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keras-team/keras | machine-learning | 20,378 | keras.io examples using model.stateless_call aren't setting training=True and aren't managing non_trainable state | both the examples
* https://keras.io/guides/writing_a_custom_training_loop_in_jax/
* https://keras.io/guides/distributed_training_with_jax/
have a training loop that uses `stateless_call`, but neither sets `training=True` ( which means they are using the `training=False` behaviour )
i guess it's not strictly relevant to the first example, which has no interesting non_trainables, but the second example has batch norm and dropout.
additionally i don't see where this non_trainable state would be managed?
* re: batchnorm; each replica might have the same params, but is getting a different batch, so would collect different batch norm stats after the stateless_call. these aren't being aggregated?
* re: dropout; the RNGKeys for dropout are in the non_trainable, but never change. shouldn't these be split after each forward pass?
| closed | 2024-10-18T12:28:37Z | 2024-10-18T21:14:00Z | https://github.com/keras-team/keras/issues/20378 | [] | matpalm | 2 |
AutoViML/AutoViz | scikit-learn | 90 | Cannot even import autoviz? | Not able to even import autoviz after installation. Thought to try it, but doesn't work.
Please see the screenshot below:
| closed | 2023-08-01T08:31:48Z | 2023-08-08T14:57:39Z | https://github.com/AutoViML/AutoViz/issues/90 | [] | mkumar73 | 2 |
MolSSI/cookiecutter-cms | pytest | 143 | Where to write the dependency? | Sorry this is a very simple question, I have selected the
```
Select dependency_source:
1 - conda-forge
```
I wonder where should I add dependencies for the installation?
Normally, I would add the dependencies to `install_requires` in `setup.py`, but that would mean that the dependency is installed via pip.
I wonder where should I put the dependencies? | closed | 2021-10-28T12:38:08Z | 2021-10-28T16:41:01Z | https://github.com/MolSSI/cookiecutter-cms/issues/143 | [] | xiki-tempula | 4 |
huggingface/datasets | nlp | 6,537 | Adding support for netCDF (*.nc) files | ### Feature request
netCDF (*.nc) is a file format for storing multidimensional scientific data, which is used by packages like `xarray` (labelled multi-dimensional arrays in Python). It would be nice to have native support for netCDF in `datasets`.
### Motivation
When uploading *.nc files onto Huggingface Hub through the `datasets` API, I would like to be able to preview the dataset without converting it to another format.
### Your contribution
I can submit a PR, provided I have the time. | open | 2023-12-27T09:27:29Z | 2023-12-27T20:46:53Z | https://github.com/huggingface/datasets/issues/6537 | [
"enhancement"
] | shermansiu | 3 |
ultralytics/ultralytics | pytorch | 19,243 | Run failed when using nms in batch infer | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
### Ultralytics YOLO Component
Export
### Bug
I want to convert the model to ONNX format and integrate NMS into the model. However, after conversion, when I try to perform batch inference with dynamic shapes, I get a Split error. When I change the batch size during conversion, the error numbers also change accordingly. It only works correctly when the preset batch size matches the inference batch size. Is this because some tensor values were converted to constants somewhere? I hope to get an answer.
err message:
```
[E:onnxruntime:, sequential_executor.cc:516 ExecuteKernel] Non-zero status code returned while running Split node. Name:'/Split_2' Status Message: Cannot split using values in 'split' attribute. Axis=0 Input shape={3,8400} NumOutputs=1 Num entries in 'split' (must equal number of outputs) was 1 Sum of sizes in 'split' (must equal size of selected axis) was 1
```
the split node:
<img width="740" alt="Image" src="https://github.com/user-attachments/assets/a99745fd-0b79-4d2d-b6ed-efc33ddeb666" />
### Environment
ultralytics 8.3.74
torch 2.4.0
onnx 1.17.0
onnxruntime-gpu 1.19.0
opencv-python 4.10.0.84
python 3.12.4
### Minimal Reproducible Example
export code:
```
from ultralytics import YOLO
import cv2
import numpy as np
model = YOLO('yolov11n-face.pt') # load a pretrained YOLOv8n detection model
image = cv2.imread('../datasets/face_test/image.jpg')
results = model((image, image2, image3, image4)) # predict on an image
model.export(format="onnx", nms=True, dynamic=True, simplify=True)
```
test code:
```
image = cv2.imread('../datasets/face_test/image1.jpg')
image2 = cv2.imread('../datasets/face_test/image2.jpg')
results = model((image, image2)) # predict on an image
output_path = 'yolov11n-face.onnx'
image_list = [image, image2]
w = torch.Tensor([i.shape[0] for i in image_list]).to(model.device)
h = torch.Tensor([i.shape[1] for i in image_list]).to(model.device)
in_tensor = preprocess(image_list) # (2, 640, 640, 3)
onnx_model = onnx.load(output_path)
onnx.checker.check_model(onnx_model)
ort_session = onnxruntime.InferenceSession(output_path)
ort_inputs = {
ort_session.get_inputs()[0].name: to_numpy(in_tensor.cpu())
}
# print(ort_inputs)
for _ in range(1):
ort_outs = ort_session.run(None, ort_inputs)
print('onnx out:')
print(ort_outs)
```
### Additional
_No response_
### Are you willing to submit a PR?
- [ ] Yes I'd like to help by submitting a PR! | closed | 2025-02-14T08:43:59Z | 2025-02-18T13:31:04Z | https://github.com/ultralytics/ultralytics/issues/19243 | [
"bug",
"fixed",
"exports"
] | sajomanaka | 11 |
timkpaine/lantern | plotly | 141 | add replay for lantern live | closed | 2018-01-24T15:13:58Z | 2018-09-13T18:48:46Z | https://github.com/timkpaine/lantern/issues/141 | [
"feature"
] | timkpaine | 1 |
|
syrupy-project/syrupy | pytest | 764 | Colored 1.5 broke syrupy | **Describe the bug**
I run `poetry update` this morning and it updated `colored` to 1.5. Which is a legit update according to pyrupy `pyproject.toml`. From the change log of `colored` I can see they renamed some functions like `bg` to `back` and `fg` to `fore`. These changes broke syrupy.
<!-- A clear and concise description of what the bug is. -->
**To reproduce**
Update to colored 1.5 and try using syrupy in a CI pipeline on github/gitlab
<!--
Steps to reproduce the behavior:
1. Setup '...'
2. Run command '....'
3. See error
-->
**Expected behavior**
<!-- A clear and concise description of what you expected to happen. -->
It should not break or it should not allow to update to 1.5
**Screenshots**
<!-- If applicable, add screenshots to help explain your problem. -->
**Environment (please complete the following information):**
- OS: I am not sure whatever my CI is using, I think Alpine
- Syrupy Version: 3.0.6
- Python Version: 3.11
**Additional context**
<!-- Add any other context about the problem here. -->
| closed | 2023-06-19T07:46:07Z | 2023-06-20T11:44:49Z | https://github.com/syrupy-project/syrupy/issues/764 | [] | dfioravanti | 4 |
serengil/deepface | deep-learning | 945 | Get Landmarks | Dear all,
maybe I am too stupid to find it. How can I get the Landmarks when I am using extract_faces?
kr
Dirk | closed | 2024-01-08T10:36:16Z | 2024-01-08T10:59:55Z | https://github.com/serengil/deepface/issues/945 | [
"question"
] | DirkWuerdemann | 1 |
jacobgil/pytorch-grad-cam | computer-vision | 545 | `SemanticSegmentationTarget` throws tensor device casting error | ## Bug
The following code
```python
targets = [
SemanticSegmentationTarget(2, np.ones((128, 128), dtype=np.float32))
]
with GradCAM(model=m, target_layers=l, reshape_transform=reshape_transform) as cam:
mask = cam(input_tensor=timg, targets=targets)
```
throws a RuntimeError
```
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, mps:0 and cpu!
```
## Full Stack Trace
```
{
"name": "RuntimeError",
"message": "Expected all tensors to be on the same device, but found at least two devices, mps:0 and cpu!",
"stack": "---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[5], line 30
24 targets = [
25 SemanticSegmentationTarget(2, np.ones((128, 128), dtype=np.float32))
26 # SemanticSegmentationTarget(10, np.ones((128, 128), dtype=np.float32))
27 ]
29 with GradCAM(model=m, target_layers=l, reshape_transform=reshape_transform) as cam:
---> 30 mask = cam(
31 input_tensor=timg,
32 targets=targets)
34 pt.imshow(show_on_image(img, mask[0]))
File ~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/pytorch_grad_cam/base_cam.py:186, in BaseCAM.__call__(self, input_tensor, targets, aug_smooth, eigen_smooth)
183 if aug_smooth is True:
184 return self.forward_augmentation_smoothing(input_tensor, targets, eigen_smooth)
--> 186 return self.forward(input_tensor, targets, eigen_smooth)
File ~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/pytorch_grad_cam/base_cam.py:98, in BaseCAM.forward(self, input_tensor, targets, eigen_smooth)
96 if self.uses_gradients:
97 self.model.zero_grad()
---> 98 loss = sum([target(output) for target, output in zip(targets, outputs)])
99 loss.backward(retain_graph=True)
101 # In most of the saliency attribution papers, the saliency is
102 # computed with a single target layer.
103 # Commonly it is the last convolutional layer.
(...)
108 # use all conv layers for example, all Batchnorm layers,
109 # or something else.
File ~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/pytorch_grad_cam/utils/model_targets.py:68, in SemanticSegmentationTarget.__call__(self, model_output)
67 def __call__(self, model_output):
---> 68 return (model_output[self.category, :, :] * self.mask).sum()
69 return (model_output[self.category, :, :] * self.mask.to(model_output.device)).sum()
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, mps:0 and cpu!"
}
```
## Version
grad-cam==1.5.4
on **Python 3.12.3**
| closed | 2024-12-12T09:11:55Z | 2024-12-12T09:54:00Z | https://github.com/jacobgil/pytorch-grad-cam/issues/545 | [] | shreyass-ranganatha | 0 |
deepset-ai/haystack | pytorch | 8,289 | Remove deprecated `SentenceWindowRetrieval` component | It was left as a stubbed component in 2.4 to aid backward compatibility with existing pipelines. It should be removed in 2.5. | closed | 2024-08-26T12:21:59Z | 2024-08-28T08:04:53Z | https://github.com/deepset-ai/haystack/issues/8289 | [
"P2",
"2.x"
] | shadeMe | 0 |
hankcs/HanLP | nlp | 693 | 人名识别导致分词错误 | <!--
注意事项和版本号必填，否则不回复。若希望尽快得到回复，请按模板认真填写，谢谢合作。
-->
## 注意事项
请确认下列注意事项：
* 我已仔细阅读下列文档，都没有找到答案：
- [首页文档](https://github.com/hankcs/HanLP)
- [wiki](https://github.com/hankcs/HanLP/wiki)
- [常见问题](https://github.com/hankcs/HanLP/wiki/FAQ)
* 我已经通过[Google](https://www.google.com/#newwindow=1&q=HanLP)和[issue区检索功能](https://github.com/hankcs/HanLP/issues)搜索了我的问题，也没有找到答案。
* 我明白开源社区是出于兴趣爱好聚集起来的自由社区，不承担任何责任或义务。我会礼貌发言，向每一个帮助我的人表示感谢。
* [x] 我在此括号内输入x打钩，代表上述事项确认完毕。
## 版本号
<!-- 发行版请注明jar文件名去掉拓展名的部分；GitHub仓库版请注明master还是portable分支 -->
当前最新版本号是：1.5.2
我使用的版本是：1.3.4
<!--以上属于必填项，以下可自由发挥-->
## 我的问题
句子 ：
```
商改后要买另外的保险保费怎么算
```
分词结果：
```
[商改后/nr, 要买/nz, 另外/c, 的/ude1, 保险/n, 保费/n, 怎么/ryv, 算/v]
```
其中 商改后 被识别为一个人名
## 复现问题
<!-- 你是如何操作导致产生问题的？比如修改了代码？修改了词典或模型？-->
### 步骤
1. 首先……
2. 然后……
3. 接着……
### 触发代码
```
public void testIssue1234() throws Exception
{
System.out.println(StandardTokenizer.segment("商改后要买另外的保险保费怎么算"));
}
```
### 期望输出
<!-- 你希望输出什么样的正确结果？-->
```
[商改/v，后/, 要买/nz, 另外/c, 的/ude1, 保险/n, 保费/n, 怎么/ryv, 算/v]
```
### 实际输出
```
[商改后/nr, 要买/nz, 另外/c, 的/ude1, 保险/n, 保费/n, 怎么/ryv, 算/v]
```
## 其他信息
<!-- 任何可能有用的信息，包括截图、日志、配置文件、相关issue等等。-->
| closed | 2017-11-27T03:27:56Z | 2017-12-02T04:22:36Z | https://github.com/hankcs/HanLP/issues/693 | [
"improvement"
] | iwaller | 3 |
scrapy/scrapy | python | 6,024 | Problem with Twisted AttributeError: 'EPollReactor' object has no attribute '_handleSignals' | ### Description
Since the release of Twisted 23.8.0 recently, I've had problems running Scrapy. I had to fix to the previous version, Twisted==22.10.0
### Steps to Reproduce
1. Install Scrapy in a new environment
2. Run .start() from an CrawlerProcess object
**Expected behavior:** Scrapy to crawl
**Actual behavior:**
**Reproduces how often:** Always in the new version
### Versions
Scrapy>=2.9.0
### Additional context
I've seen this fix on [stackoverflow](https://stackoverflow.com/questions/76995567/error-when-crawl-data-epollreactor-object-has-no-attribute-handlesignals)
| closed | 2023-08-29T00:04:21Z | 2023-10-17T13:08:25Z | https://github.com/scrapy/scrapy/issues/6024 | [
"bug"
] | RobertoHigor | 8 |
ckan/ckan | api | 7,639 | Solution: Integration with Tableau, Power BI and Data Studio | **Problem research and evidence**
1. Integration with top data visualization tools was suggested during the discussion: https://github.com/ckan/ckan/issues/7499 @jqnatividad concluded that it's an optimal way to enable top data visualization experience.
2. During the research there were 3 respondents that mentioned integrations with BI Tools:
- Customizable dashboard - linking Tableau, Power BI, Google Data Studio
- Make it easier to send data to Tableau, Power BI
- Integration with Tableau
⚡️ If you have a datastore you can integrate a BI tool.
**Solution hypothesis**
Solution should provide a view of Tableau, Power BI and Data Studio on CKAN pages. It'll enable publishers enrich datasets published with visualizations and stories based on them.
Solution should enable data sending to data visualization tool with from CKAN interface.
**Solution description**
TBD
**Questions to consider**
Is this change going to break current installations?
Can we provide a backwards compatibility?
How easy is gonna be for current implementations to migrate to this new release?
Do current versions of CKAN have the adequate resources/support to migrate to this new version?
Are we going to change the database schema?
Are we going to change the API?
Are we going to deprecate Interfaces? | open | 2023-06-10T15:48:27Z | 2023-08-01T06:44:58Z | https://github.com/ckan/ckan/issues/7639 | [
"To Discuss"
] | thegostev | 3 |
microsoft/nni | data-science | 5,702 | How to use loss instead of accuray in HPO | **Describe the issue**: I just started using nni and followed the /hpo_quickstart_pytorch/ example to inplement.
But, instead of accuracy, I need to thave the main monitor and optimize for the loss, and the lower loss, the better, obviously.
I used nni.report_intermediate_result(valLossSingle) to report the loss. I think I need to let the tuner know that this is actually loss, rather than accuracy. How to do that?
**Environment**:
- NNI version: 2.7
- Training service (local|remote|pai|aml|etc): local
- Client OS: ubuntu
- Server OS (for remote mode only):
- Python version: 3.10
- PyTorch/TensorFlow version: 2
- Is conda/virtualenv/venv used?: conda
- Is running in Docker?: no
**Configuration**:
- Experiment config (remember to remove secrets!):
- Search space:
**Log message**:
- nnimanager.log:
- dispatcher.log:
- nnictl stdout and stderr:
<!--
Where can you find the log files:
LOG: https://github.com/microsoft/nni/blob/master/docs/en_US/Tutorial/HowToDebug.md#experiment-root-director
STDOUT/STDERR: https://nni.readthedocs.io/en/stable/reference/nnictl.html#nnictl-log-stdout
-->
**How to reproduce it?**: | open | 2023-10-27T06:48:29Z | 2023-10-27T06:48:29Z | https://github.com/microsoft/nni/issues/5702 | [] | 0i0i0i | 0 |
tflearn/tflearn | data-science | 1,071 | tflearn Installation issues. | Hello,
I've been trying to install tflearn module for a while now and haven't been able to do so. I have checked most documentations and other problems but none seem to be usefull.
I am using anaconda extension. I have tensorflow installed, it's current installed version is 1.8 and as far as I understood it should be higher or equal to 1.0.
I can't use anaconda navigator for some reason but the prompt is working just fine and that's where I've been trying.
