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https://api.github.com/repos/huggingface/datasets/issues/6080 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6080/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6080/comments | https://api.github.com/repos/huggingface/datasets/issues/6080/events | https://github.com/huggingface/datasets/pull/6080 | 1,822,667,554 | PR_kwDODunzps5WdL4K | 6,080 | Remove README link to deprecated Colab notebook | {
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https://api.github.com/repos/huggingface/datasets/issues/6079 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6079/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6079/comments | https://api.github.com/repos/huggingface/datasets/issues/6079/events | https://github.com/huggingface/datasets/issues/6079 | 1,822,597,471 | I_kwDODunzps5soqFf | 6,079 | Iterating over DataLoader based on HF datasets is stuck forever | {
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} | [] | open | false | null | [] | null | 11 | "2023-07-26T14:52:37" | "2023-07-26T19:14:16" | null | NONE | null | null | null | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here?
### Steps to reproduce the bug
```
train_dataset = load_dataset(
"parquet", data_files = {'train': tr_data_path + '*.parquet'},
split = 'train',
collate_fn = streaming_data_collate_fn,
streaming = True
).with_format('torch')
train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0)
t = time.time()
iter_ = 0
for batch in train_dataloader:
iter_ += 1
if iter_ == 1000:
break
print (time.time() - t)
```
### Expected behavior
The snippet should work normally and load the next batch of data.
### Environment info
datasets: '2.14.0'
pyarrow: '12.0.0'
torch: '2.0.0'
Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0]
!uname -r
5.10.178-162.673.amzn2.x86_64 | {
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https://api.github.com/repos/huggingface/datasets/issues/6078 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6078/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6078/comments | https://api.github.com/repos/huggingface/datasets/issues/6078/events | https://github.com/huggingface/datasets/issues/6078 | 1,822,501,472 | I_kwDODunzps5soSpg | 6,078 | resume_download with streaming=True | {
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} | [] | open | false | null | [] | null | 1 | "2023-07-26T14:08:22" | "2023-07-26T15:35:25" | null | NONE | null | null | null | ### Describe the bug
I used:
```
dataset = load_dataset(
"oscar-corpus/OSCAR-2201",
token=True,
language="fr",
streaming=True,
split="train"
)
```
Unfortunately, the server had a problem during the training process. I saved the step my training stopped at.
But how can I resume download from step 1_000_´000 without re-streaming all the first 1 million docs of the dataset?
`download_config=DownloadConfig(resume_download=True)` seems to not work with streaming=True.
### Steps to reproduce the bug
```
from datasets import load_dataset, DownloadConfig
dataset = load_dataset(
"oscar-corpus/OSCAR-2201",
token=True,
language="fr",
streaming=True, # optional
split="train",
download_config=DownloadConfig(resume_download=True)
)
# interupt the run and try to relaunch it => this restart from scratch
```
### Expected behavior
I would expect a parameter to start streaming from a given index in the dataset.
### Environment info
- `datasets` version: 2.14.0
- Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6077 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6077/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6077/comments | https://api.github.com/repos/huggingface/datasets/issues/6077/events | https://github.com/huggingface/datasets/issues/6077 | 1,822,486,810 | I_kwDODunzps5soPEa | 6,077 | Mapping gets stuck at 99% | {
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} | [] | open | false | null | [] | null | 1 | "2023-07-26T14:00:40" | "2023-07-26T18:29:10" | null | CONTRIBUTOR | null | null | null | ### Describe the bug
Hi !
I'm currently working with a large (~150GB) unnormalized dataset at work.
The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it.
I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset.
The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why.
Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me.
### Steps to reproduce the bug
I'm able to reproduce the problem using the following scripts:
```python
# random_data.py
import datasets
import torch
_VERSION = "1.0.0"
class RandomDataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
version=_VERSION,
supervised_keys=None,
features=datasets.Features(
{
"positions": datasets.Array2D(
shape=(30000, 3),
dtype="float32",
),
"normals": datasets.Array2D(
shape=(30000, 3),
dtype="float32",
),
"features": datasets.Array2D(
shape=(30000, 6),
dtype="float32",
),
"scalars": datasets.Sequence(
feature=datasets.Value("float32"),
length=20,
),
},
),
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, # type: ignore
gen_kwargs={"nb_samples": 1000},
),
datasets.SplitGenerator(
name=datasets.Split.TEST, # type: ignore
gen_kwargs={"nb_samples": 100},
),
]
def _generate_examples(self, nb_samples: int):
for idx in range(nb_samples):
yield idx, {
"positions": torch.rand(30000, 3),
"normals": torch.rand(30000, 3),
"features": torch.rand(30000, 6),
"scalars": torch.rand(20),
}
```
```python
# main.py
import datasets
import torch
def compute_mean_std(
dataset: datasets.Dataset,
) -> dict[str, torch.Tensor]:
"""Compute the mean and standard deviation of each feature of the dataset.
Args:
dataset (`Dataset`): A huggingface dataset.
Returns:
dict: A dictionary containing the mean and standard deviation of each feature.
"""
result = {}
for key in dataset:
# extract data from dataset
data: torch.Tensor = dataset[key] # type: ignore
# reshape data, from (a, ..., b, c) -> (*, c)
data = data.reshape(-1, data.shape[-1])
# compute mean and std
mean = data.mean(dim=0) # (c)
std = data.std(dim=0) # (c)
# store in result
result[key] = torch.stack((mean, std))
return result
def apply_mean_std(
dataset: datasets.Dataset,
mean_std: datasets.Dataset,
) -> dict[str, torch.Tensor]:
"""Normalize the dataset using the mean and standard deviation of each feature.
Args:
dataset (`Dataset`): A huggingface dataset.
mean_std (`Dataset`): A huggingface dataset containing the mean and standard deviation of each feature.
Returns:
dict: A dictionary containing the normalized dataset.
"""
result = {}
for key in mean_std.column_names:
# extract data from dataset
data: torch.Tensor = dataset[key] # type: ignore
# extract mean and std from dict
mean = mean_std[key][0] # type: ignore
std = mean_std[key][1] # type: ignore
# normalize data
normalized_data = (data - mean) / std
result[key] = normalized_data
return result
# hack to force the map function to use the entire dataset
MAX_MAP_BATCH_SIZE = 1_000_000_000
# get dataset
ds = datasets.load_dataset(
path="random_data.py",
split="train",
).with_format("torch")
# compute mean/std of each feature
mean_std = ds.map(
desc="Computing mean/std", # type: ignore
remove_columns=ds.column_names, # type: ignore
function=compute_mean_std,
batch_size=MAX_MAP_BATCH_SIZE,
batched=True,
)
# normalize each feature of the dataset
ds_normalized = ds.map(
desc="Applying mean/std", # type: ignore
function=apply_mean_std,
batched=False,
fn_kwargs={
"mean_std": mean_std,
},
)
```
### Expected behavior
Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster.
