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https://api.github.com/repos/huggingface/datasets/issues/5962 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5962/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5962/comments | https://api.github.com/repos/huggingface/datasets/issues/5962/events | https://github.com/huggingface/datasets/issues/5962 | 1,761,589,882 | I_kwDODunzps5o_7p6 | 5,962 | Issue with train_test_split maintaining the same underlying PyArrow Table | {
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} | null | null | null | ### Describe the bug
I've been using the train_test_split method in the datasets module to split my HuggingFace Dataset into separate training, validation, and testing subsets. However, I've noticed an issue where the split datasets appear to maintain the same underlying PyArrow Table.
### Steps to reproduce the bug
1. Load any dataset ```dataset = load_dataset("lhoestq/demo1")```
2. Try the next code:
```python
from datasets import Dataset, DatasetDict
train_size = 0.6
split_train = dataset["train"].train_test_split(
train_size=train_size,
)
separate_dataset_dict = DatasetDict({
"train": split_train["train"],
"test": split_train["test"],
})
```
3. The next code ```print(separate_dataset_dict)``` when printing the dataset it gives the indication that they have 3 and 2 rows respectively.
4. But the next code:
```python
print(len(separate_dataset_dict["train"].data['id']))
print(len(separate_dataset_dict["test"].data['id']))
```
Indicates that both tables still have 5 rows.
### Expected behavior
However, I've noticed that train_test_split["train"].data, test_val_split["train"].data, and test_val_split["test"].data are identical, suggesting that they all point to the same underlying PyArrow Table. This means that the split datasets are not independent, as I expected.
I believe this is a bug in the train_test_split implementation, as I would expect this function to return datasets with separate underlying PyArrow Tables. Could you please help me understand if this is expected behavior, or if there's a workaround to create truly independent split datasets?
I would appreciate any assistance with this issue. Thank you.
### Environment info
I tried in Colab:
- `datasets` version: 2.13.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
and my PC:
- `datasets` version: 2.13.0
- Platform: Linux-5.15.107+-x86_64-with-glibc2.31
- Python version: 3.10.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 9.0.0
- Pandas version: 1.5.3 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5961 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5961/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5961/comments | https://api.github.com/repos/huggingface/datasets/issues/5961/events | https://github.com/huggingface/datasets/issues/5961 | 1,758,525,111 | I_kwDODunzps5o0Pa3 | 5,961 | IterableDataset: split by node and map may preprocess samples that will be skipped anyway | {
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} | null | null | null | There are two ways an iterable dataset can be split by node:
1. if the number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU
2. otherwise, each GPU iterate on the data and at the end keeps 1 sample out of n(GPUs) - skipping the others.
In case 2. it's therefore possible to have the same examples passed to `prepare_dataset` for each GPU.
This doesn't sound optimized though, because it runs the preprocessing on samples that won't be used in the end.
Could you open a new issue so that we can discuss about this and find a solution ?
_Originally posted by @lhoestq in https://github.com/huggingface/datasets/issues/5360#issuecomment-1592729051_
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} | null | null | null | ### Describe the bug
Currently, The server is off-line. I am using the glue metric from the local file downloaded from the hub.
I download / cached datasets using `load_dataset('glue','sst2', cache_dir='/xxx')` to cache them and then in the off-line mode, I use `load_dataset('xxx/glue.py','sst2', cache_dir='/xxx')`. I can successfully reuse cached datasets.
My problem is about the load_metric.
When I run `load_dataset('xxx/glue_metric.py','sst2',cache_dir='/xxx')` , it returns
` File "xx/lib64/python3.9/site-packages/datasets/utils/deprecation_utils.py", line 46, in wrapper
return deprecated_function(*args, **kwargs)
File "xx//lib64/python3.9/site-packages/datasets/load.py", line 1392, in load_metric
metric = metric_cls(
TypeError: 'NoneType' object is not callable`
Thanks in advance for help!
### Steps to reproduce the bug
N/A
### Expected behavior
N/A
### Environment info
`datasets == 2.12.0` | {
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I also fixed a warning message by not using format_type= in with_format() | {
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} | null | null | null | ### Describe the bug
I am using 'load_dataset 'loads a JSON file, but I found a strange bug: an error will be reported when the length of the JSON file exceeds 160000 (uncertain exact number). I have checked the data through the following code and there are no issues. So I cannot determine the true reason for this error.
The data is a list containing a dictionary. As follows:
[
{'input': 'someting...', 'target': 'someting...', 'type': 'someting...', 'history': ['someting...', ...]},
...
]
### Steps to reproduce the bug
```
import json
from datasets import load_dataset
path = "target.json"
temp_path = "temp.json"
with open(path, "r") as f:
data = json.load(f)
print(f"\n-------the JSON file length is: {len(data)}-------\n")
with open(temp_path, "w") as f:
json.dump(data[:160000], f)
dataset = load_dataset("json", data_files=temp_path)
print("\n-------This works when the JSON file length is 160000-------\n")
with open(temp_path, "w") as f:
json.dump(data[160000:], f)
dataset = load_dataset("json", data_files=temp_path)
print("\n-------This works and eliminates data issues-------\n")
with open(temp_path, "w") as f:
json.dump(data[:170000], f)
dataset = load_dataset("json", data_files=temp_path)
```
### Expected behavior
```
-------the JSON file length is: 173049-------
Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-acf3c7f418c5f4b4/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...
Downloading data files: 100%|███████████████████| 1/1 [00:00<00:00, 3328.81it/s]
Extracting data files: 100%|█████████████████████| 1/1 [00:00<00:00, 639.47it/s]
Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-acf3c7f418c5f4b4/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.
100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 265.85it/s]
-------This works when the JSON file length is 160000-------
Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-a42f04b263ceea6a/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...
Downloading data files: 100%|███████████████████| 1/1 [00:00<00:00, 2038.05it/s]
Extracting data files: 100%|█████████████████████| 1/1 [00:00<00:00, 794.83it/s]
Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-a42f04b263ceea6a/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.
100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 681.00it/s]
-------This works and eliminates data issues-------
Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-63f391c89599c7b0/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...
Downloading data files: 100%|███████████████████| 1/1 [00:00<00:00, 3682.44it/s]
Extracting data files: 100%|█████████████████████| 1/1 [00:00<00:00, 788.70it/s]
Generating train split: 0 examples [00:00, ? examples/s]Failed to read file '/home/lakala/hjc/code/pycode/glm/temp.json' with error <class 'pyarrow.lib.ArrowInvalid'>: cannot mix list and non-list, non-null values
Traceback (most recent call last):
File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
for _, table in generator:
File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(f"Not able to read records in the JSON file at {file}.") from None
ValueError: Not able to read records in the JSON file at /home/lakala/hjc/code/pycode/glm/temp.json.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/lakala/hjc/code/pycode/glm/test.py", line 22, in <module>
dataset = load_dataset("json", data_files=temp_path)
File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/load.py", line 1797, in load_dataset
builder_instance.download_and_prepare(
File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 890, in download_and_prepare
self._download_and_prepare(
File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 985, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 1746, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 1891, 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
```
### Environment info
```
Ubuntu==22.04
python==3.8
pytorch-transformers==1.2.0
transformers== 4.27.1
datasets==2.12.0
numpy==1.24.3
pandas==1.5.3
``` | {
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<img width="1292" alt="image" src="https://github.com/huggingface/datasets/assets/42851186/270fe5bc-1739-4878-b7bc-ab6d35336d4d">
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https://api.github.com/repos/huggingface/datasets/issues/5953 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5953/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5953/comments | https://api.github.com/repos/huggingface/datasets/issues/5953/events | https://github.com/huggingface/datasets/issues/5953 | 1,756,520,523 | I_kwDODunzps5osmBL | 5,953 | Bad error message when trying to download gated dataset | {
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} | [] | closed | false | null | [] | null | 8 | 2023-06-14T10:03:39 | 2023-06-14T16:36:51 | 2023-06-14T12:26:32 | CONTRIBUTOR | null | {
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} | null | null | null | ### Describe the bug
When I attempt to download a model from the Hub that is gated without being logged in, I get a nice error message. E.g.:
E.g.
```sh
Repository Not Found for url: https://huggingface.co/api/models/DeepFloyd/IF-I-XL-v1.0.
Please make sure you specified the correct `repo_id` and `repo_type`.
If you are trying to access a private or gated repo, make sure you are authenticated.
Invalid username or password..
Will try to load from local cache.
```
If I do the same for a gated dataset on the Hub, I'm not gated a nice error message IMO:
```sh
File ~/hf/lib/python3.10/site-packages/fsspec/implementations/http.py:430, in HTTPFileSystem._info(self, url, **kwargs)
427 except Exception as exc:
428 if policy == "get":
429 # If get failed, then raise a FileNotFoundError
--> 430 raise FileNotFoundError(url) from exc
431 logger.debug(str(exc))
433 return {"name": url, "size": None, **info, "type": "file"}
FileNotFoundError: https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0/resolve/main/n_shards.json
```
### Steps to reproduce the bug
```
huggingface-cli logout
```
and then:
```py
from datasets import load_dataset, Audio
# English
stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True)
stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
en_sample = next(iter(stream_data))["audio"]["array"]
# Swahili
stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "sw", split="test", streaming=True)
stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
sw_sample = next(iter(stream_data))["audio"]["array"]
```
### Expected behavior
Better error message
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.12.0
- Platform: Linux-6.2.0-76060200-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.16.0.dev0
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/5951 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5951/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5951/comments | https://api.github.com/repos/huggingface/datasets/issues/5951/events | https://github.com/huggingface/datasets/issues/5951 | 1,756,363,546 | I_kwDODunzps5or_sa | 5,951 | What is the Right way to use discofuse dataset?? | {
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**Below is the following way, as per my understanding , Is it correct :question: :question:**
The **columns/features from `DiscoFuse dataset`** that will be the **input to the `encoder` and `decoder`** are:
[Click here for Dataset link](https://huggingface.co/datasets/discofuse/viewer/discofuse-wikipedia/train?row=6)
1. **coherent_first_sentence**
2. **coherent_second_sentence**
3. **incoherent_first_sentence**
4. **incoherent_second_sentence**
[Click here for Dataset link](https://huggingface.co/datasets/discofuse/viewer/discofuse-wikipedia/train?row=6)
The **`encoder` will take these four columns as input and encode them into a sequence of hidden states. The `decoder` will then take these hidden states as input and decode them into a new sentence that fuses the two original sentences together.**
The **discourse type, connective_string, has_coref_type_pronoun, and has_coref_type_nominal columns will not be used as input to the encoder or decoder.** These columns are used to provide additional information about the dataset, but they are not necessary for the task of sentence fusion.
Please correct me if I am wrong; otherwise, if this understanding is right, how shall I implement this task practically? | {
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https://api.github.com/repos/huggingface/datasets/issues/5950 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5950/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5950/comments | https://api.github.com/repos/huggingface/datasets/issues/5950/events | https://github.com/huggingface/datasets/issues/5950 | 1,755,197,946 | I_kwDODunzps5onjH6 | 5,950 | Support for data with instance-wise dictionary as features | {
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} | null | null | null | ### Feature request
I notice that when loading data instances with feature type of python dictionary, the dictionary keys would be broadcast so that every instance has the same set of keys. Please see an example in the Motivation section.
It is possible to avoid this behavior, i.e., load dictionary features as it is and do not broadcast the keys among instances? Please note that these dictionaries would have to be processed dynamically at each training iteration into strings (and tokenized).
### Motivation
I am trying to load a dataset from a json file. Each instance of the dataset has a feature that is a dictionary but its keys depend on the instance. Every two instances may have different keys. For example, imagine a dataset that contains a set of math expressions from a bunch of mutually redundant expressions:
```
{
"index": 0,
"feature": {
"2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"],
...
}
},
...
{
"index": 9999,
"feature": {
"x >= 6": ["x >= 6", "x >= 0", "x >= -1"],
...
}
},
...
```
When directly loading the dataset using `data = load_dataset("json", data_files=file_paths, split='train')`, each instance would have all the keys from other instances and None as values. That is, instance of index 0 becomes:
```
{
"index": 0,
"feature": {
"2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"],
...
"x >= 6": None, # keys from other instances
...
}
},
```
This is not desirable. Moreover, issue would be raised if I attempt to combine two such datasets using `data = concatenate_datasets(multi_datasets)`, perhaps because their dictionary features contain different keys.
A solution I can think of is to store the dictionary features as a long string, and evaluate it later. Please kindly suggest any other solution using existing methods of datasets.
### Your contribution
N/A | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5949 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5949/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5949/comments | https://api.github.com/repos/huggingface/datasets/issues/5949/events | https://github.com/huggingface/datasets/pull/5949 | 1,754,843,717 | PR_kwDODunzps5S4oPC | 5,949 | Replace metadata utils with `huggingface_hub`'s RepoCard API | {
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} | Use `huggingface_hub`'s RepoCard API instead of `DatasetMetadata` for modifying the card's YAML, and deprecate `datasets.utils.metadata` and `datasets.utils.readme`.
After removing these modules, we can also delete `datasets.utils.resources` since the moon landing repo now stores its own version of these resources for the metadata UI.
PS: this change requires bumping `huggingface_hub` to 0.13.0 (Transformers requires 0.14.0, so should be ok) | {
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https://api.github.com/repos/huggingface/datasets/issues/5948 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5948/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5948/comments | https://api.github.com/repos/huggingface/datasets/issues/5948/events | https://github.com/huggingface/datasets/pull/5948 | 1,754,794,611 | PR_kwDODunzps5S4dUt | 5,948 | Fix sequence of array support for most dtype | {
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} | Fixes #5936
Also, a related fix to #5927 | {
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https://api.github.com/repos/huggingface/datasets/issues/5947 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5947/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5947/comments | https://api.github.com/repos/huggingface/datasets/issues/5947/events | https://github.com/huggingface/datasets/issues/5947 | 1,754,359,316 | I_kwDODunzps5okWYU | 5,947 | Return the audio filename when decoding fails due to corrupt files | {
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} | null | null | null | ### Feature request
Return the audio filename when the audio decoding fails. Although currently there are some checks for mp3 and opus formats with the library version there are still cases when the audio decoding could fail, eg. Corrupt file.
### Motivation
When you try to load an object file dataset and the decoding fails you can't know which file is corrupt
```
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7f5ab7e38290>: Format not recognised.
