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https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
Well I just sharded the original commonVoice dataset and pushed a small chunk of it in a private rep
What did go wrong?
Holen Sie sich Outlook für iOS<https://aka.ms/o0ukef>
________________________________
Von: Quentin Lhoest ***@***.***>
Gesendet: Tuesday, November 22, 2022 3:03:40 PM
An: huggingface/datasets ***@***.***>
Cc: capsabogdan ***@***.***>; Author ***@***.***>
Betreff: Re: [huggingface/datasets] Bug in downloading common_voice data and snall chunk of it to one's own hub (Issue #5276)
Sounds like one of the file is not a valid one, can you make sure you uploaded valid mp3 files ?
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Reply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5276#issuecomment-1323727434>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/ALSIFOAPAL2V4TBJTSPMAULWJTHDZANCNFSM6AAAAAASHQJ63U>.
You are receiving this because you authored the thread.Message ID: ***@***.***>
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 112 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
It should be all good then !
Could you share a link to your repository for me to investigate what went wrong ?
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 23 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
https://huggingface.co/datasets/DTU54DL/common-voice-test16k
Am Di., 22. Nov. 2022 um 16:43 Uhr schrieb Quentin Lhoest <
***@***.***>:
> It should be all good then !
> Could you share a link to your repository for me to investigate what went
> wrong ?
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1323876682>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ALSIFOEUJRZWXAM7DYA5VJDWJTS3NANCNFSM6AAAAAASHQJ63U>
> .
> You are receiving this because you authored the thread.Message ID:
> ***@***.***>
>
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 76 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
I see ! This is a bug with MP3 files.
When we store audio data in parquet, we store the bytes and the file name. From the file name extension we know if it's a WAV, an MP3 or else. But here it looks like the paths are all None.
It looks like it comes from here:
https://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/features/audio.py#L212
Cc @polinaeterna maybe we should simply put the file name instead of None values ?
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 73 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
@lhoestq I remember we wanted to avoid storing redundant data but maybe it's not that crucial indeed to store one more string value.
Or we can store paths only for mp3s, considering that for other formats we don't have such a problem with reading from bytes without format specified.
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 49 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
[
-0.333806574344635,
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
thanks for the help!
can I do anything on my side? we are doing a DL project and we need the
data really quick.
thanks
bogdan
> Message ID: ***@***.***>
>
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 31 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
I opened a pull requests here: https://github.com/huggingface/datasets/pull/5285, we'll do a new release soon with this fix.
Otherwise if you're really in a hurry you can install `datasets` from this PR
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 30 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
any idea on what's going wrong here?
thanks
Am So., 27. Nov. 2022 um 13:53 Uhr schrieb Bogdan Capsa <
***@***.***>:
> [image: image.png]
>
>> Message ID: ***@***.***>
>>
>
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 31 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
I have attached this screenshot above . can u pls help? So can not pip from pull request
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 19 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
The pull request has been merged on `main`.
You can install `datasets` from `main` using
```
pip install git+https://github.com/huggingface/datasets.git
```
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 20 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
I've tried to load this dataset DTU54DL/common-voice-test16k, but am
getting the same error.
So the bug fix will fix only if I upload a new dataset, or also loading
previously uploaded datasets?
thanks
Am Mo., 28. Nov. 2022 um 19:51 Uhr schrieb Quentin Lhoest <
***@***.***>:
> The pull request has been merged on main.
> You can install datasets from main using
>
> pip install git+https://github.com/huggingface/datasets.git
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1329587334>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ALSIFOCNYYIGHM2EX3ZIO6DWKT5MXANCNFSM6AAAAAASHQJ63U>
> .
> You are receiving this because you were mentioned.Message ID:
> ***@***.***>
>
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 103 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
> So the bug fix will fix only if I upload a new dataset, or also loading
previously uploaded datasets?
You have to reupload the dataset, sorry for the inconvenience
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 30 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5276
|
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
thank you so much for the help! works like a charm!
Am Di., 29. Nov. 2022 um 12:15 Uhr schrieb Quentin Lhoest <
***@***.***>:
> So the bug fix will fix only if I upload a new dataset, or also loading
> previously uploaded datasets?
>
> You have to reupload the dataset, sorry for the inconvenience
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1330468393>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ALSIFOBKEFZO57BAKY4IGW3WKXQUZANCNFSM6AAAAAASHQJ63U>
> .
> You are receiving this because you were mentioned.Message ID:
> ***@***.***>
>
|
### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11
| 92 |
Bug in downloading common_voice data and snall chunk of it to one's own hub
|
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] |
https://github.com/huggingface/datasets/issues/5275
|
YAML integer keys are not preserved Hub server-side
|
@huggingface/datasets if you agree, I can make the bulk edit on the Hub to fix integer keys into strings.
|
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
| 19 |
YAML integer keys are not preserved Hub server-side
|
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] |
https://github.com/huggingface/datasets/issues/5275
|
YAML integer keys are not preserved Hub server-side
|
FYI there are still 2k+ weekly users on `datasets` 2.6.1 which doesn't support the string label format for class labels. And among those, some are using datasets with class labels like imdb (60 users), conllpp (40), msra_ner (40), peoples_daily_enr (40), weibo_ner (30), conll2003 (20), etc. And renaming to string would break these users code.
|
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
| 54 |
YAML integer keys are not preserved Hub server-side
|
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] |
https://github.com/huggingface/datasets/issues/5275
|
YAML integer keys are not preserved Hub server-side
|
but isn't `datasets 2.6.1` downloading files from the Hub with the corresponding tag? I thought we had something like this before
|
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
| 21 |
YAML integer keys are not preserved Hub server-side
|
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] |
https://github.com/huggingface/datasets/issues/5275
|
YAML integer keys are not preserved Hub server-side
|
We're using `main` as models do. Some datasets need to be updated from time to time, e.g. when a link to download the data is dead.
But yea a year ago we had those tags, we just ended up not using them
|
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
| 42 |
YAML integer keys are not preserved Hub server-side
|
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] |
https://github.com/huggingface/datasets/issues/5275
|
YAML integer keys are not preserved Hub server-side
|
I opened https://github.com/huggingface/datasets/issues/5406 to communicate on this. Let me know what you think, and if it sounds good to you I can pin this issue
|
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
| 25 |
YAML integer keys are not preserved Hub server-side
|
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] |
https://github.com/huggingface/datasets/issues/5275
|
YAML integer keys are not preserved Hub server-side
|
So, is it OK to make the bulk edit on the Hub now or should we wait longer? If the latter, how long?
|
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
| 23 |
YAML integer keys are not preserved Hub server-side
|
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] |
https://github.com/huggingface/datasets/issues/5275
|
YAML integer keys are not preserved Hub server-side
|
I think we can do it. If you want to be extra cautious you can do it for all datasets except imdb and conllpp for now which are actively used by 2.6.1 users. For those two we can keep the YAML like this for some more time, or alternatively use the old dataset_infos.json file
|
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
| 54 |
YAML integer keys are not preserved Hub server-side
|
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] |
https://github.com/huggingface/datasets/issues/5275
|
YAML integer keys are not preserved Hub server-side
|
The bulk edit of canonical datasets (except imdb and conllpp) is running.
See e.g.: https://huggingface.co/datasets/acronym_identification/discussions/3
EDITED:
Done, except for "universal_morphologies", where I get
```
HTTPError: 413 Client Error: Payload Too Large for url: https://huggingface.co/api/validate-yaml
```
Also not done for the datasets missing matadata "dataset_info":
- mc4: https://huggingface.co/datasets/mc4/discussions/3
- the_pile: https://huggingface.co/datasets/the_pile/discussions/6
- timit_asr: https://huggingface.co/datasets/timit_asr/discussions/1
|
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
| 53 |
YAML integer keys are not preserved Hub server-side
|
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] |
https://github.com/huggingface/datasets/issues/5275
|
YAML integer keys are not preserved Hub server-side
|
@lhoestq, there are 6 community datasets with YAML integer keys in their `dataset_info` `class_label`:
- indonlp/indonlu
- rcds/swiss_judgment_prediction
- Jean-Baptiste/wikiner_fr
- Bingsu/Cat_and_Dog
- taskydata/tasky_or_not
- RCC-MSU/collection3
Maybe we could open a PR on them as well?
|
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
| 36 |
YAML integer keys are not preserved Hub server-side
|
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] |
https://github.com/huggingface/datasets/issues/5275
|
YAML integer keys are not preserved Hub server-side
|
Let's do this then:
- [x] [indonlp/indonlu](https://huggingface.co/datasets/indonlp/indonlu/discussions/3)
- [x] rcds/swiss_judgment_prediction
- [x] Jean-Baptiste/wikiner_fr
- [x] Bingsu/Cat_and_Dog -> merged
- [x] taskydata/tasky_or_not (was already using quotes)
- [x] RCC-MSU/collection3
EDIT: all done :)
|
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
| 32 |
YAML integer keys are not preserved Hub server-side
|
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] |
https://github.com/huggingface/datasets/issues/5275
|
YAML integer keys are not preserved Hub server-side
|
@lhoestq I was not asking you to do it, but asking if you agree me to do it... :man_facepalming:
As I self-assigned this issue... :sweat_smile:
|
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
| 25 |
YAML integer keys are not preserved Hub server-side
|
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] |
https://github.com/huggingface/datasets/issues/5274
|
load_dataset possibly broken for gated datasets?
|
Btw, thanks very much for finding the hub rollback temporary fix and bringing the issue to our attention @KhoomeiK!
|
### Describe the bug
When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:
```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
165 if repo_id.count("/") > 1:
166 raise HFValidationError(
--> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
168 f" '{repo_id}'. Use `repo_type` argument if needed."
169 )
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```
### Steps to reproduce the bug
Install requirements:
```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```
Then:
```
from datasets import load_dataset
auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```
### Expected behavior
Downloading of the datset
### Environment info
Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing
| 19 |
load_dataset possibly broken for gated datasets?
|
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] |
https://github.com/huggingface/datasets/issues/5274
|
load_dataset possibly broken for gated datasets?
|
I see the same issue when calling `load_dataset('poloclub/diffusiondb', 'large_random_1k')` with `datasets==2.7.1` and `huggingface-hub=0.11.0`. No issue with `datasets=2.6.1` and `huggingface_hub==0.10.1`.
https://github.com/poloclub/diffusiondb/issues/7
|
### Describe the bug
When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:
```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
165 if repo_id.count("/") > 1:
166 raise HFValidationError(
--> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
168 f" '{repo_id}'. Use `repo_type` argument if needed."