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/6076 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6076/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6076/comments | https://api.github.com/repos/huggingface/datasets/issues/6076/events | https://github.com/huggingface/datasets/pull/6076 | 1,822,345,597 | PR_kwDODunzps5WcGVR | 6,076 | No gzip encoding from github | {
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} | [] | open | false | null | [] | null | 2 | "2023-07-26T12:46:07" | "2023-07-26T14:01:21" | null | MEMBER | null | false | {
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} | Don't accept gzip encoding from github, otherwise some files are not streamable + seekable.
fix https://huggingface.co/datasets/code_x_glue_cc_code_to_code_trans/discussions/2#64c0e0c1a04a514ba6303e84
and making sure https://github.com/huggingface/datasets/issues/2918 works as well | {
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https://api.github.com/repos/huggingface/datasets/issues/6075 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6075/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6075/comments | https://api.github.com/repos/huggingface/datasets/issues/6075/events | https://github.com/huggingface/datasets/issues/6075 | 1,822,341,398 | I_kwDODunzps5snrkW | 6,075 | Error loading music files using `load_dataset` | {
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} | [] | closed | false | null | [] | null | 2 | "2023-07-26T12:44:05" | "2023-07-26T13:08:08" | "2023-07-26T13:08:08" | NONE | null | null | null | ### Describe the bug
I tried to load a music file using `datasets.load_dataset()` from the repository - https://huggingface.co/datasets/susnato/pop2piano_real_music_test
I got the following error -
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__
return self._getitem(key)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2788, in _getitem
formatted_output = format_table(
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 629, in format_table
return formatter(pa_table, query_type=query_type)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 398, in __call__
return self.format_column(pa_table)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 442, in format_column
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 218, in decode_column
return self.features.decode_column(column, column_name) if self.features else column
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in decode_column
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in <listcomp>
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1325, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/audio.py", line 184, in decode_example
array, sampling_rate = sf.read(f)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 372, in read
with SoundFile(file, 'r', samplerate, channels,
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 740, in __init__
self._file = self._open(file, mode_int, closefd)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1264, in _open
_error_check(_snd.sf_error(file_ptr),
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1455, in _error_check
raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace'))
RuntimeError: Error opening <_io.BufferedReader name='/home/susnato/.cache/huggingface/datasets/downloads/d2b09cb974b967b13f91553297c40c0f02f3c0d4c8356350743598ff48d6f29e'>: Format not recognised.
```
### Steps to reproduce the bug
Code to reproduce the error -
```python
from datasets import load_dataset
ds = load_dataset("susnato/pop2piano_real_music_test", split="test")
print(ds[0])
```
### Expected behavior
I should be able to read the music file without any error.
### Environment info
- `datasets` version: 2.14.0
- Platform: Linux-5.19.0-50-generic-x86_64-with-glibc2.35
- Python version: 3.9.16
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/6074 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6074/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6074/comments | https://api.github.com/repos/huggingface/datasets/issues/6074/events | https://github.com/huggingface/datasets/pull/6074 | 1,822,299,128 | PR_kwDODunzps5Wb8O_ | 6,074 | Misc doc improvements | {
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} | [] | open | false | null | [] | null | 2 | "2023-07-26T12:20:54" | "2023-07-26T14:42:56" | null | CONTRIBUTOR | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6074",
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} | Removes the warning about requiring to write a dataset loading script to define multiple configurations, as the README YAML can be used instead (for simple cases). Also, deletes the section about using the `BatchSampler` in `torch<=1.12.1` to speed up loading, as `torch 1.12.1` is over a year old (and `torch 2.0` has been out for a while). | {
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https://api.github.com/repos/huggingface/datasets/issues/6073 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6073/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6073/comments | https://api.github.com/repos/huggingface/datasets/issues/6073/events | https://github.com/huggingface/datasets/issues/6073 | 1,822,167,804 | I_kwDODunzps5snBL8 | 6,073 | version2.3.2 load_dataset()data_files can't include .xxxx in path | {
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} | [] | open | false | null | [] | null | 1 | "2023-07-26T11:09:31" | "2023-07-26T12:34:45" | null | NONE | null | null | null | ### Describe the bug
First, I cd workdir.
Then, I just use load_dataset("json", data_file={"train":"/a/b/c/.d/train/train.json", "test":"/a/b/c/.d/train/test.json"})
that couldn't work and
<FileNotFoundError: Unable to find
'/a/b/c/.d/train/train.jsonl' at
/a/b/c/.d/>
And I debug, it is fine in version2.1.2
So there maybe a bug in path join.
Here is the whole bug report:
/x/datasets/loa │
│ d.py:1656 in load_dataset │
│ │
│ 1653 │ ignore_verifications = ignore_verifications or save_infos │
│ 1654 │ │
│ 1655 │ # Create a dataset builder │
│ ❱ 1656 │ builder_instance = load_dataset_builder( │
│ 1657 │ │ path=path, │
│ 1658 │ │ name=name, │
│ 1659 │ │ data_dir=data_dir, │
│ │
│ x/datasets/loa │
│ d.py:1439 in load_dataset_builder │
│ │
│ 1436 │ if use_auth_token is not None: │
│ 1437 │ │ download_config = download_config.copy() if download_config e │
│ 1438 │ │ download_config.use_auth_token = use_auth_token │
│ ❱ 1439 │ dataset_module = dataset_module_factory( │
│ 1440 │ │ path, │
│ 1441 │ │ revision=revision, │
│ 1442 │ │ download_config=download_config, │
│ │
│ x/datasets/loa │
│ d.py:1097 in dataset_module_factory │
│ │
│ 1094 │ │
│ 1095 │ # Try packaged │
│ 1096 │ if path in _PACKAGED_DATASETS_MODULES: │
│ ❱ 1097 │ │ return PackagedDatasetModuleFactory( │
│ 1098 │ │ │ path, │
│ 1099 │ │ │ data_dir=data_dir, │
│ 1100 │ │ │ data_files=data_files, │
│ │
│x/datasets/loa │
│ d.py:743 in get_module │
│ │
│ 740 │ │ │ if self.data_dir is not None │
│ 741 │ │ │ else get_patterns_locally(str(Path().resolve())) │
│ 742 │ │ ) │
│ ❱ 743 │ │ data_files = DataFilesDict.from_local_or_remote( │
│ 744 │ │ │ patterns, │
│ 745 │ │ │ use_auth_token=self.download_config.use_auth_token, │
│ 746 │ │ │ base_path=str(Path(self.data_dir).resolve()) if self.data │
│ │
│ x/datasets/dat │
│ a_files.py:590 in from_local_or_remote │
│ │
│ 587 │ │ out = cls() │
│ 588 │ │ for key, patterns_for_key in patterns.items(): │
│ 589 │ │ │ out[key] = ( │
│ ❱ 590 │ │ │ │ DataFilesList.from_local_or_remote( │
│ 591 │ │ │ │ │ patterns_for_key, │
│ 592 │ │ │ │ │ base_path=base_path, │
│ 593 │ │ │ │ │ allowed_extensions=allowed_extensions, │
│ │
│ /x/datasets/dat │
│ a_files.py:558 in from_local_or_remote │
│ │
│ 555 │ │ use_auth_token: Optional[Union[bool, str]] = None, │
│ 556 │ ) -> "DataFilesList": │
│ 557 │ │ base_path = base_path if base_path is not None else str(Path() │
│ ❱ 558 │ │ data_files = resolve_patterns_locally_or_by_urls(base_path, pa │
│ 559 │ │ origin_metadata = _get_origin_metadata_locally_or_by_urls(data │
│ 560 │ │ return cls(data_files, origin_metadata) │
│ 561 │
│ │
│ /x/datasets/dat │
│ a_files.py:195 in resolve_patterns_locally_or_by_urls │
│ │
│ 192 │ │ if is_remote_url(pattern): │
│ 193 │ │ │ data_files.append(Url(pattern)) │
│ 194 │ │ else: │
│ ❱ 195 │ │ │ for path in _resolve_single_pattern_locally(base_path, pat │
│ 196 │ │ │ │ data_files.append(path) │
│ 197 │ │
│ 198 │ if not data_files: │
│ │
│ /x/datasets/dat │
│ a_files.py:145 in _resolve_single_pattern_locally │
│ │
│ 142 │ │ error_msg = f"Unable to find '{pattern}' at {Path(base_path).r │
│ 143 │ │ if allowed_extensions is not None: │
│ 144 │ │ │ error_msg += f" with any supported extension {list(allowed │
│ ❱ 145 │ │ raise FileNotFoundError(error_msg) │
│ 146 │ return sorted(out) │
│ 147
### Steps to reproduce the bug
1. Version=2.3.2
2. In shell, cd workdir.(cd /a/b/c/.d/)
3. load_dataset("json", data_file={"train":"/a/b/c/.d/train/train.json", "test":"/a/b/c/.d/train/test.json"})
### Expected behavior
fix it please~
### Environment info
2.3.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/6072 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6072/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6072/comments | https://api.github.com/repos/huggingface/datasets/issues/6072/events | https://github.com/huggingface/datasets/pull/6072 | 1,822,123,560 | PR_kwDODunzps5WbWFN | 6,072 | Fix fsspec storage_options from load_dataset | {
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} | [] | open | false | null | [] | null | 3 | "2023-07-26T10:44:23" | "2023-07-26T19:26:48" | null | MEMBER | null | false | {
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} | close https://github.com/huggingface/datasets/issues/6071 | {
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https://api.github.com/repos/huggingface/datasets/issues/6071 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6071/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6071/comments | https://api.github.com/repos/huggingface/datasets/issues/6071/events | https://github.com/huggingface/datasets/issues/6071 | 1,821,990,749 | I_kwDODunzps5smV9d | 6,071 | storage_options provided to load_dataset not fully piping through since datasets 2.14.0 | {
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} | [] | open | false | null | [] | null | 2 | "2023-07-26T09:37:20" | "2023-07-26T11:04:35" | null | NONE | null | null | null | ### Describe the bug
Since the latest release of `datasets` (`2.14.0`), custom filesystem `storage_options` passed to `load_dataset()` do not seem to propagate through all the way - leading to problems if loading data files that need those options to be set.
