base_path, info, features, use_auth_token, repo_id, data_files, data_dir, name, **config_kwargs)
309 # prepare info: DatasetInfo are a standardized dataclass across all datasets
310 # Prefill datasetinfo
311 if info is None:
--> 312 info = self.get_exported_dataset_info()
313 info.update(self._info())
314 info.builder_name = self.name
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/builder.py:412, in DatasetBuilder.get_exported_dataset_info(self)
400 def get_exported_dataset_info(self) -> DatasetInfo:
401 """Empty DatasetInfo if doesn't exist
402
403 Example:
(...)
410 ```
411 """
--> 412 return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/builder.py:398, in DatasetBuilder.get_all_exported_dataset_infos(cls)
385 @classmethod
386 def get_all_exported_dataset_infos(cls) -> DatasetInfosDict:
387 """Empty dict if doesn't exist
388
389 Example:
(...)
396 ```
397 """
--> 398 return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/info.py:370, in DatasetInfosDict.from_directory(cls, dataset_infos_dir)
368 dataset_metadata = DatasetMetadata.from_readme(Path(dataset_infos_dir) / "README.md")
369 if "dataset_info" in dataset_metadata:
--> 370 return cls.from_metadata(dataset_metadata)
371 if os.path.exists(os.path.join(dataset_infos_dir, config.DATASETDICT_INFOS_FILENAME)):
372 # this is just to have backward compatibility with dataset_infos.json files
373 with open(os.path.join(dataset_infos_dir, config.DATASETDICT_INFOS_FILENAME), encoding="utf-8") as f:
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/info.py:396, in DatasetInfosDict.from_metadata(cls, dataset_metadata)
387 return cls(
388 {
389 dataset_info_yaml_dict.get("config_name", "default"): DatasetInfo._from_yaml_dict(
(...)
393 }
394 )
395 else:
--> 396 dataset_info = DatasetInfo._from_yaml_dict(dataset_metadata["dataset_info"])
397 dataset_info.config_name = dataset_metadata["dataset_info"].get("config_name", "default")
398 return cls({dataset_info.config_name: dataset_info})
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/info.py:332, in DatasetInfo._from_yaml_dict(cls, yaml_data)
330 yaml_data = copy.deepcopy(yaml_data)
331 if yaml_data.get("features") is not None:
--> 332 yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
333 if yaml_data.get("splits") is not None:
334 yaml_data["splits"] = SplitDict._from_yaml_list(yaml_data["splits"])
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1745, in Features._from_yaml_list(cls, yaml_data)
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1741, in Features._from_yaml_list.<locals>.from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1741, in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1736, in Features._from_yaml_list.<locals>.from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1738, in Features._from_yaml_list.<locals>.from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1706, in Features._from_yaml_list.<locals>.unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
1709 )
1710 feature["class_label"]["names"] = [feature["class_label"]["names"][label_id] for label_id in label_ids]
TypeError: can only concatenate str (not "int") to str
```
The same issue happens when I try to load `go-emotions` multi class classification dataset. Could somebody guide me on how to fix this issue?
### Steps to reproduce the bug
Run the following code snippet in a python script/ notebook cell:
```
from datasets import load_dataset
yelp_dataset = load_dataset("yelp_review_full")
yelp_dataset
```
### Expected behavior
The dataset should be loaded perfectly, which showing the train, test and unsupervised splits with the basic data statistics
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-124-generic-x86_64-with-glibc2.31
- Python version: 3.10.9
- PyArrow version: 8.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5669 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5669/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5669/comments | https://api.github.com/repos/huggingface/datasets/issues/5669/events | https://github.com/huggingface/datasets/issues/5669 | 1,638,070,046 | I_kwDODunzps5hovce | 5,669 | Almost identical datasets, huge performance difference | {
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"total": 0,
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} | null | null | null | ### Describe the bug
I am struggling to understand (huge) performance difference between two datasets that are almost identical.
### Steps to reproduce the bug
# Fast (normal) dataset speed:
```python
import cv2
from datasets import load_dataset
from torch.utils.data import DataLoader
dataset = load_dataset("beans", split="train")
for x in DataLoader(dataset.with_format("torch"), batch_size=16, shuffle=True, num_workers=8):
pass
```
The above pass over the dataset takes about 1.5 seconds on my computer.
