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serengil/deepface
|
machine-learning
| 924
|
Unable to find good face match with deepface and Annoy
|
Hi
I am using following code from Serengil's tutorial (you tube), for finding best face match. The embeddings are from deepface and Annoy's ANNS based search is used for find best matching face.
This code does not give good matching face, as was hightlighted in Serengil's you tube video.
Looking for help about why this code would not give best matching face.
Thanks
import os
from deepface.commons import functions
from keras_facenet import FaceNet
from deepface.basemodels import Facenet
from annoy import AnnoyIndex
import matplotlib.pyplot as plt
import random
import time
facial_images = []
for root, directories, files in os.walk("../../deepface/deepface/tests/dataset"):
for file in files:
if ('.jpg' in files):
exact_path = root + files
facial_images.append(exact_path)
embedder = FaceNet()
model = Facenet.loadModel()
representations = []
min = 100000000000
max = -10000000000
for face in facial_images:
img = functions.preprocess_face(img = face, target_size = (160,160))
# embedding = embedder.embeddings(img)
embedding = model.predict(img)[0,:]
temp = embedding.min()
if (min > temp):
min = temp
temp = embedding.max()
if (max < temp):
max = temp
representation = []
representation.append(img)
representation.append(embedding)
representations.append(representation)
#synthetic data to enlarge data size
for i in range(len(representations), 100000):
filename = "dummy_%d.jpg" % i
vector = [random.gauss(min, max) for z in range(128)]
dummy_item = []
dummy_item.append(filename)
dummy_item.append(vector)
representations.append(dummy_item)
t = AnnoyIndex(128, 'euclidean')
for i in range(len(representations)):
vector = representations[i][1]
t.add_item(i, vector)
t.build(3)
idx = 0
k = 2
start = time.time()
neighbors = t.get_nns_by_item(idx,k)
end = time.time()
print ("get_nns_by_item took " , end-start, " seconds");
print (neighbors)
|
closed
|
2023-12-20T08:22:34Z
|
2023-12-20T08:46:59Z
|
https://github.com/serengil/deepface/issues/924
|
[
"question"
] |
dumbogeorge
| 2
|
mwaskom/seaborn
|
matplotlib
| 2,821
|
Calling `sns.heatmap()` changes matplotlib rcParams
|
See the following example
```python
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
mpl.rcParams["figure.dpi"] = 120
mpl.rcParams["figure.facecolor"] = "white"
mpl.rcParams["figure.figsize"] = (9, 6)
data = sns.load_dataset("iris")
print(mpl.rcParams["figure.dpi"])
print(mpl.rcParams["figure.facecolor"])
print(mpl.rcParams["figure.figsize"])
#120.0
#white
#[9.0, 6.0]
fig, ax = plt.subplots()
sns.heatmap(data.corr(), vmin=-1, vmax=1, center=0, annot=True, linewidths=4, ax=ax);
print(mpl.rcParams["figure.dpi"])
print(mpl.rcParams["figure.facecolor"])
print(mpl.rcParams["figure.figsize"])
#72.0
#(1, 1, 1, 0)
#[6.0, 4.0]
```
If I call again
```python
mpl.rcParams["figure.dpi"] = 120
mpl.rcParams["figure.facecolor"] = "white"
mpl.rcParams["figure.figsize"] = (9, 6)
```
then it works fine, but I don't know why it changes the rcParams.
**Edit** These are the versions being used
```
Last updated: Wed May 25 2022
Python implementation: CPython
Python version : 3.9.12
IPython version : 8.3.0
matplotlib: 3.5.2
seaborn : 0.11.2
sys : 3.9.12 | packaged by conda-forge | (main, Mar 24 2022, 23:25:59)
[GCC 10.3.0]
Watermark: 2.3.0
```
|
closed
|
2022-05-25T19:16:45Z
|
2022-05-27T11:13:29Z
|
https://github.com/mwaskom/seaborn/issues/2821
|
[] |
tomicapretto
| 2
|
strawberry-graphql/strawberry
|
fastapi
| 3,790
|
Incorrect typing for the `type` decorator
|
## Describe the Bug
The type function is decorated with
```
@dataclass_transform(
order_default=True, kw_only_default=True, field_specifiers=(field, StrawberryField)
)
```
Therefore mypy treats classes decorated with type as being dataclasses with ordering functions.
In particular, defining `__gt__` on such a class will be treated as an error by mypy.
However, type (that is the underlying `_wrap_dataclass` function) do not do anything to ensure the dataclass actually has order functions defined.
I see multiple solutions:
- Removing the `order_default=True` part of the `dataclass_transform` decorating `type`
- Enforcing the `order=True` in `_wrap_dataclass`
- Allowing the caller to pass dataclass kwargs (as per [my previous issue](https://github.com/strawberry-graphql/strawberry/issues/2688))
## System Information
- Operating system: Ubuntu 24.04
- Strawberry version (if applicable): 0.256.1
## Additional Context
Code samples to be clear on the issue
```
@strawberry.type
class MyClass:
attr: str
k = MyClass(attr="abc")
j = MyClass(attr="def")
j > k # TypeError: '<' not supported between instances of 'MyClass' and 'MyClass'
```
```
@strawberry.type
class MyClass:
attr: str
def __gt__(self, other):
return self.attr > other.attr
k = MyClass(attr="abc")
j = MyClass(attr="def")
j > k # True
# When running mypy
error: You may not have a custom "__gt__" method when "order" is True [misc]
```
|
open
|
2025-02-21T11:26:35Z
|
2025-02-21T11:28:42Z
|
https://github.com/strawberry-graphql/strawberry/issues/3790
|
[
"bug"
] |
Corentin-Bravo
| 0
|
521xueweihan/HelloGitHub
|
python
| 2,078
|
【开源自荐】类似 rz / sz，支持 tmux 的文件传输工具 trzsz ( trz / tsz )
|
## 项目推荐
- 项目地址：https://github.com/trzsz/trzsz
- 类别：Python
- 项目后续更新计划：
* 支持 mac 以外的其他平台（例如 windows），要 SecureCRT、Xshell 等像 iTerm2 一样支持 [coprocesses](https://iterm2.com/documentation-coprocesses.html) 才能搞。
* 将进度条改成模态框，防止在文件传输过程中误触键盘导致传输失败，需要 iTerm2 支持显示 [mac 进度条](https://developer.apple.com/library/archive/documentation/LanguagesUtilities/Conceptual/MacAutomationScriptingGuide/DisplayProgress.html) 才能搞。
* 使用 tmux 控制模式时，支持二进制上传和下载，需要 tmux 和 iTerm2 支持一些 future 才好搞。
* 使用 tmux 普通模式时，现已支持二进制下载，支持二进制上传需要 tmux 支持输入设置成 latin1 字符集，或者找到个稳定可靠的办法，将被 tmux 转成 UTF-8 的数据转换回原始的二进制数据。
- 项目描述：
[trzsz](https://trzsz.github.io/) 是一个简单的文件传输工具，和 lrzsz ( rz / sz ) 类似但支持 tmux，和 iTerm2 一起使用，并且有一个不错的进度条。
- 推荐理由：
* 登录远程电脑时用 tmux 保持会话，但 tmux 不支持用 rz / sz 上传和下载文件，这就很不方便了。
* 既然 tmux 不愿意支持 rz / sz ，那我们就设计一个新的 trz / tsz ( [trzsz](https://github.com/trzsz/trzsz) ) 去支持 tmux 。
* 不管经过多少跳才登录到远程服务器，都可以方便地上传和下载文件，没有 scp 需要中转的麻烦。
* 原来 rz / sz 的实现比较简陋，连个进度条都没有，上传和下载文件不知道还要多久才能完成，又或者卡住了也不知道。
* trzsz ( trz / tsz ) 有一个不错的进度条，虽然现在还不是模态框，但也能清楚地看到上传和下载的进度。
- 截图：
* 上传文件示例 
* 下载文件示例 
|
closed
|
2022-01-16T08:50:39Z
|
2022-01-28T01:21:25Z
|
https://github.com/521xueweihan/HelloGitHub/issues/2078
|
[
"已发布",
"Python 项目"
] |
lonnywong
| 1
|
DistrictDataLabs/yellowbrick
|
scikit-learn
| 1,014
|
Using your own models with yellow brick
|
**Describe the issue**
If you create your own clustering algorithm that follows the sklearn pattern, is there anything I need to know to allow users to use the additional clustering methods to extend this package to my own sklearn like models?
|
closed
|
2020-01-29T22:48:21Z
|
2020-02-26T14:28:46Z
|
https://github.com/DistrictDataLabs/yellowbrick/issues/1014
|
[
"type: question"
] |
achapkowski
| 3
|
fa0311/TwitterInternalAPIDocument
|
graphql
| 660
|
Any idea how long each guest token is valid for?
|
I am using endpoints which can be viewed in incognito mode. I see each ip has a 95 request limit for a 13 minute window. But any idea how long a guest token remains valid before it starts giving 403?
I am caching the guest token in order to minimize requests but in production ended up getting 403 errors after sometime.
any idea @fa0311 ?
|
open
|
2024-10-14T04:49:27Z
|
2024-10-14T07:53:56Z
|
https://github.com/fa0311/TwitterInternalAPIDocument/issues/660
|
[] |
abhranil26
| 3
|
httpie/cli
|
python
| 1,402
|
can httpie support JSON5 (JSON for Humans) input?
|
## Checklist
- [x] I've searched for similar feature requests.
---
## Enhancement request
trying to call httpie with post body including raw valid js objects,
http <url> metrics:='[{name: "activeUsers"}]'
got error:
'metrics:=[{name: "activeUsers"}]': Expecting property name enclosed in double quotes: line 1 column 3 (char 2)
as expected;
but always expected tools like `httpie` could/should accept more format for flexibility,
and there is a project for that, called JSON5 (https://json5.org/) JSON for Humans, it allows many features, and some would be most useful for `httpie`:
1. Object keys may be an ECMAScript 5.1 IdentifierName, with single quote, or without quote, if unambiguous, like my example: `metrics:='[{name: "activeUsers"}]'` hope `httpie` can recognize it and auto-translate to valid json,
2. Strings may be single quoted,
3. Numbers may be hexadecimal, or scientific format, like `1e3` `28e6` ?
## Additional information, screenshots, or code examples
JSON5 Python libraries:
1. https://pypi.org/project/json5/
6. https://pyjson5.readthedocs.io/
|
open
|
2022-05-16T07:35:20Z
|
2022-05-16T07:52:38Z
|
https://github.com/httpie/cli/issues/1402
|
[
"enhancement",
"needs product design"
] |
tx0c
| 1
|
trevorstephens/gplearn
|
scikit-learn
| 32
|
Include logic regression
|
New estimator, needs much more research to see how/if it fits into `gplearn`'s API. No milestone yet. [Citation](http://kooperberg.fhcrc.org/logic/documents/logic-regression.pdf)
Add boolean/logical functions, conditional functions and potential to input a binary input dataset
|
closed
|
2017-04-27T10:29:19Z
|
2020-02-13T11:32:51Z
|
https://github.com/trevorstephens/gplearn/issues/32
|
[
"enhancement"
] |
trevorstephens
| 3
|
django-oscar/django-oscar
|
django
| 3,921
|
Unable to access oscar on https://example.com:8443/oscar
|
### Issue Summary
I followed [this guide](https://worldoscar.org/knowledge-base/oscar-19-installation/?epkb_post_type_1=oscar-19-installation) and I managed to install the most recent version [oscar_emr19-66~1881.deb](https://sourceforge.net/projects/oscarmcmaster/files/Oscar%20Debian%2BUbuntu%20deb%20Package/oscar_emr19-66~1881.deb/download) on Ubuntu 22.04. After a successful installation, I was able to arrive at the below stages:
When I `run less /usr/share/oscar-emr/README.txt` , I got:
### The problem
When I visited https://[example.com](https://example.com:8443/oscar):8443/oscar I got:
### Technical details
```
java -version
openjdk version "11.0.15" 2022-04-19
OpenJDK Runtime Environment (build 11.0.15+10-Ubuntu-0ubuntu0.20.04.1)
OpenJDK 64-Bit Server VM (build 11.0.15+10-Ubuntu-0ubuntu0.20.04.1, mixed mode, sharing)
```
* Python version: When I run `python --version`, I got: `-bash: python: command not found`. But `pip3 --version` returned `pip 20.0.2 from /usr/lib/python3/dist-packages/pip (python 3.8)`
* Django version: When I run `pip show django | grep Version`, I got `WARNING: Package(s) not found: django`
* Oscar version: When I run `pip show django-oscar | grep Version`, I got `WARNING: Package(s) not found: django-oscar`
### Help needed
How can I fix my installation to get this up and running?
|
closed
|
2022-05-01T16:35:58Z
|
2022-05-02T04:03:02Z
|
https://github.com/django-oscar/django-oscar/issues/3921
|
[] |
jessicana
| 1
|
dgtlmoon/changedetection.io
|
web-scraping
| 2,174
|
[feature] Sort tags / groups by alphabet
|
**Version and OS**
0.45.14 on Linux/Docker
**Is your feature request related to a problem? Please describe.**
To keep an overview about my watch jobs, I added tags / groups on them. In the meantime I work with 14 tags, and they are sorted by date created.
**Describe the solution you'd like**
It would be helpful, if the tags are sorted by alphabet automatically. Or if we had at least the ability to sort them manually.
**Describe the use-case and give concrete real-world examples**
<img width="714" alt="Bildschirmfoto 2024-02-10 um 09 20 11" src="https://github.com/dgtlmoon/changedetection.io/assets/3201804/ccf5be77-34a3-4385-9519-a5e1bd8b5b91">
<img width="694" alt="Bildschirmfoto 2024-02-10 um 09 20 42" src="https://github.com/dgtlmoon/changedetection.io/assets/3201804/c84d57d9-23c5-4e21-b6a2-1dd7aaa395a1">
**Additional context**
BTW, isn't tags the better term here than groups? From my experience, an item can have multiple tags attached, but can only be in one group at the same time.
|
closed
|
2024-02-10T08:21:00Z
|
2024-03-10T10:10:57Z
|
https://github.com/dgtlmoon/changedetection.io/issues/2174
|
[
"enhancement"
] |
plangin
| 3
|
deezer/spleeter
|
deep-learning
| 186
|
por favor su ayuda me sale esto
|
|
closed
|
2019-12-16T22:48:17Z
|
2019-12-18T14:18:10Z
|
https://github.com/deezer/spleeter/issues/186
|
[
"bug",
"invalid"
] |
excel77
| 1
|
predict-idlab/plotly-resampler
|
data-visualization
| 60
|
`FigureResampler` replace not working as it should when using a `go.Figure`
|
|
closed
|
2022-05-16T07:44:20Z
|
2022-05-16T15:34:17Z
|
https://github.com/predict-idlab/plotly-resampler/issues/60
|
[
"bug"
] |
jonasvdd
| 1
|
robotframework/robotframework
|
automation
| 4,803
|
Async support to dynamic and hybrid library APIs
|
For our Lib we are using framework that is async and we have to await for results.
our lib works similar to Remote Lib to get the keywords and arguments but communication is done using asyncio.
with RF 6.1 run_keyword can be made async but functions for getting keywords/arguments/etc. can't.
as a workaround I'm getting context with EXECUTION_CONTEXTS.current and run async operations with context.asynchronous.run_until_complete().
this works but if the implementation of EXECUTION_CONTEXTS changes the library will break.
would it be possible to add async to get_* functions in Dynamic & Hybrid Lib interfaces?
|
closed
|
2023-06-22T09:26:53Z
|
2023-11-27T12:28:17Z
|
https://github.com/robotframework/robotframework/issues/4803
|
[
"enhancement",
"priority: high",
"alpha 2",
"acknowledge"
] |
WisniewskiP
| 9
|
autogluon/autogluon
|
scikit-learn
| 4,900
|
[tabular] Add `num_cpus`, `num_gpus` to `predictor.predict`
|
Related: #4871
We should add ways for user to control num_cpus and num_gpus during model inference.