`import tflearn
curses is not supported on this machine (please install/reinstall curses for an optimal experience)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\AppData\Local\Continuum\anaconda3\lib\site-packages\tflearn-0.3.2-py3.6.egg\tflearn\__init__.py", line 62, in <module>
File "C:\Users\AppData\Local\Continuum\anaconda3\lib\site-packages\tflearn-0.3.2-py3.6.egg\tflearn\datasets\__init__.py", line 1, in <module>
# -*- coding: utf-8 -*-
File "C:\Users\AppData\Local\Continuum\anaconda3\lib\site-packages\tflearn-0.3.2-py3.6.egg\tflearn\datasets\cifar10.py", line 16, in <module>
File "C:\Users\AppData\Local\Continuum\anaconda3\lib\site-packages\tflearn-0.3.2-py3.6.egg\tflearn\data_utils.py", line 7, in <module>
File "C:\Users\AppData\Local\Continuum\anaconda3\lib\site-packages\PIL\Image.py", line 60, in <module>
from . import _imaging as core
ImportError: DLL load failed: The specified module could not be found.`
That's the error that comes up when I try to import it from the prompt. It seems like it is installed but not importable
`~\AppData\Local\Continuum\anaconda3\lib\site-packages\PIL\Image.py in <module>()
58 # Also note that Image.core is not a publicly documented interface,
59 # and should be considered private and subject to change.
---> 60 from . import _imaging as core
61 if PILLOW_VERSION != getattr(core, 'PILLOW_VERSION', None):
62 raise ImportError("The _imaging extension was built for another "
ImportError: DLL load failed: The specified module could not be found.`
And that's a jupyter's notebook import error.
Please guide me!
| closed | 2018-07-03T13:03:30Z | 2018-07-04T11:29:42Z | https://github.com/tflearn/tflearn/issues/1071 | [] | Nadedic | 1 |
fastapiutils/fastapi-utils | fastapi | 340 | [BUG] Note to the installation guide | Hi! your instalation [guide](https://fastapi-utils.davidmontague.xyz/#installation) differs your guide in [github](https://github.com/dmontagu/fastapi-utils?tab=readme-ov-file#installation)
<img width="1089" alt="image" src="https://github.com/user-attachments/assets/226b09f3-44f2-46ba-876a-0c15d64788b7">
<img width="1025" alt="image" src="https://github.com/user-attachments/assets/8b0a1071-20ea-40d3-afe5-19ba6d97ce15">
| closed | 2024-10-20T09:42:59Z | 2024-11-11T14:18:23Z | https://github.com/fastapiutils/fastapi-utils/issues/340 | [
"bug"
] | ArtyomIvlev | 1 |
apache/airflow | data-science | 47,452 | Rename DAG (from models/dag) to SchedulerDAG -- or remove it! | ### Body
It is easily confused with the DAG in task sdk.
Ideally we can remove. At least rename it.
comment from ash
> That should be renamed as SchedulerDAG really -- (or possibly it can be removed now) -- it's a) a compat import while that part isn't complete, and b) where scheduler specific DAG functions go
### Committer
- [x] I acknowledge that I am a maintainer/committer of the Apache Airflow project. | open | 2025-03-06T15:01:54Z | 2025-03-06T15:04:11Z | https://github.com/apache/airflow/issues/47452 | [
"area:Scheduler",
"kind:meta"
] | dstandish | 0 |
pydata/pandas-datareader | pandas | 9 | TST: make sure tests work on python 2.6 | closed | 2015-01-22T05:34:58Z | 2015-01-22T05:53:37Z | https://github.com/pydata/pandas-datareader/issues/9 | [] | davidastephens | 0 |
|
deezer/spleeter | tensorflow | 677 | [Discussion] can we convert the pretrained model to saved model format? | can we directly download the model and convert to saved model format, then load from Cpp API? | closed | 2021-11-09T12:51:14Z | 2021-11-11T11:43:38Z | https://github.com/deezer/spleeter/issues/677 | [
"question"
] | imiskolee | 0 |
autokey/autokey | automation | 36 | Help menu/main page on github have dead links | Google Code closed down. As such the FAQ/Online Help/Scripting Help links lead to dead sites. They probably should be changed to Archive.org links or hosted elsewhere preferably. The Paypal link is defunct as well.
| closed | 2016-10-20T09:38:54Z | 2016-11-08T05:12:09Z | https://github.com/autokey/autokey/issues/36 | [] | pensive-fuzz | 1 |
JaidedAI/EasyOCR | pytorch | 923 | AttributeError: '_MultiProcessingDataLoaderIter' object has no attribute 'next' | Hi.
I am currently trying to train my own model.
I obtained the dataset as required and modified the config file. However, I get this error when trying to train.
I have already tried to decrease workers in config file.
I also tried to modify the dataset.py file, line 101 from
image, text = data_loader_iter.next()
to
image, text = next(data_loader_iter)
However, the error persist.
Thanks | closed | 2023-01-03T17:19:37Z | 2023-01-14T09:02:52Z | https://github.com/JaidedAI/EasyOCR/issues/923 | [] | proclaim5584 | 3 |
newpanjing/simpleui | django | 206 | models中图片字段展示 | 模型中我存储的图片路径为“/blog/static/images/blog/timg_PrPLvaa.jpg”，这是我访问静态资源路径，但是后台显示该字段时是用a标签包裹，自动拼接的路径不对，“http://192.168.3.28:5058/admin/blog/article/1/change/blog/static/images/blog/timg_PrPLvaa.jpg”，前边拼接的可以设计成可以让用户自定义修改，要不然点击访问不了该图片。再不然也可以不自动进行拼接，让用户保存完整链接。
**留下你的联系方式，以便与你取得联系**
QQ：13716.7399
| closed | 2019-12-23T08:53:06Z | 2019-12-26T09:23:05Z | https://github.com/newpanjing/simpleui/issues/206 | [
"enhancement"
] | User-Name-Chao | 0 |
vaexio/vaex | data-science | 1,968 | [BUG-REPORT] to_pandas_df giving error Urgent!!! | @JovanVeljanoski @maartenbreddels Hey There Can you please help me in this, its little Urgent
I'm trying to do the conversion of vaex dataframe to pandas dataframe but its giving me error shared down below. At first I've taken two dataframes and joined them on more than column with merging solution and after that I've tried converting it to pandas data frame.
Both the dataframes have these columns and datatypes:
one data frame is named as df and another is named as product
first image is of df dataframe:
second image is for product dataframe
These two tables are joined using below code:
After this we get dataframe Named megedStuff which gives us the joins. Also many time I saw vaex is not good at handling empty table cells or nan values or missing values.
When I try to convert this mergedStuff to pandas df it shows below error and also I'm attaching data with this. Maybe after converting it to csvs it will work fine but I'm not sure.
[product.csv](https://github.com/vaexio/vaex/files/8231496/product.csv)
[df.csv](https://github.com/vaexio/vaex/files/8231497/df.csv)
ArrowIndexErrorTraceback (most recent call last)
<ipython-input-11-968fb59ca07f> in <module>
----> 1 mergedStuff1 = mergedStuff.to_pandas_df()
/opt/conda/lib/python3.7/site-packages/vaex/dataframe.py in to_pandas_df(self, column_names, selection, strings, virtual, index_name, parallel, chunk_size, array_type)
3242 return iterator()
3243 else:
-> 3244 return create_pdf(self.to_dict(column_names=column_names, selection=selection, parallel=parallel, array_type=array_type))
3245
3246 @docsubst
/opt/conda/lib/python3.7/site-packages/vaex/dataframe.py in to_dict(self, column_names, selection, strings, virtual, parallel, chunk_size, array_type)
3157 yield i1, i2, dict(list(zip(column_names, [array_types.convert(chunk, array_type) for chunk in chunks])))
3158 return iterator()
-> 3159 return dict(list(zip(column_names, [array_types.convert(chunk, array_type) for chunk in self.evaluate(column_names, selection=selection, parallel=parallel)])))
3160
3161 @_hidden
/opt/conda/lib/python3.7/site-packages/vaex/dataframe.py in evaluate(self, expression, i1, i2, out, selection, filtered, array_type, parallel, chunk_size, progress)
2996 return self.evaluate_iterator(expression, s1=i1, s2=i2, out=out, selection=selection, filtered=filtered, array_type=array_type, parallel=parallel, chunk_size=chunk_size, progress=progress)
2997 else:
-> 2998 return self._evaluate_implementation(expression, i1=i1, i2=i2, out=out, selection=selection, filtered=filtered, array_type=array_type, parallel=parallel, chunk_size=chunk_size, progress=progress)
2999
3000 @docsubst
/opt/conda/lib/python3.7/site-packages/vaex/dataframe.py in _evaluate_implementation(self, expression, i1, i2, out, selection, filtered, array_type, parallel, chunk_size, raw, progress)
6351 arrays[expression][i1:i2] = blocks[i]
6352 if expression_to_evaluate:
-> 6353 df.map_reduce(assign, lambda *_: None, expression_to_evaluate, progress=progress, ignore_filter=False, selection=selection, pre_filter=use_filter, info=True, to_numpy=False, name="evaluate")
6354 def finalize_result(expression):
6355 expression_obj = expression
/opt/conda/lib/python3.7/site-packages/vaex/dataframe.py in map_reduce(self, map, reduce, arguments, progress, delay, info, to_numpy, ignore_filter, pre_filter, name, selection)
427 progressbar.add_task(task, f'map reduce: {name}')
428 task = self.executor.schedule(task)
--> 429 return self._delay(delay, task)
430
431 def apply(self, f, arguments=None, vectorize=False, multiprocessing=True):
/opt/conda/lib/python3.7/site-packages/vaex/dataframe.py in _delay(self, delay, task, progressbar)
1686 return task
1687 else:
-> 1688 self.execute()
1689 return task.get()
1690
/opt/conda/lib/python3.7/site-packages/vaex/dataframe.py in execute(self)
410 print(repr(task))
411 if self.executor.tasks:
--> 412 self.executor.execute()
413
414 async def execute_async(self):
/opt/conda/lib/python3.7/site-packages/vaex/execution.py in execute(self)
306
307 def execute(self):
--> 308 for _ in self.execute_generator():
309 pass # just eat all elements
310
/opt/conda/lib/python3.7/site-packages/vaex/execution.py in execute_generator(self, use_async)
433 dataset.row_count,
434 progress=progress,
--> 435 cancel=lambda: self._cancel(run), unpack=True, run=run, use_async=use_async)
436 duration_wallclock = time.time() - t0
437 logger.debug("executing took %r seconds", duration_wallclock)
/opt/conda/lib/python3.7/site-packages/vaex/multithreading.py in map(self, callable, iterator, count, on_error, progress, cancel, unpack, use_async, **kwargs_extra)
104 iterator = (loop.run_in_executor(self, lambda value=value: wrapped(value)) for value in cancellable_iter())
105 else:
--> 106 iterator = super(ThreadPoolIndex, self).map(wrapped, cancellable_iter())
107 total = 0
108 iterator = iter(buffer(iterator, self._max_workers + 3))
/opt/conda/lib/python3.7/concurrent/futures/_base.py in map(self, fn, timeout, chunksize, *iterables)
573 end_time = timeout + time.monotonic()
574
--> 575 fs = [self.submit(fn, *args) for args in zip(*iterables)]
576
577 # Yield must be hidden in closure so that the futures are submitted
/opt/conda/lib/python3.7/concurrent/futures/_base.py in <listcomp>(.0)
573 end_time = timeout + time.monotonic()
574
--> 575 fs = [self.submit(fn, *args) for args in zip(*iterables)]
576
577 # Yield must be hidden in closure so that the futures are submitted
/opt/conda/lib/python3.7/site-packages/vaex/multithreading.py in cancellable_iter()
90 chunk_iterator = iterator
91 def cancellable_iter():
---> 92 for value in chunk_iterator:
93 yield value
94 if cancelled:
/opt/conda/lib/python3.7/site-packages/vaex/dataset.py in chunk_iterator(self, columns, chunk_size, reverse)
647 raise KeyError(f'Oops, you tried to get column {name}, but you renamed it to {rename}')
648 columns = [self.reverse.get(name, name) for name in columns]
--> 649 for i1, i2, chunks in self.original.chunk_iterator(columns, chunk_size, reverse=reverse):
650 yield i1, i2, {self.renaming.get(name, name): ar for name, ar in chunks.items()}
651
/opt/conda/lib/python3.7/site-packages/vaex/dataset.py in chunk_iterator(self, columns, chunk_size, reverse)
1165 if column in self._dropped_names:
1166 raise KeyError(f'Oops, you tried to get column {column} while it is actually dropped')
-> 1167 yield from self.original.chunk_iterator(columns, chunk_size=chunk_size, reverse=reverse)
1168
1169 def hashed(self):
/opt/conda/lib/python3.7/site-packages/vaex/join.py in chunk_iterator(self, *args, **kwargs)
62
63 def chunk_iterator(self, *args, **kwargs):
---> 64 yield from self.original.chunk_iterator(*args, **kwargs)
65
66 def hashed(self):
/opt/conda/lib/python3.7/site-packages/vaex/dataset.py in chunk_iterator(self, columns, chunk_size, reverse)
1235 for (i1, i2, ichunks), (j1, j2, jchunks) in zip(
1236 self.left.chunk_iterator(columns_left, chunk_size, reverse=reverse),
-> 1237 self.right.chunk_iterator(columns_right, chunk_size, reverse=reverse)):
1238 # TODO: if one of the datasets does not respect the chunk_size (e.g. parquet)
1239 # this might fail
/opt/conda/lib/python3.7/site-packages/vaex/dataset.py in chunk_iterator(self, columns, chunk_size, reverse)
908 # TODO: we may be able to do this slightly more efficient by first
909 # materializing the columns
--> 910 yield from self._default_chunk_iterator(self._columns, columns, chunk_size, reverse=reverse)
911
912 def slice(self, start, end):
/opt/conda/lib/python3.7/site-packages/vaex/dataset.py in _default_chunk_iterator(self, array_map, columns, chunk_size, reverse)
506 def _default_chunk_iterator(self, array_map, columns, chunk_size, reverse=False):
507 for i1, i2, reader in self._default_lazy_chunk_iterator(array_map, columns, chunk_size, reverse):
--> 508 yield i1, i2, reader()
509
510 @abstractmethod
/opt/conda/lib/python3.7/site-packages/vaex/dataset.py in reader(i1, i2)
497 i2 = min((i + 1) * chunk_size, self.row_count)
498 def reader(i1=i1, i2=i2):
--> 499 chunks = {k: array_map[k][i1:i2] for k in columns}
500 length = i2 - i1
501 for name, chunk in chunks.items():
/opt/conda/lib/python3.7/site-packages/vaex/dataset.py in <dictcomp>(.0)
497 i2 = min((i + 1) * chunk_size, self.row_count)
498 def reader(i1=i1, i2=i2):
--> 499 chunks = {k: array_map[k][i1:i2] for k in columns}
500 length = i2 - i1
501 for name, chunk in chunks.items():
/opt/conda/lib/python3.7/site-packages/vaex/column.py in __getitem__(self, slice)
375 take_indices[mask] = 0
376 if isinstance(ar_unfiltered, supported_arrow_array_types):
--> 377 ar = ar_unfiltered.take(vaex.array_types.to_arrow(take_indices))
378 else:
379 ar = ar_unfiltered[take_indices]
/opt/conda/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.Array.take()
/opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in take(data, indices, boundscheck, memory_pool)
452 """
453 options = TakeOptions(boundscheck=boundscheck)
--> 454 return call_function('take', [data, indices], options, memory_pool)
455
456
/opt/conda/lib/python3.7/site-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function()
/opt/conda/lib/python3.7/site-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call()
/opt/conda/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
/opt/conda/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowIndexError: Index 21245 out of bounds | closed | 2022-03-11T11:31:22Z | 2022-05-27T13:26:09Z | https://github.com/vaexio/vaex/issues/1968 | [
"difficult to reproduce"
] | ashsharma96 | 3 |
OFA-Sys/Chinese-CLIP | nlp | 331 | 能否通过文本向量化中加入指令优化图片召回率 | 目前能否能通过在文本向量化中加入类似“为这个句子生成向量以用于检索图片：”，的指令来优化图片的召回率 | open | 2024-07-22T06:41:48Z | 2024-07-22T06:41:48Z | https://github.com/OFA-Sys/Chinese-CLIP/issues/331 | [] | Amphetaminewei | 0 |
mitmproxy/pdoc | api | 246 | `IndentationError` when using a multi-line decorator for a class method | #### Problem Description
I encounter an `IndentationError` when trying to generate the documentation of a class which contains a multi-line decorator on a class method.
I believe that this is a limitation of the [`_dedent` method](https://github.com/mitmproxy/pdoc/blob/main/pdoc/doc_ast.py#L210) but I do not have a good proposal for a solution yet. If we can discuss and figure out a good approach on how to resolve this I am happy to help with fixing the issue :+1:
What would need to happen is that the actual function definition also gets dedented rather than simply the line following the decorator. See the example below.