I think this is because of the new `_prepare_path_and_storage_options()` (https://github.com/huggingface/datasets/pull/6028), which returns the right `storage_options` to use given a path and a `DownloadConfig` - but which might not be taking into account the extra `storage_options` explicitly provided e.g. through `load_dataset()`
### Steps to reproduce the bug
```python
import fsspec
import pandas as pd
import datasets
# Generate mock parquet file
data_files = "demo.parquet"
pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}).to_parquet(data_files)
_storage_options = {"x": 1, "y": 2}
fs = fsspec.filesystem("file", **_storage_options)
dataset = datasets.load_dataset(
"parquet",
data_files=data_files,
storage_options=fs.storage_options
)
```
Looking at the `storage_options` resolved here:
https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L331
they end up being `{}`, instead of propagating through the `storage_options` that were provided to `load_dataset` (`fs.storage_options`). As these then get used for the filesystem operation a few lines below
https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L339
the call will fail if the user-provided `storage_options` were needed.
---
A temporary workaround that seemed to work locally to bypass the problem was to bundle a duplicate of the `storage_options` into the `download_config`, so that they make their way all the way to `_prepare_path_and_storage_options()` and get extracted correctly:
```python
dataset = datasets.load_dataset(
"parquet",
data_files=data_files,
storage_options=fs.storage_options,
download_config=datasets.DownloadConfig(storage_options={fs.protocol: fs.storage_options}),
)
```
### Expected behavior
`storage_options` provided to `load_dataset` take effect in all backend filesystem operations.
### Environment info
datasets==2.14.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6070 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6070/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6070/comments | https://api.github.com/repos/huggingface/datasets/issues/6070/events | https://github.com/huggingface/datasets/pull/6070 | 1,820,836,330 | PR_kwDODunzps5WXDLc | 6,070 | Fix Quickstart notebook link | {
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} | [] | closed | false | null | [] | null | 3 | "2023-07-25T17:48:37" | "2023-07-25T18:19:01" | "2023-07-25T18:10:16" | CONTRIBUTOR | null | false | {
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} | Reported in https://github.com/huggingface/datasets/pull/5902#issuecomment-1649885621 (cc @alvarobartt) | {
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} | [] | open | false | null | [] | null | 5 | "2023-07-25T17:45:50" | "2023-07-26T17:33:49" | null | NONE | null | null | null | ### Describe the bug
I've loaded a local image dataset with:
`ds = laod_dataset("imagefolder", data_dir=path-to-data)`
And defined a transform to process the data, following the Datasets docs.
However, I get a keyError error, indicating there's no "image" key in my dataset. When I printed out the example_batch sent to the transformation function, it shows only the labels are being sent to the function.
For some reason, the images are not in the example batches.
### Steps to reproduce the bug
I'm using the latest stable version of datasets
### Expected behavior
I expect the example_batches to contain both images and labels
### Environment info
I'm using the latest stable version of datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/6066 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6066/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6066/comments | https://api.github.com/repos/huggingface/datasets/issues/6066/events | https://github.com/huggingface/datasets/issues/6066 | 1,819,717,542 | I_kwDODunzps5sdq-m | 6,066 | AttributeError: '_tqdm_cls' object has no attribute '_lock' | {
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} | [] | closed | false | null | [] | null | 7 | "2023-07-25T07:24:36" | "2023-07-26T10:56:25" | "2023-07-26T10:56:24" | NONE | null | null | null | ### Describe the bug
```python
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/load.py", line 1034, in get_module
data_files = DataFilesDict.from_patterns(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 671, in from_patterns
DataFilesList.from_patterns(
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 586, in from_patterns
origin_metadata = _get_origin_metadata(data_files, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 502, in _get_origin_metadata
return thread_map(
^^^^^^^^^^^
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 70, in thread_map
return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 48, in _executor_map
with ensure_lock(tqdm_class, lock_name=lock_name) as lk:
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/contextlib.py", line 144, in __exit__
next(self.gen)
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 25, in ensure_lock
del tqdm_class._lock
^^^^^^^^^^^^^^^^
AttributeError: '_tqdm_cls' object has no attribute '_lock'
```
### Steps to reproduce the bug
Happens ocasionally.
### Expected behavior
I added a print in tqdm `ensure_lock()`, got a `ensure_lock <datasets.utils.logging._tqdm_cls object at 0x16dddead0> ` print.
According to the code in https://github.com/tqdm/tqdm/blob/master/tqdm/contrib/concurrent.py#L24
```python
@contextmanager
def ensure_lock(tqdm_class, lock_name=""):
"""get (create if necessary) and then restore `tqdm_class`'s lock"""
print("ensure_lock", tqdm_class, lock_name)
old_lock = getattr(tqdm_class, '_lock', None) # don't create a new lock
lock = old_lock or tqdm_class.get_lock() # maybe create a new lock
lock = getattr(lock, lock_name, lock) # maybe subtype
tqdm_class.set_lock(lock)
yield lock
if old_lock is None:
del tqdm_class._lock # <-- It tries to del the `_lock` attribute from tqdm_class.
else:
tqdm_class.set_lock(old_lock)
```
But, huggingface datasets `datasets.utils.logging._tqdm_cls` does not have the field `_lock`: https://github.com/huggingface/datasets/blob/main/src/datasets/utils/logging.py#L205
```python
class _tqdm_cls:
def __call__(self, *args, disable=False, **kwargs):
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*args, **kwargs)
else:
return EmptyTqdm(*args, **kwargs)
def set_lock(self, *args, **kwargs):
self._lock = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*args, **kwargs)
def get_lock(self):
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
```
### Environment info
Python 3.11.4
tqdm '4.65.0'
datasets master | {
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https://api.github.com/repos/huggingface/datasets/issues/6065 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6065/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6065/comments | https://api.github.com/repos/huggingface/datasets/issues/6065/events | https://github.com/huggingface/datasets/pull/6065 | 1,819,334,932 | PR_kwDODunzps5WR8jI | 6,065 | Add column type guessing from map return function | {
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} | [] | closed | false | null | [] | null | 5 | "2023-07-25T00:34:17" | "2023-07-26T15:13:45" | "2023-07-26T15:13:44" | NONE | null | false | {
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} | As discussed [here](https://github.com/huggingface/datasets/issues/5965), there are some cases where datasets is unable to automatically promote columns during mapping. The fix is to explicitly provide a `features` definition so pyarrow can configure itself with the right column types from the outset.