169 )
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```
### Steps to reproduce the bug
Install requirements:
```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```
Then:
```
from datasets import load_dataset
auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```
### Expected behavior
Downloading of the datset
### Environment info
Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing
| 20 |
load_dataset possibly broken for gated datasets?
|
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] |
https://github.com/huggingface/datasets/issues/5274
|
load_dataset possibly broken for gated datasets?
|
I fixed my issue by specifying `repo_type` in `hf_hub_url()`. https://github.com/poloclub/diffusiondb/commit/9eb91c79aaca98b0515a0ce45778b8af65b84652
I opened a PR on the Winoground's repo: https://huggingface.co/datasets/facebook/winoground/discussions/2
|
### Describe the bug
When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:
```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
165 if repo_id.count("/") > 1:
166 raise HFValidationError(
--> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
168 f" '{repo_id}'. Use `repo_type` argument if needed."
169 )
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```
### Steps to reproduce the bug
Install requirements:
```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```
Then:
```
from datasets import load_dataset
auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```
### Expected behavior
Downloading of the datset
### Environment info
Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing
| 19 |
load_dataset possibly broken for gated datasets?
|
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] |
https://github.com/huggingface/datasets/issues/5274
|
load_dataset possibly broken for gated datasets?
|
This is a bug in the script, indeed. The most robust fix is to use a relative path instead of `hf_hub_url`, which does not depend on `huggingface_hub`'s version 🙂. I've opened a PR here: https://huggingface.co/datasets/facebook/winoground/discussions/3.
|
### Describe the bug
When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:
```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
165 if repo_id.count("/") > 1:
166 raise HFValidationError(
--> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
168 f" '{repo_id}'. Use `repo_type` argument if needed."
169 )
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```
### Steps to reproduce the bug
Install requirements:
```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```
Then:
```
from datasets import load_dataset
auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```
### Expected behavior
Downloading of the datset
### Environment info
Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing
| 35 |
load_dataset possibly broken for gated datasets?
|
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-0.2850203812122345,
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0.1339835822582245,
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0.2659738063812256,
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] |
https://github.com/huggingface/datasets/issues/5274
|
load_dataset possibly broken for gated datasets?
|
huggingface_hub.utils._validators.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name':
tokenizer = AutoTokenizer.from_pretrained(ARGS.model_path, trust_remote_code=True)
Please handle automatically for local path and repo name inside, otherwise users always get confused about this
|
### Describe the bug
When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:
```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
165 if repo_id.count("/") > 1:
166 raise HFValidationError(
--> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
168 f" '{repo_id}'. Use `repo_type` argument if needed."
169 )
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```
### Steps to reproduce the bug
Install requirements:
```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```
Then:
```
from datasets import load_dataset
auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```
### Expected behavior
Downloading of the datset
### Environment info
Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing
| 32 |
load_dataset possibly broken for gated datasets?
|
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] |
https://github.com/huggingface/datasets/issues/5274
|
load_dataset possibly broken for gated datasets?
|
I think I'm also hitting this error, trying to load a model from a local path.
|
### Describe the bug
When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:
```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
165 if repo_id.count("/") > 1:
166 raise HFValidationError(
--> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
168 f" '{repo_id}'. Use `repo_type` argument if needed."
169 )
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```
### Steps to reproduce the bug
Install requirements:
```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```
Then:
```
from datasets import load_dataset
auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```
### Expected behavior
Downloading of the datset
### Environment info
Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing
| 16 |
load_dataset possibly broken for gated datasets?
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
Hi ! We're using the Arrow format for the datasets, and PyArrow tensors are not part of the Arrow format AFAIK:
> There is no direct support in the arrow columnar format to store Tensors as column values.
source: https://github.com/apache/arrow/issues/4802#issuecomment-508494694
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 40 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
@wesm @rok its been around three years. any updates, regarding dataset arrow tensor support? 🙏 I know you must be very busy, would appreciate to learn what is the state of art. I saw the PR is still open [#8510](https://github.com/apache/arrow/pull/8510)
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 40 |
Use pyarrow Tensor dtype
|
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-0.5509958863258362,
-0.4347775876522064
] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
Hey @franz101 & @lhoestq!
There is a plan and a PR to create an [ExtensionArray of Tensors](https://github.com/apache/arrow/pull/8510) of equal sizes as well as a plan to do the same for Tensors of different sizes [ARROW-8714](https://issues.apache.org/jira/browse/ARROW-8714).
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 35 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
The work stalled a little because it was not clear where TensorArray would live. However Arrow community recently agreed to make a [well-known-extension-type document](https://lists.apache.org/thread/sxd5fhc42hb6svs79t3fd79gkqj83pfh) and I would like https://github.com/apache/arrow/pull/8510 to land there and add an implementation to C++/Python + another language. Is that something you would find beneficial to you?
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 50 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
that is a great update, thank you.
it looks like this feature would benefit datasets implementation of [ArrayExtensionArray](https://github.com/huggingface/datasets/blob/9f2ff14673cac1f1ad56d80221a793f5938b68c7/src/datasets/features/features.py#L585-L641). Is that correct @eladsegal @lhoestq?
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 23 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
TensorArray sounds great ! Looking forward to it :)
We've had our own ExtensionArray for fixed shape tensors for a while now, hoping to see something more standardized by the arrow community.
Also super interested in the extension array for tensors of different sizes cc @mariosasko
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 46 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
@rok Thanks for keeping us updated! I think it's best to introduce a new feature type that would use this extension type under the hood. I'll create an issue to discuss the design with the community in the coming days.
Also, is there a tentative time frame for the variable-shape Tensor extension type?
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 53 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
@mariosasko please tag me in the discussion, perhaps I can contribute.
As for the [variable shape tensor array](https://github.com/apache/arrow/issues/24868) - I'd be interested in working on it but didn't see much interest in community yet. Are you saying `huggingface/datasets` could use it?
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 41 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
pyarrow 12 is out 🎉, will have a look if I can work on it for the ExtensionArray
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 18 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
I think these two issues need to be fixed first on the Arrow side before adding the tensor feature type here: https://github.com/apache/arrow/issues/35573 and https://github.com/apache/arrow/issues/35599.
@rok We've had a couple of requests for supporting variable-shape tensors on the forum/GH, but I did not manage to find the concrete issues using the search. TF/TFDS (and PyTorch with the `nested_tensor` API) support them, so it makes sense for us to do the same eventually (the Ray project has an [extension](https://github.com/ray-project/ray/blob/42a8d1489b37243f203120899a23d919dc85bf2a/python/ray/air/util/tensor_extensions/arrow.py#L634) type to support this case)
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 82 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
> @rok We've had a couple of requests for supporting variable-shape tensors on the forum/GH, but I did not manage to find the concrete issues using the search. TF/TFDS (and PyTorch with the `nested_tensor` API) support them, so it makes sense for us to do the same eventually (the Ray project has an [extension](https://github.com/ray-project/ray/blob/42a8d1489b37243f203120899a23d919dc85bf2a/python/ray/air/util/tensor_extensions/arrow.py#L634) type to support this case)
That does make sense indeed. We should probably also be careful about memory layout to enable zero-copy interface to TF/PyTorch.
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 79 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
Not with with the Arrow format, and therefore not in `datasets`. But they released a new [FixedShapeTensorArray](https://arrow.apache.org/docs/python/extending_types.html#fixed-size-tensor) to store tensors in Arrow format. We plan to support this in `datasets` at one point !
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 34 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
There is also an open issue to enable the conversion of `pyarrow.Tensor` to `pyarrow.FixedShapeTensorType`: https://github.com/apache/arrow/issues/35068. This way one could indirectly use `pyarrow.Tensor` in Arrow format.
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 25 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5272
|
Use pyarrow Tensor dtype
|
We started a [mailing list discussion](https://lists.apache.org/thread/qc9qho0fg5ph1dns4hjq56hp4tj7rk1k) about potential `VariableShapeTensor` extension array, please check it out and give feedback. For more details here's also a PR https://github.com/apache/arrow/pull/37166.
|
### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
| 26 |
Use pyarrow Tensor dtype
|
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] |
https://github.com/huggingface/datasets/issues/5270
|
When len(_URLS) > 16, download will hang
|
It can fix the bug temporarily.
```python
from datasets import DownloadConfig
config = DownloadConfig(num_proc=8)
In [5]: dataset = load_dataset('Freed-Wu/kodak', split='test', download_config=config)
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/6cf51f2b3d686d24a33fe86945f9e16802def212325f9345cf3cbb1b9f5f4a57...
Downloading data files #4: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.39obj/s]
Downloading data files #2: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.38obj/s]
Downloading data files #3: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.13obj/s]
Downloading data files #7: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.09obj/s]
Downloading data files #5: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.08obj/s]
Downloading data files #0: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.08obj/s]
Downloading data files #1: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:10<00:00, 3.36s/obj]
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 492k/492k [00:01<00:00, 253kB/s]
Downloading data files #6: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:13<00:00, 4.63s/obj]
Extracting data files #0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1407.17obj/s]
Extracting data files #1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1325.91obj/s]
Extracting data files #3: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1524.46obj/s]
Extracting data files #2: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1404.66obj/s]
Extracting data files #4: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1538.63obj/s]
Extracting data files #6: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1711.73obj/s]
Extracting data files #7: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 2144.33obj/s]
Extracting data files #5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1964.85obj/s]
Dataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/6cf51f2b3d686d24a33fe86945f9e16802def212325f9345cf3cbb1b9f5f4a57. Subsequent calls will reuse this data.
```
|
### Describe the bug
```python
In [9]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 1.88MB/s]
[11/19/22 22:16:21] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/bd1cc3434212e3e654f7e16ad618f8a1470b5982b086c91b1d6bc7187183c6e9...
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:02<00:00, 239kB/s]
#10: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.06s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 534k/534k [00:02<00:00, 193kB/s]
#14: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.37s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 692k/692k [00:02<00:00, 269kB/s]
#12: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.44s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 566k/566k [00:02<00:00, 210kB/s]
#5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 613k/613k [00:02<00:00, 235kB/s]
#13: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 786k/786k [00:02<00:00, 342kB/s]
#3: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.60s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 619k/619k [00:02<00:00, 254kB/s]
#4: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.68s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 737k/737k [00:02<00:00, 271kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 788k/788k [00:02<00:00, 285kB/s]
#6: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 618k/618k [00:04<00:00, 153kB/s]
#0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:11<00:00, 5.69s/obj]
^CProcess ForkPoolWorker-47:
Process ForkPoolWorker-46:
Process ForkPoolWorker-36:
Process ForkPoolWorker-38:██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Process ForkPoolWorker-37:
Process ForkPoolWorker-45:
Process ForkPoolWorker-39:
Process ForkPoolWorker-43:
Process ForkPoolWorker-33:
Process ForkPoolWorker-18:
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 365, in get
res = self._reader.recv_bytes()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/connection.py", line 221, in recv_bytes
buf = self._recv_bytes(maxlength)
KeyboardInterrupt
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/connection.py", line 419, in _recv_bytes
buf = self._recv(4)
File "/usr/lib/python3.10/multiprocessing/connection.py", line 384, in _recv
chunk = read(handle, remaining)
KeyboardInterrupt
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
Process ForkPoolWorker-20:
Process ForkPoolWorker-44:
Process ForkPoolWorker-22:
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#1: 0%| | 0/2 [03:00<?, ?obj/s]
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 659, in get_from_cache
http_get(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 442, in http_get
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
KeyboardInterrupt
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#3: 0%| | 0/2 [03:00<?, ?obj/s]
#11: 0%| | 0/1 [00:49<?, ?obj/s]
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in send
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in <listcomp>
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 266, in resolve_redirects
resp = self.send(
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#5: 0%| | 0/1 [03:00<?, ?obj/s]
KeyboardInterrupt
Process ForkPoolWorker-42:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#9: 0%| | 0/1 [00:51<?, ?obj/s]
```
### Steps to reproduce the bug
```python
"""Kodak.
Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import datasets
NUMBER = 17
_DESCRIPTION = """\
The pictures below link to lossless, true color (24 bits per pixel, aka "full
color") images. It is my understanding they have been released by the Eastman
Kodak Company for unrestricted usage. Many sites use them as a standard test
suite for compression testing, etc. Prior to this site, they were only
available in the Sun Raster format via ftp. This meant that the images could
not be previewed before downloading. Since their release, however, the lossless
PNG format has been incorporated into all the major browsers. Since PNG
supports 24-bit lossless color (which GIF and JPEG do not), it is now possible
to offer this browser-friendly access to the images.
"""
_HOMEPAGE = "https://r0k.us/graphics/kodak/"
_LICENSE = "GPLv3"
_URLS = [
f"https://github.com/MohamedBakrAli/Kodak-Lossless-True-Color-Image-Suite/raw/master/PhotoCD_PCD0992/{i}.png"
for i in range(1, 1 + NUMBER)
]
class Kodak(datasets.GeneratorBasedBuilder):
"""Kodak datasets."""
VERSION = datasets.Version("0.0.1")
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Return SplitGenerators."""
file_paths = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"file_paths": file_paths,
},
),
]
def _generate_examples(self, file_paths):
"""Yield examples."""
for file_path in file_paths:
yield file_path, {"image": file_path}
```
### Expected behavior
When `len(_URLS) < 16`, it works.
```python
In [3]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 3.02MB/s]
[11/19/22 22:04:28] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 593k/593k [00:00<00:00, 2.88MB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 621k/621k [00:03<00:00, 166kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:01<00:00, 366kB/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:13<00:00, 1.18it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 3832.38it/s]
Dataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475. Subsequent calls will reuse this data.
```
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-6.0.8-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.8
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
| 176 |
When len(_URLS) > 16, download will hang
|
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] |
https://github.com/huggingface/datasets/issues/5270
|
When len(_URLS) > 16, download will hang
|
Thanks for reporting ! This sounds like an issue with python multiprocessing. If we switch to multithreading for the downloads it should be much more robust - let me know if this is something you'd like to contribute, I'd be happy to help and give you some pointers
|
### Describe the bug
```python
In [9]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 1.88MB/s]
[11/19/22 22:16:21] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/bd1cc3434212e3e654f7e16ad618f8a1470b5982b086c91b1d6bc7187183c6e9...
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:02<00:00, 239kB/s]
#10: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.06s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 534k/534k [00:02<00:00, 193kB/s]
#14: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.37s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 692k/692k [00:02<00:00, 269kB/s]
#12: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.44s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 566k/566k [00:02<00:00, 210kB/s]
#5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 613k/613k [00:02<00:00, 235kB/s]
#13: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 786k/786k [00:02<00:00, 342kB/s]
#3: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.60s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 619k/619k [00:02<00:00, 254kB/s]
#4: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.68s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 737k/737k [00:02<00:00, 271kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 788k/788k [00:02<00:00, 285kB/s]
#6: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 618k/618k [00:04<00:00, 153kB/s]
#0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:11<00:00, 5.69s/obj]
^CProcess ForkPoolWorker-47:
Process ForkPoolWorker-46:
Process ForkPoolWorker-36:
Process ForkPoolWorker-38:██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Process ForkPoolWorker-37:
Process ForkPoolWorker-45:
Process ForkPoolWorker-39:
Process ForkPoolWorker-43:
Process ForkPoolWorker-33:
Process ForkPoolWorker-18:
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 365, in get
res = self._reader.recv_bytes()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/connection.py", line 221, in recv_bytes
buf = self._recv_bytes(maxlength)
KeyboardInterrupt
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/connection.py", line 419, in _recv_bytes
buf = self._recv(4)
File "/usr/lib/python3.10/multiprocessing/connection.py", line 384, in _recv
chunk = read(handle, remaining)
KeyboardInterrupt
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
Process ForkPoolWorker-20:
Process ForkPoolWorker-44:
Process ForkPoolWorker-22:
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#1: 0%| | 0/2 [03:00<?, ?obj/s]
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 659, in get_from_cache
http_get(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 442, in http_get
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
KeyboardInterrupt
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#3: 0%| | 0/2 [03:00<?, ?obj/s]
#11: 0%| | 0/1 [00:49<?, ?obj/s]
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in send
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in <listcomp>
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 266, in resolve_redirects
resp = self.send(
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#5: 0%| | 0/1 [03:00<?, ?obj/s]
KeyboardInterrupt
Process ForkPoolWorker-42:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#9: 0%| | 0/1 [00:51<?, ?obj/s]
```
### Steps to reproduce the bug
```python
"""Kodak.
Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import datasets
NUMBER = 17
_DESCRIPTION = """\
The pictures below link to lossless, true color (24 bits per pixel, aka "full
color") images. It is my understanding they have been released by the Eastman
Kodak Company for unrestricted usage. Many sites use them as a standard test
suite for compression testing, etc. Prior to this site, they were only
available in the Sun Raster format via ftp. This meant that the images could
not be previewed before downloading. Since their release, however, the lossless
PNG format has been incorporated into all the major browsers. Since PNG
supports 24-bit lossless color (which GIF and JPEG do not), it is now possible
to offer this browser-friendly access to the images.
"""
_HOMEPAGE = "https://r0k.us/graphics/kodak/"
_LICENSE = "GPLv3"
_URLS = [
f"https://github.com/MohamedBakrAli/Kodak-Lossless-True-Color-Image-Suite/raw/master/PhotoCD_PCD0992/{i}.png"
for i in range(1, 1 + NUMBER)
]
class Kodak(datasets.GeneratorBasedBuilder):
"""Kodak datasets."""
VERSION = datasets.Version("0.0.1")
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Return SplitGenerators."""
file_paths = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"file_paths": file_paths,
},
),
]
def _generate_examples(self, file_paths):
"""Yield examples."""
for file_path in file_paths:
yield file_path, {"image": file_path}
```
### Expected behavior
When `len(_URLS) < 16`, it works.
```python
In [3]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 3.02MB/s]
[11/19/22 22:04:28] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 593k/593k [00:00<00:00, 2.88MB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 621k/621k [00:03<00:00, 166kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:01<00:00, 366kB/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:13<00:00, 1.18it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 3832.38it/s]
Dataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475. Subsequent calls will reuse this data.
```
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-6.0.8-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.8
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
| 48 |
When len(_URLS) > 16, download will hang
|
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] |
https://github.com/huggingface/datasets/issues/5270
|
When len(_URLS) > 16, download will hang
|
> an issue with python multiprocessing
If it is an issue with multiprocessing, should we report it to upstream?
|
### Describe the bug
```python
In [9]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 1.88MB/s]
[11/19/22 22:16:21] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/bd1cc3434212e3e654f7e16ad618f8a1470b5982b086c91b1d6bc7187183c6e9...
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:02<00:00, 239kB/s]
#10: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.06s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 534k/534k [00:02<00:00, 193kB/s]
#14: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.37s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 692k/692k [00:02<00:00, 269kB/s]
#12: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.44s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 566k/566k [00:02<00:00, 210kB/s]
#5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 613k/613k [00:02<00:00, 235kB/s]
#13: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 786k/786k [00:02<00:00, 342kB/s]
#3: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.60s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 619k/619k [00:02<00:00, 254kB/s]
#4: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.68s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 737k/737k [00:02<00:00, 271kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 788k/788k [00:02<00:00, 285kB/s]
#6: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 618k/618k [00:04<00:00, 153kB/s]
#0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:11<00:00, 5.69s/obj]
^CProcess ForkPoolWorker-47:
Process ForkPoolWorker-46:
Process ForkPoolWorker-36:
Process ForkPoolWorker-38:██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Process ForkPoolWorker-37:
Process ForkPoolWorker-45:
Process ForkPoolWorker-39:
Process ForkPoolWorker-43:
Process ForkPoolWorker-33:
Process ForkPoolWorker-18:
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 365, in get
res = self._reader.recv_bytes()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/connection.py", line 221, in recv_bytes
buf = self._recv_bytes(maxlength)
KeyboardInterrupt
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/connection.py", line 419, in _recv_bytes
buf = self._recv(4)
File "/usr/lib/python3.10/multiprocessing/connection.py", line 384, in _recv
chunk = read(handle, remaining)
KeyboardInterrupt
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
Process ForkPoolWorker-20:
Process ForkPoolWorker-44:
Process ForkPoolWorker-22:
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#1: 0%| | 0/2 [03:00<?, ?obj/s]
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 659, in get_from_cache
http_get(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 442, in http_get
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
KeyboardInterrupt
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#3: 0%| | 0/2 [03:00<?, ?obj/s]
#11: 0%| | 0/1 [00:49<?, ?obj/s]
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in send
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in <listcomp>
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 266, in resolve_redirects
resp = self.send(
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#5: 0%| | 0/1 [03:00<?, ?obj/s]
KeyboardInterrupt
Process ForkPoolWorker-42:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#9: 0%| | 0/1 [00:51<?, ?obj/s]
```
### Steps to reproduce the bug
```python
"""Kodak.
Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import datasets
NUMBER = 17
_DESCRIPTION = """\
The pictures below link to lossless, true color (24 bits per pixel, aka "full
color") images. It is my understanding they have been released by the Eastman
Kodak Company for unrestricted usage. Many sites use them as a standard test
suite for compression testing, etc. Prior to this site, they were only
available in the Sun Raster format via ftp. This meant that the images could
not be previewed before downloading. Since their release, however, the lossless
PNG format has been incorporated into all the major browsers. Since PNG
supports 24-bit lossless color (which GIF and JPEG do not), it is now possible
to offer this browser-friendly access to the images.
"""
_HOMEPAGE = "https://r0k.us/graphics/kodak/"
_LICENSE = "GPLv3"
_URLS = [
f"https://github.com/MohamedBakrAli/Kodak-Lossless-True-Color-Image-Suite/raw/master/PhotoCD_PCD0992/{i}.png"
for i in range(1, 1 + NUMBER)
]
class Kodak(datasets.GeneratorBasedBuilder):
"""Kodak datasets."""