#### Steps to reproduce the behavior:
I was able to break down the issue to the following example:
```python
import pytest
class TestDummy:
"""Test Dummy."""
@pytest.mark.parametrize(
"arg,string",
[
["hello", "world"],
],
)
def test_dummy(self, arg, string):
"""Dummy test method."""
assert arg != string
```
<details>
<summary>
Using `pdoc -o tmp example.py` generates the following error:
</summary>
```python-traceback
Traceback (most recent call last):
File "/home/max/Git/cobib/.direnv/python-3.9.2/bin/pdoc", line 33, in <module>
sys.exit(load_entry_point('pdoc', 'console_scripts', 'pdoc')())
File "/home/max/Git/pdoc/pdoc/__main__.py", line 153, in cli
pdoc.pdoc(
File "/home/max/Git/pdoc/pdoc/__init__.py", line 386, in pdoc
write(doc.Module(m))
File "/home/max/Git/pdoc/pdoc/__init__.py", line 355, in write
outfile.write_bytes(r(mod).encode())
File "/home/max/Git/pdoc/pdoc/__init__.py", line 367, in r
return render.html_module(module=mod, all_modules=all_modules)
File "/home/max/Git/pdoc/pdoc/render.py", line 70, in html_module
return env.select_template(
File "/home/max/Git/cobib/.direnv/python-3.9.2/lib/python3.9/site-packages/jinja2/environment.py", line 1090, in render
self.environment.handle_exception()
File "/home/max/Git/cobib/.direnv/python-3.9.2/lib/python3.9/site-packages/jinja2/environment.py", line 832, in handle_exception
reraise(*rewrite_traceback_stack(source=source))
File "/home/max/Git/cobib/.direnv/python-3.9.2/lib/python3.9/site-packages/jinja2/_compat.py", line 28, in reraise
raise value.with_traceback(tb)
File "/home/max/Git/pdoc/pdoc/templates/default/module.html.jinja2", line 650, in top-level template code
{%- if loop.nextitem %}.{% endif -%}
File "/home/max/Git/pdoc/pdoc/templates/frame.html.jinja2", line 18, in top-level template code
<body>{% block body %}{% endblock %}</body>
File "/home/max/Git/pdoc/pdoc/templates/default/module.html.jinja2", line 722, in block "body"
{% block module_contents %}
File "/home/max/Git/pdoc/pdoc/templates/default/module.html.jinja2", line 729, in block "module_contents"
{{ member(m) }}
File "/home/max/Git/cobib/.direnv/python-3.9.2/lib/python3.9/site-packages/jinja2/runtime.py", line 679, in _invoke
rv = self._func(*arguments)
File "/home/max/Git/pdoc/pdoc/templates/default/module.html.jinja2", line 557, in template
{{ function(doc) }}
File "/home/max/Git/cobib/.direnv/python-3.9.2/lib/python3.9/site-packages/jinja2/runtime.py", line 679, in _invoke
rv = self._func(*arguments)
File "/home/max/Git/pdoc/pdoc/templates/default/module.html.jinja2", line 523, in template
{{ decorators(fn) }}
File "/home/max/Git/cobib/.direnv/python-3.9.2/lib/python3.9/site-packages/jinja2/runtime.py", line 679, in _invoke
rv = self._func(*arguments)
File "/home/max/Git/pdoc/pdoc/templates/default/module.html.jinja2", line 514, in template
{% for d in doc.decorators if not d.startswith("@_") %}
File "/home/max/Git/cobib/.direnv/python-3.9.2/lib/python3.9/site-packages/jinja2/environment.py", line 471, in getattr
return getattr(obj, attribute)
File "/usr/lib/python3.9/functools.py", line 969, in __get__
val = self.func(instance)
File "/home/max/Git/pdoc/pdoc/doc.py", line 767, in decorators
for t in doc_ast.parse(obj).decorator_list:
File "/home/max/Git/pdoc/pdoc/doc_ast.py", line 60, in parse
return _parse_function(src)
File "/home/max/Git/pdoc/pdoc/doc_ast.py", line 196, in _parse_function
tree = ast.parse(_dedent(source))
File "/usr/lib/python3.9/ast.py", line 50, in parse
return compile(source, filename, mode, flags,
File "<unknown>", line 7
def test_dummy(self, arg, string):
IndentationError: unexpected indent
```
</details>
For additional details, this is what `_dedent` generates at the moment:
```python
@pytest.mark.parametrize(
"arg,string",
[
["hello", "world"],
],
)
def test_dummy(self, arg, string):
"""Dummy test method."""
assert arg != string
```
The dedented string is not causing any problems but the remaining indent of the `def test_dummy` line eventually causes the error to arise.
#### System Information
A fresh checkout of `main`:
```
% pdoc --version
pdoc: 6.4.2 (+15, commit 8d133d2)
Python: 3.9.2
Platform: Linux-5.11.11-arch1-1-x86_64-with-glibc2.33
```
| closed | 2021-04-10T15:20:00Z | 2021-04-11T06:49:26Z | https://github.com/mitmproxy/pdoc/issues/246 | [
"bug"
] | mrossinek | 0 |
Lightning-AI/pytorch-lightning | data-science | 20,253 | Cannot turn off sampler injection at inference time. | ### Bug description
I want to use a custom distributed batch sampler at inference time.
The sampler looks like this:
```python
class DistributedInferenceBatchSampler(DistributedSampler):
def __init__(self, dataset: Dataset,
batch_size: int = 1,
num_replicas: Optional[int] = None,
rank: Optional[int] = None,
shuffle: bool = False,
seed: int = 0,
drop_last: bool = False,
) -> None:
super().__init__(dataset, num_replicas=num_replicas, rank=rank,
shuffle=shuffle, seed=seed, drop_last=drop_last)
# do stuff
# sort data indices by datapoint length, batch up
# subsample batches for current rank
self.batches = # nested list [[b1_1, b1_2, ...], [b2_1, b2_2, ...], ...]
def __iter__(self) -> Iterator[T_co]:
return iter(self.batches)
def __len__(self) -> int:
return len(self.batches)
```
I use this dataloader inside my data module:
```python
class DataModule(LightningDataModule):
# ...
def predict_dataloader(self, rank=None, num_replicas=None):
# define Dataset 'data'
bsampler = DistributedInferenceBatchSampler(dataset=data,
batch_size=4,
num_replicas=num_replicas,
rank=rank)
data_loader = DataLoader(data,
batch_sampler=bsampler)
return data_loader
```
I'm running inference like so:
```python
trainer = Trainer( strategy='auto',
use_distributed_sampler=False, # using custom distributed batchsampler
accelerator=gpu,
deterministic=False,
enable_progress_bar=True,
enable_model_summary=True,
devices=devices
)
trainer.predict(model=self._model, datamodule=self._datamodule)
```
However, Lightning tries to replace my batch sampler despite the `use_distributed_sample=False` flag because it always does so in predict mode, and fails because the sampler doesn't have the same signature as a Pytorch `BatchSampler`.
I've tried wrapping my custom `DistributedInferenceBatchSampler` like so:
```python
class BatchSamplerWrapper(BatchSampler):
def __init__(self, sampler, batch_size=1, drop_last=False):
self.sampler = sampler
self.batch_size = batch_size # ignored
self.drop_last = drop_last # ignored
def __iter__(self):
for batch in self.sampler:
yield batch
def __len__(self):
return len(self.sampler)
class DataModule(LightningDataModule):
# ...
def predict_dataloader(self, rank=None, num_replicas=None):
# define Dataset 'data'
bsampler = DistributedInferenceBatchSampler(dataset=data,
batch_size=4,
num_replicas=num_replicas,
rank=rank)
wrapper = BatchSamplerWrapper(bsampler, batch_size=4, drop_last=False)
data_loader = DataLoader(data,
batch_sampler=wrapper)
return data_loader
```
However, Lightning replaces my `bsampler` inside the wrapper with a `torch.utils.data.sampler.SequentialSampler` which leads to `BatchSamplerWrapper.__iter__()` not having the intended behaviour. It returns an `int` rather than a list of `int`s, leading to:
```
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Error executing job with overrides: []
Traceback (most recent call last):
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/codebase/IgFlow/multiflow/experiments/inference.py", line 18, in sample
run.sample()
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/codebase/IgFlow/multiflow/experiments/model_run.py", line 125, in sample
trainer.predict(model=self._model, datamodule=self._datamodule)
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 864, in predict
return call._call_and_handle_interrupt(
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 102, in launch
return function(*args, **kwargs)
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 903, in _predict_impl
results = self._run(model, ckpt_path=ckpt_path)
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 989, in _run
results = self._run_stage()
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1030, in _run_stage
return self.predict_loop.run()
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/loops/utilities.py", line 182, in _decorator
return loop_run(self, *args, **kwargs)
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py", line 119, in run
batch, batch_idx, dataloader_idx = next(data_fetcher)
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 127, in __next__
batch = super().__next__()
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 56, in __next__
batch = next(self.iterator)
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 326, in __next__
out = next(self._iterator)
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 132, in __next__
out = next(self.iterators[0])
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
data = self._next_data()
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1345, in _next_data
return self._process_data(data)
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data
data.reraise()
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/torch/_utils.py", line 644, in reraise
raise exception
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "/data/nagagpu03/not-backed-up/nvme00/vavourakis/miniforge3/envs/mflow/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
TypeError: 'int' object is not iterable
```
I just want to turn off this sampler-replacing behaviour. I have a similar setup during training (rather than inference) and that works fine (no wrappers required, either).
### What version are you seeing the problem on?
v2.1
### How to reproduce the bug
_No response_
### Error messages and logs
```
# Error messages and logs here please
```
### Environment
_No response_
### More info
_No response_ | open | 2024-09-06T11:29:47Z | 2024-09-06T11:30:00Z | https://github.com/Lightning-AI/pytorch-lightning/issues/20253 | [
"bug",
"needs triage",
"ver: 2.1.x"
] | ovavourakis | 0 |
seleniumbase/SeleniumBase | web-scraping | 3,570 | Intermittent proxy-with-auth not being set before page load | ## Intermittent proxy-with-auth not being set before page load
Since the Manifest V3 extension conversion, there is a short delay before the auth is set, which may lead to an auth prompt appearing if navigating to a page immediately after the web browser is opened when using proxy-with-auth. This delay is a fraction of a second. It can be avoided by added a tiny delay after the web browser is opened (before navigating to a URL). | closed | 2025-02-26T17:23:29Z | 2025-02-26T22:43:31Z | https://github.com/seleniumbase/SeleniumBase/issues/3570 | [
"bug"
] | mdmintz | 1 |
pytest-dev/pytest-qt | pytest | 303 | Use PyPI token in GitHub actions | I just noticed this:
https://github.com/pytest-dev/pytest-qt/blob/e8080068d3ed215051231d5d5fd43fd1268e4eaa/.github/workflows/main.yml#L83-L86
It looks like https://github.com/pypa/pypi-support/issues/98 was taken care of. | closed | 2020-05-08T15:22:15Z | 2020-08-12T20:35:16Z | https://github.com/pytest-dev/pytest-qt/issues/303 | [] | The-Compiler | 1 |
home-assistant/core | asyncio | 140,838 | SmartThing Washer start/stop | ### The problem
Can't start or stop the washing machine with the switch component included in home assistant integration.
The command should use the samsungce.washerOperatingState capability instead of the switch. With the command start and pause or cancel.
### What version of Home Assistant Core has the issue?
core-2025.3.3
### What was the last working version of Home Assistant Core?
_No response_
### What type of installation are you running?
Home Assistant OS
### Integration causing the issue
SmartThings
### Link to integration documentation on our website
https://www.home-assistant.io/integrations/smartthings
### Diagnostics information
_No response_
### Example YAML snippet
```yaml
```
### Anything in the logs that might be useful for us?
```txt
```
### Additional information
_No response_ | closed | 2025-03-17T23:17:58Z | 2025-03-19T15:20:21Z | https://github.com/home-assistant/core/issues/140838 | [
"integration: smartthings"
] | kimzrn | 2 |
python-gino/gino | sqlalchemy | 12 | Support additional table arguments | Simply follow the SQLAlchemy ORM way, support `__table_args__`:
http://docs.sqlalchemy.org/en/latest/orm/extensions/declarative/table_config.html | closed | 2017-07-22T02:00:21Z | 2017-08-04T06:07:24Z | https://github.com/python-gino/gino/issues/12 | [
"help wanted",
"task"
] | fantix | 0 |
man-group/arctic | pandas | 189 | ChunkStore - Delete does not update metadata properly | does not update rows or chunk count when a chunk_range is specified
| closed | 2016-08-04T19:15:44Z | 2016-08-05T13:57:50Z | https://github.com/man-group/arctic/issues/189 | [] | bmoscon | 1 |
allenai/allennlp | pytorch | 5,308 | TextFieldTensors are not supported as input to a model Head | <!--
Please fill this template entirely and do not erase any of it.
We reserve the right to close without a response bug reports which are incomplete.
If you have a question rather than a bug, please ask on [Stack Overflow](https://stackoverflow.com/questions/tagged/allennlp) rather than posting an issue here.
-->
## Checklist
<!-- To check an item on the list replace [ ] with [x]. -->
- [x] I have verified that the issue exists against the `main` branch of AllenNLP.
- [x] I have read the relevant section in the [contribution guide](https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md#bug-fixes-and-new-features) on reporting bugs.
- [x] I have checked the [issues list](https://github.com/allenai/allennlp/issues) for similar or identical bug reports.
- [x] I have checked the [pull requests list](https://github.com/allenai/allennlp/pulls) for existing proposed fixes.
- [x] I have checked the [CHANGELOG](https://github.com/allenai/allennlp/blob/main/CHANGELOG.md) and the [commit log](https://github.com/allenai/allennlp/commits/main) to find out if the bug was already fixed in the main branch.
- [x] I have included in the "Description" section below a traceback from any exceptions related to this bug.
- [x] I have included in the "Related issues or possible duplicates" section beloew all related issues and possible duplicate issues (If there are none, check this box anyway).
- [x] I have included in the "Environment" section below the name of the operating system and Python version that I was using when I discovered this bug.
- [x] I have included in the "Environment" section below the output of `pip freeze`.
- [ ] I have included in the "Steps to reproduce" section below a minimally reproducible example.
## Description
<!-- Please provide a clear and concise description of what the bug is here. -->
I'm trying to pass a `TextFieldTensor` to a `Head` using the `multitask` Model.
For context, the `ClassifierHead` doesn't support `TextFieldTensors` for sequence decoding tasks. I created a simple head which inherits from the `AutoRegressiveSeqDecoder`, and puts the encoder text and mask into a dictionary before passing to the forward of the decoder.
```python
@Head.register("seq_decoder_tokens")
class SeqDecoderTokensHead(AutoRegressiveSeqDecoder):
@overrides
def forward(
self,
encoded_text: torch.Tensor,
encoded_text_mask: torch.Tensor,
target_tokens: TextFieldTensors = None,
) -> dict[str, torch.Tensor]:
encoder_out = {
"encoder_outputs": encoded_text,
"source_mask": encoded_text_mask,
}
return super().forward(encoder_out, target_tokens)
```
The error, as in the traceback below, is with the `make_inputs_for_task` function.
https://github.com/allenai/allennlp/blob/7d4a67263d7a210aca22d4f2b03e8568d3c34a48/allennlp/models/multitask.py#L114-L119
From the type annotation, I'm assuming that it might not be supported by the model so I might be barking up the wrong tree? I assumed that I could just pass the variable to the Head and it would work.