```
### Your contribution
Make a PR to Add exceptions for LIbsndfileError to return the audio filename or path when soundfile decoding fails. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5946 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5946/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5946/comments | https://api.github.com/repos/huggingface/datasets/issues/5946/events | https://github.com/huggingface/datasets/issues/5946 | 1,754,234,469 | I_kwDODunzps5oj35l | 5,946 | IndexError Not Solving -> IndexError: Invalid key: ?? is out of bounds for size 0 or ?? | {
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in <cell line: 1>:1 │
│ │
│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1537 in train │
│ │
│ 1534 │ │ inner_training_loop = find_executable_batch_size( │
│ 1535 │ │ │ self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size │
│ 1536 │ │ ) │
│ ❱ 1537 │ │ return inner_training_loop( │
│ 1538 │ │ │ args=args, │
│ 1539 │ │ │ resume_from_checkpoint=resume_from_checkpoint, │
│ 1540 │ │ │ trial=trial, │
│ │
│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1789 in _inner_training_loop │
│ │
│ 1786 │ │ │ │ rng_to_sync = True │
│ 1787 │ │ │ │
│ 1788 │ │ │ step = -1 │
│ ❱ 1789 │ │ │ for step, inputs in enumerate(epoch_iterator): │
│ 1790 │ │ │ │ total_batched_samples += 1 │
│ 1791 │ │ │ │ if rng_to_sync: │
│ 1792 │ │ │ │ │ self._load_rng_state(resume_from_checkpoint) │
│ │
│ /usr/local/lib/python3.10/dist-packages/accelerate/data_loader.py:377 in __iter__ │
│ │
│ 374 │ │ dataloader_iter = super().__iter__() │
│ 375 │ │ # We iterate one batch ahead to check when we are at the end │
│ 376 │ │ try: │
│ ❱ 377 │ │ │ current_batch = next(dataloader_iter) │
│ 378 │ │ except StopIteration: │
│ 379 │ │ │ yield │
│ 380 │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:633 in __next__ │
│ │
│ 630 │ │ │ if self._sampler_iter is None: │
│ 631 │ │ │ │ # TODO(https://github.com/pytorch/pytorch/issues/76750) │
│ 632 │ │ │ │ self._reset() # type: ignore[call-arg] │
│ ❱ 633 │ │ │ data = self._next_data() │
│ 634 │ │ │ self._num_yielded += 1 │
│ 635 │ │ │ if self._dataset_kind == _DatasetKind.Iterable and \ │
│ 636 │ │ │ │ │ self._IterableDataset_len_called is not None and \ │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:677 in _next_data │
│ │
│ 674 │ │
│ 675 │ def _next_data(self): │
│ 676 │ │ index = self._next_index() # may raise StopIteration │
│ ❱ 677 │ │ data = self._dataset_fetcher.fetch(index) # may raise StopIteration │
│ 678 │ │ if self._pin_memory: │
│ 679 │ │ │ data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) │
│ 680 │ │ return data │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py:49 in fetch │
│ │
│ 46 │ def fetch(self, possibly_batched_index): │
│ 47 │ │ if self.auto_collation: │
│ 48 │ │ │ if hasattr(self.dataset, "__getitems__") and self.dataset.__getitems__: │
│ ❱ 49 │ │ │ │ data = self.dataset.__getitems__(possibly_batched_index) │
│ 50 │ │ │ else: │
│ 51 │ │ │ │ data = [self.dataset[idx] for idx in possibly_batched_index] │
│ 52 │ │ else: │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2782 in __getitems__ │
│ │
│ 2779 │ │
│ 2780 │ def __getitems__(self, keys: List) -> List: │
│ 2781 │ │ """Can be used to get a batch using a list of integers indices.""" │
│ ❱ 2782 │ │ batch = self.__getitem__(keys) │
│ 2783 │ │ n_examples = len(batch[next(iter(batch))]) │
│ 2784 │ │ return [{col: array[i] for col, array in batch.items()} for i in range(n_example │
│ 2785 │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2778 in __getitem__ │
│ │
│ 2775 │ │
│ 2776 │ def __getitem__(self, key): # noqa: F811 │
│ 2777 │ │ """Can be used to index columns (by string names) or rows (by integer index or i │
│ ❱ 2778 │ │ return self._getitem(key) │
│ 2779 │ │
│ 2780 │ def __getitems__(self, keys: List) -> List: │
│ 2781 │ │ """Can be used to get a batch using a list of integers indices.""" │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2762 in _getitem │
│ │
│ 2759 │ │ format_kwargs = kwargs["format_kwargs"] if "format_kwargs" in kwargs else self._ │
│ 2760 │ │ format_kwargs = format_kwargs if format_kwargs is not None else {} │
│ 2761 │ │ formatter = get_formatter(format_type, features=self._info.features, **format_kw │
│ ❱ 2762 │ │ pa_subtable = query_table(self._data, key, indices=self._indices if self._indice │
│ 2763 │ │ formatted_output = format_table( │
│ 2764 │ │ │ pa_subtable, key, formatter=formatter, format_columns=format_columns, output │
│ 2765 │ │ ) │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:578 in query_table │
│ │
│ 575 │ │ _check_valid_column_key(key, table.column_names) │
│ 576 │ else: │
│ 577 │ │ size = indices.num_rows if indices is not None else table.num_rows │
│ ❱ 578 │ │ _check_valid_index_key(key, size) │
│ 579 │ # Query the main table │
│ 580 │ if indices is None: │
│ 581 │ │ pa_subtable = _query_table(table, key) │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:531 in │
│ _check_valid_index_key │
│ │
│ 528 │ │ │ _check_valid_index_key(min(key), size=size) │
│ 529 │ elif isinstance(key, Iterable): │
│ 530 │ │ if len(key) > 0: │
│ ❱ 531 │ │ │ _check_valid_index_key(int(max(key)), size=size) │
│ 532 │ │ │ _check_valid_index_key(int(min(key)), size=size) │
│ 533 │ else: │
│ 534 │ │ _raise_bad_key_type(key) │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:521 in │
│ _check_valid_index_key │
│ │
│ 518 def _check_valid_index_key(key: Union[int, slice, range, Iterable], size: int) -> None: │
│ 519 │ if isinstance(key, int): │
│ 520 │ │ if (key < 0 and key + size < 0) or (key >= size): │
│ ❱ 521 │ │ │ raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") │
│ 522 │ │ return │
│ 523 │ elif isinstance(key, slice): │
│ 524 │ │ pass
### Steps to reproduce the bug
``
import json
import os
from pprint import pprint
import bitsandbytes as bnb
import pandas as pd
import torch
import torch.nn as nn
import transformers
from datasets import Dataset,load_dataset
from peft import (
LoraConfig,
PeftConfig,
PeftModel,
get_peft_model,
prepare_model_for_kbit_training
)
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
MODEL_NAME = "tiiuae/falcon-7b"
bnb_config = BitsAndBytesConfig(
load_in_4bit = True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
device_map = "auto",
trust_remote_code = True,
quantization_config = bnb_config
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
tokenizer.pad_token = tokenizer.eos_token
model.gradient_checkpointing_enable()
model = prepare_model_for_kbit_training(model)
config = LoraConfig(
r = 16,
lora_alpha = 32,
target_modules = ["query_key_value"],
lora_dropout = 0.05,
bias = "none",
task_type = "CASUAL_LM"
)
model = get_peft_model(model,config)
print_trainable_parameters(model)
def generate_prompt(data_point):
return f"""
<human>: {data_point["question"]}
<assistant>: {data_point["answer"]}
""".strip()
def generate_and_tokenize_prompt(data_point):
full_prompt = generate_prompt(data_point)
tokenized_full_prompt = tokenizer(full_prompt, padding = True, truncation = True,return_tensors = None)
return dict({
"input_ids" : tokenized_full_prompt["input_ids"],
"attention_mask" : tokenized_full_prompt["attention_mask"]
})
data = data["train"].shuffle().map(generate_and_tokenize_prompt, batched = False)
OUTPUT_DIR = "experiments"
trainings_args = transformers.TrainingArguments(
per_device_train_batch_size = 1,
gradient_accumulation_steps = 4,
num_train_epochs = 1,
learning_rate = 2e-4,
fp16 = True,
save_total_limit = 3,
logging_steps = 1,
output_dir = OUTPUT_DIR,
max_steps = 80,
optim = "paged_adamw_8bit",
lr_scheduler_type = "cosine",
warmup_ratio = 0.05,
#remove_unused_columns=True
)
trainer = transformers.Trainer(
model = model,
train_dataset = data,
args = trainings_args,
data_collator = transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
)
model.config.use_cache = False
trainer.train()
IndexError: Invalid key: 32 is out of bounds for size 0
DataSet Format is like :
[{"question": "How can I create an account?", "answer": "To create an account, click on the 'Sign Up' button on the top right corner of our website and follow the instructions to complete the registration process."}, .... ]
### Expected behavior
-
### Environment info
!pip install -q pip
!pip install -q bitsandbytes==0.39.0
!pip install -q torch==2.0.1
!pip install -q git+https://github.com/huggingface/transformers.git
!pip install -q git+https://github.com/huggingface/peft.git
!pip install -q git+https://github.com/huggingface/accelerate.git
!pip install -q datasets
!pip install -q loralib==0.1.1
!pip install -q einops==0.6.1
import json
import os
from pprint import pprint
import bitsandbytes as bnb
import pandas as pd
import torch
import torch.nn as nn
import transformers
from datasets import Dataset,load_dataset
from peft import (
LoraConfig,
PeftConfig,
PeftModel,
get_peft_model,
prepare_model_for_kbit_training
)
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/5945 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5945/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5945/comments | https://api.github.com/repos/huggingface/datasets/issues/5945/events | https://github.com/huggingface/datasets/issues/5945 | 1,754,084,577 | I_kwDODunzps5ojTTh | 5,945 | Failing to upload dataset to the hub | {
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} | null | null | null | ### Describe the bug
Trying to upload a dataset of hundreds of thousands of audio samples (the total volume is not very large, 60 gb) to the hub with push_to_hub, it doesn't work.
From time to time one piece of the data (parquet) gets pushed and then I get RemoteDisconnected even though my internet is stable.
Please help.
I'm trying to upload the dataset for almost a week.
Thanks
### Steps to reproduce the bug
not relevant
### Expected behavior
Be able to upload thedataset
### Environment info
python: 3.9 | {
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https://api.github.com/repos/huggingface/datasets/issues/5944 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5944/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5944/comments | https://api.github.com/repos/huggingface/datasets/issues/5944/events | https://github.com/huggingface/datasets/pull/5944 | 1,752,882,200 | PR_kwDODunzps5Sx7O4 | 5,944 | Arrow dataset builder to be able to load and stream Arrow datasets | {
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} | [] | closed | false | null | [] | null | 4 | 2023-06-12T14:21:49 | 2023-06-13T17:36:02 | 2023-06-13T17:29:01 | CONTRIBUTOR | null | {
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} | This adds a Arrow dataset builder to be able to load and stream from already preprocessed Arrow files.
It's related to https://github.com/huggingface/datasets/issues/3035 | {
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https://api.github.com/repos/huggingface/datasets/issues/5942 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5942/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5942/comments | https://api.github.com/repos/huggingface/datasets/issues/5942/events | https://github.com/huggingface/datasets/pull/5942 | 1,752,021,681 | PR_kwDODunzps5Su-V4 | 5,942 | Pass datasets-cli additional args as kwargs to DatasetBuilder in `run_beam.py` | {
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} | Hi,
Following this <https://discuss.huggingface.co/t/how-to-preprocess-a-wikipedia-dataset-using-dataflowrunner/41991/3>, here is a simple PR to pass any additional args to datasets-cli as kwargs in the DatasetBuilder in `run_beam.py`.
I also took the liberty to add missing setup steps to the `beam.mdx` docs in order to help everyone.
@lhoestq | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5941 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5941/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5941/comments | https://api.github.com/repos/huggingface/datasets/issues/5941/events | https://github.com/huggingface/datasets/issues/5941 | 1,751,838,897 | I_kwDODunzps5oavCx | 5,941 | Load Data Sets Too Slow In Train Seq2seq Model | {
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} | null | null | null | ### Describe the bug
step 'Generating train split' in load_dataset is too slow：
### Steps to reproduce the bug
Data： own data，16K16B Mono wav
Oficial Script:[ run_speech_recognition_seq2seq.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py)
Add Code：
if data_args.data_path is not None:
print(data_args.data_path)
raw_datasets = load_dataset("audiofolder", data_dir=data_args.data_path, cache_dir=model_args.cache_dir)
raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000))
raw_datasets = raw_datasets["train"].train_test_split(test_size=0.005, shuffle=True)
（change cache_dir to other path ，ex:/DATA/cache）
### Expected behavior
load data fast,at least 1000+
`Generating train split: 387875 examples [32:24:45, 1154.83 examples/s]`
### Environment info
- `transformers` version: 4.28.0.dev0
- Platform: Linux-5.4.0-149-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.16
- Huggingface_hub version: 0.13.2
- PyTorch version (GPU?): 1.13.1+cu116 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in> | {
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https://api.github.com/repos/huggingface/datasets/issues/5990 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5990/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5990/comments | https://api.github.com/repos/huggingface/datasets/issues/5990/events | https://github.com/huggingface/datasets/issues/5990 | 1,774,389,854 | I_kwDODunzps5pwwpe | 5,990 | Pushing a large dataset on the hub consistently hangs | {
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} | null | null | null | ### Describe the bug
Once I have locally built a large dataset that I want to push to hub, I use the recommended approach of .push_to_hub to get the dataset on the hub, and after pushing a few shards, it consistently hangs. This has happened over 40 times over the past week, and despite my best efforts to try and catch this happening and kill a process and restart, it seems to be extremely time wasting -- so I came to you to report this and to seek help.
I already tried installing hf_transfer, but it doesn't support Byte file uploads so I uninstalled it.
### Reproduction
```python
import multiprocessing as mp
import pathlib
from math import ceil
import datasets
import numpy as np
from tqdm.auto import tqdm
from tali.data.data import select_subtitles_between_timestamps
from tali.utils import load_json
tali_dataset_dir = "/data/"
if __name__ == "__main__":
full_dataset = datasets.load_dataset(
"Antreas/TALI", num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir
)
def data_generator(set_name, percentage: float = 1.0):
dataset = full_dataset[set_name]
for item in tqdm(dataset):
video_list = item["youtube_content_video"]
video_list = np.random.choice(
video_list, int(ceil(len(video_list) * percentage))
)
if len(video_list) == 0:
continue
captions = item["youtube_subtitle_text"]
captions = select_subtitles_between_timestamps(
subtitle_dict=load_json(
captions.replace(
"/data/",
tali_dataset_dir,
)
),
starting_timestamp=0,
ending_timestamp=100000000,
)
for video_path in video_list:
temp_path = video_path.replace("/data/", tali_dataset_dir)
video_path_actual: pathlib.Path = pathlib.Path(temp_path)
if video_path_actual.exists():
item["youtube_content_video"] = open(video_path_actual, "rb").read()
item["youtube_subtitle_text"] = captions
yield item
train_generator = lambda: data_generator("train", percentage=0.1)
val_generator = lambda: data_generator("val")
test_generator = lambda: data_generator("test")
train_data = datasets.Dataset.from_generator(
train_generator,
num_proc=mp.cpu_count(),
writer_batch_size=5000,
cache_dir=tali_dataset_dir,
)
val_data = datasets.Dataset.from_generator(
val_generator,
writer_batch_size=5000,
num_proc=mp.cpu_count(),
cache_dir=tali_dataset_dir,
)
test_data = datasets.Dataset.from_generator(
test_generator,
writer_batch_size=5000,
num_proc=mp.cpu_count(),
cache_dir=tali_dataset_dir,
)
dataset = datasets.DatasetDict(
{
"train": train_data,
"val": val_data,
"test": test_data,
}
)
succesful_competion = False
while not succesful_competion:
try:
dataset.push_to_hub(repo_id="Antreas/TALI-small", max_shard_size="5GB")
succesful_competion = True
except Exception as e:
print(e)
```
### Logs
```shell
Pushing dataset shards to the dataset hub: 33%|██████████████████████████████████████▎ | 7/21 [24:33<49:06, 210.45s/it]
Error while uploading 'data/val-00007-of-00021-6b216a984af1a4c8.parquet' to the Hub.
Pushing split train to the Hub.
Resuming upload of the dataset shards.
Pushing dataset shards to the dataset hub: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 46/46 [42:10<00:00, 55.01s/it]
Pushing split val to the Hub.
Resuming upload of the dataset shards.
