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https://github.com/huggingface/datasets/issues/3242
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Adding ANERcorp-CAMeLLab dataset
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Adding ANERcorp dataset
## Adding a Dataset
- **Name:** *ANERcorp-CAMeLLab*
- **Description:** *Since its creation in 2008, the ANERcorp dataset (Benajiba & Rosso, 2008) has been a standard reference used by Arabic named entity recognition researchers around the world. However, over time, this dataset was copied over from user to user, modified slightly here and there, and split in many different configurations that made it hard to compare fairly across papers and systems.
In 2020, a group of researchers from CAMeL Lab (Habash, Alhafni and Oudah), and Mind Lab (Antoun and Baly) met with the creator of the corpus, Yassine Benajiba, to consult with him and collectively agree on an exact split, and accepted minor corrections from the original dataset. Bashar Alhafni from CAMeL Lab working with Nizar Habash implemented the decisions provided in this release.*
- **Paper:** *(a) Benajiba, Yassine, Paolo Rosso, and José Miguel Benedí Ruiz. "Anersys: An Arabic named entity recognition system based on maximum entropy." In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 143-153. Springer, Berlin, Heidelberg, 2007.
(b)Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. "CAMeL Tools: An Open Source Python Toolkit, for Arabic Natural Language Processing." In Proceedings of the Conference on Language Resources and Evaluation (LREC 2020), Marseille, 2020.*
- **Data:** *https://camel.abudhabi.nyu.edu/anercorp/*
- **Motivation:** This is the standard dataset for evaluating NER performance in Arabic*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| null | 248 |
Adding ANERcorp-CAMeLLab dataset
|
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] |
https://github.com/huggingface/datasets/issues/3240
|
Couldn't reach data file for disaster_response_messages
|
It looks like the dataset isn't available anymore on appen.com
The CSV files appear to still be available at https://www.kaggle.com/landlord/multilingual-disaster-response-messages though. It says that the data are under the CC0 license so I guess we can host the dataset elsewhere instead ?
|
## Describe the bug
Following command gives an ConnectionError.
## Steps to reproduce the bug
```python
disaster = load_dataset('disaster_response_messages')
```
## Error
```
ConnectionError: Couldn't reach https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv
```
## Expected results
It should load dataset without an error
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:
| 42 |
Couldn't reach data file for disaster_response_messages
|
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] |
https://github.com/huggingface/datasets/issues/3236
|
Loading of datasets changed in #3110 returns no examples
|
Hi @eladsegal, thanks for reporting.
I am sorry, but I can't reproduce the bug:
```
In [1]: from datasets import load_dataset
In [2]: ds = load_dataset("qasper")
Downloading: 5.11kB [00:00, ?B/s]
Downloading and preparing dataset qasper/qasper (download: 9.88 MiB, generated: 35.11 MiB, post-processed: Unknown size, total: 44.99 MiB) to .cache\qasper\qasper\0.1.0\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8...
Dataset qasper downloaded and prepared to .cache\qasper\qasper\0.1.0\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8. Subsequent calls will reuse this data.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<?, ?it/s]
In [3]: ds
Out[3]:
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
This makes me suspect that the origin of the problem might be the cache: I didn't have this dataset in my cache, although I guess you already had it, before the code change introduced by #3110.
@lhoestq might it be possible that the code change introduced by #3110 makes "inaccessible" all previously cached TAR-based datasets?
- Before the caching system downloaded and extracted the tar dataset
- Now it only downloads the tar dataset (no extraction is done)
|
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 173 |
Loading of datasets changed in #3110 returns no examples
|
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] |
https://github.com/huggingface/datasets/issues/3236
|
Loading of datasets changed in #3110 returns no examples
|
I can't reproduce either in my environment (macos, python 3.7).
In your case it generates zero examples. This can only happen if the extraction of the TAR archive doesn't output the right filenames. Indeed if the `qasper` script can't find the right file to load, it's currently ignored and it returns zero examples. This case was not even considered when #3110 was developed since we considered the file names to be deterministic - and not depend on your environment.
Therefore here is my hypothesis:
- either the cache is corrupted somehow with an empty TAR archive
- OR I suspect that the issue comes from python 3.8
|
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 107 |
Loading of datasets changed in #3110 returns no examples
|
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] |
https://github.com/huggingface/datasets/issues/3236
|
Loading of datasets changed in #3110 returns no examples
|
I just tried again on python 3.8 and I was able to reproduce the issue. Let me work on a fix
|
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 21 |
Loading of datasets changed in #3110 returns no examples
|
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] |
https://github.com/huggingface/datasets/issues/3236
|
Loading of datasets changed in #3110 returns no examples
|
Ok I found the issue. It's not related to python 3.8 in itself though. This issue happens because your local installation of `datasets` is outdated compared to the changes to datasets in #3110
To fix this you just have to pull the latest changes from `master` :)
Let me know if that helps !
--------------
Here are more details about my investigation:
It's possible to reproduce this issue if you use `datasets<=1.15.1` or before b6469baa22c174b3906c631802a7016fedea6780 and if you load the dataset after revision b6469baa22c174b3906c631802a7016fedea6780. This is because `dl_manager.iter_archive` had issues at that time (and it was not used anywhere anyway).
In particular it was returning the absolute path to extracted files instead of the relative path of the file inside the archive. This was an issue because `dl_manager.iter_archive` isn't supposed to extract the TAR archive. Instead, it iterates over all the files inside the archive, without creating a directory with the extracted content.
Therefore if you want to use the datasets on `master`, make sure that you have an up-to-date local installation of `datasets` as well, or you may face incompatibilities like this.
|
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 183 |
Loading of datasets changed in #3110 returns no examples
|
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] |
https://github.com/huggingface/datasets/issues/3236
|
Loading of datasets changed in #3110 returns no examples
|
Thanks!
But what about code that is already using older version of datasets?
The reason I encountered this issue was that suddenly one of my repos with version 1.12.1 started getting 0 examples.
I handled it by adding `revision` to `load_dataset`, but I guess it would still be an issue for other users who doesn't know this.
|
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 57 |
Loading of datasets changed in #3110 returns no examples
|
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] |
https://github.com/huggingface/datasets/issues/3236
|
Loading of datasets changed in #3110 returns no examples
|
Hi, in 1.12.1 it uses the dataset scripts from that time, not the one on master.
It only uses the datasets from master if you installed `datasets` from source, or if the dataset isn't available in your local version (in this case it shows a warning and it loads from master).
|
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 51 |
Loading of datasets changed in #3110 returns no examples
|
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] |
https://github.com/huggingface/datasets/issues/3236
|
Loading of datasets changed in #3110 returns no examples
|
OK, I understand the issue a bit better now.
I see I wasn't on 1.12.1, but on 1.12.1.dev0 and since it is a dev version it uses master.
So users that use an old dev version must specify revision or else they'll encounter this problem.
BTW, when I opened the issue I installed the latest master version with
```
pip install git+git://github.com/huggingface/datasets@master#egg=datasets
```
and also used `download_mode="force_redownload"`, and it still returned 0 examples.
Now I deleted all of the cache and ran the code again, and it worked.
I'm not sure what exactly happened here, but looks like it was due to a mix of an unofficial version and its cache.
Thanks again!
|
## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
| 113 |
Loading of datasets changed in #3110 returns no examples
|
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] |
https://github.com/huggingface/datasets/issues/3232
|
The Xsum datasets seems not able to download.
|
> Hi ! On my side the URL is working fine, could you try again ?
I try it again and cannot download the file (might because of my location). Could you please provide another download link(such as google drive)? :>
|
## Describe the bug
The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.
## Steps to reproduce the bug
```python
load_dataset('xsum')
```
## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```
| 41 |
The Xsum datasets seems not able to download.
|
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] |
https://github.com/huggingface/datasets/issues/3232
|
The Xsum datasets seems not able to download.
|
I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.
|
## Describe the bug
The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.
## Steps to reproduce the bug
```python
load_dataset('xsum')
```
## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```
| 41 |
The Xsum datasets seems not able to download.
|
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] |
https://github.com/huggingface/datasets/issues/3232
|
The Xsum datasets seems not able to download.
|
> I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.
:> ok. Thanks for your reply.
|
## Describe the bug
The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.
## Steps to reproduce the bug
```python
load_dataset('xsum')
```
## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```
| 48 |
The Xsum datasets seems not able to download.
|
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] |
https://github.com/huggingface/datasets/issues/3227
|
Error in `Json(datasets.ArrowBasedBuilder)` class
|
I have additionally identified the source of the error, being that [this condition](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L124-L126) in the file
`python3.8/site-packages/datasets/packaged_modules/json/json.py` is not being entered correctly:
```python
if (
isinstance(e, pa.ArrowInvalid)
and "straddling" not in str(e)
or block_size > len(batch)
):
```
From what I can tell, in my case the block_size simply needs to be increased, but the error message does not contain "straddling" so the condition does trigger correctly and we fail to reach [the line to increase block_size](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L135).
Changing the condition above to simply
```python
if (
block_size > len(batch)
):
```
Fixes the error for me. I'm happy to create a PR containing this fix if the developers deem the other conditions unnecessary.
|
## Describe the bug
When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.
## Steps to reproduce the bug
Create a folder that contains the following:
```
.
├── testdata
│ └── mydata.json
└── test.py
```
Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)
:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!
`test.py` simply contains:
```python
from datasets import load_dataset
my_dataset = load_dataset("testdata")
```
To reproduce the error, simply run
```
python test.py
```
## Expected results
The data should load correctly without error.
## Actual results
The dataset builder fails with:
```
Using custom data configuration testdata-d490389b8ab4fd82
Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s]
Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0
Traceback (most recent call last):
File "test.py", line 28, in <module>
my_dataset = load_dataset("testdata")
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split
for key, table in utils.tqdm(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__
for obj in iterable:
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.0
| 113 |
Error in `Json(datasets.ArrowBasedBuilder)` class
|
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] |
https://github.com/huggingface/datasets/issues/3227
|
Error in `Json(datasets.ArrowBasedBuilder)` class
|
Hi ! I think the issue comes from the fact that your JSON file is not a valid JSON Lines file.