I'm not sure this is the best solution but I've made a fork with one possible solution, if it helps?
https://github.com/amitkparekh/allennlp/commit/f1e0630d83ed0d459f1efa167e4b2bad76f80bb2
<details>
<summary><b>Python traceback:</b></summary>
<p>
<!-- Paste the traceback from any exception (if there was one) in between the next two lines below -->
```
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/models/multitask.py", line 125, in <listcomp>
return [whole_batch_input[i] for i in task_indices[task]]
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/models/multitask.py", line 125, in make_inputs_for_task
return [whole_batch_input[i] for i in task_indices[task]]
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/models/multitask.py", line 138, in <dictcomp>
head_arguments = {key: make_inputs_for_task(head_name, value) for key, value in head_arguments.items()}
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/models/multitask.py", line 138, in forward
head_arguments = {key: make_inputs_for_task(head_name, value) for key, value in head_arguments.items()}
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/training/gradient_descent_trainer.py", line 351, in batch_outputs
output_dict = self._pytorch_model(**batch)
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/training/gradient_descent_trainer.py", line 458, in _train_epoch
batch_outputs = self.batch_outputs(batch, for_training=True)
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/training/gradient_descent_trainer.py", line 727, in _try_train
train_metrics = self._train_epoch(epoch)
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/training/gradient_descent_trainer.py", line 706, in train
metrics, epoch = self._try_train()
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/commands/train.py", line 543, in run
return self.trainer.train()
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/commands/train.py", line 470, in _train_worker
metrics = train_loop.run()
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/commands/train.py", line 240, in train_model
model = _train_worker(
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/commands/train.py", line 171, in train_model_from_file
return train_model(
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/commands/train.py", line 111, in train_model_from_args
train_model_from_file(
File "/Users/amit/Develop/pokerface/.venv/lib/python3.9/site-packages/allennlp/commands/__init__.py", line 121, in main
args.func(args)
File "/Users/amit/Develop/pokerface/scraps/debug_allennlp.py", line 35, in <module>
main()
File "/usr/local/Cellar/python@3.9/3.9.5/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/usr/local/Cellar/python@3.9/3.9.5/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/usr/local/Cellar/python@3.9/3.9.5/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 268, in run_path
return _run_module_code(code, init_globals, run_name,
File "/usr/local/Cellar/python@3.9/3.9.5/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/usr/local/Cellar/python@3.9/3.9.5/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 197, in _run_module_as_main (Current frame)
return _run_code(code, main_globals, None,
```
</p>
</details>
## Related issues or possible duplicates
- None
## Environment
<!-- Provide the name of operating system below (e.g. OS X, Linux) -->
OS: OS X
<!-- Provide the Python version you were using (e.g. 3.7.1) -->
Python version: 3.9.5
<details>
<summary><b>Output of <code>pip freeze</code>:</b></summary>
<p>
<!-- Paste the output of `pip freeze` in between the next two lines below -->
```
allennlp @ file:///Users/amit/Library/Caches/pypoetry/artifacts/b1/ea/84/1d6e28cb885e4d55ac8939f78e408f7deb86ea845630a64f10d7700a62/allennlp-2.5.0-py3-none-any.whl
allennlp-models @ file:///Users/amit/Library/Caches/pypoetry/artifacts/77/31/1f/2ef3c0ed6db0dc41de744411f56b478ab042f091dda6cd079b72b12c53/allennlp_models-2.5.0-py3-none-any.whl
appdirs @ file:///Users/amit/Library/Caches/pypoetry/artifacts/47/cf/4f/4ef02fb715aa36daeebad18cc5570126159c659c41c7b5ec46a7387d9b/appdirs-1.4.4-py2.py3-none-any.whl
appnope @ file:///Users/amit/Library/Caches/pypoetry/artifacts/fd/ae/b8/2382438588ec752ed602c2dcab2a0678b30ff15f2d9a30267ff97ecd64/appnope-0.1.2-py2.py3-none-any.whl
argon2-cffi @ file:///Users/amit/Library/Caches/pypoetry/artifacts/3c/87/0e/fc0a0440e3e84e11c88d9d2d049f9aff2fdc4e7493dd19af67381b1d05/argon2_cffi-20.1.0-cp37-abi3-macosx_10_6_intel.whl
astor @ file:///Users/amit/Library/Caches/pypoetry/artifacts/1f/03/36/982d1222edac5e8cb8ac6e0464249747fa800d4fb04728a99153ecfe4d/astor-0.8.1-py2.py3-none-any.whl
async-generator @ file:///Users/amit/Library/Caches/pypoetry/artifacts/fe/72/db/709736555b02c2d1ae90038b7b05138b15e24edde3aa7556fc2507a90f/async_generator-1.10-py3-none-any.whl
attrs @ file:///Users/amit/Library/Caches/pypoetry/artifacts/6f/a9/ee/569c37f69a8c365ee41d2340aeac0214ee8c0086b8d8db43a21545204b/attrs-21.2.0-py2.py3-none-any.whl
backcall @ file:///Users/amit/Library/Caches/pypoetry/artifacts/43/8e/e8/4e598704edf6fb4a53d552ea511c04e9958dcf850897760e5387878b99/backcall-0.2.0-py2.py3-none-any.whl
backports.csv @ file:///Users/amit/Library/Caches/pypoetry/artifacts/30/84/1a/81a42cff31ce7f0b7a86ab54e2cbcb610d96fa8b735d63bdb7251e91cb/backports.csv-1.0.7-py2.py3-none-any.whl
bandit @ file:///Users/amit/Library/Caches/pypoetry/artifacts/6a/05/4f/98680ab175e4b595c2d1b775974c208b6b20c05448a52944374c2db4b0/bandit-1.7.0-py3-none-any.whl
beartype @ file:///Users/amit/Library/Caches/pypoetry/artifacts/c3/fe/bd/c04bccf2fa951904264c6dc16cbdff35bc2aec170d65b9afb879841dfa/beartype-0.7.1-py3-none-any.whl
beautifulsoup4 @ file:///Users/amit/Library/Caches/pypoetry/artifacts/eb/47/47/287c1b8a386f9437d562f9221ae959756bc5bbfcd541c38c17968dfe8a/beautifulsoup4-4.9.3-py3-none-any.whl
black @ file:///Users/amit/Library/Caches/pypoetry/artifacts/6a/38/11/b77f947a81ed86d08787f98d076035bedb21abd3d3c57129221264c3ea/black-21.6b0-py3-none-any.whl
bleach @ file:///Users/amit/Library/Caches/pypoetry/artifacts/15/bd/f0/eaed67c8e6d37dda902603474339528f29bde3f7ecc2bb4b8874e0da87/bleach-3.3.0-py2.py3-none-any.whl
blis @ file:///Users/amit/Library/Caches/pypoetry/artifacts/96/b3/59/60398be6c97784bb3ba6ef5d76e86ce6bc530e27ca83de6d106b5dadd3/blis-0.7.4-cp39-cp39-macosx_10_9_x86_64.whl
boto3 @ file:///Users/amit/Library/Caches/pypoetry/artifacts/5e/59/0d/9ce4b54813b37237c51d31bb0d3c0853123334f6309a2088f81ec49c03/boto3-1.17.109-py2.py3-none-any.whl
botocore @ file:///Users/amit/Library/Caches/pypoetry/artifacts/34/8e/85/14e136ebe48249de4155082aa1b2aa772ec3f7d415dd1c60067ed3a2ad/botocore-1.20.109-py2.py3-none-any.whl
cachetools @ file:///Users/amit/Library/Caches/pypoetry/artifacts/04/ca/d7/8af05dc8ccae1212d4151afd99960369c2415b26bc13ed3bbb288c4f5a/cachetools-4.2.2-py3-none-any.whl
catalogue @ file:///Users/amit/Library/Caches/pypoetry/artifacts/af/cf/ab/153c9326701d6adc746e129e3e05991a55bd43b2f19e3c52d7ccc9a7b4/catalogue-1.0.0-py2.py3-none-any.whl
certifi @ file:///Users/amit/Library/Caches/pypoetry/artifacts/cd/2c/dc/e5bfda594e18f3f1e9af9f11e13581014d821425f325f3220b3ed2c337/certifi-2021.5.30-py2.py3-none-any.whl
cffi @ file:///Users/amit/Library/Caches/pypoetry/artifacts/6e/aa/04/2c3c9401654c8f5580dc8965817a99e8ad464a0987e17149061aadfcbf/cffi-1.14.6-cp39-cp39-macosx_10_9_x86_64.whl
cfgv @ file:///Users/amit/Library/Caches/pypoetry/artifacts/6b/52/b7/27617ac43f25c9962779813593809288745c414fd878b968cc3d91ca6c/cfgv-3.3.0-py2.py3-none-any.whl
chardet @ file:///Users/amit/Library/Caches/pypoetry/artifacts/11/63/f9/797eda27963177a6b75a340f62aa194d462ea69e6b0dbb77a651fa2b62/chardet-4.0.0-py2.py3-none-any.whl
checklist @ file:///Users/amit/Library/Caches/pypoetry/artifacts/04/84/f3/1324eec13577715f52121b3073ab37792c08483aae7faa7d22b7dd5e1d/checklist-0.0.11.tar.gz
cheroot @ file:///Users/amit/Library/Caches/pypoetry/artifacts/3c/cf/87/c9bb0e3b0d4c43affeb8f9714d5791b61c97750bc3a2ee35d276d425b5/cheroot-8.5.2-py2.py3-none-any.whl
CherryPy @ file:///Users/amit/Library/Caches/pypoetry/artifacts/dd/d5/0c/5289a45f52e9aa001f7b8c2b9c792377e79ad572bc759f1a692d160818/CherryPy-18.6.1-py2.py3-none-any.whl
click @ file:///Users/amit/Library/Caches/pypoetry/artifacts/ae/32/83/e159324c1bd58177322f4e45f598d500fe22544bff20f53f55cf749da8/click-8.0.1-py3-none-any.whl
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cryptography @ file:///Users/amit/Library/Caches/pypoetry/artifacts/c7/6d/36/40b404429242377c0424cb9957a04887a4be7399a62c1505197845c09f/cryptography-3.4.7-cp36-abi3-macosx_10_10_x86_64.whl
cymem @ file:///Users/amit/Library/Caches/pypoetry/artifacts/b8/f4/20/752f31c5ea9976672f58722949ff87b94832958712b6e3ba60e831421e/cymem-2.0.5-cp39-cp39-macosx_10_9_x86_64.whl
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eradicate @ file:///Users/amit/Library/Caches/pypoetry/artifacts/12/ce/ac/197035fe6d51568abb7ea160f5ad416d2164a2010005e8356b8229e550/eradicate-2.0.0.tar.gz
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filelock @ file:///Users/amit/Library/Caches/pypoetry/artifacts/7e/c4/97/2cfbeab3cc292d0b4290cb7cab0b969b3002dc24f6dd5944cbe340e684/filelock-3.0.12-py3-none-any.whl
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pokerface==0.1.0
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promise @ file:///Users/amit/Library/Caches/pypoetry/artifacts/d6/c6/43/95f1e737b1dd79d3a5ac6cfb264a889716bab4cd9d28a9bc8c69591d53/promise-2.3.tar.gz
prompt-toolkit @ file:///Users/amit/Library/Caches/pypoetry/artifacts/c0/cc/91/2b2506bf1a53afc81d06682da5dce00b5f700a51ee2a8452dc8b98af15/prompt_toolkit-3.0.19-py3-none-any.whl
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rsa @ file:///Users/amit/Library/Caches/pypoetry/artifacts/27/21/1f/fea99b1c1766c11c2c47dd961d7773ebab5c6acbf730200bd2e021b836/rsa-4.7.2-py3-none-any.whl
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shortuuid @ file:///Users/amit/Library/Caches/pypoetry/artifacts/80/85/8d/5bdb9fbab8b4fc7bd9599a4982cac0ae2498f4c863d13869d4e1e7b722/shortuuid-1.0.1-py3-none-any.whl
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tempora @ file:///Users/amit/Library/Caches/pypoetry/artifacts/b9/f8/26/4304b7c3157148ded2a9f388c0c3e6b1c0016147ff5995928d8db2ce8b/tempora-4.1.1-py3-none-any.whl
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termcolor @ file:///Users/amit/Library/Caches/pypoetry/artifacts/a2/5d/c7/e4ccb3b3bb8d3e3aff995fb6ffb12cfc78bbc8affa283907ee5eb5a5a5/termcolor-1.1.0.tar.gz
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tokenizers @ file:///Users/amit/Library/Caches/pypoetry/artifacts/e3/67/87/626827d63f69f9c1357c553076ccf0ee9721c9764ef434fa3654531f3f/tokenizers-0.10.3-cp39-cp39-macosx_10_11_x86_64.whl
toml @ file:///Users/amit/Library/Caches/pypoetry/artifacts/6b/6a/c9/53b19f7870a77d855e8b05ecdc98193944e5d246dafe11bbcad850ecba/toml-0.10.2-py2.py3-none-any.whl
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widgetsnbextension @ file:///Users/amit/Library/Caches/pypoetry/artifacts/eb/b0/c5/e9e106309ddf8d2cbebcd3c9f2c2be8c7c7346f58d8ff7ace4196371d8/widgetsnbextension-3.5.1-py2.py3-none-any.whl
word2number @ file:///Users/amit/Library/Caches/pypoetry/artifacts/91/7b/91/fd4e6b1580eb2a2f0bb8b725ba137628acb0adb21522a3ff9d69e6f5e1/word2number-1.1.zip
zc.lockfile @ file:///Users/amit/Library/Caches/pypoetry/artifacts/b5/a8/c8/e94e98335e585be92e35e5d07dd8a75e5c2e7774c8bd24410160f9cfe0/zc.lockfile-2.0-py2.py3-none-any.whl
```
</p>
</details>
## Steps to reproduce
I wasn't sure how to do this step but I will if I'm not making any sense and you'd like me to.
<details>
<summary><b>Example source:</b></summary>
<p>
<!-- Add a fully runnable example in between the next two lines below that will reproduce the bug -->
```
```
</p>
</details>
| closed | 2021-07-12T15:44:45Z | 2021-07-26T18:05:23Z | https://github.com/allenai/allennlp/issues/5308 | [
"bug"
] | amitkparekh | 3 |
AUTOMATIC1111/stable-diffusion-webui | deep-learning | 15,322 | [Bug]: Checkpoint / LoRA gallery slows down the browser | ### Checklist
- [X] The issue exists after disabling all extensions
- [X] The issue exists on a clean installation of webui
- [ ] The issue is caused by an extension, but I believe it is caused by a bug in the webui
- [X] The issue exists in the current version of the webui
- [X] The issue has not been reported before recently
- [ ] The issue has been reported before but has not been fixed yet
### What happened?
Since the last release, the checkpoint / LoRA galleries slow down Firefox to the point that Firefox shows a "This site slows down Firefox [stop]" banner at top of the page, if you have many checkpoints/LoRA.
### Steps to reproduce the problem
1. Have many checkpoints and/or LoRA
2. Click the Checkpoints or LoRa tab
### What should have happened?
Before the last release, it had no noticable delay. Now it takes quite some time until the tab switches, the webui gets unresponsive and Firefox warns that the site is slowing down the browser.
If wonder if it previously had some kind of lazy-loading in place that was removed or similar things. I do not even need to scroll in the last, changing the tab from Generation to Lora alone is enough to slow down the browser before I even see the first rectangle. Most Lora do not have preview images but only the standard grey placeholder.
### What browsers do you use to access the UI ?
Mozilla Firefox
### Sysinfo
(can add later if needed as I would need to remove sensitive information)
### Console logs
```Shell
(I don't see anything relevant)
```
### Additional information
_No response_ | open | 2024-03-19T10:01:20Z | 2024-03-19T10:01:20Z | https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/15322 | [
"bug-report"
] | allo- | 0 |
oegedijk/explainerdashboard | dash | 101 | It is able to support RNN models like LSTM? (3D array INPUT) | It is possible to adapt this set of dashboards for RNN-based models, such as LSTM networks.
The main problem I am encountering is in the input dataset which, being pre-processed, does not have the traditional linear structure, but is produced by the application of window-based techniques, for example.
The INPUT it's a 3D array
| closed | 2021-03-15T17:20:02Z | 2021-05-04T07:54:09Z | https://github.com/oegedijk/explainerdashboard/issues/101 | [] | Alex891 | 5 |
junyanz/pytorch-CycleGAN-and-pix2pix | pytorch | 1,207 | Question: Why does a Pixel Discriminator work? | As far as I understand the Pixel Discriminator is equivalent to a PatchGAN of size 1x1, meaning the decoder tries to decide if each pixel is real or fake. But without taking into account surrounding pixels, what information does it use to assign a label and how is it also able to recover high frequency contents of the images?
I have been working on a project and the only stable training I could get was using the pixel discriminator. With the patch gan the discriminator started off too good compared to the generator and the network couldn't learn. Hence my curiosity. | open | 2020-12-03T16:40:41Z | 2020-12-17T03:40:59Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1207 | [] | dpelacani | 3 |
OFA-Sys/Chinese-CLIP | nlp | 15 | CN-CLIPViT-B/16 模型在Flickr30K-CN数据集上的zero-shot结果复现不出来 | 你好，我使用你们开源的CN-CLIPViT-B/16 模型在MUGE上复现的结果和你们汇报的结果差不多
但是在Flickr30K-CN数据集上的结果差很远，不知道问题出在哪里，麻烦帮忙看下，谢谢。
| closed | 2022-11-18T11:30:07Z | 2022-11-28T06:30:05Z | https://github.com/OFA-Sys/Chinese-CLIP/issues/15 | [
"solved in expectation",
"已解决待验证"
] | ddNTP | 17 |
slackapi/bolt-python | fastapi | 1,032 | Rename `logger` to `logger_` in `slack_bolt.kwargs_injection.args.Args` | It is common practice to define a variable called `logger` in a script. However, the `slack_bolt.kwargs_injection.args.Args` function used as a decorator also defines `logger`, so if you want to call your normal logger in your script you have to give it a different name. It can also be unexpected behaviour, as the author may think they are calling the `logger` from their module, but actually they are calling a different logger instance altogether.
I understand this would be a breaking change, but I think it would improve usability.
