html_url stringlengths 48 51 | title stringlengths 5 280 | comments stringlengths 63 51.8k | body stringlengths 0 36.2k ⌀ | comment_length int64 16 1.52k | text stringlengths 159 54.1k | embeddings listlengths 768 768 |
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https://github.com/huggingface/datasets/issues/4632 | 'sort' method sorts one column only | Hi ! `ds.sort()` does sort the full dataset, not just one column:
```python
from datasets import *
ds = Dataset.from_dict({"foo": [3, 2, 1], "bar": ["c", "b", "a"]})
print(d.sort("foo").to_pandas()
# foo bar
# 0 1 a
# 1 2 b
# 2 3 c
```
What made you think it was not the case ? Did you experience a situation where it was only sorting one column ? | The 'sort' method changes the order of one column only (the one defined by the argument 'column'), thus creating a mismatch between a sample fields. I would expect it to change the order of the samples as a whole, based on the 'column' order. | 67 | 'sort' method sorts one column only
The 'sort' method changes the order of one column only (the one defined by the argument 'column'), thus creating a mismatch between a sample fields. I would expect it to change the order of the samples as a whole, based on the 'column' order.
Hi ! `ds.sort()` does sort the full dataset, not just one column:
```python
from datasets import *
ds = Dataset.from_dict({"foo": [3, 2, 1], "bar": ["c", "b", "a"]})
print(d.sort("foo").to_pandas()
# foo bar
# 0 1 a
# 1 2 b
# 2 3 c
```
What made you think it was not the case ? Did you experience a situation where it was only sorting one column ? | [
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https://github.com/huggingface/datasets/issues/4632 | 'sort' method sorts one column only | Hi! thank you for your quick reply!
I wanted to sort the `cnn_dailymail` dataset by the length of the labels (num of characters). I added a new column to the dataset (`ds.add_column`) with the lengths and then sorted by this new column. Only the new length column was sorted, the reset left in their original order. | The 'sort' method changes the order of one column only (the one defined by the argument 'column'), thus creating a mismatch between a sample fields. I would expect it to change the order of the samples as a whole, based on the 'column' order. | 56 | 'sort' method sorts one column only
The 'sort' method changes the order of one column only (the one defined by the argument 'column'), thus creating a mismatch between a sample fields. I would expect it to change the order of the samples as a whole, based on the 'column' order.
Hi! thank you for your quick reply!
I wanted to sort the `cnn_dailymail` dataset by the length of the labels (num of characters). I added a new column to the dataset (`ds.add_column`) with the lengths and then sorted by this new column. Only the new length column was sorted, the reset left in their original order. | [
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https://github.com/huggingface/datasets/issues/4623 | Loading MNIST as Pytorch Dataset | Hi ! We haven't implemented the conversion from images data to PyTorch tensors yet I think
cc @mariosasko | ## Describe the bug
Conversion of MNIST dataset to pytorch fails with bug
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mnist", split="train")
dataset.set_format('torch')
dataset[0]
print()
```
## Expected results
Expect to see torch tensors image and label
## Actual results
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module>
dataset[0]
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__
return self._getitem(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem
formatted_output = format_table(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table
return formatter(pa_table, query_type=query_type)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__
return self.format_row(pa_table)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row
return self.recursive_tensorize(row)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested
mapped = [
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp>
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested
return function(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize
return self._tensorize(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize
if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
python-BaseException
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Windows-10-10.0.22579-SP0
- Python version: 3.9.2
- PyArrow version: 8.0.0
- Pandas version: 1.4.1
| 18 | Loading MNIST as Pytorch Dataset
## Describe the bug
Conversion of MNIST dataset to pytorch fails with bug
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mnist", split="train")
dataset.set_format('torch')
dataset[0]
print()
```
## Expected results
Expect to see torch tensors image and label
## Actual results
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module>
dataset[0]
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__
return self._getitem(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem
formatted_output = format_table(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table
return formatter(pa_table, query_type=query_type)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__
return self.format_row(pa_table)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row
return self.recursive_tensorize(row)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested
mapped = [
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp>
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested
return function(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize
return self._tensorize(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize
if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
python-BaseException
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Windows-10-10.0.22579-SP0
- Python version: 3.9.2
- PyArrow version: 8.0.0
- Pandas version: 1.4.1
Hi ! We haven't implemented the conversion from images data to PyTorch tensors yet I think
cc @mariosasko | [
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] |
https://github.com/huggingface/datasets/issues/4623 | Loading MNIST as Pytorch Dataset | So I understand:
set_format() does not properly do the conversion to pytorch tensors from PIL images.
So that someone who stumbles on this can use the package:
```python
dataset = load_dataset("mnist", split="train")
def transform_func(examples):
examples["image"] = [np.array(img) for img in examples["image"]]
return examples
dataset = dataset.with_transform(transform_func)
dataset[0]
``` | ## Describe the bug
Conversion of MNIST dataset to pytorch fails with bug
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mnist", split="train")
dataset.set_format('torch')
dataset[0]
print()
```
## Expected results
Expect to see torch tensors image and label
## Actual results
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module>
dataset[0]
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__
return self._getitem(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem
formatted_output = format_table(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table
return formatter(pa_table, query_type=query_type)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__
return self.format_row(pa_table)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row
return self.recursive_tensorize(row)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested
mapped = [
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp>
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested
return function(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize
return self._tensorize(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize
if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
python-BaseException
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Windows-10-10.0.22579-SP0
- Python version: 3.9.2
- PyArrow version: 8.0.0
- Pandas version: 1.4.1
| 48 | Loading MNIST as Pytorch Dataset
## Describe the bug
Conversion of MNIST dataset to pytorch fails with bug
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mnist", split="train")
dataset.set_format('torch')
dataset[0]
print()
```
## Expected results
Expect to see torch tensors image and label
## Actual results
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module>
dataset[0]
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__
return self._getitem(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem
formatted_output = format_table(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table
return formatter(pa_table, query_type=query_type)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__
return self.format_row(pa_table)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row
return self.recursive_tensorize(row)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested
mapped = [
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp>
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested
return function(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize
return self._tensorize(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize
if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
python-BaseException
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Windows-10-10.0.22579-SP0
- Python version: 3.9.2
- PyArrow version: 8.0.0
- Pandas version: 1.4.1
So I understand:
set_format() does not properly do the conversion to pytorch tensors from PIL images.
So that someone who stumbles on this can use the package:
```python
dataset = load_dataset("mnist", split="train")
def transform_func(examples):
examples["image"] = [np.array(img) for img in examples["image"]]
return examples
dataset = dataset.with_transform(transform_func)
dataset[0]
``` | [
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https://github.com/huggingface/datasets/issues/4623 | Loading MNIST as Pytorch Dataset | This then appears to work with pytorch dataloaders as:
```
dataloader=torch.utils.data.DataLoader(dataset,batch_size=1)
```
and tensorflow as:
```
dataset=dataset.to_tf_dataset(batch_size=1)
``` | ## Describe the bug
Conversion of MNIST dataset to pytorch fails with bug
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mnist", split="train")
dataset.set_format('torch')
dataset[0]
print()
```
## Expected results
Expect to see torch tensors image and label
## Actual results
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module>
dataset[0]
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__
return self._getitem(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem
formatted_output = format_table(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table
return formatter(pa_table, query_type=query_type)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__
return self.format_row(pa_table)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row
return self.recursive_tensorize(row)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested
mapped = [
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp>
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested
return function(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize
return self._tensorize(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize
if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
python-BaseException
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Windows-10-10.0.22579-SP0
- Python version: 3.9.2
- PyArrow version: 8.0.0
- Pandas version: 1.4.1
| 18 | Loading MNIST as Pytorch Dataset
## Describe the bug
Conversion of MNIST dataset to pytorch fails with bug
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mnist", split="train")
dataset.set_format('torch')
dataset[0]
print()
```
## Expected results
Expect to see torch tensors image and label
## Actual results
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module>
dataset[0]
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__
return self._getitem(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem
formatted_output = format_table(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table
return formatter(pa_table, query_type=query_type)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__
return self.format_row(pa_table)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row
return self.recursive_tensorize(row)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested
mapped = [
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp>
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested
return function(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize
return self._tensorize(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize
if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
python-BaseException
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Windows-10-10.0.22579-SP0
- Python version: 3.9.2
- PyArrow version: 8.0.0
- Pandas version: 1.4.1
This then appears to work with pytorch dataloaders as:
```
dataloader=torch.utils.data.DataLoader(dataset,batch_size=1)
```
and tensorflow as:
```
dataset=dataset.to_tf_dataset(batch_size=1)
``` | [
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https://github.com/huggingface/datasets/issues/4623 | Loading MNIST as Pytorch Dataset | Hi! `set_transform`/`with_transform` is indeed the correct solution for the conversion. Improving this part of the API is one of the things I'm working on currently, so stay tuned! | ## Describe the bug
Conversion of MNIST dataset to pytorch fails with bug
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mnist", split="train")
dataset.set_format('torch')
dataset[0]
print()
```
## Expected results
Expect to see torch tensors image and label
## Actual results
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module>
dataset[0]
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__
return self._getitem(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem
formatted_output = format_table(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table
return formatter(pa_table, query_type=query_type)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__
return self.format_row(pa_table)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row
return self.recursive_tensorize(row)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested
mapped = [
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp>
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested
return function(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize
return self._tensorize(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize
if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
python-BaseException
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Windows-10-10.0.22579-SP0
- Python version: 3.9.2
- PyArrow version: 8.0.0
- Pandas version: 1.4.1
| 28 | Loading MNIST as Pytorch Dataset
## Describe the bug
Conversion of MNIST dataset to pytorch fails with bug
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mnist", split="train")
dataset.set_format('torch')
dataset[0]
print()
```
## Expected results
Expect to see torch tensors image and label
## Actual results
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module>
dataset[0]
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__
return self._getitem(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem
formatted_output = format_table(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table
return formatter(pa_table, query_type=query_type)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__
return self.format_row(pa_table)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row
return self.recursive_tensorize(row)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested
mapped = [
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp>
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested
return function(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize
return self._tensorize(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize
if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
python-BaseException
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Windows-10-10.0.22579-SP0
- Python version: 3.9.2
- PyArrow version: 8.0.0
- Pandas version: 1.4.1
Hi! `set_transform`/`with_transform` is indeed the correct solution for the conversion. Improving this part of the API is one of the things I'm working on currently, so stay tuned! | [
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https://github.com/huggingface/datasets/issues/4619 | np arrays get turned into native lists | If you add the line `dataset2.set_format('np')` before calling `dataset2[0]['tmp']` it should return `np.ndarray`.
I believe internally it will not store it as a list, it is only returning a list when you index it.
```
In [1]: import datasets, numpy as np
In [2]: dataset = datasets.load_dataset("glue", "mrpc")["validation"]
In [3]: dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
In [4]: dataset2[0]["tmp"]
Out[4]: [0.5]
In [5]: dataset2.set_format('np')
In [6]: dataset2[0]["tmp"]
Out[6]: array([0.5])
``` | ## Describe the bug
When attaching an `np.array` field, it seems that it automatically gets turned into a list (see below). Why is this happening? Could it lose precision? Is there a way to make sure this doesn't happen?
## Steps to reproduce the bug
```python
>>> import datasets, numpy as np
>>> dataset = datasets.load_dataset("glue", "mrpc")["validation"]
Reusing dataset glue (...)
100%|███████████████████████████████████████████████| 3/3 [00:00<00:00, 1360.61it/s]
>>> dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
100%|██████████████████████████████████████████| 408/408 [00:00<00:00, 10819.97ex/s]
>>> dataset2[0]["tmp"]
[0.5]
>>> type(dataset2[0]["tmp"])
<class 'list'>
```
## Expected results
`dataset2[0]["tmp"]` should be an `np.ndarray`.
## Actual results
It's a list.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: mac, though I'm pretty sure it happens on a linux machine too
- Python version: 3.9.7
- PyArrow version: 6.0.1
| 71 | np arrays get turned into native lists
## Describe the bug
When attaching an `np.array` field, it seems that it automatically gets turned into a list (see below). Why is this happening? Could it lose precision? Is there a way to make sure this doesn't happen?
## Steps to reproduce the bug
```python
>>> import datasets, numpy as np
>>> dataset = datasets.load_dataset("glue", "mrpc")["validation"]
Reusing dataset glue (...)
100%|███████████████████████████████████████████████| 3/3 [00:00<00:00, 1360.61it/s]
>>> dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
100%|██████████████████████████████████████████| 408/408 [00:00<00:00, 10819.97ex/s]
>>> dataset2[0]["tmp"]
[0.5]
>>> type(dataset2[0]["tmp"])
<class 'list'>
```
## Expected results
`dataset2[0]["tmp"]` should be an `np.ndarray`.
## Actual results
It's a list.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: mac, though I'm pretty sure it happens on a linux machine too
- Python version: 3.9.7
- PyArrow version: 6.0.1
If you add the line `dataset2.set_format('np')` before calling `dataset2[0]['tmp']` it should return `np.ndarray`.
I believe internally it will not store it as a list, it is only returning a list when you index it.
```
In [1]: import datasets, numpy as np
In [2]: dataset = datasets.load_dataset("glue", "mrpc")["validation"]
In [3]: dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
In [4]: dataset2[0]["tmp"]
Out[4]: [0.5]
In [5]: dataset2.set_format('np')
In [6]: dataset2[0]["tmp"]
Out[6]: array([0.5])
``` | [
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https://github.com/huggingface/datasets/issues/4619 | np arrays get turned into native lists | I see, thanks! Any idea if the default numpy → list conversion might cause precision loss? | ## Describe the bug
When attaching an `np.array` field, it seems that it automatically gets turned into a list (see below). Why is this happening? Could it lose precision? Is there a way to make sure this doesn't happen?
## Steps to reproduce the bug
```python
>>> import datasets, numpy as np
>>> dataset = datasets.load_dataset("glue", "mrpc")["validation"]
Reusing dataset glue (...)
100%|███████████████████████████████████████████████| 3/3 [00:00<00:00, 1360.61it/s]
>>> dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
100%|██████████████████████████████████████████| 408/408 [00:00<00:00, 10819.97ex/s]
>>> dataset2[0]["tmp"]
[0.5]
>>> type(dataset2[0]["tmp"])
<class 'list'>
```
## Expected results
`dataset2[0]["tmp"]` should be an `np.ndarray`.
## Actual results
It's a list.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: mac, though I'm pretty sure it happens on a linux machine too
- Python version: 3.9.7
- PyArrow version: 6.0.1
| 16 | np arrays get turned into native lists
## Describe the bug
When attaching an `np.array` field, it seems that it automatically gets turned into a list (see below). Why is this happening? Could it lose precision? Is there a way to make sure this doesn't happen?
## Steps to reproduce the bug
```python
>>> import datasets, numpy as np
>>> dataset = datasets.load_dataset("glue", "mrpc")["validation"]
Reusing dataset glue (...)
100%|███████████████████████████████████████████████| 3/3 [00:00<00:00, 1360.61it/s]
>>> dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
100%|██████████████████████████████████████████| 408/408 [00:00<00:00, 10819.97ex/s]
>>> dataset2[0]["tmp"]
[0.5]
>>> type(dataset2[0]["tmp"])
<class 'list'>
```
## Expected results
`dataset2[0]["tmp"]` should be an `np.ndarray`.
## Actual results
It's a list.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: mac, though I'm pretty sure it happens on a linux machine too
- Python version: 3.9.7
- PyArrow version: 6.0.1
I see, thanks! Any idea if the default numpy → list conversion might cause precision loss? | [
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https://github.com/huggingface/datasets/issues/4619 | np arrays get turned into native lists | I'm not super familiar with our datasets works internally, but I think your `np` array will be stored in a `pyarrow` format, and then you take a view of this as a python array. In which case, I think the precision should be preserved. | ## Describe the bug
When attaching an `np.array` field, it seems that it automatically gets turned into a list (see below). Why is this happening? Could it lose precision? Is there a way to make sure this doesn't happen?
## Steps to reproduce the bug
```python
>>> import datasets, numpy as np
>>> dataset = datasets.load_dataset("glue", "mrpc")["validation"]
Reusing dataset glue (...)
100%|███████████████████████████████████████████████| 3/3 [00:00<00:00, 1360.61it/s]
>>> dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
100%|██████████████████████████████████████████| 408/408 [00:00<00:00, 10819.97ex/s]
>>> dataset2[0]["tmp"]
[0.5]
>>> type(dataset2[0]["tmp"])
<class 'list'>
```
## Expected results
`dataset2[0]["tmp"]` should be an `np.ndarray`.
## Actual results
It's a list.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: mac, though I'm pretty sure it happens on a linux machine too
- Python version: 3.9.7
- PyArrow version: 6.0.1
| 44 | np arrays get turned into native lists
## Describe the bug
When attaching an `np.array` field, it seems that it automatically gets turned into a list (see below). Why is this happening? Could it lose precision? Is there a way to make sure this doesn't happen?
## Steps to reproduce the bug
```python
>>> import datasets, numpy as np
>>> dataset = datasets.load_dataset("glue", "mrpc")["validation"]
Reusing dataset glue (...)
100%|███████████████████████████████████████████████| 3/3 [00:00<00:00, 1360.61it/s]
>>> dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
100%|██████████████████████████████████████████| 408/408 [00:00<00:00, 10819.97ex/s]
>>> dataset2[0]["tmp"]
[0.5]
>>> type(dataset2[0]["tmp"])
<class 'list'>
```
## Expected results
`dataset2[0]["tmp"]` should be an `np.ndarray`.
## Actual results
It's a list.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: mac, though I'm pretty sure it happens on a linux machine too
- Python version: 3.9.7
- PyArrow version: 6.0.1
I'm not super familiar with our datasets works internally, but I think your `np` array will be stored in a `pyarrow` format, and then you take a view of this as a python array. In which case, I think the precision should be preserved. | [
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https://github.com/huggingface/datasets/issues/4618 | contribute data loading for object detection datasets with yolo data format | Hi! The `imagefolder` script is already quite complex, so a standalone script sounds better. Also, I suggest we create an org on the Hub (e.g. `hf-loaders`) and store such scripts there for easier maintenance rather than having them as packaged modules (IMO only very generic loaders should be packaged). WDYT @lhoestq @albertvillanova @polinaeterna? | **Is your feature request related to a problem? Please describe.**
At the moment, HF datasets loads [image classification datasets](https://huggingface.co/docs/datasets/image_process) out-of-the-box. There could be a data loader for loading standard object detection datasets ([original discussion here](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/2))
**Describe the solution you'd like**
I wrote a [custom script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py) to load dataset which has YOLO data format.
**Describe alternatives you've considered**
The script can either be a standalone dataset builder, or a modified version of `ImageFolder`
**Additional context**
I would be happy to contribute to this, but I would do it at a very slow pace (maybe a month or two) as I have my exams approaching 😄
| 53 | contribute data loading for object detection datasets with yolo data format
**Is your feature request related to a problem? Please describe.**
At the moment, HF datasets loads [image classification datasets](https://huggingface.co/docs/datasets/image_process) out-of-the-box. There could be a data loader for loading standard object detection datasets ([original discussion here](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/2))
**Describe the solution you'd like**
I wrote a [custom script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py) to load dataset which has YOLO data format.
**Describe alternatives you've considered**
The script can either be a standalone dataset builder, or a modified version of `ImageFolder`
**Additional context**
I would be happy to contribute to this, but I would do it at a very slow pace (maybe a month or two) as I have my exams approaching 😄
Hi! The `imagefolder` script is already quite complex, so a standalone script sounds better. Also, I suggest we create an org on the Hub (e.g. `hf-loaders`) and store such scripts there for easier maintenance rather than having them as packaged modules (IMO only very generic loaders should be packaged). WDYT @lhoestq @albertvillanova @polinaeterna? | [
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https://github.com/huggingface/datasets/issues/4618 | contribute data loading for object detection datasets with yolo data format | Thank you for the suggestion @mariosasko . I agree with the point, but I have a few doubts
1. How would the user access the script if it's not a part of the core codebase?
2. Could you direct me as to what will be the tasks I have to do to contribute to the code? As per my understanding, it would be like
1. Create a new org "hf-loaders" and add you (and more HF people) to the org
2. Add data loader script as a (model?)
3. Test it with a dataset on HF hub
3. We should maybe brainstorm as to which public datasets have this format (YOLO type) and are the most important ones to test the script with. We can even add the datasets on HF Hub alongside the script | **Is your feature request related to a problem? Please describe.**
At the moment, HF datasets loads [image classification datasets](https://huggingface.co/docs/datasets/image_process) out-of-the-box. There could be a data loader for loading standard object detection datasets ([original discussion here](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/2))
**Describe the solution you'd like**
I wrote a [custom script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py) to load dataset which has YOLO data format.