However, if I re-create (almost) the same dataset, the sweep takes HUGE amount of time: 15 minutes. Steps to reproduce:
```python
def transform(example):
example["image2"] = cv2.imread(example["image_file_path"])
return example
dataset2 = dataset.map(transform, remove_columns=["image"])
for x in DataLoader(dataset2.with_format("torch"), batch_size=16, shuffle=True, num_workers=8):
pass
```
### Expected behavior
Same timings
### Environment info
python==3.10.9
datasets==2.10.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5668 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5668/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5668/comments | https://api.github.com/repos/huggingface/datasets/issues/5668/events | https://github.com/huggingface/datasets/pull/5668 | 1,638,018,598 | PR_kwDODunzps5MwuIp | 5,668 | Support for downloading only provided split | {
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} | We can pass split to `_split_generators()`.
But I'm not sure if it's possible to solve cache issues, mostly with `dataset_info.json` | null | {
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- #5663
we should revert the temporary pin on tensorflow introduced by:
- #5664 | {
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] | open | false | null | [] | null | 11 | 2023-03-23T09:53:04 | 2025-04-05T15:10:05 | null | CONTRIBUTOR | null | {
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} | null | null | null | ### Feature request
It'd be great to have a lazy push to hub, similar to the lazy loading we have with `IterableDataset`.
Suppose you'd like to filter [LAION](https://huggingface.co/datasets/laion/laion400m) based on certain conditions, but as LAION doesn't fit into your disk, you'd like to leverage streaming:
```
from datasets import load_dataset
dataset = load_dataset("laion/laion400m", streaming=True, split="train")
```
Then you could filter the dataset based on certain conditions:
```
filtered_dataset = dataset.filter(lambda example: example['HEIGHT'] > 400)
```
In order to persist this dataset and push it back to the hub, one currently needs to first load the entire filtered dataset on disk and then push:
```
from datasets import Dataset
Dataset.from_generator(filtered_dataset.__iter__).push_to_hub(...)
```
It would be great if we can instead lazy push to the data to the hub (basically stream the data to the hub), not being limited by our disk size:
```
filtered_dataset.push_to_hub("my-filtered-dataset")
```
### Motivation
This feature would be very useful for people that want to filter huge datasets without having to load the entire dataset or a filtered version thereof on their local disk.
### Your contribution
Happy to test out a PR :) | null | {
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} | As a hotfix for our CI, temporarily pin `tensorflow` upper version:
- In Python 3.10, tensorflow-2.12.0 also installs `jax`
Fix #5663
Until root cause is fixed. | {
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```
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_jax_in_memory - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_jax_on_disk - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_audio - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_device - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_image - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_jnp_array_kwargs - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/features/test_features.py::CastToPythonObjectsTest::test_cast_to_python_objects_jax - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
===== 8 failed, 2147 passed, 10 skipped, 37 warnings in 228.69s (0:03:48) ======
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} | After ruff-0.0.258 release, the C416 rule was updated with unnecessary dict comprehensions. See:
- https://github.com/charliermarsh/ruff/releases/tag/v0.0.258
- https://github.com/charliermarsh/ruff/pull/3605
This PR fixes one unnecessary dict comprehension in our code: no need to unpack and re-pack the tuple values.
Fix #5661 | {
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```
src/datasets/arrow_dataset.py:3267:35: C416 [*] Unnecessary `dict` comprehension (rewrite using `dict()`)
Found 1 error.
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5660 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5660/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5660/comments | https://api.github.com/repos/huggingface/datasets/issues/5660/events | https://github.com/huggingface/datasets/issues/5660 | 1,635,543,646 | I_kwDODunzps5hfGpe | 5,660 | integration with imbalanced-learn | {
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] | closed | false | null | [] | null | 1 | 2023-03-22T11:05:17 | 2023-07-06T18:10:15 | 2023-07-06T18:10:15 | NONE | null | {
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} | null | null | null | ### Feature request
Wouldn't it be great if the various class balancing operations from imbalanced-learn were available as part of datasets?
### Motivation
I'm trying to use imbalanced-learn to balance a dataset, but it's not clear how to get the two to interoperate - what would be great would be some examples. I've looked online, asked gpt-4, but so far not making much progress.