Each example should be on one single line.
Can you try fixing the format to have one line per example and try again ?
|
## Describe the bug
When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.
## Steps to reproduce the bug
Create a folder that contains the following:
```
.
├── testdata
│ └── mydata.json
└── test.py
```
Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)
:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!
`test.py` simply contains:
```python
from datasets import load_dataset
my_dataset = load_dataset("testdata")
```
To reproduce the error, simply run
```
python test.py
```
## Expected results
The data should load correctly without error.
## Actual results
The dataset builder fails with:
```
Using custom data configuration testdata-d490389b8ab4fd82
Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s]
Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0
Traceback (most recent call last):
File "test.py", line 28, in <module>
my_dataset = load_dataset("testdata")
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split
for key, table in utils.tqdm(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__
for obj in iterable:
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.0
| 45 |
Error in `Json(datasets.ArrowBasedBuilder)` class
|
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] |
https://github.com/huggingface/datasets/issues/3227
|
Error in `Json(datasets.ArrowBasedBuilder)` class
|
:open_mouth: you're right, that did it! I just put everything on a single line (my file only has a single example) and that fixed the error. Thank you so much!
|
## Describe the bug
When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.
## Steps to reproduce the bug
Create a folder that contains the following:
```
.
├── testdata
│ └── mydata.json
└── test.py
```
Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)
:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!
`test.py` simply contains:
```python
from datasets import load_dataset
my_dataset = load_dataset("testdata")
```
To reproduce the error, simply run
```
python test.py
```
## Expected results
The data should load correctly without error.
## Actual results
The dataset builder fails with:
```
Using custom data configuration testdata-d490389b8ab4fd82
Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s]
Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0
Traceback (most recent call last):
File "test.py", line 28, in <module>
my_dataset = load_dataset("testdata")
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split
for key, table in utils.tqdm(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__
for obj in iterable:
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.0
| 30 |
Error in `Json(datasets.ArrowBasedBuilder)` class
|
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] |
https://github.com/huggingface/datasets/issues/3210
|
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py
|
Hi ! Do you have some kind of proxy in your browser that gives you access to internet ?
Maybe you're having this error because you don't have access to this URL from python ?
|
when I use python examples/pytorch/translation/run_translation.py --model_name_or_path examples/pytorch/translation/opus-mt-en-ro --do_train --do_eval --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config_name ro-en --output_dir /tmp/tst-translation --per_device_train_batch_size=4 --per_device_eval_batch_size=4 --overwrite_output_dir --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue?
| 35 |
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py
|
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] |
https://github.com/huggingface/datasets/issues/3210
|
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py
|
You don't need authentication to access those github hosted files
Please check that you can access this URL from your browser and also from your terminal
|
when I use python examples/pytorch/translation/run_translation.py --model_name_or_path examples/pytorch/translation/opus-mt-en-ro --do_train --do_eval --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config_name ro-en --output_dir /tmp/tst-translation --per_device_train_batch_size=4 --per_device_eval_batch_size=4 --overwrite_output_dir --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue?
| 26 |
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py
|
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] |
https://github.com/huggingface/datasets/issues/3204
|
FileNotFoundError for TupleIE dataste
|
@mariosasko @lhoestq Could you give me an update on how to load the dataset after the fix?
Thanks.
|
Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`
returns a FileNotFound error. Is the data not available?
Many thanks.
| 18 |
FileNotFoundError for TupleIE dataste
|
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] |
https://github.com/huggingface/datasets/issues/3204
|
FileNotFoundError for TupleIE dataste
|
Hi @arda-vianai,
first, you can try:
```python
import datasets
dataset = datasets.load_dataset('tuple_ie', 'all', revision="master")
```
If this doesn't work, your version of `datasets` is missing some features that are required to run the dataset script, so install the master version with the following command:
```
pip install git+https://github.com/huggingface/datasets.git
```
and then:
```python
import datasets
dataset = datasets.load_dataset('tuple_ie', 'all')
```
should work (even without `revision`).
|
Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`
returns a FileNotFound error. Is the data not available?
Many thanks.
| 64 |
FileNotFoundError for TupleIE dataste
|
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] |
https://github.com/huggingface/datasets/issues/3204
|
FileNotFoundError for TupleIE dataste
|
@mariosasko
Thanks, it is working now. I actually did that before but I didn't restart the kernel. I restarted it and it works now. My bad!!!
Many thanks and great job!
-arda
|
Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`
returns a FileNotFound error. Is the data not available?
Many thanks.
| 32 |
FileNotFoundError for TupleIE dataste
|
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] |
https://github.com/huggingface/datasets/issues/3191
|
Dataset viewer issue for '*compguesswhat*'
|
```python
>>> import datasets
>>> dataset = datasets.load_dataset('compguesswhat', name='compguesswhat-original',split='train', streaming=True)
>>> next(iter(dataset))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 497, in __iter__
for key, example in self._iter():
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 494, in _iter
yield from ex_iterable
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 87, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/compguesswhat/4d08b9e0a8d1cf036c9626c93be4a759fdd9fcce050ea503ea14b075e830c799/compguesswhat.py", line 251, in _generate_examples
with gzip.open(filepath) as in_file:
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/gzip.py", line 58, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/gzip.py", line 173, in __init__
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: 'zip://compguesswhat-original/0.2.0/compguesswhat.train.jsonl.gz::https://www.dropbox.com/s/l0nc13udml6vs0w/compguesswhat-original.zip?dl=1'
```
It's an issue with the streaming mode. Note that normal mode is used by the dataset viewer when streaming is failing, but only for the smallest datasets. This dataset is above the limit, hence the error.
Same case as https://github.com/huggingface/datasets/issues/3186#issuecomment-1096549774.
|
## Dataset viewer issue for '*compguesswhat*'
**Link:** https://huggingface.co/datasets/compguesswhat
File not found
Am I the one who added this dataset ? No
| 137 |
Dataset viewer issue for '*compguesswhat*'
|
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] |
https://github.com/huggingface/datasets/issues/3191
|
Dataset viewer issue for '*compguesswhat*'
|
There is an issue with the URLs of their data files: https://www.dropbox.com/s/l0nc13udml6vs0w/compguesswhat-original.zip?dl=1
> Dropbox Error: That didn't work for some reason
Error reported to their repo:
- https://github.com/CompGuessWhat/compguesswhat.github.io/issues/1
|
## Dataset viewer issue for '*compguesswhat*'
**Link:** https://huggingface.co/datasets/compguesswhat
File not found
Am I the one who added this dataset ? No
| 28 |
Dataset viewer issue for '*compguesswhat*'
|
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] |
https://github.com/huggingface/datasets/issues/3190
|
combination of shuffle and filter results in a bug
|
Hi ! There was a regression in `datasets` 1.12 that introduced this bug. It has been fixed in #3019 in 1.13
Can you try to update `datasets` and try again ?
|
## Describe the bug
Hi,
I would like to shuffle a dataset, then filter it based on each existing label. however, the combination of `filter`, `shuffle` seems to results in a bug. In the minimal example below, as you see in the filtered results, the filtered labels are not unique, meaning filter has not worked. Any suggestions as a temporary fix is appreciated @lhoestq.
Thanks.
Best regards
Rabeeh
## Steps to reproduce the bug
```python
import numpy as np
import datasets
datasets = datasets.load_dataset('super_glue', 'rte', script_version="master")
shuffled_data = datasets["train"].shuffle(seed=42)
for label in range(2):
print("label ", label)
data = shuffled_data.filter(lambda example: int(example['label']) == label)
print("length ", len(data), np.unique(data['label']))
```
## Expected results
Filtering per label, should only return the data with that specific label.
## Actual results
As you can see, filtered data per label, has still two labels of [0, 1]
```
label 0
length 1249 [0 1]
label 1
length 1241 [0 1]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: linux
- Python version: 3.7.11
- PyArrow version: 5.0.0
| 31 |
combination of shuffle and filter results in a bug
|
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] |
https://github.com/huggingface/datasets/issues/3189
|
conll2003 incorrect label explanation
|
Hi @BramVanroy,
since these fields are of type `ClassLabel` (you can check this with `dset.features`), you can inspect the possible values with:
```python
dset.features[field_name].feature.names # .feature because it's a sequence of labels
```
and to find the mapping between names and integers, use:
```python
dset.features[field_name].feature.int2str(value_or_values_list) # map integer value to string value
# or
dset.features[field_name].feature.str2int(value_or_values_list) # map string value to integer value
```
|
In the [conll2003](https://huggingface.co/datasets/conll2003#data-fields) README, the labels are described as follows
> - `id`: a `string` feature.
> - `tokens`: a `list` of `string` features.
> - `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4).
> - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).
> - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4) `B-LOC` (5), `I-LOC` (6) `B-MISC` (7), `I-MISC` (8).
First of all, it would be great if we can get a list of ALL possible pos_tags.
Second, the chunk tags labels cannot be correct. The description says the values go from 0 to 4 whereas the data shows values from at least 11 to 21 and 0.
EDIT: not really a bug, sorry for mistagging.
| 63 |
conll2003 incorrect label explanation
|
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] |
https://github.com/huggingface/datasets/issues/3188
|
conll2002 issues
|
Hi ! Thanks for reporting :)
This is related to https://github.com/huggingface/datasets/issues/2742, I'm working on it. It should fix the viewer for around 80 datasets.
|
**Link:** https://huggingface.co/datasets/conll2002
The dataset viewer throws a server error when trying to preview the dataset.
```
Message: Extraction protocol 'train' for file at 'https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/esp.train' is not implemented yet
```
In addition, the "point of contact" has encoding issues and does not work when clicked.