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00, 1.55ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:23<00:00, 23.51s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.39ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:30<00:00, 30.19s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.28ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:24<00:00, 24.08s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.42ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:23<00:00, 23.97s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.49ba/s]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.54ba/s^
Upload 1 LFS files: 0%| | 0/1 [04:42<?, ?it/s]
Pushing dataset shards to the dataset hub: 52%|████████████████████████████████████████████████████████████▏ | 11/21 [17:23<15:48, 94.82s/it]
That's where it got stuck
```
### System info
```shell
- huggingface_hub version: 0.15.1
- Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.35
- Python version: 3.10.11
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /root/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: Antreas
- Configured git credential helpers: store
- FastAI: N/A
- Tensorflow: N/A
- Torch: 2.1.0.dev20230606+cu121
- Jinja2: 3.1.2
- Graphviz: N/A
- Pydot: N/A
- Pillow: 9.5.0
- hf_transfer: N/A
- gradio: N/A
- numpy: 1.24.3
- ENDPOINT: https://huggingface.co
- HUGGINGFACE_HUB_CACHE: /root/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /root/.cache/huggingface/assets
- HF_TOKEN_PATH: /root/.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_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
```
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https://api.github.com/repos/huggingface/datasets/issues/5939 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5939/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5939/comments | https://api.github.com/repos/huggingface/datasets/issues/5939/events | https://github.com/huggingface/datasets/issues/5939 | 1,749,955,883 | I_kwDODunzps5oTjUr | 5,939 | . | {
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https://api.github.com/repos/huggingface/datasets/issues/5938 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5938/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5938/comments | https://api.github.com/repos/huggingface/datasets/issues/5938/events | https://github.com/huggingface/datasets/pull/5938 | 1,749,462,851 | PR_kwDODunzps5SmbkI | 5,938 | Make get_from_cache use custom temp filename that is locked | {
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} | This PR ensures that the temporary filename created is the same as the one that is locked, while writing to the cache.
This PR stops using `tempfile` to generate the temporary filename.
Additionally, the behavior now is aligned for both `resume_download` `True` and `False`.
Refactor temp_file_manager so that it uses the filename that is locked:
- Use: `cache_path + ".incomplete"`, when the locked one is `cache_path + ".lock"`
Before it was using `tempfile` inside `cache_dir`, which was not locked: although very improbable name collision (8 random characters), this was not impossible when huge number of multiple processes.
Maybe related to "Stale file handle" issues caused by `tempfile`:
- [ ] https://huggingface.co/datasets/tapaco/discussions/4
- [ ] https://huggingface.co/datasets/xcsr/discussions/1
- [ ] https://huggingface.co/datasets/covost2/discussions/3
```
Error code: ConfigNamesError
Exception: OSError
Message: [Errno 116] Stale file handle
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 61, in compute_config_names_response
for config in sorted(get_dataset_config_names(path=dataset, use_auth_token=use_auth_token))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1219, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1188, in dataset_module_factory
return HubDatasetModuleFactoryWithScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 907, in get_module
dataset_readme_path = self.download_dataset_readme_file()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 896, in download_dataset_readme_file
return cached_path(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 183, in cached_path
output_path = get_from_cache(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 611, in get_from_cache
http_get(
File "/usr/local/lib/python3.9/tempfile.py", line 496, in __exit__
result = self.file.__exit__(exc, value, tb)
OSError: [Errno 116] Stale file handle
```
- the stale file handle error can be raised when `tempfile` tries to close (when exiting its context manager) a filename that has been already closed by other process
- note that `tempfile` filenames are randomly generated but not locked in our code
CC: @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/5937 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5937/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5937/comments | https://api.github.com/repos/huggingface/datasets/issues/5937/events | https://github.com/huggingface/datasets/pull/5937 | 1,749,388,597 | PR_kwDODunzps5SmLIs | 5,937 | Avoid parallel redownload in cache | {
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https://api.github.com/repos/huggingface/datasets/issues/5936 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5936/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5936/comments | https://api.github.com/repos/huggingface/datasets/issues/5936/events | https://github.com/huggingface/datasets/issues/5936 | 1,748,424,388 | I_kwDODunzps5oNtbE | 5,936 | Sequence of array not supported for most dtype | {
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} | null | null | null | ### Describe the bug
Create a dataset composed of sequence of array fails for most dtypes (see code below).
### Steps to reproduce the bug
```python
from datasets import Sequence, Array2D, Features, Dataset
import numpy as np
for dtype in [
"bool", # ok
"int8", # failed
"int16", # failed
"int32", # failed
"int64", # ok
"uint8", # failed
"uint16", # failed
"uint32", # failed
"uint64", # failed
"float16", # failed
"float32", # failed
"float64", # ok
]:
features = Features({"foo": Sequence(Array2D(dtype=dtype, shape=(2, 2)))})
sequence = [
[[1.0, 2.0], [3.0, 4.0]],
[[5.0, 6.0], [7.0, 8.0]],
]
array = np.array(sequence, dtype=dtype)
try:
dataset = Dataset.from_dict({"foo": [array]}, features=features)
except Exception as e:
print(f"Failed for dtype={dtype}")
```
Traceback for `dtype="int8"`:
```
Traceback (most recent call last):
File "/home/qgallouedec/datasets/a.py", line 29, in <module>
raise e
File "/home/qgallouedec/datasets/a.py", line 26, in <module>
dataset = Dataset.from_dict({"foo": [array]}, features=features)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 899, in from_dict
pa_table = InMemoryTable.from_pydict(mapping=mapping)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 799, in from_pydict
return cls(pa.Table.from_pydict(*args, **kwargs))
File "pyarrow/table.pxi", line 3725, in pyarrow.lib.Table.from_pydict
File "pyarrow/table.pxi", line 5254, in pyarrow.lib._from_pydict
File "pyarrow/array.pxi", line 350, in pyarrow.lib.asarray
File "pyarrow/array.pxi", line 236, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/arrow_writer.py", line 204, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper
return func(array, *args, **kwargs)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 2091, in cast_array_to_feature
casted_values = _c(array.values, feature.feature)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper
return func(array, *args, **kwargs)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 2139, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper
return func(array, *args, **kwargs)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1967, in array_cast
return pa_type.wrap_array(array)
File "pyarrow/types.pxi", line 879, in pyarrow.lib.BaseExtensionType.wrap_array
TypeError: Incompatible storage type for extension<arrow.py_extension_type<Array2DExtensionType>>: expected list<item: list<item: int8>>, got list<item: list<item: int64>>
```
### Expected behavior
Not to fail.
### Environment info
- Python 3.10.6
- datasets: master branch
- Numpy: 1.23.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/5935 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5935/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5935/comments | https://api.github.com/repos/huggingface/datasets/issues/5935/events | https://github.com/huggingface/datasets/pull/5935 | 1,748,090,220 | PR_kwDODunzps5Sh9Mg | 5,935 | Better row group size in push_to_hub | {
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} | This is a very simple change that improves `to_parquet` to use a more reasonable row group size for image and audio datasets.
This is especially useful for `push_to_hub` and will provide a better experience with the dataset viewer on HF | {
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https://api.github.com/repos/huggingface/datasets/issues/5934 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5934/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5934/comments | https://api.github.com/repos/huggingface/datasets/issues/5934/events | https://github.com/huggingface/datasets/pull/5934 | 1,747,904,840 | PR_kwDODunzps5ShUxQ | 5,934 | Modify levels of some logging messages | {
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https://api.github.com/repos/huggingface/datasets/issues/5933 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5933/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5933/comments | https://api.github.com/repos/huggingface/datasets/issues/5933/events | https://github.com/huggingface/datasets/pull/5933 | 1,747,382,500 | PR_kwDODunzps5Sfi5J | 5,933 | Fix `to_numpy` when None values in the sequence | {
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} | Closes #5927
I've realized that the error was overlooked during testing due to the presence of only one None value in the sequence.
Unfortunately, it was the only case where the function works as expected. When the sequence contained more than one None value, the function failed. Consequently, I've updated the tests to include sequences with multiple None values. | {
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https://api.github.com/repos/huggingface/datasets/issues/5932 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5932/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5932/comments | https://api.github.com/repos/huggingface/datasets/issues/5932/events | https://github.com/huggingface/datasets/pull/5932 | 1,746,249,161 | PR_kwDODunzps5Sbrzo | 5,932 | [doc build] Use secrets | {
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https://api.github.com/repos/huggingface/datasets/issues/5931 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5931/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5931/comments | https://api.github.com/repos/huggingface/datasets/issues/5931/events | https://github.com/huggingface/datasets/issues/5931 | 1,745,408,784 | I_kwDODunzps5oCNMQ | 5,931 | `datasets.map` not reusing cached copy by default | {
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} | [] | closed | false | null | [] | null | 1 | 2023-06-07T09:03:33 | 2023-06-21T16:15:40 | 2023-06-21T16:15:40 | CONTRIBUTOR | null | {
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} | null | null | null | ### Describe the bug
When I load the dataset from local directory, it's cached copy is picked up after first time. However, for `map` operation, the operation is applied again and cached copy is not picked up. Is there any way to pick cached copy instead of processing it again? The only solution I could think of was to use `save_to_disk` after my last transform and then use that in my DataLoader pipeline. Are there any other solutions for the same?
One more thing, my dataset is occupying 6GB storage memory after I use `map`, is there any way I can reduce that memory usage?
### Steps to reproduce the bug
```
# make sure that dataset decodes audio with correct sampling rate
dataset_sampling_rate = next(iter(self.raw_datasets.values())).features["audio"].sampling_rate
if dataset_sampling_rate != self.feature_extractor.sampling_rate:
self.raw_datasets = self.raw_datasets.cast_column(
"audio", datasets.features.Audio(sampling_rate=self.feature_extractor.sampling_rate)
)
vectorized_datasets = self.raw_datasets.map(
self.prepare_dataset,
remove_columns=next(iter(self.raw_datasets.values())).column_names,
num_proc=self.num_workers,
desc="preprocess datasets",
)
# filter data that is longer than max_input_length
self.vectorized_datasets = vectorized_datasets.filter(
self.is_audio_in_length_range,
num_proc=self.num_workers,
input_columns=["input_length"],
)
def prepare_dataset(self, batch):
# load audio
sample = batch["audio"]
inputs = self.feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
batch["input_values"] = inputs.input_values[0]
batch["input_length"] = len(batch["input_values"])
batch["labels"] = self.tokenizer(batch["target_text"]).input_ids
return batch
```
### Expected behavior
`map` to use cached copy and if possible an alternative technique to reduce memory usage after using `map`
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-3.10.0-1160.71.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.16
- 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/5930 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5930/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5930/comments | https://api.github.com/repos/huggingface/datasets/issues/5930/events | https://github.com/huggingface/datasets/issues/5930 | 1,745,184,395 | I_kwDODunzps5oBWaL | 5,930 | loading private custom dataset script - authentication error | {
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} | [] | closed | false | null | [] | null | 1 | 2023-06-07T06:58:23 | 2023-06-15T14:49:21 | 2023-06-15T14:49:20 | NONE | null | {
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} | null | null | null | ### Describe the bug
Train model with my custom dataset stored in HuggingFace and loaded with the loading script requires authentication but I am not sure how ?
I am logged in in the terminal, in the browser. I receive this error:
/python3.8/site-packages/datasets/utils/file_utils.py", line 566, in get_from_cache
raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels `(ConnectionError('Unauthorized for URL `https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels. Please use the parameter `**`use_auth_token=True`**` after logging in with `**`huggingface-cli login`**`'))
when I added: `use_auth_token=True` and logged in via terminal then I received error:
or the same error in different format:
raise ConnectionError(f"`Couldn't reach {url} (error {response.status_code}`)")
ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels (`error 401`)
### Steps to reproduce the bug
1. cloned transformers library locally:
https://huggingface.co/docs/transformers/v4.15.0/examples :
> git clone https://github.com/huggingface/transformers
> cd transformers
> pip install .
> cd /transformers/examples/pytorch/audio-classification
> pip install -r requirements.txt
2. created **loading script**
> https://huggingface.co/docs/datasets/dataset_script added next to dataset:
3. uploaded **private custom dataset** with loading script to HuggingFace
> https://huggingface.co/docs/datasets/dataset_script
4. added dataset loading script to **local directory** in the above cloned transformers library:
> cd /transformers/examples/pytorch/audio-classification
5. logged in to HuggingFace on local terminal with :
> **huggingface-cli login**
6. run the model with the custom dataset stored on HuggingFace with code: https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/README.md
cd /transformers/examples/pytorch/audio-classification
> python run_audio_classification.py \
> --model_name_or_path facebook/wav2vec2-base \
> --output_dir l/users/flck/outputs/wav2vec2-base-s \
> --overwrite_output_dir \
> --dataset_name s \
> --dataset_config_name s \
> --remove_unused_columns False \
> --do_train \
> --do_eval \
> --fp16 \
> --learning_rate 3e-5 \
> --max_length_seconds 1 \
> --attention_mask False \
> --warmup_ratio 0.1 \
> --num_train_epochs 5 \
> --per_device_train_batch_size 32 \
> --gradient_accumulation_steps 4 \
> --per_device_eval_batch_size 32 \
> --dataloader_num_workers 4 \
> --logging_strategy steps \
> --logging_steps 10 \
> --evaluation_strategy epoch \
> --save_strategy epoch \
> --load_best_model_at_end True \
> --metric_for_best_model accuracy \
> --save_total_limit 3 \
> --seed 0 \
> --push_to_hub \
> **--use_auth_token=True**
### Expected behavior
Be able to train a model the https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/ run_audio_classification.py with private custom dataset stored on HuggingFace.
### Environment info
- datasets version: 2.12.0
- `transformers` version: 4.30.0.dev0
- Platform: Linux-5.4.204-ql-generic-12.0-19-x86_64-with-glibc2.17
- Python version: 3.8.12
- Huggingface_hub version: 0.15.1
- Safetensors version: 0.3.1
- PyTorch version (GPU?): 2.0.1+cu117 (True)
Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] torch==2.0.1
[pip3] torchaudio==2.0.2
[conda] numpy 1.24.3 pypi_0 pypi
[conda] torch 2.0.1 pypi_0 pypi
[conda] torchaudio 2.0.2 pypi_0 pypi
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https://api.github.com/repos/huggingface/datasets/issues/5929 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5929/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5929/comments | https://api.github.com/repos/huggingface/datasets/issues/5929/events | https://github.com/huggingface/datasets/issues/5929 | 1,744,478,456 | I_kwDODunzps5n-qD4 | 5,929 | Importing PyTorch reduces multiprocessing performance for map | {
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} | [] | closed | false | null | [] | null | 2 | 2023-06-06T19:42:25 | 2023-06-16T13:09:12 | 2023-06-16T13:09:12 | NONE | null | {
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} | null | null | null | ### Describe the bug
I noticed that the performance of my dataset preprocessing with `map(...,num_proc=32)` decreases when PyTorch is imported.
### Steps to reproduce the bug
I created two example scripts to reproduce this behavior:
```
import datasets
datasets.disable_caching()
from datasets import Dataset
import time
PROC=32
if __name__ == "__main__":
dataset = [True] * 10000000
dataset = Dataset.from_dict({'train': dataset})
start = time.time()
dataset.map(lambda x: x, num_proc=PROC)
end = time.time()
print(end - start)
```
Takes around 4 seconds on my machine.
While the same code, but with an `import torch`:
```
import datasets
datasets.disable_caching()
from datasets import Dataset
import time
import torch
PROC=32
if __name__ == "__main__":
dataset = [True] * 10000000
dataset = Dataset.from_dict({'train': dataset})
start = time.time()
dataset.map(lambda x: x, num_proc=PROC)
end = time.time()
print(end - start)
```
takes around 22 seconds.
### Expected behavior
I would expect that the import of torch to not have such a significant effect on the performance of map using multiprocessing.
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.35
- Python version: 3.11.3
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.2
- torch: 2.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5928 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5928/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5928/comments | https://api.github.com/repos/huggingface/datasets/issues/5928/events | https://github.com/huggingface/datasets/pull/5928 | 1,744,098,371 | PR_kwDODunzps5SUXPC | 5,928 | Fix link to quickstart docs in README.md | {
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https://api.github.com/repos/huggingface/datasets/issues/5927 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5927/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5927/comments | https://api.github.com/repos/huggingface/datasets/issues/5927/events | https://github.com/huggingface/datasets/issues/5927 | 1,744,009,032 | I_kwDODunzps5n83dI | 5,927 | `IndexError` when indexing `Sequence` of `Array2D` with `None` values | {
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} | [] | closed | false | null | [] | null | 2 | 2023-06-06T14:36:22 | 2023-06-13T12:39:39 | 2023-06-09T13:23:50 | MEMBER | null | {
"total": 0,
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} | null | null | null | ### Describe the bug
Having `None` values in a `Sequence` of `ArrayND` fails.