This also ties into adding parallel inference support.
|
open
|
2025-02-17T21:05:37Z
|
2025-02-17T21:05:37Z
|
https://github.com/autogluon/autogluon/issues/4900
|
[
"enhancement",
"module: tabular"
] |
Innixma
| 0
|
plotly/dash
|
data-visualization
| 2,765
|
When moving the cursor, it will sometimes get stuck
|
**Describe your context**
Please provide us your environment, so we can easily reproduce the issue.¨
16 core 32 Thread dual CPU server, view selected to show all cores
Running in Docker on Ubuntu Server
- if frontend related, tell us your Browser, Version and OS
- OS: Windows
- Browser Chrome
- Version 123.0.6308.0
**Describe the bug**
When moving the cursor around on the preview, especially on machines with many cores/threads the cursor will sometimes get stuck.
Video Attached
**Expected behavior**
Graph cursor should follow the mouse and not stay in a previous position
**Screenshots**
If applicable, add screenshots or screen recording to help explain your problem.
https://github.com/plotly/dash/assets/46653946/3576cae4-67ba-4d46-9b21-7af5febac7b3
|
closed
|
2024-02-18T19:45:58Z
|
2024-05-31T20:09:58Z
|
https://github.com/plotly/dash/issues/2765
|
[
"bug",
"sev-2"
] |
Spillebulle
| 1
|
deeppavlov/DeepPavlov
|
nlp
| 1,008
|
Readme for /examples
|
Please add readme with short description of provided examples in
https://github.com/deepmipt/DeepPavlov/tree/master/examples
Please also add to the readme links to other resources to learn DeepPavlov -
https://github.com/deepmipt/dp_tutorials
https://github.com/deepmipt/dp_notebooks
|
closed
|
2019-09-21T10:24:24Z
|
2019-09-26T13:54:55Z
|
https://github.com/deeppavlov/DeepPavlov/issues/1008
|
[] |
DeepPavlovAdmin
| 1
|
vitalik/django-ninja
|
rest-api
| 818
|
How to create Generic Schema for openapi?
|
### Discussed in https://github.com/vitalik/django-ninja/discussions/817
<div type='discussions-op-text'>
<sup>Originally posted by **suuperhu** August 8, 2023</sup>
**I have the following piece of code, but there is a problem with openapi page, how should I solve it?**
_Code environment :
ubuntu 20.04
python 3.10.11
django 3.2.15
django-ninja 0.22.2
pydantic 1.10.12_
```python
from typing import TypeVar, Generic, List
from django.http import HttpRequest
from ninja import Router, Query, Schema
from pydantic import Field
T1 = TypeVar('T1')
T2 = TypeVar('T2')
router = Router()
class Params(Schema):
name: str
age: int = Field(0, ge=0, le=120)
class Response(Schema, Generic[T1, T2]):
code: int
data: T1
message: T2
class Data(Schema):
a: int
b: List[str]
@router.get('demo/', response=Response[Data, str])
def list_level(request: HttpRequest, params: Params = Query(...)):
return {
'code': 200,
'data': {'a': 1, 'b': ['a', 'b', 'c']},
'message': 'good'
}
```
The openapi page:
So,How should I make the openapi page's 200 response look like this:
```json
{
"code": 0,
"data": {
"a": 0,
"b": [
"string",
"string",
"..."
]
},
"message": "string"
}
```
</div>
|
closed
|
2023-08-08T05:04:16Z
|
2023-08-08T06:46:52Z
|
https://github.com/vitalik/django-ninja/issues/818
|
[] |
hushoujier
| 1
|
huggingface/datasets
|
nlp
| 7,041
|
`sort` after `filter` unreasonably slow
|
### Describe the bug
as the tittle says ...
### Steps to reproduce the bug
`sort` seems to be normal.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
but `sort` after `filter` is extremely slow.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
ds = ds.filter(lambda x:x > 100, input_columns="k")
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
### Expected behavior
Is this a bug, or is it a misuse of the `sort` function?
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-3.10.0-1127.19.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2023.10.0
|
open
|
2024-07-12T03:29:27Z
|
2024-07-22T13:55:17Z
|
https://github.com/huggingface/datasets/issues/7041
|
[] |
Tobin-rgb
| 1
|
piskvorky/gensim
|
machine-learning
| 2,873
|
Further focus/slim keyedvectors.py module
|
Pre-#2698, `keyedvectors.py` was 2500+ lines, including functionality over-specific to other models, & redundant classes. Post-#2698, with some added generic functionality, it's still over 1800 lines.
It should shed some other grab-bag utility functions that have accumulated, & don't logically fit inside the `KeyedVectors` class.
In particular, the evaluation (analogies, word_ranks) helpers could move to their own module that takes a KV instance as an argument. (If other more-sophisticated evaluations can be contributed, as would be welcome, they should also live alongside those, rather than bloating `KeyedVectors`.)
The `get_keras_embedding` method, as its utilit is narrow to very specific uses, and is conditional on a not-necessarily install package, could go elsewhere too – either a kera-focused utilities module, or even just documentation/example code about how to convert to/from keras from `KeyedVectors.
Some of the more advanced word-vector-**using** calculations, like 'Word Mover's Distance' or 'Soft Cosine SImilarity', could move to method-specific modules that are then better documented/self-contained/optimized, without bloating the generic 'set of vectors' module. (They might be more discoverable, there, as well.)
And finally, some of the existing calculations could be unified/streamlined (especially the two variants of `most_similar()`, and some of the steps shared by multiple operations). My hope would be the module is eventually <1000 lines.
|
open
|
2020-07-06T20:00:39Z
|
2021-03-09T07:59:52Z
|
https://github.com/piskvorky/gensim/issues/2873
|
[] |
gojomo
| 8
|
3b1b/manim
|
python
| 1,442
|
Error installing manim and running a test program
|
### Describe the error
<!-- A clear and concise description of what you want to make. -->
I installed ffmpeg from the APT package manager and PyOpenGL from pip3 in a virtual environment. Then, when I tried to install manim in the same virtual environment it gave the following error
However, in the end, it said that it successfully installed manim and its dependencies.
Then, I tried to run a simple program to see if it works:
But, it just keeps running with no errors (like it is frozen).
### Code and Error
**Code**:
<!-- The code you run -->
```
class SquareImage(Scene):
def construct(self):
square = Square()
self.add(square)
```
```
manimgl manim_test.py SquareImage -s
```
**Error**:
<!-- The error traceback you get when run your code -->
```
ERROR: Command errored out with exit status 1:
command: /home/karan/manim/venv/bin/python /home/karan/manim/venv/lib/python3.8/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /tmp/tmp8troxa62
cwd: /tmp/pip-install-vtkpor5m/manimpango_d759c30457a744afbb343d792b24d300
Complete output (33 lines):
Package pangocairo was not found in the pkg-config search path.
Perhaps you should add the directory containing `pangocairo.pc'
to the PKG_CONFIG_PATH environment variable
No package 'pangocairo' found
Traceback (most recent call last):
File "setup.py", line 124, in check_min_version
check_call(command, stdout=subprocess.DEVNULL)
File "/usr/lib/python3.8/subprocess.py", line 364, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['pkg-config', '--print-errors', '--atleast-version', '1.30.0', 'pangocairo']' returned non-zero exit status 1.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/karan/manim/venv/lib/python3.8/site-packages/pip/_vendor/pep517/_in_process.py", line 280, in <module>
main()
File "/home/karan/manim/venv/lib/python3.8/site-packages/pip/_vendor/pep517/_in_process.py", line 263, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
File "/home/karan/manim/venv/lib/python3.8/site-packages/pip/_vendor/pep517/_in_process.py", line 114, in get_requires_for_build_wheel
return hook(config_settings)
File "/tmp/pip-build-env-vytjtzxg/overlay/lib/python3.8/site-packages/setuptools/build_meta.py", line 149, in get_requires_for_build_wheel
return self._get_build_requires(
File "/tmp/pip-build-env-vytjtzxg/overlay/lib/python3.8/site-packages/setuptools/build_meta.py", line 130, in _get_build_requires
self.run_setup()
File "/tmp/pip-build-env-vytjtzxg/overlay/lib/python3.8/site-packages/setuptools/build_meta.py", line 253, in run_setup
super(_BuildMetaLegacyBackend,
File "/tmp/pip-build-env-vytjtzxg/overlay/lib/python3.8/site-packages/setuptools/build_meta.py", line 145, in run_setup
exec(compile(code, __file__, 'exec'), locals())
File "setup.py", line 190, in <module>
_pkg_config.check_min_version(MINIMUM_PANGO_VERSION)
File "setup.py", line 127, in check_min_version
raise RequiredDependencyException(f"{self.name} >= {version} is required")
__main__.RequiredDependencyException: pangocairo >= 1.30.0 is required
----------------------------------------
```
### Environment
**OS System**: Linux Mint
**manim version**: master <!-- make sure you are using the latest version of master branch -->
**python version**: 3.8.5
|
open
|
2021-03-21T09:19:49Z
|
2022-06-13T16:52:39Z
|
https://github.com/3b1b/manim/issues/1442
|
[] |
kkin1995
| 6
|
tensorlayer/TensorLayer
|
tensorflow
| 680
|
Failed: TensorLayer (bfffb588)
|
*Sent by Read the Docs (readthedocs@readthedocs.org). Created by [fire](https://fire.fundersclub.com/).*
---
| TensorLayer build #7291120
---
| 
---
| Build Failed for TensorLayer (latest)
---
You can find out more about this failure here:
[TensorLayer build #7291120](https://readthedocs.org/projects/tensorlayer/builds/7291120/) \- failed
If you have questions, a good place to start is the FAQ:
<https://docs.readthedocs.io/en/latest/faq.html>
You can unsubscribe from these emails in your [Notification Settings](https://readthedocs.org/dashboard/tensorlayer/notifications/)
Keep documenting,
Read the Docs
| Read the Docs
<https://readthedocs.org>
---
|
closed
|
2018-06-04T14:24:24Z
|
2018-06-04T14:56:34Z
|
https://github.com/tensorlayer/TensorLayer/issues/680
|
[] |
fire-bot
| 0
|
RayVentura/ShortGPT
|
automation
| 70
|
🐛 [Bug]:
|
### What happened?
Step 9 _prepareBackgroundAssets
{'voiceover_audio_url': '.editing_assets/reddit_shorts_assets/b32f7d0a87944aa99dd1b826/audio_voice.wav', 'video_duration': None, 'background_video_url': 'https://rr3---sn-npoe7nez.googlevideo.com/videoplayback?expire=1690975098&ei=GufJZN7mJaei9fwPveCisA8&ip=194.156.163.133&id=o-AC0PipqKARrGGjkNooYfuCxs-noXKlpCEhSXMUGnVmtJ&itag=335&source=youtube&requiressl=yes&mh=ww&mm=31%2C26&mn=sn-npoe7nez%2Csn-ntqe6n76&ms=au%2Conr&mv=m&mvi=3&pl=24&initcwndbps=70318750&vprv=1&svpuc=1&mime=video%2Fwebm&gir=yes&clen=463772648&dur=545.611&lmt=1629834060752995&mt=1690953169&fvip=5&keepalive=yes&fexp=24007246%2C51000011%2C51000023&c=IOS&txp=5511222&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cvprv%2Csvpuc%2Cmime%2Cgir%2Cclen%2Cdur%2Clmt&sig=AOq0QJ8wRgIhANUzWaBjEcKzgNA2yiHX_42W4mkpVTLPv_64hiw9laDRAiEA2VYtKYeThTpaZIHsqSbBU8hsPz9Rkpwyb5_VXocQfDE%3D&lsparams=mh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Cinitcwndbps&lsig=AG3C_xAwRgIhAKQyMfTyaa5QifrExwyXHxaomTdA5wP4q5aICytBfRuFAiEAn7pYfppbDvlnRTNZQfRstO3gD894BpmVRo1fPKp8am0%3D', 'music_url': 'public/Music dj quads.wav'}
Error File "C:\Users\EDY\Desktop\c\shortgpt\gui\short_automation_ui.py", line 107, in create_short
for step_num, step_info in shortEngine.makeContent():
File "C:\Users\EDY\Desktop\c\shortgpt\shortGPT\engine\abstract_content_engine.py", line 70, in makeContent
self.stepDict[currentStep]()
File "C:\Users\EDY\Desktop\c\shortgpt\shortGPT\engine\content_short_engine.py", line 97, in _prepareBackgroundAssets
self.verifyParameters(
File "C:\Users\EDY\Desktop\c\shortgpt\shortGPT\engine\abstract_content_engine.py", line 55, in verifyParameters
raise Exception(f"Parameter :{key} is null")
### What type of browser are you seeing the problem on?
Chrome
### What type of Operating System are you seeing the problem on?
Windows
### Python Version
3.10
### Application Version
v0.0.1
### Expected Behavior
Step 9 _prepareBackgroundAssets
{'voiceover_audio_url': '.editing_assets/reddit_shorts_assets/b32f7d0a87944aa99dd1b826/audio_voice.wav', 'video_duration': None, 'background_video_url': 'https://rr3---sn-npoe7nez.googlevideo.com/videoplayback?expire=1690975098&ei=GufJZN7mJaei9fwPveCisA8&ip=194.156.163.133&id=o-AC0PipqKARrGGjkNooYfuCxs-noXKlpCEhSXMUGnVmtJ&itag=335&source=youtube&requiressl=yes&mh=ww&mm=31%2C26&mn=sn-npoe7nez%2Csn-ntqe6n76&ms=au%2Conr&mv=m&mvi=3&pl=24&initcwndbps=70318750&vprv=1&svpuc=1&mime=video%2Fwebm&gir=yes&clen=463772648&dur=545.611&lmt=1629834060752995&mt=1690953169&fvip=5&keepalive=yes&fexp=24007246%2C51000011%2C51000023&c=IOS&txp=5511222&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cvprv%2Csvpuc%2Cmime%2Cgir%2Cclen%2Cdur%2Clmt&sig=AOq0QJ8wRgIhANUzWaBjEcKzgNA2yiHX_42W4mkpVTLPv_64hiw9laDRAiEA2VYtKYeThTpaZIHsqSbBU8hsPz9Rkpwyb5_VXocQfDE%3D&lsparams=mh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Cinitcwndbps&lsig=AG3C_xAwRgIhAKQyMfTyaa5QifrExwyXHxaomTdA5wP4q5aICytBfRuFAiEAn7pYfppbDvlnRTNZQfRstO3gD894BpmVRo1fPKp8am0%3D', 'music_url': 'public/Music dj quads.wav'}
Error File "C:\Users\EDY\Desktop\c\shortgpt\gui\short_automation_ui.py", line 107, in create_short
for step_num, step_info in shortEngine.makeContent():
File "C:\Users\EDY\Desktop\c\shortgpt\shortGPT\engine\abstract_content_engine.py", line 70, in makeContent
self.stepDict[currentStep]()
File "C:\Users\EDY\Desktop\c\shortgpt\shortGPT\engine\content_short_engine.py", line 97, in _prepareBackgroundAssets
self.verifyParameters(
File "C:\Users\EDY\Desktop\c\shortgpt\shortGPT\engine\abstract_content_engine.py", line 55, in verifyParameters
raise Exception(f"Parameter :{key} is null")
### Error Message
_No response_
### Code to produce this issue.
_No response_
### Screenshots/Assets/Relevant links
_No response_
|
open
|
2023-08-02T05:37:02Z
|
2023-08-02T05:37:02Z
|
https://github.com/RayVentura/ShortGPT/issues/70
|
[
"bug"
] |
rpp-Little-pig
| 0
|
raphaelvallat/pingouin
|
pandas
| 209
|
pairwise_nonparametric()
|
Hi, I was looking for non-parametric pairwise tests and only found the parameter `parametric=False` of the `pairwise_ttests()` after some time from the flowcharts. This seems confusing to me. I'd suggest adding function `pairwise_nonparametric()` for this purpose mainly because of discoverability and clarity. Also pls note that the documentation to `pairwise_ttests()` just says: "Pairwise T-tests.". I'd be happy to prepare PR if that helps.