This PR provides an alternative approach, which is functionally equivalent to specifying features but a bit cleaner within a larger mapping pipeline. It allows clients to typehint the return variable coming from the mapper function - if we find one of these type annotations specified, and no explicit features have been passed in, we'll try to convert it into a Features map. If the map function runs and casting is unable to succeed, it will raise a DatasetTransformationNotAllowedError that indicates the typehint may be to blame. It works for batched and non-batched mapping functions.
Currently supported column types:
- builtins primitives: string, int, float, bool
- dictionaries, lists (nested and one-deep)
- Optional types and None-Unions (synonymous with optional types)
It's used like:
```python
class DatasetTyped(TypedDict):
texts: list[str]
def dataset_typed_map(batch) -> DatasetTyped:
return {"texts": [text.split() for text in batch["raw_text"]]}
dataset = {"raw_text": ["", "This is a test", "This is another test"]}
with Dataset.from_dict(dataset) as dset:
new_dataset = dset.map(
dataset_typed_map,
batched=True,
batch_size=1,
num_proc=1,
)
```
Open questions:
- Should logging indicate we have automatically guessed these types? Or proceed quietly until we hit an error (as is the current implementation). | {
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https://api.github.com/repos/huggingface/datasets/issues/6063 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6063/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6063/comments | https://api.github.com/repos/huggingface/datasets/issues/6063/events | https://github.com/huggingface/datasets/pull/6063 | 1,818,679,485 | PR_kwDODunzps5WPtxi | 6,063 | Release: 2.14.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6062 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6062/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6062/comments | https://api.github.com/repos/huggingface/datasets/issues/6062/events | https://github.com/huggingface/datasets/pull/6062 | 1,818,341,584 | PR_kwDODunzps5WOj62 | 6,062 | Improve `Dataset.from_list` docstring | {
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https://api.github.com/repos/huggingface/datasets/issues/6061 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6061/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6061/comments | https://api.github.com/repos/huggingface/datasets/issues/6061/events | https://github.com/huggingface/datasets/pull/6061 | 1,818,337,136 | PR_kwDODunzps5WOi79 | 6,061 | Dill 3.7 support | {
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https://api.github.com/repos/huggingface/datasets/issues/6060 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6060/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6060/comments | https://api.github.com/repos/huggingface/datasets/issues/6060/events | https://github.com/huggingface/datasets/issues/6060 | 1,816,614,120 | I_kwDODunzps5sR1To | 6,060 | Dataset.map() execute twice when in PyTorch DDP mode | {
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} | [] | open | false | null | [] | null | 3 | "2023-07-22T05:06:43" | "2023-07-24T19:29:55" | null | NONE | null | null | null | ### Describe the bug
I use `torchrun --standalone --nproc_per_node=2 train.py` to start training. And write the code following the [docs](https://huggingface.co/docs/datasets/process#distributed-usage). The trick about using `torch.distributed.barrier()` to only execute map at the main process doesn't always work. When I am training model, it will map twice. When I am running a test for dataset and dataloader (just print the batches), it can work. Their code about loading dataset are same.
And on another server with 30 CPU cores, I use 2 GPUs and it can't work neither.
I have tried to use `rank` and `local_rank` to check, they all didn't make sense.
### Steps to reproduce the bug
use `torchrun --standalone --nproc_per_node=2 train.py` or `torchrun --standalone train.py` to run
This is my code:
```python
if args.distributed and world_size > 1:
if args.local_rank > 0:
print(f"Rank {args.rank}: Gpu {args.gpu} waiting for main process to perform the mapping", force=True)
torch.distributed.barrier()
print("Mapping dataset")
dataset = dataset.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True), num_proc=8, desc="cut_reorder_keys")
dataset = dataset.map(lambda x: random_shift(x, shift_range=(-160, 0), feature_scale=16), num_proc=8, desc="random_shift")
dataset_test = dataset_test.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=False), num_proc=8, desc="cut_reorder_keys")
if args.local_rank == 0:
print("Mapping finished, loading results from main process")
torch.distributed.barrier()
```
### Expected behavior
Only the main process will execute `map`, while the sub process will load cache from disk.
### Environment info
server with 64 CPU cores (AMD Ryzen Threadripper PRO 5995WX 64-Cores) and 2 RTX 4090
- `python==3.9.16`
- `datasets==2.13.1`
- `torch==2.0.1+cu117`
- `22.04.1-Ubuntu`
server with 30 CPU cores (Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz) and 2 RTX 4090
- `python==3.9.0`
- `datasets==2.13.1`
- `torch==2.0.1+cu117`
- `Ubuntu 20.04` | {
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https://api.github.com/repos/huggingface/datasets/issues/6059 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6059/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6059/comments | https://api.github.com/repos/huggingface/datasets/issues/6059/events | https://github.com/huggingface/datasets/issues/6059 | 1,816,537,176 | I_kwDODunzps5sRihY | 6,059 | Provide ability to load label mappings from file | {
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"name": "enhancement",
"color": "a2eeef",
"default": true,
"description": "New feature or request"
}
] | open | false | null | [] | null | 0 | "2023-07-22T02:04:19" | "2023-07-22T02:04:19" | null | NONE | null | null | null | ### Feature request
My task is classification of a dataset containing a large label set that includes a hierarchy. Even ignoring the hierarchy I'm not able to find an example using `datasets` where the label names aren't hard-coded. This works find for classification of a handful of labels but ideally there would be a way of loading the name/id mappings required for `datasets.features.ClassLabel` from a file.
It is possible to pass a file to ClassLabel but I cannot see an easy way of using this with `GeneratorBasedBuilder` since `self._info` is called before the `dl_manager` is constructed so even if my dataset contains say `label_mappings.json` there's no way of loading it in order to construct the `datasets.DatasetInfo`
I can see other uses to accessing the `download_manager` from `self._info` - i.e. if the files contain a schema (i.e. `arrow` or `parquet` files) the `datasets.DatasetInfo` could be inferred.
The workaround that was suggested in the forum is to generate a `.py` file from the `label_mappings.json` and import it.
```
class TestDatasetBuilder(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["label_1", "label_2"]),
}
),
task_templates=[TextClassification(text_column="text", label_column="label")],
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
"""Generate AG News examples."""
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.DictReader(csv_file)
for id_, row in enumerate(csv_reader):
yield id_, row
```
### Motivation
Allow `datasets.DatasetInfo` to be generated based on the contents of the dataset.
### Your contribution
I'm willing to work on a PR with guidence. | {
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https://api.github.com/repos/huggingface/datasets/issues/6058 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6058/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6058/comments | https://api.github.com/repos/huggingface/datasets/issues/6058/events | https://github.com/huggingface/datasets/issues/6058 | 1,815,131,397 | I_kwDODunzps5sMLUF | 6,058 | laion-coco download error | {
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} | [] | closed | false | null | [] | null | 1 | "2023-07-21T04:24:15" | "2023-07-22T01:42:06" | "2023-07-22T01:42:06" | NONE | null | null | null | ### Describe the bug
The full trace:
```
/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/load.py:1744: FutureWarning: 'ignore_verifications' was de
precated in favor of 'verification_mode' in version 2.9.1 and will be removed in 3.0.0.
You can remove this warning by passing 'verification_mode=no_checks' instead.
warnings.warn(
Downloading and preparing dataset parquet/laion--laion-coco to /home/bian/.cache/huggingface/datasets/laion___parquet/laion--
laion-coco-cb4205d7f1863066/0.0.0/bcacc8bdaa0614a5d73d0344c813275e590940c6ea8bc569da462847103a1afd...