**Describe alternatives you've considered**
The script can either be a standalone dataset builder, or a modified version of `ImageFolder`
**Additional context**
I would be happy to contribute to this, but I would do it at a very slow pace (maybe a month or two) as I have my exams approaching 😄
| 135 | contribute data loading for object detection datasets with yolo data format
**Is your feature request related to a problem? Please describe.**
At the moment, HF datasets loads [image classification datasets](https://huggingface.co/docs/datasets/image_process) out-of-the-box. There could be a data loader for loading standard object detection datasets ([original discussion here](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/2))
**Describe the solution you'd like**
I wrote a [custom script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py) to load dataset which has YOLO data format.
**Describe alternatives you've considered**
The script can either be a standalone dataset builder, or a modified version of `ImageFolder`
**Additional context**
I would be happy to contribute to this, but I would do it at a very slow pace (maybe a month or two) as I have my exams approaching 😄
Thank you for the suggestion @mariosasko . I agree with the point, but I have a few doubts
1. How would the user access the script if it's not a part of the core codebase?
2. Could you direct me as to what will be the tasks I have to do to contribute to the code? As per my understanding, it would be like
1. Create a new org "hf-loaders" and add you (and more HF people) to the org
2. Add data loader script as a (model?)
3. Test it with a dataset on HF hub
3. We should maybe brainstorm as to which public datasets have this format (YOLO type) and are the most important ones to test the script with. We can even add the datasets on HF Hub alongside the script | [
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https://github.com/huggingface/datasets/issues/4618 | contribute data loading for object detection datasets with yolo data format | 1. Like this: `load_dataset("hf-loaders/yolo", data_files=...)`
2. The steps would be:
1. Create a new org `hf-community-loaders` (IMO a better name than "hf-loaders") and add me (as an admin)
2. Create a new dataset repo `yolo` and add the loading script to it (`yolo.py`)
3. Open a discussion to request our review
4. I like this idea. Another option is to add snippets that describe how to load such datasets using the `yolo` loader. | **Is your feature request related to a problem? Please describe.**
At the moment, HF datasets loads [image classification datasets](https://huggingface.co/docs/datasets/image_process) out-of-the-box. There could be a data loader for loading standard object detection datasets ([original discussion here](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/2))
**Describe the solution you'd like**
I wrote a [custom script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py) to load dataset which has YOLO data format.
**Describe alternatives you've considered**
The script can either be a standalone dataset builder, or a modified version of `ImageFolder`
**Additional context**
I would be happy to contribute to this, but I would do it at a very slow pace (maybe a month or two) as I have my exams approaching 😄
| 73 | contribute data loading for object detection datasets with yolo data format
**Is your feature request related to a problem? Please describe.**
At the moment, HF datasets loads [image classification datasets](https://huggingface.co/docs/datasets/image_process) out-of-the-box. There could be a data loader for loading standard object detection datasets ([original discussion here](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/2))
**Describe the solution you'd like**
I wrote a [custom script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py) to load dataset which has YOLO data format.
**Describe alternatives you've considered**
The script can either be a standalone dataset builder, or a modified version of `ImageFolder`
**Additional context**
I would be happy to contribute to this, but I would do it at a very slow pace (maybe a month or two) as I have my exams approaching 😄
1. Like this: `load_dataset("hf-loaders/yolo", data_files=...)`
2. The steps would be:
1. Create a new org `hf-community-loaders` (IMO a better name than "hf-loaders") and add me (as an admin)
2. Create a new dataset repo `yolo` and add the loading script to it (`yolo.py`)
3. Open a discussion to request our review
4. I like this idea. Another option is to add snippets that describe how to load such datasets using the `yolo` loader. | [
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https://github.com/huggingface/datasets/issues/4612 | Release 2.3.0 broke custom iterable datasets | Apparently, `fsspec` does not allow access to attribute-based modules anymore, such as `fsspec.async`.
However, this is a fairly simple fix:
- Change the import to: `from fsspec import asyn`;
- Change line 18 to: `asyn.iothread[0] = None`;
- Change line 19 to `asyn.loop[0] = None`. | ## Describe the bug
Trying to iterate examples from custom iterable dataset fails to bug introduced in `torch_iterable_dataset.py` since the release of 2.3.0.
## Steps to reproduce the bug
```python
next(iter(custom_iterable_dataset))
```
## Expected results
`next(iter(custom_iterable_dataset))` should return examples from the dataset
## Actual results
```
/usr/local/lib/python3.7/dist-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py in _set_fsspec_for_multiprocess()
16 See https://github.com/fsspec/gcsfs/issues/379
17 """
---> 18 fsspec.asyn.iothread[0] = None
19 fsspec.asyn.loop[0] = None
20
AttributeError: module 'fsspec' has no attribute 'asyn'
```
## Environment info
- `datasets` version: 2.3.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
| 45 | Release 2.3.0 broke custom iterable datasets
## Describe the bug
Trying to iterate examples from custom iterable dataset fails to bug introduced in `torch_iterable_dataset.py` since the release of 2.3.0.
## Steps to reproduce the bug
```python
next(iter(custom_iterable_dataset))
```
## Expected results
`next(iter(custom_iterable_dataset))` should return examples from the dataset
## Actual results
```
/usr/local/lib/python3.7/dist-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py in _set_fsspec_for_multiprocess()
16 See https://github.com/fsspec/gcsfs/issues/379
17 """
---> 18 fsspec.asyn.iothread[0] = None
19 fsspec.asyn.loop[0] = None
20
AttributeError: module 'fsspec' has no attribute 'asyn'
```
## Environment info
- `datasets` version: 2.3.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
Apparently, `fsspec` does not allow access to attribute-based modules anymore, such as `fsspec.async`.
However, this is a fairly simple fix:
- Change the import to: `from fsspec import asyn`;
- Change line 18 to: `asyn.iothread[0] = None`;
- Change line 19 to `asyn.loop[0] = None`. | [
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https://github.com/huggingface/datasets/issues/4612 | Release 2.3.0 broke custom iterable datasets | Hi! I think it's easier to replace `import fsspec` with `import fsspec.asyn` and leave the rest unchanged. @gugarosa Are you interested in submitting a PR? | ## Describe the bug
Trying to iterate examples from custom iterable dataset fails to bug introduced in `torch_iterable_dataset.py` since the release of 2.3.0.
## Steps to reproduce the bug
```python
next(iter(custom_iterable_dataset))
```
## Expected results
`next(iter(custom_iterable_dataset))` should return examples from the dataset
## Actual results
```
/usr/local/lib/python3.7/dist-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py in _set_fsspec_for_multiprocess()
16 See https://github.com/fsspec/gcsfs/issues/379
17 """
---> 18 fsspec.asyn.iothread[0] = None
19 fsspec.asyn.loop[0] = None
20
AttributeError: module 'fsspec' has no attribute 'asyn'
```
## Environment info
- `datasets` version: 2.3.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
| 25 | Release 2.3.0 broke custom iterable datasets
## Describe the bug
Trying to iterate examples from custom iterable dataset fails to bug introduced in `torch_iterable_dataset.py` since the release of 2.3.0.
## Steps to reproduce the bug
```python
next(iter(custom_iterable_dataset))
```
## Expected results
`next(iter(custom_iterable_dataset))` should return examples from the dataset
## Actual results
```
/usr/local/lib/python3.7/dist-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py in _set_fsspec_for_multiprocess()
16 See https://github.com/fsspec/gcsfs/issues/379
17 """
---> 18 fsspec.asyn.iothread[0] = None
19 fsspec.asyn.loop[0] = None
20
AttributeError: module 'fsspec' has no attribute 'asyn'
```
## Environment info
- `datasets` version: 2.3.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
Hi! I think it's easier to replace `import fsspec` with `import fsspec.asyn` and leave the rest unchanged. @gugarosa Are you interested in submitting a PR? | [
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] |
https://github.com/huggingface/datasets/issues/4610 | codeparrot/github-code failing to load | I believe the issue is in `codeparrot/github-code`. `base_path` param is missing - https://huggingface.co/datasets/codeparrot/github-code/blob/main/github-code.py#L169
Function definition has changed.
https://github.com/huggingface/datasets/blob/0e1c629cfb9f9ba124537ba294a0ec451584da5f/src/datasets/data_files.py#L547
@mariosasko could you please confirm my finding? And are there any changes that need to be done from my side? | ## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | 38 | codeparrot/github-code failing to load
## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
I believe the issue is in `codeparrot/github-code`. `base_path` param is missing - https://huggingface.co/datasets/codeparrot/github-code/blob/main/github-code.py#L169
Function definition has changed.
https://github.com/huggingface/datasets/blob/0e1c629cfb9f9ba124537ba294a0ec451584da5f/src/datasets/data_files.py#L547
@mariosasko could you please confirm my finding? And are there any changes that need to be done from my side? | [
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https://github.com/huggingface/datasets/issues/4610 | codeparrot/github-code failing to load | Good catch ! We recently did a breaking change in `get_patterns_in_dataset_repository`, I think we can revert it | ## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | 17 | codeparrot/github-code failing to load
## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
Good catch ! We recently did a breaking change in `get_patterns_in_dataset_repository`, I think we can revert it | [
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https://github.com/huggingface/datasets/issues/4610 | codeparrot/github-code failing to load | > Good catch ! We recently did a breaking change in `get_patterns_in_dataset_repository`, I think we can revert it
I can't wait for that releasee. Broke my application | ## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | 27 | codeparrot/github-code failing to load
## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
> Good catch ! We recently did a breaking change in `get_patterns_in_dataset_repository`, I think we can revert it
I can't wait for that releasee. Broke my application | [
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https://github.com/huggingface/datasets/issues/4610 | codeparrot/github-code failing to load | This simple workaround should fix: https://huggingface.co/datasets/codeparrot/github-code/discussions/2
`get_patterns_in_dataset_repository` can treat whether `base_path=None`, so we just need to make sure that codeparrot/github-code `_split_generators` calls with such an argument. | ## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | 26 | codeparrot/github-code failing to load
## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
This simple workaround should fix: https://huggingface.co/datasets/codeparrot/github-code/discussions/2
`get_patterns_in_dataset_repository` can treat whether `base_path=None`, so we just need to make sure that codeparrot/github-code `_split_generators` calls with such an argument. | [
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https://github.com/huggingface/datasets/issues/4610 | codeparrot/github-code failing to load | I am afraid your suggested change @gugarosa will break compatibility with older datasets versions that don't have `base_path` argument in `get_patterns_in_dataset_repository`, as a workaround while the issue gets resolved in `datasets` can you downgrade your datasets version to `<=2.1.0` ?
@lvwerra do you think we should adapt the script to check the datasets version before calling `get_patterns_in_dataset_repository`? | ## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | 57 | codeparrot/github-code failing to load
## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
I am afraid your suggested change @gugarosa will break compatibility with older datasets versions that don't have `base_path` argument in `get_patterns_in_dataset_repository`, as a workaround while the issue gets resolved in `datasets` can you downgrade your datasets version to `<=2.1.0` ?
@lvwerra do you think we should adapt the script to check the datasets version before calling `get_patterns_in_dataset_repository`? | [
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https://github.com/huggingface/datasets/issues/4610 | codeparrot/github-code failing to load | Actually I think it's just simpler to fix it in the dataset itself, let me open a PR
EDIT: PR opened here: https://huggingface.co/datasets/codeparrot/github-code/discussions/3 | ## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | 23 | codeparrot/github-code failing to load
## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
Actually I think it's just simpler to fix it in the dataset itself, let me open a PR
EDIT: PR opened here: https://huggingface.co/datasets/codeparrot/github-code/discussions/3 | [
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https://github.com/huggingface/datasets/issues/4610 | codeparrot/github-code failing to load | > I am afraid your suggested change @gugarosa will break compatibility with older datasets versions that don't have `base_path` argument in `get_patterns_in_dataset_repository`, as a workaround while the issue gets resolved in `datasets` can you downgrade your datasets version to `<=2.1.0` ?
> @lvwerra do you think we should adapt the script to check the datasets version before calling `get_patterns_in_dataset_repository`?
You are definitely right, sorry about it. I always keep forgetting that we need to keep in mind users from past versions, my bad. | ## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | 83 | codeparrot/github-code failing to load
## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
> I am afraid your suggested change @gugarosa will break compatibility with older datasets versions that don't have `base_path` argument in `get_patterns_in_dataset_repository`, as a workaround while the issue gets resolved in `datasets` can you downgrade your datasets version to `<=2.1.0` ?
> @lvwerra do you think we should adapt the script to check the datasets version before calling `get_patterns_in_dataset_repository`?
You are definitely right, sorry about it. I always keep forgetting that we need to keep in mind users from past versions, my bad. | [
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https://github.com/huggingface/datasets/issues/4609 | librispeech dataset has to download whole subset when specifing the split to use | Hi! You can use streaming to fetch only a subset of the data:
```python
raw_dataset = load_dataset("librispeech_asr", "clean", split="train.100", streaming=True)
```
Also, we plan to make it possible to download a particular split in the non-streaming mode, but this task is not easy due to how our dataset scripts are structured. | ## Describe the bug
librispeech dataset has to download whole subset when specifing the split to use
## Steps to reproduce the bug
see below
# Sample code to reproduce the bug
```
!pip install datasets
from datasets import load_dataset
raw_dataset = load_dataset("librispeech_asr", "clean", split="train.100")
```
## Expected results
The split "train.clean.100" is downloaded.
## Actual results
All four splits in "clean" subset is downloaded.
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
| 51 | librispeech dataset has to download whole subset when specifing the split to use
## Describe the bug
librispeech dataset has to download whole subset when specifing the split to use
## Steps to reproduce the bug
see below
# Sample code to reproduce the bug
```
!pip install datasets
from datasets import load_dataset
raw_dataset = load_dataset("librispeech_asr", "clean", split="train.100")
```
## Expected results
The split "train.clean.100" is downloaded.
## Actual results
All four splits in "clean" subset is downloaded.
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
Hi! You can use streaming to fetch only a subset of the data:
```python
raw_dataset = load_dataset("librispeech_asr", "clean", split="train.100", streaming=True)
```
Also, we plan to make it possible to download a particular split in the non-streaming mode, but this task is not easy due to how our dataset scripts are structured. | [
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https://github.com/huggingface/datasets/issues/4606 | evaluation result changes after `datasets` version change | Hi! The GH/no-namespace datasets versioning is synced with the version of the `datasets` lib, which means that the `wikiann` script was modified between the two compared versions. In this scenario, you can ensure reproducibility by pinning the script version, which is done by passing `revision="x.y.z"` (e.g. `revision="2.2.0"`) to `load_dataset.`
| ## Describe the bug
evaluation result changes after `datasets` version change
## Steps to reproduce the bug
1. Train a model on WikiAnn
2. reload the ckpt -> test accuracy becomes same as eval accuracy
3. such behavior is gone after downgrading `datasets`
https://colab.research.google.com/drive/1kYz7-aZRGdayaq-gDTt30tyEgsKlpYOw?usp=sharing
## Expected results
evaluation result shouldn't change before/after `datasets` version changes
## Actual results
evaluation result changes before/after `datasets` version changes
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: colab
- Python version: 3.7.13
- PyArrow version: 6.0.1
Q. How could the evaluation result change before/after `datasets` version changes? | 49 | evaluation result changes after `datasets` version change
## Describe the bug
evaluation result changes after `datasets` version change
## Steps to reproduce the bug
1. Train a model on WikiAnn
2. reload the ckpt -> test accuracy becomes same as eval accuracy
3. such behavior is gone after downgrading `datasets`
https://colab.research.google.com/drive/1kYz7-aZRGdayaq-gDTt30tyEgsKlpYOw?usp=sharing
## Expected results
evaluation result shouldn't change before/after `datasets` version changes
## Actual results
evaluation result changes before/after `datasets` version changes
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: colab
- Python version: 3.7.13
- PyArrow version: 6.0.1
Q. How could the evaluation result change before/after `datasets` version changes?
Hi! The GH/no-namespace datasets versioning is synced with the version of the `datasets` lib, which means that the `wikiann` script was modified between the two compared versions. In this scenario, you can ensure reproducibility by pinning the script version, which is done by passing `revision="x.y.z"` (e.g. `revision="2.2.0"`) to `load_dataset.`
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https://github.com/huggingface/datasets/issues/4605 | Dataset Viewer issue for boris/gis_filtered | Yes, this dataset is "gated": you first have to go to https://huggingface.co/datasets/boris/gis_filtered and click "Access repository" (if you accept to share your contact information with the repository authors). | ### Link
https://huggingface.co/datasets/boris/gis_filtered/viewer/boris--gis_filtered/train
### Description
When I try to access this from the website I get this error:
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/boris/gis_filtered/resolve/80b805053ce61d4eb487b6b8d9095d775c2c466e/data/train/0000.parquet')
If I try to load with code I also get the same issue:
```python
dataset2_train=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"],split="train",streaming=True)
dataset2_validation=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"], split="validation",streaming=True)
```
### Owner
No | 28 | Dataset Viewer issue for boris/gis_filtered
### Link
https://huggingface.co/datasets/boris/gis_filtered/viewer/boris--gis_filtered/train
### Description
When I try to access this from the website I get this error:
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/boris/gis_filtered/resolve/80b805053ce61d4eb487b6b8d9095d775c2c466e/data/train/0000.parquet')
If I try to load with code I also get the same issue:
```python
dataset2_train=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"],split="train",streaming=True)
dataset2_validation=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"], split="validation",streaming=True)
```
### Owner
No
Yes, this dataset is "gated": you first have to go to https://huggingface.co/datasets/boris/gis_filtered and click "Access repository" (if you accept to share your contact information with the repository authors). | [
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https://github.com/huggingface/datasets/issues/4605 | Dataset Viewer issue for boris/gis_filtered | I could reproduce the error, even though I provided my token and accepted the gate form. It looks like an error from `datasets` | ### Link
https://huggingface.co/datasets/boris/gis_filtered/viewer/boris--gis_filtered/train
### Description
When I try to access this from the website I get this error:
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/boris/gis_filtered/resolve/80b805053ce61d4eb487b6b8d9095d775c2c466e/data/train/0000.parquet')
If I try to load with code I also get the same issue:
```python
dataset2_train=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"],split="train",streaming=True)
dataset2_validation=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"], split="validation",streaming=True)
```
### Owner
No | 23 | Dataset Viewer issue for boris/gis_filtered
### Link
https://huggingface.co/datasets/boris/gis_filtered/viewer/boris--gis_filtered/train
### Description
When I try to access this from the website I get this error:
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/boris/gis_filtered/resolve/80b805053ce61d4eb487b6b8d9095d775c2c466e/data/train/0000.parquet')
If I try to load with code I also get the same issue:
```python
dataset2_train=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"],split="train",streaming=True)
dataset2_validation=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"], split="validation",streaming=True)
```
### Owner
No
I could reproduce the error, even though I provided my token and accepted the gate form. It looks like an error from `datasets` | [
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https://github.com/huggingface/datasets/issues/4605 | Dataset Viewer issue for boris/gis_filtered | This is indeed a bug in `datasets`. Parquet datasets in gated/private repositories can't be streamed properly, which caused the viewer to fail. I opened a PR at https://github.com/huggingface/datasets/pull/4608 | ### Link
https://huggingface.co/datasets/boris/gis_filtered/viewer/boris--gis_filtered/train
### Description
When I try to access this from the website I get this error:
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/boris/gis_filtered/resolve/80b805053ce61d4eb487b6b8d9095d775c2c466e/data/train/0000.parquet')
If I try to load with code I also get the same issue:
```python
dataset2_train=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"],split="train",streaming=True)
dataset2_validation=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"], split="validation",streaming=True)
```
### Owner
No | 28 | Dataset Viewer issue for boris/gis_filtered
### Link
https://huggingface.co/datasets/boris/gis_filtered/viewer/boris--gis_filtered/train
### Description
When I try to access this from the website I get this error:
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/boris/gis_filtered/resolve/80b805053ce61d4eb487b6b8d9095d775c2c466e/data/train/0000.parquet')
If I try to load with code I also get the same issue:
```python
dataset2_train=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"],split="train",streaming=True)
dataset2_validation=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"], split="validation",streaming=True)
```
### Owner
No
This is indeed a bug in `datasets`. Parquet datasets in gated/private repositories can't be streamed properly, which caused the viewer to fail. I opened a PR at https://github.com/huggingface/datasets/pull/4608 | [
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https://github.com/huggingface/datasets/issues/4597 | Streaming issue for financial_phrasebank | cc @huggingface/datasets: it seems like https://www.researchgate.net/ is flaky for datasets hosting (I put the "hosted-on-google-drive" tag since it's the same kind of issue I think) | ### Link
https://huggingface.co/datasets/financial_phrasebank/viewer/sentences_allagree/train
### Description
As reported by a community member using [AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/5#62bc217436d0e5d316a768f0), there seems to be a problem streaming this dataset:
```
Server error
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No | 25 | Streaming issue for financial_phrasebank
### Link
https://huggingface.co/datasets/financial_phrasebank/viewer/sentences_allagree/train
### Description
As reported by a community member using [AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/5#62bc217436d0e5d316a768f0), there seems to be a problem streaming this dataset:
```
Server error
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No
cc @huggingface/datasets: it seems like https://www.researchgate.net/ is flaky for datasets hosting (I put the "hosted-on-google-drive" tag since it's the same kind of issue I think) | [
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] |
https://github.com/huggingface/datasets/issues/4597 | Streaming issue for financial_phrasebank | License is Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0).