|
closed
|
2021-11-10T10:45:33Z
|
2022-03-12T23:51:51Z
|
https://github.com/raphaelvallat/pingouin/issues/209
|
[
"docs/testing :book:",
"IMPORTANT❗"
] |
michalkahle
| 5
|
ymcui/Chinese-BERT-wwm
|
nlp
| 146
|
RoBERTa-wwm-ext-large能不能把mlm权重补充上？
|
现在很多研究都表明MLM其实也是一个相当有用的语言模型，并不是纯粹的只有预训练的左右了，所以能不能麻烦一下把MLM的权重补上？
而且我最不能理解的就是，要是扔掉MLM的权重也就算了，为啥还要随机初始化一个放在那里，这不是容易误导人么？
|
closed
|
2020-09-21T06:22:18Z
|
2020-09-21T07:01:24Z
|
https://github.com/ymcui/Chinese-BERT-wwm/issues/146
|
[
"wontfix"
] |
bojone
| 1
|
newpanjing/simpleui
|
django
| 43
|
左侧栏收起后无全屏缩小
|
**bug描述**
简单的描述下遇到的bug：
**重现步骤**
1.
2.
3.
**环境**
1.操作系统：
2.python版本：
3.django版本：
4.simpleui版本：
**其他描述**
|
closed
|
2019-05-21T01:38:45Z
|
2019-05-21T02:30:57Z
|
https://github.com/newpanjing/simpleui/issues/43
|
[
"bug"
] |
Qianzujin
| 1
|
tqdm/tqdm
|
pandas
| 749
|
Logging from separate threads pushes tqdm bar up
|
Versions I'm using:
```
tqdm = 4.31.1
python = 3.7.3
OS: Ubuntu 19.04 (Linux: 5.0)
```
If we're processing an list of elements with a multiprocessing function, say applying `f` to the list with `pool.imap_unordered`, and log a message from within `f`, then the progress bar will be pushed up for each message logged.
An example script that produces this behavior:
```python
import logging
import multiprocessing
import time
import tqdm
# Set up tqdm logging handler
class TqdmLoggingHandler (logging.StreamHandler):
def __init__(self, stream=None):
super().__init__(stream)
def emit(self, record):
try:
msg = self.format(record)
tqdm.tqdm.write(msg)
self.flush()
except (KeyboardInterrupt, SystemExit):
raise
except:
self.handleError(record)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.handlers = []
logger.addHandler(TqdmLoggingHandler())
def func_mult(x):
logger.info('processing %s', x) # <- log from other threads
time.sleep(1)
return x
array = list(range(6))
with multiprocessing.Pool(2) as pool:
for xx in tqdm.tqdm(
pool.imap_unordered(func_mult, array),
total=len(array)):
logger.info('getting result: %s', xx) # <- log from current thread
```
This is the output I get:
Note that the `getting result` messages are printed correctly (above the bar), but the `processing` messages issued from other threads don't respect the bar and are added to the bottom of the screen instead.
Is this behavior expected or should log messages issued from other threats respect tqdm?
|
open
|
2019-05-23T12:51:57Z
|
2019-06-17T16:43:02Z
|
https://github.com/tqdm/tqdm/issues/749
|
[
"p3-enhancement 🔥",
"help wanted 🙏",
"need-feedback 📢",
"p2-bug-warning ⚠",
"synchronisation ⇶"
] |
tupini07
| 1
|
huggingface/transformers
|
nlp
| 36,217
|
Albert does not use SDPA's Flash Attention since attention mask is always created
|
### System Info
NA
### Who can help?
@ArthurZucker
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
https://github.com/huggingface/transformers/blob/dd16acb8a3e93b643aa374c9fb80749f5235c1a6/src/transformers/models/albert/modeling_albert.py#L772-L773
Since attention mask is always used by `AlbertModel`, SDPA will dispatch memory-efficient kernel instead of flash attention. The inner classes (`AlbertTransformer`, `AlbertLayerGroup`, ...) seem to handle attention_mask=None correctly, so it's only the outermost class is problematic.
### Expected behavior
Flash attention should be used when attention_mask=None
|
closed
|
2025-02-15T15:40:58Z
|
2025-02-16T02:36:30Z
|
https://github.com/huggingface/transformers/issues/36217
|
[
"bug"
] |
gau-nernst
| 1
|
plotly/dash-component-boilerplate
|
dash
| 62
|
capitalize component name
|
hi, while creating a new project it asks for component_name, if i give a name that starts with lowercase, it creates react component with lowercase, which is wrong from react component standards. React component must starts with uppercase.
|
open
|
2019-03-13T01:14:51Z
|
2019-03-13T01:14:51Z
|
https://github.com/plotly/dash-component-boilerplate/issues/62
|
[] |
rajeevmaster
| 0
|
igorbenav/FastAPI-boilerplate
|
sqlalchemy
| 138
|
DB session from a worker function?
|
Hey, Im trying for 2 days now to make this work,
How do I pass the session from an endpoint to the background worker function?
My idea is to insert a record in the db from my endpoint then process it from the background function.
*Edit*
I made it work by creating another function with contextmanager
```
@asynccontextmanager
async def async_get_context() -> AsyncSession:
async_session = local_session
async with async_session() as db:
yield db
```
|
closed
|
2024-05-21T13:16:39Z
|
2024-06-02T14:05:30Z
|
https://github.com/igorbenav/FastAPI-boilerplate/issues/138
|
[
"documentation",
"enhancement"
] |
kaStoyanov
| 4
|
ultralytics/yolov5
|
machine-learning
| 13,519
|
Detection with Torch Hub Failing
|
### 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
I have ML project on Python 3.7.8, it was working fine, but recently I am getting error when loading model with torch.hub.load
File "C:\Users\Strange/.cache\torch\hub\ultralytics_yolov5_master\models\common.py", line 39, in <module>
from utils.dataloaders import exif_transpose, letterbox
File "C:\Users\Strange/.cache\torch\hub\ultralytics_yolov5_master\utils\dataloaders.py", line 776
if mosaic := self.mosaic and random.random() < hyp["mosaic"]:
I know yolov5 require python > 3.8, but all current dependencies in my project is of 3.7.8, please suggest workaround to work with it
### Additional
_No response_
|
closed
|
2025-02-25T04:56:37Z
|
2025-02-27T23:58:53Z
|
https://github.com/ultralytics/yolov5/issues/13519
|
[
"question",
"dependencies",
"detect"
] |
llavkush
| 4
|
WeblateOrg/weblate
|
django
| 14,232
|
Expose 'add comment' API endpoint
|
### Describe the problem
We're migrating from another platform to Weblate, and would like to take along our comments. It's currently not possible to do this easily/programmatically. Consequently, we're using valuable contributor feedback on our strings.
### Describe the solution you would like
We'd like to have an API endpoint that allows to post a comment on a given string. Matching:
* If we provide the email address of the poster, and it exists as user on Weblate, the comment should be associated with that user.
* We'd like to provide the string key to identify the relevant string on Weblate.
### Describe alternatives you have considered
I wondered whether to request the following, but concluded that the benefits don't outweigh the (technical) challenges: If we provide a timestamp (in the past), it is maintained so that when viewing the comment in context there is additional metadata to consider whether the comment is still relevant.
### Screenshots
_No response_
### Additional context
_No response_
|
open
|
2025-03-16T11:51:08Z
|
2025-03-17T18:52:56Z
|
https://github.com/WeblateOrg/weblate/issues/14232
|
[
"enhancement",
"undecided",
"Area: API"
] |
keunes
| 2
|
graphql-python/graphene-django
|
graphql
| 1,116
|
graphene-neo4j
|
I will be glad if you update graphene-neo4j with Django 3.
|
closed
|
2021-02-15T19:17:27Z
|
2021-02-16T06:12:03Z
|
https://github.com/graphql-python/graphene-django/issues/1116
|
[
"🐛bug"
] |
MajidHeydari
| 1
|
3b1b/manim
|
python
| 2,097
|
Example Gallery Bug when Rendering High Quality
|
### Describe the bug
For the Gallery Example with https://docs.manim.community/en/stable/examples.html#pointwithtrace
**Code**:
```py
from manim import *
class PointWithTrace(Scene):
def construct(self):
path = VMobject()
dot = Dot()
path.set_points_as_corners([dot.get_center(), dot.get_center()])
def update_path(path):
previous_path = path.copy()
previous_path.add_points_as_corners([dot.get_center()])
path.become(previous_path)
path.add_updater(update_path)
self.add(path, dot)
self.play(Rotating(dot, radians=PI, about_point=RIGHT, run_time=2))
self.wait()
self.play(dot.animate.shift(UP))
self.play(dot.animate.shift(LEFT))
self.wait()
```
**Wrong display or Error traceback**:
### Additional context
The circular arc is drawn correctly, but once it is done, it deletes the whole arc and continuous as if the path started from the origin completing the drawing. This doesn't happen with low quality, but happens when you use high quality.
|
open
|
2024-01-29T04:51:34Z
|
2024-01-29T04:53:52Z
|
https://github.com/3b1b/manim/issues/2097
|
[
"bug"
] |
wmstack
| 1
|
ivy-llc/ivy
|
pytorch
| 28,339
|
Fix Frontend Failing Test: paddle - math.tensorflow.math.argmin
|
To-do List: https://github.com/unifyai/ivy/issues/27500
|
closed
|
2024-02-20T08:12:51Z
|
2024-02-20T10:21:20Z
|
https://github.com/ivy-llc/ivy/issues/28339
|
[
"Sub Task"
] |
Sai-Suraj-27
| 0
|
gevent/gevent
|
asyncio
| 1,962
|
ImportError: cannot import name 'match_hostname' from 'ssl' (/usr/lib/python3.12/ssl.py)
|
* gevent version: 22.10.2 - fedora package
* Python version: 3.12.0b3
* Operating System: Fedora rawhide
### Description:
While trying to build the sphinx documentation for the x2go python module:
```
+ make -C docs SPHINXBUILD=/usr/bin/sphinx-build-3 html
make: Entering directory '/builddir/build/BUILD/python-x2go-0.6.1.3/docs'
/usr/bin/sphinx-build-3 -b html -d build/doctrees source build/html
Running Sphinx v6.1.3
Configuration error:
There is a programmable error in your configuration file:
Traceback (most recent call last):
File "/usr/lib/python3.12/site-packages/sphinx/config.py", line 351, in eval_config_file
exec(code, namespace) # NoQA: S102
^^^^^^^^^^^^^^^^^^^^^
File "/builddir/build/BUILD/python-x2go-0.6.1.3/docs/source/conf.py", line 22, in <module>
import x2go
File "/builddir/build/BUILD/python-x2go-0.6.1.3/x2go/__init__.py", line 42, in <module>
monkey.patch_all()
File "/usr/lib/python3.12/site-packages/gevent/monkey.py", line 1279, in patch_all
patch_ssl(_warnings=_warnings, _first_time=first_time)
File "/usr/lib/python3.12/site-packages/gevent/monkey.py", line 200, in ignores
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/site-packages/gevent/monkey.py", line 1044, in patch_ssl
gevent_mod, _ = _patch_module('ssl', _warnings=_warnings)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/site-packages/gevent/monkey.py", line 462, in _patch_module
gevent_module, target_module, target_module_name = _check_availability(name)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/site-packages/gevent/monkey.py", line 448, in _check_availability
gevent_module = getattr(__import__('gevent.' + name), name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/site-packages/gevent/ssl.py", line 32, in <module>
from gevent import _ssl3 as _source # pragma: no cover
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/site-packages/gevent/_ssl3.py", line 53, in <module>
from ssl import match_hostname
ImportError: cannot import name 'match_hostname' from 'ssl' (/usr/lib/python3.12/ssl.py)
```
|
closed
|
2023-06-26T23:33:53Z
|
2023-07-10T15:30:56Z
|
https://github.com/gevent/gevent/issues/1962
|
[] |
opoplawski
| 1
|
sinaptik-ai/pandas-ai
|
data-science
| 686
|
TypeError: 'NoneType' object is not callable . Retrying Unfortunately, I was not able to answer your question, because of the following error: No code found in the response
|
#### Error:-/usr/local/lib/python3.10/dist-packages/pandasai/llm/starcoder.py:28: UserWarning: Starcoder is deprecated and will be removed in a future release.
Please use langchain.llms.HuggingFaceHub instead, although please be
aware that it may perform poorly.
warnings.warn(
/usr/local/lib/python3.10/dist-packages/pandasai/llm/falcon.py:29: UserWarning: Falcon is deprecated and will be removed in a future release.