### Your contribution
If I can get this working myself I can submit a PR with example code to go in the docs | {
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https://api.github.com/repos/huggingface/datasets/issues/5659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5659/comments | https://api.github.com/repos/huggingface/datasets/issues/5659/events | https://github.com/huggingface/datasets/issues/5659 | 1,635,447,540 | I_kwDODunzps5hevL0 | 5,659 | [Audio] Soundfile/libsndfile requirements too stringent for decoding mp3 files | {
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} | null | null | null | ### Describe the bug
I'm encountering several issues trying to load mp3 audio files using `datasets` on a TPU v4.
The PR https://github.com/huggingface/datasets/pull/5573 updated the audio loading logic to rely solely on the `soundfile`/`libsndfile` libraries for loading audio samples, regardless of their file type.
The installation guide suggests that `libsndfile` is bundled in when `soundfile` is pip installed:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/docs/source/installation.md?plain=1#L70-L71
However, just pip installing `soundfile==0.12.1` throws an error that `libsndfile` is missing:
```
pip install soundfile==0.12.1
```
Then:
```python
>>> soundfile
>>> soundfile.__libsndfile_version__
```
<details>
<summary> Traceback (most recent call last): </summary>
```
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 161, in <module>
import _soundfile_data # ImportError if this doesn't exist
ModuleNotFoundError: No module named '_soundfile_data'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 170, in <module>
raise OSError('sndfile library not found using ctypes.util.find_library')
OSError: sndfile library not found using ctypes.util.find_library
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 192, in <module>
_snd = _ffi.dlopen(_explicit_libname)
OSError: cannot load library 'libsndfile.so': libsndfile.so: cannot open shared object file: No such file or directory
```
</details>
Thus, I've followed the official instructions for installing the `soundfile` package from https://github.com/bastibe/python-soundfile#installation, which states that `libsndfile` needs to be installed separately as:
```
pip install --upgrade soundfile
sudo apt install libsndfile1
```
We can now import `soundfile`:
```python
>>> import soundfile
>>> soundfile.__version__
'0.12.1'
>>> soundfile.__libsndfile_version__
'1.0.28'
```
We see that we have `soundfile==0.12.1`, which matches the `datasets[audio]` package constraints:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/setup.py#L144-L147
But we have `libsndfile==1.0.28`, which is too low for decoding mp3 files:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/src/datasets/config.py#L136-L138
Updating/upgrading the `libsndfile` doesn't change this:
```
sudo apt-get update
sudo apt-get upgrade
```
Is there any other suggestion for how to get a compatible `libsndfile` version? Currently, the version bundled with Ubuntu `apt-get` is too low for decoding mp3 files.
Maybe we could add this under `setup.py` such that we install the correct `libsndfile` version when we do `pip install datasets[audio]`? IMO this would help circumvent such version issues.
### Steps to reproduce the bug
Environment described above. Loading mp3 files:
```python
from datasets import load_dataset
common_voice_es = load_dataset("common_voice", "es", split="validation", streaming=True)
print(next(iter(common_voice_es)))
```
```python
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[4], line 2
1 common_voice_es = load_dataset("common_voice", "es", split="validation", streaming=True)
----> 2 print(next(iter(common_voice_es)))
File ~/datasets/src/datasets/iterable_dataset.py:941, in IterableDataset.__iter__(self)
937 for key, example in ex_iterable:
938 if self.features:
939 # `IterableDataset` automatically fills missing columns with None.
940 # This is done with `_apply_feature_types_on_example`.
--> 941 yield _apply_feature_types_on_example(
942 example, self.features, token_per_repo_id=self._token_per_repo_id
943 )
944 else:
945 yield example
File ~/datasets/src/datasets/iterable_dataset.py:700, in _apply_feature_types_on_example(example, features, token_per_repo_id)
698 encoded_example = features.encode_example(example)
699 # Decode example for Audio feature, e.g.
--> 700 decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
701 return decoded_example
File ~/datasets/src/datasets/features/features.py:1864, in Features.decode_example(self, example, token_per_repo_id)
1850 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1851 """Decode example with custom feature decoding.
1852
1853 Args:
(...)
1861 `dict[str, Any]`
1862 """
-> 1864 return {
1865 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1866 if self._column_requires_decoding[column_name]
1867 else value
1868 for column_name, (feature, value) in zip_dict(
1869 {key: value for key, value in self.items() if key in example}, example
1870 )
1871 }
File ~/datasets/src/datasets/features/features.py:1865, in <dictcomp>(.0)
1850 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1851 """Decode example with custom feature decoding.