Downloading data: 100%|█| 1.89G/1.89G [04:57<00:00,
Downloading data files: 100%|█| 1/1 [04:59<00:00, 2
Extracting data files: 100%|█| 1/1 [00:00<00:00, 13
Generating train split: 0 examples [00:00, ? examples/s]<_io.BufferedReader
name='/home/bian/.cache/huggingface/datasets/downlo
ads/26d7a016d25bbd9443115cfa3092136e8eb2f1f5bcd4154
0cb9234572927f04c'>
Traceback (most recent call last):
File "/home/bian/data/ZOC/download_laion_coco.py", line 4, in <module>
dataset = load_dataset("laion/laion-coco", ignore_verifications=True)
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/load.py", line 1791, in load_dataset
builder_instance.download_and_prepare(
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 891, in download_and_prepare
self._download_and_prepare(
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 986, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/bian/anaconda3/envs/sd/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/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 1842, in _prepare_split_single
generator = self._generate_tables(**gen_kwargs)
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 67, in
_generate_tables
parquet_file = pq.ParquetFile(f)
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/pyarrow/parquet/core.py", line 323, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1227, in pyarrow._parquet.ParquetReader.open
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file
.
```
I have carefully followed the instructions in #5264 but still get the same error.
Other helpful information:
```
ds = load_dataset("parquet", data_files=
...: "https://huggingface.co/datasets/laion/l
...: aion-coco/resolve/d22869de3ccd39dfec1507
...: f7ded32e4a518dad24/part-00000-2256f782-1
...: 26f-4dc6-b9c6-e6757637749d-c000.snappy.p
...: arquet")
Found cached dataset parquet (/home/bian/.cache/huggingface/datasets/parquet/default-a02eea00aeb08b0e/0.0.0/bb8ccf89d9ee38581ff5e51506d721a9b37f14df8090dc9b2d8fb4a40957833f)
100%|██████████████| 1/1 [00:00<00:00, 4.55it/s]
```
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("laion/laion-coco", ignore_verifications=True/False)
```
### Expected behavior
Properly load Laion-coco dataset
### Environment info
datasets==2.11.0 torch==1.12.1 python 3.10 | {
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https://api.github.com/repos/huggingface/datasets/issues/6057 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6057/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6057/comments | https://api.github.com/repos/huggingface/datasets/issues/6057/events | https://github.com/huggingface/datasets/issues/6057 | 1,815,100,151 | I_kwDODunzps5sMDr3 | 6,057 | Why is the speed difference of gen example so big? | {
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} | [] | open | false | null | [] | null | 1 | "2023-07-21T03:34:49" | "2023-07-21T16:41:09" | null | NONE | null | null | null | ```python
def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir):
with open(metadata_path, 'r') as file:
metadata = json.load(file)
for idx, item in enumerate(metadata):
image_path = item.get('image_path')
text_content = item.get('text_content')
image_data = open(image_path, "rb").read()
yield idx, {
"text": text_content,
"image": {
"path": image_path,
"bytes": image_data,
},
"conditioning_image": {
"path": image_path,
"bytes": image_data,
},
}
```
Hello,
I use the above function to deal with my local data set, but I am very surprised that the speed at which I generate example is very different. When I start a training task, **sometimes 1000examples/s, sometimes only 10examples/s.**
I'm not saying that speed is changing all the time. I mean, the reading speed is different in different training, which will cause me to start training over and over again until the speed of this generation of examples is normal.
| {
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https://api.github.com/repos/huggingface/datasets/issues/6056 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6056/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6056/comments | https://api.github.com/repos/huggingface/datasets/issues/6056/events | https://github.com/huggingface/datasets/pull/6056 | 1,815,086,963 | PR_kwDODunzps5WD4RY | 6,056 | Implement proper checkpointing for dataset uploading with resume function that does not require remapping shards that have already been uploaded | {
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} | Context: issue #5990
In order to implement the checkpointing, I introduce a metadata folder that keeps one yaml file for each set that one is uploading. This yaml keeps track of what shards have already been uploaded, and which one the idx of the latest one was. Using this information I am then able to easily get the push_to_hub function to retrieve on demand past history of uploads and continue mapping and uploading from where it was left off. | {
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https://api.github.com/repos/huggingface/datasets/issues/6055 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6055/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6055/comments | https://api.github.com/repos/huggingface/datasets/issues/6055/events | https://github.com/huggingface/datasets/issues/6055 | 1,813,524,145 | I_kwDODunzps5sGC6x | 6,055 | Fix host URL in The Pile datasets | {
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} | [] | open | false | null | [] | null | 0 | "2023-07-20T09:08:52" | "2023-07-20T09:09:37" | null | NONE | null | null | null | ### Describe the bug
In #3627 and #5543, you tried to fix the host URL in The Pile datasets. But both URLs are not working now:
`HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst`
And
`ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))`
### Steps to reproduce the bug
```
from datasets import load_dataset
# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://mystic.the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```
Result:
`ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))`
And
```
from datasets import load_dataset
# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```
Result:
`HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst`
### Expected behavior
Downloading as normal.
### Environment info
Environment info
`datasets` version: 2.9.0
Platform: Windows
Python version: 3.9.13
| {
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https://api.github.com/repos/huggingface/datasets/issues/6054 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6054/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6054/comments | https://api.github.com/repos/huggingface/datasets/issues/6054/events | https://github.com/huggingface/datasets/issues/6054 | 1,813,271,304 | I_kwDODunzps5sFFMI | 6,054 | Multi-processed `Dataset.map` slows down a lot when `import torch` | {
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] | closed | false | null | [] | null | 1 | "2023-07-20T06:36:14" | "2023-07-21T15:19:37" | "2023-07-21T15:19:37" | NONE | null | null | null | ### Describe the bug
When using `Dataset.map` with `num_proc > 1`, the speed slows down much if I add `import torch` to the start of the script even though I don't use it.
I'm not sure if it's `torch` only or if any other package that is "large" will also cause the same result.
BTW, `import lightning` also slows it down.
Below are the progress bars of `Dataset.map`, the only difference between them is with or without `import torch`, but the speed varies by 6-7 times.
- without `import torch` 
- with `import torch` 
### Steps to reproduce the bug
Below is the code I used, but I don't think the dataset and the mapping function have much to do with the phenomenon.
```python3
from datasets import load_from_disk, disable_caching
from transformers import AutoTokenizer
# import torch
# import lightning
def rearrange_datapoints(
batch,
tokenizer,
sequence_length,
):
datapoints = []
input_ids = []
for x in batch['input_ids']:
input_ids += x
while len(input_ids) >= sequence_length:
datapoint = input_ids[:sequence_length]
datapoints.append(datapoint)
input_ids[:sequence_length] = []
if input_ids:
paddings = [-1] * (sequence_length - len(input_ids))
datapoint = paddings + input_ids if tokenizer.padding_side == 'left' else input_ids + paddings
datapoints.append(datapoint)
batch['input_ids'] = datapoints
return batch
if __name__ == '__main__':
disable_caching()
tokenizer = AutoTokenizer.from_pretrained('...', use_fast=False)
dataset = load_from_disk('...')
dataset = dataset.map(
rearrange_datapoints,
fn_kwargs=dict(
tokenizer=tokenizer,
sequence_length=2048,
),
batched=True,
num_proc=8,
)
```
### Expected behavior
The multi-processed `Dataset.map` function speed between with and without `import torch` should be the same.
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-3.10.0-1127.el7.x86_64-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/6053 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6053/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6053/comments | https://api.github.com/repos/huggingface/datasets/issues/6053/events | https://github.com/huggingface/datasets/issues/6053 | 1,812,635,902 | I_kwDODunzps5sCqD- | 6,053 | Change package name from "datasets" to something less generic | {
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] | open | false | null | [] | null | 0 | "2023-07-19T19:53:28" | "2023-07-19T19:55:04" | null | NONE | null | null | null | ### Feature request
I'm repeatedly finding myself in situations where I want to have a package called `datasets.py` or `evaluate.py` in my code and can't because those names are being taken up by Huggingface packages. While I can understand how (even from the user's perspective) it's aesthetically pleasing to have nice terse library names, ultimately a library hogging simple names like this is something I find short-sighted, impractical and at my most irritable, frankly rude.