Please use langchain.llms.HuggingFaceHub instead, although please be
aware that it may perform poorly.
warnings.warn(
WARNING:pandasai.helpers.logger:Error of executing code
WARNING:pandasai.helpers.logger:Failed to execute code with a correction framework [retry number: 1]
ERROR:pandasai.helpers.logger:Failed with error: Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/pandasai/smart_datalake/__init__.py", line 394, in chat
result = self._code_manager.execute_code(
File "/usr/local/lib/python3.10/dist-packages/pandasai/helpers/code_manager.py", line 276, in execute_code
return analyze_data(self._get_originals(dfs))
TypeError: 'NoneType' object is not callable
. Retrying
Unfortunately, I was not able to answer your question, because of the following error:
No code found in the response
Unfortunately, I was not able to answer your question, because of the following error:
#### Code:--
```python
df = pd.DataFrame({
"country": [
"United States",
"United Kingdom",
"France",
"Germany",
"Italy",
"Spain",
"Canada",
"Australia",
"Japan",
"China",
],
"gdp": [
19294482071552,
2891615567872,
2411255037952,
3435817336832,
1745433788416,
1181205135360,
1607402389504,
1490967855104,
4380756541440,
14631844184064,
],
"happiness_index": [6.94, 7.16, 6.66, 7.07, 6.38, 6.4, 7.23, 7.22, 5.87, 5.12],
})
from pandasai import SmartDataframe
from pandasai.llm import Starcoder, Falcon
starcoder_llm = Starcoder(api_token=token)
falcon_llm = Falcon(api_token=token)
df1 = SmartDataframe(df, config={"llm": starcoder_llm})
df2 = SmartDataframe(df, config={"llm": falcon_llm})
print(df1.chat("Which country has the highest GDP?"))
print(df2.chat("Which one is the unhappiest country?"))```
|
closed
|
2023-10-25T12:07:05Z
|
2024-06-01T00:20:12Z
|
https://github.com/sinaptik-ai/pandas-ai/issues/686
|
[] |
jaysinhpadhiyar
| 1
|
matplotlib/matplotlib
|
data-science
| 29,489
|
[Bug]: Systematic test failures with ubuntu-22.04-arm pipeline
|
### Bug summary
```
__________________________ test_errorbar_limits[svg] ___________________________
[gw2] linux -- Python 3.12.8 /opt/hostedtoolcache/Python/3.12.8/arm64/bin/python
args = ()
kwds = {'extension': 'svg', 'request': <FixtureRequest for <Function test_errorbar_limits[svg]>>}
@wraps(func)
def inner(*args, **kwds):
with self._recreate_cm():
> return func(*args, **kwds)
E matplotlib.testing.exceptions.ImageComparisonFailure: images not close (RMS 0.002):
E result_images/test_axes/errorbar_limits_svg.png
E result_images/test_axes/errorbar_limits-expected_svg.png
E result_images/test_axes/errorbar_limits_svg-failed-diff.png
/opt/hostedtoolcache/Python/3.12.8/arm64/lib/python3.12/contextlib.py:81: ImageComparisonFailure
```
and
```
_____________________________ test_get_font_names ______________________________
[gw3] linux -- Python 3.12.8 /opt/hostedtoolcache/Python/3.12.8/arm64/bin/python
@pytest.mark.skipif(sys.platform == 'win32', reason='Linux or OS only')
def test_get_font_names():
paths_mpl = [cbook._get_data_path('fonts', subdir) for subdir in ['ttf']]
fonts_mpl = findSystemFonts(paths_mpl, fontext='ttf')
fonts_system = findSystemFonts(fontext='ttf')
ttf_fonts = []
for path in fonts_mpl + fonts_system:
try:
font = ft2font.FT2Font(path)
prop = ttfFontProperty(font)
ttf_fonts.append(prop.name)
except Exception:
pass
available_fonts = sorted(list(set(ttf_fonts)))
mpl_font_names = sorted(fontManager.get_font_names())
> assert set(available_fonts) == set(mpl_font_names)
E AssertionError: assert {'C059', 'D05...ns Mono', ...} == {'C059', 'D05...ns Mono', ...}
E
E Extra items in the right set:
E 'Liberation Serif'
E 'Liberation Sans Narrow'
E 'Liberation Sans'
E 'Liberation Mono'
[...]
```
|
closed
|
2025-01-20T11:12:23Z
|
2025-01-21T23:05:49Z
|
https://github.com/matplotlib/matplotlib/issues/29489
|
[
"CI: testing"
] |
timhoffm
| 3
|
Gerapy/Gerapy
|
django
| 74
|
在用admin进行user和group添加和修改时会出现TypeError
|
前端显示：
后台报错：
主要原因是，在/server/core/TransformMiddleware中对所有request格式进行了转换，所以django自带处理方法中会出现类型错误。我目前的解决办法是加入判断，屏蔽掉来自admin的请求，初步测试可行。
> class TransformMiddleware(MiddlewareMixin):
def __call__(self, request):
"""
Change request body to str type
:param request:
:return:
"""
if isinstance(request.body, bytes):
if not 'admin' in request.path:
data = getattr(request, '_body', request.body)
request._body = data.decode('utf-8')
response = self.get_response(request)
return response
不过这个解决方案只针对了admin，不知道会不会有别的类似的问题。
|
open
|
2018-07-26T04:37:27Z
|
2018-07-26T04:37:27Z
|
https://github.com/Gerapy/Gerapy/issues/74
|
[] |
Fesbruk
| 0
|
MilesCranmer/PySR
|
scikit-learn
| 2
|
[Question] Pure Julia package
|
Hi, Is there a plan to have pure Julia API and expose it Julia package?
|
closed
|
2020-09-24T22:42:20Z
|
2021-01-18T09:24:06Z
|
https://github.com/MilesCranmer/PySR/issues/2
|
[
"implemented"
] |
sheevy
| 13
|
hbldh/bleak
|
asyncio
| 1,419
|
Qt timer can't stop bleak's notify.
|
* bleak version:0.20.0
* Python version: 3.11.4
* Operating System: windows 11
### Description
I integrated Bleak into a Qt application. I found that when using Qtimer to close the notification, there will be a problem and notify cannot be stopped.
### What I Did
My code
```python
class MainWindow(QMainWindow):
# device discovery signal
devices_discovery_signal = Signal()
devices_discovery_end_signal = Signal()
connect_signal = Signal()
disconnect_signal = Signal()
start_notify_signal = Signal()
start_notify_end_signal = Signal()
stop_notify_signal = Signal()
stop_notify_end_signal = Signal()
def __init__(self):
super().__init__()
self.devices = []
self.devices_addr = []
self.ui = Ui_VTCSensorEvaluation()
self.ui.setupUi(self)
self.ui.pushButton_3.clicked.connect(self.async_start_device_discovery)
self.ui.pushButton.clicked.connect(self.connect_device_click)
self.ui.pushButton_2.clicked.connect(self.start_notify_click)
self.ui.pushButton_2.setEnabled(False)
self.myclass = myclass()
self.update_timer = QTimer(self)
self.ui.pushButton_5.clicked.connect(self.calculate_data_bias)
self.bias_num = 0
self.call_num = 0
self.Ble = Ble_inst(self.update_device_list ,self.connect_status_update,self.receive_data_cb)
self.ui.ini_ui()
def update_data_timer_func(self):
datas = self.myclass.ret_cali_data()
if(datas == None):
return
self.ui.sensor_message_update("acc = {},{},{}".format(round(datas[0],2),round(datas[1],2),round(datas[2],2)))
self.ui.sensor_message_update("mag = {},{},{}".format(round(datas[3],2),round(datas[4],2),round(datas[5],2)))
self.ui.sensor_message_update("gyro = {},{},{}".format(round(datas[6],2),round(datas[7],2),round(datas[8],2)))
self.ui.sensor_message_update("")
return
def close_notify(self):
self.start_notify_end_signal.emit()
self.stop_notify_signal.emit()
self.stop_notify_end_signal.emit()
return
def update_device_list(self,dev_list):
self.ui.common_message_update("search the device")
self.ui.comboBox.clear()
self.ui.comboBox.addItems(dev_list)
self.devices_discovery_end_signal.emit()
return
@Slot()
def async_start_device_discovery(self):
self.devices_discovery_signal.emit()
return
def connect_status_update(self,flag):
self.ui.pushButton.setEnabled(flag)
self.ui.pushButton_2.setEnabled(not(flag))
self.ui.pushButton_2.setText("Run Compass")
if(flag == False):
self.ui.common_message_update("Connect Successfully....")
else :
self.ui.common_message_update("Connect Failed.....")
return
def receive_data_cb(self,ax,ay,az,mx,my,mz,gx,gy,gz):
self.myclass.do_cali_data(ax,ay,az,mx,my,mz,gx,gy,gz)
# print("acc data:",round(ax,2),round(ay,2),round(az,2))
# print("mag data:",round(mx,2),round(my,2),round(mz,2))
# print("gyro data:",round(gx,2),round(gy,2),round(gz,2))
return
def connect_device_click(self):
self.ui.pushButton.setEnabled(False)
ind = self.ui.comboBox.currentIndex()
if(ind == 0):
return
self.Ble.set_con_index(ind)
self.ui.common_message_update("connect the device({})".format(self.ui.comboBox.currentText()))
self.connect_signal.emit()
return
def start_notify_click(self):
print("click Run")
if(self.ui.pushButton_2.text() == "Run Compass"):
print("Run select A")
self.ui.common_message_update("start the notification....")
self.stop_notify_end_signal.emit()
self.start_notify_signal.emit()
self.ui.pushButton_2.setText("Stop Compass")
self.update_timer.timeout.connect(self.update_data_timer_func)
self.update_timer.start(10)
# self.start_notify_end_signal.emit()
elif(self.ui.pushButton_2.text() == "Stop Compass"):
print("Run select B")
self.ui.common_message_update("stop the notification....")
self.close_notify()
self.ui.pushButton_2.setText("Run Compass")
self.update_timer.stop()
return
def calculate_data_bias(self):
tmp_txt = self.ui.plainTextEdit_30.toPlainText()
try:
self.bias_num = int(tmp_txt)
if(self.bias_num > 1024):
self.ui.common_message_update("The number is bigger than 1024.")
return
if(self.bias_num <= 0):
self.ui.common_message_update("The number is less than 1.")
return
except:
self.ui.common_message_update("Text cannot be converted to a number")
return
self.call_num = 0
self.myclass.init_bias_container()
self.start_notify_signal.emit()
self.update_timer.timeout.connect(self.close_notify)
self.update_timer.start(1000)
return
class Ble_inst(QObject):
def __init__(self, update_func ,connect_status_func,rec_func):
super().__init__()
self.devices_addr = []
self.devices = []
self.client = bleak.BleakClient("FF:FF:FF:FF:FF:FF")
self.list_upadte = update_func
self.con_status_func = connect_status_func
self.con_index = -1
self.recieve_data = rec_func
return
async def add_device(self):
devices = await bleak.BleakScanner.discover()
self.devices = [""]
self.devices_addr = [""]
for d in devices:
if(d.name != None):
self.devices.append(d.name)
self.devices_addr.append(d.address)
print(d.name)
self.list_upadte(self.devices)
def disconnect_cb(self,client):
self.con_status_func(True)
print("connect loss....")
return
def set_con_index(self,index):
self.con_index = index
return
async def connect_device_func(self):
self.client = bleak.BleakClient(self.devices_addr[self.con_index],disconnected_callback=self.disconnect_cb)
await self.client.connect()
if(self.client.is_connected):
self.con_status_func(False)
return
def receive_notify_cb(self,uuid,barray):
........
return
async def stop_notify(self):
print("stop notify....")
await self.client.stop_notify(target_char)
return
async def start_notify(self):
print("start notify.....")
await self.client.start_notify(target_char,self.receive_notify_cb)
return
```
### Logs
Error Log
```console
Task exception was never retrieved
future: <Task finished name='Task-38' coro=<Ble_inst.stop_notify() done, defined at d:\workstation\VTC_display\Ble_instance.py:71> exception=KeyError(15)>
Traceback (most recent call last):
File "d:\workstation\VTC_display\Ble_instance.py", line 73, in stop_notify
await self.client.stop_notify(target_char)
File "C:\Users\isly8\anaconda3\Lib\site-packages\bleak\__init__.py", line 852, in stop_notify
await self._backend.stop_notify(char_specifier)
File "C:\Users\isly8\anaconda3\Lib\site-packages\bleak\backends\winrt\client.py", line 1020, in stop_notify
event_handler_token = self._notification_callbacks.pop(characteristic.handle)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
KeyError: 15
Task exception was never retrieved
future: <Task finished name='Task-39' coro=<Ble_inst.stop_notify() done, defined at d:\workstation\VTC_display\Ble_instance.py:71> exception=KeyError(15)>
Traceback (most recent call last):
File "d:\workstation\VTC_display\Ble_instance.py", line 73, in stop_notify
await self.client.stop_notify(target_char)
File "C:\Users\isly8\anaconda3\Lib\site-packages\bleak\__init__.py", line 852, in stop_notify
await self._backend.stop_notify(char_specifier)
File "C:\Users\isly8\anaconda3\Lib\site-packages\bleak\backends\winrt\client.py", line 1020, in stop_notify
event_handler_token = self._notification_callbacks.pop(characteristic.handle)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
KeyError: 15
```
|
closed
|
2023-09-15T01:26:02Z
|
2023-09-15T01:48:48Z
|
https://github.com/hbldh/bleak/issues/1419
|
[] |
ChienHao-Hung
| 0
|
microsoft/nni
|
pytorch
| 5,019
|
where is scripts.compression_mnist_model
|
**Describe the issue**:
When I tried to run the demo from the doc here https://nni.readthedocs.io/en/stable/tutorials/quantization_speedup.html, I could not found `scripts.compression_mnist_model` in
`from scripts.compression_mnist_model import TorchModel, trainer, evaluator, device, test_trt`
**Environment**:
- NNI version: 2.8
- Training service (local|remote|pai|aml|etc):
- Client OS: linux
- Server OS (for remote mode only):
- Python version: 3.8
- PyTorch/TensorFlow version:
- Is conda/virtualenv/venv used?:
- Is running in Docker?:
**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?**:
|
closed
|
2022-07-25T23:37:38Z
|
2022-11-23T03:09:31Z
|
https://github.com/microsoft/nni/issues/5019
|
[
"user raised",
"documentation",
"support",
"ModelSpeedup",
"v2.9.1"
] |
james20141606
| 4
|
neuml/txtai
|
nlp
| 544
|
Add support for custom scoring instances
|
Add support for custom scoring instances.
See implementations in ANNFactory, DatabaseFactory and GraphFactory.
|
closed
|
2023-09-06T21:21:06Z
|
2023-09-06T21:25:06Z
|
https://github.com/neuml/txtai/issues/544
|
[] |
davidmezzetti
| 0
|
LAION-AI/Open-Assistant
|
python
| 3,630
|
Add Persian QA Dataset
|
After fine-tuning on [Farsi data](https://github.com/LAION-AI/Open-Assistant/pull/3629), I think adding QA Datasets like [this one](https://github.com/sajjjadayobi/PersianQA) can be helpful.
If the teams want to add support for Farsi, I will be glad to contribute and add this dataset in the standard format.
|
closed
|
2023-08-02T10:30:53Z
|
2023-08-03T19:24:31Z
|
https://github.com/LAION-AI/Open-Assistant/issues/3630
|
[] |
pourmand1376
| 0
|
adbar/trafilatura
|
web-scraping
| 712
|
setup: use `pyproject.toml` file
|
This is now standard for Python 3.8+ versions.
|
closed
|
2024-10-07T10:33:18Z
|
2024-10-10T11:12:05Z
|
https://github.com/adbar/trafilatura/issues/712
|
[
"maintenance"
] |
adbar
| 0
|
Ehco1996/django-sspanel
|
django
| 925
|
有一个请求不知道是什么 找不到，404
|
**问题的描述**
有一个请求不知道是什么 找不到，404
**项目的配置文件**
**如何复现**
**相关截图/log**
**其他信息**
|
closed
|
2024-03-06T08:39:24Z
|
2024-03-10T00:20:20Z
|
https://github.com/Ehco1996/django-sspanel/issues/925
|
[] |
wangxingsheng
| 1
|
tableau/server-client-python
|
rest-api
| 861
|
Extracting "Who has seen the view"?
|
Hi,
I looked through the documentation, maybe it's there but i couldn't find it.
Is there a way to extract the breakdown of "Who has seen this view" for each view?