### Steps to reproduce the bug
```python
from datasets import Array2D, Dataset, Features, Sequence
data = [
[
[[0]],
None,
None,
]
]
feature = Sequence(Array2D((1, 1), dtype="int64"))
dataset = Dataset.from_dict({"a": data}, features=Features({"a": feature}))
dataset[0] # error raised only when indexing
```
```
Traceback (most recent call last):
File "/Users/quentingallouedec/gia/c.py", line 13, in <module>
dataset[0] # error raised only when indexing
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2658, in __getitem__
return self._getitem(key)
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2643, in _getitem
formatted_output = format_table(
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 634, in format_table
return formatter(pa_table, query_type=query_type)
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 406, in __call__
return self.format_row(pa_table)
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 441, in format_row
row = self.python_arrow_extractor().extract_row(pa_table)
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 144, in extract_row
return _unnest(pa_table.to_pydict())
File "pyarrow/table.pxi", line 4146, in pyarrow.lib.Table.to_pydict
File "pyarrow/table.pxi", line 1312, in pyarrow.lib.ChunkedArray.to_pylist
File "pyarrow/array.pxi", line 1521, in pyarrow.lib.Array.to_pylist
File "pyarrow/scalar.pxi", line 675, in pyarrow.lib.ListScalar.as_py
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/features/features.py", line 760, in to_pylist
return self.to_numpy(zero_copy_only=zero_copy_only).tolist()
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/features/features.py", line 725, in to_numpy
numpy_arr = np.insert(numpy_arr.astype(np.float64), null_indices, np.nan, axis=0)
File "<__array_function__ internals>", line 200, in insert
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/numpy/lib/function_base.py", line 5426, in insert
old_mask[indices] = False
IndexError: index 3 is out of bounds for axis 0 with size 3
```
AFAIK, the problem only occurs when you use a `Sequence` of `ArrayND`.
I strongly suspect that the problem comes from this line, or `np.insert` is misused:
https://github.com/huggingface/datasets/blob/02ee418831aba68d0be93227bce8b3f42ef8980f/src/datasets/features/features.py#L729
To put t simply, you want something that do that:
```python
import numpy as np
numpy_arr = np.zeros((1, 1, 1))
null_indices = np.array([1, 2])
np.insert(numpy_arr, null_indices, np.nan, axis=0)
# raise an error, instead of outputting
# array([[[ 0.]],
# [[nan]],
# [[nan]]])
```
### Expected behavior
The previous code should not raise an error.
### Environment info
- Python 3.10.11
- datasets 2.10.0
- pyarrow 12.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5926 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5926/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5926/comments | https://api.github.com/repos/huggingface/datasets/issues/5926/events | https://github.com/huggingface/datasets/issues/5926 | 1,743,922,028 | I_kwDODunzps5n8iNs | 5,926 | Uncaught exception when generating the splits from a dataset that miss data | {
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] | null | 1 | 2023-06-06T13:51:01 | 2023-06-07T07:53:16 | null | COLLABORATOR | null | {
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} | null | null | null | ### Describe the bug
Dataset https://huggingface.co/datasets/blog_authorship_corpus has an issue with its hosting platform, since https://drive.google.com/u/0/uc?id=1cGy4RNDV87ZHEXbiozABr9gsSrZpPaPz&export=download returns 404 error.
But when trying to generate the split names, we get an exception which is now correctly caught.
Seen originally in https://github.com/huggingface/datasets-server/blob/adbdcd6710ffed4e2eb2e4cd905b5e0dff530a15/services/worker/src/worker/job_runners/config/parquet_and_info.py#L435
### Steps to reproduce the bug
```python
>>> from datasets import StreamingDownloadManager, load_dataset_builder
>>> builder = load_dataset_builder(path="blog_authorship_corpus")
Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.60k/5.60k [00:00<00:00, 23.1MB/s]
Downloading metadata: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.81k/2.81k [00:00<00:00, 14.7MB/s]
Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.30k/7.30k [00:00<00:00, 30.8MB/s]
>>> dl_manager = StreamingDownloadManager(base_path=builder.base_path)
>>> builder._split_generators(dl_manager)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/blog_authorship_corpus/6f5d78241afd8313111956f877a57db7a0e9fc6718255dc85df0928197feb683/blog_authorship_corpus.py", line 79, in _split_generators
data = dl_manager.download_and_extract(_DATA_URL)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1087, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1039, in extract
urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 435, in map_nested
return function(data_struct)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1044, in _extract
protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 433, in _get_extraction_protocol
with fsspec.open(urlpath, **kwargs) as f:
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 439, in open
return open_files(
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 194, in __getitem__
out = super().__getitem__(item)
IndexError: list index out of range
```
### Expected behavior
We should have an Exception raised by the datasets library.
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.19.0-1026-aws-x86_64-with-glibc2.35
- Python version: 3.9.15
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5925 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5925/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5925/comments | https://api.github.com/repos/huggingface/datasets/issues/5925/events | https://github.com/huggingface/datasets/issues/5925 | 1,741,941,436 | I_kwDODunzps5n0-q8 | 5,925 | Breaking API change in datasets.list_datasets caused by change in HfApi.list_datasets | {
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} | [] | closed | false | null | [] | null | 0 | 2023-06-05T14:46:04 | 2023-06-19T17:22:43 | 2023-06-19T17:22:43 | NONE | null | {
"total": 0,
"completed": 0,
"percent_completed": 0
} | null | null | null | ### Describe the bug
Hi all,
after an update of the `datasets` library, we observer crashes in our code. We relied on `datasets.list_datasets` returning a `list`. Now, after the API of the HfApi.list_datasets was changed and it returns a `list` instead of an `Iterable`, the `datasets.list_datasets` now sometimes returns a `list` and somesimes an `Iterable`.
It would be helpful to indicate that by the return type of the `datasets.list_datasets` function.
Thanks,
Martin
### Steps to reproduce the bug
Here, the code crashed after we updated the `datasets` library:
```python
# list_datasets no longer returns a list, which leads to an error when one tries to slice it
for datasets.list_datasets(with_details=True)[:limit]:
...
```
### Expected behavior
It would be helpful to indicate that by the return type of the `datasets.list_datasets` function.
### Environment info
Ubuntu 22.04
datasets 2.12.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5924 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5924/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5924/comments | https://api.github.com/repos/huggingface/datasets/issues/5924/events | https://github.com/huggingface/datasets/pull/5924 | 1,738,889,236 | PR_kwDODunzps5SCiFv | 5,924 | Add parallel module using joblib for Spark | {
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https://api.github.com/repos/huggingface/datasets/issues/5923 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5923/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5923/comments | https://api.github.com/repos/huggingface/datasets/issues/5923/events | https://github.com/huggingface/datasets/issues/5923 | 1,737,436,227 | I_kwDODunzps5njyxD | 5,923 | Cannot import datasets - ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility | {
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} | null | null | null | ### Describe the bug
When trying to import datasets, I get a pyarrow ValueError:
Traceback (most recent call last):
File "/Users/edward/test/test.py", line 1, in <module>
import datasets
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/__init__.py", line 43, in <module>
from .arrow_dataset import Dataset
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 65, in <module>
from .arrow_reader import ArrowReader
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/arrow_reader.py", line 28, in <module>
import pyarrow.parquet as pq
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/parquet/__init__.py", line 20, in <module>
from .core import *
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 45, in <module>
from pyarrow.fs import (LocalFileSystem, FileSystem, FileType,
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/fs.py", line 49, in <module>
from pyarrow._gcsfs import GcsFileSystem # noqa
File "pyarrow/_gcsfs.pyx", line 1, in init pyarrow._gcsfs
ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject
### Steps to reproduce the bug
`import datasets`
### Expected behavior
Successful import
### Environment info
Conda environment, MacOS
python 3.9.12
datasets 2.12.0
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https://api.github.com/repos/huggingface/datasets/issues/5922 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5922/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5922/comments | https://api.github.com/repos/huggingface/datasets/issues/5922/events | https://github.com/huggingface/datasets/issues/5922 | 1,736,898,953 | I_kwDODunzps5nhvmJ | 5,922 | Length of table does not accurately reflect the split | {
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} | null | null | null | ### Describe the bug
I load a Huggingface Dataset and do `train_test_split`. I'm expecting the underlying table for the dataset to also be split, but it's not.
### Steps to reproduce the bug
### Expected behavior
The expected behavior is when `len(hf_dataset["train"].data)` should match the length of the train split, and not be the entire unsplit dataset.
### Environment info
datasets 2.10.1
python 3.10.11 | {
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https://api.github.com/repos/huggingface/datasets/issues/5921 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5921/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5921/comments | https://api.github.com/repos/huggingface/datasets/issues/5921/events | https://github.com/huggingface/datasets/pull/5921 | 1,736,563,023 | PR_kwDODunzps5R6j-y | 5,921 | Fix streaming parquet with image feature in schema | {
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https://api.github.com/repos/huggingface/datasets/issues/5920 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5920/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5920/comments | https://api.github.com/repos/huggingface/datasets/issues/5920/events | https://github.com/huggingface/datasets/pull/5920 | 1,736,196,991 | PR_kwDODunzps5R5TRB | 5,920 | Optimize IterableDataset.from_file using ArrowExamplesIterable | {
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https://api.github.com/repos/huggingface/datasets/issues/5919 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5919/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5919/comments | https://api.github.com/repos/huggingface/datasets/issues/5919/events | https://github.com/huggingface/datasets/pull/5919 | 1,735,519,227 | PR_kwDODunzps5R2_EK | 5,919 | add support for storage_options for load_dataset API | {
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} | to solve the issue in #5880
1. add s3 support in the link check step, previous we only check `http` and `https`,
2. change the parameter of `use_auth_token` to `download_config` to support both `storage_options` and `use_auth_token` parameter when trying to handle(list, open, read, etc,.) the remote files.
3. integrate the check part's duplicate code to make adding or deleting other sources easier. | {
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https://api.github.com/repos/huggingface/datasets/issues/5918 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5918/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5918/comments | https://api.github.com/repos/huggingface/datasets/issues/5918/events | https://github.com/huggingface/datasets/issues/5918 | 1,735,313,549 | I_kwDODunzps5nbsiN | 5,918 | File not found for audio dataset | {
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} | null | null | null | ### Describe the bug
After loading an audio dataset, and looking at a sample entry, the `path` element, which is supposed to be the path to the audio file, doesn't actually exist.
### Steps to reproduce the bug
Run bug.py:
```py
import os.path
from datasets import load_dataset
def run() -> None:
cv13 = load_dataset(
"mozilla-foundation/common_voice_13_0",
"hi",
split="train",
)
print(cv13[0])
audio_file = cv13[0]["path"]
if not os.path.exists(audio_file):
raise ValueError(f'File {audio_file} does not exist.')
if __name__ == "__main__":
run()
```
The result (on my machine):
```json
{'client_id': '0f018a99663f33afbb7d38aee281fb1afcfd07f9e7acd00383f604e1e17c38d6ed8adf1bd2ccbf927a52c5adefb8ac4b158ce27a7c2ed9581e71202eb302dfb3', 'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'audio': {'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'array': array([ 6.46234854e-26, -1.35709319e-25, -8.07793567e-26, ...,
1.06425944e-07, 4.46417090e-08, 2.61451660e-09]), 'sampling_rate': 48000}, 'sentence': 'हमने उसका जन्मदिन मनाया।', 'up_votes': 2, 'down_votes': 0, 'age': '', 'gender': '', 'accent': '', 'locale': 'hi', 'segment': '' ', 'variant': ''}
```
```txt
Traceback (most recent call last):
File "F:\eo-reco\bug.py", line 18, in <module>
run()
File "F:\eo-reco\bug.py", line 15, in run
raise ValueError(f'File {audio_file} does not exist.')
ValueError: File C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\common_voice_hi_26008353.mp3 does not exist.
```
### Expected behavior
The `path` element points to the correct file, which happens to be:
```
C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\hi_train_0\common_voice_hi_26008353.mp3
```
That is, there's an extra directory `hi_train_0` that is not in the `path` element.
### Environment info
- `datasets` version: 2.12.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.11.3
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
- | null | {
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} | Related to:
- #5850 | {
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} | Raise an error in `DatasetBuilder.as_dataset` when `file_format != "arrow"` (and fix the docstring)
Fix #5874 | {
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} | null | null | null | ### Describe the bug
When using the `filter` or `map` function to preprocess a dataset, a ValueError is encountered with the error message "array is too big; arr.size * arr.dtype.itemsize is larger than the maximum possible size."
Detailed error message:
Traceback (most recent call last):
File "data_processing.py", line 26, in <module>
processed_dataset[split] = samromur_children[split].map(prepare_dataset, cache_file_name=cache_dict[split],writer_batch_size = 50)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2405, in map
desc=desc,
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 557, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 524, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/fingerprint.py", line 480, in wrapper
out = func(self, *args, **kwargs)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2756, in _map_single
example = apply_function_on_filtered_inputs(example, i, offset=offset)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2655, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2347, in decorated
result = f(decorated_item, *args, **kwargs)
File "data_processing.py", line 11, in prepare_dataset
audio = batch["audio"]
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 123, in __getitem__
value = decode_nested_example(self.features[key], value) if value is not None else None
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/features.py", line 1260, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/audio.py", line 156, in decode_example
array, sampling_rate = self._decode_non_mp3_path_like(path, token_per_repo_id=token_per_repo_id)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/audio.py", line 257, in _decode_non_mp3_path_like
array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/librosa/core/audio.py", line 176, in load
y, sr_native = __soundfile_load(path, offset, duration, dtype)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/librosa/core/audio.py", line 222, in __soundfile_load
y = sf_desc.read(frames=frame_duration, dtype=dtype, always_2d=False).T
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/soundfile.py", line 891, in read
out = self._create_empty_array(frames, always_2d, dtype)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/soundfile.py", line 1323, in _create_empty_array
return np.empty(shape, dtype, order='C')
ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.
### Steps to reproduce the bug
```python
from datasets import load_dataset, DatasetDict
from transformers import WhisperFeatureExtractor
from transformers import WhisperTokenizer
samromur_children= load_dataset("language-and-voice-lab/samromur_children")
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="icelandic", task="transcribe")
def prepare_dataset(batch):
# load and resample audio data from 48 to 16kHz
audio = batch["audio"]
# compute log-Mel input features from input audio array
batch["input_features"] = feature_extractor(audio["array"], sampling_rate=16000).input_features[0]
# encode target text to label ids
batch["labels"] = tokenizer(batch["normalized_text"]).input_ids
return batch
cache_dict = {"train": "./cache/audio_train.cache", \
"validation": "./cache/audio_validation.cache", \
"test": "./cache/audio_test.cache"}
filter_cache_dict = {"train": "./cache/filter_train.arrow", \
"validation": "./cache/filter_validation.arrow", \
"test": "./cache/filter_test.arrow"}
print("before filtering")
print(samromur_children)
#filter the dataset to only include examples with more than 2 seconds of audio
samromur_children = samromur_children.filter(lambda example: example["audio"]["array"].shape[0] > 16000*2, cache_file_names=filter_cache_dict)
print("after filtering")
print(samromur_children)
processed_dataset = DatasetDict()
# processed_dataset = samromur_children.map(prepare_dataset, cache_file_names=cache_dict, num_proc=10,)
for split in ["train", "validation", "test"]:
processed_dataset[split] = samromur_children[split].map(prepare_dataset, cache_file_name=cache_dict[split])
```
### Expected behavior
The dataset is successfully processed and ready to train the model.