### Category (place an `x` in each of the `[ ]`)
* [ ] **slack_bolt.App** and/or its core components
* [ ] **slack_bolt.async_app.AsyncApp** and/or its core components
* [ ] Adapters in **slack_bolt.adapter**
* [x] Others
## Requirements
Please read the [Contributing guidelines](https://github.com/slackapi/bolt-python/blob/main/.github/contributing.md) and [Code of Conduct](https://slackhq.github.io/code-of-conduct) before creating this issue or pull request. By submitting, you are agreeing to those rules.
| closed | 2024-02-22T10:50:55Z | 2024-02-28T00:26:15Z | https://github.com/slackapi/bolt-python/issues/1032 | [
"enhancement",
"good first issue"
] | ChrisHills463 | 7 |
stanfordnlp/stanza | nlp | 1,340 | Dependency parsing: is there some way to discourage multiple `nsubj` dependents? | A very weird English tree produced by Stanza 1.6.0 in the [demo](http://stanza.run/):
> My cousin my extremely rude colleague admired last year chewed the chicken enthusiastically.
<img width="1171" alt="image" src="https://github.com/stanfordnlp/stanza/assets/985263/a6468f67-9d09-4ad5-8f3e-4cbc92d08e6b">
In UD, no word is allowed to have multiple (plain) `nsubj` dependents. But "admired" has two.
Is there a recommended alternate training or decoding method in Stanza that could avoid this sort of problem? | open | 2024-01-30T04:00:00Z | 2025-02-22T18:20:13Z | https://github.com/stanfordnlp/stanza/issues/1340 | [
"question"
] | nschneid | 8 |
Yorko/mlcourse.ai | data-science | 711 | No demo assignment in topic-3 | [jupyter_english/topic03_decision_trees_kNN](https://github.com/Yorko/mlcourse.ai/tree/main/jupyter_english/topic03_decision_trees_kNN) and [jupyter_french/topic03_decision_trees_kNN](https://github.com/Yorko/mlcourse.ai/tree/main/jupyter_french/topic03_decision_trees_kNN) has no .ipynb files for the demo assignment and solution to it.
Can you add them to the repo? | closed | 2022-09-05T15:33:33Z | 2022-09-07T14:26:24Z | https://github.com/Yorko/mlcourse.ai/issues/711 | [] | aulasau | 3 |
cleanlab/cleanlab | data-science | 401 | Add tutorial notebook to docs.cleanlab.ai | Make tutorial notebook appear on docs.cleanlab.ai (and verify it looks good on the website) by adding it to:
1) https://github.com/cleanlab/cleanlab/blob/master/docs/source/tutorials/index.rst
2) https://github.com/cleanlab/cleanlab/blob/master/docs/source/index.rst | closed | 2022-09-06T00:52:03Z | 2022-09-09T01:27:29Z | https://github.com/cleanlab/cleanlab/issues/401 | [] | elisno | 0 |
gee-community/geemap | streamlit | 1,952 | No layer style UI for ee.ImageCollection | ### Environment Information
Colab
Mon Mar 25 21:27:30 2024 UTC
OS Linux CPU(s) 2 Machine x86_64
Architecture 64bit RAM 12.7 GiB Environment IPython
Python 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]
geemap 0.32.0 ee 0.1.394 ipyleaflet 0.18.2
folium 0.14.0 jupyterlab Module not found notebook 6.5.5
ipyevents 2.0.2 geopandas 0.13.2
### Description
The layer visualization editing tool is not available when the input to `Map.add_layer` is an `ee.ImageCollection`. If an image collection is passed, I believe `.mosaic()` is called so that image tiles can be requested for viewing. Since we're visualizing an `ee.Image` object, we should be able to set and apply visualization parameters (consistent with Code Editor behavior).
### What I Did
1. Add an `ee.ImageCollection` object to a `Map` with `add_layer`
2. Display the map and click layer gear icon to set visualization properties (no tool "vis params are uneditable")
3. Mosaic the `ee.ImageCollection` first and then add to a map
4. Display the new map and click layer gear icon to set visualization properties (tool appears and WAI).
```py
# [NEW CELL] Construct an ee.ImageCollection.
col = ee.ImageCollection([ee.Image(1).byte(), ee.Image(10).byte()])
# [NEW CELL] Display the ee.ImageCollection.
m0 = geemap.Map()
m0.add_layer(col)
m0
# [NEW CELL] Display the .mosaic() of the ee.ImageCollection.
m1 = geemap.Map()
m1.add_layer(col.mosaic())
m1
```
| closed | 2024-03-25T21:48:17Z | 2024-03-26T02:42:30Z | https://github.com/gee-community/geemap/issues/1952 | [
"bug"
] | jdbcode | 0 |
pytest-dev/pytest-django | pytest | 499 | Making django_db_blocker un-opt-outable is zealous and lame | Use of `django_db_blocker` to cripple use of the database outside of specially tagged tests and fixtures interferes with Django model `get_or_create` calls at import time, which is how some systems ensure the presence of fundamental data across environments.
Making this behavior not configurable is disruptive and annoying. It results in the need to patch the patcher, a la
```
# settings/test.py
from pytest_django import plugin
plugin._blocking_manager = mock.MagicMock()
```
Please consider a means to disable this behavior through settings. | closed | 2017-07-28T22:42:23Z | 2018-03-09T09:46:13Z | https://github.com/pytest-dev/pytest-django/issues/499 | [] | jamesob | 6 |
mwaskom/seaborn | data-science | 3,392 | Swarmplot and markers | I am exploring the `swarmplot` functionality provided by seaborn. I tried to follow [stackoverflow suggestions](https://stackoverflow.com/questions/52878845/swarmplot-with-hue-affecting-marker-beyond-color) on changing the default marker for groups, but without success.
Looking at the documentation of the functionality provided by `swarmplot`, it says "additional parameters are passed to the `scatter` functions giving as an example:
```python
import seaborn as sns
tips = sns.load_dataset("tips")
sns.swarmplot(
data=tips, x="total_bill", y="day",
marker="x", linewidth=1,
)
```
However, combining this with e.g. `hue`, does not give the expected result (and I found a hint in #1595 that it is also not possible:
```python
sns.swarmplot(
data=tips, x="total_bill", y="day",
marker="x", linewidth=1,
hue='sex'
)
```
I would have expected that the crosses are now in blue and orange, but this is not the case. Happy to have a look at the code or update the documentation, but a hint where to start would be great. | closed | 2023-06-19T13:29:03Z | 2023-06-19T15:53:40Z | https://github.com/mwaskom/seaborn/issues/3392 | [] | enryH | 4 |
Buuntu/fastapi-react | fastapi | 100 | Form validation errors on Sign Up and Login pages | I found a bug where the frontend errors out when you press the submit button on empty fields for the login and sign up pages.
Here is a gif of the error for the sign up page. The same error occurred with login page as well.
| closed | 2020-08-07T04:52:52Z | 2020-08-13T02:48:20Z | https://github.com/Buuntu/fastapi-react/issues/100 | [] | adamnieto | 2 |
noirbizarre/flask-restplus | flask | 95 | Namespaces should be lazy registerable | As @frol suggested, for big APIs splitted on multiple files, Namespace should be the only class to import.
As Blueprints for Flask, it should provide all necessary methods to not require the API instance in the file and to be lazy registerable.
The target is being able to write:
``` python
# feature.py
from flask_restplus import Namespace, Resource
ns = Namespace('feature', 'Some namespace')
model = ns.model('Model', {})
@ns.route('/')
class MyFeature(Resource):
@ns.marshal_with(model)
def get(self):
pass
```
``` python
# api.py
from flask_restplus import Api
from feature import ns
api = Api()
api.register_namespace(ns)
```
| closed | 2015-11-10T08:54:30Z | 2016-01-17T19:22:48Z | https://github.com/noirbizarre/flask-restplus/issues/95 | [
"enhancement"
] | noirbizarre | 8 |
pytorch/pytorch | python | 149,223 | Inconsistent results in using torch.jit.script API from API documentation. | ### 🐛 Describe the bug
Expects an AttributeError is raised since the ignored_method is skipped in compiling as per API documentation.
```python
def test_script_module_with_ignored_method(self):
class IgnoredMethodModule(nn.Module):
def forward(self, x):
return x * 2
@torch.jit.ignore
def ignored_method(self, x):
return x * 3
module = IgnoredMethodModule()
scripted_module = torch.jit.script(module)
input_tensor = torch.tensor(5)
self.assertEqual(scripted_module(input_tensor), module(input_tensor))
# Ensure ignored method is not part of the scripted module
with self.assertRaises(AttributeError):
scripted_module.ignored_method(input_tensor)
```
```
======================================================================
FAIL: test_script_module_with_ignored_method (__main__.TestTorchJitScript)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/user/projects/api_guided_testgen/out/bug_detect_gpt4o/exec/basic_rag_apidoc/torch/torch.jit.script.py", line 70, in test_script_module_with_ignored_method
scripted_module.ignored_method(input_tensor)
AssertionError: AttributeError not raised
```
### Versions
Collecting environment information...
PyTorch version: 2.5.0
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.5.0-1ubuntu1~22.04) 9.5.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.22.1
Libc version: glibc-2.35
Python version: 3.9.21 (main, Dec 11 2024, 16:24:11) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 39 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 4
On-line CPU(s) list: 0-3
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) E-2224G CPU @ 3.50GHz
CPU family: 6
Model: 158
Thread(s) per core: 1
Core(s) per socket: 4
Socket(s): 1
Stepping: 10
CPU max MHz: 4700.0000
CPU min MHz: 800.0000
BogoMIPS: 6999.82
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb pti ssbd ibrs ibpb stibp tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp vnmi md_clear flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 128 KiB (4 instances)
L1i cache: 128 KiB (4 instances)
L2 cache: 1 MiB (4 instances)
L3 cache: 8 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-3
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled
Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT disabled
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT disabled
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT disabled
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; IBRS; IBPB conditional; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Mitigation; Microcode
Vulnerability Tsx async abort: Mitigation; TSX disabled
Versions of relevant libraries:
[pip3] numpy==2.0.1
[pip3] torch==2.5.0
[pip3] torchaudio==2.5.0
[pip3] torchvision==0.20.0
[conda] blas 1.0 mkl
[conda] cpuonly 2.0 0 pytorch
[conda] ffmpeg 4.3 hf484d3e_0 pytorch
[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
[conda] mkl 2023.1.0 h213fc3f_46344
[conda] mkl-service 2.4.0 py39h5eee18b_2
[conda] mkl_fft 1.3.11 py39h5eee18b_0
[conda] mkl_random 1.2.8 py39h1128e8f_0
[conda] numpy 2.0.1 py39h5f9d8c6_1
[conda] numpy-base 2.0.1 py39hb5e798b_1
[conda] pytorch 2.5.0 py3.9_cpu_0 pytorch
[conda] pytorch-mutex 1.0 cpu pytorch
[conda] torchaudio 2.5.0 py39_cpu pytorch
[conda] torchvision 0.20.0 py39_cpu pytorch
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | open | 2025-03-14T20:54:32Z | 2025-03-14T23:20:43Z | https://github.com/pytorch/pytorch/issues/149223 | [
"oncall: jit"
] | sjh0849 | 0 |
mwaskom/seaborn | data-visualization | 3,769 | Smaller mark width for overlapping data with `so.Hist(common_bins=False)` | When doing a `so.Hist(common_bins=False)`, if the bins for each group overlap, the width calculated for each mark is smaller that it should be.
Here's a minimal working example, where I have a dataset `A`, and its x-shifted version `B = A + shift`. In each row, I'm plotting a different shift, and when they start overlapping, the bar width is smaller than the bin width.
```python
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn.objects as so
def plot(ax, shift: float):
data = np.random.default_rng(0).normal(size=50)
df = pd.DataFrame({"A": data, "B": data + shift}).melt()
return (
so.Plot(df, x="value", color="variable")
.add(so.Bars(), so.Hist(common_bins=False))
.on(ax)
.plot()
)
shifts = [4.5, 4, 3.9, 3.8]
fig, axes = plt.subplots(len(shifts), sharex=True, gridspec_kw={"hspace": 0})
for ax, shift in zip(axes, shifts):
plot(ax, shift)
ax.set(ylabel=f"{shift = }")
```
I could trace it to this width calculation:
https://github.com/mwaskom/seaborn/blob/b4e5f8d261d6d5524a00b7dd35e00a40e4855872/seaborn/_core/plot.py#L1453
which ends up running the following line for all groups as one:
https://github.com/mwaskom/seaborn/blob/b4e5f8d261d6d5524a00b7dd35e00a40e4855872/seaborn/_core/scales.py#L467
If the bin edges are `[0, 1, 2]` and `[0.5, 1.5, 2.5]` for each group, it calculates the bin width from `[0, 0.5, 1, 1.5, ...]` and finds a width of 0.5 instead of a width of 1.
Maybe this is not a bug but something by design when there is overlap between marks?
In case it is a bug, I could contribute a fix, but would probably need some direction as to where to fix it.
Thanks! | open | 2024-10-19T21:33:56Z | 2024-10-24T12:11:39Z | https://github.com/mwaskom/seaborn/issues/3769 | [] | maurosilber | 1 |
albumentations-team/albumentations | machine-learning | 1,633 | [Tech debt] Improve interface for CoarseDropout | * `min_holes`, `max_holes` => `num_holes_range`
* `min_height`, `max_height` => `hole_height_range`
* `min_width`, `max_width` => `hole_width_range`
=>
We can update transform to use new signature, keep old as working, but mark as deprecated.
----
PR could be similar to https://github.com/albumentations-team/albumentations/pull/1615 | closed | 2024-04-05T19:29:31Z | 2024-04-19T22:01:33Z | https://github.com/albumentations-team/albumentations/issues/1633 | [
"good first issue",
"Tech debt"
] | ternaus | 1 |
databricks/koalas | pandas | 2,228 | gotImport Error | ImportError Traceback (most recent call last)
Cell In[8], line 4
2 import numpy as np
3 from collections.abc import Iterable
----> 4 import databricks.koalas as ks
5 import matplotlib.pyplot as plt
7 from pyspark import SparkContext, SparkConf
File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\databricks\koalas\__init__.py:74
66 raise RuntimeError(
67 "Please explicitly unset 'ARROW_PRE_0_15_IPC_FORMAT' environment variable in both "
68 "driver and executor sides. It is required to set this environment variable only "
69 "when you use pyarrow>=0.15 and pyspark<3.0."
70 )
73 from databricks.koalas.frame import DataFrame
---> 74 from databricks.koalas.indexes import Index, MultiIndex
75 from databricks.koalas.series import Series
76 from databricks.koalas.typedef import pandas_wraps
File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\databricks\koalas\indexes.py:51
49 from databricks.koalas.frame import DataFrame
50 from databricks.koalas.missing.indexes import _MissingPandasLikeIndex, _MissingPandasLikeMultiIndex
---> 51 from databricks.koalas.series import Series, _col
52 from databricks.koalas.utils import (
53 compare_allow_null,
54 compare_disallow_null,
(...)
61 validate_bool_kwarg,
62 )
63 from databricks.koalas.internal import _InternalFrame, NATURAL_ORDER_COLUMN_NAME
File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\databricks\koalas\series.py:22
20 import re
21 import inspect
---> 22 from collections import Iterable, OrderedDict
23 from functools import partial, wraps, reduce
24 from typing import Any, Generic, List, Optional, Tuple, TypeVar, Union
ImportError: cannot import name 'Iterable' from 'collections' (C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2800.0_x64__qbz5n2kfra8p0\lib\collections\__init__.py) | open | 2023-03-22T08:55:33Z | 2024-07-05T08:52:28Z | https://github.com/databricks/koalas/issues/2228 | [] | ParthRMehta | 1 |
tensorpack/tensorpack | tensorflow | 923 | Monitor tensors every batch | Default epoch monitors for training are quite useful.
Could you give an example of how to monitor one tensor's value **every batch** ？(and write it to tensorboard file.)
Thanks a lot.
| closed | 2018-10-05T20:19:07Z | 2018-10-25T20:32:44Z | https://github.com/tensorpack/tensorpack/issues/923 | [
"duplicate",
"usage"
] | XinDongol | 4 |
deepset-ai/haystack | nlp | 8,932 | Remove folder from core integrations https://github.com/deepset-ai/haystack-core-integrations/tree/main/nodes | closed | 2025-02-25T10:56:39Z | 2025-02-25T15:26:30Z | https://github.com/deepset-ai/haystack/issues/8932 | [
"P2"
] | julian-risch | 0 |
|
huggingface/datasets | nlp | 6,655 | Cannot load the dataset go_emotions | ### Describe the bug
When I run the following code I get an exception;
`go_emotions = load_dataset("go_emotions")`
> AttributeError Traceback (most recent call last)
Cell In[6], [line 1](vscode-notebook-cell:?execution_count=6&line=1)
----> [1](vscode-notebook-cell:?execution_count=6&line=1) go_emotions = load_dataset("go_emotions")
[2](vscode-notebook-cell:?execution_count=6&line=2) data = go_emotions.data
File [c:\Users\hijik\anaconda3\Lib\site-packages\datasets\load.py:2523](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2523), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
[2518](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2518) verification_mode = VerificationMode(
[2519](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2519) (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
[2520](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2520) )
[2522](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2522) # Create a dataset builder
-> [2523](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2523) builder_instance = load_dataset_builder(
[2524](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2524) path=path,
[2525](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2525) name=name,
[2526](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2526) data_dir=data_dir,
[2527](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2527) data_files=data_files,
[2528](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2528) cache_dir=cache_dir,
[2529](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2529) features=features,
[2530](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2530) download_config=download_config,
[2531](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2531) download_mode=download_mode,
[2532](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2532) revision=revision,
[2533](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2533) token=token,
[2534](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2534) storage_options=storage_options,
[2535](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2535) trust_remote_code=trust_remote_code,
[2536](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2536) _require_default_config_name=name is None,
...