Thanks in advance!
|
open
|
2021-07-12T08:23:19Z
|
2021-11-08T06:13:15Z
|
https://github.com/tableau/server-client-python/issues/861
|
[
"enhancement",
"Server-Side Enhancement"
] |
Zestsx
| 2
|
jumpserver/jumpserver
|
django
| 14,217
|
[Bug] 配置LDAP会自动还原
|
### 产品版本
v4.1.0
### 版本类型
- [X] 社区版
- [ ] 企业版
- [ ] 企业试用版
### 安装方式
- [ ] 在线安装 (一键命令安装)
- [ ] 离线包安装
- [ ] All-in-One
- [ ] 1Panel
- [X] Kubernetes
- [ ] 源码安装
### 环境信息
helm 包安装
### 🐛 缺陷描述
admin账号登陆后，配置ldap登陆。配置信息填写完毕、开启ldap功能，选择提交。测试链接正常，可以获取到用户数；测试ldap用户登陆提示Authentication failed (before login check failed): not enabled auth ldap。刷新页面再看即可发现ldap开关已被关闭，配置信息已被还原。
### 复现步骤
admin账号登陆后，配置ldap登陆。配置信息填写完毕、开启ldap功能，选择提交。测试链接正常，可以获取到用户数；测试ldap用户登陆提示Authentication failed (before login check failed): not enabled auth ldap。刷新页面再看即可发现ldap开关已被关闭，配置信息已被还原。
### 期望结果
能够正常保存ldap信息，不被还原。
### 补充信息
_No response_
### 尝试过的解决方案
_No response_
|
closed
|
2024-09-23T06:45:24Z
|
2024-09-26T10:01:03Z
|
https://github.com/jumpserver/jumpserver/issues/14217
|
[
"🐛 Bug"
] |
yulinor
| 6
|
strawberry-graphql/strawberry
|
asyncio
| 3,430
|
Schema basics docs
|
https://strawberry.rocks/docs/general/schema-basics returns 500 error
|
closed
|
2024-04-02T08:58:46Z
|
2025-03-20T15:56:39Z
|
https://github.com/strawberry-graphql/strawberry/issues/3430
|
[
"bug"
] |
lorddaedra
| 2
|
dmlc/gluon-cv
|
computer-vision
| 1,582
|
Top-1 accuracy on UCF101 dataset is bad?
|
I trained `i3d_nl5_resnet50_v1` on `UCF101` datasets with `Kinetics400` pretrained, the acc: `top-1=85.2%, top-5=95.4%`.
Params: `clip_len=32, input=224x224, lr=0.01, batch_size=8`.
It seems not so good, what is the possible reason?
|
closed
|
2021-01-07T03:19:58Z
|
2021-01-08T02:41:31Z
|
https://github.com/dmlc/gluon-cv/issues/1582
|
[] |
Tramac
| 3
|
HIT-SCIR/ltp
|
nlp
| 261
|
编译完成后在lib目录下并看不到segmentor_jni.so呀?
|
ltp编译完成后有个lib目录里面有很多so文件, 但是并看不到segmentor_jni.so等ltp4j使用的so?
这是为什么? 是因为ltp4j太久没人维护了吗?
|
closed
|
2017-11-11T16:07:55Z
|
2018-01-23T12:56:06Z
|
https://github.com/HIT-SCIR/ltp/issues/261
|
[] |
ambjlon
| 1
|
PeterL1n/RobustVideoMatting
|
computer-vision
| 127
|
CUA
|
closed
|
2022-01-12T11:35:10Z
|
2022-01-14T06:39:23Z
|
https://github.com/PeterL1n/RobustVideoMatting/issues/127
|
[] |
HarrytheOrange
| 0
|
|
plotly/dash-core-components
|
dash
| 475
|
box plot issue with dcc.Graph?
|
Getting a few reports in the community forum that look valid, but I haven't attempted reproducing yet: https://community.plot.ly/t/boxplot-in-dash/19623/4
|
open
|
2019-03-05T15:00:28Z
|
2019-03-05T15:00:28Z
|
https://github.com/plotly/dash-core-components/issues/475
|
[] |
chriddyp
| 0
|
gradio-app/gradio
|
python
| 10,255
|
Bug of gr.ImageMask save image
|
### Describe the bug
Hi, author.
I use gr.ImageMask met a bug. Wish you can solve this problem.
I set of ImageMask width and height, in the page save image from gr.ImageMask, but image size is compressed, not the width and height of the original upload.
### Have you searched existing issues? 🔎
- [X] I have searched and found no existing issues
### Reproduction
```python
import gradio as gr
with gr.Blocks() as demo:
with gr.Row():
im = gr.ImageMask(
type="numpy",
interactive=True,
height=150,
width=500
)
```
### Screenshot
_No response_
### Logs
_No response_
### System Info
```shell
gradio：5.9.1
```
### Severity
I can work around it
|
closed
|
2024-12-26T09:42:37Z
|
2025-01-22T18:35:03Z
|
https://github.com/gradio-app/gradio/issues/10255
|
[
"bug",
"🖼️ ImageEditor"
] |
yaosheng216
| 1
|
satwikkansal/wtfpython
|
python
| 137
|
Is copy or reference in self recursion?
|
Hello, I not found in this repository about this grammar. so, I open this issue.
My CPython version is 3.7.1
- list recursion (reference )
```python
class C:
def f1(self, a):
if a[0][0] == 0:
return a
else:
a[0][0] -= 1
self.f1(a)
return a
print(C().f1([[5, 2],[3, 4]]))
```
result:
```bash
>>> [[0, 2], [3, 4]]
```
- variable recursion (copy)
```python
class C:
def f1(self, a):
if a == 0:
return a
else:
a -= 1
self.f1(a)
return a
print(C().f1(3))
```
result
```bash
>>> 2
```
Just defining functions is the same.
Thank you.
|
closed
|
2019-09-09T04:14:31Z
|
2019-12-21T17:08:04Z
|
https://github.com/satwikkansal/wtfpython/issues/137
|
[
"new snippets"
] |
daidai21
| 1
|
plotly/dash-cytoscape
|
plotly
| 175
|
Graph nodes flocking to single point
|
#### Description
Nodes in graphs are shown correctly for a second before flocking to a single point, usually happens when inserting Cyto-components using callbacks. It is independent of any particular layout. This happens often.
Looks like this:
<img src="https://i.ibb.co/6WV2BF3/Screenshot-2022-05-17-at-16-15-32.png" alt="Screenshot-2022-05-17-at-16-15-32" border="0">
#### Steps/Code to Reproduce
Not deterministic. Happens when having multiple graphs, shown at different times in the Dash app.
#### Expected Results
A graph with properly placed nodes.
#### Actual Results
This.
<img src="https://i.ibb.co/6WV2BF3/Screenshot-2022-05-17-at-16-15-32.png" alt="Screenshot-2022-05-17-at-16-15-32" border="0">
#### Versions
```
Dash 2.3.1
/Users/niels/Documents/GitHub/test/test.py:3: UserWarning:
The dash_html_components package is deprecated. Please replace
`import dash_html_components as html` with `from dash import html`
import dash_html_components; print("Dash Core Components", dash_html_components.__version__)
Dash Core Components 2.0.2
/Users/niels/Documents/GitHub/test/test.py:4: UserWarning:
The dash_core_components package is deprecated. Please replace
`import dash_core_components as dcc` with `from dash import dcc`
import dash_core_components; print("Dash HTML Components", dash_core_components.__version__)
Dash HTML Components 2.3.0
Dash Renderer 1.9.1
Dash HTML Components 0.2.0
```
|
open
|
2022-05-17T14:21:44Z
|
2022-05-24T15:48:35Z
|
https://github.com/plotly/dash-cytoscape/issues/175
|
[] |
nilq
| 3
|
aio-libs/aiomysql
|
asyncio
| 979
|
aiomysql raise InvalidRequestError: Cannot release a connection with not finished transaction
|
### Describe the bug
When the database connection runs out and a new request is sent, an error occurd and the whole application goes down!
in the aiomysql/utils.py, line 137
```python3
async def __aexit__(self, exc_type, exc, tb):
try:
await self._pool.release(self._conn)
finally:
self._pool = None
self._conn = None
```
why release the whole connection pool?
### To Reproduce
```python3
import asyncio
import base64
import aiohttp.web_routedef
from aiohttp import web
from aiomysql.sa import create_engine
route = aiohttp.web.RouteTableDef()
@route.get('/')
async def test(request):
async with request.app.db.acquire() as conn:
trans = await conn.begin()
await asyncio.sleep(60)
await trans.commit()
return web.Response(text="ok")
@route.get('/test')
async def test(request):
async with request.app.db.acquire() as conn:
trans = await conn.begin()
await trans.close()
return web.Response(text="not ok")
async def mysql_context(app):
engine = await create_engine(
db='fmp_new',
user='root',
password="lyp82nlf",
host="localhost",
maxsize=1,
echo=True,
pool_recycle=1
)
app.db = engine
yield
app.db.close()
await app.db.wait_closed()
async def init_app():
app = web.Application()
app.add_routes(route)
app.cleanup_ctx.append(mysql_context)
return app
def main():
app = init_app()
web.run_app(app, port=9999)
if __name__ == '__main__':
main()
```
### Expected behavior
when a new request arrives , If the connection is exhausted, then wait for it release
### Logs/tracebacks
```python-traceback
Error handling request
Traceback (most recent call last):
File "test_aiomysql.py", line 20, in test
await asyncio.sleep(60)
File "/usr/lib/python3.7/asyncio/tasks.py", line 595, in sleep
return await future
concurrent.futures._base.CancelledError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/yangshen/Envs/fmp_37/lib/python3.7/site-packages/aiohttp/web_protocol.py", line 422, in _handle_request
resp = await self._request_handler(request)
File "/home/yangshen/Envs/fmp_37/lib/python3.7/site-packages/aiohttp/web_app.py", line 499, in _handle
resp = await handler(request)
File "test_aiomysql.py", line 21, in test
await trans.commit()
File "/home/yangshen/Envs/fmp_37/lib/python3.7/site-packages/aiomysql/utils.py", line 139, in __aexit__
await self._pool.release(self._conn)
File "/home/yangshen/Envs/fmp_37/lib/python3.7/site-packages/aiomysql/sa/engine.py", line 163, in release
raise InvalidRequestError("Cannot release a connection with "
aiomysql.sa.exc.InvalidRequestError: Cannot release a connection with not finished transaction
```
### Python Version
```console
$ python --version
3.7.5
```
### aiomysql Version
```console
$ python -m pip show aiomysql
0.1.0
```
### PyMySQL Version
```console
$ python -m pip show PyMySQL
1.1.0
```
### SQLAlchemy Version
```console
$ python -m pip show sqlalchemy
1.4.49
```
### OS
ubuntu 18.04
### Database type and version
```console
5.7.39-0ubuntu0.18.04.2
```
### Additional context
_No response_
### Code of Conduct
- [X] I agree to follow the aio-libs Code of Conduct
|
open
|
2024-03-07T09:01:24Z
|
2024-03-07T09:06:15Z
|
https://github.com/aio-libs/aiomysql/issues/979
|
[
"bug"
] |
ShownYoung
| 0
|
KevinMusgrave/pytorch-metric-learning
|
computer-vision
| 370
|
Does library supports load data from from torch.utils.data.Dataset?
|
closed
|
2021-10-05T07:14:17Z
|
2021-10-05T09:49:20Z
|
https://github.com/KevinMusgrave/pytorch-metric-learning/issues/370
|
[] |
KennyTC
| 1
|
|
wandb/wandb
|
tensorflow
| 9,198
|
[Bug-App]: Sweep UI (Parallel Coordinates Plot, Parameter Importance Plot, etc.) Missing in W&B Dashboard
|
### Describe the bug
Description:
I ran the following code from the sweep tutorial using the W&B library. While the logged score is visible on the W&B website, I cannot see the Sweep UI features such as the parallel coordinates plot or parameter importance plot.
Code:
```
# Import the W&B Python Library and log into W&B
import wandb
wandb.login()
# 1: Define objective/training function
def objective(config):
score = config.x**3 + config.y
return score
def main():
wandb.init(project="my-first-sweep")
score = objective(wandb.config)
wandb.log({"score": score})
# 2: Define the search space
sweep_configuration = {
"method": "random",
"metric": {"goal": "minimize", "name": "score"},
"parameters": {
"x": {"max": 0.1, "min": 0.01},
"y": {"values": [1, 3, 7]},
},
}
# 3: Start the sweep
sweep_id = wandb.sweep(sweep=sweep_configuration, project="my-first-sweep")
wandb.agent(sweep_id, function=main, count=10)
```
Here is the capture of the sweep web UI.
<img width="1845" alt="Image" src="https://github.com/user-attachments/assets/5e15e4fc-236c-407d-a374-0016c91058d4" />
|
closed
|
2025-01-07T12:15:07Z
|
2025-01-09T12:36:42Z
|
https://github.com/wandb/wandb/issues/9198
|
[
"ty:bug",
"c:sweeps",
"a:app"
] |
hspark1212
| 2
|
tartiflette/tartiflette
|
graphql
| 549
|
Setup/teardown is failing when doing automated tests
|
This is a bug report about doing setup/teardown for running automated tests with Tartiflette (and tartiflette-aiohttp). It seems that the first test succeeds, but subsequent tests fail. I've created a repository with a minimal reproducible test case, which is here: https://github.com/singingwolfboy/tartiflette-test-bug
* [x] **Explain with a simple sentence the expected behavior** (above)
* [x] **Tartiflette version:** 1.4.1
* [x] **Python version:** 3.10.0
* [x] **Executed in docker:** Yes
* [x] **Dockerfile sample:** https://github.com/singingwolfboy/tartiflette-test-bug/blob/main/Dockerfile
* [x] **GraphQL Schema & Query:** unrelated
* [x] **Is it a regression from a previous versions?** unknown
* [x] **Stack trace**: in the README of the repo (click on "Here's an example of the test failure output" to see it)
|
open
|
2021-12-02T17:54:09Z
|
2021-12-02T17:54:09Z
|
https://github.com/tartiflette/tartiflette/issues/549
|
[] |
singingwolfboy
| 0
|
httpie/cli
|
python
| 966
|
[Docs] `cv@` & `=@` are getting replaced in docsite with [email-protected]
|
Clicking on the link in `[email-protected]` takes me to https://httpie.org/cdn-cgi/l/email-protection
|
closed
|
2020-09-17T19:50:28Z
|
2020-09-20T06:36:16Z
|
https://github.com/httpie/cli/issues/966
|
[] |
jskrzypek
| 1
|
netbox-community/netbox
|
django
| 17,940
|
"Tagged VLANs" multiselect in Interface incomplete
|
### Deployment Type
Self-hosted
### Triage priority
N/A
### NetBox Version
v4.1.4
### Python Version
3.10
### Steps to Reproduce
Create more than 100 VLANs.
Create a Device with an Interface.
Set the 802.1q mode to tagged.
Open the "Tagged VLANs" multiselect and select VLANs.
### Expected Behavior
All existing VLANs show up and can be selected.
### Observed Behavior
VLANS 1 to 100 show up. VLAN 101 and above do not show up.
|
closed
|
2024-11-06T09:23:59Z
|
2025-02-06T03:03:46Z
|
https://github.com/netbox-community/netbox/issues/17940
|
[] |
georg-again
| 2
|
matplotlib/mplfinance
|
matplotlib
| 368
|
Point and figure [pnf] - Reversal param
|
Hello,
Is it possible to change the PNF param "reversal" the same way as i can do with "box_size"? Is it even possible?