We can host their data on the Hub. | ### Link
https://huggingface.co/datasets/financial_phrasebank/viewer/sentences_allagree/train
### Description
As reported by a community member using [AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/5#62bc217436d0e5d316a768f0), there seems to be a problem streaming this dataset:
```
Server error
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No | 18 | Streaming issue for financial_phrasebank
### Link
https://huggingface.co/datasets/financial_phrasebank/viewer/sentences_allagree/train
### Description
As reported by a community member using [AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/5#62bc217436d0e5d316a768f0), there seems to be a problem streaming this dataset:
```
Server error
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No
License is Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0).
We can host their data on the Hub. | [
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] |
https://github.com/huggingface/datasets/issues/4596 | Dataset Viewer issue for universal_dependencies | Finally fixed! We updated the dataset viewer and it fixed the issue.
https://huggingface.co/datasets/universal_dependencies/viewer/aqz_tudet/train
<img width="1561" alt="Capture d’écran 2022-09-07 à 13 29 18" src="https://user-images.githubusercontent.com/1676121/188867795-4f7dd438-d4f2-46cd-8a92-20a37fb2d6bc.png">
| ### Link
https://huggingface.co/datasets/universal_dependencies
### Description
invalid json response body at https://datasets-server.huggingface.co/splits?dataset=universal_dependencies reason: Unexpected token I in JSON at position 0
### Owner
_No response_ | 23 | Dataset Viewer issue for universal_dependencies
### Link
https://huggingface.co/datasets/universal_dependencies
### Description
invalid json response body at https://datasets-server.huggingface.co/splits?dataset=universal_dependencies reason: Unexpected token I in JSON at position 0
### Owner
_No response_
Finally fixed! We updated the dataset viewer and it fixed the issue.
https://huggingface.co/datasets/universal_dependencies/viewer/aqz_tudet/train
<img width="1561" alt="Capture d’écran 2022-09-07 à 13 29 18" src="https://user-images.githubusercontent.com/1676121/188867795-4f7dd438-d4f2-46cd-8a92-20a37fb2d6bc.png">
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] |
https://github.com/huggingface/datasets/issues/4595 | Dataset Viewer issue with False positive PII redaction | The value is in the data, it's not an issue with the "dataset-viewer".
<img width="1161" alt="Capture d’écran 2022-06-29 à 10 25 51" src="https://user-images.githubusercontent.com/1676121/176389325-4d2a9a7f-1583-45b8-aa7a-960ffaa6a36a.png">
Maybe open a PR: https://huggingface.co/datasets/cakiki/rosetta-code/discussions
| ### Link
https://huggingface.co/datasets/cakiki/rosetta-code
### Description
Hello, I just noticed an entry being redacted that shouldn't have been:
`RootMeanSquare@Range[10]` is being displayed as `[email protected][10]`
### Owner
_No response_ | 28 | Dataset Viewer issue with False positive PII redaction
### Link
https://huggingface.co/datasets/cakiki/rosetta-code
### Description
Hello, I just noticed an entry being redacted that shouldn't have been:
`RootMeanSquare@Range[10]` is being displayed as `[email protected][10]`
### Owner
_No response_
The value is in the data, it's not an issue with the "dataset-viewer".
<img width="1161" alt="Capture d’écran 2022-06-29 à 10 25 51" src="https://user-images.githubusercontent.com/1676121/176389325-4d2a9a7f-1583-45b8-aa7a-960ffaa6a36a.png">
Maybe open a PR: https://huggingface.co/datasets/cakiki/rosetta-code/discussions
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https://github.com/huggingface/datasets/issues/4595 | Dataset Viewer issue with False positive PII redaction | This was indeed a scraping issue which I assumed was a display issue; sorry about that! | ### Link
https://huggingface.co/datasets/cakiki/rosetta-code
### Description
Hello, I just noticed an entry being redacted that shouldn't have been:
`RootMeanSquare@Range[10]` is being displayed as `[email protected][10]`
### Owner
_No response_ | 16 | Dataset Viewer issue with False positive PII redaction
### Link
https://huggingface.co/datasets/cakiki/rosetta-code
### Description
Hello, I just noticed an entry being redacted that shouldn't have been:
`RootMeanSquare@Range[10]` is being displayed as `[email protected][10]`
### Owner
_No response_
This was indeed a scraping issue which I assumed was a display issue; sorry about that! | [
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https://github.com/huggingface/datasets/issues/4592 | Issue with jalFaizy/detect_chess_pieces when running datasets-cli test | Hi @faizankshaikh
Please note that we have recently launched the Community feature, specifically targeted to create Discussions (about issues/questions/asking-for-help) on each Dataset on the Hub:
- Blog post: https://huggingface.co/blog/community-update
- Docs: https://huggingface.co/docs/hub/repositories-pull-requests-discussions
The Discussion tab for your "jalFaizy/detect_chess_pieces" dataset is here: https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions
You can use it to ask for help by pinging the Datasets maintainers: see our docs here: https://huggingface.co/docs/datasets/master/en/share#ask-for-a-help-and-reviews
I'm transferring this discussion to your Discussion tab and trying to address it: https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/1 | ### Link
https://huggingface.co/datasets/jalFaizy/detect_chess_pieces
### Description
I am trying to write a appropriate data loader for [a custom dataset](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces) using [this script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py)
When I run the command
`$ datasets-cli test "D:\workspace\HF\detect_chess_pieces" --save_infos --all_configs`
It gives the following error
```
Using custom data configuration default
Traceback (most recent call last):
File "c:\users\faiza\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\faiza\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\faiza\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 7, in <module>
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 132, in run
for j, builder in enumerate(get_builders()):
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 125, in get_builders
yield builder_cls(
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 1148, in __init__
super().__init__(*args, **kwargs)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 306, in __init__
info = self.get_exported_dataset_info()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 405, in get_exported_dataset_info
return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 390, in get_all_exported_dataset_infos
return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 309, in from_directory
dataset_infos_dict = {
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 310, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 272, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 160, in __post_init__
templates = [
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 161, in <listcomp>
template if isinstance(template, TaskTemplate) else task_template_from_dict(template)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\tasks\__init__.py", line 43, in task_template_from_dict
return template.from_dict(task_template_dict)
AttributeError: 'NoneType' object has no attribute 'from_dict'
```
My assumption is that there is some kind of issue in how the "task_templates" are read, because even if I keep them as None, or not include the argument at all, the same error occurs
### Owner
Yes | 74 | Issue with jalFaizy/detect_chess_pieces when running datasets-cli test
### Link
https://huggingface.co/datasets/jalFaizy/detect_chess_pieces
### Description
I am trying to write a appropriate data loader for [a custom dataset](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces) using [this script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py)
When I run the command
`$ datasets-cli test "D:\workspace\HF\detect_chess_pieces" --save_infos --all_configs`
It gives the following error
```
Using custom data configuration default
Traceback (most recent call last):
File "c:\users\faiza\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\faiza\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\faiza\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 7, in <module>
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 132, in run
for j, builder in enumerate(get_builders()):
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 125, in get_builders
yield builder_cls(
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 1148, in __init__
super().__init__(*args, **kwargs)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 306, in __init__
info = self.get_exported_dataset_info()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 405, in get_exported_dataset_info
return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 390, in get_all_exported_dataset_infos
return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 309, in from_directory
dataset_infos_dict = {
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 310, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 272, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 160, in __post_init__
templates = [
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 161, in <listcomp>
template if isinstance(template, TaskTemplate) else task_template_from_dict(template)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\tasks\__init__.py", line 43, in task_template_from_dict
return template.from_dict(task_template_dict)
AttributeError: 'NoneType' object has no attribute 'from_dict'
```
My assumption is that there is some kind of issue in how the "task_templates" are read, because even if I keep them as None, or not include the argument at all, the same error occurs
### Owner
Yes
Hi @faizankshaikh
Please note that we have recently launched the Community feature, specifically targeted to create Discussions (about issues/questions/asking-for-help) on each Dataset on the Hub:
- Blog post: https://huggingface.co/blog/community-update
- Docs: https://huggingface.co/docs/hub/repositories-pull-requests-discussions
The Discussion tab for your "jalFaizy/detect_chess_pieces" dataset is here: https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions
You can use it to ask for help by pinging the Datasets maintainers: see our docs here: https://huggingface.co/docs/datasets/master/en/share#ask-for-a-help-and-reviews
I'm transferring this discussion to your Discussion tab and trying to address it: https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/1 | [
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https://github.com/huggingface/datasets/issues/4592 | Issue with jalFaizy/detect_chess_pieces when running datasets-cli test | Thank you @albertvillanova , I will keep that in mind.
Just a quick note - I posted the issue on Github because the dataset viewer suggested me to "open an issue for direct support". Maybe it can be updated with your suggestion
| ### Link
https://huggingface.co/datasets/jalFaizy/detect_chess_pieces
### Description
I am trying to write a appropriate data loader for [a custom dataset](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces) using [this script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py)
When I run the command
`$ datasets-cli test "D:\workspace\HF\detect_chess_pieces" --save_infos --all_configs`
It gives the following error
```
Using custom data configuration default
Traceback (most recent call last):
File "c:\users\faiza\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\faiza\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\faiza\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 7, in <module>
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 132, in run
for j, builder in enumerate(get_builders()):
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 125, in get_builders
yield builder_cls(
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 1148, in __init__
super().__init__(*args, **kwargs)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 306, in __init__
info = self.get_exported_dataset_info()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 405, in get_exported_dataset_info
return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 390, in get_all_exported_dataset_infos
return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 309, in from_directory
dataset_infos_dict = {
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 310, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 272, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 160, in __post_init__
templates = [
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 161, in <listcomp>
template if isinstance(template, TaskTemplate) else task_template_from_dict(template)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\tasks\__init__.py", line 43, in task_template_from_dict
return template.from_dict(task_template_dict)
AttributeError: 'NoneType' object has no attribute 'from_dict'
```
My assumption is that there is some kind of issue in how the "task_templates" are read, because even if I keep them as None, or not include the argument at all, the same error occurs
### Owner
Yes | 43 | Issue with jalFaizy/detect_chess_pieces when running datasets-cli test
### Link
https://huggingface.co/datasets/jalFaizy/detect_chess_pieces
### Description
I am trying to write a appropriate data loader for [a custom dataset](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces) using [this script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py)
When I run the command
`$ datasets-cli test "D:\workspace\HF\detect_chess_pieces" --save_infos --all_configs`
It gives the following error
```
Using custom data configuration default
Traceback (most recent call last):
File "c:\users\faiza\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\faiza\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\faiza\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 7, in <module>
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 132, in run
for j, builder in enumerate(get_builders()):
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 125, in get_builders
yield builder_cls(
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 1148, in __init__
super().__init__(*args, **kwargs)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 306, in __init__
info = self.get_exported_dataset_info()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 405, in get_exported_dataset_info
return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 390, in get_all_exported_dataset_infos
return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 309, in from_directory
dataset_infos_dict = {
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 310, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 272, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 160, in __post_init__
templates = [
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 161, in <listcomp>
template if isinstance(template, TaskTemplate) else task_template_from_dict(template)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\tasks\__init__.py", line 43, in task_template_from_dict
return template.from_dict(task_template_dict)
AttributeError: 'NoneType' object has no attribute 'from_dict'
```
My assumption is that there is some kind of issue in how the "task_templates" are read, because even if I keep them as None, or not include the argument at all, the same error occurs
### Owner
Yes
Thank you @albertvillanova , I will keep that in mind.
Just a quick note - I posted the issue on Github because the dataset viewer suggested me to "open an issue for direct support". Maybe it can be updated with your suggestion
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] |
https://github.com/huggingface/datasets/issues/4592 | Issue with jalFaizy/detect_chess_pieces when running datasets-cli test | Thank you pointing this out: yes, definitely, we should fix the error message. We are working on this. | ### Link
https://huggingface.co/datasets/jalFaizy/detect_chess_pieces
### Description
I am trying to write a appropriate data loader for [a custom dataset](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces) using [this script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py)
When I run the command
`$ datasets-cli test "D:\workspace\HF\detect_chess_pieces" --save_infos --all_configs`
It gives the following error
```
Using custom data configuration default
Traceback (most recent call last):
File "c:\users\faiza\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\faiza\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\faiza\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 7, in <module>
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 132, in run
for j, builder in enumerate(get_builders()):
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 125, in get_builders
yield builder_cls(
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 1148, in __init__
super().__init__(*args, **kwargs)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 306, in __init__
info = self.get_exported_dataset_info()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 405, in get_exported_dataset_info
return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 390, in get_all_exported_dataset_infos
return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 309, in from_directory
dataset_infos_dict = {
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 310, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 272, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 160, in __post_init__
templates = [
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 161, in <listcomp>
template if isinstance(template, TaskTemplate) else task_template_from_dict(template)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\tasks\__init__.py", line 43, in task_template_from_dict
return template.from_dict(task_template_dict)
AttributeError: 'NoneType' object has no attribute 'from_dict'
```
My assumption is that there is some kind of issue in how the "task_templates" are read, because even if I keep them as None, or not include the argument at all, the same error occurs
### Owner
Yes | 18 | Issue with jalFaizy/detect_chess_pieces when running datasets-cli test
### Link
https://huggingface.co/datasets/jalFaizy/detect_chess_pieces
### Description
I am trying to write a appropriate data loader for [a custom dataset](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces) using [this script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py)
When I run the command
`$ datasets-cli test "D:\workspace\HF\detect_chess_pieces" --save_infos --all_configs`
It gives the following error
```
Using custom data configuration default
Traceback (most recent call last):
File "c:\users\faiza\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\faiza\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\faiza\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 7, in <module>
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 132, in run
for j, builder in enumerate(get_builders()):
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 125, in get_builders
yield builder_cls(
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 1148, in __init__
super().__init__(*args, **kwargs)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 306, in __init__
info = self.get_exported_dataset_info()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 405, in get_exported_dataset_info
return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 390, in get_all_exported_dataset_infos
return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 309, in from_directory
dataset_infos_dict = {
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 310, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 272, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 160, in __post_init__
templates = [
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 161, in <listcomp>
template if isinstance(template, TaskTemplate) else task_template_from_dict(template)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\tasks\__init__.py", line 43, in task_template_from_dict
return template.from_dict(task_template_dict)
AttributeError: 'NoneType' object has no attribute 'from_dict'
```
My assumption is that there is some kind of issue in how the "task_templates" are read, because even if I keep them as None, or not include the argument at all, the same error occurs
### Owner
Yes
Thank you pointing this out: yes, definitely, we should fix the error message. We are working on this. | [
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https://github.com/huggingface/datasets/issues/4591 | Can't push Images to hub with manual Dataset | Hi, thanks for reporting! This issue stems from the changes introduced in https://github.com/huggingface/datasets/pull/4282 (cc @lhoestq), in which list casts are ignored if they don't change the list type (required to preserve `null` values). And `push_to_hub` does a special cast to embed external image files but doesn't change the types, hence the failure. | ## Describe the bug
If I create a dataset including an 'Image' feature manually, when pushing to hub decoded images are not pushed,
instead it looks for image where image local path is/used to be.
This doesn't (at least didn't used to) happen with imagefolder. I want to build dataset manually because it is complicated.
This happens even though the dataset is looking like decoded images:
and I use `embed_external_files=True` while `push_to_hub` (same with false)
## Steps to reproduce the bug
```python
from PIL import Image
from datasets import Image as ImageFeature
from datasets import Features,Dataset
#manually create dataset
feats=Features(
{
"images": [ImageFeature()], #same even if explicitly ImageFeature(decode=True)
"input_image": ImageFeature(),
}
)
test_data={"images":[[Image.open("test.jpg"),Image.open("test.jpg"),Image.open("test.jpg")]], "input_image":[Image.open("test.jpg")]}
test_dataset=Dataset.from_dict(test_data,features=feats)
print(test_dataset)
test_dataset.push_to_hub("ceyda/image_test_public",private=False,token="",embed_external_files=True)
# clear cache rm -r ~/.cache/huggingface
# remove "test.jpg" # remove to see that it is looking for image on the local path
test_dataset=load_dataset("ceyda/image_test_public",use_auth_token="")
print(test_dataset)
print(test_dataset['train'][0])
```
## Expected results
should be able to push image bytes if dataset has `Image(decode=True)`
## Actual results
errors because it is trying to decode file from the non existing local path.
```
----> print(test_dataset['train'][0])
File ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py:2154, in Dataset.__getitem__(self, key)
2152 def __getitem__(self, key): # noqa: F811
2153 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2154 return self._getitem(
2155 key,
2156 )
File ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py:2139, in Dataset._getitem(self, key, decoded, **kwargs)
2137 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
2138 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2139 formatted_output = format_table(
2140 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
2141 )
2142 return formatted_output
File ~/.local/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
...
-> 3068 fp = builtins.open(filename, "rb")
3069 exclusive_fp = True
3071 try:
FileNotFoundError: [Errno 2] No such file or directory: 'test.jpg'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-1074-azure-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| 52 | Can't push Images to hub with manual Dataset
## Describe the bug
If I create a dataset including an 'Image' feature manually, when pushing to hub decoded images are not pushed,
instead it looks for image where image local path is/used to be.
This doesn't (at least didn't used to) happen with imagefolder. I want to build dataset manually because it is complicated.
This happens even though the dataset is looking like decoded images:
and I use `embed_external_files=True` while `push_to_hub` (same with false)
## Steps to reproduce the bug
```python
from PIL import Image
from datasets import Image as ImageFeature
from datasets import Features,Dataset
#manually create dataset
feats=Features(
{
"images": [ImageFeature()], #same even if explicitly ImageFeature(decode=True)
"input_image": ImageFeature(),
}
)
test_data={"images":[[Image.open("test.jpg"),Image.open("test.jpg"),Image.open("test.jpg")]], "input_image":[Image.open("test.jpg")]}
test_dataset=Dataset.from_dict(test_data,features=feats)
print(test_dataset)
test_dataset.push_to_hub("ceyda/image_test_public",private=False,token="",embed_external_files=True)
# clear cache rm -r ~/.cache/huggingface
# remove "test.jpg" # remove to see that it is looking for image on the local path
test_dataset=load_dataset("ceyda/image_test_public",use_auth_token="")
print(test_dataset)
print(test_dataset['train'][0])
```
## Expected results
should be able to push image bytes if dataset has `Image(decode=True)`
## Actual results
errors because it is trying to decode file from the non existing local path.
```
----> print(test_dataset['train'][0])
File ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py:2154, in Dataset.__getitem__(self, key)
2152 def __getitem__(self, key): # noqa: F811
2153 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2154 return self._getitem(
2155 key,
2156 )
File ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py:2139, in Dataset._getitem(self, key, decoded, **kwargs)
2137 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
2138 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2139 formatted_output = format_table(
2140 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
2141 )
2142 return formatted_output
File ~/.local/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
...