VERSION = datasets.Version("0.0.1")
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Return SplitGenerators."""
file_paths = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"file_paths": file_paths,
},
),
]
def _generate_examples(self, file_paths):
"""Yield examples."""
for file_path in file_paths:
yield file_path, {"image": file_path}
```
### Expected behavior
When `len(_URLS) < 16`, it works.
```python
In [3]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 3.02MB/s]
[11/19/22 22:04:28] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 593k/593k [00:00<00:00, 2.88MB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 621k/621k [00:03<00:00, 166kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:01<00:00, 366kB/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:13<00:00, 1.18it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 3832.38it/s]
Dataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475. Subsequent calls will reuse this data.
```
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-6.0.8-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.8
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
| 19 |
When len(_URLS) > 16, download will hang
|
[
-0.35618311166763306,
-0.15406537055969238,
-0.2573458254337311,
0.18303023278713226,
0.23311255872249603,
-0.07892145961523056,
0.029426762834191322,
0.046859659254550934,
0.26982203125953674,
0.20958082377910614,
-0.10404272377490997,
-0.32338833808898926,
-0.06664596498012543,
-0.2741364538669586,
-0.27228811383247375,
0.2237529158592224,
-0.12956349551677704,
-0.02941983938217163,
-0.2225925177335739,
0.10779544711112976,
-0.450708270072937,
0.2663893699645996,
-0.0013458734611049294,
-0.2235354483127594,
0.22345121204853058,
0.2350965142250061,
0.2799101173877716,
0.1861484944820404,
-0.3113683760166168,
-0.49473798274993896,
-0.15827827155590057,
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0.2895098924636841,
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] |
https://github.com/huggingface/datasets/issues/5270
|
When len(_URLS) > 16, download will hang
|
Debugging this would require quite some work in my opinion, and I've often failed to make reproducible examples, since it's pretty correlated to one's environment + hardware. So I wouldn't spend too much time on this unless we manage to reproduce this on another machine consistently.
Instead I'd encourage a more pragmatic fix that is: not create tons of processes (on regular machines it may slow things down anyway), and instead use multithreading by default.
|
### Describe the bug
```python
In [9]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 1.88MB/s]
[11/19/22 22:16:21] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/bd1cc3434212e3e654f7e16ad618f8a1470b5982b086c91b1d6bc7187183c6e9...
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:02<00:00, 239kB/s]
#10: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.06s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 534k/534k [00:02<00:00, 193kB/s]
#14: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.37s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 692k/692k [00:02<00:00, 269kB/s]
#12: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.44s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 566k/566k [00:02<00:00, 210kB/s]
#5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 613k/613k [00:02<00:00, 235kB/s]
#13: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 786k/786k [00:02<00:00, 342kB/s]
#3: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.60s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 619k/619k [00:02<00:00, 254kB/s]
#4: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.68s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 737k/737k [00:02<00:00, 271kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 788k/788k [00:02<00:00, 285kB/s]
#6: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 618k/618k [00:04<00:00, 153kB/s]
#0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:11<00:00, 5.69s/obj]
^CProcess ForkPoolWorker-47:
Process ForkPoolWorker-46:
Process ForkPoolWorker-36:
Process ForkPoolWorker-38:██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Process ForkPoolWorker-37:
Process ForkPoolWorker-45:
Process ForkPoolWorker-39:
Process ForkPoolWorker-43:
Process ForkPoolWorker-33:
Process ForkPoolWorker-18:
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 365, in get
res = self._reader.recv_bytes()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/connection.py", line 221, in recv_bytes
buf = self._recv_bytes(maxlength)
KeyboardInterrupt
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/connection.py", line 419, in _recv_bytes
buf = self._recv(4)
File "/usr/lib/python3.10/multiprocessing/connection.py", line 384, in _recv
chunk = read(handle, remaining)
KeyboardInterrupt
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
Process ForkPoolWorker-20:
Process ForkPoolWorker-44:
Process ForkPoolWorker-22:
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#1: 0%| | 0/2 [03:00<?, ?obj/s]
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 659, in get_from_cache
http_get(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 442, in http_get
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
KeyboardInterrupt
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#3: 0%| | 0/2 [03:00<?, ?obj/s]
#11: 0%| | 0/1 [00:49<?, ?obj/s]
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in send
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in <listcomp>
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 266, in resolve_redirects
resp = self.send(
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#5: 0%| | 0/1 [03:00<?, ?obj/s]
KeyboardInterrupt
Process ForkPoolWorker-42:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#9: 0%| | 0/1 [00:51<?, ?obj/s]
```
### Steps to reproduce the bug
```python
"""Kodak.
Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import datasets
NUMBER = 17
_DESCRIPTION = """\
The pictures below link to lossless, true color (24 bits per pixel, aka "full
color") images. It is my understanding they have been released by the Eastman
Kodak Company for unrestricted usage. Many sites use them as a standard test
suite for compression testing, etc. Prior to this site, they were only
available in the Sun Raster format via ftp. This meant that the images could
not be previewed before downloading. Since their release, however, the lossless
PNG format has been incorporated into all the major browsers. Since PNG
supports 24-bit lossless color (which GIF and JPEG do not), it is now possible
to offer this browser-friendly access to the images.
"""
_HOMEPAGE = "https://r0k.us/graphics/kodak/"
_LICENSE = "GPLv3"
_URLS = [
f"https://github.com/MohamedBakrAli/Kodak-Lossless-True-Color-Image-Suite/raw/master/PhotoCD_PCD0992/{i}.png"
for i in range(1, 1 + NUMBER)
]
class Kodak(datasets.GeneratorBasedBuilder):
"""Kodak datasets."""
VERSION = datasets.Version("0.0.1")
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Return SplitGenerators."""
file_paths = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"file_paths": file_paths,
},
),
]
def _generate_examples(self, file_paths):
"""Yield examples."""
for file_path in file_paths:
yield file_path, {"image": file_path}
```
### Expected behavior
When `len(_URLS) < 16`, it works.
```python
In [3]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 3.02MB/s]
[11/19/22 22:04:28] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 593k/593k [00:00<00:00, 2.88MB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 621k/621k [00:03<00:00, 166kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:01<00:00, 366kB/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:13<00:00, 1.18it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 3832.38it/s]
Dataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475. Subsequent calls will reuse this data.
```
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-6.0.8-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.8
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
| 75 |
When len(_URLS) > 16, download will hang
|
[
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0.29948967695236206,
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0.4033485949039459,
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0.24993933737277985,
0.18757475912570953,
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0.09352591633796692,
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0.008858812041580677,
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] |
https://github.com/huggingface/datasets/issues/5270
|
When len(_URLS) > 16, download will hang
|
I am not expert of python. I hear about python has GIL, which result in multi processing is worse than multi threading. So I am not sure if this change makes sense?
And if this is a bug of multi processing, why not report to upstream and let them fix? And even if change it to multi threading, how can we make sure it can truly fix this problem?
|
### Describe the bug
```python
In [9]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 1.88MB/s]
[11/19/22 22:16:21] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/bd1cc3434212e3e654f7e16ad618f8a1470b5982b086c91b1d6bc7187183c6e9...
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:02<00:00, 239kB/s]
#10: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.06s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 534k/534k [00:02<00:00, 193kB/s]
#14: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.37s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 692k/692k [00:02<00:00, 269kB/s]
#12: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.44s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 566k/566k [00:02<00:00, 210kB/s]
#5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
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#0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:11<00:00, 5.69s/obj]
^CProcess ForkPoolWorker-47:
Process ForkPoolWorker-46:
Process ForkPoolWorker-36:
Process ForkPoolWorker-38:██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Process ForkPoolWorker-37:
Process ForkPoolWorker-45:
Process ForkPoolWorker-39:
Process ForkPoolWorker-43:
Process ForkPoolWorker-33:
Process ForkPoolWorker-18:
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 365, in get
res = self._reader.recv_bytes()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/connection.py", line 221, in recv_bytes
buf = self._recv_bytes(maxlength)
KeyboardInterrupt
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/connection.py", line 419, in _recv_bytes
buf = self._recv(4)
File "/usr/lib/python3.10/multiprocessing/connection.py", line 384, in _recv
chunk = read(handle, remaining)
KeyboardInterrupt
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
Process ForkPoolWorker-20:
Process ForkPoolWorker-44:
Process ForkPoolWorker-22:
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#1: 0%| | 0/2 [03:00<?, ?obj/s]
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 659, in get_from_cache
http_get(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 442, in http_get
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
KeyboardInterrupt
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#3: 0%| | 0/2 [03:00<?, ?obj/s]
#11: 0%| | 0/1 [00:49<?, ?obj/s]
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in send
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in <listcomp>
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 266, in resolve_redirects
resp = self.send(
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#5: 0%| | 0/1 [03:00<?, ?obj/s]
KeyboardInterrupt
Process ForkPoolWorker-42:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#9: 0%| | 0/1 [00:51<?, ?obj/s]
```
### Steps to reproduce the bug
```python
"""Kodak.
Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import datasets
NUMBER = 17
_DESCRIPTION = """\
The pictures below link to lossless, true color (24 bits per pixel, aka "full
color") images. It is my understanding they have been released by the Eastman
Kodak Company for unrestricted usage. Many sites use them as a standard test
suite for compression testing, etc. Prior to this site, they were only
available in the Sun Raster format via ftp. This meant that the images could
not be previewed before downloading. Since their release, however, the lossless
PNG format has been incorporated into all the major browsers. Since PNG
supports 24-bit lossless color (which GIF and JPEG do not), it is now possible
to offer this browser-friendly access to the images.
"""
_HOMEPAGE = "https://r0k.us/graphics/kodak/"
_LICENSE = "GPLv3"
_URLS = [
f"https://github.com/MohamedBakrAli/Kodak-Lossless-True-Color-Image-Suite/raw/master/PhotoCD_PCD0992/{i}.png"
for i in range(1, 1 + NUMBER)
]
class Kodak(datasets.GeneratorBasedBuilder):
"""Kodak datasets."""
VERSION = datasets.Version("0.0.1")
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Return SplitGenerators."""
file_paths = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"file_paths": file_paths,
},
),
]
def _generate_examples(self, file_paths):
"""Yield examples."""
for file_path in file_paths:
yield file_path, {"image": file_path}
```
### Expected behavior
When `len(_URLS) < 16`, it works.
```python
In [3]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 3.02MB/s]
[11/19/22 22:04:28] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 593k/593k [00:00<00:00, 2.88MB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 621k/621k [00:03<00:00, 166kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:01<00:00, 366kB/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:13<00:00, 1.18it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 3832.38it/s]
Dataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475. Subsequent calls will reuse this data.
```
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-6.0.8-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.8
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
| 69 |
When len(_URLS) > 16, download will hang
|
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] |
https://github.com/huggingface/datasets/issues/5270
|
When len(_URLS) > 16, download will hang
|
> Just my 2c. No offense.
sure np ^^
> I hear about python has GIL, which result in multi processing is worse than multi threading. So I am not sure if this change makes sense?