My preference would be a pattern like what you get with all the other big libraries like numpy or pandas:
```
import huggingface as hf
# hf.transformers, hf.datasets, hf.evaluate
```
or things like
```
import huggingface.transformers as tf
# tf.load_model(), etc
```
If this isn't possible for some technical reason, at least just call the packages something like `hf_transformers` and so on.
I realize this is a very big change that's probably been discussed internally already, but I'm making this issue and sister issues on each huggingface project just to start the conversation and begin tracking community feeling on the matter, since I suspect I'm not the only one who feels like this.
Sorry if this has been requested already on this issue tracker, I couldn't find anything looking for terms like "package name".
Sister issues:
- [transformers](https://github.com/huggingface/transformers/issues/24934)
- **datasets**
- [evaluate](https://github.com/huggingface/evaluate/issues/476)
### Motivation
Not taking up package names the user is likely to want to use.
### Your contribution
No - more a matter of internal discussion among core library authors. | {
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https://api.github.com/repos/huggingface/datasets/issues/6052 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6052/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6052/comments | https://api.github.com/repos/huggingface/datasets/issues/6052/events | https://github.com/huggingface/datasets/pull/6052 | 1,812,145,100 | PR_kwDODunzps5V5yOi | 6,052 | Remove `HfFileSystem` and deprecate `S3FileSystem` | {
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} | Remove the legacy `HfFileSystem` and deprecate `S3FileSystem`
cc @philschmid for the SageMaker scripts/notebooks that still use `datasets`' `S3FileSystem` | {
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https://api.github.com/repos/huggingface/datasets/issues/6051 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6051/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6051/comments | https://api.github.com/repos/huggingface/datasets/issues/6051/events | https://github.com/huggingface/datasets/issues/6051 | 1,811,549,650 | I_kwDODunzps5r-g3S | 6,051 | Skipping shard in the remote repo and resume upload | {
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} | [] | closed | false | null | [] | null | 2 | "2023-07-19T09:25:26" | "2023-07-20T18:16:01" | "2023-07-20T18:16:00" | NONE | null | null | null | ### Describe the bug
For some reason when I try to resume the upload of my dataset, it is very slow to reach the index of the shard from which to resume the uploading.
From my understanding, the problem is in this part of the code:
arrow_dataset.py
```python
for index, shard in logging.tqdm(
enumerate(itertools.chain([first_shard], shards_iter)),
desc="Pushing dataset shards to the dataset hub",
total=num_shards,
disable=not logging.is_progress_bar_enabled(),
):
shard_path_in_repo = path_in_repo(index, shard)
# Upload a shard only if it doesn't already exist in the repository
if shard_path_in_repo not in data_files:
```
In particular, iterating the generator is slow during the call:
```python
self._select_contiguous(start, length, new_fingerprint=new_fingerprint)
```
I wonder if it is possible to avoid calling this function for shards that are already uploaded and just start from the correct shard index.
### Steps to reproduce the bug
1. Start the upload
```python
dataset = load_dataset("imagefolder", data_dir=DATA_DIR, split="train", drop_labels=True)
dataset.push_to_hub("repo/name")
```
2. Stop and restart the upload after hundreds of shards
### Expected behavior
Skip the uploaded shards faster.
### Environment info
- `datasets` version: 2.5.1
- Platform: Linux-4.18.0-193.el8.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.16
- PyArrow version: 12.0.1
- Pandas version: 2.0.2
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https://api.github.com/repos/huggingface/datasets/issues/6049 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6049/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6049/comments | https://api.github.com/repos/huggingface/datasets/issues/6049/events | https://github.com/huggingface/datasets/pull/6049 | 1,810,378,706 | PR_kwDODunzps5Vz1pd | 6,049 | Update `ruff` version in pre-commit config | {
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https://api.github.com/repos/huggingface/datasets/issues/6048 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6048/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6048/comments | https://api.github.com/repos/huggingface/datasets/issues/6048/events | https://github.com/huggingface/datasets/issues/6048 | 1,809,629,346 | I_kwDODunzps5r3MCi | 6,048 | when i use datasets.load_dataset, i encounter the http connect error! | {
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`common_voice_test = load_dataset("audiofolder", data_dir="./dataset/",cache_dir="./cache",split=datasets.Split.TEST)`
when i run the code above, i got the error as below:
--------------------------------------------
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f299ed082e0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))")))
--------------------------------------------------
My all data is on local machine, why does it need to connect the internet? how can i fix it, because my machine cannot connect the internet.
### Steps to reproduce the bug
1
### Expected behavior
no error when i use the load_dataset func
### Environment info
python=3.8.15 | {
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https://github.com/huggingface/transformers/pull/24867#discussion_r1266519626 | {
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Using the HTTP_PROXY and HTTPS_PROXY environment variables works though since we use aiohttp to call the HF Hub.
This can be implemented in `_prepare_single_hop_path_and_storage_options`.
Though ideally the `HfFileSystem` could support passing at least the proxies | {
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} | To make it easier to understand for users.
They can use "path" to specify a single path, <s>or "paths" to use a list of paths.</s>
Glob patterns are still supported though
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https://api.github.com/repos/huggingface/datasets/issues/6043 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6043/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6043/comments | https://api.github.com/repos/huggingface/datasets/issues/6043/events | https://github.com/huggingface/datasets/issues/6043 | 1,807,771,750 | I_kwDODunzps5rwGhm | 6,043 | Compression kwargs have no effect when saving datasets as csv | {
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} | [] | open | false | null | [] | null | 3 | "2023-07-17T13:19:21" | "2023-07-22T17:34:18" | null | NONE | null | null | null | ### Describe the bug
Attempting to save a dataset as a compressed csv file, the compression kwargs provided to `.to_csv()` that get piped to panda's `pandas.DataFrame.to_csv` do not have any effect - resulting in the dataset not getting compressed.
A warning is raised if explicitly providing a `compression` kwarg, but no warnings are raised if relying on the defaults. This can lead to datasets secretly not getting compressed for users expecting the behaviour to match panda's `.to_csv()`, where the compression format is automatically inferred from the destination path suffix.
### Steps to reproduce the bug
```python
# dataset is not compressed (but at least a warning is emitted)
import datasets
dataset = datasets.load_dataset("rotten_tomatoes", split="train")
dataset.to_csv("uncompressed.csv")
print(os.path.getsize("uncompressed.csv")) # 1008607
dataset.to_csv("compressed.csv.gz", compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1})
print(os.path.getsize("compressed.csv.gz")) # 1008607
```
```shell
>>>
RuntimeWarning: compression has no effect when passing a non-binary object as input.
csv_str = batch.to_pandas().to_csv(
```
```python
# dataset is not compressed and no warnings are emitted
dataset.to_csv("compressed.csv.gz")
print(os.path.getsize("compressed.csv.gz")) # 1008607
# compare with
dataset.to_pandas().to_csv("pandas.csv.gz")
print(os.path.getsize("pandas.csv.gz")) # 418561
```
---
I think that this is because behind the scenes `pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`, but users that are providing a path-like to `datasets.Dataset.to_csv` are likely not to expect / know that - leading to a mismatch in their understanding of the expected behaviour of the `compression` kwarg.