### Environment info
Python version: 3.7.13
datasets package version: 2.4.0
librosa package version: 0.10.0.post2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5913 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5913/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5913/comments | https://api.github.com/repos/huggingface/datasets/issues/5913/events | https://github.com/huggingface/datasets/issues/5913 | 1,731,427,484 | I_kwDODunzps5nM3yc | 5,913 | I tried to load a custom dataset using the following statement: dataset = load_dataset('json', data_files=data_files). The dataset contains 50 million text-image pairs, but an error occurred. | {
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"site_admin": false
} | [] | closed | false | null | [] | null | 2 | 2023-05-30T02:55:26 | 2023-07-24T12:00:38 | 2023-07-24T12:00:38 | NONE | null | {
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} | null | null | null | ### Describe the bug
File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
Downloading and preparing dataset json/default to /home/kas/diffusers/examples/dreambooth/cache_data/datasets/json/default-acf423d8c6ef99d0/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data files: 100%|██████████| 1/1 [00:00<00:00, 84.35it/s]
Extracting data files: 0%| | 0/1 [00:00<?, ?it/s] for _, table in generator:
File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 114, in _generate_tables
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
File "pyarrow/_json.pyx", line 258, in pyarrow._json.read_json
Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 27.72it/s]
Generating train split: 0 examples [00:00, ? examples/s] File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 125, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2390448764
### Steps to reproduce the bug
1、data_files = ["1.json", "2.json", "3.json"]
2、dataset = load_dataset('json', data_files=data_files)
### Expected behavior
Read the dataset normally.
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-4.15.0-29-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5912 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5912/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5912/comments | https://api.github.com/repos/huggingface/datasets/issues/5912/events | https://github.com/huggingface/datasets/issues/5912 | 1,730,299,852 | I_kwDODunzps5nIkfM | 5,912 | Missing elements in `map` a batched dataset | {
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} | [] | closed | false | null | [] | null | 1 | 2023-05-29T08:09:19 | 2023-07-26T15:48:15 | 2023-07-26T15:48:15 | NONE | null | {
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} | null | null | null | ### Describe the bug
As outlined [here](https://discuss.huggingface.co/t/length-error-using-map-with-datasets/40969/3?u=sachin), the following collate function drops 5 out of possible 6 elements in the batch (it is 6 because out of the eight, two are bad links in laion). A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here.
The weirdest part is when inspecting the sizes of the tensors as shown below, both `tokenized_captions["input_ids"]` and `image_features` show the correct shapes. Simply the output only has one element (with the batch dimension squeezed out).
```python
class CollateFn:
def get_image(self, url):
try:
response = requests.get(url)
return Image.open(io.BytesIO(response.content)).convert("RGB")
except PIL.UnidentifiedImageError:
logger.info(f"Reading error: Could not transform f{url}")
return None
except requests.exceptions.ConnectionError:
logger.info(f"Connection error: Could not transform f{url}")
return None
def __call__(self, batch):
images = [self.get_image(url) for url in batch["url"]]
captions = [caption for caption, image in zip(batch["caption"], images) if image is not None]
images = [image for image in images if image is not None]
tokenized_captions = tokenizer(
captions,
padding="max_length",
truncation=True,
max_length=tokenizer.model_max_length,
return_tensors="pt",
)
image_features = torch.stack([torch.Tensor(feature_extractor(image)["pixel_values"][0]) for image in images])
# import pdb; pdb.set_trace()
return {"input_ids": tokenized_captions["input_ids"], "images": image_features}
collate_fn = CollateFn()
laion_ds = datasets.load_dataset("laion/laion400m", split="train", streaming=True)
laion_ds_batched = laion_ds.map(collate_fn, batched=True, batch_size=8, remove_columns=next(iter(laion_ds)).keys())
```
### Steps to reproduce the bug
A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here.
### Expected behavior
Would expect `next(iter(laion_ds_batched))` to produce two tensors of shape `(batch_size, 77)` and `batch_size, image_shape`.
### Environment info
datasets==2.12.0
python==3.10 | {
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https://api.github.com/repos/huggingface/datasets/issues/5910 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5910/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5910/comments | https://api.github.com/repos/huggingface/datasets/issues/5910/events | https://github.com/huggingface/datasets/issues/5910 | 1,728,909,790 | I_kwDODunzps5nDRHe | 5,910 | Cannot use both set_format and set_transform | {
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} | [] | closed | false | null | [] | null | 5 | 2023-05-27T19:22:23 | 2023-07-09T21:40:54 | 2023-06-16T14:41:24 | NONE | null | {
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} | null | null | null | ### Describe the bug
I need to process some data using the set_transform method but I also need the data to be formatted for pytorch before processing it.
I don't see anywhere in the documentation something that says that both methods cannot be used at the same time.
### Steps to reproduce the bug
```
from datasets import load_dataset
ds = load_dataset("mnist", split="train")
ds.set_format(type="torch")
def transform(entry):
return entry["image"].double()
ds.set_transform(transform)
print(ds[0])
```
### Expected behavior
It should print the pytorch tensor image as a double, but it errors because "entry" in the transform function doesn't receive a pytorch tensor to begin with, it receives a PIL Image -> entry.double() errors because entry isn't a pytorch tensor.
### Environment info
Latest versions.
### Note:
It would be at least handy to have access to a function that can do the dataset.set_format in the set_transform function.
Something like:
```
from datasets import load_dataset, do_format
ds = load_dataset("mnist", split="train")
def transform(entry):
entry = do_format(entry, type="torch")
return entry["image"].double()
ds.set_transform(transform)
print(ds[0])
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5909 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5909/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5909/comments | https://api.github.com/repos/huggingface/datasets/issues/5909/events | https://github.com/huggingface/datasets/pull/5909 | 1,728,900,068 | PR_kwDODunzps5Rgga6 | 5,909 | Use more efficient and idiomatic way to construct list. | {
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} | Using `*` is ~2X faster according to [benchmark](https://colab.research.google.com/gist/ttsugriy/c964a2604edf70c41911b10335729b6a/for-vs-mult.ipynb) with just 4 patterns. This doesn't matter much since this tiny difference is not going to be noticeable, but why not? | {
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https://api.github.com/repos/huggingface/datasets/issues/5908 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5908/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5908/comments | https://api.github.com/repos/huggingface/datasets/issues/5908/events | https://github.com/huggingface/datasets/issues/5908 | 1,728,653,935 | I_kwDODunzps5nCSpv | 5,908 | Unbearably slow sorting on big mapped datasets | {
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} | null | null | null | ### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially with bigger datasets (wasn't able to sort 700k lines at all, with flattening takes about a minute).
### Steps to reproduce the bug
```Python
from datasets import load_dataset
import time
dataset = load_dataset("xnli", "en", split="train")
dataset = dataset.shard(10, 0)
print(len(dataset))
t = time.time()
# dataset = dataset.flatten_indices() # uncomment this line and it's fast
dataset = dataset.sort("label", reverse=True, load_from_cache_file=False)
print(f"finished in {time.time() - t:.4f} seconds")
```
### Expected behavior
Expect sorting to take the same or less time than flattening and then sorting.
### Environment info
- `datasets` version: 2.12.1.dev0 (same with 2.12.0 too)
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.10.10
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5907 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5907/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5907/comments | https://api.github.com/repos/huggingface/datasets/issues/5907/events | https://github.com/huggingface/datasets/pull/5907 | 1,728,648,560 | PR_kwDODunzps5RfqUU | 5,907 | Add `flatten_indices` to `DatasetDict` | {
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} | [] | closed | false | null | [] | null | 2 | 2023-05-27T10:55:44 | 2023-06-01T11:46:35 | 2023-06-01T11:39:36 | CONTRIBUTOR | null | {
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} | Add `flatten_indices` to `DatasetDict` for convinience | {
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https://api.github.com/repos/huggingface/datasets/issues/5906 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5906/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5906/comments | https://api.github.com/repos/huggingface/datasets/issues/5906/events | https://github.com/huggingface/datasets/issues/5906 | 1,728,171,113 | I_kwDODunzps5nAcxp | 5,906 | Could you unpin responses version? | {
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} | null | null | null | ### Describe the bug
Could you unpin [this](https://github.com/huggingface/datasets/blob/main/setup.py#L139) or move it to test requirements? This is a testing library and we also use it for our tests as well. We do not want to use a very outdated version.
### Steps to reproduce the bug
could not install this library due to dependency conflict.
### Expected behavior
can install datasets
### Environment info
linux 64 | {
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https://api.github.com/repos/huggingface/datasets/issues/5905 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5905/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5905/comments | https://api.github.com/repos/huggingface/datasets/issues/5905/events | https://github.com/huggingface/datasets/issues/5905 | 1,727,541,392 | I_kwDODunzps5m-DCQ | 5,905 | Offer an alternative to Iterable Dataset that allows lazy loading and processing while skipping batches efficiently | {
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] | open | false | null | [] | null | 1 | 2023-05-26T12:33:02 | 2023-06-15T13:34:18 | null | CONTRIBUTOR | null | {
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} | null | null | null | ### Feature request
I would like a way to resume training from a checkpoint without waiting for a very long time when using an iterable dataset.
### Motivation
I am training models on the speech-recognition task. I have very large datasets that I can't comfortably store on a disk and also quite computationally intensive audio processing to do. As a result I want to load data from my remote when it is needed and perform all processing on the fly.
I am currently using the iterable dataset feature of _datasets_. It does everything I need with one exception. My issue is that when resuming training at a step n, we have to download all the data and perform the processing of steps < n, just to get the iterable at the right step. In my case it takes almost as long as training for the same steps, which make resuming training from a checkpoint useless in practice.
I understand that the nature of iterators make it probably nearly impossible to quickly resume training.
I thought about a possible solution nonetheless :
I could in fact index my large dataset and make it a mapped dataset. Then I could use set_transform to perform the processing on the fly. Finally, if I'm not mistaken, the _accelerate_ package allows to [skip steps efficiently](https://github.com/huggingface/accelerate/blob/a73898027a211c3f6dc4460351b0ec246aa824aa/src/accelerate/data_loader.py#L827) for a mapped dataset.
Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there.
If not, I could do it using a plain _Pytorch_ dataset. Then I would need to convert it to a _datasets_' dataset to get all the features of _datasets_. Is it something possible ?
### Your contribution
I could provide a PR to allow lazy loading of mapped dataset or the conversion of a mapped _Pytorch_ dataset into a _Datasets_ dataset if you think it is an useful new feature. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5904 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5904/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5904/comments | https://api.github.com/repos/huggingface/datasets/issues/5904/events | https://github.com/huggingface/datasets/pull/5904 | 1,727,415,626 | PR_kwDODunzps5Rbfks | 5,904 | Validate name parameter in make_file_instructions | {
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} | Validate `name` parameter in `make_file_instructions`.
This way users get more informative error messages, instead of:
```stacktrace
.../huggingface/datasets/src/datasets/arrow_reader.py in make_file_instructions(name, split_infos, instruction, filetype_suffix, prefix_path)
110 name2len = {info.name: info.num_examples for info in split_infos}
111 name2shard_lengths = {info.name: info.shard_lengths for info in split_infos}
--> 112 name2filenames = {
113 info.name: filenames_for_dataset_split(
114 path=prefix_path,
.../huggingface/datasets/src/datasets/arrow_reader.py in <dictcomp>(.0)
111 name2shard_lengths = {info.name: info.shard_lengths for info in split_infos}
112 name2filenames = {
--> 113 info.name: filenames_for_dataset_split(
114 path=prefix_path,
115 dataset_name=name,
.../huggingface/datasets/src/datasets/naming.py in filenames_for_dataset_split(path, dataset_name, split, filetype_suffix, shard_lengths)
68
69 def filenames_for_dataset_split(path, dataset_name, split, filetype_suffix=None, shard_lengths=None):
---> 70 prefix = filename_prefix_for_split(dataset_name, split)
71 prefix = os.path.join(path, prefix)
72
.../huggingface/datasets/src/datasets/naming.py in filename_prefix_for_split(name, split)
52
53 def filename_prefix_for_split(name, split):
---> 54 if os.path.basename(name) != name:
55 raise ValueError(f"Should be a dataset name, not a path: {name}")
56 if not re.match(_split_re, split):
.../lib/python3.9/posixpath.py in basename(p)
140 def basename(p):
141 """Returns the final component of a pathname"""
--> 142 p = os.fspath(p)
143 sep = _get_sep(p)
144 i = p.rfind(sep) + 1
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
Related to #5895. | {
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https://api.github.com/repos/huggingface/datasets/issues/5903 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5903/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5903/comments | https://api.github.com/repos/huggingface/datasets/issues/5903/events | https://github.com/huggingface/datasets/pull/5903 | 1,727,372,549 | PR_kwDODunzps5RbV82 | 5,903 | Relax `ci.yml` trigger for `pull_request` based on modified paths | {
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} | ## What's in this PR?
As of a previous PR at #5902, I've seen that the CI was automatically trigger on any file, in that case when modifying a Jupyter Notebook (.ipynb), which IMO could be skipped, as the modification on the Jupyter Notebook has no effect/impact on the `ci.yml` outcome. So this PR controls the paths that trigger the `ci.yml` to avoid wasting resources when not needed.
## What's pending in this PR?
I would like to confirm whether this should affect both `push` and `pull_request`, since just modifications in those files won't change the `ci.yml` outcome, so maybe it's worth skipping it too in the `push` trigger. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5902 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5902/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5902/comments | https://api.github.com/repos/huggingface/datasets/issues/5902/events | https://github.com/huggingface/datasets/pull/5902 | 1,727,342,194 | PR_kwDODunzps5RbPS9 | 5,902 | Fix `Overview.ipynb` & detach Jupyter Notebooks from `datasets` repository | {
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} | ## What's in this PR?
This PR solves #5887 since there was a mismatch between the tokenizer and the model used, since the tokenizer was `bert-base-cased` while the model was `distilbert-base-case` both for the PyTorch and TensorFlow alternatives. Since DistilBERT doesn't use/need the `token_type_ids`, the `**batch` was failing, as the batch contained `input_ids`, `attention_mask`, `token_type_ids`, `start_positions` and `end_positions`, and `token_type_ids` was not required.
Besides that, at the end `seqeval` was being used to evaluate the model predictions, and just `evaluate` was being installed, so I've also included the `seqeval` installation.
Finally, I've re-run everything in Google Colab, and every cell was successfully executed!
## What was done on top of the original PR?
Based on the comments from @mariosasko and @stevhliu, I've updated the contents of this PR to also review the `quickstart.mdx` and update what was needed, besides that, we may eventually move the `Overview.ipynb` dataset to `huggingface/notebooks` following @stevhliu suggestions. | {
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https://api.github.com/repos/huggingface/datasets/issues/5901 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5901/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5901/comments | https://api.github.com/repos/huggingface/datasets/issues/5901/events | https://github.com/huggingface/datasets/pull/5901 | 1,727,179,016 | PR_kwDODunzps5Rarux | 5,901 | Make prepare_split more robust if errors in metadata dataset_info splits | {
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} | This PR uses `split_generator.split_info` as default value for `split_info` if any exception is raised while trying to get `split_generator.name` from `self.info.splits` (this may happen if there is any error in the metadata dataset_info splits).
Please note that `split_info` is only used by the logger.