1852
1853 Args:
(...)
1861 `dict[str, Any]`
1862 """
1864 return {
-> 1865 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1866 if self._column_requires_decoding[column_name]
1867 else value
1868 for column_name, (feature, value) in zip_dict(
1869 {key: value for key, value in self.items() if key in example}, example
1870 )
1871 }
File ~/datasets/src/datasets/features/features.py:1308, in decode_nested_example(schema, obj, token_per_repo_id)
1305 elif isinstance(schema, (Audio, Image)):
1306 # we pass the token to read and decode files from private repositories in streaming mode
1307 if obj is not None and schema.decode:
-> 1308 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
1309 return obj
File ~/datasets/src/datasets/features/audio.py:167, in Audio.decode_example(self, value, token_per_repo_id)
162 raise RuntimeError(
163 "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
164 'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '
165 )
166 elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
--> 167 raise RuntimeError(
168 "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
169 'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '
170 )
172 if file is None:
173 token_per_repo_id = token_per_repo_id or {}
RuntimeError: Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`.
```
### Expected behavior
Load mp3 files!
### Environment info
- `datasets` version: 2.10.2.dev0
- Platform: Linux-5.13.0-1023-gcp-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.13.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- Soundfile version: 0.12.1
- Libsndfile version: 1.0.28 | {
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https://api.github.com/repos/huggingface/datasets/issues/5658 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5658/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5658/comments | https://api.github.com/repos/huggingface/datasets/issues/5658/events | https://github.com/huggingface/datasets/pull/5658 | 1,634,867,204 | PR_kwDODunzps5MmJe0 | 5,658 | docs: Update num_shards docs to mention num_proc on Dataset and DatasetDict | {
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} | Closes #5653
@mariosasko | {
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https://api.github.com/repos/huggingface/datasets/issues/5656 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5656/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5656/comments | https://api.github.com/repos/huggingface/datasets/issues/5656/events | https://github.com/huggingface/datasets/pull/5656 | 1,634,156,563 | PR_kwDODunzps5Mjxoo | 5,656 | Fix `fsspec.open` when using an HTTP proxy | {
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} | Most HTTP(S) downloads from this library support proxy automatically by reading the `HTTP_PROXY` environment variable (et al.) because `requests` is widely used. However, in some parts of the code, `fsspec` is used, which in turn uses `aiohttp` for HTTP(S) requests (as opposed to `requests`), which in turn doesn't support reading proxy env variables by default. This PR enables reading them automatically.
Read [aiohttp docs on using proxies](https://docs.aiohttp.org/en/stable/client_advanced.html?highlight=trust_env#proxy-support).
For context, [the Python library requests](https://requests.readthedocs.io/en/latest/user/advanced/?highlight=http_proxy#proxies) and [the official Python library via `urllib.urlopen` support this automatically by default](https://docs.python.org/3/library/urllib.request.html#urllib.request.urlopen). Many (most common ones?) programs also do the same, including cURL, APT, Wget, and many others. | {
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https://api.github.com/repos/huggingface/datasets/issues/5655 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5655/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5655/comments | https://api.github.com/repos/huggingface/datasets/issues/5655/events | https://github.com/huggingface/datasets/pull/5655 | 1,634,030,017 | PR_kwDODunzps5MjWYy | 5,655 | Improve features decoding in to_iterable_dataset | {
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} | Following discussion at https://github.com/huggingface/datasets/pull/5589
Right now `to_iterable_dataset` on images/audio hurts iterable dataset performance a lot (e.g. x4 slower because it encodes+decodes images/audios unnecessarily).
I fixed it by providing a generator that yields undecoded examples | {
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https://api.github.com/repos/huggingface/datasets/issues/5654 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5654/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5654/comments | https://api.github.com/repos/huggingface/datasets/issues/5654/events | https://github.com/huggingface/datasets/issues/5654 | 1,633,523,705 | I_kwDODunzps5hXZf5 | 5,654 | Offset overflow when executing Dataset.map | {
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} | null | null | null | ### Describe the bug
Hi, I'm trying to use `.map` method to cache multiple random crops from the image to speed up data processing during training, as the image size is too big.