Here the bottleneck speed is the bandwidth used to download the files. When downloading, the GIL is released, so multithreading gives the same speed as multiprocessing.
> And if this is a bug of multi processing, why not report to upstream and let them fix?
Usually to fix a bug it's important to be able to reproduce it. This way you can share it, experiment with it, and then make sure it's fixed. Here I'm afraid it's not easy to reproduce. Though I think that spawning too many processes for your machine can lead to this kind of issues.
> And even if change it to multi threading, how can we make sure it can truly fix this problem?
Multithreading is more robust in python because IIRC there are less locks involved which are often the cause of code hanging for no reason.
|
### Describe the bug
```python
In [9]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 1.88MB/s]
[11/19/22 22:16:21] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/bd1cc3434212e3e654f7e16ad618f8a1470b5982b086c91b1d6bc7187183c6e9...
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:02<00:00, 239kB/s]
#10: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.06s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 534k/534k [00:02<00:00, 193kB/s]
#14: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.37s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 692k/692k [00:02<00:00, 269kB/s]
#12: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.44s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 566k/566k [00:02<00:00, 210kB/s]
#5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 613k/613k [00:02<00:00, 235kB/s]
#13: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 786k/786k [00:02<00:00, 342kB/s]
#3: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.60s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 619k/619k [00:02<00:00, 254kB/s]
#4: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.68s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 737k/737k [00:02<00:00, 271kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 788k/788k [00:02<00:00, 285kB/s]
#6: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 618k/618k [00:04<00:00, 153kB/s]
#0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:11<00:00, 5.69s/obj]
^CProcess ForkPoolWorker-47:
Process ForkPoolWorker-46:
Process ForkPoolWorker-36:
Process ForkPoolWorker-38:██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Process ForkPoolWorker-37:
Process ForkPoolWorker-45:
Process ForkPoolWorker-39:
Process ForkPoolWorker-43:
Process ForkPoolWorker-33:
Process ForkPoolWorker-18:
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 365, in get
res = self._reader.recv_bytes()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/connection.py", line 221, in recv_bytes
buf = self._recv_bytes(maxlength)
KeyboardInterrupt
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/connection.py", line 419, in _recv_bytes
buf = self._recv(4)
File "/usr/lib/python3.10/multiprocessing/connection.py", line 384, in _recv
chunk = read(handle, remaining)
KeyboardInterrupt
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
Process ForkPoolWorker-20:
Process ForkPoolWorker-44:
Process ForkPoolWorker-22:
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#1: 0%| | 0/2 [03:00<?, ?obj/s]
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 659, in get_from_cache
http_get(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 442, in http_get
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
KeyboardInterrupt
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#3: 0%| | 0/2 [03:00<?, ?obj/s]
#11: 0%| | 0/1 [00:49<?, ?obj/s]
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in send
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in <listcomp>
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 266, in resolve_redirects
resp = self.send(
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#5: 0%| | 0/1 [03:00<?, ?obj/s]
KeyboardInterrupt
Process ForkPoolWorker-42:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#9: 0%| | 0/1 [00:51<?, ?obj/s]
```
### Steps to reproduce the bug
```python
"""Kodak.
Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import datasets
NUMBER = 17
_DESCRIPTION = """\
The pictures below link to lossless, true color (24 bits per pixel, aka "full
color") images. It is my understanding they have been released by the Eastman
Kodak Company for unrestricted usage. Many sites use them as a standard test
suite for compression testing, etc. Prior to this site, they were only
available in the Sun Raster format via ftp. This meant that the images could
not be previewed before downloading. Since their release, however, the lossless
PNG format has been incorporated into all the major browsers. Since PNG
supports 24-bit lossless color (which GIF and JPEG do not), it is now possible
to offer this browser-friendly access to the images.
"""
_HOMEPAGE = "https://r0k.us/graphics/kodak/"
_LICENSE = "GPLv3"
_URLS = [
f"https://github.com/MohamedBakrAli/Kodak-Lossless-True-Color-Image-Suite/raw/master/PhotoCD_PCD0992/{i}.png"
for i in range(1, 1 + NUMBER)
]
class Kodak(datasets.GeneratorBasedBuilder):
"""Kodak datasets."""
VERSION = datasets.Version("0.0.1")
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Return SplitGenerators."""
file_paths = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"file_paths": file_paths,
},
),
]
def _generate_examples(self, file_paths):
"""Yield examples."""
for file_path in file_paths:
yield file_path, {"image": file_path}
```
### Expected behavior
When `len(_URLS) < 16`, it works.
```python
In [3]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 3.02MB/s]
[11/19/22 22:04:28] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 593k/593k [00:00<00:00, 2.88MB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 621k/621k [00:03<00:00, 166kB/s]
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Dataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475. Subsequent calls will reuse this data.
```
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-6.0.8-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.8
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
| 179 |
When len(_URLS) > 16, download will hang
|
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] |
https://github.com/huggingface/datasets/issues/5265
|
Get an IterableDataset from a map-style Dataset
|
I think `stream` could be misleading since the data is not being streamed from remote endpoints (one could think that's the case when they see `load_dataset` followed by `stream`). Hence, I prefer the second option.
PS: When we resolve https://github.com/huggingface/datasets/issues/4542, we could add `as_tf_dataset` to the API for consistency and deprecate `to_tf_dataset`.
|
This is useful to leverage iterable datasets specific features like:
- fast approximate shuffling
- lazy map, filter etc.
Iterating over the resulting iterable dataset should be at least as fast at iterating over the map-style dataset.
Here are some ideas regarding the API:
```python
# 1.
# - consistency with load_dataset(..., streaming=True)
# - gives intuition that map/filter/etc. are done on-the-fly
ids = ds.stream()
# 2.
# - more explicit on the output type
# - but maybe sounds like a conversion tool rather than a step in a processing pipeline
ids = ds.as_iterable_dataset()
```
| 52 |
Get an IterableDataset from a map-style Dataset
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
Could you share the full stack trace please ?
Can you also try running this code ? It can be useful to determine if the issue comes from `datasets` or `fsspec` (streaming) or `pyarrow` (parquet reading):
```python
ds = load_dataset("parquet", data_files=a_parquet_file_url, use_auth_token=True)
```
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 43 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
Here's the full trace
```
Traceback (most recent call last):
File "/home/loubna_huggingface_co/load.py", line 15, in <module>
ds_all = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java",use_auth_token=True, split="train", revision="v1.1.a1")
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py", line 1742, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py", line 814, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py", line 905, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py", line 1502, in _prepare_split
for key, table in logging.tqdm(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 67, in _generate_tables
parquet_file = pq.ParquetFile(f)
File "/opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py", line 286, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1227, in pyarrow._parquet.ParquetReader.open
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
when running
```python
ds = load_dataset("parquet", data_files="https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/blob/v1.1.a1/data/java/data_0000.parquet", use_auth_token=True)
```
I get 401 error, but that's the case for the python subset too which I can load properly
```
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py", line 1719, in load_dataset
builder_instance = load_dataset_builder(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py", line 1497, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py", line 1134, in dataset_module_factory
return PackagedDatasetModuleFactory(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py", line 707, in get_module
data_files = DataFilesDict.from_local_or_remote(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py", line 795, in from_local_or_remote
DataFilesList.from_local_or_remote(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py", line 764, in from_local_or_remote
origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py", line 710, in _get_origin_metadata_locally_or_by_urls
return thread_map(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py", line 94, in thread_map
return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
File "/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py", line 76, in _executor_map
return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))
File "/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py", line 1183, in __iter__
for obj in iterable:
File "/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py", line 609, in result_iterator
yield fs.pop().result()
File "/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py", line 446, in result
return self.__get_result()
File "/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result
raise self._exception
File "/opt/conda/envs/venv/lib/python3.9/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py", line 701, in _get_single_origin_metadata_locally_or_by_urls
return (request_etag(data_file, use_auth_token=use_auth_token),)
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 411, in request_etag
response.raise_for_status()
File "/opt/conda/envs/venv/lib/python3.9/site-packages/requests/models.py", line 960, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/blob/v1.1.a1/data/python/data_0000.parquet```
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 301 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
Can you check you used the right token ? You shouldn't get a 401 using your token
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 17 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
I checked it’s the right token, when loading the full dataset I get the error after data extraction so I can access the files.
```
Downloading and preparing dataset parquet/bigcode--the-stack-dedup-pjj to /home/loubna_huggingface_co/.cache/huggingface/datasets/bigcode___parquet/bigcode--the-stack-dedup-pjj-872ffac7f4bb46ca/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...
Downloading data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 22.38it/s]
Extracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 49.91it/s]
Traceback (most recent call last):
File "/home/loubna_huggingface_co/load_ds.py", line 5, in <module>
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", use_auth_token=True,split="train", revision="v1.1.a1")
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py", line 1742, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py", line 814, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py", line 905, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py", line 1502, in _prepare_split
for key, table in logging.tqdm(
File "/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 67, in _generate_tables
parquet_file = pq.ParquetFile(f)
File "/opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py", line 286, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1227, in pyarrow._parquet.ParquetReader.open
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
Could it be that I'm using a wrong url, I just copied it from the address bar
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 172 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
The URL is wrong indeed, the right one is the one with "resolve" (the one you get when clicking on "download")- otherwise you try to download an html page ;)
```
https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/resolve/v1.1.a1/data/java/data_0000.parquet
```
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 33 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
Ah thanks! So I tried it with the first parquet file and it works, is there a way to know which parquet file was causing the issue since there are a lot of shards?
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 34 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
I think you have to try them all :/
Alternatively you can add a try/catch in `parquet.py` in `datasets` to raise the name of the file that fails at doing `parquet_file = pq.ParquetFile(f)` when you run your initial code
```python
load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
```
but it will still iterate on all the files until it fails
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 58 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
I did find the file, and I get the same error as before
```
Downloading data files: 100%|███████████████████| 1/1 [00:00<00:00, 8160.12it/s]
Extracting data files: 100%|████████████████████| 1/1 [00:00<00:00, 1447.81it/s]
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
Input In [22], in <cell line: 7>()
4 data_features = (data["train"].features)
6 url = "/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7"
----> 7 data = load_dataset("parquet",
8 data_files=url,
9 split="train",
10 features=data_features,
11 use_auth_token=True)
File /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py:1742, 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)
1739 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1741 # Download and prepare data
-> 1742 builder_instance.download_and_prepare(
1743 download_config=download_config,
1744 download_mode=download_mode,
1745 ignore_verifications=ignore_verifications,
1746 try_from_hf_gcs=try_from_hf_gcs,
1747 use_auth_token=use_auth_token,
1748 )
1750 # Build dataset for splits
1751 keep_in_memory = (
1752 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1753 )
File /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:814, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, storage_options, **download_and_prepare_kwargs)
808 if not downloaded_from_gcs:
809 prepare_split_kwargs = {
810 "file_format": file_format,
811 "max_shard_size": max_shard_size,
812 **download_and_prepare_kwargs,
813 }
--> 814 self._download_and_prepare(
815 dl_manager=dl_manager,
816 verify_infos=verify_infos,
817 **prepare_split_kwargs,
818 **download_and_prepare_kwargs,
819 )
820 # Sync info
821 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:905, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
901 split_dict.add(split_generator.split_info)
903 try:
904 # Prepare split will record examples associated to the split
--> 905 self._prepare_split(split_generator, **prepare_split_kwargs)
906 except OSError as e:
907 raise OSError(
908 "Cannot find data file. "
909 + (self.manual_download_instructions or "")
910 + "\nOriginal error:\n"
911 + str(e)
912 ) from None
File /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:1502, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, max_shard_size)
1500 total_num_examples, total_num_bytes = 0, 0
1501 try:
-> 1502 for key, table in logging.tqdm(
1503 generator,
1504 unit=" tables",
1505 leave=False,
1506 disable=not logging.is_progress_bar_enabled(),
1507 ):
1508 if max_shard_size is not None and writer._num_bytes > max_shard_size:
1509 num_examples, num_bytes = writer.finalize()
File /opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py:1195, in tqdm.__iter__(self)
1192 time = self._time
1194 try:
-> 1195 for obj in iterable:
1196 yield obj
1197 # Update and possibly print the progressbar.