`mpf.plot(df, type='pnf', pnf_params=dict(box_size=7))`
|
closed
|
2021-04-01T14:54:53Z
|
2021-04-25T18:06:50Z
|
https://github.com/matplotlib/mplfinance/issues/368
|
[
"enhancement",
"released"
] |
heytechv
| 4
|
amisadmin/fastapi-amis-admin
|
sqlalchemy
| 179
|
添加search_fields模糊搜索字段后报错
|
不添加页面正常显示，添加后页面为空报错
变量变成了[~]$样式
|
closed
|
2024-07-25T09:01:58Z
|
2024-08-02T02:12:03Z
|
https://github.com/amisadmin/fastapi-amis-admin/issues/179
|
[] |
zeroChen00
| 1
|
pandas-dev/pandas
|
python
| 60,656
|
ENH: Add `date_format` and `date_unit` to `to_dict` similar to what exists in `to_json`
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
`df.to_dict` is often (maybe even mostly) to post data via REST API.
My usual workflow for posting data looks like this:
```
df = pd.DataFrame({"date": pd.to_daterange("2021-01-01", "2024-01-01")})
df["values"] = 1
df["date"] = df["date"].map(lambda x: x.isoformat())
df = df.to_dict("records")
df["some_key"] = "some_value"
response= requests.post(url, data=df)
```
Ideally, the datetime to string could happen inside of `to_dict` via an optional `date_format` and `date_unit` parameter similar to `df.to_json`.
However, `df.to_json` is not a suitable alternative because often I need to add an additional key as seen above.
If this seems like an appropriate new feature, I will dig into how to implement this.
### Feature Description
Add 2 new parameters to `to_dict`:
1. `date_format`. Possible values: None, Epoch, isoformat. Default: None. If None, do nothing.
2. `date_unit`. One of ‘s’, ‘ms’, ‘us’, ‘ns’ . Default ms
### Alternative Solutions
None really
### Additional Context
Code for to_dict: https://github.com/pandas-dev/pandas/blob/1be26374dd7ef43bc709c4bc6db2daf7bfd606c8/pandas/core/methods/to_dict.py#L56
to_json: https://github.com/pandas-dev/pandas/blob/v2.2.3/pandas/core/generic.py#L2428-L2717
|
open
|
2025-01-04T10:06:33Z
|
2025-01-04T14:19:10Z
|
https://github.com/pandas-dev/pandas/issues/60656
|
[
"Enhancement",
"IO Data",
"Closing Candidate"
] |
lucasjamar
| 1
|
huggingface/datasets
|
computer-vision
| 6,689
|
.load_dataset() method defaults to zstandard
|
### Describe the bug
Regardless of what method I use, datasets defaults to zstandard for unpacking my datasets.
This is poor behavior, because not only is zstandard not a dependency in the huggingface package (and therefore, your dataset loading will be interrupted while it asks you to install the package), but it happens on datasets that are uploaded in json format too, meaning the dataset loader will attempt to convert the data to a zstandard compatible format, and THEN try to unpackage it.
My 4tb drive runs out of room when using zstandard on slimpajama. It loads fine on 1.5tb when using json, however I lack the understanding of the "magic numbers" system used to select the unpackaging algorithm, so I can't push a change myself.
Commenting out this line, in "/datasets/utils/extract.py" fixes the issue, and causes SlimPajama to properly extract using rational amounts of storage, however it completely disables zstandard, which is probably undesirable behavior. Someone with an understanding of the "magic numbers" system should probably take a pass over this issue.
```
class Extractor:
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
extractors: Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
#"zstd": ZstdExtractor, # This line needs to go, in order for datasets to work w/o non-dependent packages
"rar": RarExtractor,
"bz2": Bzip2Extractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": Lz4Extractor, # <Added version="2.4.0"/>
}
```
### Steps to reproduce the bug
'''
from datasaets import load_dataset
load_dataset(path="/cerebras/SlimPajama-627B")
'''
This alone should trigger the error on any system that does not have zstandard pip installed.
### Expected behavior
This repository (which is encoded in json format, not zstandard) should check whether zstandard is installed before defaulting to it. Additionally, using zstandard should not use more than 3x the required space that other extraction mechanisms use.
### Environment info
- `datasets` version: 2.17.1
- Platform: Linux-6.5.0-18-generic-x86_64-with-glibc2.35
- Python version: 3.12.0
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0
|
closed
|
2024-02-22T17:39:27Z
|
2024-03-07T14:54:16Z
|
https://github.com/huggingface/datasets/issues/6689
|
[] |
ElleLeonne
| 4
|
scrapfly/scrapfly-scrapers
|
web-scraping
| 5
|
Failed to register account
|
remind must be work email
|
closed
|
2023-10-12T08:24:41Z
|
2023-10-12T08:56:40Z
|
https://github.com/scrapfly/scrapfly-scrapers/issues/5
|
[
"question"
] |
lovelifelovejava
| 0
|
microsoft/UFO
|
automation
| 129
|
AttributeError: 'AppAgentProcessor' object has no attribute 'update_step'. Did you mean: 'update_cost'?
|
I tried setting up and running the UFO first time with a basic task of sending an email to a gmail address with a simple question.
UFO does everything as expected until the last step when it asks the user for permission to send the email. That's where I received the error. Full traceback is pasted below:
[Input Required:] UFO🛸 will apply click_input(button='left', double=False) on the [Send] item. Please confirm whether to proceed or not. Please input Y or N.
Y
Traceback (most recent call last):
File "C:\Users\anaconda3\envs\ufo\lib\runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\anaconda3\envs\ufo\lib\runpy.py", line 86, in _run_code
exec(code, run_globals)
File "C:\Users\code\UFO\ufo\__main__.py", line 7, in <module>
ufo.main()
File "C:\Users\code\UFO\ufo\ufo.py", line 56, in main
clients.run_all()
File "C:\Users\code\UFO\ufo\module\client.py", line 28, in run_all
session.run()
File "C:\Users\code\UFO\ufo\module\sessions\session.py", line 103, in run
super().run()
File "C:\Users\code\UFO\ufo\module\basic.py", line 355, in run
round.run()
File "C:\Users\code\UFO\ufo\module\basic.py", line 104, in run
self.agent.handle(self.context)
File "C:\Users\code\UFO\ufo\agents\agent\basic.py", line 224, in handle
self.state.handle(self, context)
File "C:\Users\code\UFO\ufo\agents\states\app_agent_state.py", line 315, in handle
agent.process_resume()
File "C:\Users\code\UFO\ufo\agents\agent\basic.py", line 237, in process_resume
self.processor.resume()
File "C:\Users\code\UFO\ufo\agents\processors\basic.py", line 152, in resume
self.update_step()
AttributeError: 'AppAgentProcessor' object has no attribute 'update_step'. Did you mean: 'update_cost'?
Currently on Windows 11 using Anaconda Virtual Env with python 3.10.15
Also tried python 3.13 but same error.
Any assistance would be appreciated, thanks
|
open
|
2024-10-27T10:39:11Z
|
2024-11-23T13:00:25Z
|
https://github.com/microsoft/UFO/issues/129
|
[] |
HamzaAsiff
| 8
|
adbar/trafilatura
|
web-scraping
| 786
|
Slow extraction after recent PRs
|
Hi @unsleepy22, I just ran tests on the benchmark with `pyinstrument tests/comparison_small.py` and there seems to be an issue with your PR which improve the results but have a major cost in terms of timing (say 3-4x as slow overall).
The problem seems to be in `determine_returnstring` > `xmltotxt` > `process_element`. My guess is that a slow test with descendants or ancestors is run recursively although there is no need.
The function `is_in_table_cell()` is a potential culprit, its use should be limited to cases where it's absolutely necessary and/or there should be a better way to perform the test after all.
|
closed
|
2025-02-10T16:38:09Z
|
2025-02-17T16:29:13Z
|
https://github.com/adbar/trafilatura/issues/786
|
[
"bug"
] |
adbar
| 0
|
piskvorky/gensim
|
data-science
| 3,295
|
Get coverage to work properly under Github Actions
|
See the suggestions here: https://github.com/RaRe-Technologies/gensim/pull/3286#issuecomment-1050561253
|
open
|
2022-02-25T07:01:04Z
|
2022-02-25T07:13:39Z
|
https://github.com/piskvorky/gensim/issues/3295
|
[
"housekeeping"
] |
mpenkov
| 0
|
miguelgrinberg/flasky
|
flask
| 148
|
Fix manage.py path problem on windows
|
> covdir = os.path.join(basedir, 'tmp/coverage')
> COV.html_report(directory=covdir)
> print('HTML version: file://%s/index.html' % covdir)
There is some problems on _windows_, coz path is '\'.
|
closed
|
2016-05-29T04:44:46Z
|
2017-12-10T20:04:19Z
|
https://github.com/miguelgrinberg/flasky/issues/148
|
[
"bug"
] |
viprs
| 1
|
Asabeneh/30-Days-Of-Python
|
matplotlib
| 437
|
Python Program
|
closed
|
2023-08-23T10:27:38Z
|
2023-08-23T10:28:38Z
|
https://github.com/Asabeneh/30-Days-Of-Python/issues/437
|
[] |
xozayn
| 0
|
|
suitenumerique/docs
|
django
| 332
|
Web accessibility problems on document edition page
|
## Bug Report
**Problematic behavior**
Here is the detailed analysis by Antoine : https://www.loom.com/share/3c9642546c2c4e5391b2ce04a5c3df93
|
open
|
2024-10-14T08:10:00Z
|
2024-12-11T14:11:07Z
|
https://github.com/suitenumerique/docs/issues/332
|
[
"good first issue",
"frontend"
] |
virgile-dev
| 1
|
tfranzel/drf-spectacular
|
rest-api
| 904
|
How can I use pagination in extend_schema_field?
|
I have a field in my serializer:
```
messages = serializers.SerializerMethodField()
```
and corresponding method:
```
def get_messages(self, obj):
```
that returns a list of paginated messages. I use `LimitOffsetPagination` class and my custom `MessageSerializer` serializer. I wish to define a type for this method, by using `extend_schema_field` decorator.
what I've done is:
```
@extend_schema_field(MessageSerializer(many=True))
def get_messages(self, obj):
```
but this type is incorrect because data returned by this method is paginated. How can I declare the type for this method taking into the account paginator class?
|
closed
|
2022-12-23T09:18:55Z
|
2022-12-23T16:06:27Z
|
https://github.com/tfranzel/drf-spectacular/issues/904
|
[] |
adybionka
| 2
|
coqui-ai/TTS
|
deep-learning
| 2,350
|
ValueError(" [!] Unkown encoder type.")
|
### Describe the bug
I am trying to load the pretrained glowtts model from the repo but I am stuck with this error when ***setup_model()*** function runs.
***ValueError(" [!] Unkown encoder type.")***
### To Reproduce
to reproduce simply load the config for the glowtts model and run setup_model:
config = load_config(path_to_config)
model = setup_model(config)
### Expected behavior
Traceback (most recent call last):
File "C:\TTS\run_glowtts_hifigan.py", line 72, in <module>
model = setup_model(C)
File "c:\TTS\tts\models\__init__.py", line 13, in setup_model
model = MyModel.init_from_config(config, samples)
File "c:\tts\TTS\tts\models\glow_tts.py", line 557, in init_from_config
return GlowTTS(new_config, ap, tokenizer, speaker_manager)
File "c:\TTS\tts\models\glow_tts.py", line 80, in __init__
self.encoder = Encoder(
File "c:\TTS\tts\layers\glow_tts\encoder.py", line 132, in __init__
raise ValueError(" [!] Unkown encoder type.")
ValueError: [!] Unkown encoder type.
### Logs
_No response_
### Environment
```shell
Env:
Cuda 11.6
Conda 4.5.12
Python 3.8
Pytorch 1.13.1
TTS version 0.11.1
```
### Additional context
Anyone who can explain this behavior?
|
closed
|
2023-02-16T15:34:23Z
|
2023-03-25T23:58:54Z
|
https://github.com/coqui-ai/TTS/issues/2350
|
[
"bug",
"wontfix"
] |
nicholasguimaraes
| 1
|
onnx/onnx
|
deep-learning
| 5,869
|
Cannot install on windows 10 with pip - `test_data_set_0` folder is missing
|
# Bug Report
### Is the issue related to model conversion?
<!-- If the ONNX checker reports issues with this model then this is most probably related to the converter used to convert the original framework model to ONNX. Please create this bug in the appropriate converter's GitHub repo (pytorch, tensorflow-onnx, sklearn-onnx, keras-onnx, onnxmltools) to get the best help. -->
### Describe the bug
<!-- Please describe the bug clearly and concisely -->
When trying to install it via `pip install onnx`, I get the following error:
```
ERROR: Could not install packages due to an OSError: [WinError 3] The system cannot find the path specified: 'C:\\Users\\hrger\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\onnx\\backend\\test\\data\\node\\test_averagepool_3d_dilations_large_count_include_pad_is_0_ceil_mode_is_False\\test_data_set_0'
```
upon `cd`ing to `C:\\Users\\hrger\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\onnx\\backend\\test\\data\\node\\test_averagepool_3d_dilations_large_count_include_pad_is_0_ceil_mode_is_False`, it has only one file `model.onnx`:
```
Mode LastWriteTime Length Name
---- ------------- ------ ----
-a---- 19/01/2024 20:05 303 model.onnx
```
### System information
<!--
- OS Platform and Distribution (*e.g. Linux Ubuntu 20.04*):
- ONNX version (*e.g. 1.13*):
- Python version:
- GCC/Compiler version (if compiling from source):
- CMake version:
- Protobuf version:
- Visual Studio version (if applicable):-->
- OS Platform and Distribution: WIndows 10 Professional 22H2
- ONNX version: 1.15.0
- Python version: 3.10.11
### Reproduction instructions
<!--
- Describe the code to reproduce the behavior.
```
import onnx
model = onnx.load('model.onnx')
...
```
- Attach the ONNX model to the issue (where applicable)-->
`pip instsall onnx`
### Expected behavior
<!-- A clear and concise description of what you expected to happen. -->
It should install successfully.
### Notes
<!-- Any additional information -->
|
closed
|
2024-01-19T20:11:58Z
|
2024-01-25T14:20:05Z
|
https://github.com/onnx/onnx/issues/5869
|
[
"bug"
] |
Grsz
| 3
|
plotly/dash
|
dash
| 2,497
|
select certain columns / rows that are False when cell_selectable=False
|
When I use dashtable with cell_selectable=False, all cells are not selectable. I am wondering if we can have the option to choose certain columns / rows that are not selectable? In my case, I only want 1 column that is selectable. Other columns should be disabled.
Using dash 2.9
|
open
|
2023-04-05T05:42:18Z
|
2024-08-13T19:31:23Z
|
https://github.com/plotly/dash/issues/2497
|
[
"feature",
"P3"
] |
slyin87
| 0
|
chiphuyen/stanford-tensorflow-tutorials
|
tensorflow
| 30
|
advise to add a Jupyter Notebook
|
hi, here is a suggestion, how about add a "Jupyter Notebook" in example PATH?
|
open
|
2017-06-18T09:05:43Z
|
2017-07-11T17:47:48Z
|
https://github.com/chiphuyen/stanford-tensorflow-tutorials/issues/30
|
[] |
DoneHome
| 1
|
satwikkansal/wtfpython
|
python
| 219
|
Explain meaning of asterisk in heading titles
|
A lot of the headings end in a asterisk e.g. "First things first! *" does but "Strings can be tricky sometimes" does not. This suggests a footnote but after quite a bit of searching I can't find one. Perhaps it means something else like "new" or "reader contributed". Please explain on the page otherwise there's not much point it being there and it's a bit frustrating.
|
closed
|
2020-08-24T09:24:05Z
|
2020-08-24T14:41:05Z
|
https://github.com/satwikkansal/wtfpython/issues/219
|
[] |
arthur-tacca
| 1
|
onnx/onnx
|
pytorch
| 6,168
|
Any operation to convert "Tile" to "Slice"
|
I am working on some Low-Rank Adaptation (LoRA) models, in which two set of `torch.nn.Parameters` are initialized and then multiplied into a complete weight matrix of Conv/Attention during inference.