---> [63](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:63) if issubclass(obj_type, transformers.PreTrainedTokenizerBase):
[64](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:64) pklregister(obj_type)(_save_transformersPreTrainedTokenizerBase)
[66](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:66) # Unwrap `torch.compile`-ed functions
AttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase'
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?10bc0728-6947-456e-9a3e-f056872b04c6) or open in a [text editor](command:workbench.action.openLargeOutput?10bc0728-6947-456e-9a3e-f056872b04c6). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Steps to reproduce the bug
```
from datasets import load_dataset
go_emotions = load_dataset("go_emotions")
```
### Expected behavior
Should simply load the variable with the data from the file
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.16.1
- Platform: Windows-10-10.0.22631-SP0
- Python version: 3.11.4
- `huggingface_hub` version: 0.20.3
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- `fsspec` version: 2023.10.0 | open | 2024-02-09T12:15:39Z | 2024-02-12T09:35:55Z | https://github.com/huggingface/datasets/issues/6655 | [] | arame | 4 |
sqlalchemy/alembic | sqlalchemy | 387 | Foreign key relationships are not created when use_alter is used. | **Migrated issue, originally created by tomkcook ([@tomkcook](https://github.com/tomkcook))**
The basic problem is that when a column is marked as ForeignKey and the use_alter option is set to True, the foreign key constraint is not created in the database. The database is postgresql 9.5.
```
$ pip freeze
alembic==0.8.7
click==6.6
Flask==0.11.1
Flask-Alembic==2.0.1
Flask-Login==0.3.2
Flask-SQLAlchemy==2.1
GeoAlchemy2==0.3.0
gunicorn==19.6.0
itsdangerous==0.24
Jinja2==2.8
Mako==1.0.4
MarkupSafe==0.23
pkg-resources==0.0.0
psycopg2==2.6.2
python-editor==1.0.1
SQLAlchemy==1.0.15
Werkzeug==0.11.11
```
My test application is attached. Briefly, I have these models:
```
from test import db
from sqlalchemy.dialects.postgres import UUID
from sqlalchemy.sql import func
class User(db.Model):
id = db.Column(UUID, primary_key = True, default=func.uuid_generate_v1())
name = db.Column(db.String)
class Post(db.Model):
id = db.Column(UUID, primary_key = True, default=func.uuid_generate_v1())
name = db.Column(db.String)
owner_id = db.Column(UUID, db.ForeignKey('user.id', use_alter=True))
owner = db.relationship("User", backref=db.backref('posts', lazy='dynamic'))
if __name__ == '__main__':
me = User(name = 'me')
db.session.add(me)
db.session.add(Post(name = 'My Post', owner = me))
db.session.commit()
```
When I generate a revision I get this:
```
""".
Revision ID: f4f67ed72eac
Revises:
Create Date: 2016-09-12 13:26:02.697400
"""
# revision identifiers, used by Alembic.
revision = 'f4f67ed72eac'
down_revision = None
branch_labels = ('default',)
depends_on = None
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
def upgrade():
### commands auto generated by Alembic - please adjust! ###
op.create_table('post',
sa.Column('id', postgresql.UUID(), nullable=False),
sa.Column('name', sa.String(), nullable=True),
sa.Column('owner_id', postgresql.UUID(), nullable=True),
sa.ForeignKeyConstraint(['owner_id'], ['test.user.id'], use_alter=True),
sa.PrimaryKeyConstraint('id'),
schema='test'
)
op.create_table('user',
sa.Column('id', postgresql.UUID(), nullable=False),
sa.Column('name', sa.String(), nullable=True),
sa.PrimaryKeyConstraint('id'),
schema='test'
)
### end Alembic commands ###
def downgrade():
### commands auto generated by Alembic - please adjust! ###
op.drop_table('user', schema='test')
op.drop_table('post', schema='test')
### end Alembic commands ###
```
As you can see, there is a ForeignKeyConstraint created. But when I run alembic upgrade to that version, the table in the database doesn't have any foreign key constraints:
```
test=> \d user
Table "test.user"
Column | Type | Modifiers
--------+-------------------+-----------
id | uuid | not null
name | character varying |
Indexes:
"user_pkey" PRIMARY KEY, btree (id)
test=> \d post
Table "test.post"
Column | Type | Modifiers
----------+-------------------+-----------
id | uuid | not null
name | character varying |
owner_id | uuid |
Indexes:
"post_pkey" PRIMARY KEY, btree (id)
```
If the `use_alter` flag is omitted, then foreign key constraints are created correctly.
----------------------------------------
Attachments: [test.tar.bz2](../wiki/imported_issue_attachments/387/test.tar.bz2)
| closed | 2016-09-12T13:42:35Z | 2022-07-07T14:33:56Z | https://github.com/sqlalchemy/alembic/issues/387 | [
"bug",
"duplicate",
"recursive FK issue"
] | sqlalchemy-bot | 3 |
ultrafunkamsterdam/undetected-chromedriver | automation | 1,026 | Disable javascript not working | Hey there, I tried to disable the javascript running on the undetected_chromedriver but without luck.
This command does not work:
`self.chrome_options.add_argument('--disable-javascript')`
This alternative is not supported by the undetected_chromedriver
` prefs = {'profile.default_content_setting_values': {
'images': 2,
'javascript': 2,
'geolocation': 2,
'popups': 2,
'cookies': 2}}
self.chrome_options.add_experimental_option("prefs", prefs)
`
Not sure if there's another way to do it, but both methods above work on normal selenium chrome.
Thank you. | closed | 2023-02-03T17:25:35Z | 2023-02-03T17:51:52Z | https://github.com/ultrafunkamsterdam/undetected-chromedriver/issues/1026 | [] | ZhangPeng4242 | 0 |
JoeanAmier/TikTokDownloader | api | 152 | 【建议】main 启动的时候添加一下启动参数 | python mian.py 5 直接web页面启动 | open | 2024-02-06T12:25:15Z | 2024-02-06T14:26:46Z | https://github.com/JoeanAmier/TikTokDownloader/issues/152 | [] | scccy | 2 |
Asabeneh/30-Days-Of-Python | flask | 306 | No license file | The `license.md` file helps with how your files can be edited and even forked also, guides how pull request can be done on your main branch.
It should be added.
I can work on it. | open | 2022-10-01T23:49:00Z | 2024-04-09T02:08:23Z | https://github.com/Asabeneh/30-Days-Of-Python/issues/306 | [] | chemben17 | 2 |
allenai/allennlp | nlp | 4,974 | Publish allennlp-server to pypi | It's annoying that it's separate. It doesn't get proper test coverage this way. We should either support this feature properly, or not at all. | closed | 2021-02-12T01:03:36Z | 2021-04-06T18:29:48Z | https://github.com/allenai/allennlp/issues/4974 | [
"Feature request"
] | dirkgr | 14 |
scikit-image/scikit-image | computer-vision | 7,607 | Missing scikit-image nightly wheels on MacOS | We installed the nightlies in Dask CI (usually from here: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple), but we are getting errors that the wheels can't be found there anymore (and seem to be gone), is this intended and if yes is there another source? | closed | 2024-11-13T15:40:32Z | 2024-11-15T19:03:19Z | https://github.com/scikit-image/scikit-image/issues/7607 | [
":warning: Priority",
":robot: type: Infrastructure"
] | phofl | 6 |
huggingface/datasets | computer-vision | 7,193 | Support of num_workers (multiprocessing) in map for IterableDataset | ### Feature request
Currently, IterableDataset doesn't support setting num_worker in .map(), which results in slow processing here. Could we add support for it? As .map() can be run in the batch fashion (e.g., batch_size is default to 1000 in datasets), it seems to be doable for IterableDataset as the regular Dataset.
### Motivation
Improving data processing efficiency
### Your contribution
Testing | open | 2024-10-02T18:34:04Z | 2024-10-03T09:54:15Z | https://github.com/huggingface/datasets/issues/7193 | [
"enhancement"
] | getao | 1 |
schemathesis/schemathesis | pytest | 2,409 | [BUG] Response validation fails when using authentication and custom session | ### Checklist
- [x] I checked the [FAQ section](https://schemathesis.readthedocs.io/en/stable/faq.html#frequently-asked-questions) of the documentation
- [x] I looked for similar issues in the [issue tracker](https://github.com/schemathesis/schemathesis/issues)
- [x] I am using the latest version of Schemathesis
### Describe the bug
Validating responses fails when an endpoint specifies a `security` parameter and a custom test client is passed as the `session` argument to `case.call_and_validate()`, such as in [this example from the documentation](https://schemathesis.readthedocs.io/en/stable/auth.html#custom-test-client-in-python-tests).
### To Reproduce
🚨 **Mandatory** 🚨: Steps to reproduce the behavior:
Running this test example with `pytest` should reproduce the issue:
```python
from typing import Annotated
from fastapi import FastAPI, Security
from fastapi.security import APIKeyHeader
from starlette_testclient import TestClient
import schemathesis
app = FastAPI()
@app.get("/", responses={200: {"model": {}}, 403: {"model": {}}})
def root(api_key: Annotated[str, Security(APIKeyHeader(name="x-api-key"))]):
return {"message": "Hello world"}
schemathesis.experimental.OPEN_API_3_1.enable()
schema = schemathesis.from_asgi("/openapi.json", app)
@schema.parametrize()
def test_api(case):
client = TestClient(app)
case.call_and_validate(session=client)
```
This gives me the following error:
```
FAILED tests/test_schema.py::test_api[GET /] - requests.exceptions.ConnectionError: HTTPConnectionPool(host='localhost', port=80): Max retries exceeded with url: / (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7c2e41ffa6...
```
This is the OpenAPI schema:
```json
{
"openapi": "3.1.0",
"info": {
"title": "FastAPI",
"version": "0.1.0"
},
"paths": {
"/": {
"get": {
"summary": "Root",
"operationId": "root__get",
"responses": {
"200": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": {}
}
}
},
"403": {
"description": "Forbidden"
}
},
"security": [
{
"APIKeyHeader": []
}
]
}
}
},
"components": {
"securitySchemes": {
"APIKeyHeader": {
"type": "apiKey",
"in": "header",
"name": "x-api-key"
}
}
}
}
```
### Expected behavior
The test given above should pass
### Environment
```
- OS: Linux
- Python version: 3.12
- Schemathesis version: 3.34.1
- Spec version: 3.1.0
```
### Additional context
I believe the issue is in this check: https://github.com/schemathesis/schemathesis/blob/master/src/schemathesis/specs/openapi/checks.py#L351 The check is creating a `requests.Session` which will lead to an actual HTTP call. It should be using the specified session instead.
| closed | 2024-08-20T08:02:45Z | 2024-08-20T19:39:17Z | https://github.com/schemathesis/schemathesis/issues/2409 | [
"Priority: High",
"Type: Bug"
] | flacerdk | 2 |
errbotio/errbot | automation | 946 | Command & response translations/Internationalization | ### I am...
* [ ] Reporting a bug
* [X] Suggesting a new feature
* [ ] Requesting help with running my bot
* [ ] Requesting help writing plugins
* [ ] Here about something else
### I am running...
* Errbot version: 4.3.5
* OS version: Fedora 25
* Python version: 3.5.2
* Using a virtual environment: yes
### Suggested Feature...
Could we make some kind of decorator that allow us to setup a command alias or translation?
In multi language environment can be useful if we can simplify add some kind of option to translate the command names.
Additionally could be useful the option of make the output of a command translatable based on the User or MUC preference.
Or maybe a initially simple approach that let us run multiple instances of the same bot in different languages. I think that this is not-so-hard(tm) to be done for the responses part, but still requires some work in the command name and params translations. | closed | 2017-01-19T23:09:54Z | 2019-01-05T17:07:54Z | https://github.com/errbotio/errbot/issues/946 | [] | qlixed | 1 |
deezer/spleeter | deep-learning | 600 | Cannot find reference 'get_default_audio_adapter' in 'adapter.py' | <!-- Please respect the title [Discussion] tag. -->
from spleeter.audio.adapter import get_default_audio_adapter but my IDE reported that the reference cannot be found | closed | 2021-03-30T01:03:18Z | 2021-04-02T13:42:46Z | https://github.com/deezer/spleeter/issues/600 | [
"question"
] | cong-x-p | 1 |
nvbn/thefuck | python | 1,318 | _ | _ | closed | 2022-07-05T08:42:36Z | 2022-07-05T14:56:57Z | https://github.com/nvbn/thefuck/issues/1318 | [] | 3Rafal | 0 |
christabor/flask_jsondash | plotly | 9 | Need delete widget option | Doesn't exist.
| closed | 2016-05-03T20:28:19Z | 2016-05-03T21:45:29Z | https://github.com/christabor/flask_jsondash/issues/9 | [] | christabor | 0 |
inducer/pudb | pytest | 48 | Screen flashes at each step | This has always been an issue for me. Each time I step through a line of code, the screen flashes. If the line takes a long time to complete, all I can see is my original prompt (bash or IPython or whatever). This is annoying when stepping through code, because the flashing is disorienting, and it's also annoying when executing a long line of code, because I can no longer see what is executing until it is done. This also happens a lot if there are print statements, as the statement is printed at the "original prompt".
Is this easily fixable, or is it an Urwid issue? I haven't looked at the code yet, but maybe just the screen redrawing commands need to be reorganized a little bit. Can you also reproduce this?
| open | 2012-11-12T00:48:42Z | 2024-08-12T21:49:10Z | https://github.com/inducer/pudb/issues/48 | [
"Bug"
] | asmeurer | 43 |
Evil0ctal/Douyin_TikTok_Download_API | fastapi | 564 | [BUG] 获取直播流失败，恳请老师解答一下 | <img width="783" alt="Image" src="https://github.com/user-attachments/assets/b7d4a6c3-2486-49c0-ac65-cfee3c1da4c6" /> | open | 2025-02-26T05:33:16Z | 2025-02-26T05:33:16Z | https://github.com/Evil0ctal/Douyin_TikTok_Download_API/issues/564 | [
"BUG"
] | Dilemmacpy | 0 |
CorentinJ/Real-Time-Voice-Cloning | deep-learning | 1,196 | Hybrid modeling | I have one question. Is it possible to use the Tortoise tts model together with that to enhance its results? | open | 2023-04-17T15:35:44Z | 2023-04-17T15:35:44Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/1196 | [] | Tortoise17 | 0 |
Asabeneh/30-Days-Of-Python | matplotlib | 413 | loops | i cant do it:
Find the ten most spoken languages from the data
Find the 10 most populated countries in the world | closed | 2023-07-03T17:24:50Z | 2023-07-03T17:38:34Z | https://github.com/Asabeneh/30-Days-Of-Python/issues/413 | [] | Bidmajnoon | 0 |
davidsandberg/facenet | tensorflow | 1,150 | How to combine two models to a single pb file? | Dear,
My goal is adding more dataset to the pre-trained model to increase the accuracy.
1. I freezed the graph to get the 1st model named **pre-trained-model.pb**
2. Then I trained another model based on my own images followed [this topic](https://github.com/davidsandberg/facenet/wiki/Classifier-training-of-inception-resnet-v1) named **new-model.pb**
How can I merge these 2 model into one **final-model.pb**? or can I feed the 1st model to the second model to produce the final single model (pb)? | open | 2020-04-10T04:37:07Z | 2022-05-20T16:07:53Z | https://github.com/davidsandberg/facenet/issues/1150 | [] | tongvantruong | 1 |
Urinx/WeixinBot | api | 146 | 运行weixin.py报错诶 | ImportError: No module named requests_toolbelt.multipart.encoder | open | 2017-01-18T08:21:48Z | 2017-04-05T08:56:20Z | https://github.com/Urinx/WeixinBot/issues/146 | [] | zy00000 | 2 |
aidlearning/AidLearning-FrameWork | jupyter | 142 | Unfavorable language? How to change the language fully to english !? | Dear Devs, it is a pleasure to use such incredible app, but there are some things which needs to be resolved, the gui is quite good, but many settings are written in Chinese language which is unfavorable to many, pls kindly try to change the whole language system into English which will be appreciated!
Looking forward for your actions.