Am I the one who added this dataset ? No, @lhoestq did
| 24 |
conll2002 issues
|
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] |
https://github.com/huggingface/datasets/issues/3188
|
conll2002 issues
|
Ah, hadn't seen that sorry.
The scrambled "point of contact" is a separate issue though, I think.
|
**Link:** https://huggingface.co/datasets/conll2002
The dataset viewer throws a server error when trying to preview the dataset.
```
Message: Extraction protocol 'train' for file at 'https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/esp.train' is not implemented yet
```
In addition, the "point of contact" has encoding issues and does not work when clicked.
Am I the one who added this dataset ? No, @lhoestq did
| 17 |
conll2002 issues
|
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] |
https://github.com/huggingface/datasets/issues/3186
|
Dataset viewer for nli_tr
|
It's an issue with the streaming mode:
```python
>>> import datasets
>>> dataset = datasets.load_dataset('nli_tr', name='snli_tr',split='test', streaming=True)
>>> next(iter(dataset))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 497, in __iter__
for key, example in self._iter():
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 494, in _iter
yield from ex_iterable
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 87, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/nli_tr/c2ddd0c0a70caddac6a81c2dae5ca7939f00060d517d08f1983927818dba6521/nli_tr.py", line 155, in _generate_examples
with codecs.open(filepath, encoding="utf-8") as f:
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/codecs.py", line 905, in open
file = builtins.open(filename, mode, buffering)
FileNotFoundError: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_test.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip'
```
Note that normal mode is used by the dataset viewer when streaming is failing, but only for the smallest datasets. `nli_tr` is above the limit, hence the error.
|
## Dataset viewer issue for '*nli_tr*'
**Link:** https://huggingface.co/datasets/nli_tr
Hello,
Thank you for the new dataset preview feature that will help the users to view the datasets online.
We just noticed that the dataset viewer widget in the `nli_tr` dataset shows the error below. The error must be due to a temporary problem that may have blocked access to the dataset through the dataset viewer. But the dataset is currently accessible through the link in the error message. May we kindly ask if it would be possible to rerun the job so that it can access the dataset for the dataset viewer function?
Thank you.
Emrah
------------------------------------------
Server Error
Status code: 404
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_train.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip
------------------------------------------
Am I the one who added this dataset ? Yes
| 119 |
Dataset viewer for nli_tr
|
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] |
https://github.com/huggingface/datasets/issues/3186
|
Dataset viewer for nli_tr
|
Apparently there is an issue with the data source URLs: Server Not Found
- https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip
We are contacting the authors to ask them:
@e-budur you are one of the authors: are you aware of the issue with the URLs of your data ?
|
## Dataset viewer issue for '*nli_tr*'
**Link:** https://huggingface.co/datasets/nli_tr
Hello,
Thank you for the new dataset preview feature that will help the users to view the datasets online.
We just noticed that the dataset viewer widget in the `nli_tr` dataset shows the error below. The error must be due to a temporary problem that may have blocked access to the dataset through the dataset viewer. But the dataset is currently accessible through the link in the error message. May we kindly ask if it would be possible to rerun the job so that it can access the dataset for the dataset viewer function?
Thank you.
Emrah
------------------------------------------
Server Error
Status code: 404
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_train.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip
------------------------------------------
Am I the one who added this dataset ? Yes
| 43 |
Dataset viewer for nli_tr
|
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] |
https://github.com/huggingface/datasets/issues/3185
|
7z dataset preview not implemented?
|
It's a bug in the dataset viewer: the dataset cannot be downloaded in streaming mode, but since the dataset is relatively small, the dataset viewer should have fallback to normal mode. Working on a fix.
|
## Dataset viewer issue for dataset 'samsum'
**Link:** https://huggingface.co/datasets/samsum
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol '7z' for file at 'https://arxiv.org/src/1911.12237v2/anc/corpus.7z' is not implemented yet
| 35 |
7z dataset preview not implemented?
|
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] |
https://github.com/huggingface/datasets/issues/3181
|
`None` converted to `"None"` when loading a dataset
|
Hi @eladsegal, thanks for reporting.
@mariosasko I saw you are already working on this, but maybe my comment will be useful to you.
All values are casted to their corresponding feature type (including `None` values). For example if the feature type is `Value("bool")`, `None` is casted to `False`.
It is true that strings were an exception, but this was recently fixed by @lhoestq (see #3158).
|
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 65 |
`None` converted to `"None"` when loading a dataset
|
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] |
https://github.com/huggingface/datasets/issues/3181
|
`None` converted to `"None"` when loading a dataset
|
Thanks for reporting.
This is actually a breaking change that I think can cause issues when users preprocess their data. String columns used to be nullable. Maybe we can correct https://github.com/huggingface/datasets/pull/3158 to keep the None values and avoid this breaking change ?
EDIT: the other types (bool, int, etc) can also become nullable IMO
|
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 54 |
`None` converted to `"None"` when loading a dataset
|
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] |
https://github.com/huggingface/datasets/issues/3181
|
`None` converted to `"None"` when loading a dataset
|
So what would be the best way to handle a feature that can have a null value in some of the instances? So far I used `None`.
Using the empty string won't be a good option, as it can be an actual value in the data and is not the same as not having a value at all.
|
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 58 |
`None` converted to `"None"` when loading a dataset
|
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] |
https://github.com/huggingface/datasets/issues/3181
|
`None` converted to `"None"` when loading a dataset
|
Hi @eladsegal,
Use `None`. As @albertvillanova correctly pointed out, this change in conversion was introduced (by mistake) in #3158. To avoid it, install the earlier revision with:
```
pip install git+https://github.com/huggingface/datasets.git@8107844ec0e7add005db0585c772ee20adc01a5e
```
I'm making all the feature types nullable as we speak, and the fix will be merged probably early next week.
|
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 52 |
`None` converted to `"None"` when loading a dataset
|
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] |
https://github.com/huggingface/datasets/issues/3181
|
`None` converted to `"None"` when loading a dataset
|
https://github.com/huggingface/datasets/pull/3195 fixed it, we'll do a new release soon :)
For now feel free to install `datasets` from the master branch
|
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 21 |
`None` converted to `"None"` when loading a dataset
|
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] |
https://github.com/huggingface/datasets/issues/3181
|
`None` converted to `"None"` when loading a dataset
|
Thanks, but unfortunately looks like it isn't fixed yet 😢
[notebook for 1.14.0](https://colab.research.google.com/drive/1SV3sFXPJMWSQgbm4pr9Y1Q8OJ4JYKcDo?usp=sharing)
[notebook for master](https://colab.research.google.com/drive/145wDpuO74MmsuI0SVLcI1IswG6aHpyhi?usp=sharing)
|
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 16 |
`None` converted to `"None"` when loading a dataset
|
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0.3200947940349579,
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0.27806100249290466,
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] |
https://github.com/huggingface/datasets/issues/3181
|
`None` converted to `"None"` when loading a dataset
|
Oh, sorry. I deleted the fix by accident when I was resolving a merge conflict. Let me fix this real quick.
|
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 21 |
`None` converted to `"None"` when loading a dataset
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
After some digging, I found that this is caused by `dill` and using `recurse=True)` when trying to dump the object. The problem also occurs without multiprocessing. I can only find [the following information](https://dill.readthedocs.io/en/latest/dill.html#dill._dill.dumps) about this:
> If recurse=True, then objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary. This is needed for functions defined via exec().
In the utils, this is explicitly enabled
https://github.com/huggingface/datasets/blob/df63614223bf1dd1feb267d39d741bada613352c/src/datasets/utils/py_utils.py#L327-L330
Is this really necessary? Is there a way around it? Also pinging the spaCy team in case this is easy to solve on their end. (I hope so.)
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 108 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
Hi ! Thanks for reporting
Yes `recurse=True` is necessary to be able to hash all the objects that are passed to the `map` function
EDIT: hopefully this object can be serializable soon, but otherwise we can consider adding more control to the user on how to hash objects that are not serializable (as mentioned in https://github.com/huggingface/datasets/issues/3044#issuecomment-948818210)
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 56 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
I submitted a PR to spacy that should fix this issue (linked above). I'll leave this open until that PR is merged.
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 22 |
"Property couldn't be hashed properly" even though fully picklable
|
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0.3591448664665222,
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-0.17948631942272186,
-0.09173077344894409,
-0.13466796278953552,
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0.47821369767189026,
-0.11404625326395035,
-0.15819652378559113,
0.14496955275535583,
0.06133649870753288
] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
@lhoestq After some testing I find that even with the updated spaCy, no cache files are used. I do not get any warnings though, but I can see that map is run every time I run the code. Do you have thoughts about why? If you want to try the tests below, make sure to install spaCy from [here](https://github.com/BramVanroy/spaCy) and installing the base model with `python -m spacy download en_core_web_sm`.