Fix #5895 if passed `verification_mode="no_checks"`:
```python
ds = load_dataset(
"ArmelR/stack-exchange-instruction",
data_dir="data/finetune",
split="train",
verification_mode="no_checks",
revision="c609f1caade5cfbf3b9fe9cfa17d7cb000b457bd",
)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5900 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5900/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5900/comments | https://api.github.com/repos/huggingface/datasets/issues/5900/events | https://github.com/huggingface/datasets/pull/5900 | 1,727,129,617 | PR_kwDODunzps5RahTR | 5,900 | Fix minor typo in docs loading.mdx | {
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https://api.github.com/repos/huggingface/datasets/issues/5899 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5899/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5899/comments | https://api.github.com/repos/huggingface/datasets/issues/5899/events | https://github.com/huggingface/datasets/pull/5899 | 1,726,279,011 | PR_kwDODunzps5RXods | 5,899 | canonicalize data dir in config ID hash | {
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} | fixes #5871
The second commit is optional but improves readability. | {
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https://api.github.com/repos/huggingface/datasets/issues/5898 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5898/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5898/comments | https://api.github.com/repos/huggingface/datasets/issues/5898/events | https://github.com/huggingface/datasets/issues/5898 | 1,726,190,481 | I_kwDODunzps5m45OR | 5,898 | Loading The flores data set for specific language | {
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} | null | null | null | ### Describe the bug
I am trying to load the Flores data set
the code which is given is
```
from datasets import load_dataset
dataset = load_dataset("facebook/flores")
```
This gives the error of config name
""ValueError: Config name is missing"
Now if I add some config it gives me the some error
"HFValidationError: Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are forbidden, '-' and '.' cannot start or end the name, max length is 96: 'facebook/flores, 'ace_Arab''.
"
How I can load the data of the specific language ?
Couldn't find any tutorial
any one can help me out?
### Steps to reproduce the bug
step one load the data set
`from datasets import load_dataset
dataset = load_dataset("facebook/flores")`
it gives the error of config
once config is given
it gives the error of
"HFValidationError: Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are forbidden, '-' and '.' cannot start or end the name, max length is 96: 'facebook/flores, 'ace_Arab''.
"
### Expected behavior
Data set should be loaded but I am receiving error
### Environment info
Datasets , python , | {
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} | [] | closed | false | null | [] | null | 4 | 2023-05-25T16:26:33 | 2023-05-26T12:22:04 | 2023-05-26T11:57:16 | COLLABORATOR | null | {
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} | Fix cast on sliced `FixedSizeListArray`s.
Fix #5866 | {
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https://api.github.com/repos/huggingface/datasets/issues/5896 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5896/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5896/comments | https://api.github.com/repos/huggingface/datasets/issues/5896/events | https://github.com/huggingface/datasets/issues/5896 | 1,726,022,500 | I_kwDODunzps5m4QNk | 5,896 | HuggingFace does not cache downloaded files aggressively/early enough | {
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} | [] | closed | false | null | [] | null | 2 | 2023-05-25T15:14:36 | 2024-03-15T15:36:07 | 2024-03-15T15:36:07 | NONE | null | {
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} | null | null | null | ### Describe the bug
I wrote the following script:
```
import datasets
dataset = datasets.load.load_dataset("wikipedia", "20220301.en", split="train[:10000]")
```
I ran it and spent 90 minutes downloading a 20GB file. Then I saw:
```
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20.3G/20.3G [1:30:29<00:00, 3.73MB/s]
Traceback (most recent call last):
File "/home/jack/Code/Projects/Transformers/Codebase/main.py", line 5, in <module>
dataset = datasets.load.load_dataset("wikipedia", "20220301.en", split="train[:10000]")
File "/home/jack/.local/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset
builder_instance.download_and_prepare(
File "/home/jack/.local/lib/python3.10/site-packages/datasets/builder.py", line 883, in download_and_prepare
self._save_info()
File "/home/jack/.local/lib/python3.10/site-packages/datasets/builder.py", line 2037, in _save_info
import apache_beam as beam
ModuleNotFoundError: No module named 'apache_beam'
```
And the 20GB of data was seemingly instantly gone forever, because when I ran the script again, it had to do the download again.
### Steps to reproduce the bug
See above
### Expected behavior
See above
### Environment info
datasets 2.10.1
Python 3.10 | {
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https://api.github.com/repos/huggingface/datasets/issues/5895 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5895/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5895/comments | https://api.github.com/repos/huggingface/datasets/issues/5895/events | https://github.com/huggingface/datasets/issues/5895 | 1,725,467,252 | I_kwDODunzps5m2Ip0 | 5,895 | The dir name and split strings are confused when loading ArmelR/stack-exchange-instruction dataset | {
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"type": "User",
"user_view_type": "public",
"site_admin": false
} | [] | closed | false | null | [] | null | 2 | 2023-05-25T09:39:06 | 2023-05-29T02:32:12 | 2023-05-29T02:32:12 | NONE | null | {
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} | null | null | null | ### Describe the bug
When I load the ArmelR/stack-exchange-instruction dataset, I encounter a bug that may be raised by confusing the dir name string and the split string about the dataset.
When I use the script "datasets.load_dataset('ArmelR/stack-exchange-instruction', data_dir="data/finetune", split="train", use_auth_token=True)", it fails. But it succeeds when I add the "streaming = True" parameter.
The website of the dataset is https://huggingface.co/datasets/ArmelR/stack-exchange-instruction/ .
The traceback logs are as below:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/load.py", line 1797, in load_dataset
builder_instance.download_and_prepare(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 890, in download_and_prepare
self._download_and_prepare(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 985, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 1706, in _prepare_split
split_info = self.info.splits[split_generator.name]
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/splits.py", line 530, in __getitem__
instructions = make_file_instructions(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/arrow_reader.py", line 112, in make_file_instructions
name2filenames = {
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/arrow_reader.py", line 113, in <dictcomp>
info.name: filenames_for_dataset_split(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/naming.py", line 70, in filenames_for_dataset_split
prefix = filename_prefix_for_split(dataset_name, split)
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/naming.py", line 54, in filename_prefix_for_split
if os.path.basename(name) != name:
File "/home/xxx/miniconda3/envs/code/lib/python3.9/posixpath.py", line 142, in basename
p = os.fspath(p)
TypeError: expected str, bytes or os.PathLike object, not NoneType
### Steps to reproduce the bug
1. import datasets library function: ```from datasets import load_dataset```
2. load dataset: ```ds=load_dataset('ArmelR/stack-exchange-instruction', data_dir="data/finetune", split="train", use_auth_token=True)```
### Expected behavior
The dataset can be loaded successfully without the streaming setting.
### Environment info
Linux,
python=3.9
datasets=2.12.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5894 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5894/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5894/comments | https://api.github.com/repos/huggingface/datasets/issues/5894/events | https://github.com/huggingface/datasets/pull/5894 | 1,724,774,910 | PR_kwDODunzps5RSjot | 5,894 | Force overwrite existing filesystem protocol | {
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} | Fix #5876 | {
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https://api.github.com/repos/huggingface/datasets/issues/5893 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5893/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5893/comments | https://api.github.com/repos/huggingface/datasets/issues/5893/events | https://github.com/huggingface/datasets/pull/5893 | 1,722,519,056 | PR_kwDODunzps5RK40K | 5,893 | Load cached dataset as iterable | {
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} | To be used to train models it allows to load an IterableDataset from the cached Arrow file.
See https://github.com/huggingface/datasets/issues/5481 | {
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When a user access requests are enabled, and new requests are set to Manual Review, the dataset owner should be notified of the pending requests. However, instead, currently nothing happens, and so the dataset request can go unanswered for quite some time until the owner happens to check that particular dataset's Settings pane.
### Steps to reproduce the bug
1. Enable a dataset's user access requests
2. Set to Manual Review
3. Ask another HF user to request access to the dataset
4. Dataset owner is not notified
### Expected behavior
The dataset owner should receive some kind of notification, perhaps in their HF site inbox, or by email, when a dataset access request is made and manual review is enabled.
### Environment info
n/a | {
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} | Fix #1774, fix #5875
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> DatasetDict({
train: Dataset({
features: ['input', 'output'],
num_rows: 4500
})
test: Dataset({
features: ['input', 'output'],
num_rows: 500
})
})
**# input (in-correct sentence)**
`data['train'][0]['input']`
**>>** 'We are meet sunday 10am12pmET in Crown Heights Brooklyn New York'
**# output (correct sentence)**
`data['train'][0]['output']`
**>>** 'We meet Sundays 10am-12pmET in Crown Heights, Brooklyn, New York.'
**I Want to align the output tokens with input**
```
`# tokenize both inputs and targets
def tokenize_fn(batch):
# tokenize the input sequence first
# this populates input_ids, attention_mask, etc.
tokenized_inputs = tokenizer(
batch['input']
)
labels_batch = tokenizer.tokenize(batch['output']) # original targets
aligned_labels_batch = []
for i, labels in enumerate(labels_batch):
word_ids = tokenized_inputs[i].word_ids()
aligned_labels_batch.append(align_targets(labels, word_ids)) # align_targets is another user defined function which is been called here
# recall: the 'target' must be stored in key called 'labels'
tokenized_inputs['labels'] = aligned_labels_batch
return tokenized_inputs`
```
```
data.map(
tokenize_fn,
batched=True,
remove_columns=data['train'].column_names,
)
```
When this user defined function is mapped to every records of train and test batch am getting following error:
**1.** **raise DatasetTransformationNotAllowedError(
3457 "Using `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn't create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it."**
**2.** **TypeError: TextEncodeInput must be Union[TextInputSequence, Tuple[InputSequence, InputSequence]]** | null | {
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} | null | null | null | ### Describe the bug
### Steps to reproduce the bug
Use link as reference document written https://colab.research.google.com/github/huggingface/datasets/blob/main/notebooks/Overview.ipynb#scrollTo=biqDH9vpvSVz
```python
# Now let's train our model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.train().to(device)
for i, batch in enumerate(dataloader):
batch.to(device)
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
model.zero_grad()
print(f'Step {i} - loss: {loss:.3}')
if i > 5:
break
```
Error
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-44-7040b885f382>](https://localhost:8080/#) in <cell line: 5>()
5 for i, batch in enumerate(dataloader):
6 batch.to(device)
----> 7 outputs = model(**batch)
8 loss = outputs.loss
9 loss.backward()
[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs)
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
TypeError: DistilBertForQuestionAnswering.forward() got an unexpected keyword argument 'token_type_ids'
```
https://github.com/huggingface/datasets/assets/1328316/5d8b1d61-9337-4d59-8423-4f37f834c156
### Expected behavior
Run success on Google Colab (free)
### Environment info
Windows 11 x64, Google Colab free (my Google Drive just empty about 200 MB, but I don't think it cause problem) | {
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when i used Dataset.map api to process data concurrently, i found that
it gets slower and slower as it gets closer to completion. Then i read the source code of arrow_dataset.py and found that it shard the dataset and use multiprocessing pool to execute each shard.It may cause the slowest task to drag out the entire program's execution time,especially when processing huge dataset.
### Motivation
using work-stealing algorithm instead of sharding and parallel computing to optimize performance.
### Your contribution
just an idea. | null | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I recently chose to use huggingface hub as the home for a large multi modal dataset I've been building. https://huggingface.co/datasets/Antreas/TALI
It combines images, text, audio and video. Now, I could very easily upload a dataset made via datasets.Dataset.from_generator, as long as it did not include video files. I found that including .mp4 files in the entries would not auto-upload those files.
Hence I tried to upload them myself. I quickly found out that uploading many small files is a very bad way to use git lfs, and that it would take ages, so, I resorted to using 7z to pack them all up. But then I had a new problem.
My dataset had a size of 1.9TB. Trying to upload such a large file with the default huggingface_hub API always resulted in time outs etc. So I decided to split the large files into chunks of 5GB each and reupload.
So, eventually it all worked out. But now the dataset can't be properly and natively used by the datasets API because of all the needed preprocessing -- and furthermore the hub is unable to visualize things.
**Describe the solution you'd like**
A native way to upload large datasets that include .mp4 or other video types.
**Describe alternatives you've considered**
Already explained earlier
**Additional context**
https://huggingface.co/datasets/Antreas/TALI
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} | null | null | null | ### Describe the bug
When loading any dataset that contains a column with strings that are not ASCII-compatible, looping over those records raises the following exception e.g. for `é` character `UnicodeEncodeError: 'ascii' codec can't encode character '\xe9' in position 0: ordinal not in range(128)`.
### Steps to reproduce the bug
Running the following script will eventually fail, when reaching to the batch that contains non-ASCII compatible strings.
```python
from datasets import load_dataset
ds = load_dataset("imdb", split="train")
tfds = ds.to_tf_dataset(batch_size=16)
for batch in tfds:
print(batch)
>>> UnicodeEncodeError: 'ascii' codec can't encode character '\xe9' in position 0: ordinal not in range(128)
```
### Expected behavior
The following script to run properly, making sure that the strings are either `numpy.unicode_` or `numpy.string` instead of `numpy.bytes_` since some characters are not ASCII compatible and that would lead to an issue when applying the `map`.
```python
from datasets import load_dataset
ds = load_dataset("imdb", split="train")
tfds = ds.to_tf_dataset(batch_size=16)
for batch in tfds:
print(batch)
```
### Environment info
- `datasets` version: 2.12.1.dev0
- Platform: macOS-13.3.1-arm64-arm-64bit
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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} | ## What's in this PR?
This PR addresses some minor fixes and general improvements in the `to_tf_dataset` method of `datasets.Dataset`, to convert a 🤗HuggingFace Dataset as a TensorFlow Dataset.
The main bug solved in this PR comes with the string-encoding, since for safety purposes the internal conversion of `numpy.arrays` when `dtype` is unicode/string, is to convert it into `numpy.bytes`, more information in the docstring of https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#L210. That's triggered when using `tensorflow.numpy_function` as it's applying another type cast besides the one that `datasets` does, so the casting is applied at least twice per entry/batch. So this means that the definition of the `numpy.unicode_` dtype when the data in the batch is a string, is ignored, and replaced by `numpy.bytes_`.
Besides that, some other minor things have been fixed:
* Made `batch_size` an optional parameter in `to_tf_dataset`
* Map the `tensorflow` output dtypes just once, and not in every `tf.function` call during `map`
* Keep `numpy` formatting in the `datasets.Dataset` if already formatted like it, no need to format it again as `numpy`
* Docstring indentation in `dataset_to_tf` and `multiprocess_dataset_to_tf`
## What's missing in this PR?
I can include some integration tests if needed, to validate that `batch_size` is optional, and that the tensors in the TF-Dataset can be looped over with no issues as before. | {
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} | null | null | null | ### Describe the bug
Context: we're splitting an iterable dataset by node and then passing it to a torch data loader with multiple workers
When we iterate over it for 5 steps, we don't get an error
When we instead iterate over it for 8 steps, we get an `IndexError` when fetching the data if we have too many workers
### Steps to reproduce the bug
Here, we have 2 JAX processes (`jax.process_count() = 2`) which we split the dataset over. The dataset loading script can be found here: https://huggingface.co/datasets/distil-whisper/librispeech_asr/blob/c6a1e805cbfeed5057400ac5937327d7e30281b8/librispeech_asr.py#L310
<details>
<summary> Code to reproduce </summary>
```python
from datasets import load_dataset
import jax
from datasets.distributed import split_dataset_by_node
from torch.utils.data import DataLoader
from tqdm import tqdm
# load an example dataset (https://huggingface.co/datasets/distil-whisper/librispeech_asr)
dataset = load_dataset("distil-whisper/librispeech_asr", "all", split="train.clean.100", streaming=True)
# just keep the text column -> no need to define a collator
dataset_text = dataset.remove_columns(set(dataset.features.keys()) - {"text"})
# define some constants
batch_size = 256
num_examples = 5 # works for 5 examples, doesn't for 8
num_workers = dataset_text.n_shards
# try with multiple workers
dataloader = DataLoader(dataset_text, batch_size=batch_size, num_workers=num_workers, drop_last=True)
for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Multiple workers"):
if i == num_examples:
break
# try splitting by node (we can't do this with `dataset_text` since `split_dataset_by_node` expects the Audio column for an ASR dataset)
dataset = split_dataset_by_node(dataset, rank=jax.process_index(), world_size=jax.process_count())
# remove the text column again
dataset_text = dataset.remove_columns(set(dataset.features.keys()) - {"text"})
dataloader = DataLoader(dataset_text, batch_size=16, num_workers=num_workers // 2, drop_last=True)
for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Split by node"):
if i == num_examples:
break
# too many workers
dataloader = DataLoader(dataset_text, batch_size=256, num_workers=num_workers, drop_last=True)
for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Too many workers"):
if i == num_examples:
break
```
</details>
<details>
<summary> With 5 examples: </summary>
```
Multiple workers: 100%|███████████████████████████████████████████████████████████████████| 5/5 [00:16<00:00, 3.33s/it]
Assigning 7 shards (or data sources) of the dataset to each node.