Thanks
| closed | 2020-12-12T07:00:25Z | 2023-02-20T04:14:26Z | https://github.com/aidlearning/AidLearning-FrameWork/issues/142 | [] | Ryan-Blade | 3 |
Nemo2011/bilibili-api | api | 207 | 【求助】有关于判断done_geetest() | **Python 版本：** 3.10.7
**模块版本：** 15.0.0
**运行环境：** Windows
---
我是一个初学者,正在尝试写一个我自己的bilibili相关的想法来锻炼自己的能力
我参考本库的文档设计了一个窗口,其他一切正常,但我不知道如何在主函数中判断done_geetest()来关闭极验的窗口随后获取验证码.
我一开始想到的就是使用while true来循环判断并根据if来break,但很显然这会导致线程阻塞并使窗口崩溃
随后我想到使用async进行异步处理,但相关的教程过于繁琐导致我找不到实际需要的部分
我也进行了一些尝试,但实际上还是阻塞了
如果有大佬愿意帮助解决不胜感激
| closed | 2023-02-19T17:23:14Z | 2023-02-20T11:02:34Z | https://github.com/Nemo2011/bilibili-api/issues/207 | [
"question"
] | yhzcake | 3 |
iterative/dvc | machine-learning | 10,653 | Dvc commit operation is too slow | # Bug Report
## Description
I use git and dvc to manage my training datasets, which consists of thousands of jsonl files.
After I modify several jsonl files, I use `dvc status && dvc commit`. `dvc status` operation is completed quickly (I know dvc will only hash files once until it gets modified. Here only several jsonl files are modified so `dvc status` operation cost little). However, `dvc commit` operation cost a lot of time.
While `dvc commit` is executing, I see lots of "Checking out xxx/xxx/xxx.jsonl" shows in the terminal, and I believe those jsonl files are not modified. Why dvc need to check out files that are not modified?
### Expected
Assume two files `a.jsonl` and `b.jsonl` are modified, I think `dvc commit` should equal to `dvc add a.jsonl b.jsonl`. However, it seems that `dvc commit` will check out all files tracked by dvc.
I expect `dvc commit` operation skip files which are not modified, so it can be completed quickly.
### Environment information
**Output of `dvc doctor`:**
```console
$ dvc doctor
DVC version: 3.55.2 (pip)
-------------------------
Platform: Python 3.9.19 on Linux-5.14.0-284.25.1.el9_2.x86_64-x86_64-with-glibc2.31
Subprojects:
dvc_data = 3.16.5
dvc_objects = 5.1.0
dvc_render = 1.0.2
dvc_task = 0.4.0
scmrepo = 3.3.7
Supports:
http (aiohttp = 3.10.5, aiohttp-retry = 2.8.3),
https (aiohttp = 3.10.5, aiohttp-retry = 2.8.3),
s3 (s3fs = 2024.6.1, boto3 = 1.35.7)
Config:
Global: /mnt/afs/jiangtan/.config/dvc
System: /etc/xdg/dvc
Cache types: hardlink, symlink
Cache directory: fuse.quarkfs_client on quarkfs_client
Caches: local
Remotes: s3
Workspace directory: fuse.quarkfs_client on quarkfs_client
Repo: dvc, git
Repo.site_cache_dir: /path/to/repo/.dvc/site_cache_dir/repo/eddf3641719990517f0cfc808ea33376
```
| open | 2024-12-17T08:15:02Z | 2024-12-22T18:52:26Z | https://github.com/iterative/dvc/issues/10653 | [
"performance",
"triage"
] | jiangtann | 10 |
Significant-Gravitas/AutoGPT | python | 8,807 | blocks and graphs don't work | ### ⚠️ Search for existing issues first ⚠️
- [X] I have searched the existing issues, and there is no existing issue for my problem
### Which Operating System are you using?
Windows
### Which version of AutoGPT are you using?
Latest Release
### What LLM Provider do you use?
Azure
### Which area covers your issue best?
Installation and setup
### What commit or version are you using?
13da8af170602005b7a51ae527c388758825ed15
### Describe your issue.
After going to the frontend page of AutoGPT, my blocks and graphs do not work
### Upload Activity Log Content
(venv) PS G:\Stable-diffusion\AutoGPT\autogpt_platform\frontend> npm run dev
> frontend@0.3.3 dev
> next dev
▲ Next.js 14.2.13
- Local: http://localhost:5151
- Environments: .env.local
- Experiments (use with caution):
· instrumentationHook
✓ Starting...
○ Compiling /instrumentation ...
✓ Compiled /instrumentation in 3.2s (1256 modules)
✓ Ready in 7.9s
✓ Compiled /src/middleware in 353ms (562 modules)
○ Compiling / ...
✓ Compiled / in 14.8s (7445 modules)
Supabase auth error: AuthSessionMissingError: Auth session missing!
at eval (webpack-internal:///(rsc)/./node_modules/@supabase/auth-js/dist/module/GoTrueClient.js:885:59)
at SupabaseAuthClient._useSession (webpack-internal:///(rsc)/./node_modules/@supabase/auth-js/dist/module/GoTrueClient.js:787:26)
at async SupabaseAuthClient._getUser (webpack-internal:///(rsc)/./node_modules/@supabase/auth-js/dist/module/GoTrueClient.js:877:20)
at async eval (webpack-internal:///(rsc)/./node_modules/@supabase/auth-js/dist/module/GoTrueClient.js:864:20)
at async eval (webpack-internal:///(rsc)/./node_modules/@supabase/auth-js/dist/module/GoTrueClient.js:732:28) {
__isAuthError: true,
status: 400,
code: undefined
}
GET / 200 in 15947ms
⚠ The "images.domains" configuration is deprecated. Please use "images.remotePatterns" configuration instead.
○ Compiling /login ...
✓ Compiled /login in 4.9s (8595 modules)
Supabase auth error: AuthSessionMissingError: Auth session missing!
at eval (webpack-internal:///(rsc)/./node_modules/@supabase/auth-js/dist/module/GoTrueClient.js:885:59)
at SupabaseAuthClient._useSession (webpack-internal:///(rsc)/./node_modules/@supabase/auth-js/dist/module/GoTrueClient.js:787:26)
at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
at async SupabaseAuthClient._getUser (webpack-internal:///(rsc)/./node_modules/@supabase/auth-js/dist/module/GoTrueClient.js:877:20)
at async eval (webpack-internal:///(rsc)/./node_modules/@supabase/auth-js/dist/module/GoTrueClient.js:864:20)
at async eval (webpack-internal:///(rsc)/./node_modules/@supabase/auth-js/dist/module/GoTrueClient.js:732:28) {
__isAuthError: true,
status: 400,
code: undefined
}
GET /login?_rsc=v3pub 200 in 191ms
○ Compiling /signup ...
✓ Compiled /signup in 1627ms (8591 modules)
GET / 200 in 63ms
POST /signup 303 in 838ms
○ Compiling /build ...
✓ Compiled /build in 3.4s (9538 modules)
### Upload Error Log Content
_No response_ | closed | 2024-11-27T12:20:54Z | 2024-12-05T20:07:56Z | https://github.com/Significant-Gravitas/AutoGPT/issues/8807 | [] | amesemyta1 | 3 |
STVIR/pysot | computer-vision | 280 | 如何确定训练RPN的次数？ | 请问如何确定训练RPN的次数？麻烦详细说明一下
谢谢 | closed | 2020-01-14T08:53:46Z | 2020-01-19T06:26:42Z | https://github.com/STVIR/pysot/issues/280 | [] | ghost | 1 |
huggingface/pytorch-image-models | pytorch | 1,300 | nexterov in LARS | https://github.com/rwightman/pytorch-image-models/blob/75144395739162a153dae5628320b85b5895634b/timm/optim/lars.py#L70-L73
Why set the `nesterov` as False when calling `__setstate__`? | closed | 2022-06-13T06:50:48Z | 2022-06-14T04:18:17Z | https://github.com/huggingface/pytorch-image-models/issues/1300 | [] | weigq | 3 |
3b1b/manim | python | 1,656 | GLib-GIO-WARNING!!!(Acer Predator) | Describe the error
Run for a few seconds and then Interrupt
Code and Error
Code:
manimgl example_scenes.py GraphExample
Error:
GLib-GIO-WARNING **: 17:58:47.474: Unexpectedly, UWP app `AcerIncorporated.PredatorSenseV30_3.0.3162.0_x64__48frkmn4z8aw4' (AUMId`AcerIncorporated.PredatorSenseV30_48frkmn4z8aw4!CentenialConvert') supports 1 extensions but has no verbs
Environment
OS System: win10
manim version: master
python version:python3.8
@3b1b @bhbr @TonyCrane @eulertour @Sridhar3b1b Waiting for your help. Please Guide me | open | 2021-10-19T12:33:39Z | 2021-10-20T15:38:45Z | https://github.com/3b1b/manim/issues/1656 | [] | vaibhawkhemka | 3 |
tfranzel/drf-spectacular | rest-api | 474 | SimpleJWT integration doesn't take into account the settings AUTH_HEADER_NAME | Hi, just figuring this out today because I had to use a custom header for simplejwt auth
**Describe the bug**
SimpleJwt can be configured by using a dict in the settings.py of a django project.
`SIMPLE_JWT = {
'AUTH_HEADER_NAME' : "HTTP_X_TOKEN" # translate to X-token as header.
}`
But the current implementation doesn't take this settings into account :
`class SimpleJWTScheme(OpenApiAuthenticationExtension):
target_class = 'rest_framework_simplejwt.authentication.JWTAuthentication'
name = 'jwtAuth'
def get_security_definition(self, auto_schema):
from rest_framework_simplejwt.settings import api_settings
if len(api_settings.AUTH_HEADER_TYPES) > 1:
warn(
f'OpenAPI3 can only have one "bearerFormat". JWT Settings specify '
f'{api_settings.AUTH_HEADER_TYPES}. Using the first one.'
)
return {
'type': 'http',
'scheme': 'bearer',
'bearerFormat': api_settings.AUTH_HEADER_TYPES[0],
}`
Where the return should become something I guess like
`{'type':'apiKey', 'in': 'header', 'name': api_settings.SIMPLE_JWT['AUTH_HEADER_NAME']}`
**To Reproduce**
Change simplejwt header setting.
**Expected behavior**
Authentication should scheme should follow simplejwt settings.
I may have time to make a PR if needed.
| closed | 2021-08-02T12:13:57Z | 2021-08-25T13:15:35Z | https://github.com/tfranzel/drf-spectacular/issues/474 | [
"bug",
"fix confirmation pending"
] | Smixi | 14 |
yuka-friends/Windrecorder | streamlit | 153 | bug: 录制时关机或关闭Windrecoder会导致录像文件损坏 | Shutting down or closing Windrecoder during recording might cause the video file to be damaged. | **运行环境**：Win10 Home 22H2, AMD 5800H, 录制编码器 `AMD_h265`, 录制比特率 `150kbps`
**bug描述**：录制时关机或关闭Windrecoder会导致录像文件损坏，而且OCR模块会正常更改文件名为 `xxx-OCRED.mp4`，potplayer无法打开视频文件。
个人推测是因为使用mp4封装文件导致文件损坏，可以尝试使用mkv录制，再在索引文件前封装为mp4（OBS推荐做法）
Edit:相同码率下mkv视频好像比mp4糊一些 | open | 2024-04-24T07:13:06Z | 2024-07-11T09:44:20Z | https://github.com/yuka-friends/Windrecorder/issues/153 | [
"bug",
"P0"
] | RTLiang | 1 |
man-group/arctic | pandas | 542 | Publish arctic releases via conda-forge | Conda is increasingly used in the data science community. Conda-forge provides build infrastructure for many project. Aim to get arctic built automatically for conda:
https://conda-forge.org/#about | closed | 2018-04-20T08:38:06Z | 2019-11-18T11:05:50Z | https://github.com/man-group/arctic/issues/542 | [
"hackathon",
"medium",
"build-and-release"
] | jamesblackburn | 2 |
neuml/txtai | nlp | 365 | Update transcription notebook | Update transcription notebook to incorporate latest models available in transformers added with #362 | closed | 2022-10-17T16:26:03Z | 2022-10-17T16:53:22Z | https://github.com/neuml/txtai/issues/365 | [] | davidmezzetti | 0 |
ultralytics/yolov5 | machine-learning | 13,330 | about save-txt in yolov5-seg | ### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
### Question
when activating "save-txt" in yolov5-seg.py, a txt with the coordinates of the predicted region is saved, but i found that the coordinates seem not to be in sequence, that is to say when i use fillpoly in opencv, the coordinates seem unable to form a polygon like the one of prediction. is there a way to make the coordinates in sequence?
我发现启用save-txt后保存的包含预测分割区域的txt里的坐标似乎不是按顺序的（指坐标的保存顺序不是围着分割区域的）？用opencv的fillpoly填充出来的也跟预测的区域不一样。有办法把坐标变成按顺序的吗？
### Additional
_No response_ | open | 2024-09-23T09:40:49Z | 2024-10-27T13:30:36Z | https://github.com/ultralytics/yolov5/issues/13330 | [
"question"
] | Powerfulidot | 5 |
ultralytics/ultralytics | python | 19,091 | Export to NCNN format no longer works with Ultralytics 8.3.71 and torch 2.6.0+cpu on Raspberry Pi 4 | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
### Ultralytics YOLO Component
Export
### Bug
The latest version of `ultralytics 8.3.71` in conjunction with `torch-2.6.0+cpu` causes the `yolo export model=yolo11n.pt format=ncnn` to longer work. It results in the following output, and the model does not get converted to NCNN format. If you downgrade the libraries to `ultralytics 8.3.70` and `torch-2.5.1+cpu`, it does work.
Can you please resolve this issue? Is there any way I can help?
```
yolo export model=my_model.pt format=ncnn
Ultralytics 8.3.71 Python-3.11.2 torch-2.6.0+cpu CPU (Cortex-A72)
YOLO11n summary (fused): 238 layers, 2,583,127 parameters, 0 gradients, 6.3 GFLOPs
PyTorch: starting from 'my_model.pt' with input shape (1, 3, 480, 480) BCHW and output shape(s) (1, 9, 4725) (5.2 MB)
TorchScript: starting export with torch 2.6.0+cpu...
Illegal instruction
```
Thanks to the Ultralytics team for all your great work on this library!
### Environment
(venv) evan@raspberrypi:~/yolo/yolo3 $ yolo checks
Ultralytics 8.3.71 🚀 Python-3.11.2 torch-2.6.0+cpu CPU (Cortex-A72)
Setup complete ✅ (4 CPUs, 7.6 GB RAM, 12.0/27.8 GB disk)
OS Linux-6.6.62+rpt-rpi-v8-aarch64-with-glibc2.36
Environment Linux
Python 3.11.2
Install pip
RAM 7.63 GB
Disk 12.0/27.8 GB
CPU Cortex-A72
CPU count 4
GPU None
GPU count None
CUDA None
numpy ✅ 2.1.1<=2.1.1,>=1.23.0
matplotlib ✅ 3.10.0>=3.3.0
opencv-python ✅ 4.11.0.86>=4.6.0
pillow ✅ 11.1.0>=7.1.2
pyyaml ✅ 6.0.2>=5.3.1
requests ✅ 2.32.3>=2.23.0
scipy ✅ 1.15.1>=1.4.1
torch ✅ 2.6.0>=1.8.0
torch ✅ 2.6.0!=2.4.0,>=1.8.0; sys_platform == "win32"
torchvision ✅ 0.21.0>=0.9.0
tqdm ✅ 4.67.1>=4.64.0
psutil ✅ 6.1.1
py-cpuinfo ✅ 9.0.0
pandas ✅ 2.2.3>=1.1.4
seaborn ✅ 0.13.2>=0.11.0
ultralytics-thop ✅ 2.0.14>=2.0.0
### Minimal Reproducible Example
**Steps to reproduce**
This is all run on a Raspberry Pi 4b 8GB using 64-bit Raspberry Pi OS version 12 (Bookworm). The OS is freshly installed on an SD cardusing Raspberry Pi Imager.
Open a terminal and run the following commands:
1. `sudo apt update && sudo apt upgrade -y`
2. `mkdir yolo && cd yolo`
3. `python3 -m venv venv`
4. `source venv/bin/activate`
5. `pip install ultralytics ncnn`
6. `yolo export model=yolo11n.pt format=ncnn`
The error will occur upon running that last command. The error no longer occurs if you downgrade using `pip install ultralytics==8.3.70 torch==2.5.1`.