-> 3068 fp = builtins.open(filename, "rb")
3069 exclusive_fp = True
3071 try:
FileNotFoundError: [Errno 2] No such file or directory: 'test.jpg'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-1074-azure-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
Hi, thanks for reporting! This issue stems from the changes introduced in https://github.com/huggingface/datasets/pull/4282 (cc @lhoestq), in which list casts are ignored if they don't change the list type (required to preserve `null` values). And `push_to_hub` does a special cast to embed external image files but doesn't change the types, hence the failure. | [
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] |
https://github.com/huggingface/datasets/issues/4581 | Dataset Viewer issue for pn_summary | Note that I refreshed twice this dataset, and I still have (another) error on one of the splits
```
Status code: 400
Exception: ClientResponseError
Message: 403, message='Forbidden', url=URL('https://doc-14-4c-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/pgotjmcuh77q0lk7p44rparfrhv459kp/1656403650000/11771870722949762109/*/16OgJ_OrfzUF_i3ftLjFn9kpcyoi7UJeO?e=download')
```
Like the three splits are processed in parallel by the workers, I imagine that the Google hosting is rate-limiting us.
cc @albertvillanova
| ### Link
https://huggingface.co/datasets/pn_summary/viewer/1.0.0/validation
### Description
Getting an index error on the `validation` and `test` splits:
```
Server error
Status code: 400
Exception: IndexError
Message: list index out of range
```
### Owner
No | 51 | Dataset Viewer issue for pn_summary
### Link
https://huggingface.co/datasets/pn_summary/viewer/1.0.0/validation
### Description
Getting an index error on the `validation` and `test` splits:
```
Server error
Status code: 400
Exception: IndexError
Message: list index out of range
```
### Owner
No
Note that I refreshed twice this dataset, and I still have (another) error on one of the splits
```
Status code: 400
Exception: ClientResponseError
Message: 403, message='Forbidden', url=URL('https://doc-14-4c-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/pgotjmcuh77q0lk7p44rparfrhv459kp/1656403650000/11771870722949762109/*/16OgJ_OrfzUF_i3ftLjFn9kpcyoi7UJeO?e=download')
```
Like the three splits are processed in parallel by the workers, I imagine that the Google hosting is rate-limiting us.
cc @albertvillanova
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https://github.com/huggingface/datasets/issues/4580 | Dataset Viewer issue for multi_news | Thanks for reporting, @lewtun.
I forced the refreshing of the preview and it worked OK for train and validation splits.
I guess the error has to do with the data files being hosted at Google Drive: this gives errors when requested automatically using scripts.
We should host them to fix the error. Let's see if the license allows that. | ### Link
https://huggingface.co/datasets/multi_news
### Description
Not sure what the index error is referring to here:
```
Status code: 400
Exception: IndexError
Message: list index out of range
```
### Owner
No | 59 | Dataset Viewer issue for multi_news
### Link
https://huggingface.co/datasets/multi_news
### Description
Not sure what the index error is referring to here:
```
Status code: 400
Exception: IndexError
Message: list index out of range
```
### Owner
No
Thanks for reporting, @lewtun.
I forced the refreshing of the preview and it worked OK for train and validation splits.
I guess the error has to do with the data files being hosted at Google Drive: this gives errors when requested automatically using scripts.
We should host them to fix the error. Let's see if the license allows that. | [
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] |
https://github.com/huggingface/datasets/issues/4578 | [Multi Configs] Use directories to differentiate between subsets/configurations | How to set new split names, instead of train/test/validation? For example, I have a local dataset, consists of several subsets, named "A", "B", and "C". How can I create a huggingface dataset, with splits A/B/C ?
The document in https://huggingface.co/docs/datasets/dataset_script only tells me how to create datasets with subsets that is hosted on another server. How to do it if my datasets are local? | Currently to define several subsets/configurations of your dataset, you need to use a dataset script.
However it would be nice to have a no-code way to to this.
For example we could specify different configurations of a dataset (for example, if a dataset contains different languages) with one directory per configuration.
These structures are not supported right now, but would be nice to have:
```
my_dataset_repository/
├── README.md
├── en/
│ ├── train.csv
│ └── test.csv
└── fr/
├── train.csv
└── test.csv
```
Or with one directory per split:
```
my_dataset_repository/
├── README.md
├── en/
│ ├── train/
│ │ ├── shard_0.csv
│ │ └── shard_1.csv
│ └── test/
│ ├── shard_0.csv
│ └── shard_1.csv
└── fr/
├── train/
│ ├── shard_0.csv
│ └── shard_1.csv
└── test/
├── shard_0.csv
└── shard_1.csv
```
cc @stevhliu @albertvillanova
This can be specified in the README as YAML with
```
configs:
- config_name: en
data_dir: en
- config_name: fr
data_dir: fr
``` | 64 | [Multi Configs] Use directories to differentiate between subsets/configurations
Currently to define several subsets/configurations of your dataset, you need to use a dataset script.
However it would be nice to have a no-code way to to this.
For example we could specify different configurations of a dataset (for example, if a dataset contains different languages) with one directory per configuration.
These structures are not supported right now, but would be nice to have:
```
my_dataset_repository/
├── README.md
├── en/
│ ├── train.csv
│ └── test.csv
└── fr/
├── train.csv
└── test.csv
```
Or with one directory per split:
```
my_dataset_repository/
├── README.md
├── en/
│ ├── train/
│ │ ├── shard_0.csv
│ │ └── shard_1.csv
│ └── test/
│ ├── shard_0.csv
│ └── shard_1.csv
└── fr/
├── train/
│ ├── shard_0.csv
│ └── shard_1.csv
└── test/
├── shard_0.csv
└── shard_1.csv
```
cc @stevhliu @albertvillanova
This can be specified in the README as YAML with
```
configs:
- config_name: en
data_dir: en
- config_name: fr
data_dir: fr
```
How to set new split names, instead of train/test/validation? For example, I have a local dataset, consists of several subsets, named "A", "B", and "C". How can I create a huggingface dataset, with splits A/B/C ?
The document in https://huggingface.co/docs/datasets/dataset_script only tells me how to create datasets with subsets that is hosted on another server. How to do it if my datasets are local? | [
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https://github.com/huggingface/datasets/issues/4578 | [Multi Configs] Use directories to differentiate between subsets/configurations | > The document in https://huggingface.co/docs/datasets/dataset_script only tells me how to create datasets with subsets that is hosted on another server. How to do it if my datasets are local?
It works the same - you just need to use local paths instead of URLs | Currently to define several subsets/configurations of your dataset, you need to use a dataset script.
However it would be nice to have a no-code way to to this.
For example we could specify different configurations of a dataset (for example, if a dataset contains different languages) with one directory per configuration.
These structures are not supported right now, but would be nice to have:
```
my_dataset_repository/
├── README.md
├── en/
│ ├── train.csv
│ └── test.csv
└── fr/
├── train.csv
└── test.csv
```
Or with one directory per split:
```
my_dataset_repository/
├── README.md
├── en/
│ ├── train/
│ │ ├── shard_0.csv
│ │ └── shard_1.csv
│ └── test/
│ ├── shard_0.csv
│ └── shard_1.csv
└── fr/
├── train/
│ ├── shard_0.csv
│ └── shard_1.csv
└── test/
├── shard_0.csv
└── shard_1.csv
```
cc @stevhliu @albertvillanova
This can be specified in the README as YAML with
```
configs:
- config_name: en
data_dir: en
- config_name: fr
data_dir: fr
``` | 44 | [Multi Configs] Use directories to differentiate between subsets/configurations
Currently to define several subsets/configurations of your dataset, you need to use a dataset script.
However it would be nice to have a no-code way to to this.
For example we could specify different configurations of a dataset (for example, if a dataset contains different languages) with one directory per configuration.
These structures are not supported right now, but would be nice to have:
```
my_dataset_repository/
├── README.md
├── en/
│ ├── train.csv
│ └── test.csv
└── fr/
├── train.csv
└── test.csv
```
Or with one directory per split:
```
my_dataset_repository/
├── README.md
├── en/
│ ├── train/
│ │ ├── shard_0.csv
│ │ └── shard_1.csv
│ └── test/
│ ├── shard_0.csv
│ └── shard_1.csv
└── fr/
├── train/
│ ├── shard_0.csv
│ └── shard_1.csv
└── test/
├── shard_0.csv
└── shard_1.csv
```
cc @stevhliu @albertvillanova
This can be specified in the README as YAML with
```
configs:
- config_name: en
data_dir: en
- config_name: fr
data_dir: fr
```
> The document in https://huggingface.co/docs/datasets/dataset_script only tells me how to create datasets with subsets that is hosted on another server. How to do it if my datasets are local?
It works the same - you just need to use local paths instead of URLs | [
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https://github.com/huggingface/datasets/issues/4575 | Problem about wmt17 zh-en dataset | @winterfell2021 Hi, I wonder where the code you provided should be added. I tried to add them in the `datasets/table.py` in `array_cast` function, however, the 'zh' item is none. | It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.dataset, "zh-en")
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 1684, in load_dataset
use_auth_token=use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1221, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1215, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 533, in finalize
self.write_examples_on_file()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 410, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 503, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1846, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1756, in array_cast
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
TypeError: Couldn't cast array of type
struct<c[hn]: string, en: string, zh: string>
to
struct<en: string, zh: string>
```
So the solution of this problem is to change the original array manually:
```
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if 'zh' not in vo:
tmp['zh'] = vo['c[hn]']
else:
tmp['zh'] = vo['zh']
data_list.append(tmp)
array = pa.array(data_list, type=pa.struct([
pa.field('en', pa.string()),
pa.field('zh', pa.string()),
]))
```
Therefore, maybe a correct version of original casia2015 file need to be updated | 29 | Problem about wmt17 zh-en dataset
It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.dataset, "zh-en")
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 1684, in load_dataset
use_auth_token=use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1221, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1215, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 533, in finalize
self.write_examples_on_file()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 410, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 503, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1846, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1756, in array_cast
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
TypeError: Couldn't cast array of type
struct<c[hn]: string, en: string, zh: string>
to
struct<en: string, zh: string>
```
So the solution of this problem is to change the original array manually:
```
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if 'zh' not in vo:
tmp['zh'] = vo['c[hn]']
else:
tmp['zh'] = vo['zh']
data_list.append(tmp)
array = pa.array(data_list, type=pa.struct([
pa.field('en', pa.string()),
pa.field('zh', pa.string()),
]))
```
Therefore, maybe a correct version of original casia2015 file need to be updated
@winterfell2021 Hi, I wonder where the code you provided should be added. I tried to add them in the `datasets/table.py` in `array_cast` function, however, the 'zh' item is none. | [
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] |
https://github.com/huggingface/datasets/issues/4575 | Problem about wmt17 zh-en dataset | I found some 'zh' item is none while 'c[hn]' is not.
So the code may change to:
```python
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if vo.get('zh'):
tmp['zh'] = vo['zh']
else:
tmp['zh'] = vo['c[hn]']
data_list.append(tmp)
array = pa.array(data_list, type=pa.struct([
pa.field('en', pa.string()),
pa.field('zh', pa.string()),
]))
``` | It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.dataset, "zh-en")
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 1684, in load_dataset
use_auth_token=use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1221, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1215, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 533, in finalize
self.write_examples_on_file()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 410, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 503, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1846, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1756, in array_cast
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
TypeError: Couldn't cast array of type
struct<c[hn]: string, en: string, zh: string>
to
struct<en: string, zh: string>
```
So the solution of this problem is to change the original array manually:
```
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if 'zh' not in vo:
tmp['zh'] = vo['c[hn]']
else:
tmp['zh'] = vo['zh']
data_list.append(tmp)
array = pa.array(data_list, type=pa.struct([
pa.field('en', pa.string()),
pa.field('zh', pa.string()),
]))
```
Therefore, maybe a correct version of original casia2015 file need to be updated | 58 | Problem about wmt17 zh-en dataset
It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.dataset, "zh-en")
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 1684, in load_dataset
use_auth_token=use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1221, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1215, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 533, in finalize
self.write_examples_on_file()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 410, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 503, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1846, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1756, in array_cast
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
TypeError: Couldn't cast array of type
struct<c[hn]: string, en: string, zh: string>
to
struct<en: string, zh: string>
```
So the solution of this problem is to change the original array manually:
```
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if 'zh' not in vo:
tmp['zh'] = vo['c[hn]']
else:
tmp['zh'] = vo['zh']
data_list.append(tmp)
array = pa.array(data_list, type=pa.struct([
pa.field('en', pa.string()),
pa.field('zh', pa.string()),
]))
```
Therefore, maybe a correct version of original casia2015 file need to be updated
I found some 'zh' item is none while 'c[hn]' is not.
So the code may change to:
```python
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if vo.get('zh'):
tmp['zh'] = vo['zh']
else:
tmp['zh'] = vo['c[hn]']
data_list.append(tmp)
array = pa.array(data_list, type=pa.struct([
pa.field('en', pa.string()),
pa.field('zh', pa.string()),
]))
``` | [
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https://github.com/huggingface/datasets/issues/4575 | Problem about wmt17 zh-en dataset | I just pushed a fix, we'll do a new release of `datasets` soon to include this fix. In the meantime you can use the fixed dataset by passing `revision="main"` to `load_dataset` | It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.dataset, "zh-en")
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 1684, in load_dataset
use_auth_token=use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1221, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1215, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 533, in finalize
self.write_examples_on_file()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 410, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 503, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1846, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1756, in array_cast
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
TypeError: Couldn't cast array of type
struct<c[hn]: string, en: string, zh: string>
to
struct<en: string, zh: string>
```
So the solution of this problem is to change the original array manually:
```
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if 'zh' not in vo:
tmp['zh'] = vo['c[hn]']
else:
tmp['zh'] = vo['zh']
data_list.append(tmp)
array = pa.array(data_list, type=pa.struct([
pa.field('en', pa.string()),
pa.field('zh', pa.string()),
]))
```
Therefore, maybe a correct version of original casia2015 file need to be updated | 31 | Problem about wmt17 zh-en dataset
It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.dataset, "zh-en")
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 1684, in load_dataset
use_auth_token=use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1221, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1215, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 533, in finalize
self.write_examples_on_file()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 410, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 503, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1846, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1756, in array_cast
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
TypeError: Couldn't cast array of type
struct<c[hn]: string, en: string, zh: string>
to
struct<en: string, zh: string>
```
So the solution of this problem is to change the original array manually:
```
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if 'zh' not in vo:
tmp['zh'] = vo['c[hn]']
else:
tmp['zh'] = vo['zh']
data_list.append(tmp)
array = pa.array(data_list, type=pa.struct([
pa.field('en', pa.string()),
pa.field('zh', pa.string()),
]))
```
Therefore, maybe a correct version of original casia2015 file need to be updated
I just pushed a fix, we'll do a new release of `datasets` soon to include this fix. In the meantime you can use the fixed dataset by passing `revision="main"` to `load_dataset` | [
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https://github.com/huggingface/datasets/issues/4572 | Dataset Viewer issue for mlsum | Thanks for reporting, @lewtun.
After investigation, it seems that the server https://gitlab.lip6.fr does not allow HTTP Range requests.
We are trying to find a workaround... | ### Link
https://huggingface.co/datasets/mlsum/viewer/de/train
### Description
There's seems to be a problem with the download / streaming of this dataset:
```
Server error
Status code: 400
Exception: BadZipFile
Message: File is not a zip file
```
### Owner
No | 25 | Dataset Viewer issue for mlsum
### Link
https://huggingface.co/datasets/mlsum/viewer/de/train
### Description
There's seems to be a problem with the download / streaming of this dataset:
```
Server error
Status code: 400
Exception: BadZipFile
Message: File is not a zip file
```
### Owner
No
Thanks for reporting, @lewtun.
After investigation, it seems that the server https://gitlab.lip6.fr does not allow HTTP Range requests.
We are trying to find a workaround... | [
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] |
https://github.com/huggingface/datasets/issues/4571 | move under the facebook org? | I'm just wondering why we don't have this dataset under:
- the `facebook` namespace
- or the canonical dataset `flores`: why does this only have 2 languages? | ### Link
https://huggingface.co/datasets/gsarti/flores_101
### Description
It seems like streaming isn't supported for this dataset:
```
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://dl.fbaipublicfiles.com/flores101/dataset/flores101_dataset.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
### Owner
No | 27 | move under the facebook org?
### Link
https://huggingface.co/datasets/gsarti/flores_101
### Description
It seems like streaming isn't supported for this dataset:
```
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://dl.fbaipublicfiles.com/flores101/dataset/flores101_dataset.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
### Owner
No
I'm just wondering why we don't have this dataset under:
- the `facebook` namespace
- or the canonical dataset `flores`: why does this only have 2 languages? | [
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https://github.com/huggingface/datasets/issues/4570 | Dataset sharding non-contiguous? | This was silly; I was sure I'd looked for a `contiguous` argument, and was certain there wasn't one the first time I looked :smile:
Sorry about that. | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.
## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
shard = dataset.shard(num_shards=num_shards, index=shard_index)
shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```
## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example
## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| 27 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.
## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
shard = dataset.shard(num_shards=num_shards, index=shard_index)
shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```
## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example
## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
This was silly; I was sure I'd looked for a `contiguous` argument, and was certain there wasn't one the first time I looked :smile:
Sorry about that. | [
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https://github.com/huggingface/datasets/issues/4570 | Dataset sharding non-contiguous? | Hi! You can pass `contiguous=True` to `.shard()` get contiguous shards. More info on this and the default behavior can be found in the [docs](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.shard).
EDIT: Answered as you closed the thread 😄 | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.
## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
shard = dataset.shard(num_shards=num_shards, index=shard_index)
shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```
## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example
## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| 32 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.
## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
shard = dataset.shard(num_shards=num_shards, index=shard_index)
shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```
## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example
## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
Hi! You can pass `contiguous=True` to `.shard()` get contiguous shards. More info on this and the default behavior can be found in the [docs](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.shard).
EDIT: Answered as you closed the thread 😄 | [
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https://github.com/huggingface/datasets/issues/4570 | Dataset sharding non-contiguous? | Hahaha I'm sorry; my excuse is: it's Sunday. (Which makes me all the more grateful for your response :smiley: | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.
## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
shard = dataset.shard(num_shards=num_shards, index=shard_index)
shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```
## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example
## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| 19 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.
## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
shard = dataset.shard(num_shards=num_shards, index=shard_index)
shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```
## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example
## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
Hahaha I'm sorry; my excuse is: it's Sunday. (Which makes me all the more grateful for your response :smiley: | [
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https://github.com/huggingface/datasets/issues/4570 | Dataset sharding non-contiguous? | @mariosasko Sorry for reviving this, but I was curious as to why `contiguous=False` was the default. This might be a personal bias, but I feel that a user would expect the opposite to be the default. :thinking: | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.
## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
shard = dataset.shard(num_shards=num_shards, index=shard_index)
shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```
## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example
## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| 37 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.
## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
shard = dataset.shard(num_shards=num_shards, index=shard_index)
shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```
## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example
## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
@mariosasko Sorry for reviving this, but I was curious as to why `contiguous=False` was the default. This might be a personal bias, but I feel that a user would expect the opposite to be the default. :thinking: | [
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] |
https://github.com/huggingface/datasets/issues/4569 | Dataset Viewer issue for sst2 | Hi @lewtun, thanks for reporting.
I have checked locally and refreshed the preview and it seems working smooth now:
```python
In [8]: ds
Out[8]:
DatasetDict({
train: Dataset({
features: ['idx', 'sentence', 'label'],
num_rows: 67349
})
validation: Dataset({
features: ['idx', 'sentence', 'label'],
num_rows: 872
})
test: Dataset({
features: ['idx', 'sentence', 'label'],
num_rows: 1821
})
})
```
Could you confirm? | ### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No | 57 | Dataset Viewer issue for sst2
### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No
Hi @lewtun, thanks for reporting.
I have checked locally and refreshed the preview and it seems working smooth now:
```python
In [8]: ds
Out[8]:
DatasetDict({
train: Dataset({
features: ['idx', 'sentence', 'label'],
num_rows: 67349
})
validation: Dataset({
features: ['idx', 'sentence', 'label'],
num_rows: 872
})
test: Dataset({
features: ['idx', 'sentence', 'label'],
num_rows: 1821
})
})
```
Could you confirm? | [
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https://github.com/huggingface/datasets/issues/4569 | Dataset Viewer issue for sst2 | Thanks @albertvillanova - it is indeed working now (not sure what caused the error in the first place). Closing this :) | ### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No | 21 | Dataset Viewer issue for sst2
### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No
Thanks @albertvillanova - it is indeed working now (not sure what caused the error in the first place). Closing this :) | [
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] |
https://github.com/huggingface/datasets/issues/4568 | XNLI cache reload is very slow | Hi,
Could you tell us how you are running this code?