### Expected behavior
The dataset to be saved as a compressed csv file when providing a `compression` kwarg, or when relying on the default `compression='infer'`
### Environment info
`datasets == 2.13.1`
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} | To have Splits, Configurations and Builder parameters at the same doc level | {
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https://api.github.com/repos/huggingface/datasets/issues/6040 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6040/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6040/comments | https://api.github.com/repos/huggingface/datasets/issues/6040/events | https://github.com/huggingface/datasets/pull/6040 | 1,807,410,238 | PR_kwDODunzps5VptVf | 6,040 | Fix legacy_dataset_infos | {
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} | [] | closed | false | null | [] | null | 3 | "2023-07-17T09:56:21" | "2023-07-17T10:24:34" | "2023-07-17T10:16:03" | MEMBER | null | false | {
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} | was causing transformers CI to fail
https://circleci.com/gh/huggingface/transformers/855105 | {
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https://api.github.com/repos/huggingface/datasets/issues/6039 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6039/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6039/comments | https://api.github.com/repos/huggingface/datasets/issues/6039/events | https://github.com/huggingface/datasets/issues/6039 | 1,806,508,451 | I_kwDODunzps5rrSGj | 6,039 | Loading column subset from parquet file produces error since version 2.13 | {
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} | [] | closed | false | null | [] | null | 0 | "2023-07-16T09:13:07" | "2023-07-24T14:35:04" | "2023-07-24T14:35:04" | NONE | null | null | null | ### Describe the bug
`load_dataset` allows loading a subset of columns from a parquet file with the `columns` argument. Since version 2.13, this produces the following error:
```
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
for _, table in generator:
File "/usr/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
raise ValueError(
ValueError: Tried to load parquet data with columns '['sepal_length']' with mismatching features '{'sepal_length': Value(dtype='float64', id=None), 'sepal_width': Value(dtype='float64', id=None), 'petal_length': Value(dtype='float64', id=None), 'petal_width': Value(dtype='float64', id=None), 'species': Value(dtype='string', id=None)}'
```
This seems to occur because `datasets` is checking whether the columns in the schema exactly match the provided list of columns, instead of whether they are a subset.
### Steps to reproduce the bug
```python
# Prepare some sample data
import pandas as pd
iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
iris.to_parquet('iris.parquet')
# ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
print(iris.columns)
# Load data with datasets
from datasets import load_dataset
# Load full parquet file
dataset = load_dataset('parquet', data_files='iris.parquet')
# Load column subset; throws error for datasets>=2.13
dataset = load_dataset('parquet', data_files='iris.parquet', columns=['sepal_length'])
```
### Expected behavior
No error should be thrown and the given column subset should be loaded.
### Environment info
- `datasets` version: 2.13.0
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.9
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/6038 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6038/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6038/comments | https://api.github.com/repos/huggingface/datasets/issues/6038/events | https://github.com/huggingface/datasets/issues/6038 | 1,805,960,244 | I_kwDODunzps5rpMQ0 | 6,038 | File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare if str(split_generator.split_info.name).lower() == "all": AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'? | {
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} | [] | closed | false | null | [] | null | 1 | "2023-07-15T07:58:08" | "2023-07-24T11:54:15" | "2023-07-24T11:54:15" | NONE | null | null | null | Hi, I use the code below to load local file
```
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
# urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(_URLs)
print(data_dir)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir["train"]),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir["dev"]),
"split": "dev",
},
),
]
```
and error occured
```
Traceback (most recent call last):
File "/home/zhizhou/data1/zhanghao/huggingface/FineTuning_Transformer/load_local_dataset.py", line 2, in <module>
dataset = load_dataset("./QA_script.py",data_files='/home/zhizhou/.cache/huggingface/datasets/conversatiom_corps/part_file.json')
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/load.py", line 1809, in load_dataset
builder_instance.download_and_prepare(
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 909, in download_and_prepare
self._download_and_prepare(
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 1670, in _download_and_prepare
super()._download_and_prepare(
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare
if str(split_generator.split_info.name).lower() == "all":
AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'?
```
Could you help me? | {
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https://api.github.com/repos/huggingface/datasets/issues/6037 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6037/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6037/comments | https://api.github.com/repos/huggingface/datasets/issues/6037/events | https://github.com/huggingface/datasets/issues/6037 | 1,805,887,184 | I_kwDODunzps5ro6bQ | 6,037 | Documentation links to examples are broken | {
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} | [] | closed | false | null | [] | null | 2 | "2023-07-15T04:54:50" | "2023-07-17T22:35:14" | "2023-07-17T15:10:32" | NONE | null | null | null | ### Describe the bug
The links at the bottom of [add_dataset](https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html) to examples of specific datasets are all broken, for example
- text classification: [ag_news](https://github.com/huggingface/datasets/blob/master/datasets/ag_news/ag_news.py) (original data are in csv files)
### Steps to reproduce the bug
Click on links to examples from latest documentation
### Expected behavior
Links should be up to date - it might be more stable to link to https://huggingface.co/datasets/ag_news/blob/main/ag_news.py
### Environment info
dataset v1.2.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/6036 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6036/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6036/comments | https://api.github.com/repos/huggingface/datasets/issues/6036/events | https://github.com/huggingface/datasets/pull/6036 | 1,805,138,898 | PR_kwDODunzps5ViKc4 | 6,036 | Deprecate search API | {
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} | [] | open | false | null | [] | null | 8 | "2023-07-14T16:22:09" | "2023-07-21T19:53:51" | null | CONTRIBUTOR | null | false | {
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} | The Search API only supports Faiss and ElasticSearch as vector stores, is somewhat difficult to maintain (e.g., it still doesn't support ElasticSeach 8.0, difficult testing, ...), does not have the best design (adds a bunch of methods to the `Dataset` class that are only useful after creating an index), the usage doesn't seem to be significant and is not integrated with the Hub. Since we have no plans/bandwidth to improve it and better alternatives such as `langchain` and `docarray` exist, I think it should be deprecated (and eventually removed).
If we decide to deprecate/remove it, the following usage instances need to be addressed:
* [Course](https://github.com/huggingface/course/blob/0018bb434204d9750a03592cb0d4e846093218d8/chapters/en/chapter5/6.mdx#L342 ) and [Blog](https://github.com/huggingface/blog/blob/4897c6f73d4492a0955ade503281711d01840e09/image-search-datasets.md?plain=1#L252) - calling the FAISS API directly should be OK in these instances as it's pretty simple to use for basic scenarios. Alternatively, we can use `langchain`, but this adds an extra dependency
* [Transformers](https://github.com/huggingface/transformers/blob/50726f9ea7afc6113da617f8f4ca1ab264a5e28a/src/transformers/models/rag/retrieval_rag.py#L183) - we can use the FAISS API directly and store the index as a separate attribute (and instead of building the `wiki_dpr` index each time the dataset is generated, we can generate it once and push it to the Hub repo, and then read it from there
cc @huggingface/datasets @LysandreJik for the opinion | {
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https://api.github.com/repos/huggingface/datasets/issues/6035 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6035/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6035/comments | https://api.github.com/repos/huggingface/datasets/issues/6035/events | https://github.com/huggingface/datasets/pull/6035 | 1,805,087,687 | PR_kwDODunzps5Vh_QR | 6,035 | Dataset representation | {
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https://api.github.com/repos/huggingface/datasets/issues/6034 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6034/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6034/comments | https://api.github.com/repos/huggingface/datasets/issues/6034/events | https://github.com/huggingface/datasets/issues/6034 | 1,804,501,361 | I_kwDODunzps5rjoFx | 6,034 | load_dataset hangs on WSL | {
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load_dataset simply hangs. It happens once every ~5 times, and interestingly hangs for a multiple of 5 minutes (hangs for 5/10/15 minutes). Using the profiler in PyCharm shows that it spends the time at <method 'connect' of '_socket.socket' objects>. However, a local cache is available so I am not sure why socket is needed. ([profiler result](https://ibb.co/0Btbbp8))
It only happens on WSL for me. It works for native Windows and my MacBook. (cache quickly recognized and loaded within a second).