Split by node: 100%|██████████████████████████████████████████████████████████████████████| 5/5 [00:13<00:00, 2.76s/it]
Assigning 7 shards (or data sources) of the dataset to each node.
Too many dataloader workers: 14 (max is dataset.n_shards=7). Stopping 7 dataloader workers.
To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary t
o have a number of workers greater than dataset.n_shards=7. To enable more parallelism, please split the dataset in more
files than 7.
Too many workers: 100%|███████████████████████████████████████████████████████████████████| 5/5 [00:15<00:00, 3.03s/it]
```
</details>
<details>
<summary> With 7 examples: </summary>
```
Multiple workers: 100%|███████████████████████████████████████████████████████████████████| 8/8 [00:13<00:00, 1.71s/it]
Assigning 7 shards (or data sources) of the dataset to each node.
Split by node: 100%|██████████████████████████████████████████████████████████████████████| 8/8 [00:11<00:00, 1.38s/it]
Assigning 7 shards (or data sources) of the dataset to each node.
Too many dataloader workers: 14 (max is dataset.n_shards=7). Stopping 7 dataloader workers.
To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary to have a number of workers greater than dataset.n_shards=7. To enable more parallelism, please split the dataset in more files than 7.
Too many workers: 88%|██████████████████████████████████████████████████████████▋ | 7/8 [00:13<00:01, 1.89s/it]
Traceback (most recent call last):
File "distil-whisper/test_librispeech.py", line 36, in <module>
for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Too many workers"):
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/tqdm/std.py", line 1178, in __iter__
for obj in iterable:
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
data = self._next_data()
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1325, in _next_data
return self._process_data(data)
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data
data.reraise()
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/_utils.py", line 644, in reraise
raise exception
IndexError: Caught IndexError in DataLoader worker process 7.
Original Traceback (most recent call last):
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 986, in __iter__
yield from self._iter_pytorch(ex_iterable)
File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 920, in _iter_pytorch
for key, example in ex_iterable.shard_data_sources(worker_info.id, worker_info.num_workers):
File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 540, in shard_data_sources
self.ex_iterable.shard_data_sources(worker_id, num_workers),
File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 796, in shard_data_sources
self.ex_iterable.shard_data_sources(worker_id, num_workers),
File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 126, in shard_data_sources
requested_gen_kwargs = _merge_gen_kwargs([gen_kwargs_list[i] for i in shard_indices])
File "/home/sanchitgandhi/datasets/src/datasets/utils/sharding.py", line 76, in _merge_gen_kwargs
for key in gen_kwargs_list[0]
IndexError: list index out of range
```
</details>
### Expected behavior
Should pass for both 5 and 7 examples
### Environment info
- `datasets` version: 2.12.1.dev0
- Platform: Linux-5.13.0-1023-gcp-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1 | null | {
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} | null | null | null | ### Describe the bug
I have a JSON file in my s3 file system(minio), I can use load_dataset to get the file link, but I can't iterate it
<img width="816" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/cc0778d3-36f3-45b5-ac68-4e7c664c2ed0">
<img width="1144" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/76872af3-8b3c-42ff-9f55-528c920a7af1">
we can change 4 lines to fix this bug, you can check whether it is ok for us.
<img width="941" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/5a22155a-ece7-496c-8506-047e5c235cd3">
### Steps to reproduce the bug
1. storage a file in you s3 file system
2. use load_dataset to read it through streaming
3. iterate it
### Expected behavior
can iterate it successfully
### Environment info
- `datasets` version: 2.12.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
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"total": 0,
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} | null | null | null | ### Feature request
Add support for prefetching the next n batches through iterabledataset to reduce batch loading bottleneck in training loop.
### Motivation
The primary motivation behind this is to use hardware accelerators alongside a streaming dataset. This is required when you are in a low ram or low disk space setting as well as quick iteration where you're iterating though different accelerator environments (e.x changing ec2 instances quickly to figure out batch/sec for a particular architecture).
Currently, using the IterableDataset results in accelerators becoming basically useless due to the massive bottleneck induced by the dataset lazy loading/transform/mapping.
I've considered two alternatives:
PyTorch dataloader that handles this. However, I'm using jax, and I believe this is a piece of functionality that should live in the stream class.
Replicating the "num_workers" part of the PyTorch DataLoader to eagerly load batches and apply the transform so Arrow caching will automatically cache results and make them accessible.
### Your contribution
I may or may not have time to do this. Currently, I've written the basic multiprocessor approach to handle the eager DataLoader for my own use case with code that's not integrated to datasets. I'd definitely see this as being the default over the regular Dataset for most people given that they wouldn't have to wait on the datasets while also not worrying about performance. | null | {
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} | null | null | null | ### Feature request
It would be great if there would be support for high performance, highly scalable text deduplication algorithms as part of the datasets library.
### Motivation
Motivated by this blog post https://huggingface.co/blog/dedup and this library https://github.com/google-research/deduplicate-text-datasets, but slightly frustrated by how its not very easy to work with these tools I am proposing this feature.
### Your contribution
I would be happy to contribute to the development effort of this feature. would love to collaborate with others in the development effort. | null | {
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} | null | null | null | ### Describe the bug
Hello,
I am currently working on a project where both [DataLab](https://github.com/ExpressAI/DataLab) and [datasets](https://github.com/huggingface/datasets) are subdependencies.
I noticed that I cannot import both libraries, as they both register FileSystems in `fsspec`, expecting the FileSystems not being registered before.
When running the code below, I get the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\__init__.py", line 28, in <module>
from datalabs.arrow_dataset import concatenate_datasets, Dataset
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\arrow_dataset.py", line 60, in <module>
from datalabs.arrow_writer import ArrowWriter, OptimizedTypedSequence
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\arrow_writer.py", line 28, in <module>
from datalabs.features import (
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\features\__init__.py", line 2, in <module>
from datalabs.features.audio import Audio
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\features\audio.py", line 21, in <module>
from datalabs.utils.streaming_download_manager import xopen
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\utils\streaming_download_manager.py", line 16, in <module>
from datalabs.filesystems import COMPRESSION_FILESYSTEMS
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\filesystems\__init__.py", line 37, in <module>
fsspec.register_implementation(fs_class.protocol, fs_class)
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\fsspec\registry.py", line 51, in register_implementation
raise ValueError(
ValueError: Name (bz2) already in the registry and clobber is False
```
I think as simple solution would be to just set `clobber=True` in https://github.com/huggingface/datasets/blob/main/src/datasets/filesystems/__init__.py#L28. This allows the register to discard previous registrations. This should work, as the datalabs FileSystems are copies of the datasets FileSystems. However, I don't know if it is guaranteed to be compatible with other libraries that might use the same protocols.
I am linking the symmetric issue on [DataLab](https://github.com/ExpressAI/DataLab/issues/425) as ideally the issue is solved in both libraries the same way. Otherwise, it could lead to different behaviors depending on which library gets imported first.
### Steps to reproduce the bug
1. Run `pip install datalabs==0.4.15 datasets==2.12.0`
2. Run the following python code:
```
import datalabs
import datasets
```
### Expected behavior
It should be possible to import both libraries without getting a Value Error
### Environment info
datalabs==0.4.15
datasets==2.12.0
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https://api.github.com/repos/huggingface/datasets/issues/5875 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5875/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5875/comments | https://api.github.com/repos/huggingface/datasets/issues/5875/events | https://github.com/huggingface/datasets/issues/5875 | 1,716,770,394 | I_kwDODunzps5mU9Za | 5,875 | Why split slicing doesn't behave like list slicing ? | {
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} | null | null | null | ### Describe the bug
If I want to get the first 10 samples of my dataset, I can do :
```
ds = datasets.load_dataset('mnist', split='train[:10]')
```
But if I exceed the number of samples in the dataset, an exception is raised :
```
ds = datasets.load_dataset('mnist', split='train[:999999999]')
```
> ValueError: Requested slice [:999999999] incompatible with 60000 examples.
### Steps to reproduce the bug
```
ds = datasets.load_dataset('mnist', split='train[:999999999]')
```
### Expected behavior
I would expect it to behave like python lists (no exception raised, the whole list is kept) :
```
d = list(range(1000))[:999999]
print(len(d)) # > 1000
```
### Environment info
- `datasets` version: 2.9.0
- Platform: macOS-12.6-arm64-arm-64bit
- Python version: 3.9.12
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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} | null | null | null | ### Describe the bug
I used a custom builder to ``download_and_prepare`` a dataset. The first (very minor) issue is that the doc seems to suggest ``download_and_prepare`` will return the dataset, while it does not ([builder.py](https://github.com/huggingface/datasets/blob/main/src/datasets/builder.py#L718-L738)).
```
>>> from datasets import load_dataset_builder
>>> builder = load_dataset_builder("rotten_tomatoes")
>>> ds = builder.download_and_prepare("./output_dir", file_format="parquet")
```
The main issue I am facing is loading the dataset from those parquet files. I used the `as_dataset` method suggested by the doc, however it returns:
`
FileNotFoundError: [Errno 2] Failed to open local file 'output_dir/__main__-train-00000-of-00245.arrow'. Detail:
[errno 2] No such file or directory.
`
### Steps to reproduce the bug
1. Create a custom builder of some sort: `builder = CustomBuilder()`.
2. Run `download_and_prepare` with the parquet format: `builder.download_and_prepare("./output_dir", file_format="parquet")`.
3. Run `dataset = builder.as_dataset()`.
### Expected behavior
I guess I'd expect `as_dataset` to generate the dataset in arrow format if it has to, or to suggest an alternative way to load the dataset (I've also tried other methods with `load_dataset` to no avail, probably due to misunderstandings on my part).
### Environment info
```
- `datasets` version: 2.12.0
- Platform: Linux-5.15.0-1027-gcp-x86_64-with-glibc2.31
- Python version: 3.10.0
- Huggingface_hub version: 0.14.1
- PyArrow version: 8.0.0
- Pandas version: 1.5.3
``` | {
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} | null | null | null | ### Feature request
Add an environment variable to replace the default lock file path.
### Motivation
Usually, dataset path is a read-only path while the lock file needs to be modified each time. It would be convenient if the path can be reset individually.
### Your contribution
```/src/datasets/utils/filelock.py
class UnixFileLock(BaseFileLock):
def __init__(self, lock_file, timeout=-1, max_filename_length=None):
#-------------------
if os.getenv('DS_TMP_PATH'):
file_name = str(lock_file).split('/')[-1]
dataset_tmp_path = os.getenv('DS_TMP_PATH')
lock_file = os.path.join(dataset_tmp_path, file_name)
#-------------------
max_filename_length = os.statvfs(os.path.dirname(lock_file)).f_namemax
super().__init__(lock_file, timeout=timeout, max_filename_length=max_filename_length)
```
A simple demo is as upper. Thanks. | null | {
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} | Fix the `infer_module_for_data_files` and `infer_module_for_data_files_in_archives` functions when passed a data file name with uppercase extension, e.g. `filename.TXT`.
Before, `None` module was returned. | {
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} | null | null | null | ### Describe the bug
I am working with the `recipe_nlg` dataset, which requires manual download. Once it's downloaded, I've noticed that the hash in the custom data configuration is different if I add a trailing `/` to my `data_dir`. It took me a while to notice that the hashes were different, and to understand that that was the cause of my dataset being processed anew instead of the cached version being used.
### Steps to reproduce the bug
1. Follow the steps to manually download the `recipe_nlg` dataset to `/data/recipenlg`.
2. Load it using `load_dataset`, once without a trailing slash and once with one:
```python
>>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg")
Using custom data configuration default-082278caeea85765
Downloading and preparing dataset recipe_nlg/default to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74...
Dataset recipe_nlg downloaded and prepared to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.10s/it]
DatasetDict({
train: Dataset({
features: ['id', 'title', 'ingredients', 'directions', 'link', 'source', 'ner'],
num_rows: 2231142
})
})
>>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg/")
Using custom data configuration default-83e87680785d0493
Downloading and preparing dataset recipe_nlg/default to /home/user/.cache/huggingface/datasets/recipe_nlg/default-83e87680785d0493/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74...
Generating train split: 1%| | 12701/2231142 [00:04<13:15, 2790.25 examples/s
^C
```
3. Observe that the hash suffix in the custom data configuration changes due to the altered string.
### Expected behavior
I think I would expect the hash to remain constant if it actually points to the same location on disk. I would expect the use of `os.path.normpath` to canonicalize the paths.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5870 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5870/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5870/comments | https://api.github.com/repos/huggingface/datasets/issues/5870/events | https://github.com/huggingface/datasets/issues/5870 | 1,712,156,282 | I_kwDODunzps5mDW56 | 5,870 | Behaviour difference between datasets.map and IterableDatasets.map | {
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} | null | null | null | ### Describe the bug
All the examples in all the docs mentioned throughout huggingface datasets correspond to datasets object, and not IterableDatasets object. At one point of time, they might have been in sync, but the code for datasets version >=2.9.0 is very different as compared to the docs.
I basically need to .map() a transform on images in an iterable dataset, which was made using a custom databuilder config.
This works very good in map-styles datasets, but the .map() fails in IterableDatasets, show behvaiour as such:
"pixel_values" key not found, KeyError in examples object/dict passed into transform function for map, which works fine with map style, even as batch.
In iterable style, the object/dict passed into map() paramter callable function is completely different as what is mentioned in all examples.
Please look into this. Thank you
My databuilder class is inherited as such:
def _info(self):
print ("Config: ",self.config.__dict__.keys())
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"labels": datasets.Sequence(datasets.Value("uint16")),
# "labels_name": datasets.Value("string"),
# "pixel_values": datasets.Array3D(shape=(3, 1280, 960), dtype="float32"),
"pixel_values": datasets.Array3D(shape=(1280, 960, 3), dtype="uint8"),
"image_s3_path": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="none",
citation="",
)
def _split_generators(self, dl_manager):
records_train = list(db.mini_set.find({'split':'train'},{'image_s3_path':1, 'ocwen_template_name':1}))[:10000]
records_val = list(db.mini_set.find({'split':'val'},{'image_s3_path':1, 'ocwen_template_name':1}))[:1000]
# print (len(records),self.config.num_shards)
# shard_size_train = len(records_train)//self.config.num_shards
# sharded_records_train = [records_train[i:i+shard_size_train] for i in range(0,len(records_train),shard_size_train)]
# shard_size_val = len(records_val)//self.config.num_shards
# sharded_records_val = [records_val[i:i+shard_size_val] for i in range(0,len(records_val),shard_size_val)]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"records":records_train} # passing list of records, for sharding to take over
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"records":records_val} # passing list of records, for sharding to take over
),
]
def _generate_examples(self, records):
# print ("Generating examples for [{}] shards".format(len(shards)))
# initiate_db_connection()
# records = list(db.mini_set.find({'split':split},{'image_s3_path':1, 'ocwen_template_name':1}))[:10]
id_ = 0
# for records in shards:
for i,rec in enumerate(records):
img_local_path = fetch_file(rec['image_s3_path'],self.config.buffer_dir)
# t = self.config.processor(Image.open(img_local_path), random_padding=True, return_tensors="np").pixel_values.squeeze()
# print (t.shape, type(t),type(t[0][0][0]))
# sys.exit()
pvs = np.array(Image.open(img_local_path).resize((1280,960))) # image object is wxh, so resize as per that, numpy array of it is hxwxc, transposing to cxwxh
# pvs = self.config.processor(Image.open(img_local_path), random_padding=True, return_tensors="np").pixel_values.astype(np.float16).squeeze()
# print (type(pvs[0][0][0]))
lblids = self.config.processor.tokenizer('<s_class>'+rec['ocwen_template_name']+'</s_class>'+'</s>', add_special_tokens=False, padding=False, truncation=False, return_tensors="np")["input_ids"].squeeze(0) # take padding later, as per batch collating
# print (len(lblids),type(lblids[0]))
# print (type(pvs),pvs.shape,type(pvs[0][0][0]), type(lblids))
yield id_, {"labels":lblids,"pixel_values":pvs,"image_s3_path":rec['image_s3_path']}
id_+=1
os.remove(img_local_path)
and I load it inside my trainer script as such
`ds = load_dataset("/tmp/DonutDS/dataset/", split="train", streaming=True) # iterable dataset, where .map() falls`
or also as
`ds = load_from_disk('/tmp/DonutDS/dataset/') #map style dataset`
Thank you to the team for having such a great library, and for this bug fix in advance!