1198 # Note: does not call self.update(1) for speed optimisation.
File /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py:67, in Parquet._generate_tables(self, files)
65 for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
66 with open(file, "rb") as f:
---> 67 parquet_file = pq.ParquetFile(f)
68 try:
69 for batch_idx, record_batch in enumerate(
70 parquet_file.iter_batches(batch_size=self.config.batch_size, columns=self.config.columns)
71 ):
File /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py:286, in ParquetFile.__init__(self, source, metadata, common_metadata, read_dictionary, memory_map, buffer_size, pre_buffer, coerce_int96_timestamp_unit, decryption_properties, thrift_string_size_limit, thrift_container_size_limit)
280 def __init__(self, source, *, metadata=None, common_metadata=None,
281 read_dictionary=None, memory_map=False, buffer_size=0,
282 pre_buffer=False, coerce_int96_timestamp_unit=None,
283 decryption_properties=None, thrift_string_size_limit=None,
284 thrift_container_size_limit=None):
285 self.reader = ParquetReader()
--> 286 self.reader.open(
287 source, use_memory_map=memory_map,
288 buffer_size=buffer_size, pre_buffer=pre_buffer,
289 read_dictionary=read_dictionary, metadata=metadata,
290 coerce_int96_timestamp_unit=coerce_int96_timestamp_unit,
291 decryption_properties=decryption_properties,
292 thrift_string_size_limit=thrift_string_size_limit,
293 thrift_container_size_limit=thrift_container_size_limit,
294 )
295 self.common_metadata = common_metadata
296 self._nested_paths_by_prefix = self._build_nested_paths()
File /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/_parquet.pyx:1227, in pyarrow._parquet.ParquetReader.open()
File /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/error.pxi:100, in pyarrow.lib.check_status()
ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 465 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
Can you check the JSON file associated to `/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7` ? In the JSON file we can know from where it was downloaded
You can find it at `/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7.json`
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 28 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
I'm able to load it properly using
```python
ds = load_dataset("parquet", data_files=a_parquet_file_url, use_auth_token=token)
```
My guess is that your download was corrupted. Please delete `93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7` and `93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7.json` locally and try again
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 31 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
That worked, thanks! But I thought if something went wrong with a download `datasets` creates new cache for all the files, that's not the case? (at some point I even changed dataset versions so it was still using that cache?)
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 40 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
Cool !
> But I thought if something went wrong with a download datasets creates new cache for all the files
We don't perform integrity verifications if we don't know in advance the hash of the file to download.
> at some point I even changed dataset versions so it was still using that cache?
`datasets` caches the files by URL and ETag. If the content of a file changes, then the ETag changes and so it redownloads the file
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 80 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5264
|
`datasets` can't read a Parquet file in Python 3.9.13
|
I experience the same error in v 2.12.0. But found out it was due to one column from polars was a categorical dtype (related to the error from #5706. Temporarily resolved it by casting the column to str instead.
|
### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
| 39 |
`datasets` can't read a Parquet file in Python 3.9.13
|
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] |
https://github.com/huggingface/datasets/issues/5262
|
AttributeError: 'Value' object has no attribute 'names'
|
Hi ! It looks like your "isDif" column is a Sequence of Value("string"), not a Sequence of ClassLabel.
You can convert your Value("string") feature type to a ClassLabel feature type this way:
```python
from datasets import ClassLabel, Sequence
# provide the label_names yourself
label_names = [...]
# OR get them from the dataset
label_names = sorted(set(label for labels in raw_datasets["train"]["isDif"] for label in labels))
# Cast to ClassLabel
raw_datasets = raw_datasets.cast_column("isDif", Sequence(ClassLabel(names=label_names)))
```
|
Hello
I'm trying to build a model for custom token classification
I already followed the token classification course on huggingface
while adapting the code to my work, this message occures :
'Value' object has no attribute 'names'
Here's my code:
`raw_datasets`
generates
DatasetDict({
train: Dataset({
features: ['isDisf', 'pos', 'tokens', 'id'],
num_rows: 14
})
})
`raw_datasets["train"][3]["isDisf"]`
generates
['B_RM', 'I_RM', 'I_RM', 'B_RP', 'I_RP', 'O', 'O']
`dis_feature = raw_datasets["train"].features["isDisf"]
dis_feature`
generates
Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)
and
`label_names = dis_feature.feature.names
label_names`
generates
AttributeError Traceback (most recent call last)
[<ipython-input-28-972fd54a869a>](https://localhost:8080/#) in <module>
----> 1 label_names = dis_feature.feature.names
2 label_names
AttributeError: 'Value' object has
AttributeError: 'Value' object has no attribute 'names'
Thank you for your help
| 73 |
AttributeError: 'Value' object has no attribute 'names'
|
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] |
https://github.com/huggingface/datasets/issues/5261
|
Add PubTables-1M
|
cc @albertvillanova the author would like to add this dataset to the hub: https://github.com/microsoft/table-transformer/issues/68#issuecomment-1319114621. Could you help him out?
|
### Name
PubTables-1M
### Paper
https://openaccess.thecvf.com/content/CVPR2022/html/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.html
### Data
https://github.com/microsoft/table-transformer
### Motivation
Table Transformer is now available in 🤗 Transformer, and it was trained on PubTables-1M. It's a large dataset for table extraction and structure recognition in unstructured documents.
| 19 |
Add PubTables-1M
|
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] |
https://github.com/huggingface/datasets/issues/5260
|
consumer-finance-complaints dataset not loading
|
I have opened an issue in that dataset Community tab on the Hub: https://huggingface.co/datasets/consumer-finance-complaints/discussions/1
Please note that in the meantime, you can load the dataset by passing `ignore_verifications=True`:
```python
>>> ds = load_dataset("consumer-finance-complaints", ignore_verifications=True)
>>> ds
DatasetDict({
train: Dataset({
features: ['Date Received', 'Product', 'Sub Product', 'Issue', 'Sub Issue', 'Complaint Text', 'Company Public Response', 'Company', 'State', 'Zip Code', 'Tags', 'Consumer Consent Provided', 'Submitted via', 'Date Sent To Company', 'Company Response To Consumer', 'Timely Response', 'Consumer Disputed', 'Complaint ID'],
num_rows: 3079747
})
})
```
|
### Describe the bug
Error during dataset loading
### Steps to reproduce the bug
```
>>> import datasets
>>> cf_raw = datasets.load_dataset("consumer-finance-complaints")
Downloading builder script: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8.42k/8.42k [00:00<00:00, 3.33MB/s]
Downloading metadata: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.60k/5.60k [00:00<00:00, 2.90MB/s]
Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16.6k/16.6k [00:00<00:00, 510kB/s]
Downloading and preparing dataset consumer-finance-complaints/default to /root/.cache/huggingface/datasets/consumer-finance-complaints/default/0.0.0/30e483d37fb4b25bb98cad1bfd2dc48f6ed6d1f3371eb4568c625a61d1a79b69...
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 511M/511M [00:04<00:00, 103MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare
super()._download_and_prepare(
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 931, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=1605177353, num_examples=2455765, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=2043641693, num_examples=3079747, shard_lengths=[721000, 656000, 788000, 846000, 68747], dataset_name='consumer-finance-complaints')}]
```
### Expected behavior
dataset should load
### Environment info
>>> datasets.__version__
'2.7.0'
Python 3.8.10
"Ubuntu 20.04.4 LTS"
| 82 |
consumer-finance-complaints dataset not loading
|
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] |
https://github.com/huggingface/datasets/issues/5259
|
datasets 2.7 introduces sharding error
|
I notice a comment in the code says:
`Having lists of different sizes makes sharding ambigious, raise an error in this case until we decide how to define sharding without ambiguity for users`
... which suggests this update was pushed knowing that it might break some things. But, it didn't seem to have a useful error message of an argument that could be passed to avoid the error.
|
### Describe the bug
dataset fails to load with runtime error
`RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.`
### Steps to reproduce the bug
With datasets[audio] 2.7 loaded, and logged into hugging face,
`data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)`
creates the error.
Full stack trace:
```---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
[<ipython-input-7-8cb9ca0f79f0>](https://localhost:8080/#) in <module>
----> 1 data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)
5 frames
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) 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, num_proc, **config_kwargs)
1745 try_from_hf_gcs=try_from_hf_gcs,
1746 use_auth_token=use_auth_token,
-> 1747 num_proc=num_proc,
1748 )
1749
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
824 verify_infos=verify_infos,
825 **prepare_split_kwargs,
--> 826 **download_and_prepare_kwargs,
827 )
828 # Sync info
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
1554 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs):
1555 super()._download_and_prepare(
-> 1556 dl_manager, verify_infos, check_duplicate_keys=verify_infos, **prepare_splits_kwargs
1557 )
1558
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
911 try:
912 # Prepare split will record examples associated to the split
--> 913 self._prepare_split(split_generator, **prepare_split_kwargs)
914 except OSError as e:
915 raise OSError(
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1362 fpath = path_join(self._output_dir, fname)
1363
-> 1364 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
1365 if num_input_shards <= 1 and num_proc is not None:
1366 logger.warning(
[/usr/local/lib/python3.7/dist-packages/datasets/utils/sharding.py](https://localhost:8080/#) in _number_of_shards_in_gen_kwargs(gen_kwargs)
16 + "\n".join(f"\t- key {key} has length {length}" for key, length in lists_lengths.items())
17 + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
---> 18 + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
19 )
20 )
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.```
### Expected behavior
the dataset loads in datasets version 2.6.1 and should load with datasets 2.7
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
| 68 |
datasets 2.7 introduces sharding error
|
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] |
https://github.com/huggingface/datasets/issues/5259
|
datasets 2.7 introduces sharding error
|
Sorry for the inconvenience, I opened a PR in your repo to fix this: https://huggingface.co/datasets/sil-ai/bloom-speech/discussions/2
Basically we've always considered lists in `gen_kwargs` to be a shard list that we can split and pass into different workers to generate the dataset (e.g. if you pass `num_proc=` in `load_dataset()` to generate the dataset in parallel), but it was documented only recently
|
### Describe the bug
dataset fails to load with runtime error
`RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.`
### Steps to reproduce the bug
With datasets[audio] 2.7 loaded, and logged into hugging face,
`data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)`
creates the error.