I noticed that, the weight of original Conv is converted into `Slice` type, while the torch.nn.Parameters designed for LoRA Conv/Attention are converted into `Tile` type.
Is it possible to convert the self-designed `torch.nn.Parameters` into `Slice` type, or is it possible to convert any `Tile` type into `Slice` type?
|
closed
|
2024-06-11T07:29:08Z
|
2024-07-10T05:41:22Z
|
https://github.com/onnx/onnx/issues/6168
|
[
"question"
] |
zw-xxx
| 2
|
pyg-team/pytorch_geometric
|
deep-learning
| 9,734
|
`SetTransformerAggregation` has parameter incorrectly written
|
### 📚 Describe the documentation issue
The page for the `nn.aggr.SetTransformerAggregation` class has a typo, where the parameter `layer_norm` is shown as `norm`.
### Suggest a potential alternative/fix
I will open a PR with an edit to that particular docstring.
|
closed
|
2024-10-25T19:20:18Z
|
2024-10-28T13:59:57Z
|
https://github.com/pyg-team/pytorch_geometric/issues/9734
|
[
"documentation"
] |
eurunuela
| 1
|
BeanieODM/beanie
|
pydantic
| 922
|
Concerns and Suggestions Regarding Beanie Library
|
I wanted to raise some concerns regarding the current state of the Beanie library, particularly in its role as an async ODM in Python.
As a user of the library, I've noticed that the maintainer seems to be unavailable, which has raised some concerns about its suitability for use in our production environment. Additionally, I've observed that there are numerous open issues and pull requests that often take a month or longer to be reviewed.
In light of these observations, I believe it might be beneficial to explore some potential solutions to improve the maintenance and responsiveness of the project. Here are a few suggestions:
1. **Consider Turning the Project into an Organization:** By transitioning the project to an organization, it could attract more contributors and potentially alleviate the burden on a single maintainer.
2. **Engage with MongoDB:** Since Beanie is closely related to MongoDB, it might be worthwhile to reach out to the MongoDB community for support or collaboration opportunities.
3. **Expand the Maintainer Team:** Adding more maintainers to the project could help distribute the workload and improve the speed of issue resolution and pull request reviews.
I believe implementing one or more of these suggestions could greatly benefit the Beanie library and its users. I'm eager to hear your thoughts and discuss potential next steps.
|
closed
|
2024-04-23T17:43:49Z
|
2024-10-08T17:06:22Z
|
https://github.com/BeanieODM/beanie/issues/922
|
[
"Stale"
] |
alm0ra
| 10
|
seleniumbase/SeleniumBase
|
pytest
| 3,351
|
how can I keep the browser open and keep it open?
|
how can I keep the browser open and keep it open so that I can continue the secondary operation without having to restart the browser every time?
|
closed
|
2024-12-19T04:23:13Z
|
2025-01-12T14:16:55Z
|
https://github.com/seleniumbase/SeleniumBase/issues/3351
|
[
"duplicate",
"question"
] |
quyunet
| 6
|
ymcui/Chinese-BERT-wwm
|
nlp
| 111
|
MaskedLM的head能开源吗？
|
现在提供的模型只包含WWM fine tune 完成的BERT模型。能同时提供论文中用来fine tune 的MLM的linear head 吗？
|
closed
|
2020-04-27T00:45:34Z
|
2020-04-28T16:46:09Z
|
https://github.com/ymcui/Chinese-BERT-wwm/issues/111
|
[] |
snakeztc
| 3
|
ckan/ckan
|
api
| 8,409
|
Accessibility: <a> tags without href
|
Hi, there are a couple of `<a>` tags without `href` attributes in some of the templates. For example, https://github.com/ckan/ckan/blob/5117fbaeee5a60cf62e42abde2dcef19c747505a/ckan/templates/organization/snippets/info.html#L67
As per https://accessibleweb.com/question-answer/link-element-still-accessible-without-href/
> Links that are built with anchor elements (`<a>`) are not accessible without an `href` value. The `href` value determines what page or content a user will be directed to once the link is activated. If the `href` value is left blank, then the link may appear to be broken to users and may also cause confusing screen reader announcements.
Instead of using `<a>` for these buttons, the best option here would be to use a `<form>` tag:
```html
<form method=post action=... hx-post=...>
<input class="btn btn-danger" type=submit value=Unfollow>
</form>
```
This ensures two things:
1. Accessiblity. Screen readers 'understand' and announce forms as actionable items on the page.
2. Progressive enhancement. The follow/unfollow actions will work even with JavaScript disabled (they will just be normal form submits).
|
open
|
2024-08-22T04:32:40Z
|
2024-08-22T13:02:19Z
|
https://github.com/ckan/ckan/issues/8409
|
[] |
yawaramin
| 0
|
iperov/DeepFaceLab
|
deep-learning
| 847
|
Inconsistent iterations times and issues with RW override in new version.
|
Expected behavior:
Correct me if I'm wrong but the recent updates should be overriding random warp setting state to disabled (n) as long as pretrain is enabled (y).
Actual behavior:
Despite this override update it seems like the behavior is not how one would expect, either explanation or bug fix is required, whenever random warp is set to disabled (n) overall VRAM usage is at about 9,3GB (batch 14, 224 DF-UD 288/80/80/16, optimizer set to GPU, LR disabled, GTX 1080Ti 11GB, Windows 10, 2004 update, display running of the same GPU) whereas when random warp is enabled (y) VRAM usage is close to 10,6GB.
I also tested this very same model on older version of DFL before the RW override during pretrain (before July 26), I've tested it being enabled/disabled couple times just to be sure and sure enough, old version same VRAM usage, new one with RW enabled, 10,6 even saw 10,7GB VRAM usage after a minute of training.
Also overall iterations since few updated ago have the issue where every nth one will be higher, usually twice but not always, for me it was usually all consistent -/+ 50-100ms, after some update it changed and then every 16th iteration was about twice of the previous 15 (600ms for 15 iterration, 1200ms at 16 and then back to 600ms) and now it seems to have gotten even worse, CPU usage is pinned at 100% (before it was lower) and while at the beginning it still behaves the same (16th iter twice of the rest) after few minutes it gets worse and then either every 3rd or 4th iteration is like 1000ms, 1500ms, even 2200 or up to 3000ms. It's worse with new update and RW enabled, bit better with it disabled or enabled/disabled on older version (probably due to that lower vram usage). I'm thinking it might be the issue with my CPU being tad weak since it's pinned at 95-100% all the time, but this wasn't the case before, is the -U or -D (or -UD) heavier on CPU? Or some other thing made it heavier on CPU overall?
Shouldn't then this override update keep RW disabled all the time when pretrain is enabled? Why higher VRAM usage? What's with the iteration times being all over the place, this is not an issue just for me, there have been many people who reported the same issue over past month.
UPDATE: It seems like I was able to resolve this issue, not sure why but after I've downloaded the newest 08.02 release and ran it once it fixed it in the version I had and now VRAM usage after RW override update is the same no matter the RW setting (about 9,3-9,4 GB). I will now leave it to run for few minutes to see if maybe the iteration time issue was fixed or at least improved.
Steps to reproduce:
Download newest version of DFL (08.02) and run a model with or without random warp enabled, observe VRAM usage and iteration times.
Other issues/questions:
Recent updates also changed behavior of random warp, changed value in warp.py, introduced more changes in the code as well as forced it to be disable during pretraining without even a simple explanation as to why, this raises couple questions about how models should be now correctly trained:
1. Does this mean pretrain is not required at all or just for pretraining?
2. How does this change affect lower resolution models that still use base DF and LIAE as well as -U variants?
3. Do models still correctly generalize with RW being disabled during pretraining?
4. Is changing batch size still allowed during pretraining/training or should it remain now at mostly the same setting throughout the training?
5. How should LR be used now that RW is disabled, previous updated mentioned workflow like this: RW:Y LR:N -> RW:Y LR:Y -> RW:N LR:???? (enable before GAN). Should LR be used at all during pretraining?
Other relevant information:
GTX 1080Ti, 16GB of RAM, 4 core, 8 thread i7 4,1GHZ when load on all 8 threads
Windows 10 64bit with 2004 update, display running of the GPU
DF-UD 224 Full Face model, dims: 288/80/80/16, Pretrain mode enabled
|
open
|
2020-08-03T12:39:58Z
|
2023-06-08T23:12:09Z
|
https://github.com/iperov/DeepFaceLab/issues/847
|
[] |
ThomasBardem
| 6
|
xonsh/xonsh
|
data-science
| 4,960
|
Web config tool includes \r in prompt
|
When using the web config (xonfig web), newlines are interpreted as "\r\n". On macOS (and Linux, I assume) this adds a "^M" to the prompt, before the newline.
## xonfig
```
+------------------+--------------------------------+
| xonsh | 0.13.3 |
| Python | 3.10.7 |
| PLY | 3.11 |
| have readline | True |
| prompt toolkit | 3.0.31 |
| shell type | prompt_toolkit |
| history backend | json |
| pygments | 2.13.0 |
| on posix | True |
| on linux | False |
| on darwin | True |
| on windows | False |
| on cygwin | False |
| on msys2 | False |
| is superuser | False |
| default encoding | utf-8 |
| xonsh encoding | utf-8 |
| encoding errors | surrogateescape |
| xontrib | [] |
+------------------+--------------------------------+
```
## Expected Behavior
I would expect the "\r" to be stripped from the text field before the prompt is set.
## Current Behavior
I was able to confirm that the "\r" is being added to the prompt variable by checking my .xonshrc after using the web config. Removing this value removes the "^M" from my prompt.
## Steps to Reproduce
* Run xonfig web from your shell.
* Select a multi-line prompt, and set your .xonshrc
* Exit and restart xonsh. You'll see a "^M" before the new line.
## For community
⬇️ **Please click the 👍 reaction instead of leaving a `+1` or 👍 comment**
|
closed
|
2022-10-04T22:43:21Z
|
2022-10-25T14:07:21Z
|
https://github.com/xonsh/xonsh/issues/4960
|
[
"good first issue",
"xonfig-web"
] |
JoBrad
| 2
|
SYSTRAN/faster-whisper
|
deep-learning
| 1,231
|
Problem installing on macOS with Python 3.13.1
|
This seems to be an issue with the av library being locked to a specific version. I can install av 14.1.0 just fine, but I believe faster-whisper is requiring a version below 13. I am on macOS in a virtualenv with nothing else installed and python 3.13.1.
|
open
|
2025-01-28T12:02:16Z
|
2025-01-30T16:58:02Z
|
https://github.com/SYSTRAN/faster-whisper/issues/1231
|
[] |
creyD
| 1
|
PokeAPI/pokeapi
|
graphql
| 1,178
|
Title: is_default Field Incorrect for Pokémon Form 10441
|
# `is_default` Field Incorrect for Pokémon Form `10441`
## Description
The `is_default` field in the `/api/v2/pokemon-form/10441/` endpoint is incorrectly set to `false`, despite being the only form associated with Pokémon `10272` (`ursaluna-bloodmoon`).
According to the [documentation for Pokémon forms](https://pokeapi.co/docs/v2#pokemon-forms), the `is_default` field should be `true` for exactly one form per Pokémon. Since this Pokémon has only one form, it should logically be marked as the default form.
---
## Steps to Reproduce
1. Fetch the Pokémon data from `/api/v2/pokemon/10272/`:
```json
{
"forms": [
{
"name": "ursaluna-bloodmoon",
"url": "https://pokeapi.co/api/v2/pokemon-form/10441/"
}
]
}
```
2. Fetch the form data from `/api/v2/pokemon-form/10441/`:
```json
{
"id": 10441,
"is_battle_only": false,
"is_default": false
}
```
3. Observe that the `is_default` field is set to `false`.
---
## Expected Behavior
Since form `10441` is the only form for Pokémon `10272`, the `is_default` field should be `true` to align with the documentation.
---
## Observed Behavior
The `is_default` field is incorrectly set to `false`.
---
## Suggestion
Update the `is_default` field for `/api/v2/pokemon-form/10441/` to `true` to reflect the correct behavior for the only form of this Pokémon.
|
open
|
2024-12-25T20:25:03Z
|
2024-12-26T09:41:46Z
|
https://github.com/PokeAPI/pokeapi/issues/1178
|
[] |
Ferlow
| 1
|
Farama-Foundation/PettingZoo
|
api
| 1,143
|
[Bug Report] MPE SimpleEnv continuous actions are the "other way"
|
### Describe the bug
At the moment the simple env action.u computations are opposite to the discrete environment's. In the current setup when the agents recieve [0, 1, 0, 1, 0] for example they start moving to the top right, instead of the expected bottom left, based on `Agent and adversary action space: [no_action, move_left, move_right, move_down, move_up]`
### Code example
```shell
# simple_env.py from line 206
if self.continuous_actions:
# Process continuous action as in OpenAI MPE
agent.action.u[0] += action[0][1] - action[0][2] # Here
agent.action.u[1] += action[0][3] - action[0][4] # And here
else:
# process discrete action
if action[0] == 1:
agent.action.u[0] = -1.0
if action[0] == 2:
agent.action.u[0] = +1.0
if action[0] == 3:
agent.action.u[1] = -1.0
if action[0] == 4:
agent.action.u[1] = +1.0
```
### System info
_No response_
### Additional context
_No response_
### Checklist
- [X] I have checked that there is no similar [issue](https://github.com/Farama-Foundation/PettingZoo/issues) in the repo
|
closed
|
2023-12-02T13:14:48Z
|
2023-12-07T14:42:28Z
|
https://github.com/Farama-Foundation/PettingZoo/issues/1143
|
[
"bug"
] |
mrxaxen
| 3
|
Nike-Inc/koheesio
|
pydantic
| 136
|
[MAJOR] rename SynchronizeDeltaToSnowflakeTask to SynchronizeDeltaToSnowflakeStep in a future major release
|
Does it make sense to rename it SynchronizeDeltaToSnowflakeStep in a future major release?
_Originally posted by @riccamini in https://github.com/Nike-Inc/koheesio/pull/97#discussion_r1860832343_
|
open
|
2024-11-29T14:07:41Z
|
2024-11-29T14:07:56Z
|
https://github.com/Nike-Inc/koheesio/issues/136
|
[
"postponed"
] |
dannymeijer
| 0
|
flairNLP/flair
|
nlp
| 3,626
|
[Question]: Is it possible to add UMLS Metathesaurus as custom linking model
|
### Question
On https://flairnlp.github.io/flair/v0.15.1/tutorial/tutorial-hunflair2/customize-linking.html there is example on how to add Human Phenotype Ontology.