I tested on my machine (M1 Mac). And it is running fine both on and off internet.
<img width="1033" alt="Screen Shot 2022-07-03 at 1 32 25 AM" src="https://user-images.githubusercontent.com/8711912/177026364-4ad7cedb-e524-4513-97f7-7961bbb34c90.png">
Tested on both stable and dev version. | ### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet.
```
---------------------------------------------------------------------------
gaierror Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
/opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
71
---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
73 af, socktype, proto, canonname, sa = res
/opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags)
751 addrlist = []
--> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
753 af, socktype, proto, canonname, sa = res
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_33/3594208039.py in <module>
----> 1 load_dataset("xnli", "en")
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1673 revision=revision,
1674 use_auth_token=use_auth_token,
-> 1675 **config_kwargs,
1676 )
1677
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1494 download_mode=download_mode,
1495 data_dir=data_dir,
-> 1496 data_files=data_files,
1497 )
1498
/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1182 download_config=download_config,
1183 download_mode=download_mode,
-> 1184 dynamic_modules_path=dynamic_modules_path,
1185 ).get_module()
1186 elif path.count("/") == 1: # community dataset on the Hub
/opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path)
506 self.dynamic_modules_path = dynamic_modules_path
507 assert self.name.count("/") == 0
--> 508 increase_load_count(name, resource_type="dataset")
509
510 def download_loading_script(self, revision: Optional[str]) -> str:
/opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type)
166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS:
167 try:
--> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset"))
169 except Exception:
170 pass
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries)
93 return http_head(
94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset),
---> 95 max_retries=max_retries,
96 )
97
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
445 allow_redirects=allow_redirects,
446 timeout=timeout,
--> 447 max_retries=max_retries,
448 )
449 return response
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
366 tries += 1
367 try:
--> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
369 success = True
370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
527 }
528 send_kwargs.update(settings)
--> 529 resp = self.send(prep, **send_kwargs)
530
531 return resp
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs)
643
644 # Send the request
--> 645 r = adapter.send(request, **kwargs)
646
647 # Total elapsed time of the request (approximately)
/opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 decode_content=False,
449 retries=self.max_retries,
--> 450 timeout=timeout
451 )
452
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
708 body=body,
709 headers=headers,
--> 710 chunked=chunked,
711 )
712
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
384 # Trigger any extra validation we need to do.
385 try:
--> 386 self._validate_conn(conn)
387 except (SocketTimeout, BaseSSLError) as e:
388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
1038 # Force connect early to allow us to validate the connection.
1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
-> 1040 conn.connect()
1041
1042 if not conn.is_verified:
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self)
356 def connect(self):
357 # Add certificate verification
--> 358 self.sock = conn = self._new_conn()
359 hostname = self.host
360 tls_in_tls = False
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
173 try:
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
177
KeyboardInterrupt:
``` | 46 | XNLI cache reload is very slow
### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet.
```
---------------------------------------------------------------------------
gaierror Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
/opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
71
---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
73 af, socktype, proto, canonname, sa = res
/opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags)
751 addrlist = []
--> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
753 af, socktype, proto, canonname, sa = res
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_33/3594208039.py in <module>
----> 1 load_dataset("xnli", "en")
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1673 revision=revision,
1674 use_auth_token=use_auth_token,
-> 1675 **config_kwargs,
1676 )
1677
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1494 download_mode=download_mode,
1495 data_dir=data_dir,
-> 1496 data_files=data_files,
1497 )
1498
/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1182 download_config=download_config,
1183 download_mode=download_mode,
-> 1184 dynamic_modules_path=dynamic_modules_path,
1185 ).get_module()
1186 elif path.count("/") == 1: # community dataset on the Hub
/opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path)
506 self.dynamic_modules_path = dynamic_modules_path
507 assert self.name.count("/") == 0
--> 508 increase_load_count(name, resource_type="dataset")
509
510 def download_loading_script(self, revision: Optional[str]) -> str:
/opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type)
166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS:
167 try:
--> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset"))
169 except Exception:
170 pass
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries)
93 return http_head(
94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset),
---> 95 max_retries=max_retries,
96 )
97
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
445 allow_redirects=allow_redirects,
446 timeout=timeout,
--> 447 max_retries=max_retries,
448 )
449 return response
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
366 tries += 1
367 try:
--> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
369 success = True
370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
527 }
528 send_kwargs.update(settings)
--> 529 resp = self.send(prep, **send_kwargs)
530
531 return resp
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs)
643
644 # Send the request
--> 645 r = adapter.send(request, **kwargs)
646
647 # Total elapsed time of the request (approximately)
/opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 decode_content=False,
449 retries=self.max_retries,
--> 450 timeout=timeout
451 )
452
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
708 body=body,
709 headers=headers,
--> 710 chunked=chunked,
711 )
712
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
384 # Trigger any extra validation we need to do.
385 try:
--> 386 self._validate_conn(conn)
387 except (SocketTimeout, BaseSSLError) as e:
388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
1038 # Force connect early to allow us to validate the connection.
1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
-> 1040 conn.connect()
1041
1042 if not conn.is_verified:
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self)
356 def connect(self):
357 # Add certificate verification
--> 358 self.sock = conn = self._new_conn()
359 hostname = self.host
360 tls_in_tls = False
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
173 try:
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
177
KeyboardInterrupt:
```
Hi,
Could you tell us how you are running this code?
I tested on my machine (M1 Mac). And it is running fine both on and off internet.
<img width="1033" alt="Screen Shot 2022-07-03 at 1 32 25 AM" src="https://user-images.githubusercontent.com/8711912/177026364-4ad7cedb-e524-4513-97f7-7961bbb34c90.png">
Tested on both stable and dev version. | [
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0.058431729674339294,
0.006329108029603958,
-0.04914678633213043,
0.02681650221347809,
-0.23214468359947205,
0.19745635986328125,
0.2437468022108078,
0.18252934515476227,
-0.08453693985939026,
0.04058806598186493,
0.3258123993873596,
0.08739824593067169,
-0.19389866292476654,
-0.24720534682273865,
0.3096849322319031,
-0.006662540137767792,
0.3069881200790405,
0.3138149380683899,
0.051395490765571594,
-0.17268377542495728,
0.1520869880914688,
-0.15480655431747437,
-0.2129465490579605,
-0.3084140717983246,
0.18944396078586578,
0.3437804579734802,
0.12138383090496063,
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0.15921485424041748,
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0.21469977498054504,
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0.7069826722145081,
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0.14158564805984497,
0.2294345498085022,
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0.0636095479130745,
0.04767075181007385,
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0.07188894599676132,
0.3706740736961365,
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0.33429425954818726,
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0.1873793751001358,
-0.30833736062049866,
0.17692628502845764,
0.1652759611606598,
0.0681203305721283,
-0.10848097503185272,
0.23804612457752228,
0.28737542033195496,
0.0937037467956543,
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-0.06862454861402512,
-0.17766007781028748,
-0.19345737993717194,
-0.13677185773849487
] |
https://github.com/huggingface/datasets/issues/4568 | XNLI cache reload is very slow | Sure, I was running it on a Linux machine.
I found that if I turn the Internet off, it would still try to make a HTTPS call which would slow down the cache loading. If you can't reproduce then we can close the issue. | ### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet.
```
---------------------------------------------------------------------------
gaierror Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
/opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
71
---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
73 af, socktype, proto, canonname, sa = res
/opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags)
751 addrlist = []
--> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
753 af, socktype, proto, canonname, sa = res
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_33/3594208039.py in <module>
----> 1 load_dataset("xnli", "en")
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1673 revision=revision,
1674 use_auth_token=use_auth_token,
-> 1675 **config_kwargs,
1676 )
1677
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1494 download_mode=download_mode,
1495 data_dir=data_dir,
-> 1496 data_files=data_files,
1497 )
1498
/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1182 download_config=download_config,
1183 download_mode=download_mode,
-> 1184 dynamic_modules_path=dynamic_modules_path,
1185 ).get_module()
1186 elif path.count("/") == 1: # community dataset on the Hub
/opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path)
506 self.dynamic_modules_path = dynamic_modules_path
507 assert self.name.count("/") == 0
--> 508 increase_load_count(name, resource_type="dataset")
509
510 def download_loading_script(self, revision: Optional[str]) -> str:
/opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type)
166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS:
167 try:
--> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset"))
169 except Exception:
170 pass
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries)
93 return http_head(
94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset),
---> 95 max_retries=max_retries,
96 )
97
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
445 allow_redirects=allow_redirects,
446 timeout=timeout,
--> 447 max_retries=max_retries,
448 )
449 return response
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
366 tries += 1
367 try:
--> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
369 success = True
370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
527 }
528 send_kwargs.update(settings)
--> 529 resp = self.send(prep, **send_kwargs)
530
531 return resp
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs)
643
644 # Send the request
--> 645 r = adapter.send(request, **kwargs)
646
647 # Total elapsed time of the request (approximately)
/opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 decode_content=False,
449 retries=self.max_retries,
--> 450 timeout=timeout
451 )
452
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
708 body=body,
709 headers=headers,
--> 710 chunked=chunked,
711 )
712
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
384 # Trigger any extra validation we need to do.
385 try:
--> 386 self._validate_conn(conn)
387 except (SocketTimeout, BaseSSLError) as e:
388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
1038 # Force connect early to allow us to validate the connection.
1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
-> 1040 conn.connect()
1041
1042 if not conn.is_verified:
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self)
356 def connect(self):
357 # Add certificate verification
--> 358 self.sock = conn = self._new_conn()
359 hostname = self.host
360 tls_in_tls = False
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
173 try:
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
177
KeyboardInterrupt:
``` | 44 | XNLI cache reload is very slow
### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet.
```
---------------------------------------------------------------------------
gaierror Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
/opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
71
---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
73 af, socktype, proto, canonname, sa = res
/opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags)
751 addrlist = []
--> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
753 af, socktype, proto, canonname, sa = res
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_33/3594208039.py in <module>
----> 1 load_dataset("xnli", "en")
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1673 revision=revision,
1674 use_auth_token=use_auth_token,
-> 1675 **config_kwargs,
1676 )
1677
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1494 download_mode=download_mode,
1495 data_dir=data_dir,
-> 1496 data_files=data_files,
1497 )
1498
/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1182 download_config=download_config,
1183 download_mode=download_mode,
-> 1184 dynamic_modules_path=dynamic_modules_path,
1185 ).get_module()
1186 elif path.count("/") == 1: # community dataset on the Hub
/opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path)
506 self.dynamic_modules_path = dynamic_modules_path
507 assert self.name.count("/") == 0
--> 508 increase_load_count(name, resource_type="dataset")
509
510 def download_loading_script(self, revision: Optional[str]) -> str:
/opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type)
166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS:
167 try:
--> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset"))
169 except Exception:
170 pass
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries)
93 return http_head(
94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset),
---> 95 max_retries=max_retries,
96 )
97
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
445 allow_redirects=allow_redirects,
446 timeout=timeout,
--> 447 max_retries=max_retries,
448 )
449 return response
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
366 tries += 1
367 try:
--> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
369 success = True
370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
527 }
528 send_kwargs.update(settings)
--> 529 resp = self.send(prep, **send_kwargs)
530
531 return resp
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs)
643
644 # Send the request
--> 645 r = adapter.send(request, **kwargs)
646
647 # Total elapsed time of the request (approximately)
/opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 decode_content=False,
449 retries=self.max_retries,
--> 450 timeout=timeout
451 )
452
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
708 body=body,
709 headers=headers,
--> 710 chunked=chunked,
711 )
712
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
384 # Trigger any extra validation we need to do.
385 try:
--> 386 self._validate_conn(conn)
387 except (SocketTimeout, BaseSSLError) as e:
388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
1038 # Force connect early to allow us to validate the connection.
1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
-> 1040 conn.connect()
1041
1042 if not conn.is_verified:
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self)
356 def connect(self):
357 # Add certificate verification
--> 358 self.sock = conn = self._new_conn()
359 hostname = self.host
360 tls_in_tls = False
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
173 try:
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
177
KeyboardInterrupt:
```
Sure, I was running it on a Linux machine.
I found that if I turn the Internet off, it would still try to make a HTTPS call which would slow down the cache loading. If you can't reproduce then we can close the issue. | [
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] |
https://github.com/huggingface/datasets/issues/4568 | XNLI cache reload is very slow | Hi @Muennighoff! You can set the env variable `HF_DATASETS_OFFLINE` to `1` to avoid this behavior in offline mode. More info is available [here](https://huggingface.co/docs/datasets/master/en/loading#offline). | ### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet.
```
---------------------------------------------------------------------------
gaierror Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
/opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
71
---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
73 af, socktype, proto, canonname, sa = res
/opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags)
751 addrlist = []
--> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
753 af, socktype, proto, canonname, sa = res
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_33/3594208039.py in <module>
----> 1 load_dataset("xnli", "en")
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1673 revision=revision,
1674 use_auth_token=use_auth_token,
-> 1675 **config_kwargs,
1676 )
1677
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1494 download_mode=download_mode,
1495 data_dir=data_dir,
-> 1496 data_files=data_files,
1497 )
1498
/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1182 download_config=download_config,
1183 download_mode=download_mode,
-> 1184 dynamic_modules_path=dynamic_modules_path,
1185 ).get_module()
1186 elif path.count("/") == 1: # community dataset on the Hub
/opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path)
506 self.dynamic_modules_path = dynamic_modules_path
507 assert self.name.count("/") == 0
--> 508 increase_load_count(name, resource_type="dataset")
509
510 def download_loading_script(self, revision: Optional[str]) -> str:
/opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type)
166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS:
167 try:
--> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset"))
169 except Exception:
170 pass
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries)
93 return http_head(
94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset),
---> 95 max_retries=max_retries,
96 )
97
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
445 allow_redirects=allow_redirects,
446 timeout=timeout,
--> 447 max_retries=max_retries,
448 )
449 return response
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
366 tries += 1
367 try:
--> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
369 success = True
370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
527 }
528 send_kwargs.update(settings)
--> 529 resp = self.send(prep, **send_kwargs)
530
531 return resp
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs)
643
644 # Send the request
--> 645 r = adapter.send(request, **kwargs)
646
647 # Total elapsed time of the request (approximately)
/opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 decode_content=False,
449 retries=self.max_retries,
--> 450 timeout=timeout
451 )
452
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
708 body=body,
709 headers=headers,
--> 710 chunked=chunked,
711 )
712
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
384 # Trigger any extra validation we need to do.
385 try:
--> 386 self._validate_conn(conn)
387 except (SocketTimeout, BaseSSLError) as e:
388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
1038 # Force connect early to allow us to validate the connection.
1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
-> 1040 conn.connect()
1041
1042 if not conn.is_verified:
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self)
356 def connect(self):
357 # Add certificate verification
--> 358 self.sock = conn = self._new_conn()
359 hostname = self.host
360 tls_in_tls = False
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
173 try:
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
177
KeyboardInterrupt:
``` | 23 | XNLI cache reload is very slow
### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet.
```
---------------------------------------------------------------------------
gaierror Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
/opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
71
---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
73 af, socktype, proto, canonname, sa = res
/opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags)
751 addrlist = []
--> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
753 af, socktype, proto, canonname, sa = res
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_33/3594208039.py in <module>
----> 1 load_dataset("xnli", "en")
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1673 revision=revision,
1674 use_auth_token=use_auth_token,
-> 1675 **config_kwargs,
1676 )
1677
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1494 download_mode=download_mode,
1495 data_dir=data_dir,
-> 1496 data_files=data_files,
1497 )
1498
/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1182 download_config=download_config,
1183 download_mode=download_mode,
-> 1184 dynamic_modules_path=dynamic_modules_path,
1185 ).get_module()
1186 elif path.count("/") == 1: # community dataset on the Hub
/opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path)
506 self.dynamic_modules_path = dynamic_modules_path
507 assert self.name.count("/") == 0
--> 508 increase_load_count(name, resource_type="dataset")
509
510 def download_loading_script(self, revision: Optional[str]) -> str:
/opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type)
166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS:
167 try:
--> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset"))
169 except Exception:
170 pass
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries)
93 return http_head(
94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset),
---> 95 max_retries=max_retries,
96 )
97
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
445 allow_redirects=allow_redirects,
446 timeout=timeout,
--> 447 max_retries=max_retries,
448 )
449 return response
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
366 tries += 1
367 try:
--> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
369 success = True
370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
527 }
528 send_kwargs.update(settings)
--> 529 resp = self.send(prep, **send_kwargs)
530
531 return resp
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs)
643
644 # Send the request
--> 645 r = adapter.send(request, **kwargs)
646
647 # Total elapsed time of the request (approximately)
/opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 decode_content=False,
449 retries=self.max_retries,
--> 450 timeout=timeout
451 )
452
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
708 body=body,
709 headers=headers,
--> 710 chunked=chunked,
711 )
712
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
384 # Trigger any extra validation we need to do.
385 try:
--> 386 self._validate_conn(conn)
387 except (SocketTimeout, BaseSSLError) as e:
388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
1038 # Force connect early to allow us to validate the connection.
1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
-> 1040 conn.connect()
1041
1042 if not conn.is_verified:
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self)
356 def connect(self):
357 # Add certificate verification
--> 358 self.sock = conn = self._new_conn()
359 hostname = self.host
360 tls_in_tls = False
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
173 try:
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
177
KeyboardInterrupt:
```
Hi @Muennighoff! You can set the env variable `HF_DATASETS_OFFLINE` to `1` to avoid this behavior in offline mode. More info is available [here](https://huggingface.co/docs/datasets/master/en/loading#offline). | [
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https://github.com/huggingface/datasets/issues/4566 | Document link #load_dataset_enhancing_performance points to nowhere | Hi! This is indeed the link the docstring should point to. Are you interested in submitting a PR to fix this? | ## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dataset_enhancing_performance) link [here](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.load_from_disk.keep_in_memory) points to nowhere, I guess it should point to https://huggingface.co/docs/datasets/v2.3.2/en/cache#improve-performance?
| 21 | Document link #load_dataset_enhancing_performance points to nowhere
## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dataset_enhancing_performance) link [here](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.load_from_disk.keep_in_memory) points to nowhere, I guess it should point to https://huggingface.co/docs/datasets/v2.3.2/en/cache#improve-performance?
Hi! This is indeed the link the docstring should point to. Are you interested in submitting a PR to fix this? | [
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] |
https://github.com/huggingface/datasets/issues/4566 | Document link #load_dataset_enhancing_performance points to nowhere | https://github.com/huggingface/datasets/blame/master/docs/source/cache.mdx#L93
There seems already an anchor here. Somehow it doesn't work. I am not very familiar with how this online documentation works. | ## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dataset_enhancing_performance) link [here](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.load_from_disk.keep_in_memory) points to nowhere, I guess it should point to https://huggingface.co/docs/datasets/v2.3.2/en/cache#improve-performance?
| 22 | Document link #load_dataset_enhancing_performance points to nowhere
## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dataset_enhancing_performance) link [here](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.load_from_disk.keep_in_memory) points to nowhere, I guess it should point to https://huggingface.co/docs/datasets/v2.3.2/en/cache#improve-performance?
https://github.com/huggingface/datasets/blame/master/docs/source/cache.mdx#L93
There seems already an anchor here. Somehow it doesn't work. I am not very familiar with how this online documentation works. | [
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https://github.com/huggingface/datasets/issues/4562 | Dataset Viewer issue for allocine | Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content *sequentially* (no random access). | ### Link
https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attribute 'readable'
```
### Owner
No | 38 | Dataset Viewer issue for allocine
### Link
https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attribute 'readable'
```
### Owner
No
Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content *sequentially* (no random access). | [
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https://github.com/huggingface/datasets/issues/4562 | Dataset Viewer issue for allocine | > Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content _sequentially_ (no random access).
Ah thanks for the clarification! I'll look out for this next time and implement the fix myself :) | ### Link
https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attribute 'readable'
```
### Owner
No | 57 | Dataset Viewer issue for allocine
### Link
https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attribute 'readable'
```
### Owner
No
> Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content _sequentially_ (no random access).
Ah thanks for the clarification! I'll look out for this next time and implement the fix myself :) | [
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] |
https://github.com/huggingface/datasets/issues/4550 | imdb source error | Thanks for reporting, @Muhtasham.