### Steps to reproduce the bug
Above config can allow one to reproduce the said bug
### Expected behavior
.map() should show some consistency b/w map-style and iterable-style datasets, or atleast the docs should address iterable-style datasets behaviour and examples. I honestly do not figure the use of such docs.
### Environment info
datasets==2.9.0
transformers==4.26.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5869 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5869/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5869/comments | https://api.github.com/repos/huggingface/datasets/issues/5869/events | https://github.com/huggingface/datasets/issues/5869 | 1,711,990,003 | I_kwDODunzps5mCuTz | 5,869 | Image Encoding Issue when submitting a Parquet Dataset | {
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] | closed | false | null | [] | null | 16 | 2023-05-16T09:42:58 | 2023-06-16T12:48:38 | 2023-06-16T09:30:48 | NONE | null | {
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} | null | null | null | ### Describe the bug
Hello,
I'd like to report an issue related to pushing a dataset represented as a Parquet file to a dataset repository using Dask. Here are the details:
We attempted to load an example dataset in Parquet format from the Hugging Face (HF) filesystem using Dask with the following code snippet:
```
import dask.dataframe as dd
df = dd.read_parquet("hf://datasets/lambdalabs/pokemon-blip-captions",index=False)
```
In this dataset, the "image" column is represented as a dictionary/struct with the format:
```
df = df.compute()
df["image"].iloc[0].keys()
-> dict_keys(['bytes', 'path'])
```
I think this is the format encoded by the [`Image`](https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Image) feature extractor from datasets to format suitable for Arrow.
The next step was to push the dataset to a repository that I created:
```
dd.to_parquet(dask_df, path = "hf://datasets/philippemo/dummy_dataset/data")
```
However, after pushing the dataset using Dask, the "image" column is now represented as the encoded dictionary `(['bytes', 'path'])`, and the images are not properly visualized. You can find the dataset here: [Link to the problematic dataset](https://huggingface.co/datasets/philippemo/dummy_dataset).
It's worth noting that both the original dataset and the one submitted with Dask have the same schema with minor alterations related to metadata:
**[ Schema of original dummy example.](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/blob/main/data/train-00000-of-00001-566cc9b19d7203f8.parquet)**
```
image: struct<bytes: binary, path: null>
child 0, bytes: binary
child 1, path: null
text: string
```
**[ Schema of pushed dataset with dask](https://huggingface.co/datasets/philippemo/dummy_dataset/blob/main/data/part.0.parquet)**
```
image: struct<bytes: binary, path: null>
child 0, bytes: binary
child 1, path: null
text: string
```
This issue seems to be related to an encoding type that occurs when pushing a model to the hub. Normally, models should be represented as an HF dataset before pushing, but we are working with an example where we need to push large datasets using Dask.
Could you please provide clarification on how to resolve this issue?
Thank you!
### Reproduction
To get the schema I downloaded the parquet files and used pyarrow.parquet to read the schema
```
import pyarrow.parquet
pyarrow.parquet.read_schema(<path_to_parquet>, memory_map=True)
```
### Logs
_No response_
### System info
```shell
- huggingface_hub version: 0.14.1
- Platform: Linux-5.19.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /home/philippe/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: philippemo
- Configured git credential helpers: cache
- FastAI: N/A
- Tensorflow: N/A
- Torch: N/A
- Jinja2: 3.1.2
- Graphviz: N/A
- Pydot: N/A
- Pillow: 9.4.0
- hf_transfer: N/A
- gradio: N/A
- ENDPOINT: https://huggingface.co
- HUGGINGFACE_HUB_CACHE: /home/philippe/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /home/philippe/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/philippe/.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_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
```
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https://api.github.com/repos/huggingface/datasets/issues/5868 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5868/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5868/comments | https://api.github.com/repos/huggingface/datasets/issues/5868/events | https://github.com/huggingface/datasets/issues/5868 | 1,711,173,098 | I_kwDODunzps5l_m3q | 5,868 | Is it possible to change a cached file and 're-cache' it instead of re-generating? | {
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} | null | null | null | ### Feature request
Hi,
I have a huge cached file using `map`(over 500GB), and I want to change an attribution of each element, is there possible to do it using some method instead of re-generating, because `map` takes over 24 hours
### Motivation
For large datasets, I think it is very important because we always face the problem which is changing something in the original cache without re-generating it.
### Your contribution
For now, I can't help, sorry. | {
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https://api.github.com/repos/huggingface/datasets/issues/5867 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5867/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5867/comments | https://api.github.com/repos/huggingface/datasets/issues/5867/events | https://github.com/huggingface/datasets/pull/5867 | 1,710,656,067 | PR_kwDODunzps5QizOn | 5,867 | Add logic for hashing modules/functions optimized with `torch.compile` | {
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} | Fix https://github.com/huggingface/datasets/issues/5839
PS: The `Pickler.save` method is becoming a bit messy, so I plan to refactor the pickler a bit at some point. | {
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https://api.github.com/repos/huggingface/datasets/issues/5866 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5866/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5866/comments | https://api.github.com/repos/huggingface/datasets/issues/5866/events | https://github.com/huggingface/datasets/issues/5866 | 1,710,496,993 | I_kwDODunzps5l9Bzh | 5,866 | Issue with Sequence features | {
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Sequences features sometimes causes errors when the specified length is not -1
### Steps to reproduce the bug
```python
import numpy as np
from datasets import Features, ClassLabel, Sequence, Value, Dataset
feats = Features(**{'target': ClassLabel(names=[0, 1]),'x': Sequence(feature=Value(dtype='float64',id=None), length=2, id=None)})
Dataset.from_dict({"target": np.ones(2000).astype(int), "x": np.random.rand(2000,2)},features = feats).flatten_indices()
```
Throws:
```
TypeError: Couldn't cast array of type
fixed_size_list<item: double>[2]
to
Sequence(feature=Value(dtype='float64', id=None), length=2, id=None)
```
The same code works without any issues when `length = -1`
EDIT: The error seems to happen only when the length of the dataset is bigger than 1000 for some reason
### Expected behavior
No exception
### Environment info
- `datasets` version: 2.10.1
- Python version: 3.9.5
- PyArrow version: 11.0.0
- Pandas version: 1.4.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5865 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5865/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5865/comments | https://api.github.com/repos/huggingface/datasets/issues/5865/events | https://github.com/huggingface/datasets/pull/5865 | 1,710,455,738 | PR_kwDODunzps5QiHnw | 5,865 | Deprecate task api | {
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} | The task API is not well adopted in the ecosystem, so this PR deprecates it. The `train_eval_index` is a newer, more flexible solution that should be used instead (I think?).
These are the projects that still use the task API :
* the image classification example in Transformers: [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/pytorch/image-classification/run_image_classification_no_trainer.py#L262) and [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/tensorflow/image-classification/run_image_classification.py#L277)
* autotrain: [here](https://github.com/huggingface/autotrain-backend/blob/455e274004b56f9377d64db4ab03671508fcc4cd/zeus/zeus/run/utils.py#L666)
* api-inference-community: [here](https://github.com/huggingface/api-inference-community/blob/fb8fb29d577a5bf01c82944db745489a6d6ed3d4/manage.py#L64) (but the rest of the code does not call the `resolve_dataset` function)
So we need to update these files after the merge.
cc @lewtun | {
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https://api.github.com/repos/huggingface/datasets/issues/5864 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5864/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5864/comments | https://api.github.com/repos/huggingface/datasets/issues/5864/events | https://github.com/huggingface/datasets/issues/5864 | 1,710,450,047 | I_kwDODunzps5l82V_ | 5,864 | Slow iteration over Torch tensors | {
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} | [] | open | false | null | [] | null | 2 | 2023-05-15T16:43:58 | 2024-10-08T10:21:48 | null | NONE | null | {
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} | null | null | null | ### Describe the bug
I have a problem related to this [issue](https://github.com/huggingface/datasets/issues/5841): I get a way slower iteration when using a Torch dataloader if I use vanilla Numpy tensors or if I first apply a ToTensor transform to the input. In particular, it takes 5 seconds to iterate over the vanilla input and ~30s after the transformation.
### Steps to reproduce the bug
Here is the minimum code to reproduce the problem
```python
import numpy as np
from datasets import Dataset, DatasetDict, load_dataset, Array3D, Image, Features
from torch.utils.data import DataLoader
from tqdm import tqdm
import torchvision
from torchvision.transforms import ToTensor, Normalize
#################################
# Without transform
#################################
train_dataset = load_dataset(
'cifar100',
split='train',
use_auth_token=True,
)
train_dataset.set_format(type="numpy", columns=["img", "fine_label"])
train_loader= DataLoader(
train_dataset,
batch_size=100,
pin_memory=False,
shuffle=True,
num_workers=8,
)
for batch in tqdm(train_loader, desc="Loading data, no transform"):
pass
#################################
# With transform
#################################
transform_func = torchvision.transforms.Compose([
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std= [0.229, 0.224, 0.225]),]
)
train_dataset = train_dataset.map(
desc=f"Preprocessing samples",
function=lambda x: {"img": transform_func(x["img"])},
)
train_dataset.set_format(type="numpy", columns=["img", "fine_label"])
train_loader= DataLoader(
train_dataset,
batch_size=100,
pin_memory=False,
shuffle=True,
num_workers=8,
)
for batch in tqdm(train_loader, desc="Loading data after transform"):
pass
```
I have also tried converting the Image column to an Array3D
```python
img_shape = train_dataset[0]["img"].shape
features = train_dataset.features.copy()
features["x"] = Array3D(shape=img_shape, dtype="float32")
train_dataset = train_dataset.map(
desc=f"Preprocessing samples",
function=lambda x: {"x": np.array(x["img"], dtype=np.uint8)},
features=features,
)
train_dataset.cast_column("x", Array3D(shape=img_shape, dtype="float32"))
train_dataset.set_format(type="numpy", columns=["x", "fine_label"])
```
but to no avail. Any clue?
### Expected behavior
The iteration should take approximately the same time with or without the transformation, as it doesn't change the shape of the input. What may be the issue here?
### Environment info
```
- `datasets` version: 2.12.0
- Platform: Linux-5.4.0-137-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5863 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5863/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5863/comments | https://api.github.com/repos/huggingface/datasets/issues/5863/events | https://github.com/huggingface/datasets/pull/5863 | 1,710,335,905 | PR_kwDODunzps5QhtlM | 5,863 | Use a new low-memory approach for tf dataset index shuffling | {
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} | This PR tries out a new approach to generating the index tensor in `to_tf_dataset`, which should reduce memory usage for very large datasets. I'll need to do some testing before merging it!
Fixes #5855 | {
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https://api.github.com/repos/huggingface/datasets/issues/5862 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5862/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5862/comments | https://api.github.com/repos/huggingface/datasets/issues/5862/events | https://github.com/huggingface/datasets/issues/5862 | 1,710,140,646 | I_kwDODunzps5l7qzm | 5,862 | IndexError: list index out of range with data hosted on Zenodo | {
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```
IndexError: list index out of range
```
See:
- huggingface/datasets-server#1151
- https://huggingface.co/datasets/reddit/discussions/5
- huggingface/datasets-server#1118
- https://huggingface.co/datasets/krr-oxford/OntoLAMA/discussions/1
- https://huggingface.co/datasets/hyperpartisan_news_detection/discussions/3
- https://huggingface.co/datasets/um005/discussions/2
- https://huggingface.co/datasets/tapaco/discussions/2
- https://huggingface.co/datasets/common_language/discussions/3
- https://huggingface.co/datasets/pass/discussions/1
After investigation:
- This happens with data files hosted on Zenodo
- Indeed, there is an underlying 429 HTTP error: Too Many Requests
Note that some time ago, it also happened with data files hosted on Google Drive. See:
- #4581
- #4580
The reason then was that there was a 403 HTTP error: Forbidden
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} | Don't create a tqdm progress bar when `disable_tqdm` is passed to `map_nested`.
On my side it sped up some iterable datasets by ~30% when `map_nested` is used extensively to recursively tensorize python dicts. | {
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} | null | null | null | ### Describe the bug
Pandas-style subset indexing on dataset does not throw an error, when maybe it should. Instead returns the first instance of the dataset regardless of index condition.
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. `squad = datasets.load_dataset("squad_v2", split="validation")`
2. `item = squad[squad['question'] == "Who was the Norse leader?"]`
or `it = squad[squad['id'] == '56ddde6b9a695914005b962b']`
3. returns the first item in the dataset, which does not satisfy the above conditions:
`{'id': '56ddde6b9a695914005b9628', 'title': 'Normans', 'context': 'The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave their name to Normandy, a region in France. They were descended from Norse ("Norman" comes from "Norseman") raiders and pirates from Denmark, Iceland and Norway who, under their leader Rollo, agreed to swear fealty to King Charles III of West Francia. Through generations of assimilation and mixing with the native Frankish and Roman-Gaulish populations, their descendants would gradually merge with the Carolingian-based cultures of West Francia. The distinct cultural and ethnic identity of the Normans emerged initially in the first half of the 10th century, and it continued to evolve over the succeeding centuries.', 'question': 'In what country is Normandy located?', 'answers': {'text': ['France', 'France', 'France', 'France'], 'answer_start': [159, 159, 159, 159]}}`
### Expected behavior
Should either throw an error message, or return the dataset item that satisfies the condition.
### Environment info
- `datasets` version: 2.9.0
- Platform: macOS-13.3.1-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5857 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5857/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5857/comments | https://api.github.com/repos/huggingface/datasets/issues/5857/events | https://github.com/huggingface/datasets/issues/5857 | 1,709,326,622 | I_kwDODunzps5l4kEe | 5,857 | Adding chemistry dataset/models in huggingface | {
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} | null | null | null | ### Feature request
Huggingface is really amazing platform for open science.
In addition to computer vision, video and NLP, would it be of interest to add chemistry/materials science dataset/models in Huggingface? Or, if its already done, can you provide some pointers.
We have been working on a comprehensive benchmark on this topic: [JARVIS-Leaderboard](https://pages.nist.gov/jarvis_leaderboard/) and I am wondering if we could contribute/integrate this project as a part of huggingface.
### Motivation
Similar to the main stream AI field, there is need of large scale benchmarks/models/infrastructure for chemistry/materials data.
### Your contribution
We can start adding datasets as our [benchmarks](https://github.com/usnistgov/jarvis_leaderboard/tree/main/jarvis_leaderboard/benchmarks) should be easily convertible to the dataset format. | {
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