Would it be possible to add UMLS Metathesaurus and how?
|
closed
|
2025-03-04T17:35:28Z
|
2025-03-22T11:01:25Z
|
https://github.com/flairNLP/flair/issues/3626
|
[
"question"
] |
darije
| 3
|
PrefectHQ/prefect
|
data-science
| 17,042
|
Allow a List of Inputs for Prefect ECS Work Pool Start Commands
|
### Bug summary
## Issue
Currently Prefect doesn't support a list of inputs as a custom start command. This functionality is natively supported by Docker and ECS.
## Example
Command: `['/bin/sh', '-c', 'python -m some.module', '&&', 'prefect flow-run execute']`
If I pass the above start command list to a Prefect ECS work pool. I get the following error:
Stacktrace:
```
Failed to submit flow run '89015a3e-c72b-4e8c-af78-c90d7521eea5' to infrastructure.
Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/prefect/workers/base.py", line 1007, in _submit_run_and_capture_errors
configuration = await self._get_configuration(flow_run)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/prefect/workers/base.py", line 1105, in _get_configuration
configuration = await self.job_configuration.from_template_and_values(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/prefect/client/utilities.py", line 99, in with_injected_client
return await fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/prefect_aws/workers/ecs_worker.py", line 422, in from_template_and_values
return cls(**populated_configuration)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pydantic/main.py", line 214, in __init__
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
pydantic_core._pydantic_core.ValidationError: 1 validation error for ECSJobConfiguration
command
Input should be a valid string [type=string_type, input_value=['/bin/sh', '-c', 'python...efect flow-run execute'], input_type=list]
For further information visit https://errors.pydantic.dev/2.10/v/string_type
```
I can't pass in the commands as a single string because Docker will only execute the first command in front of the `&&`. For example, `python -m some.module && prefect flow-run execute` will result in only `python -m some.module` being executed.
### Version info
```Text
Version: 3.1.8
API version: 0.8.4
Python version: 3.11.11
Git commit: 53a83ebc
Built: Tue, Dec 17, 2024 10:20 AM
OS/Arch: linux/x86_64
Profile: ephemeral
Server type: ephemeral
Pydantic version: 2.9.2
Server:
Database: sqlite
SQLite version: 3.34.1
Integrations:
prefect-shell: 0.3.0
prefect-dask: 0.3.2
```
### Additional context
_No response_
|
open
|
2025-02-07T14:53:33Z
|
2025-02-07T14:53:33Z
|
https://github.com/PrefectHQ/prefect/issues/17042
|
[
"bug"
] |
skohlleffel
| 0
|
Ehco1996/django-sspanel
|
django
| 717
|
数据后台无法跳转
|
**问题的描述**
我已经把页面功能启动来了，整体登录也OK，但是在看后端对接教程的时候，发现数据后台无法跳转，我看了代码里也没有在后台跳转处写跳转链接，包括html代码里也没有数据后台的添加SS链接等功能
**项目的配置文件**
**如何复现**
**相关截图/log**
**其他信息**
|
closed
|
2022-09-03T05:05:56Z
|
2022-11-22T04:10:04Z
|
https://github.com/Ehco1996/django-sspanel/issues/717
|
[
"bug"
] |
a807966224
| 2
|
danimtb/dasshio
|
dash
| 21
|
Dasshio randomly stops
|
My Dasshio add-on service randomly stops. Sometimes the dash buttons seem to stop working, but on inspecting the add-on I find that it has changed state to "stopped", with an error in the log.
The last time, this is what happened:
- I pressed a dash button once
- it correctly performed the associated action
- then I pressed it again but it didn't work
- I checked the Hass.io web interface and the add-on was in "**state: stopped**"
- I started the add-on and then it worked correctly in detecting button presses several times.
This is the log:
```
2017-12-13 07:01:35,704 | INFO | Gillette button pressed!
2017-12-13 07:01:35,705 | INFO | Request: http://homeassistant:8123/api/services/light/toggle
2017-12-13 07:01:35,810 | INFO | Status Code: 200
2017-12-13 07:01:35,811 | INFO | Successful request
Traceback (most recent call last):
File "dasshio.py", line 81, in <module>
sniff(stop_filter=arp_display, filter='arp or (udp and src port 68 and dst port 67 and src host 0.0.0.0)', store=0, count=0)
File "/usr/lib/python3.6/site-packages/scapy/sendrecv.py", line 617, in sniff
s.close()
File "/usr/lib/python3.6/site-packages/scapy/arch/linux.py", line 499, in close
set_promisc(self.ins, i, 0)
File "/usr/lib/python3.6/site-packages/scapy/arch/linux.py", line 151, in set_promisc
mreq = struct.pack("IHH8s", get_if_index(iff), PACKET_MR_PROMISC, 0, b"")
File "/usr/lib/python3.6/site-packages/scapy/arch/linux.py", line 294, in get_if_index
return int(struct.unpack("I",get_if(iff, SIOCGIFINDEX)[16:20])[0])
File "/usr/lib/python3.6/site-packages/scapy/arch/linux.py", line 288, in get_if
ifreq = ioctl(s, cmd, struct.pack("16s16x",bytes(iff,'utf-8')))
OSError: [Errno 19] No such device
Connection lost. Reconnecting…
```
This is the add-on configuration. Notice that at the moment I am actively using only the first configured button, "Gillette".
```
Description Use Amazon Dash Buttons in Home Assistant
Version 0.0.9
State stopped
Boot auto
Auto update X
Uses host network V
Builds locally V
Detached V
Options
{
"buttons": [
{
"name": "Gillette",
"address": "**:**:**:**:**:ea",
"url": "http://homeassistant:8123/api/services/light/toggle",
"headers": "{\"x-ha-access\": \"*******\"}",
"body": "{\"entity_id\": \"light.abatjour\"}"
},
{
"name": "Angelica",
"address": "**:**:**:**:**:B8",
"url": "http://homeassistant:8123/api/services/persistent_notification/create",
"headers": "{\"x-ha-access\": \"*******\"}",
"body": "{\"message\": \"prova angelica\"}"
},
{
"name": "Finish",
"address": "**:**:**:**:**:B9",
"url": "http://homeassistant:8123/api/services/persistent_notification/create",
"headers": "{\"x-ha-access\": \"*******\"}",
"body": "{\"message\": \"prova finish\"}"
},
{
"name": "Duracell",
"address": "**:**:**:**:**:A0",
"url": "http://homeassistant:8123/api/services/automation/trigger",
"headers": "{\"x-ha-access\": \"*******\"}",
"body": "{\"entity_id\":\"automation.demo_audio_e_luce\"}"
},
{
"name": "Barilla",
"address": "**:**:**:**:**:0D",
"url": "http://homeassistant:8123/api/services/persistent_notification/create",
"headers": "{\"x-ha-access\": \"*******\"}",
"body": "{\"message\": \"prova barilla\"}"
}
]
}
```
Hass.io version is 0.59.2 running on Raspberry Pi 3.
|
closed
|
2017-12-14T09:05:47Z
|
2018-03-19T23:07:21Z
|
https://github.com/danimtb/dasshio/issues/21
|
[
"bug"
] |
amagnolo
| 47
|
junyanz/pytorch-CycleGAN-and-pix2pix
|
computer-vision
| 1,485
|
AttributeError: Can't pickle local object 'get_transform.<locals>.<lambda>'
|
need some help with this problem 😭
|
open
|
2022-09-19T14:45:31Z
|
2022-09-20T20:25:16Z
|
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1485
|
[] |
Zhou248
| 1
|
Lightning-AI/pytorch-lightning
|
pytorch
| 19,553
|
Permission denied: '/usr/share/lmod/lmod/init/ksh_funcs/libcudart.so'
|
### Bug description
When using bitsandbytes==0.41, getting the following error
```
permissionError: [Errno 13] Permission denied: '/usr/share/lmod/lmod/init/ksh_funcs/libcudart.so'
```
which is also mentioned here: https://github.com/TimDettmers/bitsandbytes/issues/809
I think the issue is still here because Lightning is one version behind.
### What version are you seeing the problem on?
v2.2
### How to reproduce the bug
_No response_
### Error messages and logs
```
permissionError: [Errno 13] Permission denied: '/usr/share/lmod/lmod/init/ksh_funcs/libcudart.so'
```
### Environment
<details>
<summary>Current environment</summary>
```
#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0):
#- Lightning App Version (e.g., 0.5.2):
#- PyTorch Version (e.g., 2.0):
#- Python version (e.g., 3.9):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
#- Running environment of LightningApp (e.g. local, cloud):
```
</details>
### More info
_No response_
cc @carmocca @awaelchli
|
closed
|
2024-03-01T04:55:32Z
|
2024-03-04T15:11:32Z
|
https://github.com/Lightning-AI/pytorch-lightning/issues/19553
|
[
"bug",
"precision: bnb"
] |
dipta007
| 2
|
developmentseed/lonboard
|
data-visualization
| 313
|
[EPIC] Javascript enhancements
|
## Context
Interactive maps need to be reactive in any and all environments.
## Issue
Let's implement some JS-based improvements.
## Acceptance-Criteria
These are the tasks that need to be completed or artifacts that need to be produced.
- [ ] https://github.com/developmentseed/lonboard/issues/148
- [x] https://github.com/developmentseed/lonboard/issues/264
- [x] https://github.com/developmentseed/lonboard/issues/112
|
open
|
2024-01-11T15:46:32Z
|
2024-04-19T15:03:14Z
|
https://github.com/developmentseed/lonboard/issues/313
|
[
"javascript"
] |
emmalu
| 1
|
graphql-python/graphene-sqlalchemy
|
sqlalchemy
| 112
|
Generate Input Arguments from SQLAlchemy Class?
|
Hello,
Do you know if it's possible to generate input arguments dynamically from the SQLAlchemy class that will be transformed by the mutation?
Example:
My input arguments for a `CreatePerson` mutation look like this:
```python
class CreatePersonInput(graphene.InputObjectType):
"""Arguments to create a person."""
name = graphene.String(required=True, description="Name of the person to be created.")
height = graphene.String(default_value="unknown", description="Height of the person to be created.")
mass = graphene.String(default_value="unknown", description="Mass of the person to be created.")
hair_color = graphene.String(default_value="unknown", description="Hair color of the person to be created.")
skin_color = graphene.String(default_value="unknown", description="Skin color of the person to be created.")
eye_color = graphene.String(default_value="unknown", description="Eye color of the person to be created.")
birth_year = graphene.String(default_value="unknown", description="Birth year of the person to be created.")
gender = graphene.String(default_value="unknown", description="Gender of the person to be created.")
planet_id = graphene.ID(default_value="unknown", description="Global Id of the planet from which the person to be created comes from.")
url = graphene.String(default_value="unknown", description="URL of the person in the Star Wars API.")
class CreatePerson(graphene.Mutation):
"""Mutation to create a person."""
person = graphene.Field(lambda: People, description="Person created by this mutation.")
class Arguments:
input = CreatePersonInput(required=True)
...
```
In the meantime, the input arguments for my `UpdatePerson` mutation look like this:
```python
class UpdatePersonInput(graphene.InputObjectType):
"""Arguments to update a person."""
id = graphene.ID(required=True)
name = graphene.String()
height = graphene.String()
mass = graphene.String()
hair_color = graphene.String()
skin_color = graphene.String()
eye_color = graphene.String()
birth_year = graphene.String()
gender = graphene.String()
planet_id = graphene.ID()
url = graphene.String()
class UpdatePerson(graphene.Mutation):
"""Update a person."""
person = graphene.Field(lambda: People, description="Person updated by this mutation.")
class Arguments:
input = UpdatePersonInput(required=True)
...
```
Finally, my SQLAlchemy class look like this:
```python
class ModelPeople(Base):
"""People model."""
__tablename__ = 'people'
id = Column('id', Integer, primary_key=True)
name = Column('name', String)
height = Column('height', String)
mass = Column('mass', String)
hair_color = Column('hair_color', String)
skin_color = Column('skin_color', String)
eye_color = Column('eye_color', String)
birth_year = Column('birth_year', String)
gender = Column('gender', String)
planet_id = Column('planet_id', Integer, ForeignKey('planet.id'))
created = Column('created', String)
edited = Column('edited', String)
url = Column('url', String)
...
```
This is all pretty redundant and it would be ideal if we could just reuse the SQLAlchemy class attributes in the `InputObjectType`
|
open
|
2018-02-09T05:44:41Z
|
2018-04-24T18:13:29Z
|
https://github.com/graphql-python/graphene-sqlalchemy/issues/112
|
[] |
alexisrolland
| 3
|
schemathesis/schemathesis
|
pytest
| 2,504
|
Specifying --hypothesis-seed=# does not recreate tests with the same data
|
### 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
When running the CLI SchemaThesis tests, we cannot reproduce errors due to the changing data sent to the API. When specifying the seed, the initial several tests do use the same data, but eventually diverges into completely different data sets unique to each run.
### To Reproduce
1. Run this command twice:
st run --base-url=http://host.docker.internal:3050/api --checks=all --data-generation-method=all --target=all --max-failures=5000 --validate-schema=true --debug-output-file=/mnt/debug.json --show-trace --code-sample-style=curl --cassette-path /mnt/cassette.yaml --fixups=all --stateful=links --sanitize-output=true --contrib-unique-data --contrib-openapi-fill-missing-examples --hypothesis-max-examples=25 --hypothesis-phases=generate,explicit --hypothesis-verbosity=verbose --experimental=openapi-3.1 --generation-allow-x00=false --schemathesis-io-telemetry=false --hypothesis-seed=115111099930844871414873197742186425200 -H Authorization: Bearer TOKEN /mnt/api-specification.json'
2. Cassette file is different lengths, different number of tests were run, different data was sent to the API
Please include a minimal API schema causing this issue:
```yaml
{
"openapi": "3.0.1",
"info": {
"title": "System APIs",
"description": "DESCRIPTION REDACTED",
"version": "1.00.00"
},
"servers": [
{
"url": "/api"
}
],
"paths": {
....
```
### Expected behavior
The documentation for the run seed is sparse, but it indicates that the seed should be used to reproduce test results. We are not seeing this behavior.
### Environment
```
- OS: [Windows in Docker]
- Python version: [Using schemathesis/schemathesis:stable]
- Schemathesis version: [Using schemathesis/schemathesis:stable]
- Spec version: [e.g. Open API 3.0.1]
```
### Additional context
Where the two test runs begin to diverge, one test run will send URLs like:
uri: '[http://host.docker.internal:3050/api/PATHREDACTED?0=False&0=False&=true&%C2%94%F1%B3%9C%B3%C2%BC=0']
Whereas the equivalent other test run at the same line in the cassette file will be:
uri: '[http://host.docker.internal:3050/api/PATHREDACTED?ID=0']
|
open
|
2024-10-09T20:26:42Z
|
2024-10-20T01:56:38Z
|
https://github.com/schemathesis/schemathesis/issues/2504
|
[
"Type: Bug",
"Status: Needs Triage"
] |
hydroculator
| 2
|
pyg-team/pytorch_geometric
|
pytorch
| 8,853
|
Add Support for Pytorch 2.2
|
### 🚀 The feature, motivation and pitch
Pytorch 2.2 was released:
[Blog](https://pytorch.org/blog/pytorch2-2/)
[Release Notes](https://github.com/pytorch/pytorch/releases/tag/v2.2.0)
### Alternatives
_No response_
### Additional context
_No response_
|
closed
|
2024-02-02T09:40:55Z
|
2024-02-03T10:13:11Z
|
https://github.com/pyg-team/pytorch_geometric/issues/8853
|
[
"feature"
] |
Foisunt
| 2
|
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