```python
from functools import partial
from pathlib import Path
import spacy
from datasets import Dataset
import datasets
datasets.logging.set_verbosity_debug()
def tokenize(nlp, l):
return {"tok": [t.text for t in nlp(l["text"])]}
def main():
fin = r"some/file/with/many/lines"
lines = Path(fin).read_text(encoding="utf-8").splitlines()
nlp = spacy.load("en_core_web_sm")
ds = Dataset.from_dict({"text": lines, "text_id": list(range(len(lines)))})
tok = partial(tokenize, nlp)
ds = ds.map(tok, load_from_cache_file=True)
print(ds[0:2])
if __name__ == '__main__':
main()
```
... or with load_dataset (here I get the message that `load_dataset` can reuse the dataset, but still I see all samples being processed via the tqdm progressbar):
```python
from functools import partial
import spacy
from datasets import load_dataset
import datasets
datasets.logging.set_verbosity_debug()
def tokenize(nlp, sample):
return {"tok": [t.text for t in nlp(sample["text"])]}
def main():
fin = r"some/file/with/many/lines"
nlp = spacy.load("en_core_web_sm")
tok_func = partial(tokenize, nlp)
ds = load_dataset('text', data_files=fin)
ds = ds["train"].map(tok_func)
print(ds[0:2])
if __name__ == '__main__':
main()
```
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 207 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
It looks like every time you load `en_core_web_sm` you get a different python object:
```python
import spacy
from datasets.fingerprint import Hasher
nlp1 = spacy.load("en_core_web_sm")
nlp2 = spacy.load("en_core_web_sm")
Hasher.hash(nlp1), Hasher.hash(nlp2)
# ('f6196a33882fea3b', 'a4c676a071f266ff')
```
Here is a list of attributes that have different hashes for `nlp1` and `nlp2`:
- tagger
- parser
- entity
- pipeline (it's the list of the three attributes above)
I just took a look at the tagger for example and I found subtle differences (there may be other differences though):
```python
nlp1.tagger.model.tok2vec.embed.id, nlp2.tagger.model.tok2vec.embed.id
# (1721, 2243)
```
We can try to find all the differences and find the best way to hash those objects properly
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 109 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
Thanks for searching! I went looking, and found that this is an implementation detail of thinc
https://github.com/explosion/thinc/blob/68691e303ae68cae4bc803299016f1fc064328bf/thinc/model.py#L96-L98
Presumably (?) exactly to distinguish between different parts in memory when multiple models are loaded. Do not think that this can be changed on their end - but I will ask what exactly it is for (I'm curious).
Do you think it is overkill to write something into the hasher explicitly to deal with spaCy models? It seems like something that is beneficial to many, but I do not know if you are open to adding third-party-specific ways to deal with this. If you are, I can have a look for this specific case how we can ignore `thinc.Model.id` from the hasher.
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 119 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
It can be even simpler to hash the bytes of the pipeline instead
```python
nlp1.to_bytes() == nlp2.to_bytes() # True
```
IMO we should integrate the custom hashing for spacy models into `datasets` (we use a custom Pickler for that).
What could be done on Spacy's side instead (if they think it's nice to have) is to implement a custom pickling for these classes using `to_bytes`/`from_bytes` to have deterministic pickle dumps.
Finally I think it would be nice in the future to add an API to let `datasets` users control this kind of things. Something like being able to define your own hashing if you use complex objects.
```python
@datasets.register_hash(spacy.language.Language)
def hash_spacy_language(nlp):
return Hasher.hash(nlp.to_bytes())
```
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 114 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
I do not quite understand what you mean. as far as I can tell, using `to_bytes` does a pickle dump behind the scene (with `srsly`), recursively using `to_bytes` on the required objects. Therefore, the result of `to_bytes` is a deterministic pickle dump AFAICT. Or do you mean that you wish that using your own pickler and running `dumps(nlp)` should also be deterministic? I guess that would require `__setstate__` and `__getstate__` methods on all the objects that have to/from_bytes. I'll have a listen over at spaCy what they think, and if that would solve the issue. I'll try this locally first, if I find the time.
I agree that having the option to use a custom hasher would be useful. I like your suggestion!
EDIT: after trying some things and reading through their API, it seems that they explicitly do not want this. https://spacy.io/usage/saving-loading#pipeline
> When serializing the pipeline, keep in mind that this will only save out the binary data for the individual components to allow spaCy to restore them – not the entire objects. This is a good thing, because it makes serialization safe. But it also means that you have to take care of storing the config, which contains the pipeline configuration and all the relevant settings.
Best way forward therefore seems to implement the ability to specify a hasher depending on the objects that are pickled, as you suggested. I can work on this if that is useful. I could use some pointers as to how you would like to implement the `register_hash` functionality though. I assume using `catalogue` over at Explosion might be a good starting point.
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 271 |
"Property couldn't be hashed properly" even though fully picklable
|
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0.14496955275535583,
0.06133649870753288
] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
Interestingly, my PR does not solve the issue discussed above. The `tokenize` function hash is different on every run, because for some reason `nlp.__call__` has a different hash every time. The issue therefore seems to run much deeper than I thought. If you have any ideas, I'm all ears.
```shell
git clone https://github.com/explosion/spaCy.git
cd spaCy/
git checkout cab9209c3dfcd1b75dfe5657f10e52c4d847a3cf
cd ..
git clone https://github.com/BramVanroy/datasets.git
cd datasets
git checkout registry
pip install -e .
pip install ../spaCy
spacy download en_core_web_sm
```
```python
import spacy
from datasets import load_dataset
from datasets.fingerprint import Hasher
from datasets.utils.registry import hashers
@hashers.register(spacy.Language)
def hash_spacy_language(nlp):
return Hasher.hash(nlp.to_bytes())
def main():
fin = r"your/large/file"
nlp = spacy.load("en_core_web_sm")
# This is now always the same yay!
print(Hasher.hash(nlp))
def tokenize(l):
return {"tok": [t.text for t in nlp(l["text"])]}
ds = load_dataset("text", data_files=fin)
# But this is not...
print(Hasher.hash(tokenize))
# ... because of this
print(Hasher.hash(nlp.__call__))
ds = ds["train"].map(tokenize)
print(ds[0:2])
if __name__ == '__main__':
main()
```
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 151 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
Hi ! I just answered in your PR :) In order for your custom hashing to be used for nested objects, you must integrate it into our recursive pickler that we use for hashing.
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 34 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
I don't quite understand the design constraints of `datasets` or the script that you're running, but my usual advice is to avoid using pickle unless you _absolutely_ have to. So for instance instead of doing your `partial` over the `nlp` object itself, can you just pass the string `en_core_web_sm` in? This will mean calling `spacy.load()` inside the work function, but this is no worse than having to call `pickle.load()` on the contents of the NLP object anyway -- in fact you'll generally find `spacy.load()` faster, apart from the disk read.
If you need to pass in the bytes data and don't want to read from disk, you could do something like this:
```
msg = (nlp.lang, nlp.to_bytes())
def unpack(lang, bytes_data):
return spacy.blank(lang).from_bytes(bytes_data)
```
I think that should probably work: the Thinc `model.to_dict()` method (which is used by the `model.to_bytes()` method) doesn't pack the model's ID into the message, so the `nlp.to_bytes()` that you get shouldn't be affected by the global IDs. So you should get a clean message from `nlp.to_bytes()` that doesn't depend on the global state.
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 177 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
Hi Matthew, thanks for chiming in! We are currently implementing exactly what you suggest: `to_bytes()` as a default before pickling - but we may prefer `to_dict` to avoid double dumping.
`datasets` uses pickle dumps (actually dill) to get unique representations of processing steps (a "fingerprint" or hash). So it never needs to re-load that dump - it just needs its value to create a hash. If a fingerprint is identical to a cached fingerprint, then the result can be retrieved from the on-disk cache. (@lhoestq or @mariosasko can correct me if I'm wrong.)
I was experiencing the issue that parsing with spaCy gave me a different fingerprint on every run of the script and thus it could never load the processed dataset from cache. At first I thought the reason was that spaCy Language objects were not picklable with recursive dill, but even after [adjusting for that](https://github.com/explosion/spaCy/pull/9593) the issue persisted. @lhoestq found that this is due to the changing `id`, which you discussed [here](https://github.com/explosion/spaCy/discussions/9609#discussioncomment-1661081). So yes, you are right. On the surface there simply seems to be an incompatibility between `datasets` default caching functionality as it is currently implemented and `spacy.Language`.
The [linked PR](https://github.com/huggingface/datasets/pull/3224) aims to remedy that, though. Up to now I have put some effort into making it easier to define your own "pickling" function for a given type (and optionally any of its subclasses). That allows us to tell `datasets` that instead of doing `dill.save(nlp)` (non-deterministic), to use `dill.save(nlp.to_bytes())` (deterministic). When I find some more time, the PR [will be expanded](https://github.com/huggingface/datasets/pull/3224#issuecomment-968958528) to improve the user-experience a bit and add a built-in function to pickle `spacy.Language` as one of the defaults (using `to_bytes()`).
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 275 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
Is there a workaround for this? maybe by explicitly requesting datasets to cache the result of `.map()`?
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 17 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
Hi ! If your function is not picklable, then the fingerprint of the resulting dataset can't be computed. The fingerprint is a hash that is used by the cache to reload previously computed datasets: the dataset file is named `cache-<fingerprint>.arrow` in your dataset's cache directory.
As a workaround you can set the fingerprint that is going to be used by the cache:
```python
result = my_dataset.map(func, new_fingerprint=new_fingerprint)
```
Any future call to `map` with the same `new_fingerprint` will reload the result from the cache.
**Be careful using this though: if you change your `func`, be sure to change the `new_fingerprint` as well.**
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 102 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
I've been having an issue that might be related to this when trying to pre-tokenize a corpus and caching it for using it later in the pre-training of a RoBERTa model. I always get the following warning:
```
Dataset text downloaded and prepared to /gpfswork/rech/project/user/.cache/hf-datasets/text/default-1850886023af0077/0.0.0/acc32f2f2ef863c93c2f30c52f7df6cc9053a1c2230b8d7da0d210404683ca08. Subsequent calls will reuse this data.
Parameter 'function'=<function encode_dataset.<locals>.<lambda> at 0x14a92157b280> of the transform datasets.arrow_dataset.Dataset.filter@2.0.1 couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
```
And when I launch the pre-training the pre-tokenized corpus is not found and it is tokenized again, which makes me waste precious GPU hours.
For me, the workaround was downgrading `dill` and `multiprocess` to the following versions:
```
dill 0.3.4
multiprocess 0.70.12.2
```
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 167 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
> Hi ! If your function is not picklable, then the fingerprint of the resulting dataset can't be computed. The fingerprint is a hash that is used by the cache to reload previously computed datasets: the dataset file is named `cache-<fingerprint>.arrow` in your dataset's cache directory.