Indeed IMDB dataset is not accessible from yesterday, because the data is hosted on the data owners servers at Stanford (http://ai.stanford.edu/) and these are down due to a power outage originated by a fire: https://twitter.com/StanfordAILab/status/1539472302399623170?s=20&t=1HU1hrtaXprtn14U61P55w
As a temporary workaroud, you can load the IMDB dataset with this tweak:
```python
ds = load_dataset("imdb", revision="tmp-fix-imdb")
```
| ## Describe the bug
imdb dataset not loading
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imdb")
```
## Expected results
## Actual results
```bash
06/23/2022 14:45:18 - INFO - datasets.builder - Dataset not on Hf google storage. Downloading and preparing it from source
06/23/2022 14:46:34 - INFO - datasets.utils.file_utils - HEAD request to http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz timed out, retrying... [1.0]
.....
ConnectionError: Couldn't reach http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz (ConnectTimeout(MaxRetryError("HTTPConnectionPool(host='ai.stanford.edu', port=80): Max retries exceeded with url: /~amaas/data/sentiment/aclImdb_v1.tar.gz (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x7f2d750cf690>, 'Connection to ai.stanford.edu timed out. (connect timeout=100)'))")))
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
| 58 | imdb source error
## Describe the bug
imdb dataset not loading
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imdb")
```
## Expected results
## Actual results
```bash
06/23/2022 14:45:18 - INFO - datasets.builder - Dataset not on Hf google storage. Downloading and preparing it from source
06/23/2022 14:46:34 - INFO - datasets.utils.file_utils - HEAD request to http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz timed out, retrying... [1.0]
.....
ConnectionError: Couldn't reach http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz (ConnectTimeout(MaxRetryError("HTTPConnectionPool(host='ai.stanford.edu', port=80): Max retries exceeded with url: /~amaas/data/sentiment/aclImdb_v1.tar.gz (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x7f2d750cf690>, 'Connection to ai.stanford.edu timed out. (connect timeout=100)'))")))
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
Thanks for reporting, @Muhtasham.
Indeed IMDB dataset is not accessible from yesterday, because the data is hosted on the data owners servers at Stanford (http://ai.stanford.edu/) and these are down due to a power outage originated by a fire: https://twitter.com/StanfordAILab/status/1539472302399623170?s=20&t=1HU1hrtaXprtn14U61P55w
As a temporary workaroud, you can load the IMDB dataset with this tweak:
```python
ds = load_dataset("imdb", revision="tmp-fix-imdb")
```
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https://github.com/huggingface/datasets/issues/4548 | Metadata.jsonl for Imagefolder is ignored if it's in a parent directory to the splits directories/do not have "{split}_" prefix | I agree it would be nice to support this. It doesn't fit really well in the current data_files.py, where files of each splits are separated in different folder though, maybe we have to modify a bit the logic here.
One idea would be to extend `get_patterns_in_dataset_repository` and `get_patterns_locally` to additionally check for `metadata.json`, but feel free to comment if you have better ideas (I feel like we're reaching the limits of what the current implementation IMO, so we could think of a different way of resolving the data files if necessary) | If data contains a single `metadata.jsonl` file for several splits, it won't be included in a dataset's `data_files` and therefore ignored.
This happens when a directory is structured like as follows:
```
train/
file_1.jpg
file_2.jpg
test/
file_3.jpg
file_4.jpg
metadata.jsonl
```
or like as follows:
```
train_file_1.jpg
train_file_2.jpg
test_file_3.jpg
test_file_4.jpg
metadata.jsonl
```
The same for HF repos.
because it's ignored by the patterns [here](https://github.com/huggingface/datasets/blob/master/src/datasets/data_files.py#L29)
@lhoestq @mariosasko Do you think it's better to add this functionality in `data_files.py` or just specifically in imagefolder/audiofolder code? In `data_files.py` would me more general but I don't know if there are any other cases when that might be needed.
| 91 | Metadata.jsonl for Imagefolder is ignored if it's in a parent directory to the splits directories/do not have "{split}_" prefix
If data contains a single `metadata.jsonl` file for several splits, it won't be included in a dataset's `data_files` and therefore ignored.
This happens when a directory is structured like as follows:
```
train/
file_1.jpg
file_2.jpg
test/
file_3.jpg
file_4.jpg
metadata.jsonl
```
or like as follows:
```
train_file_1.jpg
train_file_2.jpg
test_file_3.jpg
test_file_4.jpg
metadata.jsonl
```
The same for HF repos.
because it's ignored by the patterns [here](https://github.com/huggingface/datasets/blob/master/src/datasets/data_files.py#L29)
@lhoestq @mariosasko Do you think it's better to add this functionality in `data_files.py` or just specifically in imagefolder/audiofolder code? In `data_files.py` would me more general but I don't know if there are any other cases when that might be needed.
I agree it would be nice to support this. It doesn't fit really well in the current data_files.py, where files of each splits are separated in different folder though, maybe we have to modify a bit the logic here.
One idea would be to extend `get_patterns_in_dataset_repository` and `get_patterns_locally` to additionally check for `metadata.json`, but feel free to comment if you have better ideas (I feel like we're reaching the limits of what the current implementation IMO, so we could think of a different way of resolving the data files if necessary) | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | This has so much potential to be great! Also I think you tagged some poor random dude on the internet whose name is also Joao, lol, edited that for you! | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 30 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
This has so much potential to be great! Also I think you tagged some poor random dude on the internet whose name is also Joao, lol, edited that for you! | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | Noted and I will look into the thread in detail tomorrow once I log back in. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 16 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
Noted and I will look into the thread in detail tomorrow once I log back in. | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | @lhoestq I have used TFRecords with `tf.data` for both vision and text and I can say that they are quite performant. I haven't worked with Feather yet as similarly as I have with TFRecords. If you haven't started the benchmarking script yet, I can prepare a Colab notebook that loads Feather files, converts them into a `tf.data` pipeline, and does some basic preprocessing.
But in my limited understanding, Feather might be better suited for CSV files. Not yet sure if it's good for modalities like images. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 86 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
@lhoestq I have used TFRecords with `tf.data` for both vision and text and I can say that they are quite performant. I haven't worked with Feather yet as similarly as I have with TFRecords. If you haven't started the benchmarking script yet, I can prepare a Colab notebook that loads Feather files, converts them into a `tf.data` pipeline, and does some basic preprocessing.
But in my limited understanding, Feather might be better suited for CSV files. Not yet sure if it's good for modalities like images. | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | > Not yet sure if it's good for modalities like images.
We store images pretty much the same way as tensorflow_datasets (i.e. storing the encoded image bytes, or a path to the local image, so that the image can be decoded on-the-fly), so as long as we use something similar as TFDS for image decoding it should be ok | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 59 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
> Not yet sure if it's good for modalities like images.
We store images pretty much the same way as tensorflow_datasets (i.e. storing the encoded image bytes, or a path to the local image, so that the image can be decoded on-the-fly), so as long as we use something similar as TFDS for image decoding it should be ok | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly? But it introduces an I/O redundancy of having to read the images every time.
With caching it could be somewhat mitigated but it's not a good solution for bigger image datasets. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 53 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly? But it introduces an I/O redundancy of having to read the images every time.
With caching it could be somewhat mitigated but it's not a good solution for bigger image datasets. | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | > So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly?
hopefully yes :)
I double-checked the TFDS source code and they always save the bytes actually, not the path. Anyway we'll see if we run into issues or not (as a first step we can require the bytes to be in the feather file) | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 67 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
> So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly?
hopefully yes :)
I double-checked the TFDS source code and they always save the bytes actually, not the path. Anyway we'll see if we run into issues or not (as a first step we can require the bytes to be in the feather file) | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | Yes. For images, TFDS actually prepares TFRecords first for encoding and then reuses them for every subsequent call. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 18 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
Yes. For images, TFDS actually prepares TFRecords first for encoding and then reuses them for every subsequent call. | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | @lhoestq @Rocketknight1 I worked on [this PoC](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59) that
* Creates Feather files from a medium resolution dataset (`tf_flowers`).
* Explores different options with TensorFlow IO to load the Feather files.
I haven't benchmarked those different options yet. There's also a gotcha that I have noted in the PoC. I hope it gets us started but I'm sorry if this is redundant. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 61 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
@lhoestq @Rocketknight1 I worked on [this PoC](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59) that
* Creates Feather files from a medium resolution dataset (`tf_flowers`).
* Explores different options with TensorFlow IO to load the Feather files.
I haven't benchmarked those different options yet. There's also a gotcha that I have noted in the PoC. I hope it gets us started but I'm sorry if this is redundant. | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | Cool thanks ! If I understand correctly in your PoC you store the flattened array of pixels in the feather file. This will take a lot of disk space.
Maybe we could just save the encoded bytes and let users apply a `map` to decode/transform them into the format they need for training ? Users can use tf.image to do so for example | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 63 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
Cool thanks ! If I understand correctly in your PoC you store the flattened array of pixels in the feather file. This will take a lot of disk space.
Maybe we could just save the encoded bytes and let users apply a `map` to decode/transform them into the format they need for training ? Users can use tf.image to do so for example | [
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] |
https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | @lhoestq this is what I tried:
```py
def read_image(path):
with open(path, "rb") as f:
return f.read()
total_images_written = 0
for step in tqdm.tnrange(int(math.ceil(len(image_paths) / batch_size))):
batch_image_paths = image_paths[step * batch_size : (step + 1) * batch_size]
batch_image_labels = all_integer_labels[step * batch_size : (step + 1) * batch_size]
data = [read_image(path) for path in batch_image_paths]
table = pa.Table.from_arrays([data, batch_image_labels], ["data", "labels"])
write_feather(table, f"/tmp/flowers_feather_{step}.feather", chunksize=chunk_size)
total_images_written += len(batch_image_paths)
print(f"Total images written: {total_images_written}.")
del data
```
I got the feather files done (no resizing required as you can see):
```sh
ls -lh /tmp/*.feather
-rw-r--r-- 1 sayakpaul wheel 64M Jun 24 09:28 /tmp/flowers_feather_0.feather
-rw-r--r-- 1 sayakpaul wheel 59M Jun 24 09:28 /tmp/flowers_feather_1.feather
-rw-r--r-- 1 sayakpaul wheel 51M Jun 24 09:28 /tmp/flowers_feather_2.feather
-rw-r--r-- 1 sayakpaul wheel 45M Jun 24 09:28 /tmp/flowers_feather_3.feather
```
Now there seems to be a problem with `tfio.arrow`:
```py
import tensorflow_io.arrow as arrow_io
dataset = arrow_io.ArrowFeatherDataset(
["/tmp/flowers_feather_0.feather"],
columns=(0, 1),
output_types=(tf.string, tf.int64),
output_shapes=([], []),
batch_mode="auto",
)
print(dataset.element_spec)
```
Prints:
```
(TensorSpec(shape=(None,), dtype=tf.string, name=None),
TensorSpec(shape=(None,), dtype=tf.int64, name=None))
```
But when I do `sample = next(iter(dataset))` it goes into:
```py
InternalError Traceback (most recent call last)
Input In [30], in <cell line: 1>()
----> 1 sample = next(iter(dataset))
File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py:766, in OwnedIterator.__next__(self)
764 def __next__(self):
765 try:
--> 766 return self._next_internal()
767 except errors.OutOfRangeError:
768 raise StopIteration
File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py:749, in OwnedIterator._next_internal(self)
746 # TODO(b/77291417): This runs in sync mode as iterators use an error status
747 # to communicate that there is no more data to iterate over.
748 with context.execution_mode(context.SYNC):
--> 749 ret = gen_dataset_ops.iterator_get_next(
750 self._iterator_resource,
751 output_types=self._flat_output_types,
752 output_shapes=self._flat_output_shapes)
754 try:
755 # Fast path for the case `self._structure` is not a nested structure.
756 return self._element_spec._from_compatible_tensor_list(ret) # pylint: disable=protected-access
File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/ops/gen_dataset_ops.py:3017, in iterator_get_next(iterator, output_types, output_shapes, name)
3015 return _result
3016 except _core._NotOkStatusException as e:
-> 3017 _ops.raise_from_not_ok_status(e, name)
3018 except _core._FallbackException:
3019 pass
File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:7164, in raise_from_not_ok_status(e, name)
7162 def raise_from_not_ok_status(e, name):
7163 e.message += (" name: " + name if name is not None else "")
-> 7164 raise core._status_to_exception(e) from None
InternalError: Invalid: INVALID_ARGUMENT: arrow data type 0x7ff9899d8038 is not supported: Type error: Arrow data type is not supported [Op:IteratorGetNext]
```
Some additional notes:
* I can actually decode an image encoded with `read_image()` (shown earlier):
```py
sample_image_path = image_paths[0]
encoded_image = read_image(sample_image_path)
image = tf.image.decode_png(encoded_image, 3)
print(image.shape)
```
* If the above `tf.data.Dataset` object would have succeeded my plan was to just map the decoder like so:
```py
autotune = tf.data.AUTOTUNE
dataset = dataset.map(lambda x, y: (tf.image.decode_png(x, 3), y), num_parallel_calls=autotune)
``` | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 413 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
@lhoestq this is what I tried:
```py
def read_image(path):
with open(path, "rb") as f:
return f.read()
total_images_written = 0
for step in tqdm.tnrange(int(math.ceil(len(image_paths) / batch_size))):
batch_image_paths = image_paths[step * batch_size : (step + 1) * batch_size]
batch_image_labels = all_integer_labels[step * batch_size : (step + 1) * batch_size]
data = [read_image(path) for path in batch_image_paths]
table = pa.Table.from_arrays([data, batch_image_labels], ["data", "labels"])
write_feather(table, f"/tmp/flowers_feather_{step}.feather", chunksize=chunk_size)
total_images_written += len(batch_image_paths)
print(f"Total images written: {total_images_written}.")
del data
```
I got the feather files done (no resizing required as you can see):
```sh
ls -lh /tmp/*.feather
-rw-r--r-- 1 sayakpaul wheel 64M Jun 24 09:28 /tmp/flowers_feather_0.feather
-rw-r--r-- 1 sayakpaul wheel 59M Jun 24 09:28 /tmp/flowers_feather_1.feather
-rw-r--r-- 1 sayakpaul wheel 51M Jun 24 09:28 /tmp/flowers_feather_2.feather
-rw-r--r-- 1 sayakpaul wheel 45M Jun 24 09:28 /tmp/flowers_feather_3.feather
```
Now there seems to be a problem with `tfio.arrow`:
```py
import tensorflow_io.arrow as arrow_io
dataset = arrow_io.ArrowFeatherDataset(
["/tmp/flowers_feather_0.feather"],
columns=(0, 1),
output_types=(tf.string, tf.int64),
output_shapes=([], []),
batch_mode="auto",
)
print(dataset.element_spec)
```
Prints:
```
(TensorSpec(shape=(None,), dtype=tf.string, name=None),
TensorSpec(shape=(None,), dtype=tf.int64, name=None))
```
But when I do `sample = next(iter(dataset))` it goes into:
```py
InternalError Traceback (most recent call last)
Input In [30], in <cell line: 1>()
----> 1 sample = next(iter(dataset))
File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py:766, in OwnedIterator.__next__(self)
764 def __next__(self):
765 try:
--> 766 return self._next_internal()
767 except errors.OutOfRangeError:
768 raise StopIteration
File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py:749, in OwnedIterator._next_internal(self)
746 # TODO(b/77291417): This runs in sync mode as iterators use an error status
747 # to communicate that there is no more data to iterate over.
748 with context.execution_mode(context.SYNC):
--> 749 ret = gen_dataset_ops.iterator_get_next(
750 self._iterator_resource,
751 output_types=self._flat_output_types,
752 output_shapes=self._flat_output_shapes)
754 try:
755 # Fast path for the case `self._structure` is not a nested structure.
756 return self._element_spec._from_compatible_tensor_list(ret) # pylint: disable=protected-access
File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/ops/gen_dataset_ops.py:3017, in iterator_get_next(iterator, output_types, output_shapes, name)
3015 return _result
3016 except _core._NotOkStatusException as e:
-> 3017 _ops.raise_from_not_ok_status(e, name)
3018 except _core._FallbackException:
3019 pass
File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:7164, in raise_from_not_ok_status(e, name)
7162 def raise_from_not_ok_status(e, name):
7163 e.message += (" name: " + name if name is not None else "")
-> 7164 raise core._status_to_exception(e) from None
InternalError: Invalid: INVALID_ARGUMENT: arrow data type 0x7ff9899d8038 is not supported: Type error: Arrow data type is not supported [Op:IteratorGetNext]
```
Some additional notes:
* I can actually decode an image encoded with `read_image()` (shown earlier):
```py
sample_image_path = image_paths[0]
encoded_image = read_image(sample_image_path)
image = tf.image.decode_png(encoded_image, 3)
print(image.shape)
```
* If the above `tf.data.Dataset` object would have succeeded my plan was to just map the decoder like so:
```py
autotune = tf.data.AUTOTUNE
dataset = dataset.map(lambda x, y: (tf.image.decode_png(x, 3), y), num_parallel_calls=autotune)
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | @lhoestq I think I was able to make it work in the way you were envisioning. Here's the PoC:
https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb
Some details:
* I am currently serializing the images as strings with `base64`). In comparison to the flattened arrays as before, the size of the individual feather files has reduced (144 MB -> 85 MB, largest).
* When decoding, I am first decoding the base64 string and then decoding that string (with `tf.io.decode_base64`) as an image with `tf.image.decode_png()`.
* The entire workflow (from generating the Feather files to loading them and preparing the batched `tf.data` pipeline) involves the following libraries: `pyarraow`, `tensorflow-io`, and `tensorflow`.
Cc: @Rocketknight1 @gante | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 107 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
@lhoestq I think I was able to make it work in the way you were envisioning. Here's the PoC:
https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb
Some details:
* I am currently serializing the images as strings with `base64`). In comparison to the flattened arrays as before, the size of the individual feather files has reduced (144 MB -> 85 MB, largest).
* When decoding, I am first decoding the base64 string and then decoding that string (with `tf.io.decode_base64`) as an image with `tf.image.decode_png()`.
* The entire workflow (from generating the Feather files to loading them and preparing the batched `tf.data` pipeline) involves the following libraries: `pyarraow`, `tensorflow-io`, and `tensorflow`.
Cc: @Rocketknight1 @gante | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | Cool thanks ! Too bad the Arrow binary type doesn't seem to be supported in `arrow_io.ArrowFeatherDataset` :/ We would also need it to support Arrow struct type. Indeed images in `datasets` are represented using an Arrow type
```python
pa.struct({"path": pa.string(), "bytes": pa.binary()})
```
not sure yet how hard it is to support this though.
Changing the typing on our side would create concerning breaking changes, that's why it would be awesome if it could work using these types | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 78 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
Cool thanks ! Too bad the Arrow binary type doesn't seem to be supported in `arrow_io.ArrowFeatherDataset` :/ We would also need it to support Arrow struct type. Indeed images in `datasets` are represented using an Arrow type
```python
pa.struct({"path": pa.string(), "bytes": pa.binary()})
```
not sure yet how hard it is to support this though.
Changing the typing on our side would create concerning breaking changes, that's why it would be awesome if it could work using these types | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | If the ArrowFeatherDataset doesn't yet support it, I guess our hands are a bit tied at the moment.
IIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?
```
pa.struct({"path": pa.string(), "bytes": pa.binary()})
```
In that case, `pa.binary()` isn't yet supported. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 52 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
If the ArrowFeatherDataset doesn't yet support it, I guess our hands are a bit tied at the moment.
IIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?
```
pa.struct({"path": pa.string(), "bytes": pa.binary()})
```
In that case, `pa.binary()` isn't yet supported. | [
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] |
https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | > IIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?
>
> pa.struct({"path": pa.string(), "bytes": pa.binary()})
Yea because that's the data format we're using. If we were to use base64, then we would have to process the full dataset to convert it, which can take some time. Converting to TFRecords would be simpler than converting to base64 in Feather files.
Maybe it would take too much time to be worth exploring, but according to https://github.com/tensorflow/io/issues/1361#issuecomment-819029002 it's possible to add support for binary type in ArrowFeatherDataset. What do you think ? Any other alternative in mind ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 108 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
> IIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?
>
> pa.struct({"path": pa.string(), "bytes": pa.binary()})
Yea because that's the data format we're using. If we were to use base64, then we would have to process the full dataset to convert it, which can take some time. Converting to TFRecords would be simpler than converting to base64 in Feather files.