Full stack trace:
```---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
[<ipython-input-7-8cb9ca0f79f0>](https://localhost:8080/#) in <module>
----> 1 data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)
5 frames
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) 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, num_proc, **config_kwargs)
1745 try_from_hf_gcs=try_from_hf_gcs,
1746 use_auth_token=use_auth_token,
-> 1747 num_proc=num_proc,
1748 )
1749
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
824 verify_infos=verify_infos,
825 **prepare_split_kwargs,
--> 826 **download_and_prepare_kwargs,
827 )
828 # Sync info
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
1554 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs):
1555 super()._download_and_prepare(
-> 1556 dl_manager, verify_infos, check_duplicate_keys=verify_infos, **prepare_splits_kwargs
1557 )
1558
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
911 try:
912 # Prepare split will record examples associated to the split
--> 913 self._prepare_split(split_generator, **prepare_split_kwargs)
914 except OSError as e:
915 raise OSError(
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1362 fpath = path_join(self._output_dir, fname)
1363
-> 1364 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
1365 if num_input_shards <= 1 and num_proc is not None:
1366 logger.warning(
[/usr/local/lib/python3.7/dist-packages/datasets/utils/sharding.py](https://localhost:8080/#) in _number_of_shards_in_gen_kwargs(gen_kwargs)
16 + "\n".join(f"\t- key {key} has length {length}" for key, length in lists_lengths.items())
17 + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
---> 18 + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
19 )
20 )
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.```
### Expected behavior
the dataset loads in datasets version 2.6.1 and should load with datasets 2.7
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
| 59 |
datasets 2.7 introduces sharding error
|
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] |
https://github.com/huggingface/datasets/issues/5258
|
Restore order of split names in dataset_info for canonical datasets
|
The bulk edit is running...
See for example:
- A single config: https://huggingface.co/datasets/acronym_identification/discussions/2
- Multiple configs: https://huggingface.co/datasets/babi_qa/discussions/1
|
After a bulk edit of canonical datasets to create the YAML `dataset_info` metadata, the split names were accidentally sorted alphabetically. See for example:
- https://huggingface.co/datasets/bc2gm_corpus/commit/2384629484401ecf4bb77cd808816719c424e57c
Note that this order is the one appearing in the preview of the datasets.
I'm making a bulk edit to align the order of the splits appearing in the metadata info with the order appearing in the loading script.
Related to:
- #5202
| 17 |
Restore order of split names in dataset_info for canonical datasets
|
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] |
https://github.com/huggingface/datasets/issues/5258
|
Restore order of split names in dataset_info for canonical datasets
|
TODO: Add "dataset_info" YAML metadata to:
- [x] "chr_en" has no metadata JSON file, nor "dataset_info" YAML tag in its card
- Fixing PR: https://huggingface.co/datasets/chr_en/discussions/1
- [x] "conll2000" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [x] "crime_and_punish" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [x] "dart" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [x] "iwslt2017" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [ ] "mc4" has no metadata JSON file, nor "dataset_info" YAML tag in its card
- [ ] "the_pile" has no metadata JSON file, nor "dataset_info" YAML tag in its card
- [ ] "timit_asr" has no metadata JSON file, nor "dataset_info" YAML tag in its card
|
After a bulk edit of canonical datasets to create the YAML `dataset_info` metadata, the split names were accidentally sorted alphabetically. See for example:
- https://huggingface.co/datasets/bc2gm_corpus/commit/2384629484401ecf4bb77cd808816719c424e57c
Note that this order is the one appearing in the preview of the datasets.
I'm making a bulk edit to align the order of the splits appearing in the metadata info with the order appearing in the loading script.
Related to:
- #5202
| 141 |
Restore order of split names in dataset_info for canonical datasets
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
Cool ! Let us know if you have questions or if we can help :)
I guess we'll also have to create the NYU CS Department on the Hub ?
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 30 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
> I guess we'll also have to create the NYU CS Department on the Hub ?
Yes, you're right! Let me add it to my profile first, and then we can transfer. Meanwhile, if it's recommended to loop the dataset author in here, let me know.
Also, the NYU Depth dataset seems big. Any example scripts for creating image datasets that I could refer?
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 64 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
You can check the imagenet-1k one.
PS: If the licenses allows it, it'b be nice to host the dataset as sharded TAR archives (like imagenet-1k) instead of the ZIP format they use:
- it will make streaming much faster
- ZIP compression is not well suited for images
- it will allow parallel processing of the dataset (you can pass a subset of shards to each worker)
> if it's recommended to loop the dataset author in here, let me know.
It's recommended indeed, you can send them an email once you have the dataset ready and invite them to the org on the Hub
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 105 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
> You can check the imagenet-1k one.
Where can I find the script? Are you referring to https://huggingface.co/docs/datasets/image_process ? Or is there anything more specific?
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 25 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
Update: started working on it here: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2.
I am facing an issue and I have detailed it here: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/discussions/1
Edit: The issue is gone.
However, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive?
@lhoestq
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 57 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
A Colab Notebook demonstrating the dataset loading part:
https://colab.research.google.com/gist/sayakpaul/aa0958c8d4ad8518d52a78f28044d871/scratchpad.ipynb
@osanseviero @lhoestq
I will work on a notebook to work with the dataset including data visualization.
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 25 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
@osanseviero @lhoestq things seem to work fine with the current version of the dataset [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2). Here's a notebook I developed to help with visualization: https://colab.research.google.com/drive/1K3ZU8XUPRDOYD38MQS9nreQXJYitlKSW?usp=sharing.
@lhoestq I need your help with the following:
> However, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive?
@osanseviero @lhoestq question for you:
Where should we host the dataset? I think hosting it under hf.co/datasets (that is HF is the org) is fine as we have ImageNet-1k hosted similarly. We could then reach out to Diana Wofk (author of [Fast Depth](https://github.com/dwofk/fast-depth) and the owner of the repo on which TFDS NYU Depth V2 is based) for a review. WDYT?
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 127 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
> However, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive?
First you can separate the train data and the validation data.
Then since the dataset is quite big, you can even shard the train split and the validation split in multiple TAR archives. Something around 16 archives for train and 4 for validation would be fine for example.
Also no need to gzip the TAR archives, the images are already compressed in png or jpeg.
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 98 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
> Then since the dataset is quite big, you can even shard the train split and the validation split in multiple TAR archives. Something around 16 archives for train and 4 for validation would be fine for example.
Yes, I got you. But this process seems to be manual and should be tailored for the given dataset. Do you have any script that you used to create the ImageNet-1k shards?
> Also no need to gzip the TAR archives, the images are already compressed in png or jpeg.
I was not going to do that. Not sure what brought it up.
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 101 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
> Yes, I got you. But this process seems to be manual and should be tailored for the given dataset. Do you have any script that you used to create the ImageNet-1k shards?
I don't, but I agree it'd be nice to have a script for that !
> I was not going to do that. Not sure what brought it up.
The original dataset is gzipped for some reason
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 70 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
> Where should we host the dataset? I think hosting it under hf.co/datasets (that is HF is the org) is fine as we have ImageNet-1k hosted similarly.
Maybe you can create an org for NYU Courant (this is the institute of the lab of the main author of the dataset if I'm not mistaken), and invite the authors to join.
We don't add datasets without namespace anymore
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 67 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
Updates: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/discussions/5
The entire process (preparing multiple archives, preparing data loading script, etc.) was fun and engaging, thanks to the documentation. I believe we could work on a small blog post that would work as a reference for the future contributors following this path. What say?
Cc: @lhoestq @osanseviero
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 49 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
> I believe we could work on a small blog post that would work as a reference for the future contributors following this path. What say?
@polinaeterna already mentioned it would be nice to present this process for audio (it's exactly the same), I believe it can be useful to many people
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 52 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
Cool. Let's work on that after the NYU Depth Dataset is fully in on Hub (under the appropriate org). 🤗
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 20 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
@lhoestq need to discuss something while I am adding the dataset card to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/.
As per [Papers With Code](https://paperswithcode.com/dataset/nyuv2), NYU Depth v2 is used for many different tasks:
* Monocular depth estimation
* Depth estimation
* Semantic segmentation
* Plane instance segmentation
* ...
So, while writing the supported task part of the dataset card, should we focus on all these? IMO, we could focus on just depth estimation and semantic segmentation for now since we have supported models for these two. WDYT?
Also, I am getting:
```
remote: Your push was accepted, but with warnings:
remote: - Warning: The task_ids "depth-estimation" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering
remote: ----------------------------------------------------------
remote: Please find the documentation at:
remote: https://huggingface.co/docs/hub/model-cards#model-card-metadata
```
What should be the plan of action for this?
Cc: @osanseviero
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 198 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5255
|
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
> What should be the plan of action for this?
When you merged https://github.com/huggingface/hub-docs/pull/488, there is a JS Interfaces GitHub Actions workflow that runs https://github.com/huggingface/hub-docs/actions/workflows/js-interfaces-tests.yml. It has a step called [export-task scripts](https://github.com/huggingface/hub-docs/actions/runs/3622479064/jobs/6107238948) which exports an interface you can use in `dataset`. If you look at the logs, it prints out a map. This map can replace https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/tasks.json (tasks.json was generated with this script), which should add depth estimation
|
### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it.
| 68 |
Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI
|
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] |
https://github.com/huggingface/datasets/issues/5251
|
Docs are not generated after latest release
|
After a discussion with @mishig25:
- He said that this action should be triggered if we call our release branch according to the regex `v*-release`, as transformers does
- I said that our procedure is different: our release branch is *temporary* and it is deleted just after the release PR is merged to main
- Indeed the release tag is not yet created when we make the release PR (not event when this is merged to main), but when we make the Release itself.
I was thinking that maybe we could change the triggering event: use `release` instead of `push`.
What do you think, @huggingface/datasets?
|
After the latest `datasets` release version 0.7.0, the docs were not generated.
As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action:
https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8
Related to:
- #5250
CC: @mishig25
| 105 |
Docs are not generated after latest release
|
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] |
https://github.com/huggingface/datasets/issues/5251
|
Docs are not generated after latest release
|
He says not; but the branch has no tag yet; does the doc building require the tag? Or just the version number in `__init__.py` or setup.py?
|
After the latest `datasets` release version 0.7.0, the docs were not generated.