>
> As a workaround you can set the fingerprint that is going to be used by the cache:
>
> ```python
> result = my_dataset.map(func, new_fingerprint=new_fingerprint)
> ```
>
> Any future call to `map` with the same `new_fingerprint` will reload the result from the cache.
>
> **Be careful using this though: if you change your `func`, be sure to change the `new_fingerprint` as well.**
Is the argument `new_fingerprint` available for datasetDict ? I can only use it on arrow datasets but might be useful to generalize it to DatasetDict's map as well ? @lhoestq
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 143 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
> I've been having an issue that might be related to this when trying to pre-tokenize a corpus and caching it for using it later in the pre-training of a RoBERTa model. I always get the following warning:
>
> ```
> Dataset text downloaded and prepared to /gpfswork/rech/project/user/.cache/hf-datasets/text/default-1850886023af0077/0.0.0/acc32f2f2ef863c93c2f30c52f7df6cc9053a1c2230b8d7da0d210404683ca08. Subsequent calls will reuse this data.
> Parameter 'function'=<function encode_dataset.<locals>.<lambda> at 0x14a92157b280> of the transform datasets.arrow_dataset.Dataset.filter@2.0.1 couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
> ```
>
> And when I launch the pre-training the pre-tokenized corpus is not found and it is tokenized again, which makes me waste precious GPU hours.
>
> For me, the workaround was downgrading `dill` and `multiprocess` to the following versions:
>
> ```
> dill 0.3.4
> multiprocess 0.70.12.2
> ```
This worked for me - thanks!
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 188 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
I see this has just been closed - it seems quite relevant to another tokenizer I have been trying to use, the `vinai/phobert` family of tokenizers
https://huggingface.co/vinai/phobert-base
https://huggingface.co/vinai/phobert-large
I ran into an issue where a large dataset took several hours to tokenize, the process hung, and I was unable to use the cached version of the tokenized data:
https://discuss.huggingface.co/t/cache-parallelize-long-tokenization-step/25791/3
I don't see any way to specify the hash of the tokenizer or the fingerprint of the tokenized data to use, so is the tokenized dataset basically lost at this point? Is there a good way to avoid this happening again if I retokenize the data?
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 105 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
In your case it looks like the job failed before caching the data - maybe one of the processes crashed
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 20 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
Interesting. Thanks for the observation. Any suggestions on how to start tracking that down? Perhaps run it singlethreaded and see if it crashes?
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 23 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
You can monitor your RAM and disk space in case a process dies from OOM or disk full, and when it hangs you can check how many processes are running. IIRC there are other start methods for multiprocessing in python that may show an error message if a process dies.
Running on a single process can also help debugging this indeed
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 61 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
Hi @tung-msol could you open a new issue and share the error you got and the map function you used ?
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 21 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
I faced the same problem even after using these versions for python 3.10:
> dill 0.3.4
> multiprocess 0.70.12.2
However, doing `result = my_dataset.map(func, new_fingerprint=new_fingerprint)` worked!
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 26 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3178
|
"Property couldn't be hashed properly" even though fully picklable
|
You should define your fingerprint as a string that identifies your dataset. By default it's a hash computed from the data files and on the applied transformations.
|
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
| 27 |
"Property couldn't be hashed properly" even though fully picklable
|
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] |
https://github.com/huggingface/datasets/issues/3177
|
More control over TQDM when using map/filter with multiple processes
|
Hi,
It's hard to provide an API that would cover all use-cases with tqdm in this project.
However, you can make it work by defining a custom decorator (a bit hacky tho) as follows:
```python
import datasets
def progress_only_on_rank_0(func):
def wrapper(*args, **kwargs):
rank = kwargs.get("rank")
disable_tqdm = kwargs.get("disable_tqdm", False)
disable_tqdm = True if rank is not None and rank > 0 else disable_tqdm
kwargs["disable_tqdm"] = disable_tqdm
return func(*args, **kwargs)
return wrapper
datasets.Dataset._map_single = progress_only_on_rank_0(datasets.Dataset._map_single)
```
EDIT: Ups, closed by accident.
Thanks for the provided links. `Trainer` requires this for training in multi-node distributed setting. However, `Dataset.map` doesn't support that yet.
Do you have an API for this in mind? `Dataset.map` is already bloated with the arguments, so IMO it's not a good idea to add a new arg there.
|
It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets.
```python
dataset.map(lambda examples: tokenize(examples["text"]), batched=True, num_proc=6)
```
The above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down.
```
#0: 0%| | 0/13 [00:00<?, ?ba/s]
#1: 0%| | 0/13 [00:00<?, ?ba/s]
#2: 0%| | 0/13 [00:00<?, ?ba/s]
#3: 0%| | 0/13 [00:00<?, ?ba/s]
#4: 0%| | 0/13 [00:00<?, ?ba/s]
#5: 0%| | 0/13 [00:00<?, ?ba/s]
#0: 8%| | 1/13 [00:00<?, ?ba/s]
#1: 8%| | 1/13 [00:00<?, ?ba/s]
...
```
Instead, it would be welcome if we had the option to only show the progress of rank 0.
| 129 |
More control over TQDM when using map/filter with multiple processes
|
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] |
https://github.com/huggingface/datasets/issues/3177
|
More control over TQDM when using map/filter with multiple processes
|
Inspiration may be found at `transformers`.
https://github.com/huggingface/transformers/blob/4a394cf53f05e73ab9bbb4b179a40236a5ffe45a/src/transformers/trainer.py#L1231-L1233
To get unique IDs for each worker, see https://stackoverflow.com/a/10192611/1150683
|
It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets.
```python
dataset.map(lambda examples: tokenize(examples["text"]), batched=True, num_proc=6)
```
The above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down.
```
#0: 0%| | 0/13 [00:00<?, ?ba/s]
#1: 0%| | 0/13 [00:00<?, ?ba/s]
#2: 0%| | 0/13 [00:00<?, ?ba/s]
#3: 0%| | 0/13 [00:00<?, ?ba/s]
#4: 0%| | 0/13 [00:00<?, ?ba/s]
#5: 0%| | 0/13 [00:00<?, ?ba/s]
#0: 8%| | 1/13 [00:00<?, ?ba/s]
#1: 8%| | 1/13 [00:00<?, ?ba/s]
...
```
Instead, it would be welcome if we had the option to only show the progress of rank 0.
| 16 |
More control over TQDM when using map/filter with multiple processes
|
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0.17203298211097717,
-0.13112951815128326,
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0.10796986520290375,
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0.15587404370307922
] |
https://github.com/huggingface/datasets/issues/3172
|
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
NB: even if the error is raised, the dataset is successfully cached. So restarting the script after every `map()` allows to ultimately run the whole preprocessing. But this prevents to realistically run the code over multiple nodes.
|
## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
| 37 |
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
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] |
https://github.com/huggingface/datasets/issues/3172
|
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
Hi,
It's not easy to debug the problem without the script. I may be wrong since I'm not very familiar with PyTorch Lightning, but shouldn't you preprocess the data in the `prepare_data` function of `LightningDataModule` and not in the `setup` function.
As you can't modify the module state in `prepare_data` (according to the docs), use the `cache_file_name` argument in `Dataset.map` there, and reload the processed data in `setup` with `Dataset.from_file(cache_file_name)`. If `num_proc>1`, check the docs on the `suffix_template` argument of `Dataset.map` to get an idea what the final `cache_file_names` are going to be.
Let me know if this helps.
|
## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
| 99 |
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
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] |
https://github.com/huggingface/datasets/issues/3172
|
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
Hi @mariosasko, thank you for the hint, that helped me to move forward with that issue.
I did a major refactoring of my project to disentangle my `LightningDataModule` and `Dataset`. Just FYI, it looks like:
```python
class Builder():
def __call__() -> DatasetDict:
# load and preprocess the data
return dataset
class DataModule(LightningDataModule):
def prepare_data():
self.builder()
def setup():
self.dataset = self.builder()
```
Unfortunately, the entanglement between `LightningDataModule` and `Dataset` was not the issue.
The culprit was `hydra` and a slight adjustment of the structure of my project solved this issue. The problematic project structure was:
```
src/
| - cli.py
| - training/
| -experiment.py
# code in experiment.py
def run_experiment(config):
# preprocess data and run
# code in cli.py
@hydra.main(...)
def run(config):
return run_experiment(config)
```
Moving `run()` from `clip.py` to `training.experiment.py` solved the issue with `SystemError 15`. No idea why.
Even if the traceback was referring to `Dataset.__del__`, the problem does not seem to be primarily related to `datasets`, so I will close this issue. Thank you for your help!
|
## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
| 170 |
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
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] |
https://github.com/huggingface/datasets/issues/3172
|
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
Please allow me to revive this discussion, as I have an extremely similar issue. Instead of an error, my datasets functions simply aren't caching properly. My setup is almost the same as yours, with hydra to configure my experiment parameters.
@vlievin Could you confirm if your code correctly loads the cache? If so, do you have any public code that I can reference for comparison?
I will post a full example with hydra that illustrates this problem in a little bit, probably on another thread.
|
## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
| 85 |
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
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] |
https://github.com/huggingface/datasets/issues/3172
|
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
Hello @mariomeissner, very sorry for the late reply, I hope you have found a solution to your problem!
I don't have public code at the moment. I have not experienced any other issue with hydra, even if I don't understand why changing the location of the definition of `run()` fixed the problem.