### Steps to reproduce the bug
I am using Ubuntu 22.04.2 LTS (GNU/Linux 5.15.90.1-microsoft-standard-WSL2 x86_64)
Python 3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0] on linux
>>> import datasets
>>> datasets.load_dataset('ai2_arc', 'ARC-Challenge') # hangs for 5/10/15 minutes
### Expected behavior
cache quickly recognized and loaded within a second
### Environment info
Please let me know if I should provide more environment information. | {
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https://api.github.com/repos/huggingface/datasets/issues/6033 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6033/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6033/comments | https://api.github.com/repos/huggingface/datasets/issues/6033/events | https://github.com/huggingface/datasets/issues/6033 | 1,804,482,051 | I_kwDODunzps5rjjYD | 6,033 | `map` function doesn't fully utilize `input_columns`. | {
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} | [] | closed | false | null | [] | null | 0 | "2023-07-14T08:49:28" | "2023-07-14T09:16:04" | "2023-07-14T09:16:04" | NONE | null | null | null | ### Describe the bug
I wanted to select only some columns of data.
And I thought that's why the argument `input_columns` exists.
What I expected is like this:
If there are ["a", "b", "c", "d"] columns, and if I set `input_columns=["a", "d"]`, the data will have only ["a", "d"] columns.
But it doesn't select columns.
It preserves existing columns.
The main cause is `update` function of `dictionary` type `transformed_batch`.
https://github.com/huggingface/datasets/blob/682d21e94ab1e64c11b583de39dc4c93f0101c5a/src/datasets/iterable_dataset.py#L687-L691
`transformed_batch` gets all the columns by `transformed_batch = dict(batch)`.
Even `function_args` selects `input_columns`, `update` preserves columns other than `input_columns`.
I think it should take a new dictionary with columns in `input_columns` like this:
```
# transformed_batch = dict(batch)
# transformed_batch.update(self.function(*function_args, **self.fn_kwargs)
# This is what I think correct.
transformed_batch = self.function(*function_args, **self.fn_kwargs)
```
Let me know how to use `input_columns`.
### Steps to reproduce the bug
Described all above.
### Expected behavior
Described all above.
### Environment info
datasets: 2.12
python: 3.8 | {
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https://api.github.com/repos/huggingface/datasets/issues/6032 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6032/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6032/comments | https://api.github.com/repos/huggingface/datasets/issues/6032/events | https://github.com/huggingface/datasets/issues/6032 | 1,804,358,679 | I_kwDODunzps5rjFQX | 6,032 | DownloadConfig.proxies not work when load_dataset_builder calling HfApi.dataset_info | {
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} | [] | open | false | null | [] | null | 2 | "2023-07-14T07:22:55" | "2023-07-17T04:12:45" | null | NONE | null | null | null | ### Describe the bug
```python
download_config = DownloadConfig(proxies={'https': '<my proxy>'})
builder = load_dataset_builder(..., download_config=download_config)
```
But, when getting the dataset_info from HfApi, the http requests not using the proxies.
### Steps to reproduce the bug
1. Setup proxies in DownloadConfig.
2. Call `load_dataset_build` with download_config.
3. Inspect the call stack in HfApi.dataset_info.
### Expected behavior
DownloadConfig.proxies works for getting dataset_info.
### Environment info
https://github.com/huggingface/datasets/commit/406b2212263c0d33f267e35b917f410ff6b3bc00
Python 3.11.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/6031 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6031/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6031/comments | https://api.github.com/repos/huggingface/datasets/issues/6031/events | https://github.com/huggingface/datasets/issues/6031 | 1,804,183,858 | I_kwDODunzps5riaky | 6,031 | Argument type for map function changes when using `input_columns` for `IterableDataset` | {
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} | [] | closed | false | null | [] | null | 1 | "2023-07-14T05:11:14" | "2023-07-14T14:44:15" | "2023-07-14T14:44:15" | NONE | null | null | null | ### Describe the bug
I wrote `tokenize(examples)` function as an argument for `map` function for `IterableDataset`.
It process dictionary type `examples` as a parameter.
It is used in `train_dataset = train_dataset.map(tokenize, batched=True)`
No error is raised.
And then, I found some unnecessary keys and values in `examples` so I added `input_columns` argument to `map` function to select keys and values.
It gives me an error saying
```
TypeError: tokenize() takes 1 positional argument but 3 were given.
```
The code below matters.
https://github.com/huggingface/datasets/blob/406b2212263c0d33f267e35b917f410ff6b3bc00/src/datasets/iterable_dataset.py#L687
For example, `inputs = {"a":1, "b":2, "c":3}`.
If `self.input_coluns` is `None`,
`inputs` is a dictionary type variable and `function_args` becomes a `list` of a single `dict` variable.
`function_args` becomes `[{"a":1, "b":2, "c":3}]`
Otherwise, lets say `self.input_columns = ["a", "c"]`
`[inputs[col] for col in self.input_columns]` results in `[1, 3]`.
I think it should be `[{"a":1, "c":3}]`.
I want to ask if the resulting format is intended.
Maybe I can modify `tokenize()` to have 2 parameters in this case instead of having 1 dictionary.
But this is confusing to me.
Or it should be fixed as `[{col:inputs[col] for col in self.input_columns}]`
### Steps to reproduce the bug
Run `map` function of `IterableDataset` with `input_columns` argument.
### Expected behavior
`function_args` looks better to have same format.
I think it should be `[{"a":1, "c":3}]`.
### Environment info
dataset version: 2.12
python: 3.8 | {
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https://api.github.com/repos/huggingface/datasets/issues/6030 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6030/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6030/comments | https://api.github.com/repos/huggingface/datasets/issues/6030/events | https://github.com/huggingface/datasets/pull/6030 | 1,803,864,744 | PR_kwDODunzps5Vd0ZG | 6,030 | fixed typo in comment | {
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} | [] | closed | false | null | [] | null | 2 | "2023-07-13T22:49:57" | "2023-07-14T14:21:58" | "2023-07-14T14:13:38" | CONTRIBUTOR | null | false | {
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} | This mistake was a bit confusing, so I thought it was worth sending a PR over. | {
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https://api.github.com/repos/huggingface/datasets/issues/6029 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6029/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6029/comments | https://api.github.com/repos/huggingface/datasets/issues/6029/events | https://github.com/huggingface/datasets/pull/6029 | 1,803,460,046 | PR_kwDODunzps5VcbPW | 6,029 | [docs] Fix link | {
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} | Fixes link to the builder classes :) | {
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https://api.github.com/repos/huggingface/datasets/issues/6028 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6028/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6028/comments | https://api.github.com/repos/huggingface/datasets/issues/6028/events | https://github.com/huggingface/datasets/pull/6028 | 1,803,294,981 | PR_kwDODunzps5Vb3LJ | 6,028 | Use new hffs | {
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} | Thanks to @janineguo 's work in https://github.com/huggingface/datasets/pull/5919 which was needed to support HfFileSystem.
Switching to `HfFileSystem` will help implementing optimization in data files resolution
## Implementation details
I replaced all the from_hf_repo and from_local_or_remote in data_files.py to only use a new `from_patterns` which works for any fsspec path, including hf:// paths, https:// URLs and local paths. This simplifies the codebase since there is no logic duplication anymore when it comes to data files resolution.
I added `_prepare_path_and_storage_options` which returns the right storage_options to use given a path and a `DownloadConfig`. This is the only place where the logic depends on the filesystem type that must be used.
I also removed the `get_metadata_data_files_list ` and `get_patterns_and_data_files` functions added recently, since data files resolution is now handled using a common interface.
## New features
hf:// paths are now supported in data_files
## Breaking changes
DataFilesList and DataFilesDict:
- use `str` paths instead of `Union[Path, Url]`
- require posix paths for windows paths
close https://github.com/huggingface/datasets/issues/6017 | {
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https://api.github.com/repos/huggingface/datasets/issues/6027 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6027/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6027/comments | https://api.github.com/repos/huggingface/datasets/issues/6027/events | https://github.com/huggingface/datasets/pull/6027 | 1,803,008,486 | PR_kwDODunzps5Va4g3 | 6,027 | Delete `task_templates` in `IterableDataset` when they are no longer valid | {
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} | Fix #6025 | {
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