The map function executes all iterations, and then returns the following error:
```bash
Traceback (most recent call last):
File "/home/ubuntu/miniconda3/envs/enhancement/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3353, in _map_single
writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
File "/home/ubuntu/miniconda3/envs/enhancement/lib/python3.8/site-packages/datasets/arrow_writer.py", line 582, in finalize
self.write_examples_on_file()
File "/home/ubuntu/miniconda3/envs/enhancement/lib/python3.8/site-packages/datasets/arrow_writer.py", line 446, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/home/ubuntu/miniconda3/envs/enhancement/lib/python3.8/site-packages/datasets/arrow_writer.py", line 555, in write_batch
self.write_table(pa_table, writer_batch_size)
File "/home/ubuntu/miniconda3/envs/enhancement/lib/python3.8/site-packages/datasets/arrow_writer.py", line 567, in write_table
pa_table = pa_table.combine_chunks()
File "pyarrow/table.pxi", line 3315, in pyarrow.lib.Table.combine_chunks
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
```
Here is the minimal code (`/home/datasets/DIV2K_train_HR` is just a folder of images that can be replaced by any appropriate):
### Steps to reproduce the bug
```python
from glob import glob
import torch
from datasets import Dataset, Image
from torchvision.transforms import PILToTensor, RandomCrop
file_paths = glob("/home/datasets/DIV2K_train_HR/*")
to_tensor = PILToTensor()
crop_transf = RandomCrop(size=256)
def prepare_data(example):
tensor = to_tensor(example["image"].convert("RGB"))
return {"hr": torch.stack([crop_transf(tensor) for _ in range(25)])}
train_data = Dataset.from_dict({"image": file_paths}).cast_column("image", Image())
train_data = train_data.map(
prepare_data,
cache_file_name="/home/datasets/DIV2K_train_HR_crops.tmp",
desc="Caching multiple random crops of image",
remove_columns="image",
)
print(train_data[0].keys(), train_data[0]["hr"].shape)
```
### Expected behavior
Cached file is stored at `"/home/datasets/DIV2K_train_HR_crops.tmp"`, output is `dict_keys(['hr']) torch.Size([25, 3, 256, 256])`
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.10
- Python version: 3.8.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- Pytorch version: 2.0.0+cu117
- torchvision version: 0.15.1+cu117 | null | {
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} | null | null | null | ### Describe the bug
[`num_proc`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.DatasetDict.save_to_disk.num_proc) will affect `num_shards`, but it's not documented
### Steps to reproduce the bug
Nothing to reproduce
### Expected behavior
[document of `num_shards`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.DatasetDict.save_to_disk.num_shards) explicitly says that it depends on `max_shard_size`, it should also mention `num_proc`.
### Environment info
datasets main document | {
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} | Some users (even internally at HF) are doing
```python
dset_features = dset.features
dset_features.pop(col_to_remove)
dset = dset.map(..., features=dset_features)
```
Right now this causes issues because it modifies the features dict in place before the map.
In this PR I modified `dset.features` to return a copy of the features, so that users can modify it if they want. | {
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https://api.github.com/repos/huggingface/datasets/issues/5651 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5651/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5651/comments | https://api.github.com/repos/huggingface/datasets/issues/5651/events | https://github.com/huggingface/datasets/issues/5651 | 1,631,967,509 | I_kwDODunzps5hRdkV | 5,651 | expanduser in save_to_disk | {
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] | null | 5 | 2023-03-20T12:02:18 | 2023-10-27T14:04:37 | 2023-10-27T14:04:37 | NONE | null | {
"total": 0,
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} | null | null | null | ### Describe the bug
save_to_disk() does not expand `~`
1. `dataset = load_datasets("any dataset")`
2. `dataset.save_to_disk("~/data")`
3. a folder named "~" created in current folder
4. FileNotFoundError is raised, because the expanded path does not exist (`/home/<user>/data`)
related issue https://github.com/huggingface/transformers/issues/10628
### Steps to reproduce the bug
As described above.
### Expected behavior
expanduser correctly
### Environment info
- datasets 2.10.1
- python 3.10 | {
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https://api.github.com/repos/huggingface/datasets/issues/5650 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5650/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5650/comments | https://api.github.com/repos/huggingface/datasets/issues/5650/events | https://github.com/huggingface/datasets/issues/5650 | 1,630,336,919 | I_kwDODunzps5hLPeX | 5,650 | load_dataset can't work correct with my image data | {
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} | null | null | null | I have about 20000 images in my folder which divided into 4 folders with class names.
When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked | {
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