As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action:
https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8
Related to:
- #5250
CC: @mishig25
| 26 |
Docs are not generated after latest release
|
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] |
https://github.com/huggingface/datasets/issues/5251
|
Docs are not generated after latest release
|
It uses `module.__version__` (i.e. the one defined in `__init__.py`) - no need to have a tag
https://github.com/huggingface/doc-builder/blob/81575cf081964c30ea5fd39450f4820db963f18e/src/doc_builder/commands/build.py#L69
|
After the latest `datasets` release version 0.7.0, the docs were not generated.
As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action:
https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8
Related to:
- #5250
CC: @mishig25
| 17 |
Docs are not generated after latest release
|
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] |
https://github.com/huggingface/datasets/issues/5251
|
Docs are not generated after latest release
|
Thanks, @lhoestq.
@mishig25 has manually forced the generation of the docs, that are live for 2.7.0 version: https://huggingface.co/docs/datasets/v2.7.0/en/index
|
After the latest `datasets` release version 0.7.0, the docs were not generated.
As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action:
https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8
Related to:
- #5250
CC: @mishig25
| 18 |
Docs are not generated after latest release
|
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] |
https://github.com/huggingface/datasets/issues/5251
|
Docs are not generated after latest release
|
just to confirm, is there anything I need to do from my side ? Or is everything good here ?
|
After the latest `datasets` release version 0.7.0, the docs were not generated.
As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action:
https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8
Related to:
- #5250
CC: @mishig25
| 20 |
Docs are not generated after latest release
|
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0.07576131820678711,
0.3127543032169342,
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] |
https://github.com/huggingface/datasets/issues/5245
|
Unable to rename columns in streaming dataset
|
Hi @peregilk this bug is directly related to https://github.com/huggingface/datasets/issues/3888, and still not fixed... But I'll try to have a look!
|
### Describe the bug
Trying to rename column in a streaming datasets, destroys the features object.
### Steps to reproduce the bug
The following code illustrates the error:
```
from datasets import load_dataset
dataset = load_dataset('mc4', 'en', streaming=True, split='train')
dataset.info.features
# {'text': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
dataset = dataset.rename_column("text", "content")
dataset.info.features
# This returned object is now None!
```
### Expected behavior
This should just alter the renamed column.
### Environment info
datasets 2.6.1
| 20 |
Unable to rename columns in streaming dataset
|
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] |
https://github.com/huggingface/datasets/issues/5245
|
Unable to rename columns in streaming dataset
|
Thanks @alvarobartt. It is great if you are able to fix it, but when reading the explanation it seems like it is possible to work around it.
We also tried keeping the 'info.features' and then adding a modified version back after the remove/rename. Unforutunately that leads to a dataset that is not possible to iterate over.
|
### Describe the bug
Trying to rename column in a streaming datasets, destroys the features object.
### Steps to reproduce the bug
The following code illustrates the error:
```
from datasets import load_dataset
dataset = load_dataset('mc4', 'en', streaming=True, split='train')
dataset.info.features
# {'text': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
dataset = dataset.rename_column("text", "content")
dataset.info.features
# This returned object is now None!
```
### Expected behavior
This should just alter the renamed column.
### Environment info
datasets 2.6.1
| 56 |
Unable to rename columns in streaming dataset
|
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] |
https://github.com/huggingface/datasets/issues/5245
|
Unable to rename columns in streaming dataset
|
So if you iterate over the `IterableDataset` as `next(iter(ds))` and then run `rename_columns` when checking that data it will work, but in the end, it's just renaming the column one example/batch at a time, not renaming the column name for all the entries in the dataset, which is the ideal.
|
### Describe the bug
Trying to rename column in a streaming datasets, destroys the features object.
### Steps to reproduce the bug
The following code illustrates the error:
```
from datasets import load_dataset
dataset = load_dataset('mc4', 'en', streaming=True, split='train')
dataset.info.features
# {'text': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
dataset = dataset.rename_column("text", "content")
dataset.info.features
# This returned object is now None!
```
### Expected behavior
This should just alter the renamed column.
### Environment info
datasets 2.6.1
| 50 |
Unable to rename columns in streaming dataset
|
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] |
https://github.com/huggingface/datasets/issues/5245
|
Unable to rename columns in streaming dataset
|
@alvarobartt Thanks. My use case was that I wanted to do multiple things, ie removing all unnecessary columns, renaming some valid columns, and then using cast (in my case checking if the audio is not 16K and casting it). It is just convenient to look into the info.features between each of these operations. Alternatively, I will just plan ahead...;) To me it seems like all the operations are working.
Thanks for the advice. It was very useful.
|
### Describe the bug
Trying to rename column in a streaming datasets, destroys the features object.
### Steps to reproduce the bug
The following code illustrates the error:
```
from datasets import load_dataset
dataset = load_dataset('mc4', 'en', streaming=True, split='train')
dataset.info.features
# {'text': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
dataset = dataset.rename_column("text", "content")
dataset.info.features
# This returned object is now None!
```
### Expected behavior
This should just alter the renamed column.
### Environment info
datasets 2.6.1
| 77 |
Unable to rename columns in streaming dataset
|
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] |
https://github.com/huggingface/datasets/issues/5245
|
Unable to rename columns in streaming dataset
|
If we know the features before renaming, then we know the features after renaming, so we can pass the new features to the returned dataset in `rename_column` indeed ! If anyone is interested in contributing, feel free to open a PR and I'd be happy to help / give some pointers :)
|
### Describe the bug
Trying to rename column in a streaming datasets, destroys the features object.
### Steps to reproduce the bug
The following code illustrates the error:
```
from datasets import load_dataset
dataset = load_dataset('mc4', 'en', streaming=True, split='train')
dataset.info.features
# {'text': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
dataset = dataset.rename_column("text", "content")
dataset.info.features
# This returned object is now None!
```
### Expected behavior
This should just alter the renamed column.
### Environment info
datasets 2.6.1
| 52 |
Unable to rename columns in streaming dataset
|
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] |
https://github.com/huggingface/datasets/issues/5244
|
Allow dataset streaming from private a private source when loading a dataset with a dataset loading script
|
Hi ! What kind of private source ? We're exploring adding support for cloud storage and URIs like s3://, gs:// etc. with authentication in the download manager
|
### Feature request
Add arguments to the function _get_authentication_headers_for_url_ like custom_endpoint and custom_token in order to add flexibility when downloading files from a private source.
It should also be possible to provide these arguments from the dataset loading script, maybe giving them to the dl_manager
### Motivation
It is possible to share a dataset hosted on another platform by writing a dataset loading script. It works perfectly for publicly available resources.
For resources that require authentication, you can provide a [download_custom](https://huggingface.co/docs/datasets/package_reference/builder_classes#datasets.DownloadManager) method to the download_manager.
Unfortunately, this function doesn't work with **dataset streaming**.
A solution so as to allow dataset streaming from private sources would be a more flexible _get_authentication_headers_for_url_ function.
### Your contribution
Would you be interested in this improvement ?
If so I could provide a PR. I've got something working locally, but it's not very clean, I'd need some guidance regarding integration.
| 27 |
Allow dataset streaming from private a private source when loading a dataset with a dataset loading script
|
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] |
https://github.com/huggingface/datasets/issues/5244
|
Allow dataset streaming from private a private source when loading a dataset with a dataset loading script
|
Hello! It's a google cloud storage, so gs://, but I'm using it with https.
Being able to provide a file system like [here](https://huggingface.co/docs/datasets/main/filesystems#load-serialized-datasets) would be even more practical indeed.
I've found a quite complicated workaround which consists of monkey patching all of the functions in streaming_download_manager.py to use my own _get_authentication_headers_for_url_ .
A support for this use case would be greatly appreciated!
For reference my _get_authentication_headers_for_url_ looks like this:
```
import os
from typing import Optional, Union
from datasets import config
from huggingface_hub import HfFolder
from gcsfs.credentials import GoogleCredentials
DEFAULT_PROJECT = os.environ.get("GCSFS_DEFAULT_PROJECT", "")
access = "full_control"
gcs_token = os.environ.get("GCS_TOKEN")
def get_authentication_headers_for_url(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> dict:
"""Handle the HF authentication"""
headers = {}
if url.startswith(config.HF_ENDPOINT):
if use_auth_token is False:
token = None
elif isinstance(use_auth_token, str):
token = use_auth_token
else:
token = HfFolder.get_token()
elif url.startswith("https://storage.googleapis.com"):
credentials = GoogleCredentials(DEFAULT_PROJECT, access, gcs_token)
credentials.maybe_refresh()
token = credentials.credentials.token
else:
token = None
if token:
headers["authorization"] = f"Bearer {token}"
return headers
```
|
### Feature request
Add arguments to the function _get_authentication_headers_for_url_ like custom_endpoint and custom_token in order to add flexibility when downloading files from a private source.
It should also be possible to provide these arguments from the dataset loading script, maybe giving them to the dl_manager
### Motivation
It is possible to share a dataset hosted on another platform by writing a dataset loading script. It works perfectly for publicly available resources.
For resources that require authentication, you can provide a [download_custom](https://huggingface.co/docs/datasets/package_reference/builder_classes#datasets.DownloadManager) method to the download_manager.
Unfortunately, this function doesn't work with **dataset streaming**.
A solution so as to allow dataset streaming from private sources would be a more flexible _get_authentication_headers_for_url_ function.
### Your contribution
Would you be interested in this improvement ?
If so I could provide a PR. I've got something working locally, but it's not very clean, I'd need some guidance regarding integration.
| 159 |
Allow dataset streaming from private a private source when loading a dataset with a dataset loading script
|
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] |
https://github.com/huggingface/datasets/issues/5244
|
Allow dataset streaming from private a private source when loading a dataset with a dataset loading script
|
I would be a big fan of this feature! @Hubert-Bonisseur if this doesn't become a supported feature, would you mind sharing your code? Thanks!
|
### Feature request
Add arguments to the function _get_authentication_headers_for_url_ like custom_endpoint and custom_token in order to add flexibility when downloading files from a private source.
It should also be possible to provide these arguments from the dataset loading script, maybe giving them to the dl_manager
### Motivation
It is possible to share a dataset hosted on another platform by writing a dataset loading script. It works perfectly for publicly available resources.
For resources that require authentication, you can provide a [download_custom](https://huggingface.co/docs/datasets/package_reference/builder_classes#datasets.DownloadManager) method to the download_manager.
Unfortunately, this function doesn't work with **dataset streaming**.
A solution so as to allow dataset streaming from private sources would be a more flexible _get_authentication_headers_for_url_ function.
### Your contribution
Would you be interested in this improvement ?
If so I could provide a PR. I've got something working locally, but it's not very clean, I'd need some guidance regarding integration.
| 24 |
Allow dataset streaming from private a private source when loading a dataset with a dataset loading script
|
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