Overall, I don't have issue with caching anymore, even when
1. using custom fingerprints using the argument `new_fingerprint
2. when using `num_proc>1`
|
## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
| 74 |
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
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] |
https://github.com/huggingface/datasets/issues/3172
|
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
I solved my issue by turning the map callable into a class static method, like they do in `lightning-transformers`. Very strange...
|
## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
| 21 |
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
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] |
https://github.com/huggingface/datasets/issues/3172
|
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
I have this issue with datasets v2.5.2 with Python 3.8.10 on Ubuntu 20.04.4 LTS. It does not occur when num_proc=1. When num_proc>1, it intermittently occurs and will cause process to hang. As previously mentioned, it occurs even when datasets have been previously cached. I have tried wrapping logic in a static class as suggested with @mariomeissner with no improvement.
|
## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
| 59 |
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
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] |
https://github.com/huggingface/datasets/issues/3172
|
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
Can someone provide a reproducer to help us debug this (e.g., a `hydra` repo with dummy model and data)?
|
## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
| 19 |
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
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] |
https://github.com/huggingface/datasets/issues/3172
|
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
Hi, similarly here, is there any update for this issue? Particularly having the exact same message as in #6393, not sure this is a datasets or llm-foundry issue
|
## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
| 28 |
`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
|
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] |
https://github.com/huggingface/datasets/issues/3171
|
Raise exceptions instead of using assertions for control flow
|
Adding the remaining tasks for this issue to help new code contributors.
$ cd src/datasets && ack assert -lc
- [x] commands/convert.py:1
- [x] arrow_reader.py:3
- [x] load.py:7
- [x] utils/py_utils.py:2
- [x] features/features.py:9
- [x] arrow_writer.py:7
- [x] search.py:6
- [x] table.py:1
- [x] metric.py:3
- [x] tasks/image_classification.py:1
- [x] arrow_dataset.py:17
- [x] fingerprint.py:6
- [x] io/json.py:1
- [x] io/csv.py:1
|
Motivated by https://github.com/huggingface/transformers/issues/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.
Currently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR.
| 61 |
Raise exceptions instead of using assertions for control flow
|
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] |
https://github.com/huggingface/datasets/issues/3171
|
Raise exceptions instead of using assertions for control flow
|
Hi all,
I am interested in taking up `fingerprint.py`, `search.py`, `arrow_writer.py` and `metric.py`. Will raise a PR soon!
|
Motivated by https://github.com/huggingface/transformers/issues/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.
Currently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR.
| 18 |
Raise exceptions instead of using assertions for control flow
|
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] |
https://github.com/huggingface/datasets/issues/3168
|
OpenSLR/83 is empty
|
Hi @tyrius02, thanks for reporting. I see you self-assigned this issue: are you working on this?
|
## Describe the bug
As the summary says, openslr / SLR83 / train is empty.
The dataset returned after loading indicates there are **zero** rows. The correct number should be **17877**.
## Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('openslr', 'SLR83')
```
## Expected results
```
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'sentence'],
num_rows: 17877
})
})
```
## Actual results
```
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'sentence'],
num_rows: 0
})
})
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.1.dev0 (master HEAD)
- Platform: Ubuntu 20.04
- Python version: 3.7.10
- PyArrow version: 3.0.0
| 16 |
OpenSLR/83 is empty
|
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] |
https://github.com/huggingface/datasets/issues/3168
|
OpenSLR/83 is empty
|
@albertvillanova Yes. Figured I introduced the broken config, I should fix it too.
I've got it working, but I'm struggling with one of the tests. I've started a PR so I/we can work through it.
|
## Describe the bug
As the summary says, openslr / SLR83 / train is empty.
The dataset returned after loading indicates there are **zero** rows. The correct number should be **17877**.
## Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('openslr', 'SLR83')
```
## Expected results
```
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'sentence'],
num_rows: 17877
})
})
```
## Actual results
```
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'sentence'],
num_rows: 0
})
})
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.1.dev0 (master HEAD)
- Platform: Ubuntu 20.04
- Python version: 3.7.10
- PyArrow version: 3.0.0
| 35 |
OpenSLR/83 is empty
|
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] |
https://github.com/huggingface/datasets/issues/3167
|
bookcorpusopen no longer works
|
I tried with the latest changes from #3280 on google colab and it worked fine :)
We'll do a new release soon, in the meantime you can use the updated version with:
```python
load_dataset("bookcorpusopen", revision="master")
```
|
## Describe the bug
When using the latest version of datasets (1.14.0), I cannot use the `bookcorpusopen` dataset. The process blocks always around `9924 examples [00:06, 1439.61 examples/s]` when preparing the dataset. I also noticed that after half an hour the process is automatically killed because of the RAM usage (the machine has 1TB of RAM...).
This did not happen with 1.4.1.
I tried also `rm -rf ~/.cache/huggingface` but did not help.
Changing python version between 3.7, 3.8 and 3.9 did not help too.
## Steps to reproduce the bug
```python
import datasets
d = datasets.load_dataset('bookcorpusopen')
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Linux-5.4.0-1054-aws-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 4.0.1
| 36 |
bookcorpusopen no longer works
|
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] |
https://github.com/huggingface/datasets/issues/3164
|
Add raw data files to the Hub with GitHub LFS for canonical dataset
|
Hi @zlucia, I would actually suggest hosting the dataset as a huggingface.co-hosted dataset.
The only difference with a "canonical"/legacy dataset is that it's nested under an organization (here `stanford` or `stanfordnlp` for instance – completely up to you) but then you can upload your data using git-lfs (unlike "canonical" datasets where we don't host the data)
Let me know if this fits your use case!
cc'ing @osanseviero @lhoestq and rest of the team 🤗
|
I'm interested in sharing the CaseHOLD dataset (https://arxiv.org/abs/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset](https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks!
| 74 |
Add raw data files to the Hub with GitHub LFS for canonical dataset
|
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] |
https://github.com/huggingface/datasets/issues/3164
|
Add raw data files to the Hub with GitHub LFS for canonical dataset
|
Hi @zlucia,
As @julien-c pointed out, the way to store/host raw data files in our Hub is by using what we call "community" datasets:
- either at your personal namespace: `load_dataset("zlucia/casehold")`
- or at an organization namespace: for example, if you create the organization `reglab`, then `load_dataset("reglab/casehold")`
Please note that "canonical" datasets do not normally store/host their raw data at our Hub, but in a third-party server. For "canonical" datasets, we just host the "loading script", that is, a Python script that downloads the raw data from a third-party server, creates the HuggingFace dataset from it and caches it locally.
In order to create an organization namespace in our Hub, please follow this link: https://huggingface.co/organizations/new
There are already many organizations at our Hub (complete list here: https://huggingface.co/organizations), such as:
- Stanford CRFM: https://huggingface.co/stanford-crfm
- Stanford NLP: https://huggingface.co/stanfordnlp
- Stanford CS329S: Machine Learning Systems Design: https://huggingface.co/stanford-cs329s
Also note that you in your organization namespace:
- you can add any number of members
- you can store both raw datasets and models, and those can be immediately accessed using `datasets` and `transformers`
Once you have created an organization, these are the steps to upload/host a raw dataset:
- The no-code procedure: https://huggingface.co/docs/datasets/upload_dataset.html
- Using the command line (terminal): https://huggingface.co/docs/datasets/share.html#add-a-community-dataset
Please, feel free to ping me if you have any further questions or need help.
|
I'm interested in sharing the CaseHOLD dataset (https://arxiv.org/abs/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset](https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks!
| 222 |
Add raw data files to the Hub with GitHub LFS for canonical dataset
|
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] |
https://github.com/huggingface/datasets/issues/3164
|
Add raw data files to the Hub with GitHub LFS for canonical dataset
|
Ah I see, I think I was unclear whether there were benefits to uploading a canonical dataset vs. a community provided dataset. Thanks for clarifying. I'll see if we want to create an organization namespace and otherwise, will upload the dataset under my personal namespace.
|
I'm interested in sharing the CaseHOLD dataset (https://arxiv.org/abs/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset](https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks!
| 45 |
Add raw data files to the Hub with GitHub LFS for canonical dataset
|
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] |
https://github.com/huggingface/datasets/issues/3162
|
`datasets-cli test` should work with datasets without scripts
|
> It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).
>
> I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!
why don't you try to share that info with people, so you can also save some days.
|
It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).
I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!
| 75 |
`datasets-cli test` should work with datasets without scripts
|
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] |
https://github.com/huggingface/datasets/issues/3162
|
`datasets-cli test` should work with datasets without scripts
|
Hi ! You can run the command if you download the repository
```
git clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest
```
and run the command
```
datasets-cli test DataMeasurementsTest/DataMeasurementsTest.py
```
(though on my side it doesn't manage to download the data since the dataset is private ^^)
|
It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).
I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!
| 43 |
`datasets-cli test` should work with datasets without scripts
|
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] |
https://github.com/huggingface/datasets/issues/3162
|
`datasets-cli test` should work with datasets without scripts
|
> Hi ! You can run the command if you download the repository
>
> ```
> git clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest
> ```
>
> and run the command
>
> ```
> datasets-cli test DataMeasurementsTest/DataMeasurementsTest.py
> ```
>
> (though on my side it doesn't manage to download the data since the dataset is private ^^)
Hi! Thanks for the info.
git cannot find the repository. Do you know if they have depreciated these tests and created a new one?
|
It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).
I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!
| 80 |
`datasets-cli test` should work with datasets without scripts
|
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] |
https://github.com/huggingface/datasets/issues/3162
|
`datasets-cli test` should work with datasets without scripts
|
I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`
|
It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).
I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!
| 20 |
`datasets-cli test` should work with datasets without scripts
|
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] |
https://github.com/huggingface/datasets/issues/3162
|
`datasets-cli test` should work with datasets without scripts
|
> I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`
your example repo and this page `https://huggingface.co/docs/datasets/add_dataset.html` helped me to solve.. thanks a lot
|
It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).