### Additional
_No response_
### Are you willing to submit a PR?
- [ ] Yes I'd like to help by submitting a PR! | open | 2025-02-06T02:24:31Z | 2025-03-11T18:36:30Z | https://github.com/ultralytics/ultralytics/issues/19091 | [
"bug",
"embedded",
"exports"
] | EdjeElectronics | 10 |
JaidedAI/EasyOCR | pytorch | 567 | Using custom model with different input size? (rgb: True) | I've successfully trained my own model, thank you so much for the guidance there, but now when I am trying to use the model I made with `rgb: true` (making it have an input of 3 channels) I get errors for size mismatches:
```
RuntimeError: Given groups=1, weight of size [32, 3, 3, 3], expected
input[1, 1, 64, 256] to have 3 channels, but got 1 channels instead
```
This may be due to the downloaded model, or something? I played with `download_enabled` and removing the downloaded model, that gives this error:
```
FileNotFoundError: Missing ./ocr_model/craft_mlt_25k.pth and downloads disabled
```
How do I use the reader **only using** my network? or am I trying to do something that doesn't make any sense? If my training is making great predictions, I'm confused on why I need another model?
```py
reader = easyocr.Reader(
['en'],
# here's where my custom_model.pth file is
model_storage_directory="./ocr_model",
# here's where my custom_model.py and custom_model.yaml live
user_network_directory='./ocr_network',
recog_network='custom_model'
)
reader.readtext('test.jpg')
```
I believe I set `custom_model.yaml` up properly:
```yaml
network_params:
input_channel: 3
output_channel: 256
hidden_size: 256
imgH: 64
lang_list:
- 'en'
character_list: 0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz
```
## EDIT
Doing the exact same training but with `rgb: False` and everything works, woo! am I missing out on much without rgb? | closed | 2021-10-13T00:31:36Z | 2022-12-22T02:26:54Z | https://github.com/JaidedAI/EasyOCR/issues/567 | [] | ckcollab | 4 |
ets-labs/python-dependency-injector | asyncio | 94 | Enhancement of catalogs inheritance | Example below should work properly:
``` python
"""Example of catalogs inheritance."""
import dependency_injector as di
class CatalogA(di.AbstractCatalog):
"""Example catalog A."""
p1 = di.Provider()
class CatalogB(CatalogA):
"""Example catalog B."""
p2 = di.Provider()
class CatalogC(CatalogB):
"""Example catalog C."""
p3 = di.Provider()
# `di.AbstractCatalog.providers` attribute is a dict of all available providers,
# including current catalog providers and providers that are inherited
# from parent catalogs:
assert CatalogA.providers == dict(p1=CatalogA.p1)
assert CatalogB.providers == dict(p1=CatalogA.p1, p2=CatalogB.p2)
assert CatalogC.providers == dict(p1=CatalogA.p1, p2=CatalogB.p2, p3=CatalogC.p3)
# `di.AbstractCatalog.inherited_providers` attribute is a dict of all providers that
# are inherited from parent catalogs
assert CatalogA.inherited_providers == dict()
assert CatalogB.inherited_providers == dict(p1=CatalogA.p1)
assert CatalogC.inherited_providers == dict(p1=CatalogA.p1, p2=CatalogB.p2)
# `di.AbstractCatalog.cls_providers` attribute is a dict of current catalog providers:
assert CatalogA.cls_providers == dict(p1=CatalogA.p1)
assert CatalogB.cls_providers == dict(p2=CatalogB.p2)
assert CatalogB.cls_providers == dict(p3=CatalogC.p3)
```
| closed | 2015-10-07T07:46:45Z | 2015-10-07T16:44:57Z | https://github.com/ets-labs/python-dependency-injector/issues/94 | [
"enhancement"
] | rmk135 | 0 |
ansible/awx | django | 15,631 | AWX Office Hours - 11/12/24 | # AWX Office Hours
## Proposed agenda based on topics
- https://github.com/ansible/awx/pull/15627
## What
After a successful Contributor Summit in October 2023, one of the bits of feedback we got was to host a regular time for the Automation Controller (AWX) Team to be available for your folks in the AWX Community, so we are happy to announce a new regular video meeting.
This kind of feedback loop is vital to the success of AWX and the AWX team wants to make it as easy as possible for you - our community - to get involved.
## Where & When
Our next meeting will be held on Tuesday, November 12th, 2024 at [1500 UTC](https://dateful.com/time-zone-converter?t=15:00&tz=UTC)
* [Google Meet](https://meet.google.com/vyk-dfow-cfi)
* Via Phone PIN: 842522378 [Guide](https://support.google.com/meet/answer/9518557)
This meeting is held once a month, on the second Tuesday of the month, at [1500 UTC](https://dateful.com/time-zone-converter?t=15:00&tz=UTC)
## How
Add one topic per comment in this GitHub issue
If you don't have a GitHub account, jump on [#awx:ansible.com](https://matrix.to/#/#awx:ansible.com) on Matrix and we can add the topic for you
## Talk with us
As well as the fortnightly video meeting you can join the Community (inc development team) on Matrix Chat.
* Matrix: [#awx:ansible.com](https://matrix.to/#/#awx:ansible.com) (recomended)
* libera.chat IRC: `#ansible-awx` (If you are already setup on IRC)
The Matrix & IRC channels are bridged, you'll just have a better experience on Matrix
## Links
[AWX YouTube Chanel](https://www.youtube.com/@ansible-awx)
[Previous Meeting](#15319)
[Meeting recording]()
Next Meeting
See you soon! | closed | 2024-11-12T15:24:17Z | 2024-12-10T15:46:14Z | https://github.com/ansible/awx/issues/15631 | [
"needs_triage",
"community"
] | thedoubl3j | 0 |
python-gino/gino | asyncio | 750 | [Suggestion] Docs Translation | gino would be useful project (IMO)
I have a great interest in gino project.
If I has willing to translate Korean, Can You accept my Pull request?
P.S. Happy New Year | closed | 2021-01-02T13:34:09Z | 2021-01-10T21:00:05Z | https://github.com/python-gino/gino/issues/750 | [
"feature request"
] | KimSoungRyoul | 1 |
adbar/trafilatura | web-scraping | 584 | Removing related links at end of article/sidebar on news websites? | Over here in the Media Cloud project we're seeing poor performance on the content extraction task for a variety of pages that include links to other "related" stories at the end of article content. Our use case is trying to extract only article content as text. Do you have advice on tweaks to make to improve that performance? This might be the opposite of #518, because we do _not_ want related links as part of content.
Here's sample code with real examples parsed in a way that looks very similar to our usage. The function returns true if the supplied text is included in the extracted content (the erroneous results, in our use case). Each of these incorrectly includes text that is part of a "related links" type callout that appears _after_ article content. Any advice appreciated.
```python
import trafilatura
import requests
MEDIA_CLOUD_USER_AGENT = 'Mozilla/5.0 (compatible; mediacloud academic archive; mediacloud.org)'
def is_text_in_webpage_content(txt:str, url:str) -> bool:
req = requests.get(url, headers={'User-Agent': MEDIA_CLOUD_USER_AGENT},timeout=30)
parsed = trafilatura.bare_extraction(req.text, only_with_metadata=False, url=url,
include_images=False, include_comments=False)
content_text = parsed['text']
return txt in content_text
print(is_text_in_webpage_content(
'Thai Official', # item on bottom of page in "Latest News" section
'https://www.ibtimes.co.uk/falling-inflation-shifts-focus-when-ecb-could-cut-rates-1722106'))
print(is_text_in_webpage_content(
'HIV from Terrence Higgins to Today', # <li> under the "listen on sounds" banner after article
'https://www.bbc.co.uk/sport/football/67640638'))
print(is_text_in_webpage_content(
'Madhuri Dixit', # title of an item in the featured movie below the main content area
'https://timesofindia.indiatimes.com/videos/lifestyle/fashion/10-indian-saris-every-woman-should-have-in-her-wardrobe/videoshow/105809845.cms'))
print(is_text_in_webpage_content(
'Immigration, Ukraine', # title of an item in the "most popular" sidebar content
'https://www.bfmtv.com/cote-d-azur/nice-25-personnes-expulsees-lors-d-operations-anti-squat-menees-dans-le-quartier-des-liserons_AN-202312150639.html'))
``` | open | 2024-05-03T17:19:12Z | 2024-07-17T17:37:18Z | https://github.com/adbar/trafilatura/issues/584 | [
"bug"
] | rahulbot | 3 |
plotly/dash-core-components | dash | 949 | animation_options in Graph has incorrect default value ("ease" instead of "easing") | The defaultProps for `animation_options` are `{ frame: { redraw: false, }, transition: { duration: 750, ease: 'cubic-in-out', }, }`, but `Plotly.animate` takes an "easing" argument, not "ease".
I don't see any warning in the console for this, so I don't think these arguments are being validated; I can put whatever I like in `animation_options` and never get warnings/errors.
As an aside, it would be helpful if the `dash_core_components` [docs](https://dash.plotly.com/dash-core-components/graph) mentioned that `frame.duration` has to be set at least as long as `transition.duration`, or at least linked to [https://plotly.com/javascript/animations/](https://plotly.com/javascript/animations/), as it's not immediately clear that you can't just arbitrarily set `transition.duration` to higher values. In fact the default `frame.duration` is 500, so the 750 default value here is misleading (maybe just setting `frame.duration` to 750 in the default here would at least highlight to users that it needs to be set). | open | 2021-04-08T15:23:40Z | 2021-04-08T15:23:40Z | https://github.com/plotly/dash-core-components/issues/949 | [] | slishak | 0 |
dmlc/gluon-cv | computer-vision | 1,014 | When training yolov3 with coco, how to merge dog and cat into animal categories? | When training yolov3 with coco, how to merge dog and cat into animal categories? Thx. | closed | 2019-10-30T03:11:20Z | 2021-06-07T07:04:25Z | https://github.com/dmlc/gluon-cv/issues/1014 | [
"Stale"
] | YeShangyuan | 5 |
django-import-export/django-import-export | django | 1,441 | How to add error message when `UNIQUE constraint failed` | when import twice get a long `Traceback (most recent call last):`
```py
class Employee(models.Model):
id_number = models.CharField(max_length=180, null=True, blank=True, unique=True)
```
I want to show the error like admin default error message, like `already exist Employee with this _id_number ` | closed | 2022-05-27T01:15:54Z | 2023-04-12T13:02:24Z | https://github.com/django-import-export/django-import-export/issues/1441 | [
"question"
] | wgf4242 | 4 |
xuebinqin/U-2-Net | computer-vision | 225 | Can .pth change to pytorch .pt file | Hi, may I know what kind of models you train in torch? I would like to change the .pth to pytorch .pt file, since .pth is in conflict with python directory, and I cannot open in android mobile.
Thanks. | open | 2021-07-03T03:51:52Z | 2021-07-03T10:22:04Z | https://github.com/xuebinqin/U-2-Net/issues/225 | [] | Josonlchui | 1 |
sherlock-project/sherlock | python | 1,638 | Issues while runinng | here's the output
pi@raspberrypi:~/sherlock $ python3 sherlock
Traceback (most recent call last):
File "/usr/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/pi/sherlock/sherlock/__main__.py", line 21, in <module>
import sherlock
File "/home/pi/sherlock/sherlock/sherlock.py", line 12, in <module>
import pandas as pd
File "/home/pi/.local/lib/python3.9/site-packages/pandas/__init__.py", line 16, in <module>
raise ImportError(
ImportError: Unable to import required dependencies:
numpy:
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
We have compiled some common reasons and troubleshooting tips at:
https://numpy.org/devdocs/user/troubleshooting-importerror.html
Please note and check the following:
* The Python version is: Python3.9 from "/usr/bin/python3"
* The NumPy version is: "1.23.5"
and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.
Original error was: libcblas.so.3: cannot open shared object file: No such file
or directory
please lmk how to solve it | closed | 2022-12-14T22:25:41Z | 2023-02-04T17:21:21Z | https://github.com/sherlock-project/sherlock/issues/1638 | [] | Micychalek | 1 |
davidsandberg/facenet | tensorflow | 304 | Why slim.flatten(net) before slim.dropout(net)? | when I read the model code, I found there is a layer slim.flatten(net) before net = slim.dropout(net, dropout_keep_prob, is_training=is_training, scope='Dropout'), it's different with paper.
sorry, I'm new to this area, thank you very much | closed | 2017-06-02T07:35:44Z | 2017-06-06T01:46:59Z | https://github.com/davidsandberg/facenet/issues/304 | [] | chencjiajy | 1 |
pytest-dev/pytest-html | pytest | 295 | [Request]: Can we please add the support to extend the pytest-html to pytest-bdd framework? | This plugin is absolutely working for pytest-bdd framework too. I see the passed/failed/skipped etc. However, I am wondering if we can extend support of this plugin for pytest-bdd framework? As we can see the report with the Feature as well for Feature/Gherkin steps. ?? Any suggestions/thoughts are highly appreciated. | open | 2020-04-24T22:21:43Z | 2020-10-22T22:41:20Z | https://github.com/pytest-dev/pytest-html/issues/295 | [
"enhancement",
"feature"
] | CuriousQA | 6 |
NVlabs/neuralangelo | computer-vision | 163 | The second-order analytical gradients are not all zero as described in the article. | I printed the value of the value of the second derivative, and found that the second derivative is not zero. I understand that theoretically the second derivative of trilinear interpolation should be 0, but why are the results of the code implementation inconsistent?
`gradient = torch.autograd.grad(sdf.sum(), x, create_graph=True)[0]`
`hessian = torch.autograd.grad(gradient.sum(), x, create_graph=True)[0]` | open | 2023-12-01T07:31:46Z | 2023-12-01T07:32:02Z | https://github.com/NVlabs/neuralangelo/issues/163 | [] | ckrsls | 0 |
zihangdai/xlnet | nlp | 24 | Python package | Would you be opposed to turning this into a python package? Something like this: https://github.com/monkeyhippies/xlnet/commit/f0471a242ed5dad5c4be7602b17dd0a96ad6b671
Or is it meant to just be a repo of helpful scripts? | closed | 2019-06-22T07:59:54Z | 2019-06-22T08:20:03Z | https://github.com/zihangdai/xlnet/issues/24 | [] | monkeyhippies | 0 |
keras-team/autokeras | tensorflow | 1,641 | TimeSeriesForecaster: Predict function returns ValueError | Hi,
I've been using AutoKeras for Time Series forecasting but when the model has been trained and I apply the test data, it raises: "ValueError: The prediction data requires the original training data to make predictions on subsequent data points"
but I can't put both the test data and the training data as separate arguments? Do I need to append the test data to the training data for the model to predict or is there something I'm missing?
Setup Details
- OS type and version: Windows 10
- Python: 3.9
- autokeras: 1.0.16
- keras-tuner: 1.0.4
- scikit-learn: 1.0
- numpy: 1.19.5
- pandas: 1.3.4
- tensorflow: 2.5.0 (GPU)
| open | 2021-10-23T17:37:02Z | 2023-03-01T20:25:07Z | https://github.com/keras-team/autokeras/issues/1641 | [] | sparkey63 | 5 |
activeloopai/deeplake | data-science | 2,685 | [BUG] Can NOT run deeplake python library | ### Severity
None
### Current Behavior
Can not run deeplake python library, I already tried to have fresh deployment on different version of python, langchain and deeplake.
### Steps to Reproduce
- Use conda to create and activate new virutal environment
- install all python library,
> pip`` install langchain==0.0.208 deeplake openai tiktoken
> or pip`` install langchain deeplake openai tiktoken
> or pip`` install langchain==0.0.208 deeplake==3.8.2 openai tiktoken
- try import deeplake
all failed with following code
`Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/jamesou/Documents/Projects/langchain/Learning/deeplake.py", line 28, in <module>
db = DeepLake.from_documents(docs, dataset_path=dataset_path, embedding=OpenAIEmbeddings())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/jamesou/miniforge3/envs/deep/lib/python3.11/site-packages/langchain/vectorstores/base.py", line 317, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/jamesou/miniforge3/envs/deep/lib/python3.11/site-packages/langchain/vectorstores/deeplake.py", line 736, in from_texts
deeplake_dataset = cls(
^^^^
File "/Users/jamesou/miniforge3/envs/deep/lib/python3.11/site-packages/langchain/vectorstores/deeplake.py", line 123, in __init__
raise ValueError(
ValueError: Could not import deeplake python package. Please install it with `pip install deeplake`.`
### Expected/Desired Behavior
it should be able to run successfully
### Python Version
3.11.1 or 3.11.6
### OS
OSX 13.6
### IDE
VS Code
### Packages
_No response_
### Additional Context
_No response_
### Possible Solution
_No response_
### Are you willing to submit a PR?
- [ ] I'm willing to submit a PR (Thank you!) | closed | 2023-11-05T09:15:51Z | 2023-11-29T23:49:01Z | https://github.com/activeloopai/deeplake/issues/2685 | [
"bug"
] | jamesoujj | 3 |
PokemonGoF/PokemonGo-Bot | automation | 5,931 | Looking for a quick answer? Not sure if you have found something new ? Check our Discord | [https://discord.gg/n3g5puF](https://discord.gg/n3g5puF)
:)
| open | 2017-02-22T20:13:45Z | 2018-06-11T16:01:03Z | https://github.com/PokemonGoF/PokemonGo-Bot/issues/5931 | [
"Enhancement",
"Approved"
] | pogarek | 2 |
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