Maybe it would take too much time to be worth exploring, but according to https://github.com/tensorflow/io/issues/1361#issuecomment-819029002 it's possible to add support for binary type in ArrowFeatherDataset. What do you think ? Any other alternative in mind ? | [
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] |
https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | > Maybe it would take too much time to be worth exploring, but according to https://github.com/tensorflow/io/issues/1361#issuecomment-819029002 it's possible to add support for binary type in ArrowFeatherDataset.
Should be possible as per the comment but there hasn't been any progress and it's been more than a year.
> If we were to use base64, then we would have to process the full dataset to convert it, which can take some time.
I don't understand this. I would think TFRecords would also need something similar but I need the context you're coming from.
> What do you think ? Any other alternative in mind ?
TFRecords since the TensorFlow ecosystem has developed good support for it over the years. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 117 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
> Maybe it would take too much time to be worth exploring, but according to https://github.com/tensorflow/io/issues/1361#issuecomment-819029002 it's possible to add support for binary type in ArrowFeatherDataset.
Should be possible as per the comment but there hasn't been any progress and it's been more than a year.
> If we were to use base64, then we would have to process the full dataset to convert it, which can take some time.
I don't understand this. I would think TFRecords would also need something similar but I need the context you're coming from.
> What do you think ? Any other alternative in mind ?
TFRecords since the TensorFlow ecosystem has developed good support for it over the years. | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | > I don't understand this. I would think TFRecords would also need something similar but I need the context you're coming from.
Users already have a copy of the dataset in Arrow format (we can change this to Feather). So to load the Arrow/feather files to a TF dataset we need TF IO or something like that. Otherwise the user has to convert all the files from Arrow to TFRecords to use TF data efficiently. But the conversion needs resources: CPU, disk, time. Converting the images to base64 require the same sort of resources.
So the issue we're trying to tackle is how to load the Arrow data in TF without having to convert anything ^^ | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 116 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
> I don't understand this. I would think TFRecords would also need something similar but I need the context you're coming from.
Users already have a copy of the dataset in Arrow format (we can change this to Feather). So to load the Arrow/feather files to a TF dataset we need TF IO or something like that. Otherwise the user has to convert all the files from Arrow to TFRecords to use TF data efficiently. But the conversion needs resources: CPU, disk, time. Converting the images to base64 require the same sort of resources.
So the issue we're trying to tackle is how to load the Arrow data in TF without having to convert anything ^^ | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | Yeah, it looks like in its current state the tfio support for `Feather` is incomplete, so we'd end up having to write a lot of it, or do a conversion that defeats the whole point (because if we're going to convert the whole dataset we might as well convert to `TFRecord`). | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 51 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
Yeah, it looks like in its current state the tfio support for `Feather` is incomplete, so we'd end up having to write a lot of it, or do a conversion that defeats the whole point (because if we're going to convert the whole dataset we might as well convert to `TFRecord`). | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | @lhoestq Although I think this is a dead-end for now unfortunately, because of the limitations at TF's end, we could still explore automatic conversion to TFRecord, or I could dive into refining `to_tf_dataset()` to yield unbatched samples and/or load samples with multiprocessing to improve throughput. Do you have any preferences there? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 51 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
@lhoestq Although I think this is a dead-end for now unfortunately, because of the limitations at TF's end, we could still explore automatic conversion to TFRecord, or I could dive into refining `to_tf_dataset()` to yield unbatched samples and/or load samples with multiprocessing to improve throughput. Do you have any preferences there? | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | > @lhoestq Although I think this is a dead-end for now unfortunately, because of the limitations at TF's end, we could still explore automatic conversion to TFRecord, or I could dive into refining `to_tf_dataset()` to yield unbatched samples and/or load samples with multiprocessing to improve throughput. Do you have any preferences there?
Happy to take part there @Rocketknight1. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 58 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
> @lhoestq Although I think this is a dead-end for now unfortunately, because of the limitations at TF's end, we could still explore automatic conversion to TFRecord, or I could dive into refining `to_tf_dataset()` to yield unbatched samples and/or load samples with multiprocessing to improve throughput. Do you have any preferences there?
Happy to take part there @Rocketknight1. | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | If `to_tf_dataset` can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 22 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
If `to_tf_dataset` can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ? | [
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] |
https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | > If to_tf_dataset can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ?
Sort of! A `tf.data.Dataset` is more like an iterator, and does not support sample indexing. `to_tf_dataset()` creates an iterator, but to convert that to `TFRecord`, the user would have to iterate over the whole thing and manually save the stream of samples to files. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 67 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
> If to_tf_dataset can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ?
Sort of! A `tf.data.Dataset` is more like an iterator, and does not support sample indexing. `to_tf_dataset()` creates an iterator, but to convert that to `TFRecord`, the user would have to iterate over the whole thing and manually save the stream of samples to files. | [
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] |
https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | Someone would like to try to dive into tfio to fix this ? Sounds like a good opportunity to learn what are the best ways to load a dataset for TF, and also the connections between Arrow and TF.
If we can at least have the Arrow `binary` type working for TF that would be awesome already (issue https://github.com/tensorflow/io/issues/1361)
also cc @nateraw in case you'd be interested ;) | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 68 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
Someone would like to try to dive into tfio to fix this ? Sounds like a good opportunity to learn what are the best ways to load a dataset for TF, and also the connections between Arrow and TF.
If we can at least have the Arrow `binary` type working for TF that would be awesome already (issue https://github.com/tensorflow/io/issues/1361)
also cc @nateraw in case you'd be interested ;) | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | > Sounds like a good opportunity to learn what are the best ways to load a dataset for TF
The recommended way would likely be a combination of TFRecords and `tf.data`.
Exploring the connection between Arrow and TensorFlow is definitely worth pursuing though. But I am not sure about the implications of storing images in a format supported by Arrow. I guess we'll know more once we have at least figured out the support for `binary` type for TFIO. I will spend some time on it and keep this thread updated. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 91 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
> Sounds like a good opportunity to learn what are the best ways to load a dataset for TF
The recommended way would likely be a combination of TFRecords and `tf.data`.
Exploring the connection between Arrow and TensorFlow is definitely worth pursuing though. But I am not sure about the implications of storing images in a format supported by Arrow. I guess we'll know more once we have at least figured out the support for `binary` type for TFIO. I will spend some time on it and keep this thread updated. | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | I am currently working on a fine-tuning notebook for the TFSegFormer model (Semantic Segmentation). The resolution is high for both the input images and the labels - (512, 512, 3). Here's the [Colab Notebook](https://colab.research.google.com/drive/1jAtR7Z0lYX6m6JsDI5VByh5vFaNhHIbP?usp=sharing) (it's a WIP so please bear that in mind).
I think the current implementation of `to_tf_dataset()` does create a bottleneck here since the GPU utilization is quite low. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 62 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
I am currently working on a fine-tuning notebook for the TFSegFormer model (Semantic Segmentation). The resolution is high for both the input images and the labels - (512, 512, 3). Here's the [Colab Notebook](https://colab.research.google.com/drive/1jAtR7Z0lYX6m6JsDI5VByh5vFaNhHIbP?usp=sharing) (it's a WIP so please bear that in mind).
I think the current implementation of `to_tf_dataset()` does create a bottleneck here since the GPU utilization is quite low. | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | Here's a notebook showing the performance difference: https://colab.research.google.com/gist/sayakpaul/d7ca67c90beb47e354942c9d8c0bd8ef/scratchpad.ipynb.
Note that I acknowledge that it's not an apples-to-apples comparison in many aspects (the dataset isn't the same, data serialization format isn't the same, etc.) but this is the best I could do. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 41 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
Here's a notebook showing the performance difference: https://colab.research.google.com/gist/sayakpaul/d7ca67c90beb47e354942c9d8c0bd8ef/scratchpad.ipynb.
Note that I acknowledge that it's not an apples-to-apples comparison in many aspects (the dataset isn't the same, data serialization format isn't the same, etc.) but this is the best I could do. | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | Thanks ! I think the speed difference can be partly explained: you use ds.shuffle in your dataset, which is an exact shuffling (compared to TFDS which does buffer shuffling): it slows down query time by 2x to 10x since it has to play with data that are not contiguous.
The rest of the speed difference seems to be caused by image decoding (from 330µs/image to 30ms/image) | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 66 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
Thanks ! I think the speed difference can be partly explained: you use ds.shuffle in your dataset, which is an exact shuffling (compared to TFDS which does buffer shuffling): it slows down query time by 2x to 10x since it has to play with data that are not contiguous.
The rest of the speed difference seems to be caused by image decoding (from 330µs/image to 30ms/image) | [
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https://github.com/huggingface/datasets/issues/4542 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | Fair enough. Can do one without shuffling too. But it's an important one to consider I guess. | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data | 17 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
Fair enough. Can do one without shuffling too. But it's an important one to consider I guess. | [
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https://github.com/huggingface/datasets/issues/4540 | Avoid splitting by` .py` for the file. | Hi @espoirMur, thanks for reporting.
You are right: that code line could be improved and made more generically valid.
On the other hand, I would suggest using `os.path.splitext` instead.
Are you willing to open a PR? :) | https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272
Hello,
Thanks you for this library .
I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code to my home directory.
Step to reproduce.
- If you have a home folder which ends with `.py`
- load a module with a local folder
`qa_dataset = load_dataset("src/data/build_qa_dataset.py")`
it is failed
A possible workaround would be to use pathlib at the mentioned line
` meta_path = Path(importable_local_file).parent.joinpath("metadata.json")` this can alivate the issue .
Let me what are your thought on this and I can try to fix it by A PR.
| 37 | Avoid splitting by` .py` for the file.
https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272
Hello,
Thanks you for this library .
I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code to my home directory.
Step to reproduce.
- If you have a home folder which ends with `.py`
- load a module with a local folder
`qa_dataset = load_dataset("src/data/build_qa_dataset.py")`
it is failed
A possible workaround would be to use pathlib at the mentioned line
` meta_path = Path(importable_local_file).parent.joinpath("metadata.json")` this can alivate the issue .
Let me what are your thought on this and I can try to fix it by A PR.
Hi @espoirMur, thanks for reporting.
You are right: that code line could be improved and made more generically valid.
On the other hand, I would suggest using `os.path.splitext` instead.
Are you willing to open a PR? :) | [
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] |
https://github.com/huggingface/datasets/issues/4538 | Dataset Viewer issue for Pile of Law | Thanks so much for adding the docs. I was able to successfully hide the viewer using the
```
viewer: false
```
flag in the README.md of the dataset. I'm closing the issue because this is resolved. Thanks again! | ### Link
https://huggingface.co/datasets/pile-of-law/pile-of-law
### Description
Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information?
Thanks so much!
### Owner
Yes | 38 | Dataset Viewer issue for Pile of Law
### Link
https://huggingface.co/datasets/pile-of-law/pile-of-law
### Description
Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information?
Thanks so much!
### Owner
Yes
Thanks so much for adding the docs. I was able to successfully hide the viewer using the
```
viewer: false
```
flag in the README.md of the dataset. I'm closing the issue because this is resolved. Thanks again! | [
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https://github.com/huggingface/datasets/issues/4538 | Dataset Viewer issue for Pile of Law | Just for the record:
- the doc
<img width="1430" alt="Capture d’écran 2022-06-27 à 09 29 27" src="https://user-images.githubusercontent.com/1676121/175884089-bca6c0d5-6387-473e-98ca-86a910ede4bd.png">
- the dataset main page
<img width="1134" alt="Capture d’écran 2022-06-27 à 09 29 05" src="https://user-images.githubusercontent.com/1676121/175884152-5f285bf0-3471-45de-927a-e141b00ebb33.png">
- the dataset viewer page
<img width="567" alt="Capture d’écran 2022-06-27 à 09 29 16" src="https://user-images.githubusercontent.com/1676121/175884191-ab6a297b-1c11-417e-bbde-0b7623278a79.png">
| ### Link
https://huggingface.co/datasets/pile-of-law/pile-of-law
### Description
Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information?
Thanks so much!
### Owner
Yes | 47 | Dataset Viewer issue for Pile of Law
### Link
https://huggingface.co/datasets/pile-of-law/pile-of-law
### Description
Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information?
Thanks so much!
### Owner
Yes
Just for the record:
- the doc
<img width="1430" alt="Capture d’écran 2022-06-27 à 09 29 27" src="https://user-images.githubusercontent.com/1676121/175884089-bca6c0d5-6387-473e-98ca-86a910ede4bd.png">
- the dataset main page
<img width="1134" alt="Capture d’écran 2022-06-27 à 09 29 05" src="https://user-images.githubusercontent.com/1676121/175884152-5f285bf0-3471-45de-927a-e141b00ebb33.png">
- the dataset viewer page
<img width="567" alt="Capture d’écran 2022-06-27 à 09 29 16" src="https://user-images.githubusercontent.com/1676121/175884191-ab6a297b-1c11-417e-bbde-0b7623278a79.png">
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https://github.com/huggingface/datasets/issues/4531 | Dataset Viewer issue for CSV datasets | Confirmed, it's fixed now. Thanks for reporting, and thanks @coyotte508 for fixing it
<img width="1123" alt="Capture d’écran 2022-06-21 à 10 28 05" src="https://user-images.githubusercontent.com/1676121/174753833-1b453a5a-6a90-4717-bca1-1b5fc6b75e4a.png">
| ### Link
https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin
### Description
I'm populating CSV datasets [here](https://huggingface.co/scikit-learn) but the viewer is not enabled and it looks for a dataset loading script, the datasets aren't on queue as well.
You can replicate the problem by simply uploading any CSV dataset.
### Owner
Yes | 23 | Dataset Viewer issue for CSV datasets
### Link
https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin
### Description
I'm populating CSV datasets [here](https://huggingface.co/scikit-learn) but the viewer is not enabled and it looks for a dataset loading script, the datasets aren't on queue as well.
You can replicate the problem by simply uploading any CSV dataset.
### Owner
Yes
Confirmed, it's fixed now. Thanks for reporting, and thanks @coyotte508 for fixing it
<img width="1123" alt="Capture d’écran 2022-06-21 à 10 28 05" src="https://user-images.githubusercontent.com/1676121/174753833-1b453a5a-6a90-4717-bca1-1b5fc6b75e4a.png">
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https://github.com/huggingface/datasets/issues/4529 | Ecoset | Hi! Very cool dataset! I answered your questions on the forum. Also, feel free to comment `#self-assign` on this issue to self-assign it. | ## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**
**Ecoset** was created as a clean and ecologically valid alternative to **Imagenet**.
It is a large image recognition dataset, similar to Imagenet in size and structure. However, the authors of ecoset claim several improvements over Imagenet, like:
- more ecologically valid classes (e.g. not over-focussed on distinguishing different dog breeds)
- less NSFW content
- 'pre-packed image recognition models' that come with the dataset and can be used for validation of other models.
I am working for one of the authors of the paper with the aim of bringing Ecoset to huggingface datasets. Therefore I can work on this issue personally, but could use some help from devs and experienced users if the dataset is of interest to them. I phrased some of my questions on [discuss.huggingface](https://discuss.huggingface.co/t/handling-large-image-datasets/19373).
| 23 | Ecoset
## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**
**Ecoset** was created as a clean and ecologically valid alternative to **Imagenet**.
It is a large image recognition dataset, similar to Imagenet in size and structure. However, the authors of ecoset claim several improvements over Imagenet, like:
- more ecologically valid classes (e.g. not over-focussed on distinguishing different dog breeds)
- less NSFW content
- 'pre-packed image recognition models' that come with the dataset and can be used for validation of other models.
I am working for one of the authors of the paper with the aim of bringing Ecoset to huggingface datasets. Therefore I can work on this issue personally, but could use some help from devs and experienced users if the dataset is of interest to them. I phrased some of my questions on [discuss.huggingface](https://discuss.huggingface.co/t/handling-large-image-datasets/19373).
Hi! Very cool dataset! I answered your questions on the forum. Also, feel free to comment `#self-assign` on this issue to self-assign it. | [
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https://github.com/huggingface/datasets/issues/4529 | Ecoset | Hey There, thanks for closing 🤗
Forgot the issue existed, so I didn't close it after implementing the downloader :) | ## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**
**Ecoset** was created as a clean and ecologically valid alternative to **Imagenet**.
It is a large image recognition dataset, similar to Imagenet in size and structure. However, the authors of ecoset claim several improvements over Imagenet, like:
- more ecologically valid classes (e.g. not over-focussed on distinguishing different dog breeds)
- less NSFW content
- 'pre-packed image recognition models' that come with the dataset and can be used for validation of other models.
I am working for one of the authors of the paper with the aim of bringing Ecoset to huggingface datasets. Therefore I can work on this issue personally, but could use some help from devs and experienced users if the dataset is of interest to them. I phrased some of my questions on [discuss.huggingface](https://discuss.huggingface.co/t/handling-large-image-datasets/19373).
| 20 | Ecoset
## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**
**Ecoset** was created as a clean and ecologically valid alternative to **Imagenet**.
It is a large image recognition dataset, similar to Imagenet in size and structure. However, the authors of ecoset claim several improvements over Imagenet, like:
- more ecologically valid classes (e.g. not over-focussed on distinguishing different dog breeds)
- less NSFW content
- 'pre-packed image recognition models' that come with the dataset and can be used for validation of other models.
I am working for one of the authors of the paper with the aim of bringing Ecoset to huggingface datasets. Therefore I can work on this issue personally, but could use some help from devs and experienced users if the dataset is of interest to them. I phrased some of my questions on [discuss.huggingface](https://discuss.huggingface.co/t/handling-large-image-datasets/19373).
Hey There, thanks for closing 🤗
Forgot the issue existed, so I didn't close it after implementing the downloader :) | [
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https://github.com/huggingface/datasets/issues/4528 | Memory leak when iterating a Dataset | Here is a modified repro example that makes it easier to see the leak:
```
$ cat ds2.py
import gc, sys
import time
from datasets import load_dataset
import os, psutil
process = psutil.Process(os.getpid())
print(process.memory_info().rss/2**20)
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus']
corpus = corpus.select(range(200000))
print(process.memory_info().rss/2**20)
batch = None
mem_before_start = psutil.Process(os.getpid()).memory_info().rss / 2**20
step = 20000
for i in range(0, 10*step, step):
mem_before = psutil.Process(os.getpid()).memory_info().rss / 2**20
batch = corpus[i:i+step]
import objgraph
#objgraph.show_refs([batch])
#objgraph.show_refs([corpus])
#sys.exit()
gc.collect()
mem_after = psutil.Process(os.getpid()).memory_info().rss / 2**20
print(f"{i:6d} {mem_after - mem_before:12.4f} {mem_after - mem_before_start:12.4f}")
```
Let's run:
```
$ python ds2.py
0 36.5391 36.5391
20000 10.4609 47.0000
40000 5.9766 52.9766
60000 7.8906 60.8672
80000 6.0586 66.9258
100000 8.4453 75.3711
120000 6.7422 82.1133
140000 8.5664 90.6797
160000 5.7344 96.4141
180000 8.3398 104.7539
```
You can see the last column of total RSS memory keeps on growing in MBs. The mid column is by how much it was grown during a single iteration of the repro script (20000 items) | e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
process = psutil.Process(os.getpid())
print(process.memory_info().rss) # output: 633507840 bytes
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset
print(process.memory_info().rss) # output: 698601472 bytes
logger.info("Applying method to all examples in all splits")
for i in trange(0, len(corpus), 1000):
batch = corpus[i:i+1000]
data = pyarrow.total_allocated_bytes()
if data > 0:
logger.info(f"{i}/{len(corpus)}: {data}")
print(process.memory_info().rss) # output: 3788247040 bytes
del batch
gc.collect()
print(process.memory_info().rss) # output: 3788247040 bytes
logger.info("Done...")
time.sleep(100)
```
## Expected results
Limited memory usage, and memory to be freed after processing
## Actual results
Memory leak
You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| 160 | Memory leak when iterating a Dataset
e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
process = psutil.Process(os.getpid())
print(process.memory_info().rss) # output: 633507840 bytes
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset
print(process.memory_info().rss) # output: 698601472 bytes
logger.info("Applying method to all examples in all splits")
for i in trange(0, len(corpus), 1000):
batch = corpus[i:i+1000]
data = pyarrow.total_allocated_bytes()
if data > 0:
logger.info(f"{i}/{len(corpus)}: {data}")
print(process.memory_info().rss) # output: 3788247040 bytes
del batch
gc.collect()
print(process.memory_info().rss) # output: 3788247040 bytes
logger.info("Done...")
time.sleep(100)
```
## Expected results
Limited memory usage, and memory to be freed after processing
## Actual results
Memory leak
You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
Here is a modified repro example that makes it easier to see the leak:
```
$ cat ds2.py
import gc, sys
import time
from datasets import load_dataset
import os, psutil
process = psutil.Process(os.getpid())
print(process.memory_info().rss/2**20)
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus']
corpus = corpus.select(range(200000))
print(process.memory_info().rss/2**20)
batch = None
mem_before_start = psutil.Process(os.getpid()).memory_info().rss / 2**20
step = 20000
for i in range(0, 10*step, step):
mem_before = psutil.Process(os.getpid()).memory_info().rss / 2**20
batch = corpus[i:i+step]
import objgraph
#objgraph.show_refs([batch])
#objgraph.show_refs([corpus])
#sys.exit()
gc.collect()
mem_after = psutil.Process(os.getpid()).memory_info().rss / 2**20
print(f"{i:6d} {mem_after - mem_before:12.4f} {mem_after - mem_before_start:12.4f}")
```
Let's run:
```
$ python ds2.py
0 36.5391 36.5391
20000 10.4609 47.0000
40000 5.9766 52.9766
60000 7.8906 60.8672
80000 6.0586 66.9258
100000 8.4453 75.3711
120000 6.7422 82.1133
140000 8.5664 90.6797
160000 5.7344 96.4141
180000 8.3398 104.7539
```
You can see the last column of total RSS memory keeps on growing in MBs. The mid column is by how much it was grown during a single iteration of the repro script (20000 items) | [
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https://github.com/huggingface/datasets/issues/4528 | Memory leak when iterating a Dataset | @NouamaneTazi, please check my analysis here https://github.com/huggingface/datasets/issues/4883#issuecomment-1242599722 so if you agree with my research this Issue can be closed as well.