I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!
| 35 |
`datasets-cli test` should work with datasets without scripts
|
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] |
https://github.com/huggingface/datasets/issues/3155
|
Illegal instruction (core dumped) at datasets import
|
It seems to be an issue with how conda-forge is building the binaries. It works on some machines, but not a machine with AMD Opteron 8384 processors.
|
## Describe the bug
I install datasets using conda and when I import datasets I get: "Illegal instruction (core dumped)"
## Steps to reproduce the bug
```
conda create --prefix path/to/env
conda activate path/to/env
conda install -c huggingface -c conda-forge datasets
# exits with output "Illegal instruction (core dumped)"
python -m datasets
```
## Environment info
When I run "datasets-cli env", I also get "Illegal instruction (core dumped)"
If I run the following commands:
```
conda create --prefix path/to/another/new/env
conda activate path/to/another/new/env
conda install -c huggingface transformers
transformers-cli env
```
Then I get:
- `transformers` version: 4.11.3
- Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
Let me know what additional information you need in order to debug this issue. Thanks in advance!
| 27 |
Illegal instruction (core dumped) at datasets import
|
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] |
https://github.com/huggingface/datasets/issues/3154
|
Sacrebleu unexpected behaviour/requirement for data format
|
Hi @BramVanroy!
Good question. This project relies on PyArrow (tables) to store data too big to fit in RAM. In the case of metrics, this means that the number of predictions and references has to match to form a table.
That's why your example throws an error even though it matches the schema:
```python
refs = [
['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],
['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],
] # len(refs) = 2
hyps = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'] # len(hyps) = 3
```
Instead, it should be:
```python
refs = [
['The dog bit the man.', 'The dog had bit the man.'],
['It was not unexpected.', 'No one was surprised.'],
['The man bit him first.', 'The man had bitten the dog.'],
] # len(refs) = 3
hyps = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'] # len(hyps) = 3
```
However, `sacreblue` works with the format that's described in your example, hence this part:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99
Hope you get an idea!
|
## Describe the bug
When comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https://github.com/huggingface/datasets/pull/3153).
In the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error.
## Steps to reproduce the bug
```python
import sacrebleu
import datasets
refs = [
['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],
['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],
]
hyps = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.']
expected_bleu = 48.530827
ds_bleu = datasets.load_metric("sacrebleu")
bleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score
print(bleu_score_sb, expected_bleu)
# works: 48.5308...
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
print(bleu_score_ds, expected_bleu)
# ValueError: Predictions and/or references don't match the expected format.
```
This seems to be related to how datasets forces the features format here:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99
and then manipulates the references during the compute stage here
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L119-L122
I do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https://github.com/mjpost/sacrebleu/blob/2787185dd0f8d224c72ee5a831d163c2ac711a47/sacrebleu/metrics/base.py#L229).
## Actual results
Traceback (most recent call last):
File "C:\Users\bramv\AppData\Roaming\JetBrains\PyCharm2020.3\scratches\scratch_23.py", line 23, in <module>
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
File "C:\dev\python\datasets\src\datasets\metric.py", line 392, in compute
self.add_batch(predictions=predictions, references=references)
File "C:\dev\python\datasets\src\datasets\metric.py", line 439, in add_batch
raise ValueError(
ValueError: Predictions and/or references don't match the expected format.
Expected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')},
Input predictions: ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'],
Input references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']]
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1
| 197 |
Sacrebleu unexpected behaviour/requirement for data format
|
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] |
https://github.com/huggingface/datasets/issues/3154
|
Sacrebleu unexpected behaviour/requirement for data format
|
Thanks, that makes sense. It is a bit unfortunate because it may be confusing to users since the input format is suddenly different than what they may expect from the underlying library/metric. But it is understandable due to how `datasets` works!
|
## Describe the bug
When comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https://github.com/huggingface/datasets/pull/3153).
In the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error.
## Steps to reproduce the bug
```python
import sacrebleu
import datasets
refs = [
['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],
['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],
]
hyps = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.']
expected_bleu = 48.530827
ds_bleu = datasets.load_metric("sacrebleu")
bleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score
print(bleu_score_sb, expected_bleu)
# works: 48.5308...
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
print(bleu_score_ds, expected_bleu)
# ValueError: Predictions and/or references don't match the expected format.
```
This seems to be related to how datasets forces the features format here:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99
and then manipulates the references during the compute stage here
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L119-L122
I do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https://github.com/mjpost/sacrebleu/blob/2787185dd0f8d224c72ee5a831d163c2ac711a47/sacrebleu/metrics/base.py#L229).
## Actual results
Traceback (most recent call last):
File "C:\Users\bramv\AppData\Roaming\JetBrains\PyCharm2020.3\scratches\scratch_23.py", line 23, in <module>
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
File "C:\dev\python\datasets\src\datasets\metric.py", line 392, in compute
self.add_batch(predictions=predictions, references=references)
File "C:\dev\python\datasets\src\datasets\metric.py", line 439, in add_batch
raise ValueError(
ValueError: Predictions and/or references don't match the expected format.
Expected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')},
Input predictions: ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'],
Input references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']]
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1
| 41 |
Sacrebleu unexpected behaviour/requirement for data format
|
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] |
https://github.com/huggingface/datasets/issues/3148
|
Streaming with num_workers != 0
|
I can confirm that I was able to reproduce the bug. This seems odd given that #3423 reports duplicate data retrieval when `num_workers` and `streaming` are used together, which is obviously different from what is reported here.
|
## Describe the bug
When using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.
The code owner is likely @lhoestq
## Steps to reproduce the bug
For your convenience, we've prepped a colab notebook that reproduces the bug
https://colab.research.google.com/drive/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing
```python
!pip install datasets==1.14.0
should_freeze_forever = True
# ^-- set this to True in order to freeze forever, set to False in order to work normally
import torch
from datasets import load_dataset
data = load_dataset("oscar", "unshuffled_deduplicated_bn", split="train", streaming=True)
data = data.map(lambda x: {"text": x["text"], "orig": f"oscar[{x['id']}]"}, batched=True)
data = data.shuffle(100, seed=1337)
data = data.with_format("torch")
loader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)
# v-- the code should freeze forever at this line
for i, row in enumerate(loader):
print(row)
if i > 10: break
print("DONE!")
```
## Expected results
The code should not freeze forever with num_workers=2
## Actual results
The code freezes forever with num_workers=2
## Environment info
- `datasets` version: 1.14.0 (also found in previous versions)
- Platform: google colab (also locally)
- Python version: 3.7, (also 3.8)
- PyArrow version: 3.0.0
| 37 |
Streaming with num_workers != 0
|
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] |
https://github.com/huggingface/datasets/issues/3148
|
Streaming with num_workers != 0
|
Any update? A possible solution is to have multiple arrow files as shards, and handle them like what webdatasets does.
Pytorch's new dataset RFC is supporting sharding now, which may helps avoid duplicate data under streaming mode. (https://github.com/pytorch/pytorch/blob/master/torch/utils/data/datapipes/iter/grouping.py#L13)
|
## Describe the bug
When using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.
The code owner is likely @lhoestq
## Steps to reproduce the bug
For your convenience, we've prepped a colab notebook that reproduces the bug
https://colab.research.google.com/drive/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing
```python
!pip install datasets==1.14.0
should_freeze_forever = True
# ^-- set this to True in order to freeze forever, set to False in order to work normally
import torch
from datasets import load_dataset
data = load_dataset("oscar", "unshuffled_deduplicated_bn", split="train", streaming=True)
data = data.map(lambda x: {"text": x["text"], "orig": f"oscar[{x['id']}]"}, batched=True)
data = data.shuffle(100, seed=1337)
data = data.with_format("torch")
loader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)
# v-- the code should freeze forever at this line
for i, row in enumerate(loader):
print(row)
if i > 10: break
print("DONE!")
```
## Expected results
The code should not freeze forever with num_workers=2
## Actual results
The code freezes forever with num_workers=2
## Environment info
- `datasets` version: 1.14.0 (also found in previous versions)
- Platform: google colab (also locally)
- Python version: 3.7, (also 3.8)
- PyArrow version: 3.0.0
| 39 |
Streaming with num_workers != 0
|
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] |
https://github.com/huggingface/datasets/issues/3148
|
Streaming with num_workers != 0
|
Hi ! Thanks for the insights :) Note that in streaming mode there're usually no arrow files. The data are streamed from TAR, ZIP, text, etc. files directly from the web. Though for sharded datasets we can definitely adopt a similar strategy !
|
## Describe the bug
When using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.
The code owner is likely @lhoestq
## Steps to reproduce the bug
For your convenience, we've prepped a colab notebook that reproduces the bug
https://colab.research.google.com/drive/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing
```python
!pip install datasets==1.14.0
should_freeze_forever = True
# ^-- set this to True in order to freeze forever, set to False in order to work normally
import torch
from datasets import load_dataset
data = load_dataset("oscar", "unshuffled_deduplicated_bn", split="train", streaming=True)
data = data.map(lambda x: {"text": x["text"], "orig": f"oscar[{x['id']}]"}, batched=True)
data = data.shuffle(100, seed=1337)
data = data.with_format("torch")
loader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)
# v-- the code should freeze forever at this line
for i, row in enumerate(loader):
print(row)
if i > 10: break
print("DONE!")
```
## Expected results
The code should not freeze forever with num_workers=2
## Actual results
The code freezes forever with num_workers=2
## Environment info
- `datasets` version: 1.14.0 (also found in previous versions)
- Platform: google colab (also locally)
- Python version: 3.7, (also 3.8)
- PyArrow version: 3.0.0
| 43 |
Streaming with num_workers != 0
|
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] |
https://github.com/huggingface/datasets/issues/3145
|
[when Image type will exist] provide a way to get the data as binary + filename
|
@severo I'll keep that in mind.
You can track progress on the Image feature in #3163 (still in the early stage).
|
**Is your feature request related to a problem? Please describe.**
When a dataset cell contains a value of type Image (be it from a remote URL, an Array2D/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.
Note: this issue would apply exactly the same for the `Audio` type.
**Describe the solution you'd like**
If a "cell" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:
```python
filename: str
data: bytes
```
**Describe alternatives you've considered**
A way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.
| 21 |
[when Image type will exist] provide a way to get the data as binary + filename
|
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] |
https://github.com/huggingface/datasets/issues/3145
|
[when Image type will exist] provide a way to get the data as binary + filename
|
Hi ! As discussed with @severo offline it looks like the dataset viewer already supports reading PIL images, so maybe the dataset viewer doesn't need to disable decoding after all
|
**Is your feature request related to a problem? Please describe.**
When a dataset cell contains a value of type Image (be it from a remote URL, an Array2D/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.
Note: this issue would apply exactly the same for the `Audio` type.
**Describe the solution you'd like**
If a "cell" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:
```python
filename: str
data: bytes
```
**Describe alternatives you've considered**
A way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.
| 30 |
[when Image type will exist] provide a way to get the data as binary + filename
|
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] |
https://github.com/huggingface/datasets/issues/3142
|
Provide a way to write a streamed dataset to the disk
|
Yes, I agree this feature is much needed. We could do something similar to what TF does (https://www.tensorflow.org/api_docs/python/tf/data/Dataset#cache).
Ideally, if the entire streamed dataset is consumed/cached, the generated cache should be reusable for the Arrow dataset.
|
**Is your feature request related to a problem? Please describe.**
The streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again.
**Describe the solution you'd like**
Provide a way to write the streamed rows of a dataset on the disk, and to load from it later.
**Describe alternatives you've considered**
Provide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data.
| 36 |
Provide a way to write a streamed dataset to the disk
|
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] |
https://github.com/huggingface/datasets/issues/3142
|
Provide a way to write a streamed dataset to the disk
|
@mariosasko Hi big brother,any update on this? It's 2024 with large streamed dataset loading consume too much time(exp. 2day..), really need this feature for what TF does
|
**Is your feature request related to a problem? Please describe.**
The streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again.
**Describe the solution you'd like**
Provide a way to write the streamed rows of a dataset on the disk, and to load from it later.
**Describe alternatives you've considered**
Provide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data.
| 27 |
Provide a way to write a streamed dataset to the disk
|
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] |
https://github.com/huggingface/datasets/issues/3135
|
Make inspect.get_dataset_config_names always return a non-empty list of configs
|
Hi @severo, I guess this issue requests not only to be able to access the configuration name (by using `inspect.get_dataset_config_names`), but the configuration itself as well (I mean you use the name to get the configuration afterwards, maybe using `builder_cls.builder_configs`), is this right?
|
**Is your feature request related to a problem? Please describe.**
Currently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to
**Describe the solution you'd like**
In that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check/region_1`).
https://github.com/huggingface/datasets/blob/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af/src/datasets/inspect.py#L161
| 43 |
Make inspect.get_dataset_config_names always return a non-empty list of configs
|
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] |
https://github.com/huggingface/datasets/issues/3135
|
Make inspect.get_dataset_config_names always return a non-empty list of configs
|
Yes, maybe the issue could be reformulated. As a user, I want to avoid having to manage special cases:
- I want to be able to get the names of a dataset's configs, and use them in the rest of the API (get the data, get the split names, etc).
- I don't want to have to manage datasets with named configs (`glue`) differently from datasets without named configs (`acronym_identification`, `Check/region_1`)
|
**Is your feature request related to a problem? Please describe.**
Currently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to
**Describe the solution you'd like**
In that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check/region_1`).
https://github.com/huggingface/datasets/blob/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af/src/datasets/inspect.py#L161
| 71 |
Make inspect.get_dataset_config_names always return a non-empty list of configs
|
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] |
https://github.com/huggingface/datasets/issues/3134
|
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
|
Hi,
Did you try to run the code multiple times (GitHub URLs can be down sometimes for various reasons)? I can access `https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py`, so this code is working without an error on my side.
Additionally, can you please run the `datasets-cli env` command because it seems to me that you are using the `datasets` version different from `1.12.1`?
|
datasets version: 1.12.1
`metric = datasets.load_metric('rouge')`
The error:
> ConnectionError Traceback (most recent call last)
> <ipython-input-3-dd10a0c5212f> in <module>
> ----> 1 metric = datasets.load_metric('rouge')
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
> 613 download_config=download_config,
> 614 download_mode=download_mode,
> --> 615 dataset=False,
> 616 )
> 617 metric_cls = import_main_class(module_path, dataset=False)
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)
> 328 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)
> 329 try:
> --> 330 local_path = cached_path(file_path, download_config=download_config)
> 331 except FileNotFoundError:
> 332 if script_version is not None:
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
> 296 use_etag=download_config.use_etag,
> 297 max_retries=download_config.max_retries,
> --> 298 use_auth_token=download_config.use_auth_token,
> 299 )
> 300 elif os.path.exists(url_or_filename):
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
> 603 raise FileNotFoundError("Couldn't find file at {}".format(url))
> 604 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
> --> 605 raise ConnectionError("Couldn't reach {}".format(url))
> 606
> 607 # Try a second time
>
> ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
Is there any remedy to solve the connection issue ?
| 58 |
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
|
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] |
https://github.com/huggingface/datasets/issues/3134
|
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
|
Same issue when running `metric = datasets.load_metric("accuracy")`.
Error info is:
```
metric = datasets.load_metric("accuracy")
Traceback (most recent call last):
File "<ipython-input-2-d25db38b26c5>", line 1, in <module>
metric = datasets.load_metric("accuracy")
File "D:\anaconda3\lib\site-packages\datasets\load.py", line 610, in load_metric
module_path, _ = prepare_module(
File "D:\anaconda3\lib\site-packages\datasets\load.py", line 330, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "D:\anaconda3\lib\site-packages\datasets\utils\file_utils.py", line 288, in cached_path
output_path = get_from_cache(
File "D:\anaconda3\lib\site-packages\datasets\utils\file_utils.py", line 605, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/accuracy/accuracy.py
```
My `datasets-cli env` result is as follows:
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 6.0.0
@yananchen1989 did you find a way to solve this?
|
datasets version: 1.12.1
`metric = datasets.load_metric('rouge')`
The error:
> ConnectionError Traceback (most recent call last)
> <ipython-input-3-dd10a0c5212f> in <module>
> ----> 1 metric = datasets.load_metric('rouge')
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
> 613 download_config=download_config,
> 614 download_mode=download_mode,
> --> 615 dataset=False,
> 616 )
> 617 metric_cls = import_main_class(module_path, dataset=False)
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)
> 328 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)
> 329 try:
> --> 330 local_path = cached_path(file_path, download_config=download_config)
> 331 except FileNotFoundError:
> 332 if script_version is not None:
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
> 296 use_etag=download_config.use_etag,
> 297 max_retries=download_config.max_retries,
> --> 298 use_auth_token=download_config.use_auth_token,
> 299 )
> 300 elif os.path.exists(url_or_filename):
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
> 603 raise FileNotFoundError("Couldn't find file at {}".format(url))
> 604 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
> --> 605 raise ConnectionError("Couldn't reach {}".format(url))
> 606
> 607 # Try a second time
>
> ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
Is there any remedy to solve the connection issue ?
| 103 |
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
|
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] |
https://github.com/huggingface/datasets/issues/3134
|
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
|
It seems to be able to solve this issue by adding the equivalent `accuracy.py` locally.
change `metric = datasets.load_metric("accuracy")` to `metric = datasets.load_metric(path = "./accuracy.py")`.
Copy `accuracy.py` from browser at [accuracy.py](https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/accuracy/accuracy.py)
|
datasets version: 1.12.1
`metric = datasets.load_metric('rouge')`
The error:
> ConnectionError Traceback (most recent call last)
> <ipython-input-3-dd10a0c5212f> in <module>
> ----> 1 metric = datasets.load_metric('rouge')
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
> 613 download_config=download_config,
> 614 download_mode=download_mode,
> --> 615 dataset=False,
> 616 )
> 617 metric_cls = import_main_class(module_path, dataset=False)
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)
> 328 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)
> 329 try:
> --> 330 local_path = cached_path(file_path, download_config=download_config)
> 331 except FileNotFoundError:
> 332 if script_version is not None:
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
> 296 use_etag=download_config.use_etag,
> 297 max_retries=download_config.max_retries,
> --> 298 use_auth_token=download_config.use_auth_token,
> 299 )
> 300 elif os.path.exists(url_or_filename):
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
> 603 raise FileNotFoundError("Couldn't find file at {}".format(url))
> 604 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
> --> 605 raise ConnectionError("Couldn't reach {}".format(url))
> 606
> 607 # Try a second time
>
> ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
Is there any remedy to solve the connection issue ?
| 31 |
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
|
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] |
https://github.com/huggingface/datasets/issues/3134
|
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
|
> It seems to be able to solve this issue by adding the equivalent `accuracy.py` locally. change `metric = datasets.load_metric("accuracy")` to `metric = datasets.load_metric(path = "./accuracy.py")`. Copy `accuracy.py` from browser at [accuracy.py](https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/accuracy/accuracy.py)
This is really a good way
|
datasets version: 1.12.1
`metric = datasets.load_metric('rouge')`
The error:
> ConnectionError Traceback (most recent call last)
> <ipython-input-3-dd10a0c5212f> in <module>
> ----> 1 metric = datasets.load_metric('rouge')
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
> 613 download_config=download_config,
> 614 download_mode=download_mode,
> --> 615 dataset=False,
> 616 )
> 617 metric_cls = import_main_class(module_path, dataset=False)
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)
> 328 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)
> 329 try:
> --> 330 local_path = cached_path(file_path, download_config=download_config)
> 331 except FileNotFoundError:
> 332 if script_version is not None:
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
> 296 use_etag=download_config.use_etag,
> 297 max_retries=download_config.max_retries,
> --> 298 use_auth_token=download_config.use_auth_token,
> 299 )
> 300 elif os.path.exists(url_or_filename):
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
> 603 raise FileNotFoundError("Couldn't find file at {}".format(url))
> 604 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
> --> 605 raise ConnectionError("Couldn't reach {}".format(url))
> 606
> 607 # Try a second time
>
> ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
Is there any remedy to solve the connection issue ?
| 38 |
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
|
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] |
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