I also made a suggestion at how to proceed to hunt for a real leak here https://github.com/huggingface/datasets/issues/4883#issuecomment-1242600626
you may find this one to be useful as well https://github.com/huggingface/datasets/issues/4883#issuecomment-1242597966 | e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
process = psutil.Process(os.getpid())
print(process.memory_info().rss) # output: 633507840 bytes
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset
print(process.memory_info().rss) # output: 698601472 bytes
logger.info("Applying method to all examples in all splits")
for i in trange(0, len(corpus), 1000):
batch = corpus[i:i+1000]
data = pyarrow.total_allocated_bytes()
if data > 0:
logger.info(f"{i}/{len(corpus)}: {data}")
print(process.memory_info().rss) # output: 3788247040 bytes
del batch
gc.collect()
print(process.memory_info().rss) # output: 3788247040 bytes
logger.info("Done...")
time.sleep(100)
```
## Expected results
Limited memory usage, and memory to be freed after processing
## Actual results
Memory leak
You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| 49 | Memory leak when iterating a Dataset
e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
process = psutil.Process(os.getpid())
print(process.memory_info().rss) # output: 633507840 bytes
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset
print(process.memory_info().rss) # output: 698601472 bytes
logger.info("Applying method to all examples in all splits")
for i in trange(0, len(corpus), 1000):
batch = corpus[i:i+1000]
data = pyarrow.total_allocated_bytes()
if data > 0:
logger.info(f"{i}/{len(corpus)}: {data}")
print(process.memory_info().rss) # output: 3788247040 bytes
del batch
gc.collect()
print(process.memory_info().rss) # output: 3788247040 bytes
logger.info("Done...")
time.sleep(100)
```
## Expected results
Limited memory usage, and memory to be freed after processing
## Actual results
Memory leak
You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
@NouamaneTazi, please check my analysis here https://github.com/huggingface/datasets/issues/4883#issuecomment-1242599722 so if you agree with my research this Issue can be closed as well.
I also made a suggestion at how to proceed to hunt for a real leak here https://github.com/huggingface/datasets/issues/4883#issuecomment-1242600626
you may find this one to be useful as well https://github.com/huggingface/datasets/issues/4883#issuecomment-1242597966 | [
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https://github.com/huggingface/datasets/issues/4528 | Memory leak when iterating a Dataset | Amazing job! Thanks for taking time to debug this 🤗
For my side, I tried to do some more research as well, but to no avail. https://github.com/huggingface/datasets/issues/4883#issuecomment-1243415957 | e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
process = psutil.Process(os.getpid())
print(process.memory_info().rss) # output: 633507840 bytes
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset
print(process.memory_info().rss) # output: 698601472 bytes
logger.info("Applying method to all examples in all splits")
for i in trange(0, len(corpus), 1000):
batch = corpus[i:i+1000]
data = pyarrow.total_allocated_bytes()
if data > 0:
logger.info(f"{i}/{len(corpus)}: {data}")
print(process.memory_info().rss) # output: 3788247040 bytes
del batch
gc.collect()
print(process.memory_info().rss) # output: 3788247040 bytes
logger.info("Done...")
time.sleep(100)
```
## Expected results
Limited memory usage, and memory to be freed after processing
## Actual results
Memory leak
You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| 27 | Memory leak when iterating a Dataset
e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
process = psutil.Process(os.getpid())
print(process.memory_info().rss) # output: 633507840 bytes
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset
print(process.memory_info().rss) # output: 698601472 bytes
logger.info("Applying method to all examples in all splits")
for i in trange(0, len(corpus), 1000):
batch = corpus[i:i+1000]
data = pyarrow.total_allocated_bytes()
if data > 0:
logger.info(f"{i}/{len(corpus)}: {data}")
print(process.memory_info().rss) # output: 3788247040 bytes
del batch
gc.collect()
print(process.memory_info().rss) # output: 3788247040 bytes
logger.info("Done...")
time.sleep(100)
```
## Expected results
Limited memory usage, and memory to be freed after processing
## Actual results
Memory leak
You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
Amazing job! Thanks for taking time to debug this 🤗
For my side, I tried to do some more research as well, but to no avail. https://github.com/huggingface/datasets/issues/4883#issuecomment-1243415957 | [
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https://github.com/huggingface/datasets/issues/4526 | split cache used when processing different split | I was not able to reproduce this behavior (I tried without using pytorch lightning though, since I don't know what code you ran in pytorch lightning to get this).
If you can provide a MWE that would be perfect ! :) | ## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through
```
class myDataModule:
def train_dataloader(self):
ds = load_dataset('squad', split='train')
ds = ds.map(some_function)
return [ds]
def val_dataloader(self):
ds = load_dataset('squad', split="validation")
ds = ds.map(some_function)
return [ds]
```
I don't know if it depends on `pytorch_lightning` or `datasets` but setting `ds.map(some_function, load_from_cache_file=False)` fixes the issue.
If this is not enough to replicate I will try and provide and MWE, I don't have time now so I thought I wuld open the issue first! | 41 | split cache used when processing different split
## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through
```
class myDataModule:
def train_dataloader(self):
ds = load_dataset('squad', split='train')
ds = ds.map(some_function)
return [ds]
def val_dataloader(self):
ds = load_dataset('squad', split="validation")
ds = ds.map(some_function)
return [ds]
```
I don't know if it depends on `pytorch_lightning` or `datasets` but setting `ds.map(some_function, load_from_cache_file=False)` fixes the issue.
If this is not enough to replicate I will try and provide and MWE, I don't have time now so I thought I wuld open the issue first!
I was not able to reproduce this behavior (I tried without using pytorch lightning though, since I don't know what code you ran in pytorch lightning to get this).
If you can provide a MWE that would be perfect ! :) | [
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https://github.com/huggingface/datasets/issues/4526 | split cache used when processing different split | Hi, I think the issue happened because I was loading datasets under an `if` ... `else` statement and the condition would change the dataset I would need to load but instead the cached one was always returned. However, I believe that is expected behaviour, if so I'll close the issue.
Otherwise I will try to provide a MWE | ## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through
```
class myDataModule:
def train_dataloader(self):
ds = load_dataset('squad', split='train')
ds = ds.map(some_function)
return [ds]
def val_dataloader(self):
ds = load_dataset('squad', split="validation")
ds = ds.map(some_function)
return [ds]
```
I don't know if it depends on `pytorch_lightning` or `datasets` but setting `ds.map(some_function, load_from_cache_file=False)` fixes the issue.
If this is not enough to replicate I will try and provide and MWE, I don't have time now so I thought I wuld open the issue first! | 58 | split cache used when processing different split
## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through
```
class myDataModule:
def train_dataloader(self):
ds = load_dataset('squad', split='train')
ds = ds.map(some_function)
return [ds]
def val_dataloader(self):
ds = load_dataset('squad', split="validation")
ds = ds.map(some_function)
return [ds]
```
I don't know if it depends on `pytorch_lightning` or `datasets` but setting `ds.map(some_function, load_from_cache_file=False)` fixes the issue.
If this is not enough to replicate I will try and provide and MWE, I don't have time now so I thought I wuld open the issue first!
Hi, I think the issue happened because I was loading datasets under an `if` ... `else` statement and the condition would change the dataset I would need to load but instead the cached one was always returned. However, I believe that is expected behaviour, if so I'll close the issue.
Otherwise I will try to provide a MWE | [
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https://github.com/huggingface/datasets/issues/4525 | Out of memory error on workers while running Beam+Dataflow | Some naive ideas to cope with this:
- enable more RAM on each worker
- force the spanning of more workers
- others? | ## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently workers run out of memory while processing them.
Any help/hint is welcome!
Error message:
```
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
Info from the Diagnostics tab:
```
Out of memory: Killed process 1882 (python) total-vm:6041764kB, anon-rss:3290928kB, file-rss:0kB, shmem-rss:0kB, UID:0 pgtables:9520kB oom_score_adj:900
The worker VM had to shut down one or more processes due to lack of memory.
```
## Additional information
### Stack trace
```
Traceback (most recent call last):
File "/home/albert_huggingface_co/natural_questions/venv/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/run_beam.py", line 127, in run
builder.download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 1389, in _download_and_prepare
pipeline_results.wait_until_finish()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 1667, in wait_until_finish
raise DataflowRuntimeException(
apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error:
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
### Logs
```
Error message from worker: Data channel closed, unable to receive additional data from SDK sdk-0-0
Workflow failed. Causes: S30:train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/Read+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/GroupByWindow+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/FlatMap(restore_timestamps)+train/ReadAllFromText/ReadAllFiles/Reshard/RemoveRandomKeys+train/ReadAllFromText/ReadAllFiles/ReadRange+train/Map(_parse_example)+train/Encode+train/Count N. Examples+train/Get values/Values+train/Save to parquet/Write/WriteImpl/WindowInto(WindowIntoFn)+train/Save to parquet/Write/WriteImpl/WriteBundles+train/Save to parquet/Write/WriteImpl/Pair+train/Save to parquet/Write/WriteImpl/GroupByKey/Write failed., The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors. The work item was attempted on these workers: beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: Data channel closed, unable to receive additional data from SDK sdk-0-0, beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-bwsj Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-5052 Root cause: The worker lost contact with the service.
```
| 23 | Out of memory error on workers while running Beam+Dataflow
## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently workers run out of memory while processing them.
Any help/hint is welcome!
Error message:
```
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
Info from the Diagnostics tab:
```
Out of memory: Killed process 1882 (python) total-vm:6041764kB, anon-rss:3290928kB, file-rss:0kB, shmem-rss:0kB, UID:0 pgtables:9520kB oom_score_adj:900
The worker VM had to shut down one or more processes due to lack of memory.
```
## Additional information
### Stack trace
```
Traceback (most recent call last):
File "/home/albert_huggingface_co/natural_questions/venv/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/run_beam.py", line 127, in run
builder.download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 1389, in _download_and_prepare
pipeline_results.wait_until_finish()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 1667, in wait_until_finish
raise DataflowRuntimeException(
apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error:
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
### Logs
```
Error message from worker: Data channel closed, unable to receive additional data from SDK sdk-0-0
Workflow failed. Causes: S30:train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/Read+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/GroupByWindow+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/FlatMap(restore_timestamps)+train/ReadAllFromText/ReadAllFiles/Reshard/RemoveRandomKeys+train/ReadAllFromText/ReadAllFiles/ReadRange+train/Map(_parse_example)+train/Encode+train/Count N. Examples+train/Get values/Values+train/Save to parquet/Write/WriteImpl/WindowInto(WindowIntoFn)+train/Save to parquet/Write/WriteImpl/WriteBundles+train/Save to parquet/Write/WriteImpl/Pair+train/Save to parquet/Write/WriteImpl/GroupByKey/Write failed., The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors. The work item was attempted on these workers: beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: Data channel closed, unable to receive additional data from SDK sdk-0-0, beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-bwsj Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-5052 Root cause: The worker lost contact with the service.
```
Some naive ideas to cope with this:
- enable more RAM on each worker
- force the spanning of more workers
- others? | [
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https://github.com/huggingface/datasets/issues/4525 | Out of memory error on workers while running Beam+Dataflow | @albertvillanova We were finally able to process the full NQ dataset on our machines using 600 gb with 5 workers. Maybe these numbers will work for you as well. | ## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently workers run out of memory while processing them.
Any help/hint is welcome!
Error message:
```
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
Info from the Diagnostics tab:
```
Out of memory: Killed process 1882 (python) total-vm:6041764kB, anon-rss:3290928kB, file-rss:0kB, shmem-rss:0kB, UID:0 pgtables:9520kB oom_score_adj:900
The worker VM had to shut down one or more processes due to lack of memory.
```
## Additional information
### Stack trace
```
Traceback (most recent call last):
File "/home/albert_huggingface_co/natural_questions/venv/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/run_beam.py", line 127, in run
builder.download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 1389, in _download_and_prepare
pipeline_results.wait_until_finish()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 1667, in wait_until_finish
raise DataflowRuntimeException(
apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error:
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
### Logs
```
Error message from worker: Data channel closed, unable to receive additional data from SDK sdk-0-0
Workflow failed. Causes: S30:train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/Read+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/GroupByWindow+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/FlatMap(restore_timestamps)+train/ReadAllFromText/ReadAllFiles/Reshard/RemoveRandomKeys+train/ReadAllFromText/ReadAllFiles/ReadRange+train/Map(_parse_example)+train/Encode+train/Count N. Examples+train/Get values/Values+train/Save to parquet/Write/WriteImpl/WindowInto(WindowIntoFn)+train/Save to parquet/Write/WriteImpl/WriteBundles+train/Save to parquet/Write/WriteImpl/Pair+train/Save to parquet/Write/WriteImpl/GroupByKey/Write failed., The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors. The work item was attempted on these workers: beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: Data channel closed, unable to receive additional data from SDK sdk-0-0, beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-bwsj Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-5052 Root cause: The worker lost contact with the service.
```
| 29 | Out of memory error on workers while running Beam+Dataflow
## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently workers run out of memory while processing them.
Any help/hint is welcome!
Error message:
```
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
Info from the Diagnostics tab:
```
Out of memory: Killed process 1882 (python) total-vm:6041764kB, anon-rss:3290928kB, file-rss:0kB, shmem-rss:0kB, UID:0 pgtables:9520kB oom_score_adj:900
The worker VM had to shut down one or more processes due to lack of memory.
```
## Additional information
### Stack trace
```
Traceback (most recent call last):
File "/home/albert_huggingface_co/natural_questions/venv/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/run_beam.py", line 127, in run
builder.download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 1389, in _download_and_prepare
pipeline_results.wait_until_finish()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 1667, in wait_until_finish
raise DataflowRuntimeException(
apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error:
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
### Logs
```
Error message from worker: Data channel closed, unable to receive additional data from SDK sdk-0-0
Workflow failed. Causes: S30:train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/Read+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/GroupByWindow+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/FlatMap(restore_timestamps)+train/ReadAllFromText/ReadAllFiles/Reshard/RemoveRandomKeys+train/ReadAllFromText/ReadAllFiles/ReadRange+train/Map(_parse_example)+train/Encode+train/Count N. Examples+train/Get values/Values+train/Save to parquet/Write/WriteImpl/WindowInto(WindowIntoFn)+train/Save to parquet/Write/WriteImpl/WriteBundles+train/Save to parquet/Write/WriteImpl/Pair+train/Save to parquet/Write/WriteImpl/GroupByKey/Write failed., The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors. The work item was attempted on these workers: beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: Data channel closed, unable to receive additional data from SDK sdk-0-0, beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-bwsj Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-5052 Root cause: The worker lost contact with the service.
```
@albertvillanova We were finally able to process the full NQ dataset on our machines using 600 gb with 5 workers. Maybe these numbers will work for you as well. | [
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https://github.com/huggingface/datasets/issues/4525 | Out of memory error on workers while running Beam+Dataflow | Thanks a lot for the hint, @seirasto.
I have one question: what runner did you use? Direct, Apache Flink/Nemo/Samza/Spark, Google Dataflow...? Thank you. | ## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently workers run out of memory while processing them.
Any help/hint is welcome!
Error message:
```
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
Info from the Diagnostics tab:
```
Out of memory: Killed process 1882 (python) total-vm:6041764kB, anon-rss:3290928kB, file-rss:0kB, shmem-rss:0kB, UID:0 pgtables:9520kB oom_score_adj:900
The worker VM had to shut down one or more processes due to lack of memory.
```
## Additional information
### Stack trace
```
Traceback (most recent call last):
File "/home/albert_huggingface_co/natural_questions/venv/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/run_beam.py", line 127, in run
builder.download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 1389, in _download_and_prepare
pipeline_results.wait_until_finish()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 1667, in wait_until_finish
raise DataflowRuntimeException(
apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error:
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
### Logs
```
Error message from worker: Data channel closed, unable to receive additional data from SDK sdk-0-0
Workflow failed. Causes: S30:train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/Read+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/GroupByWindow+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/FlatMap(restore_timestamps)+train/ReadAllFromText/ReadAllFiles/Reshard/RemoveRandomKeys+train/ReadAllFromText/ReadAllFiles/ReadRange+train/Map(_parse_example)+train/Encode+train/Count N. Examples+train/Get values/Values+train/Save to parquet/Write/WriteImpl/WindowInto(WindowIntoFn)+train/Save to parquet/Write/WriteImpl/WriteBundles+train/Save to parquet/Write/WriteImpl/Pair+train/Save to parquet/Write/WriteImpl/GroupByKey/Write failed., The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors. The work item was attempted on these workers: beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: Data channel closed, unable to receive additional data from SDK sdk-0-0, beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-bwsj Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-5052 Root cause: The worker lost contact with the service.
```
| 23 | Out of memory error on workers while running Beam+Dataflow
## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently workers run out of memory while processing them.
Any help/hint is welcome!
Error message:
```
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
Info from the Diagnostics tab:
```
Out of memory: Killed process 1882 (python) total-vm:6041764kB, anon-rss:3290928kB, file-rss:0kB, shmem-rss:0kB, UID:0 pgtables:9520kB oom_score_adj:900
The worker VM had to shut down one or more processes due to lack of memory.
```
## Additional information
### Stack trace
```
Traceback (most recent call last):
File "/home/albert_huggingface_co/natural_questions/venv/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/run_beam.py", line 127, in run
builder.download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 1389, in _download_and_prepare
pipeline_results.wait_until_finish()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 1667, in wait_until_finish
raise DataflowRuntimeException(
apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error:
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
### Logs
```
Error message from worker: Data channel closed, unable to receive additional data from SDK sdk-0-0
Workflow failed. Causes: S30:train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/Read+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/GroupByWindow+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/FlatMap(restore_timestamps)+train/ReadAllFromText/ReadAllFiles/Reshard/RemoveRandomKeys+train/ReadAllFromText/ReadAllFiles/ReadRange+train/Map(_parse_example)+train/Encode+train/Count N. Examples+train/Get values/Values+train/Save to parquet/Write/WriteImpl/WindowInto(WindowIntoFn)+train/Save to parquet/Write/WriteImpl/WriteBundles+train/Save to parquet/Write/WriteImpl/Pair+train/Save to parquet/Write/WriteImpl/GroupByKey/Write failed., The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors. The work item was attempted on these workers: beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: Data channel closed, unable to receive additional data from SDK sdk-0-0, beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-bwsj Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-5052 Root cause: The worker lost contact with the service.
```
Thanks a lot for the hint, @seirasto.
I have one question: what runner did you use? Direct, Apache Flink/Nemo/Samza/Spark, Google Dataflow...? Thank you. | [
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